Search Results (542)

Gliomas are notoriously difficult to treat owing to their pronounced heterogeneity and highly variable treatment responses. This reality drives the development of precise diagnostic and prognostic methods. This review explores the modern arsenal of bioinformatic tools aimed at refining diagnosis and stratifying glioma patients by different malignancy grades and types. We perform a comparative analysis of software solutions for processing whole-exome sequencing data, analyzing DNA methylation profiles, and interpreting transcriptomic data, highlighting their key advantages and limited applicability in routine clinical practice. Special emphasis is placed on the contribution of bioinformatics to fundamental oncology, as these tools aid in the discovery of new biomarker genes and potential targets for targeted therapy. The ninth section discusses the role of computational models in predicting immunotherapy efficacy. It demonstrates how integrative data analysis—including tumor mutational burden assessment, characterization of the tumor immune microenvironment, and neoantigen identification—can help identify patients who are most likely to respond to immune checkpoint inhibitors and other immunotherapeutic approaches. The obtained data provide compelling justification for including immunotherapy in standard glioma treatment protocols, provided that candidates are accurately selected based on comprehensive bioinformatic analysis. The tools discussed pave the way for transitioning from an empirical to a personalized approach in glioma patient management. However, we also note that this field remains largely in the preclinical research stage and has not yet revolutionized clinical practice. This review is intended for biological scientists and clinicians who find traditional bioinformatic tools difficult to use. Full article

Background and Objectives: Radiomics and artificial intelligence (AI) offer emerging quantitative tools for enhancing the diagnostic evaluation of testicular cancer. Conventional imaging—ultrasound (US), magnetic resonance imaging (MRI), and computed tomography (CT)—remains central to management but has limited ability to characterize tumor subtypes, detect occult nodal disease, or differentiate necrosis, teratoma, and viable tumor in post-chemotherapy residual masses. This systematic review summarizes current advances in radiomics and AI for both primary tumors and retroperitoneal disease. Materials and Methods: A systematic search of PubMed, Scopus, and Web of Science identified studies applying radiomics or AI to testicular tumors, retroperitoneal lymph nodes and post-chemotherapy residual masses. Eligible studies included quantitative imaging analyses performed on ultrasound, MRI, and CT, with optional integration of clinical or molecular biomarkers. Eighteen studies met inclusion criteria and were evaluated with respect to methodological design, diagnostic performance, and translational readiness. Results: Across modalities, radiomics demonstrated encouraging discriminatory capacity, with accuracies of 74–82% for ultrasound, 80.7–97.9% for MRI, and 71.7–85.3% for CT. CT-based radiomics for post-chemotherapy residual masses showed moderate ability to distinguish necrosis/fibrosis, teratoma, and viable germ-cell tumor, though heterogeneous methodologies and limited external validation constrained generalizability. The strongest performance was observed in multimodal approaches: integrating radiomics with clinical variables or circulating microRNAs improved accuracy by up to 12% and 15%, respectively, mirroring gains reported in other oncologic radiomics applications. Persisting variability in segmentation practices, acquisition protocols, feature extraction, and machine-learning methods highlights ongoing barriers to reproducibility. Conclusions: Radiomics and AI-enhanced frameworks represent promising adjuncts for improving the noninvasive evaluation of testicular cancer, particularly when combined with clinical or molecular biomarkers. Future progress will depend on standardized imaging protocols, harmonized radiomics pipelines, and multicenter prospective validation. With continued methodological refinement and clinical integration, radiomics may support more precise risk stratification and reduce unnecessary interventions in testicular cancer. Full article

Brain tumors, particularly glioblastoma, remain among the most lethal cancers, with limited survival benefits from current genetic and molecular-targeted approaches. Emerging evidence reveals that beyond oncogenes and mutations, neuronal plasticity, long-term potentiation, synaptic remodeling, and neurotransmitter-driven signaling play a pivotal role in shaping tumor progression and therapeutic response. This convergence of neuroscience and oncology has given rise to the field of cancer neuroscience, which explores the bidirectional interactions between neurons and malignant cells. In this review, we summarize fundamental principles of neuronal plasticity, contrasting physiological roles with pathological reprogramming in brain tumors. We highlight how tumor cells exploit synaptic input, particularly glutamatergic signaling, to enhance proliferation, invasion, and integration into neural circuits. We further discuss how neuronal-driven feedback loops contribute to therapy resistance, including chemoresistance, radioresistance, and immune evasion, mediated through pathways such as mitogen-activated protein kinase (MAPK), phosphoinositide 3-kinase/protein kinase B (PI3K/AKT), and calcium influx. The tumor microenvironment, including astrocytes, microglia, and oligodendrocyte-lineage cells, emerges as an active participant in reinforcing this neuron-tumor ecosystem. Finally, this review explores therapeutic opportunities targeting neuronal plasticity, spanning pharmacological interventions, neuromodulation approaches (transcranial magnetic stimulation (TMS), deep brain stimulation (DBS), optogenetics), and computational/artificial intelligence frameworks that model neuron tumor networks to predict personalized therapy. Also, we propose future directions integrating connect omics, neuroinformatics, and brain organoid models to refine translational strategies. Full article

Immunohistochemistry (IHC) is essential for diagnostic, prognostic, and predictive biomarker assessment in oncology, but manual interpretation is limited by subjectivity and inter-observer variability. Machine learning (ML), a computational subset of AI that allows algorithms to recognise patterns and learn from annotated datasets to make predictions or decisions, has led to advancements in digital pathology by supporting automated quantification of biomarker expression on whole-slide images (WSIs). This review evaluates the role of ML-assisted IHC scoring in the transition from validated biomarkers to the discovery of emerging prognostic and predictive IHC biomarkers for genitourinary (GU) tumours. Current applications include ML-based scoring of routinely used biomarkers such as ER/PR, HER2, mismatch repair (MMR) proteins, PD-L1, and Ki-67, demonstrating improved consistency and scalability. Emerging studies in GU cancers show that algorithms can quantify markers including androgen receptor (AR), PTEN, cytokeratins, Uroplakin II, Nectin-4 and immune checkpoint proteins, with early evidence indicating associations between ML-derived metrics and clinical outcomes. Important limitations remain, including limited availability of training datasets, variability in staining protocols, and regulatory challenges. Overall, ML-assisted IHC scoring is a reproducible and evolving approach that may support biomarker discovery and enhance precision GU oncology. Full article

This work presents the development of an Artificial Intelligence (AI)-based pipeline for patient-specific three-dimensional (3D) reconstruction from oncological magnetic resonance imaging (MRI), leveraging image-derived information to enhance the analysis process. These developments were carried out within the framework of Cella Medical Solutions, forming part of a broader initiative to improve and optimize the company’s medical-image processing pipeline. The system integrates automatic MRI sequence classification using a ResNet-based architecture and segmentation of anatomical structures with a modular nnU-Net v2 framework. The classification stage achieved over 90% accuracy and showed improved segmentation performance over prior state-of-the-art pipelines, particularly for contrast-sensitive anatomies such as the hepatic vasculature and pancreas, where dedicated vascular networks showed Dice score differences of approximately 20–22%, and for musculoskeletal structures, where the model outperformed specialized networks in several elements. In terms of computational efficiency, the complete processing of a full MRI case, including sequence classification and segmentation, required approximately four minutes on the target hardware. The integration of sequence-aware information allows for a more comprehensive understanding of MRI signals, leading to more accurate delineations than approaches without such differentiation. From a clinical perspective, the proposed method has the potential to be integrated into surgical planning workflows. The segmentation outputs were converted into a patient-specific 3D model, which was subsequently integrated into Cella’s surgical planner as a proof of concept. This process illustrates the transition from voxel-wise anatomical labels to a fully navigable 3D reconstruction, representing a step toward more robust and personalized AI-driven medical-image analysis workflows that leverage sequence-aware information for enhanced clinical utility. Full article

Background/Objectives: Contrast-induced nephropathy (CIN) is a common iatrogenic or medically induced condition among patients who receive intravenous infusion of iodinated contrast media that can cause renal insufficiency, raise the cost of care, and increase mortality risk. This study evaluated the incidence of CIN and predictors of renal function among cancer patients receiving contrast-enhanced computed tomography (CECT). Methods: A prospective, single-center longitudinal study was conducted at King Abdul-Aziz Medical City’s (Jeddah) medical imaging department from December 2021 to December 2023. Convenience sampling was used to select patients who were exposed to CECT based on data filled in the electronic medical record during the study period. Results: The final sample constituted 80 patients (47.71% attrition, mean age = 55.5 years, 58.75% male). The high attrition rate was associated with participants with incomplete records, those who were lost to follow-up, and those whose follow-up Scr was collected after 72 h from CECT administration. There was no statistically significant change in Scr following contrast exposure (mean increase 0.9 µmol/L; paired t = 1.41, p = 0.162; Wilcoxon p = 0.326). The incidence of CIN was 3.75% (3 of 80 patients; 95% confidence intervals (CI), 1.28–10.39%). Regression analysis showed no statistically significant associations between the percentage change in Scr and age, sex, baseline creatinine, or eGFR category (model R² = 0.07). No clinically meaningful predictors of CIN were identified. Conclusions: The incidence of CIN in this study’s cohort of low-risk cancer patients undergoing CECT was low, and contrast exposure did not produce significant short-term changes in renal function. These findings support the safety of modern contrast agents in oncology imaging, but multi-center studies with larger samples and more robust methods are warranted to refine CIN risk assessment in cancer patients undergoing CECT. Full article

Recent advances in oncology have contributed to a steady rise in cancer survivorship. However, many cancer therapies are associated with cardiovascular adverse events, leading to increased rates of cardiovascular morbidity and mortality. As a result, cardio-oncology has emerged as a rapidly advancing discipline that relies on multidisciplinary collaboration. Cardiovascular multimodality imaging (CVMI) is an essential diagnostic and surveillance tool for cardiovascular toxicity, along with clinical evaluation and biomarkers. CVMI plays a central role in diagnosing cancer therapy-related cardiac dysfunction (CTRCD) and myocarditis, while also supporting the assessment of vascular toxicity and arrhythmias. It is essential for baseline cardiac evaluation and continuous monitoring throughout and following cancer therapy. CVMI enables early detection of cardiovascular toxicity, facilitating prompt initiation of cardioprotective therapy and allowing cancer therapy to proceed without compromising safety. Echocardiography is the primary imaging modality for screening, diagnosing, and monitoring CTRCD. Moreover, it is the first-line imaging test for cardiac structural and functional assessment in patients who develop immune checkpoint inhibitor (ICI)-related myocarditis. Advanced imaging techniques, such as cardiac magnetic resonance (CMR), nuclear imaging, and cardiac computed tomography, may help determine the cause and severity of left ventricular dysfunction, as well as assess cardiac masses and vascular toxicity. Not least, CMR is the gold standard imaging modality to diagnose myocarditis. This article is a narrative review that focuses on the various modalities of CVMI and their applications in cardio-oncology. Since the issue addressed is very extensive, this review was designed to be concise. Full article

Background: As pediatric head and neck cancer (pHNC) incidence increases, the development of new surgical oncology techniques to reduce morbidity are essential. Intraoperative navigation (iNav) represents the most translatable technology among both the model-comparative and integrative surgical navigation technologies to optimize surgical outcomes. Methods: A scoping review of the literature was performed according to PRISMA guidelines from 1970 to present (February 2025), investigating the use of iNav in cases of pHNC. Patient case details and authors’ perception of iNav’s utility were analyzed. A single-center retrospective case series review (September 2022 to September 2025) of the senior authors’ experience employing iNav in pHNC cases was also performed. Results: The scoping review identified twenty-seven cases of pHNC from sixteen studies that both utilized iNav and met the inclusion criteria. Many of the authors commented favorably on the utility of iNav technology, while concurrently agreeing upon its limitations. The case series review identified five cases of pHNC that met the inclusion criteria. This small case series revealed a 100% R0 resection rate with the use of iNav in four pHNC resections. The fifth case used iNav for biopsy site selection. Conclusions: The results of our scoping review as well as our institutional experience with this technology demonstrate its utility in guiding surgical approach, confirming depth of resection, and navigating marginal assessment. This study was limited by incidental and incomplete reporting of iNav’s clinical application to pHNC; several extensive institutional reports had to be excluded due to insufficiently detailed data linkage. Our review builds upon the existing pediatric surgical literature, anchoring the evidentiary justification for the application of iNav to pediatric head and neck surgery. Full article

Early and reliable identification of vertebral metastases on computed tomography remains a major challenge in oncologic imaging due to the morphological complexity of metastatic lesions and the high inter-patient variability of spinal anatomy. In this study, an end-to-end interpretable radiomic-based framework was developed to automatically distinguish healthy from metastatic vertebrae using segmented DICOM data, coupled with an interactive virtual reality (VR) visualization module implemented in Unity 3D. The proposed framework integrates radiomic feature extraction and selection, informed undersampling to address class imbalance, and automatic machine learning-based classification. To facilitate interpretation, patient-specific 3D models with overlapped classifier outputs were integrated into a VR desktop application, enabling advanced exploration of patient-specific spinal models, with color-coded visualization of algorithmic predictions and expert-defined suspicious lesions. The final classification model, trained using a Random Forest algorithm and optimized via stratified 5-fold cross-validation, achieved an overall accuracy of 0.86, an Area Under the Receiver Operating Characteristic Curve of 0.91, and an F1-score of 0.81 for the metastatic class on the independent test set, achieving competitive diagnostic performance while preserving transparency and clinical interpretability. This study represents a foundational step toward intelligent, interactive, and clinically interpretable tools for the diagnosis and follow-up of spinal metastatic disease. Full article

Developing effective CAR-T cell therapy for solid tumours remains challenging because of biological barriers such as antigen escape and an immunosuppressive microenvironment. The aim of this study is to develop a mathematical model of the spatio-temporal dynamics of tumour processes in order to assess key factors that limit treatment efficacy. We propose a reaction–diffusion model described by a system of partial differential equations for the densities of tumour cells and CAR-T cells, the concentration of immune inhibitors, and the degree of antigen escape. The methods of investigation include stability analysis and numerical solution of the model using a finite-difference scheme. The simulations show that antigen escape produces a resistant tumour core and relapse after an initial regression; increasing the escape rate from $\gamma =0.001$ to $0.1$ increases the final tumour volume at $t=100$ days from approximately 35.3 a.u. to 36.2 a.u. Parameter mapping further indicates that for $\gamma \le 0.01$ tumour control can be achieved at moderate killing rates ($k_{CT}\approx 1{\mathrm{day}}^{-1}$), whereas for $\gamma \ge 0.05$ comparable control requires $k_{CT}\approx 2$–$5{\mathrm{day}}^{-1}$. Repeated CAR-T administration improves durability: the residual normalised tumour volume at $t=100$ days decreases from approximately 4.5 after a single infusion to approximately 0.9 (double) and approximately 0.5 (triple), with a saturating benefit for further intensification. We conclude that the proposed model is a valuable tool for analysing and optimising CAR-T therapy protocols, and that our results highlight the need for combined strategies aimed at overcoming antigen escape. Full article

Objective: The aim of this study was to evaluate whether intrauterine administration of autologous peripheral blood mononuclear cells (PBMCs) activated with interferon tau (IFN-τ) before embryo transfer improves implantation and pregnancy outcomes in IVF patients. Methods: This single-center, prospective, randomized, controlled trial was conducted at Nadezhda Women’s Health Hospital (Approval No.: 6/28022023). The study was registered at ClinicalTrials.gov (NCT05775198). Randomization was computer-generated with allocation concealed via sealed envelopes. Participants and statisticians were blinded to group assignment; clinicians were not. Women aged 21–50 undergoing frozen–thawed embryo transfer with euploid embryos were included. Exclusion criteria included uterine anomalies, autoimmune, oncologic conditions, infections, or use of immunosuppressants. Participants (n = 340) were randomized 1:1 to receive either intrauterine infusion of autologous PBMCs activated in vitro with IFN-τ or standard IVF care without PBMC treatment. PBMCs were cultured with recombinant IFN-τ, washed, and infused 24 h prior to single euploid blastocyst transfer. A total of 14 patients were excluded from analysis because of early dropout, leaving 326 (n = 167; n = 159) patients for modified intention-to-treat analysis. Primary outcomes included implantation rate (elevated urinary or blood hCG), clinical pregnancy (fetal heartbeat at 6–8 weeks), and live birth rates. Miscarriage rate and safety were secondary objectives. Patients were followed up until 6 weeks post pregnancy resolution. Results: In the intervention group, 38.3% of patients achieved implantation, compared to 27.7% in the controls (OR 1.6, 95% CI: 1.0–2.6, p = 0.04). Live birth rates were also significantly higher in the IFN-τ-modulated PBMC group (28.7% vs. 17.6%, OR 1.9, 95% CI: 1.1–3.2; p = 0.02). While the clinical pregnancy rate was higher, it did not reach statistical significance (34.7% vs. 25.8%, p = 0.08). There was no difference between the groups in terms of miscarriage (p = 0.4). No serious adverse events were reported after treatment, during pregnancy or in the postnatal period. Conclusions: Intrauterine treatment with IFN-τ-activated PBMCs before ET significantly improves implantation and live birth rates in IVF patients. Full article

Bladder cancer represents a significant health problem worldwide, with it being a major cause of death and characterized by frequent recurrences. Effective treatment hinges on early and accurate diagnosis; however, traditional methods are invasive, time-consuming, and subjective. In this research, we propose a transparent deep learning model based on the YOLOv11 structure to not only enhance lesion detection but also give the visual support of the model’s predictions. Five versions of YOLOv11—nano, small, medium, large, and extra large—were trained and tested by us on a comprehensive dataset of hematoxylin and eosin-stained histopathology slides with the inflammation, urothelial cell carcinoma (UCC), and invalid tissue categories. The YOLOv11-large variant turned out to be the best-performing model at the forefront of technology, with an accuracy of 97.09%, precision and recall of 95.47% each, and balanced accuracy of 96.60%. Besides the precision–recall curves (AUPRC: inflammation = 0.935, invalid = 0.852, UCC = 0.958), ROC-AUC curves (overall AUC = 0.972) and risk–coverage analysis (AUC = 0.994) were also used for detailed assessment of the model to confirm its steadiness and trustworthiness. The confusion matrix displayed the highest true positive rates in all classes and a few misclassifications, which mainly happened between inflammation and invalid samples, indicating a possible morphological overlap. Moreover, as supported by a low Expected Calibration Error (ECE), the model was in great calibration. YOLOv11 reaches higher performance while still being computationally efficient by incorporating advanced architectural features like the C3k2 block and C2PSA spatial attention module. This is a step towards the realization of the AI-assisted bladder cancer diagnostic system that is not only reliable and transparent but also scalable, presented in this work. Full article

Objectives: Primary objective was to report the feasibility, safety and efficacy of percutaneous ablation of hepatic malignant tumors that are undetectable or inconspicuous in non-enhanced computed tomography (CT) scans using an electromagnetic navigation system with a marker option. Secondary objectives included the evaluation of technical parameters including the accuracy of needle placement, the number of control CT acquisitions, and procedural duration. Methods: This prospective study (performed from 1 March 2022 until 30 November 2024) included all patients with hepatic tumors (not visible or poorly defined on non-enhanced CT) who underwent percutaneous microwave ablation (MWA). Technical efficacy was assessed with contrast-enhanced CT immediately post-ablation, and oncologic outcomes (overall and progression-free survival) were evaluated with MRI at 1, 3, and 6 months. Results: Fifteen patients (12 males, 3 females; mean age of 66 years) with 16 tumors (median diameter of 15 mm) were treated in 16 sessions. Tumor types included hepatocellular carcinoma (n = 7), colorectal metastasis (n = 4), ocular melanoma (n = 1), neuroendocrine tumor (n = 1), intrahepatic cholangiocarcinoma (n = 1), and breast cancer metastasis (n = 1). Median procedure time was 53 min, scans number was nine, needle length was 12 cm, and median deviation was 1 mm. No complications were reported. Primary efficacy rate was 94% (15/16), rising to a secondary (assisted) technique efficacy of 100% after re-ablation (one session). During median follow-up of 23 months, local tumor progression-free survival was 100%; distant progression-free survival was 80%, and two patients (13.3%) died, one being cancer-related. Conclusions: Electromagnetic navigation with a marker option enables safe, accurate, and effective MWA of inconspicuous hepatic tumors, achieving excellent local control with favorable oncologic outcomes. Full article

Background/Objectives: The sella turcica (ST) is a central craniofacial and endocrinological landmark whose morphology reflects both local skeletal development and systemic influences. Alterations in its form have been observed in various genetic syndromes, including trisomy 21 (Down syndrome, DS). Considering the characteristic craniofacial morphology of DS, this review aimed to evaluate whether individuals with DS present distinctive morphometric features and shape variants of the ST compared with non-syndromic populations and to discuss their diagnostic and clinical relevance. Methods: A systematic literature search was carried out in PubMed, the Cochrane Library, Web of Science, Wiley, MDPI, and Google Scholar on 8 May 2024. Search terms included “sella turcica,” “Down syndrome,” and “morphology.” Studies employing lateral cephalograms, cone-beam computed tomography (CBCT), or computed tomography (CT) to assess ST morphology were included when quantitative or qualitative comparisons with control groups were available. The review followed the PRISMA 2020 guidelines and was prospectively registered in PROSPERO (CRD42024580071). Results: Only six studies fulfilled the inclusion criteria. Increased ST dimensions and a predominance of U-shaped and J-shaped variants in individuals with DS compared with controls were most frequently reported. Although the studies differed in methodology, the findings consistently indicated characteristic enlargement and remodeling of the ST in trisomy 21. Conclusions: Individuals with Down syndrome exhibit distinctive sella turcica morphology characterized by increased size and specific shape variants. The evidence base remains small and heterogeneous, with few observational studies and mixed age groups and imaging modalities, which limits the strength and generalizability of the conclusions. The present study aims to provide a modern, updated systematic review of current evidence on sella turcica morphology in patients with Down syndrome, to identify reported patterns of variation, and to explore their clinical and diagnostic significance. Recognition of these features enhances diagnostic accuracy in craniofacial evaluation, facilitates comprehensive orthodontic, endocrine, and oncological assessment, and advances understanding of cranial base development within the context of genetic syndromes. Full article

The integration of molecular features into histopathological diagnoses has become central to the World Health Organization (WHO) classification of central nervous system (CNS) tumors, improving prognostic accuracy and supporting precision medicine. However, unequal access to molecular testing limits the universal application of integrated diagnosis. To address this, artificial intelligence (AI) models are being developed to predict molecular alterations directly from histological data. In gliomas, deep learning applied to whole-slide images (WSIs) of permanent sections achieves neuropathologist-level accuracy in predicting biomarkers such as IDH mutation and 1p/19q co-deletion, as well as in molecular subtype classification and outcome prediction. Recent advances extend these approaches to intraoperative cryosections, enabling real-time glioma grading, molecular prediction, and label-free tissue analysis using modalities such as stimulated Raman histology and domain-adaptive image translation. Beyond gliomas, AI-powered histology is being explored in other brain tumors, including morphology-based molecular classification of spinal cord ependymomas and intraoperative discrimination of gliomas from primary CNS lymphomas. This review summarizes current progress in AI-assisted molecular profiling prediction of brain tumors from tissue, highlighting opportunities for rapid, accurate, and globally accessible diagnostics. The integration of histology and computational methods holds promise for the development of smart AI-assisted neuro-oncology. Full article