Integrating Nanotechnology and Artificial Intelligence for Early Detection and Prognostication of Glioblastoma: A Translational Perspective

Abstract

Glioblastoma (GBM) is the most common and aggressive malignant brain tumor in adults. This review explains the connections between the genesis and progression of GBM and particular cellular tumorigenic mechanisms, such as angiogenesis, invasion, migration, growth factor overexpression, genetic instability, and apoptotic disorders, as well as possible therapeutic targets that help predict the course of the disease. Glioblastoma multiforme (GBM) diagnosis relies heavily on histopathological features, molecular markers, extracellular vesicles, neuroimaging, and biofluid-based glial tumor identification. In order to improve miRNA stability and stop the proliferation of cancer cells, nanoparticles, magnetic nanoparticles, contrast agents, gold nanoparticles, and nanoprobes are being created for use in cancer treatments, neuroimaging, and biopsy. Targeted nanoparticles can boost the strength of an MRI signal by about 28–50% when compared to healthy tissue or controls in a preclinical model like mouse lymph node metastasis. Combining the investigation of CNAs and noncoding RNAs with deep learning-driven global profiling of genes, proteins, RNAs, miRNAs, and metabolites presents exciting opportunities for creating new diagnostic markers for malignancies of the central nervous system. Artificial intelligence (AI) advances precision medicine and cancer treatment by enabling the real-time analysis of complex biological and clinical data through wearable sensors and nanosensors; optimizing drug dosages, nanomaterial design, and treatment plans; and accelerating the development of nanomedicine through high-throughput testing and predictive modeling.

1. Introduction

Gliomas, glioneuronal tumors, and neuronal tumors are divided into six families by the World Health Organization (WHO): ependymomas, glioneuronal and neuronal tumors, circumscribed astrocytic gliomas, pediatric-type diffuse low-grade gliomas, and adult-type diffuse gliomas. These are differentiated from choroid plexus tumors by their unique epithelial characteristics [1]. Glioblastoma multiforme (GBM) is the most prevalent and aggressive primary malignant brain tumor in adults, comprising about 14.5% of central nervous system tumors. Its incidence ranges from 0.6 to 5 per 100,000 people, more commonly affecting men, older adults (median diagnosis age 60–64), and white, non-Hispanic individuals. The rising incidence may relate to an aging population and enhanced diagnostic techniques, although specific causes are unclear. GBM is primarily sporadic with few risk factors, such as ionizing radiation exposure, and the prognosis remains poor, with a median survival of 10–15 months despite treatments. Outcomes vary, influenced by age, performance status, and tumor resection extent, with distinct primary and secondary subtypes sharing an aggressive clinical behavior [2,3,4,5,6].

An uncommon malignancy, glioblastoma (GBM) primarily affects older individuals and is characterized by a poor prognosis. Risk factors include exposure to ionizing radiation, genetic predispositions such as TP53 mutations, and inherited conditions like Li–Li-Fraumeni syndrome and neurofibromatosis type 1. No evidence links GBM to routine radiation exposure. The tumorigenesis of GBM involves cellular processes such as angiogenesis, invasion, and migration, with IDH mutations leading to cancer development due to the abnormal accumulation of the oncometabolite 2-HG [7,8,9,10].

Notch signaling is required for cell differentiation, proliferation, and death in a wide variety of cell types and tissues, including neurons in the central nervous system. The Notch-1, Notch-2, Notch-3, and Notch-4 receptors are important. Notch-1 is either a tumor suppressor or an oncogene [11]. When ceramides are broken down by the acid ceramidase enzyme, they promote senescence and cell death [12]. Angiogenesis of glioma stem cells and the restoration of oxygen supply are stimulated by vascular endothelial growth factor (VEGF) [13]. Glioblastoma tumors have altered, rearranged, or elevated PDGFRα gene expression, which contributes to oncogenesis [14].

By attaching extracellular signaling ligands to its extracellular domain, the transmembrane cell RTK known as the epidermal growth factor receptor (EGFR) starts several signal transduction cascades. Glioblastoma etiology is often linked to EGFR mutations [15]. The PI3K/AKT/mTOR pathway is essential for controlling the cell cycle in glioblastoma because overactivation lowers survival rates and increases tumor aggressiveness. The tumor suppressor PTEN is rendered inactive by mutations in glioblastomas [16,17]. The Smoothened protein is deactivated by the sonic hedgehog (SHH) glycoprotein, which causes glioblastomas to transition to adult stem cells [18,19]. The RB1 protein regulates the cell cycle transition from G1 to S-phase. In both primary and secondary GBMs, this pathway is commonly observed to be inactivated [20].

The DNA repair enzyme 6-methylguanine-DNA methyltransferase, which is effective in treating MGMT promoter hypermethylation disorders brought on by gene silencing, is said, by proponents of MGMT encoding, to shield cells from alkylating medications [21]. One intricate piece of cellular machinery, the proteasome, participates in a number of enzymatic activities that facilitate the breakdown and recycling of proteins and may weaken cancer cells [22]. Headaches, nausea, intracerebral hypertension, motor impairments, weight loss, confusion, and visual or verbal impairments are typical clinical presentations [23,24,25].

Figure 1 explains the RTK (Receptor tyrosine kinase) signaling pathway in Glioblastoma.

Treatment for glioblastoma and stopping its progression depend on early detection. Two disadvantages of current techniques, like histomolecular ones, are the absence of biomarkers and decreased BBB penetration. In contemporary medicine, it is essential to identify the causes of origin, development, and early diagnosis. To detect tumors and neurodegenerative diseases, imaging techniques such as MRI, PET, CT, PA, and FL are required [26].

Biomimetic nanosystems are made of different materials, including proteins, cell membranes, and disease-targeting components, and they resemble physiological conditions. The created affinity nanoprobes have a long lifespan, are highly biocompatible, and replicate the microenvironment of disease. They provide a novel way to penetrate the blood–brain barrier (BBB) and make it easier to develop multipurpose “all-in-one” probes for treatment and diagnosis. Despite difficulties with BBB permeability for contrast agents, these developments improve management aspects of glioblastoma, such as early detection, treatment plans, and monitoring therapy responses [27]. The use of cutting-edge nanotechnologies in the diagnosis of glioblastoma in its early stages is covered in this review, which also emphasizes the benefits of these materials for better diagnosis and treatment. It also covers the possibilities for creating sophisticated smart sensors for the treatment of glioblastoma, as well as the drawbacks and difficulties in creating nanodiagnostic techniques.

2. Literature Search

Extensive searches were carried out in pertinent databases in order to examine literature, papers, and research pertaining to the AI and Nanotechnology framework. The following search terms were used to search the websites of international organizations and conduct thorough searches on Scopus, ACM Digital Library, and PubMed:“ Glioblastoma” AND “Imaging” AND “Biomarkers” AND “Nanotechnology” AND “Artificial intelligence” AND “clinical decision” AND “Ethical OR law OR regulation”. Review demonstrates how AI may be integrated with medical sensors and devices based on nanotechnology, allowing for individualized monitoring, advanced clinical decision support, and evidence-based suggestions for optimizing treatment and managing disease. In order to shed light on the various technologies that have developed in this dynamic and quickly evolving field, this review attempts to provide a thorough overview of AI and nanotechnology integration within the healthcare industry. As a result, it aims to overcome the primary obstacles to integrating such technologies into clinical practice by offering a comprehensive review of the ethical and legal environment around the use of AI and nanotechnology in healthcare [28,29].

3. Current Diagnostic Modalities and Limitations

Glioblastomas are a type of tumor with well-known MRI features. They comprise various infiltrating intraparenchymal lesions. In contrast to cerebral edema, the core of the lesion appears hypointense. Neoangiogenesis is visible on perfusion-weighted imaging, but diffusion/perfusion sequences provide a more precise diagnosis [30]. Due to cellular activity, choline/creatinine and choline/N-acetylaspartate ratios in MR spectroscopy (MRS) are not specific enough to diagnose glioblastomas; however, a smaller myoinositol peak and a greater lactate and lipid peak are good indicators of glioblastoma. Apart from assessing the extent of peritumoral invasion, tests can differentiate glioblastomas from brain abscesses, lymphomas, and metastases [31]. The lack of efficient diagnostic techniques is a major issue with glioblastoma treatment; neurological testing and neuroimaging studies are the main methods used for diagnosis. The slow dispersion process of brain tumors causes delayed detection, which results in the missed clinical signs [32,33].

The prognosis for secondary glioblastoma multiforme (GBM), a type of glioma that affects young people, is better than that of primary GBM. It can be identified by a variety of molecular markers and is often found in the frontal lobe [34]. Biofluid glial tumor detection can improve the quality of life for patients. The process of genotyping circulating tumor nucleic acids makes it possible to classify cancers, evaluate genetic alterations, and calculate the prognosis and tumor burden. Clinically significant biomarkers, including IDH1/2 mutant status, MGMT promoter methylation, 1p/19q co-deletion, and ATRX loss, are used to analyze these genetic indications [35,36].

Although miRNA expression levels are useful for early disease detection, including cancer, their low specificity and selectivity render them unreliable markers for cancer diagnosis. MiRNAs are not appropriate for non-tumor pathologies because they can alter several genes. Clinical evaluation and additional molecular testing are necessary for an accurate diagnosis of glioblastoma [37]. Numerous proteins have been described, and alterations in their quantitative or qualitative makeup are currently linked to the development of cancer patients’ tumors. VEGF and angiogenesis-associated proteins (FGF-b, IGFBP-2, Ang2, EGF and others), extracellular matrix proteins (TSP1/2, TNC, Cyr61/CCN1, OPN, etc.), matrix metalloproteinases (MMP-2, MMP-9, AEG-1), cell line-associated proteins (GFAP), macrophage migration inhibitory factor (MIF), and functionally related proteins (DD-T, CD74, CD44, CXCR2 and CXCR4) are a few of these proteins that can be used for the diagnosis and prognostic assessment of glioblastoma development [38,39]. Cell lipids, metabolites, and monomers are tiny molecules that diffuse across cell membranes and enter cells. However, their low molecular weight and lack of specificity make them ineffective for diagnosis. Circulating tumor cells enter the bloodstream, with antibodies, genetic analysis, and cell size evaluation used. glioma tumor cells enter the bloodstream due to their invasiveness [40,41].

Conventional imaging technologies for glioblastoma diagnosis, including MRI, CT, and PET, are commonly utilized to characterize tumors and aid in treatment planning; however, they exhibit significant limitations. These imaging modalities often lack biological specificity, complicating the differentiation between active tumor tissue and treatment-induced alterations such as inflammation or necrosis, which may result in misdiagnosis and delayed treatment decisions. PET imaging, although capable of identifying specific biochemical markers, encounters challenges like nonspecific binding and the necessity for tracers that can penetrate the blood–brain barrier, reducing its efficacy in certain situations. Furthermore, these imaging techniques do not furnish molecular or genetic data critical for personalized therapy. For instance, while MRI can reveal abnormal regions that may not denote cancer, PET tracers such as [18F] fluoro-ethyl-L-tyrosine or [18F] Fluorodopa can aid in the diagnosis but still struggle with specificity and availability. Consequently, there is an increasing interest in alternative diagnostic approaches like liquid biopsies and biosensors, which can deliver molecular insights and facilitate less invasive, more frequent monitoring of glioblastoma [42,43,44,45].

4. Nanotechnology for Glioblastoma Diagnosis

The study of atomic and molecular characteristics and interactions at the nanoscale (1–100 nanometers) and the application of these properties in diverse fields is known as nanotechnology. Continuous advancements in nanotechnology over the past few decades have shown great promise for the detection and management of gliomas. Nanoparticles (NPs) have the potential to be helpful in a range of cancer treatments due to their small size, adaptable surface, and low level of detrimental side effects [46,47,48]. When exposed to magnetic fields, certain magnetic nanoparticles (MNPs) show high permeability and characteristic magnetism, which makes them valuable for biomarker detection and possibly crucial for the diagnosis of various illnesses, including gliomas [49,50,51]. Figure 2 shows some nanomaterials used in the diagnosis and treatment of glioblastoma.

Accurate diagnosis is crucial in treating gliomas, which grow invasively in the brain and are difficult to pinpoint with imaging methods. Gliomas are a target compound due to their strong affinity for specific binding sites and are used as therapeutic and diagnostic agents. Both passive and active targeting are employed to transport nanoparticles into biological barriers. The EPR effect is caused by passive mechanisms in the brain, such as the buildup of nanoparticles, which promote defects and deficiencies in endothelial cells [52].

Nanoparticles, such as liposomes and dendrimers, cross the blood–brain barrier to deliver chemotherapeutic agents to GBM cells, improving drug bioactivity and reducing systemic toxicity. Quantum dots aid in early imaging and real-time tracking of tumor progression [53].

Nanoparticles (NPs) can cause cytogenotoxicity in three ways: direct damage to cell surfaces and organelles, formation of toxic ions, and oxidative stress. Factors like size, shape, structure, aggregation state, surface properties, dosage, and material type influence NPs’ toxicity. To minimize cytotoxicity, in vitro and in vivo cytotoxicity testing is required for biomedical applications, considering cell type and exposure duration. The lack of sophisticated machinery for accurate and scalable nanomaterial production, the challenge of evaluating their safety and effectiveness, and the unique limitations of specific materials are some potential disadvantages that must be addressed before discussing the many benefits of nanoparticles and their uses. The creation of nanoparticles should be facilitated by factors including stability in physiological settings, biocompatibility, propensity to cross biological barriers, and low cost and high effectiveness [54].

Table 1. Nanotechnology approach for the diagnosis of glioblastoma [55,56,57,58,59,60,61,62,63].

Nanotechnology Approach	Diagnostic Application	Advantages	Challenges/Limitations	References
Nanoparticle-based MRI contrast agents	Enhanced tumor imaging, intraoperative guidance	Improved sensitivity, real-time tracking	BBB penetration, potential toxicity	[55,56,57,58]
Fluorescent nanoparticles	Tumor visualization during surgery	Increased tumor cell visibility, precision	Limited clinical translation	[55,56,58]
Gold and iron oxide nanoparticles	MRI, PET, photoacoustic imaging	Multifunctional, high contrast, targeting	Biocompatibility, clearance	[55,56,59,60]
Liposomes, dendrimers, micelles	Drug delivery + imaging (theranostics)	Dual function (diagnosis + therapy), tunability	Stability, manufacturing complexity	[59,61,62,63]
Smart/inorganic nanoparticles	Targeted molecular imaging	Specificity, surface modification	Off-target effects, immune response	[55,56,60]
Nanocarriers for liquid biopsy	Detection of circulating tumor DNA/cells	Non-invasive, early detection	Sensitivity, standardization	[56,58,62]
Nanomedicine-enabled PET/MRI agents	Multimodal imaging	Comprehensive tumor characterization	Cost, regulatory hurdles	[55,56,57]
Extracellular vesicle analysis	Biomarker discovery, monitoring	Personalized diagnosis, prognosis	Isolation techniques, reproducibility	[56,58,62]
Polymer-based nanoparticles	Targeted imaging and drug delivery	BBB penetration, controlled release	Long-term safety, scalability	[56,59,61,63]
CRISPR/Cas9 delivery via nanoparticles	Molecular diagnostics, gene editing	Precision, potential for personalized medicine	Delivery efficiency, ethical concerns	[56,59]

displays information on the nanotechnology approach for the diagnosis of glioblastoma.

4.1. Nanotechnology for MRI

Gadolinium-based contrast agents’ poor sensitivity, resolution, and background noise limit the use of current MRI techniques for glioma diagnosis. High magnetic relaxation and specificity nanoscale contrast agents have become viable substitutes as clinical requirements change, with the potential to improve tissue differentiation and MRI sensitivity. Iron oxide nanoparticles (IONPs) and manganese oxide nanoparticles (MnO NPs) are two examples of magnetic nanomaterials that work well for T1/T2 MRIs [64]. Superparamagnetic iron oxide can be used to identify low-grade gliomas because it is covalently decorated with interleukin-6 receptor-targeting peptide, which enables the particle to cross the blood–brain barrier [65]. By thermally breaking down manganese-based compounds, MnO NPs (nanoparticles) with excitation-dependent fluorescence have been created for use as nanoprobes in magnetic resonance imaging [66].

Using SPIONs modified with indocyanine (Cy7) molecules and peptides, a nanoprobe was created for MRI/NIR fluorescence dual-modality imaging that demonstrated improved tumor-homing and barrier penetration capabilities. In both ex vivo and in vivo imaging, this probe allowed for precise aggregation at glioma sites, which could enhance preoperative and intraoperative GBM imaging [67]. The design of paramagnetic or Gd-containing nanomaterials with optimal ratios to improve imaging contrast is necessary to better distinguish IDH1 mutation patients and treat GBM pseudoprogression. Furthermore, for better targeting and imaging resolution, Gd can be functionalized or encapsulated in liposomes [68].

4.1.1. Computerized Tomography Nanoprobe

PET, SPECT, and ECT are frequently used imaging methods for the diagnosis of gliomas. While conventional iodine agents have low specificity and resolution, contrast materials improve X-ray imaging to differentiate between gliomas and normal brain tissue. On the other hand, compared to iodine, gold nanoparticles provide imaging results that are more stable and clear [69]. Selecting polyethyleneimine as the template and modifying it successively with polyethylene glycol (PEG) and a glioma-specific peptide (chlorotoxin, CTX) is comparatively simple. The complex is then embedded onto gold nanoparticles (Au NP). In subcutaneous tumor models, these particles were effective as a nanoprobe for targeted SPECT/CT imaging and radionuclide therapy of glioma cells both in vitro and in vivo [70].

A nanoprobe was demonstrated to be able to penetrate the blood–brain barrier and specifically target glioma cells in a rat intracranial glioma model. Gold nanoparticles can be readily altered to improve targeted delivery and BBB penetration. By binding to target molecules and radioisotopes, they can increase the quantity of contrast agent that is delivered to tumor lesions. Furthermore, glioma imaging and treatment have made use of radioisotope-labeled nanomaterials [71]. In conclusion, radiation modalities based on nanotechnology might represent a useful innovation for delivering more accurate and thorough anatomical data.

4.1.2. Fluorescent Nanoprobe

The behavior of silicon nanoparticles (SNPs-PEG-RGD-FITC) as gliomas developed was investigated using a mouse glioma model. The study showed that these nanoparticles, which target integrin αvβ3, effectively penetrated and remained in solid gliomas after intravenous injection. This method enables the observation of the dynamics of nanomaterials in glioma targeting and infiltration in real time [72]. A new work described a glioma-targeting and redox-activatable theranostic nanoprobe (Co-NP-RGD1/1) for on-demand synergistic chemotherapy/photodynamic therapy (Chemo-PDT) of orthotopic gliomas, guided by magnetic resonance (MR) and fluorescence (FL) bimodal imaging [73].

In the presence of cRGD-targeting groups on the surface, intravenous Co-NP-RGD could be given to in situ glioma cells across the blood–brain barrier in an in vitro model, producing an increased MRI contrast signal for the localization of gliomas in the brain [73]. Based on the idea of NIR-II fluorescence imaging, a new multimodal nanoprobe of NIR-II fluorescent molecules with aggregation-induced emission (AIE) characteristics has been developed [74]. Using a mouse glioma model, they were able to do dual-modal molecular imaging of gliomas with high resolution and high signal-to-noise ratio by using NIR one-region photoacoustic imaging and NIR two-region fluorescence. Furthermore, multimodal nanoprobe guiding for identifying the intraoperative borders of gliomas has advanced significantly in other domestic and foreign investigations, offering a new guidance method for glioma resection [75].

Gold nanoparticles, Ag and Cu-doped nanoparticles, and rare Earth nanomaterials are examples of nanoparticles that have the right absorption in the infrared window and are utilized for fluorescence and NIR detection. Compared to NIR detection, QDs have exceptional chemical and photochemical stability as well as a high fluorescence quantum yield [76]. Streptavidin-PEG-CdSe/ZnS QDs coupled with biotin aptamers for in vitro and in vivo fluorescence imaging of gliomas based on particular EGFRvIII signaling [75]. Additionally, a method for real-time in vivo intraoperative visualization of GBM has been reported: plasminogen activator receptor (uPAR) linked with NIR-II nanoprobe [77].

As a preoperative marker, their findings revealed a clearly defined glioma boundary with strong fluorescence intensity. Carbon dots have garnered interest among various QDs because of their low toxicity and good biocompatibility, which makes them appropriate for use in biomedical applications. CDs generated from metformin and noted their multimodal strong fluorescence capacity to penetrate the blood–brain barrier and function as an imaging tool for brain disorders, such as the U87 cell model [78].

4.1.3. PA Imaging

PA imaging utilizes FL imaging with the spatial resolution of soft tissue imaging (ultrasound). PA imaging has been applied recently to brain illnesses, including structural visualizations, lymph node imaging, and tumor imaging [79]. Using a wide variety of nanomaterials, including Au, carbon nanomaterials, transition metal dichalcogenides 2D nanomaterials (MXenen), and semiconducting polymers, several studies examined the PA imaging of brain tumors [80]. Covalently coupled molybdenum disulfide (MoS2) nanosheets with indocyanine green (ICG) dye were able to image GBM at a depth of 3.5 mm [81].

In order to overcome the difficulties of light absorption and scattering in living tissue, the NIR-II PA imaging modules show a markedly improved signal/background ratio and improved tissue penetration. For efficient imaging of orthotopic brain tumors, a conjugated polymer (CP)-based nanoparticle (NP) has been created. Through the Stille polymerization of benzobisthiadiazole (BBT) and benzodithiophene, this NP was created using electron-deficient acceptors and donor paradigms. It was then nanoprecipitated into a DSPE-PEG2000 matrix. This design makes it possible to detect glioma tumors up to 3.4 mm below the skull with little interference from the background, indicating that NIR-II PA contrast agents may be essential for further translational studies in imaging technologies [82].

4.2. Liquid Biopsy

The diagnosis and prognosis of brain malignancies have been thoroughly investigated using neuroimaging and biopsy. A simple and minimally invasive technique is liquid biopsy (LB), which uses plasma or cerebrospinal fluid (CSF) [83]. LB is a highly invasive tissue extraction substitute that is restricted to a small tumor area and uses bodily fluids to provide information that is typically combined with a clinical diagnosis [84]. Because of the turnover and continuous release of nucleic acids into the bloodstream, LB makes it possible to monitor tumors in real time, which aids in the early detection and successful treatment of brain tumors. DNA and RNA are utilized in this context to describe malignancy, serving as biomarkers in LBs and enhancing the capacity to characterize the genetic profile of GBM. The sensitivity of these assays is dependent on the size, kind, and volume of the tumor, and LBs allow for the detection of changes in pathways [3].

Since it is close to the brain tissues and has a higher concentration of biomarkers than plasma, CSF has been studied due to the difficulties in obtaining blood fluid under optimal circumstances. According to recent research, ultrasound combined with LB raises marker concentrations and promotes noninvasive deep tissue penetration into the tumor [73,76]. Numerous microfluidics-based technologies have been studied to find events and mutations that are hard to spot using traditional imaging methods [85]. Figure 3 explained Liquid biopsy in Glioblastoma.

For cancer monitoring and early diagnosis, liquid biopsy is increasingly using a variety of biomarkers, including circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), exosomes, extracellular vesicles (EVs), and microRNAs. Clinical trials being conducted by renowned cancer centers are yielding encouraging results, especially for CTCs and ctDNA. Additionally, EVs have greatly enhanced the detection and diagnosis of a number of solid tumors [86]. These biomarkers help with disease tracking and treatment decisions by offering vital information about tumor characteristics. Liquid biopsies’ main benefits are their noninvasiveness (just blood or fluid samples are needed) and their suitability for continuous monitoring, which reduces patient risk and discomfort while enabling the identification of dynamic changes in the disease [87].

Machine learning models can identify the mutations with the highest clinical significance by classifying and assessing the discovered mutations through training. The possibility that ctDNA would be found in the plasma samples of lung cancer patients was investigated using models such as logistic regression and elastic net [88]. By analyzing specific DNA methylation segments of CpG islands, the challenge of determining the tissue from which ctDNA originates can be overcome. Since they are the first abnormalities to show up during the course of cancer development, they are reliable markers for determining the tissue origin of ctDNA. Studies that have employed machine learning to examine whole-genome sequencing data of cfDNA and other data types have found that whole-genome methylation sequences offer the highest predictive value for differentiating the tissue origin of ctDNA [89]. This does not, however, imply that brief cfDNA sequences are worthless for machine learning applications. Short sequences can greatly improve machine learning models’ diagnostic skills, according to research [90].

4.3. Extracellular Vesicles

Extracellular vehicles (EVs) are naturally occurring cellular products with a hydrophilic aqueous core and an exterior hydrophobic lipid bilayer. They are essential for maintaining cell homeostasis and play a key role in cancer development by regulating invasion, migration, drug resistance, angiogenesis induction, and proliferation. Glioma cells can produce various types of EVs, making them attractive targets for diagnostic research. EVs are crucial intercellular communication mediators and biomarkers for diagnosing illnesses and assessing treatment effectiveness. EVs are classified into various sizes, including exosomes, microvesicles, oncosomes, apoptotic bodies, and big oncosomes. They can enter bodily fluids and transport various substances [91]. They are then internalized through cellular fusion or endocytosis; several substances can enhance these internalization processes. EVs have a great ability to carry specific messages across the blood–brain barrier [92]. Since EVs can be separated from bodily fluids, including blood, urine, and saliva, they provide numerous benefits in terms of early identification and enhancing patient quality of life before a tumor develops a more serious malignant potential [93].

Extracellular vesicles (EVs) or circulating tumor nucleic acids can be used in liquid biopsies in a number of ways to increase their sensitivity and specificity. By employing dual antibody capture and photosensitizer-bead detection to target disease-specific markers like CD147, sophisticated analytical methods, including the ExoScreen assay, allow for the direct and extremely sensitive identification of cancer-derived EVs from blood without the need for intricate purification. Biomarker sensitivity and diagnostic accuracy can be further improved by combining different liquid biopsy methods (e.g., exosomes plus circulating tumor DNA) or enriching for cancer-specific exosomes. Composite signatures that improve cancer detection’s sensitivity and specificity can be found by integrating and analyzing multianalyte data from EVs and other biomarkers using machine learning [94].

4.4. MicroRNAs

MiRNAs are noncoding RNAs made up of 20–22 nucleotides that adversely control gene expression through recognition, transcription, translation, and epigenetic mechanisms. They also control the expression of numerous proteins involved in the EGFR signaling pathway. Because they are either elevated or downregulated in prognostic studies with GBM patients, many noncoding RNAs have been thought to be possible biomarkers for GBM, making them crucial diagnostic biomarkers [95]. Since miRNA is found in biofluids, stability is essential for regulation. Nanoparticles are used to carry it in order to get around this instability. Research employing miRNA in conjunction with a functionalized nanographene oxide coupled model demonstrated that it significantly improves transfection effectiveness, inhibits the growth of cancer cells more effectively, and shows promise for molecular imaging-based diagnosis [95,96]. The coupling with EVs is another tactic to increase miRNA stability, creating an effective delivery system using artificially produced nano-sized vesicles [97].

4.5. Biosensing for GBM

Although tissue and liquid biopsies are intrusive procedures, they are frequently unable to assess “real-time” tumor dynamics due to ongoing treatment. Currently, a tissue biopsy from a suspected GBM lesion, combined with histological analysis, is the gold standard diagnostic procedure for GBM [98]. In various biofluids, noncoding RNAs (ncRNAs, including microRNA, long noncoding RNA, and circular RNAs) and CNAs have been explored as potential diagnostic markers for CNS malignancies. For patients with glioma tumors, these CNAs are discharged into the biofluidic streams, such as blood and CSF, as EVs or exosomes. Because certain exosomes can be elevated in bodily fluids, they may be a noninvasive diagnostic biomarker for GBM. To do this, a variety of devices were created as biosensors for the detection of gliomas in bodily fluids, including blood and CSF, as clinical diagnostic reagents. These devices ranged from point-of-care analysis to traditional sandwich ELISA-based microarray chip technologies [99,100].

A micronuclear magnetic resonance (μNMR) functionalized microfluidic chip platform. At an early stage of GBM diagnosis, this chip may evaluate EV enrichment (EGFRvIII mRNA) using real-time RNA collection and analysis [101]. The immunomagnetic exosome RNA (iMER) setup isolates glioma-specific EVs using antibody-conjugated magnetic beads, which are lysed on chip surfaces. Before reverse transcription and polymerase chain reaction (PCR) analysis, the lysed EVs (EGFR (þ) and EGFRvIII (þ)) exosomes go through a filter that resembles a glass bead mesh. The current chip combines a custom thermocycler-based PCR setup with a portable fluorescence detector.

Another method for separating and analyzing extracellular vesicles (EVs) linked to glioblastoma multiforme (GBM) involves a fluorescence-based biosensing technique. It utilizes alternating current electrokinetic (ACE) devices to align microelectrodes with an electric field, creating dielectrophoretic (DEP) high-field zones. These zones concentrate nucleosomes and proteins, allowing for the targeted detection using fluorescent antibodies specific to exosomal proteins, such as anti-CD63. The ACE device can separate GBM EVs from raw plasma in about 30 min. Additionally, a nanoplasmonic platform employing materials like titanium nitride (TiN) has been developed to measure glioma-derived exosomes and analyze nanoparticle EVs [101,102].

Surface plasmon resonance (SPR) analysis was performed using the biotinylated antibody coupled BA-TiN plasmonic biosensor. Exosomes (30–200 nm) may be quantitatively detected and isolated from a glioma cell (U251MG) using this method. A diagnostic marker seen on the surfaces of glioma cells and exosomes produced from U251 GM is the EGFRvIII mutant protein [103]. Recently, label-free electrochemical biosensors based on Zr-based metal–organic frameworks (Zr-MOFs) were developed to collect exosomes produced from GBM. The EGFR and EGFRvIII diagnosis biomarkers were precisely targeted by the (H-C-acpacp-FALGEA-NH2) peptide ligands conjugated on the Zr-MOFs surfaces. This aids in clinical analysis and helps distinguish between normal and diseased GBM at an extremely early stage. Numerous studies have also shown that, in addition to the angiogenic proteins, GBM-derived exosomes contain mRNA, CNAs, and miRNAs, which can serve as diagnostic indicators for early-stage GBM. The CSF of both healthy individuals and GBM patients contained a variety of exosome-derived miRNAs [30,104].

The sensitivity and specificity of nanostructures, including nanoparticles, nanowires, and hybrid nanomaterials, have transformed biosensing. By manipulating these nanoengineered components, GBM-specific markers can be selectively attached, increasing detection throughput and accuracy. Data analysis, pattern recognition, and predictive modeling are all improved by artificial intelligence (AI), which raises biosensor performance. AI-TENG and other AI-assisted biosensors provide accuracy, efficiency, and self-powered functioning. Through IoT platforms, machine learning models facilitate remote monitoring and increase the accuracy of cancer detection [105,106,107].

5. AI for Multi-Omics and Radiomic Integration

AI has had a significant impact on oncology, as it has completely changed how complicated problems associated with cancer are approached. AI-powered methods have significantly improved oncologic research’s accuracy and productivity, paving the way for customized cancer therapies. Its uses are numerous and include drug development, genomic research, data mining from medical records, and analysis of cancer images [108,109].

Machine Learning Techniques in Multi-Omics Data Integration

Supervised learning in medical imaging trains models with labeled data to distinguish between benign and malignant tumors. Unsupervised learning analyzes unlabeled data to categorize patients based on genetic similarities for personalized treatments. Reinforcement learning enhances treatment strategies through trial and error. Deep learning, especially with convolutional neural networks (CNNs), excels at processing unstructured data like images for cancer analysis. For sequential data, recurrent neural networks and long short-term memory networks assist in medical record mining and genetic sequence evaluation [110]. By grouping related data points, clustering techniques like k-means and hierarchical clustering can assist in the discovery of new cancer subtypes or disease states. By projecting multi-omics data into lower-dimensional spaces, dimensionality reduction techniques like principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) make the data easier to handle for additional analysis [111].

Random Forest is an ensemble learning technique that combines several decision trees to improve prediction accuracy for classification and regression tasks on a variety of datasets. On the other hand, hierarchical clustering groups related data points into nested clusters in order to find natural data structures. Gaining insight into the main uses of these algorithms can highlight their advantages in data processing and AI-based multiomics analysis. Furthermore, through the analysis of patient omics profiles and treatment outcomes, reinforcement learning teaches models to make decisions based on environmental input, enabling the identification of the best ways to treat illnesses [112].

Multi-layer neural networks are used in deep learning, a branch of machine learning, to find intricate patterns in large, varied datasets, particularly in multiomics research. It is excellent at processing complex, non-linear data and has uses in audio, image, and natural language processing. Furthermore, deep learning can efficiently examine omics data to identify important patterns that conventional statistical techniques might overlook [113].

Deep learning architectures, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are pivotal in multi-omics research. CNNs adapt omics data as one- or two-dimensional images, while RNNs handle sequential data like time-series or transcriptomes [64]. Additional models, including deep belief networks, autoencoders, and generative adversarial networks, help identify hidden patterns and correlations in omics data. This integration of deep learning may significantly enhance our understanding of complex biological systems, potentially leading to innovative diagnostic and therapeutic strategies [114].

Graph neural networks (GNNs) are increasingly utilized in node classification, especially in predicting cancer molecular subtypes through models that examine gene-gene relationships using graph convolutional networks (GCNs). Advanced technologies enable profiling various biological “omics” data, such as transcriptomics and proteomics, providing insights into biological systems and diseases. Traditional pathological diagnoses do not reveal the molecular basis of diseases, and individual omics data tend to offer correlational rather than causal insights, limiting their use in proactive processes [115,116,117].

Deep learning is used in large-scale CRISPR screening datasets to find the best target sequences, increasing editing efficiency and reducing off-target effects. By automatically recommending gRNA designs based on comprehensive CRISPR experiments, AI-based tools speed up gene editing. Furthermore, the PinPoint test is an AI-powered blood test designed to enhance cancer referral procedures. To provide precise cancer risk predictions, it makes use of a machine learning algorithm that incorporates variables like age and sex. Medical practitioners can reduce post-pandemic assessment delays by using this decision support tool to prioritize high-risk patients for prompt evaluations [118].

In precision oncology, it is crucial to forecast how patient populations will react to treatment. To predict treatment responses, machine learning methods have been used, such as deep neural network evaluations. In order to predict drug response behavior, the MOLI method, a multi-omics late mix technique that uses deep neural networks, combines expression data, copy number variations, and somatic mutations. Additionally, MOLI is used in conjunction with data on board medications that have comparable targets [119].

Significant differences between pancreatic ductal adenocarcinoma specimens and normal tissues in terms of RNA and miRNA transcriptomics data have been found using the Support Vector Machine (SVM) and Leave-One-Out Cross-Validation (LOOCV) models. To find effective diagnostic markers, these features (chosen RNAs and miRNAs) were further integrated with miRNA target expression data. These markers were then verified in other datasets and physiologically interpreted by analyzing the relevant target genes’ pathways [120]. Finding biomarkers for glioblastoma is essential to minimize invasive procedures. A new technique using differential scanning fluorimetry has been developed, allowing ML-based algorithms to distinguish cancer patients from healthy individuals with about 92% precision, based on plasma denaturation profiles. This method requires only a simple blood test and has the potential to serve as a pan-cancer diagnostic and monitoring tool [121].

Table 2. Multiomics-based prediction of cancer prognosis [122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156].

Genomics-Based Prediction of Cancer Prognosis
Study Title	Cancer Type(s)	Approach	Key Findings	Reference
Insights for precision oncology from the integration of genomic and clinical data of 13,880 tumors from the 100,000 Genomes Cancer Programme	33 solid tumor types	Whole-genome sequencing (WGS) integrated with clinical data	Linking WGS and clinical outcomes enables survival analysis and identification of prognostic cancer genes, supporting precision oncology	[122]
Genomics to select treatment for patients with metastatic breast cancer (SAFIR02-BREAST trial)	Metastatic breast cancer	Genomic profiling for therapy selection	Targeted therapies matched to actionable genomic alterations improve progression-free survival; benefit depends on actionability level	[123]
Combining multidimensional genomic measurements for predicting cancer prognosis: observations from TCGA	Breast, glioblastoma, AML, lung SCC	mRNA, DNA methylation, miRNA, copy number	Multidimensional genomic data can improve prognosis prediction, but gene expression and clinical data are most predictive	[124]
Integrating multi-omics data through deep learning for accurate cancer prognosis prediction	15 cancer types (TCGA)	Multi-omics (genomics, transcriptomics, etc.) with deep learning	Denoising autoencoder improves prognostic accuracy; robust integration of multi-omics data identifies prognostic markers	[125]
Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis	Glioma, renal cell carcinoma	Histology + genomics (mutations, CNV, RNA-Seq)	Multimodal deep learning fusion improves survival prediction over unimodal models	[126]
Pan-cancer integrative histology-genomic analysis via multimodal deep learning	14 cancer types	Histology + genomics	Multimodal deep learning predicts outcomes and discovers prognostic features across cancers	[127]
Radiogenomics-based cancer prognosis in colorectal cancer	Colorectal cancer	Radiomics + gene expression	Combining imaging and gene expression enhances prognostic stratification	[128]
Transcriptomic-Based Prediction of Cancer Prognosis.
PETACC-8 & IDEA-France (2025)	Stage III colon cancer	3′RNA sequencing, TME, and cell cycle signatures	Developed prognostic models integrating transcriptomic signatures, improving risk stratification for recurrence	[129]
WINTHER Trial (2020)	Diverse solid tumors	RNA expression profiling	Combined genomics and transcriptomics increased actionable targets; improved patient matching to therapies	[130,131]
POG Program (2022)	Advanced/metastatic cancers	Whole genome and transcriptome analysis (WGTA)	RNA data informed 67% of treatments; 46% of WGTA-informed treatments led to clinical benefit	[132]
Pediatric Poor Prognosis Study (2024)	Pediatric cancers	WGTA	Integrating transcriptome data identified actionable variants in 96% of cases, guiding therapy	[133]
PERCEPTION (2024)	Multiple myeloma, breast, and lung cancer	Single-cell transcriptomics	scRNA-seq-based models outperformed bulk predictors in clinical response prediction	[134]
SELECT (2021)	10 cancer types	Synthetic lethality via transcriptome	Predicted therapy response in 80% of 35 clinical trials, including WINTHER	[131]
Pathology Atlas (2017)	17 cancer types	Genome-wide transcriptomics	Identified prognostic genes and created an open-access atlas for survival prediction	[135]
Epigenomics-Based Prediction of Cancer Prognosis.
Development and validation of epigenetic modification-related signals for the diagnosis and prognosis of colorectal cancer	Colorectal cancer	Predictive model using gene expression and epigenetic-related genes, validated on patient cohorts	An 8-gene epigenetic signature effectively predicts prognosis and may guide targeted therapies	[136]
Exploring the role of epigenetic regulation in cancer prognosis with the epigenetic score	Pan-cancer (TCGA datasets)	LASSO Cox model to create an epigenetic score, a nomogram integrating clinical features	Epigenetic score correlates with cancer hallmarks and predicts survival across cancer types	[137]
Epigenetic and Tumor Microenvironment for Prognosis of Patients with Gastric Cancer	Gastric cancer	Machine learning (NMF, LASSO, SVM) to identify prognostic gene signatures	Identified hub genes for prognosis; signatures performed well in survival prediction and immunotherapy response	[138]
Epigenetics in the diagnosis and prognosis of head and neck cancer: A systematic review	Head and neck squamous cell carcinoma	Systematic review of 25 studies on DNA methylation and histone modifications	Several biomarkers (e.g., DAPK, TIMP3) show promise for early detection, but more robust trials are needed	[139]
Epigenome-based cancer risk prediction: rationale, opportunities and challenges	General/Multiple cancers	Review of DNA methylation-based risk prediction tests	DNA methylation tests are promising for risk prediction, but challenges include cell-type specificity and implementation	[140]
Epigenetic alterations in the gastrointestinal tract: Current and emerging use for biomarkers of cancer	GI cancers (colorectal, liver, etc.)	Review of clinical and emerging epigenetic biomarkers	Epigenetic alterations are robust biomarkers for prognosis and are being integrated into clinical tests	[141]
Proteomics and Metabolomics-Based Prediction of Cancer Prognosis.
Metabolomic-Based Approaches for Endometrial Cancer Diagnosis and Prognosis: A Review	Endometrial	Metabolomics	Identifies metabolite biomarkers for improved diagnosis, prognosis, and monitoring	[142]
Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review	Endometrial	Proteomics and Metabolomics	Reviews diagnostic/prognostic biomarker discovery using omics and clinical data	[143]
Discovery and Validation of Clinical Biomarkers of Cancer: A Review Combining Metabolomics and Proteomics	Various	Proteomics and Metabolomics	Highlights advance in biomarker discovery and clinical translation	[144]
Development of a cancer prognostic signature based on pan-cancer proteomics	Multiple (pan-cancer)	Proteomics	Proteomics-based model accurately predicts survival across cancers	[145]
Plasma-based proteomic and metabolomic characterization of lung and lymph node metastases in cervical cancer patients	Cervical	Proteomics and Metabolomics	Identifies biomarker panels for predicting lung and lymph node metastasis	[146]
Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer	Gastric	Metabolomics and Machine Learning	Machine learning model outperforms traditional markers for diagnosis and prognosis	[147]
Radiomic-Based Prediction of Cancer Prognosis.
Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer	NSCLC	112 patients, CT-based radiomics, various modeling strategies	Random Forest and PCA improved the prediction of recurrence and survival; addressing data imbalance increased accuracy	[148]
Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer	Head and Neck	300 patients, multi-cohort, PET/CT radiomics	Radiomics plus clinical data predicted recurrence/metastasis risk; validated across cohorts	[149]
Biliary Tract Cancer at CT: A Radiomics-based Model to Predict Lymph Node Metastasis and Survival Outcomes	Biliary Tract	247 patients, CT-based, retrospective	Radiomics model predicted lymph node metastasis and survival; the high-risk group had worse outcomes	[150]
Development and Validation of CT-Based Radiomics Signature for Overall Survival Prediction in Multi-organ Cancer	Lung, Kidney, Head and Neck	905 patients, multi-organ, CT-based	Radiomics signature predicted survival across cancer types; the combined model improved accuracy	[151]
A Metabolism-Related Radiomics Signature for Predicting the Prognosis of Colorectal Cancer	Colorectal	381 patients, CT-based, LASSO regression	Radiomics score independently predicted disease-free survival; the nomogram outperformed the TNM stage	[152]
Radiomics for Survival Risk Stratification of Clinical and Pathologic Stage IA Pure-Solid Non-Small Cell Lung Cancer	NSCLC (Stage IA)	592 patients, multi-region radiomics	Multiregional radiomics signature stratified survival risk, improved over clinical predictors	[153]
Delta-radiomics features for the prediction of patient outcomes in non–small cell lung cancer	NSCLC	107 patients, longitudinal CT	Radiomics features changed during therapy; end-of-treatment features predicted response	[154]
Radiogenomics-based cancer prognosis in colorectal cancer	Colorectal	64 patients, CT radiomics + gene expression	Combined radiomics and genomics improved prognostic stratification	[128]
Systematic review and meta-analysis of radiomics-based models in NSCLC	NSCLC	40 studies, 6223 patients	Radiomics models showed modest prognostic value; need for standardization	[155]
Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis	Various	Review	Multiomics (radiomics, pathomics, genomics) enhances TME assessment and prognosis prediction	[156]

displayed information about the multiomics-based prediction of cancer prognosis.

6. Bridging Between AI and Nanotechnology

AI and nanotechnology are being combined to create new materials, devices, and systems with unprecedented capabilities. This integration accelerates the identification of new materials, enhances conductivity, reactivity, and strength, and facilitates precise control and supervision in nanofabrication and manufacturing. AI-driven techniques monitor and optimize processes, while robotic systems automate high-throughput production, enhancing productivity and efficiency [157]. AI and nanotechnology are revolutionizing drug delivery, diagnostic tools, and treatments in nanomedicine, promoting adaptable treatment regimens, real-time remedial optimization, predictive models, and data-driven insights in renewable energy production and sustainability [158].

AI uses data-driven methods, virtual screening, and predictive modeling to identify new pharmaceutical targets and therapeutic approaches. It improves patient outcomes and pharmaceutical efficacy. AI plays a critical role in nanofabrication and manufacturing, improving scalability, precision, and efficiency. Techniques use sensor data to optimize performance and track process factors in real-time [159,160].

AI uses simulation and predictive modeling to help design and optimize nanofabrication processes. By analyzing data from prior manufacturing cycles, machine learning algorithms forecast how changes to the process would affect the properties of the nanostructure. AI-powered simulations speed up the development of nanomaterials and devices by enabling virtual experimentation [161]. Through the analysis of enormous databases of attributes and performance metrics, AI greatly assists in the design and optimization of biomaterials. It anticipates traits, finds desirable materials fast, and optimizes processing parameters and mixtures [162]. Biomaterial behavior at the molecular and nanoscale levels can be studied, and biocompatibility, immunogenicity, and bioactivity can be predicted using AI-driven computer modeling and simulation. By comprehending host response and tissue regeneration mechanisms, these models direct the creation of biomaterials for biomedical applications [163].

AI enhances biomaterials for tissue engineering and drug delivery applications and helps with material design. To develop effective drug delivery systems, machine learning algorithms examine drug release kinetics, diffusion mechanisms, and cellular absorption paths. In order to guarantee reliability, consistency, and security for clinical usage, AI also assists in characterizing and monitoring the quality of biomaterials at every stage of production [164,165]. Through image analysis, disease early warning, biomarker search, precision medicine, and accelerating drug discovery and protein structure prediction, artificial intelligence is transforming the medical field [166]. Through surface adsorption, chemical interactions, and optical transduction, nanomaterials such as metal nanoparticles, graphene, and carbon nanotubes enable the selective detection of pollutants, resulting in highly sensitive and specific detection [167].

Table 3. Types of Nanoparticles, AI Methods, and Clinical Uses in Cancer Treatment [168,169,170,171,172,173,174,175,176,177].

Nanoparticle Type	AI Algorithm/Approach	Clinical Outcome/Use Case	Citations
Polymeric nanoparticles	Machine learning	Enhanced targeted drug delivery in cancer	[168,169,170]
Dendrimers	Neural networks	Improved drug loading and release profiles	[169,170]
Micelles	Optimization algorithms	Controlled drug release, reduced toxicity	[169,170]
Liposomes	Deep learning	Personalized dosing, improved therapeutic index	[169,170,171]
Protein nanoparticles	Pattern recognition	Tumor-specific targeting, better diagnostics	[169,172]
Cell membrane nanoparticles	AI-driven design	Enhanced immune evasion, longer circulation	[169,173]
Mesoporous silica nanoparticles	Predictive modeling	Increased delivery efficiency to tumors	[169,174]
Gold nanoparticles	Mathematical modeling, ML	Optimized photothermal therapy, improved imaging	[169,175]
Iron oxide nanoparticles	AI-assisted PBPK modeling	Accurate prediction of biodistribution	[169,174]
Quantum dots	Classification algorithms	Improved cancer cell identification	[169,172]
Carbon nanotubes	ANN, ML	High-accuracy cancer cell classification	[169,172]
Black phosphorus	AI-based optimization	Enhanced drug delivery, reduced side effects	[169,170]
MOF nanoparticles	QSAR, ML	Optimized structure for drug delivery	[169,171]
Exosome-mimicking nanoparticles	AI-driven nanocarrier design	Improved biocompatibility, targeted siRNA delivery	[169,173]
Multifunctional nanoparticles	ML for synergy prediction	Combined chemo/immunotherapy, reduced resistance	[173,176]
Biomimetic nanocarriers	ML, optimization	Enhanced tumor targeting, immune evasion	[169,173]
Smart nanoparticles (stimuli-responsive)	AI-powered design	Precision therapy, individualized treatment	[168,169]
Carbon nanoparticles (CNPs)	ANN, ML	Subclassification of breast cancer, >98% diagnostic accuracy	[172]
Nanoparticle-modified drugs	AI for dose optimization	Improved combination therapy outcomes	[176]
Nanoparticle-based imaging agents	AI for image analysis	Enhanced diagnostic accuracy, better treatment planning	[169,177]

display information on types of nanoparticles, AI methods, and clinical uses in cancer treatment, and

Table 4. Integration of Nanotechnology and AI Models in Glioma Diagnosis and Therapy: Applications, Clinical Status, and Examples [178,179,180,181,182,183,184,185,186].

Technology/ Model	Nanotechnology Type and Example	Application (What It Does)	Example (Specific Implementation)	Clinical Status	References
Deep learning on MRI/Pathology	Metal nanoparticles (e.g., gold, iron oxide) for enhanced imaging contrast and targeted delivery	Tumor segmentation, grading, and molecular subtyping	CNNs and transformer-based models for classifying glioma subtypes from histopathology and MRI	Clinically validated	[178,179,180,181,182]
AI on TCR NGS data	Persistent luminescence nanoparticles (e.g., TRZD: ZnGa2O4:Cr3+, Sn4+) for long-term NIR imaging and therapy	Immune repertoire-based glioma diagnosis	AI models using T-cell receptor sequencing to classify glioma with an AUC of up to 96.7%	Early clinical	[183,184]
AI model optimization	Chlorotoxin peptide-functionalized gold nanoparticles (CTX-PEI-AuNPs) for targeted SPECT/CT imaging/therapy	Deploying AI in low-resource clinical settings	Post-training optimization of ResUNet for tumor delineation, reducing latency and memory usage	Early clinical	[177,184]
Explainable AI (XAI) for prognosis	AI-guided nanomedicine design for optimizing nanoparticle properties for diagnosis and therapy	Transparent, interpretable prediction of glioma outcomes	XGBoost, SHAP, LIME, and other XAI tools for feature importance and model explanation	Preclinical	[177,185,186]
Deep learning for digital pathology	Albumin-based nanotheranostic probes (e.g., ICG/AuNR@BCNP) for multimodal imaging and phototherapy	Automated histopathological subtype classification	SD-Net_WCE (DenseNet variant) for classifying five glioma subtypes from H&E slides	Clinically validated	[179,182]
Radiomics and radiogenomics	Multifunctional metal nanoparticles for targeted drug delivery, imaging, and therapy	Linking imaging features to molecular/ genomic profiles	AI models predicting IDH mutation and 1p/19q codeletion from MRI features	Clinically validated	[179,180,184]
AI for treatment response prediction	Surface-modified nanoparticles (e.g., PEGylated, ligand-targeted) for BBB penetration and targeted delivery	Predicting therapy outcomes and recurrence	Machine learning models integrating imaging, genomics, and clinical data	Preclinical	[179,184,186]
AI-enabled nanomedicine design	AI-enabled design of nanomedicines for personalized dosing and reduced nanotoxicity	Optimizing nanomaterial properties for diagnosis/therapy	AI algorithms predicting nanomaterial interactions for improved efficacy and safety	Preclinical	[177,186]
Nanoparticle-based imaging agents	Iron oxide nanoparticles (IONPs) for MRI and therapy; gold nanoparticles for SPECT/CT imaging and therapy	Enhanced Imaging for glioma detection	Iron oxide nanoparticles (IONPs) for MRI contrast, cell tracking, and magnetic hyperthermia	Mostly preclinical	[177,186]

display information on the AI-integrated nanoparticle system for enhanced Glioma diagnosis and therapy.

7. Translational Considerations

7.1. Regulatory Landscape for AI in Genomic Medicine

As different jurisdictional regulations currently govern AI-driven multi-omics research, regulatory frameworks for AI in genetic medicine must change to ensure clinical validity, data security, and patient safety. In the US, the FDA and NIH provide oversight and have set rules for medical software that uses AI. However, because self-learning AI systems are dynamic, there are substantial regulatory challenges [187,188].

The European Union enforces the General Data Protection Regulation (GDPR), which requires informed consent and robust data protection for genetic research. High-risk AI applications, such as genomics and personalized medicine, are intended to be regulated by the EU’s Artificial Intelligence Act [189]. Asian nations like China and Japan, on the other hand, prioritize developing AI in healthcare while abiding by regional data protection regulations. China has imposed stringent data governance through the Personal Information Protection Law (PIPL), while Japan has developed ethical AI principles in line with its genomic research [190].

Despite these rules, cross-border AI-driven genomic research faces difficulties due to a lack of uniformity among international regulatory organizations. To promote international research collaboration, efforts are underway to establish international standards for the validation of AI models, the ethical application of AI, and the interoperability of multi-omics data [191]. Policymakers must strive toward standardized AI validation frameworks and create flexible regulatory laws that keep up with technological changes to promote AI-driven multi-omics research while maintaining ethical compliance and patient safety [192].

Table 5. Regulatory framework for AI-driven genomics research across different regions.

Region	Key Regulatory Frameworks	Focus Areas
United States	FDA, NIH, GINA	AI software validation, genetic privacy laws
European Union	GDPR, AI Act	Data protection, high-risk AI regulation
China	PIPL, AI Governance Initiatives	National AI strategies, genomic data security
Japan	Ethical AI Guidelines, Genome Research Laws	AI in genomics, patient data ethics

displayed information on the regulatory framework for AI-driven genomics research across different regions.

The EMA and FDA’s current protocols are inadequate for the complexity of nanomedicine and AI-driven technologies, necessitating the development of new assessment and regulatory frameworks. The varied characteristics of nanoparticles, including size, shape, surface charge, and composition, pose significant challenges because they have a substantial impact on their pharmacokinetics, safety, and efficacy when compared to traditional medications [193].

Because AI-based algorithms need to process patient-specific data in real-time, they add complexity to nanomedicine. Regulatory bodies need to establish precise rules for AI-driven nanomedicine that cover both the computational logic of AI and the characteristics of nanoparticles. Adaptive clinical trial designs that enable ongoing optimization based on real-time data are one example of this [194]. Preclinical development, where AI predicts toxicity and optimizes designs; early clinical trials using data from wearable sensors enabled by AI; safety and efficacy evaluations in a variety of populations; and post-marketing surveillance, where AI analytics are used to guarantee long-term safety and compliance, are the four phases of the clinical translation roadmap. To handle discrepancies and set precise guidelines, regulatory participation is crucial at every stage [171,177].

Cancer treatment AI frameworks are still in their infancy, which leaves developers and physicians in the dark. Transparency of AI algorithms, which are frequently viewed as “black boxes,” raises ethical questions about explainability, which is important for important decisions. There are also concerns that AI may exacerbate socioeconomic, racial, and geographic healthcare disparities. Particularly with complex treatments like novel nanomedicines, concerns regarding patient autonomy and the informed consent process must be addressed as AI becomes more prevalent in decision-making [195,196].

7.2. Challenges in Clinical Implementation and Data Standardization

Data standardization, model reproducibility, and clinician adoption are just a few of the logistical and technical obstacles that must be overcome for AI-driven multi-omics to be successfully incorporated into clinical practice. Harmonization across datasets is challenging because multi-omics datasets are generated from various experimental platforms, each with its own formats, resolutions, and noise levels [197]. Since genomic, transcriptomic, and proteomic datasets frequently lack standardized formats and metadata annotations, data interoperability is a significant problem in clinical deployment. Although data integration has improved with the introduction of international bioinformatics standards like the FAIR (Findable, Accessible, Interoperable, and Reusable) principles, disparities remain between healthcare systems and research institutes [198].

The repeatability of AI models in actual healthcare settings is another major obstacle. Although a lot of AI-driven multi-omics models work well on research datasets, sample bias and overfitting frequently limit their capacity to be applied to a variety of patient groups. Researchers must use cross-cohort validation techniques and provide reliable benchmarking datasets to evaluate AI model performance in various clinical settings in order to increase reproducibility [199]. Another obstacle to clinician adoption of AI-driven multi-omics technologies is the lack of technical proficiency among many medical practitioners to decipher AI-generated findings. The integration of AI into healthcare operations is made more difficult by the absence of user-friendly interfaces and decision-support tools. AI developers need to focus on developing clinically interpretable models and providing medical practitioners with sufficient training on how to utilize AI-driven multi-omics platforms to address this [200].

AI-driven genomic medicine implementation in low-resource healthcare settings is further complicated by issues with computational infrastructure and cost. Processing large-scale multi-omics datasets requires high-performance computer resources, which limits the availability of AI-driven solutions in developing nations. Although cloud-based AI systems have surfaced as a viable remedy, issues with data security and legal compliance need to be resolved before they can be widely used [201]. Notwithstanding these obstacles, multi-omics research powered by AI has enormous promise to revolutionize precision medicine. AI-powered genomic medicine has the potential to become a common clinical tool that improves patient outcomes, therapy tailoring, and disease diagnostics by tackling problems with data standardization, model repeatability, and clinician acceptance [202].

Table 6. Clinical Translation Gaps for Cancer Diagnosis and Treatment Using Nanotechnology and AI [203,204,205,206,207,208,209,210,211].

Translation Gap	Nanotechnology Example(s)	AI Example(s)	Citations
Limited Clinical Approvals	Liposomal doxorubicin (Doxil), albumin-bound paclitaxel (Abraxane); most nanomedicines remain preclinical	AI-driven design of nanocarriers for siRNA delivery, but few AI-optimized nanomedicines have clinical approval	[173,177,203,204]
Biological Barriers	Nanoparticles for siRNA/chemotherapy delivery face rapid degradation, poor tumor accumulation, and immune clearance	AI models predict nanoparticle–biological interactions to optimize delivery, but translation to humans is limited	[173,177,204,205]
Tumor Heterogeneity	Multifunctional nanoparticles for combined therapy (e.g., chemo-immuno- or photothermal therapy) to address tumor diversity	AI analyzes patient omics/imaging data to tailor nanomedicine, but heterogeneity complicates universal solutions	[177,206,207,208]
Safety and Toxicity Concerns	Biomimetic nanocarriers (e.g., exosome-mimicking) improve biocompatibility, but long-term human safety data lacking	AI used to predict and minimize nanotoxicity, but real-world validation is limited	[173,186,206,209]
Regulatory and Manufacturing Challenges	Few standardized protocols for nanomedicine production; scalability and reproducibility issues	AI can optimize manufacturing processes, but regulatory pathways for AI-designed nanomedicines are unclear	[177,205,210,211]
Data Integration and Patient Stratification	Nanodiagnostics and nano-imaging generate large datasets for patient profiling	AI integrates multi-omics, imaging, and clinical data for personalized therapy, but clinical implementation is rare	[177,207,208]
Gap Between Preclinical and Clinical	Most nanoformulations (e.g., targeted nanoparticles, nano-theranostics) show efficacy in animals but not in humans	AI-optimized nanomedicines are often validated only in silico or in animals, not in clinical trials	[173,177,203,206]
Incomplete Understanding of Cancer Biology	Nanoparticles are designed for targeted delivery, but incomplete knowledge of the tumor microenvironment limits success	AI helps uncover new biomarkers and drug targets, but translation to effective therapies is ongoing	[177,206,210,211]

display information on the bridging the translation gap between the AI and Nanotechnology in Cancer Therapy.

To expedite the transition of nanotechnology and artificial intelligence methods from preclinical studies to therapeutic applications, urgent research is required. Important tasks include creating standardized datasets, creating explainable AI algorithms, and giving multidisciplinary cooperation top priority. Future clinical studies should evaluate the practical performance, safety, and efficacy of AI-guided nanomedicine platforms. AI optimization for the design and functionalization of nanomaterials can enhance their medicinal efficacy and biocompatibility. By facilitating smooth clinical translation and commercialization, regulatory frameworks and guidelines can help close the gap between precision oncology laboratory innovation and patient-centered treatments [172,197].

Glioblastoma (GBM) patient outcomes are greatly improved by early detection and timely surgical intervention, according to recent clinical evidence. In particular, a better overall survival rate—28.4 months for early intervention groups versus 18.7 months for delayed treatment groups—correlates with a GBM diagnosis made within two weeks of symptom onset and surgery performed within three weeks. Higher Karnofsky performance scores indicate that patients who receive early intervention also typically maintain better functional status [212,213]. Furthermore, compared to conventional histology-based methods, prompt detection of tumor progression or recurrence via advanced imaging or molecular profiling enables more aggressive and customized management strategies, improving clinical outcomes. However, because GBM is aggressive, has a high recurrence rate, and the available treatments are ineffective, the overall prognosis is still bleak [214,215]. Although it does not significantly alter the course of the disease for the majority of patients, early detection enables patients to receive adjuvant and surgical treatments while their neurological condition is better, extending their survival and improving their quality of life. Therefore, the lack of cures for GBM limits the benefits of early detection, even though it enhances outcomes within the current therapeutic framework [212,213].

8. Current Challenges and Future Perspectives

The physical and chemical characteristics of nanoparticles that impact imaging quality, clinical viability, and diagnostic efficacy influence the selection of these particles for imaging. Gold nanoparticles are preferred for their optical and biocompatible qualities, while superparamagnetic iron oxide nanoparticles are necessary for magnetic resonance imaging because of their powerful magnetic characteristics. Other nanoparticles with paramagnetic and photoluminescent properties, such as quantum dots and manganese oxide, are selected. Regulatory approval, circulation time, and toxicity all have an impact on clinical viability. Biocompatibility, targeted delivery, quality, reproducibility, signal attenuation, low penetration depth, and separating signals from background noise are among the difficulties; clinical implementation is further complicated by regulatory barriers [213,214,215,216,217,218].

Large-scale multi-omics data integration is anticipated to be greatly improved by AI, opening up new avenues for drug development, personalized medicine, and disease etiology research. In order to address current computational and privacy issues, future developments will concentrate on federated learning, edge computing, and quantum AI. The combination of edge AI and omics research also enables real-time processing on portable diagnostics and sequencing platforms, which speeds up and lowers the cost of genomic analysis, especially in healthcare settings with limited resources [201,219].

Quantum AI is projected to revolutionize large-scale omics data processing by overcoming computational limitations in multi-omics integration, protein structure prediction, and genomic variant interpretation. Quantum-enhanced deep learning models are expected to significantly accelerate complex biological computations, thereby advancing genomic research. Additionally, AI-driven data harmonization will facilitate the integration of diverse datasets from multiple cohorts, enhancing collaboration in research. The standardization of AI-powered data pipelines will ensure replicable and clinically actionable multi-omics insights, promoting interoperability across different omics platforms [220,221,222].

Inaccurate model representation can result from an uneven distribution of data in medical AI. Methods like transfer learning and synthetic data generation are suggested to improve accuracy. Techniques like cross-institutional data collection and reinforcement learning are used to improve generalization. To lessen bias in training datasets, future studies should concentrate on interpretability, data privacy, and ethical issues. Training on a variety of datasets, worldwide validation, and real-world data testing are necessary to guarantee the robustness of AI diagnostic models. Standardized reporting, explainable AI, federated learning, and continuous monitoring to spot biases and performance problems are some examples of evaluation protocols [223,224,225].

Traditional diagnostic techniques still require specialized equipment and trained personnel, even with developments in microfluidics, nanotechnology, and artificial intelligence. This might do away with the necessity for centralized labs and pathologists. Cost-effectiveness, limited processing power, data protection, and regulatory approval are some of the difficulties with nano-AI proof-of-concept tools. Particularly in environments with limited infrastructure, interdisciplinary collaboration is required to develop affordable instruments for individualized treatment and speedy diagnostics. To fully maximize this potential, more innovation and validation are required [226].

9. Conclusions

The commercialization of nanoparticles faces FDA approval challenges due to time-consuming and labor-intensive processes. Artificial intelligence and computer-aided nanoparticle design have been significant in clinical practice. Although nanoparticles have many advantages, problems such as a lack of sophisticated equipment, safety concerns, and material limitations must be resolved. When producing nanoparticles, factors including cost, efficacy, biocompatibility, propensity to cross biological barriers, and physiological stability should be taken into account. The combination of artificial intelligence (AI) with multi-omics research has transformed precision medicine by facilitating thorough data analysis, the identification of biomarkers, and genomics-driven treatment approaches. These advancements have improved early disease diagnosis, individualized treatment planning, and drug discovery. Notable innovations include AI-powered genomic variant interpretation using deep learning to detect disease-linked mutations more accurately and machine learning-based biomarker discovery that enhances predictive and prognostic capabilities for targeted therapies. AI also facilitates systems-level understanding through multi-omics network analysis, refining risk prediction and patient stratification. Despite these strides, challenges remain in clinical translation, data harmonization, ethical concerns, and the transparency of AI models. AI’s capacity to manage vast, heterogeneous datasets enables superior predictive modeling and therapeutic targeting, particularly in complex diseases such as cancer, neurodegenerative, and metabolic disorders. Nevertheless, ensuring model interpretability, reproducibility, and fairness across diverse populations is crucial for widespread adoption in clinical practice. The responsible use of AI in healthcare requires global regulatory standards and ethical frameworks, which require collaboration among researchers, regulatory bodies, and industry stakeholders. Policymakers must be proactive, adaptable, and able to predict developments, regulate with foresight, and modify policies as needed. Ultimately, AI-powered nanotechnology research represents a transformative force in healthcare, fostering interdisciplinary collaboration and catalyzing advancements in precision medicine that promise global patient benefits.