Search Results (853)

In precision oncology research, achieving joint modeling of tumor grading and treatment response, together with interpretable mechanism analysis, based on multimodal medical imaging and clinical data remains a challenging and critical problem. From a sensing perspective, these imaging and clinical data can be regarded as heterogeneous sensor-derived signals acquired by medical imaging sensors and clinical monitoring systems, providing continuous and structured observations of tumor characteristics and patient states. Existing approaches typically rely on invasive pathological grading, while grading prediction and treatment response modeling are often conducted independently. Moreover, multimodal fusion procedures generally lack explicit structural constraints, which limits their practical utility in clinical decision-making. To address these issues, a grade-guided multimodal collaborative modeling framework was proposed. Built upon mature deep learning models, including 3D ResNet-18, MLP, and CNN–Transformer, tumor grading was incorporated as a weakly supervised prior into the processes of multimodal feature fusion and treatment response modeling, thereby enabling an integrated solution for non-invasive grading prediction, treatment response subtype discovery, and intrinsic mechanism interpretation. Through a grade-guided feature fusion mechanism, discriminative information that is highly correlated with tumor malignancy and treatment sensitivity is emphasized in the multimodal joint representation, while irrelevant features are suppressed to prevent interference with model learning. Within a unified framework, grading prediction and grade-conditioned treatment response modeling are jointly realized. Experimental results on real-world clinical datasets demonstrate that the proposed method achieved an accuracy of $84.6\%$ and a kappa coefficient of $0.81$ in the tumor-grading prediction task, indicating a high level of consistency with pathological grading. In the treatment response prediction task, the proposed model attained an AUC of $0.85$, a precision of $0.81$, and a recall of $0.79$, significantly outperforming single-modality models, conventional early-fusion models, and multimodal CNN–Transformer models without grading constraints. In addition, treatment-sensitive and treatment-resistant subtypes identified under grading conditions exhibited stable and significant stratification differences in clustering consistency and survival analysis, validating the potential value of the proposed approach for clinical risk assessment and individualized treatment decision-making. Full article

Background and Objectives: The introduction of total neoadjuvant therapy (TNT) in the preoperative stage has been associated with improved oncological outcomes. However, TNT may lead to tissue fibrosis and be accompanied by increased difficulty during surgery. Additionally, predicting tumor response to neoadjuvant therapy is crucial for identifying patients who may achieve a complete pathological response (pCR) or qualify for organ-preserving strategies. The aim of this study is to evaluate the effect of TNT versus conventional chemoradiotherapy (CRT) on tumor regression grade (TRG) and the association between preoperative carcinoembryonic antigen (CEA) levels and good tumor response. A secondary endpoint is to investigate the effect of TNT on surgical difficulty, using indirect indicators like the quality of total mesorectal excision (TME), circumferential resection margin (CRM), and achievement of R0 resection. Materials and Methods: This is a retrospective, single-center study including 93 patients with locally advanced rectal cancer who received either TNT (n = 43) or CRT (n = 50). Results: The TNT group, compared to the CRT group, demonstrated a significantly higher rate of pCR (TRG0) (37.2% vs. 18%, p = 0.038) and good tumor regression (TRG 0–1) (53.5% vs. 28%, p = 0.019). Furthermore, patients with CEA < 5 ng/mL showed significantly higher rates of good tumor response (TRG 0–1) compared to those with CEA ≥ 5 ng/mL (45.3% vs. 16.7%, p = 0.032). When further categorized by treatment type, CEA levels did not demonstrate statistically significant differences Lastly, increased surgical difficulty could not be established, as no significant differences were observed in terms of positive CRM rates, R0 resection, and TME quality between groups. Conclusions: TNT was associated with improved TRG scores compared to CRT without increasing surgical difficulty. Lower pre-treatment CEAs were linked to better tumor response, irrespective of the type of treatment. These findings support the oncological benefit of TNT and suggest that CEA may have some predictive value for treatment response. Full article

Meningiomas are the most common primary intracranial tumors, showing highly heterogeneous behavior and clinical outcomes. While the majority are benign, about one in five meningiomas are classified as higher grade (WHO Grade II–III), characterized by a more aggressive, treatment-resistant pathology. Although surgical resection remains the first-line therapy, peptide receptor radionuclide therapy is emerging as a novel and promising option for advanced, multifocal, or recurrent disease. The theranostic paradigm allows simultaneous detection and treatment of somatostatin receptor-expressing lesions using a single radiopharmaceutical. In this review, we explore the evolving role of PRRT in the management of meningiomas. We provide an integrated overview of preclinical findings—including radiosensitization mechanisms—and summarize the rapidly expanding clinical literature, which in recent years has grown both in patient numbers and in methodological sophistication. Particular emphasis is placed on advances in dosimetry, quantitative imaging, and radiomics, which are beginning to refine patient selection and improve response prediction. Together, current evidence highlights the therapeutic potential of radionuclide therapy in aggressive or refractory meningiomas and underscores the need for further prospective trials to define its optimal clinical application. Full article

Background and Objectives: Aggressive cancer development is characterized by rapid tumor growth and progressive immune dysfunction. Tumor-derived microRNAs (miRNAs) emerge as master regulators of both malignant transformation and immune evasion, making them promising therapeutic targets. Using the highly aggressive CT-26 peritoneal adenomatosis model, this study explored the potential of selective miRNA inhibition to simultaneously suppress tumor growth and overcome immunosuppression. Methods and Results: Our results revealed that inhibition of miR-155, miR-21, and miR-17 by methylsulfonyl phosphoramidate (mesyl) oligonucleotides exhibited markedly different therapeutic profiles. miR-155 inhibition demonstrated minimal efficacy. miR-21 suppression provided early tumor regression and prevented cancer-associated thymic atrophy, translating into extended survival. miR-17 inhibition displayed delayed but superior tumor growth inhibition, significantly reducing pathologically elevated polymorphonuclear myeloid-derived suppressor cell (MDSC) populations, and nearly doubled animal lifespan. Combination therapy targeting all three miRNAs integrated these complementary mechanisms, maintaining consistent anti-tumor efficacy across early and late stages while providing thymic protection and MDSC reduction. Importantly, therapeutic responses in vivo substantially exceeded predictions based on in vitro tumor cell proliferation and motility measurements, revealing critical contributions of systemic immunomodulation. Conclusions: These findings demonstrate that miRNA inhibition reshapes tumor–immune interactions, positioning anti-miRNA therapeutics as immunomodulatory agents for effective colorectal cancer treatment. Full article

Background and Objectives: This study aimed to identify clinicopathological factors associated with axillary pathological complete response (ApCR) in patients with HER2-positive breast cancer presenting with clinically node-positive disease (cN+) confirmed by biopsy who received neoadjuvant therapy (NAT), and to assess the prognostic significance of ApCR on survival outcomes. Materials and Methods: A total of 221 patients with clinically node-positive (cN+) HER2-positive invasive breast cancer, with nodal involvement confirmed by fine-needle aspiration or core needle biopsy, who received neoadjuvant therapy (NAT) and subsequently underwent surgery at three centers between January 2015 and January 2025 were retrospectively reviewed. The association between clinicopathological factors and axillary pathological complete response (ApCR) was analyzed using logistic regression. Survival analyses were performed using the Kaplan–Meier method. Results: The median follow-up duration was 34.3 months. Axillary pathological complete response (ApCR) was achieved in 67.9% of patients. The ApCR rate was higher in stage II disease compared with stage III (76.9% vs. 62.9%). Patients with HER2 3+ tumors demonstrated a higher ApCR rate (70.8%) than those with HER2 2+/FISH+ tumors (46.2%). In multivariable logistic regression, HER2 3+ status (OR = 2.745; 95% CI: 1.138–6.619; p = 0.025) and lower clinical stage (OR = 2.251; 95% CI: 1.182–4.287; p = 0.014) were independently associated with a higher likelihood of achieving ApCR. In survival analyses, the 3-year event-free survival rate was 92% (95% CI: 86–98%) in the ApCR group, compared with 75% (95% CI: 63–87%) in the non-ApCR group. Kaplan–Meier analysis demonstrated that ApCR was a significant prognostic factor for EFS (p = 0.001). Median overall survival (OS) was not reached in either group due to the limited number of death events. Conclusions: ApCR was frequent in node-positive HER2-positive breast cancer after neoadjuvant therapy. HER2 3+ status and lower clinical stage independently predicted ApCR, which in turn was associated with improved event-free survival. These findings underscore the prognostic relevance of ApCR in this setting. Full article

Background: The prognostic value of treatment-induced necrosis in soft STS remains uncertain. This study evaluated whether MRI-based changes in necrosis (Δ necrosis) between pre- and post-neoadjuvant chemotherapy scans correlate with pathologic necrosis and clinical outcomes. Methods: In this retrospective cohort, 27 patients with STS who received neoadjuvant chemotherapy and underwent pre- and post-treatment MRI were analyzed. Necrosis was graded categorically (<5%, 5–25%, 25–50%, 50–75%, 75–95%, and >95%), and Δ necrosis was calculated as the change in estimated necrosis between scans. Correlations between MRI-derived and pathologic necrosis were assessed using Spearman’s rank coefficient. Survival analyses (progression-free, local recurrence-free, and disease-specific overall survival) were performed using Kaplan–Meier and log-rank tests. Results: Post-treatment MRI necrosis moderately correlated with pathologic necrosis (ρ = 0.44, p = 0.028), whereas Δ necrosis showed a weaker, nonsignificant correlation (ρ = 0.24, p = 0.24). Neither MRI-based nor pathologic necrosis thresholds were associated with survival outcomes. Conclusions: MRI-based Δ necrosis did not predict pathologic necrosis or oncologic outcomes in STS, suggesting that radiologic changes in necrosis may not serve as reliable markers of therapeutic response. Future studies integrating quantitative imaging and standardized pathology protocols together with future exploration of molecular tools such as ctDNA are needed to refine treatment assessment in STS. Full article

Predicting neoadjuvant chemotherapy response in breast cancer remains critical for optimizing treatment strategies, yet robust predictive biomarkers are lacking. This study implemented an ensemble machine learning approach to identify a gene expression signature predicting pathological complete response (pCR) versus residual disease (RD) using bulk RNA-sequencing data from GSE163882 (138 RD, 80 pCR). We employed TMM normalization with differential expression analysis (250 genes, FDR < 0.05, |log2FC| ≥ 1), ensemble feature selection across five classifiers (Random Forest, Gradient Boosting, SVM, k-NN, and Neural Network) with 10-fold repeated cross-validation, and stacked ensemble development. Consensus selection identified a 17-gene signature consistently ranked across algorithms. The stacked ensemble achieved 0.97 AUC post-testing on hold-out test data. External validation on the independent GSE240671 cohort (37 pCR, 25 RD) following ComBat batch correction achieved ROC AUC of 0.78 and PR AUC of 0.85 with isotonic calibration, demonstrating balanced accuracy of 0.71 and 0.86 sensitivity for pCR detection. Pathway enrichment revealed associations with cell cycle regulation (E2F3, MKI67), DNA repair (BRCA2), and transcriptional control (MED1), with six priority genes (MED1, BRCA2, E2F3, PITPNB, H1-1, and FARP2) showing established breast cancer relevance. This externally validated 17-gene signature provides a biologically grounded tool for NAC response prediction in precision oncology. Full article

Autoantibodies have long been regarded as passive reflections of immune dysregulation in connective tissue diseases (CTDs). Recent advances in systems immunology and molecular pathology have fundamentally redefined them as active molecular fingerprints that delineate distinct disease endophenotypes with predictive power for clinical trajectories and therapeutic responses. Rather than mere epiphenomena, autoantibodies encode precise information about dominant immune pathways, organ tropism, and pathogenic mechanisms. This review synthesizes emerging evidence that autoantibody repertoires—defined by specificity, structural properties, and functional characteristics—stratify patients beyond traditional clinical taxonomy into discrete pathobiological subsets. Specific signatures such as anti-MDA5 in rapidly progressive interstitial lung disease, anti-RNA polymerase III in scleroderma renal crisis, and anti-Ro52/TRIM21 in systemic overlap syndromes illustrate how serological profiles predict outcomes with remarkable precision. Mechanistically, autoantibody pathogenicity is modulated by immunoglobulin isotype distribution, Fc glycosylation patterns, and tissue-specific receptor expression—variables that determine whether an antibody functions as a biomarker or pathogenic effector. The structural heterogeneity of autoantibodies, shaped by cytokine microenvironments and B-cell subset imprinting, creates a dynamic continuum between pro-inflammatory and regulatory states. The integration of serological, transcriptomic, and imaging data establishes a precision medicine framework: autoantibodies function simultaneously as disease classifiers and therapeutic guides. This endophenotype-driven approach is already influencing trial design and patient stratification in systemic lupus erythematosus, systemic sclerosis, and inflammatory myopathies, and is reshaping both clinical practice and scientific taxonomy in CTDs. Recognizing autoantibodies as endophenotypic determinants aligns disease classification with pathogenic mechanism and supports the transition towards immunologically informed therapeutic strategies. Full article

Residual microcalcifications after neoadjuvant chemotherapy (NAC) in breast cancer remain a complex diagnostic and therapeutic challenge. Although NAC has significantly improved pathologic complete response (pCR) rates and transformed surgical approaches, the persistence or evolution of microcalcifications may not accurately reflect residual disease. This discrepancy complicates radiologic interpretation, impacts surgical decision-making, and may lead to overtreatment or unnecessary mastectomies. This review synthesizes current evidence on the radiologic–pathologic correlation of post-NAC microcalcifications, their prognostic value, and their relevance to guiding surgical management in contemporary precision oncology. A narrative review of the literature was performed, focusing on imaging evolution after NAC, pathologic correlations, predictive and prognostic implications, and the role of microcalcifications in defining optimal surgical strategies, ranging from breast-conserving surgery to mastectomy. Emerging contributions from digital breast tomosynthesis, contrast-enhanced mammography (CEM), Magnetic Resonance (MR) and radiomics are also examined. Studies consistently demonstrate that residual microcalcifications are often poor predictors of viable tumor tissue after NAC. Up to half of cases with persistent calcifications may reflect minimal or absent residual invasive cancer, whereas calcifications may also persist in areas of treatment-induced necrosis or fibrosis. Reliance on calcifications alone may therefore lead to unnecessary extensive resections. Conversely, specific morphologic patterns, especially fine pleomorphic or branching calcifications, are more strongly associated with residual malignancy. Advanced imaging and radiomics show promise in improving predictive accuracy. Residual microcalcifications after NAC should not be interpreted as a direct surrogate of residual disease. A multimodal assessment integrating imaging evolution, tumor biology, and treatment response is essential to optimize surgical planning and avoid overtreatment. Precision surgery in the NAC era increasingly requires individualized decision-making supported by advanced imaging and robust radiologic–pathologic correlation. Full article

Introduction: In rectal cancer, the choice of treatment strategy depends on the tumor stage and the response to neoadjuvant therapy. Accurate assessment of tumor regression through magnetic resonance imaging (MRI) may help to guide personalized approaches, including delayed or nonoperative management. This study aimed to assess the correlations between pathological complete response (pCR) and clinical staging before and after neoadjuvant treatment in rectal cancer patients. Methods: We conducted a retrospective analysis of rectal cancer patients treated with neoadjuvant therapy followed by radical resection in our oncological surgery department between July 2012 and December 2024. Clinical staging and tumor response were assessed using MRI, focusing on T- and N-stage evaluation. Pathological complete response (pCR) was defined as the absence of tumor cells on histopathological examination. Associations between pCR and clinical variables were explored. Results: Out of a total of 1693 rectal cancer patients, 783 (46.25%) received neoadjuvant therapy, with 62 patients (7.92%) presenting pCR. The majority had tumor stage cT3 (n = 45, 72.6%) and lymph node stage cN2b (n = 25, 40.3%) before treatment. Post-treatment MRI showed complete tumor response (T0) in 20 patients (32.3%) and nodal downstaging to N0 in 34 patients (54.8%). MRI provided imaging findings that indicate a limited correlation between clinical assessment of tumor response and pathological outcome. Six patients (9.6%) developed distant metastases, and there were no local recurrences. Conclusions: While MRI provides valuable preoperative information, its accuracy in predicting pCR remains limited. Achieving pCR is a favorable prognostic indicator, but it does not eliminate the risk of distant metastasis; therefore, continued surveillance and individualized management strategies remain essential to optimize outcomes in rectal cancer patients. Full article

Intravascular imaging (IVI) was introduced 35 years ago to assess coronary artery pathology and plaque vulnerability. However, from its first applications it became apparent that it can also be useful in percutaneous coronary intervention (PCI) planning and optimizing PCI results. In the early days of PCI, IVI was used to examine the efficacy of emerging endovascular devices and the vessel wall response to therapy, while in the drug-eluting stent (DES) era, IVI was used to guide DES implantation and assess final results post-intervention. The first studies assessing the role of IVI in guiding PCI with DES have failed to demonstrate a prognostic benefit for the use of IVI; however, more recent large-scale randomized trials have underscored its value in this setting. IVI, with its high resolution, allows optimal stent sizing, prompt identification and correction of common causes of stent failure, and it has been shown that it improves outcomes in complex procedures. This review summarizes the evidence supporting the role of IVI in PCI planning in DES era, synopsizes the studies that have highlighted the value of IVI in predicting stent failure, discusses the limitations of the first randomized trials that failed to demonstrate a prognostic benefit from its use, and presents the results of the more recent large-scale outcome studies that underscored its role in complex PCI planning. Full article

Uveitis constitutes a heterogeneous group of intraocular inflammatory pathologies, including both infectious and non-infectious aetiologies, often leading to substantial morbidity and permanent loss of vision in up to 20% of the affected cases. Visual impairment is most prominent in intermediate, posterior, or panuveitis and is commonly associated with cystoid macular oedema, epiretinal membranes, macular holes, and retinal detachment. In the context of uveitis, these complications arise as a result of recurrent flare-ups or chronic inflammation, contributing to cumulative ocular damage. Pars plana vitrectomy (PPV) has an evolving role in the diagnostic and therapeutic approach to uveitis. Diagnostic PPV allows for the analysis of vitreous fluid and tissue using techniques such as PCR, flow cytometry, cytology, and cultures, providing further insights into intraocular immune responses. Therapeutic PPV can be employed for the management of structural complications associated with uveitis, in a wide spectrum of inflammatory clinical entities such as Adamantiades–Behçet disease, juvenile idiopathic arthritis, acute retinal necrosis, or ocular toxoplasmosis. Modern small-gauge and minimally invasive techniques improve visual outcomes, reduce intraocular inflammation, and may decrease reliance on systemic immunosuppression. Emerging technologies, including robot-assisted systems, are expected to enhance surgical precision and safety in the future. Despite these advances, PPV outcomes remain variable due to heterogeneity in indications, surgical techniques, and postoperative management. Prospective studies with standardized protocols, detailed subgroup analyses, and the integration of immunological profiling are needed to define which patients benefit most, optimize therapeutic strategies, and establish predictive biomarkers in uveitis management. Full article

Background: Deep brain stimulation (DBS) is increasingly understood as a precision-oriented neuromodulation therapy capable of influencing distributed basal ganglia–thalamo–cortical and cerebellothalamic networks. Although its symptomatic benefits in Parkinson’s disease, essential tremor, and dystonia are well established, the extent to which DBS supports motor learning, adaptive plasticity, and participation in rehabilitation remains insufficiently defined. Traditional interpretations of DBS as a focal or lesion-like intervention are being challenged by electrophysiological and imaging evidence demonstrating multiscale modulation of circuit dynamics. Objectives and methods: DBS may enhance rehabilitation outcomes by stabilizing pathological oscillations and reducing moment-to-moment variability in motor performance, thereby enabling more consistent task execution and more effective physiotherapy, occupational therapy, and speech–language interventions. However, direct comparative evidence demonstrating additive or synergistic effects of DBS combined with rehabilitation remains limited. As a result, this potential is not fully realized in clinical practice due to interindividual variability, limited insight into how individual circuit architecture shapes therapeutic response, and the limited specificity of current connectomic biomarkers for predicting functional gains. Results: Technological advances such as tractography-guided targeting, directional leads, sensing-enabled devices, and adaptive stimulation are expanding opportunities to align neuromodulation with individualized circuit dysfunction. Despite these developments, major conceptual and empirical gaps persist. Few controlled studies directly compare outcomes with versus without structured rehabilitation following DBS. Heterogeneity in therapeutic response and rehabilitation access further complicates the interpretation of outcomes. Clarifying these relationships is essential for developing precision-informed frameworks that integrate DBS with rehabilitative strategies, recognizing that current connectomic and physiological biomarkers remain incompletely validated for predicting functional outcomes. Conclusions: This review synthesizes mechanistic, imaging, and technological evidence to outline a network-informed perspective of DBS as a potential facilitator of rehabilitation-driven functional improvement and identifies priorities for future research aimed at optimizing durable functional restoration. Full article

Background/Objectives: Pathological complete response (pCR) is an established surrogate marker of neoadjuvant chemotherapy (NACT) efficacy in breast cancer; however, reliable predictors of pCR remain limited. Immune–inflammation- and nutrition-based biomarkers derived from routine blood tests may offer accessible tools for early assessments of treatment response. This study aimed to evaluate both baseline values and dynamic (Δ) changes in multiple immune–nutritional indices to determine their predictive performance with regard topCR. Methods: A retrospective analysis was conducted on 236 early breast cancer patients who received neoadjuvant chemotherapy. Pre-treatment (B), post-treatment (A), and Δ values were calculated for the prognostic nutritional index (PNI), advanced lung cancer inflammation index (ALI), hemoglobin–albumin–lymphocyte–platelet (HALP) score, systemic inflammation response index (SIRI), pan-immune–inflammation value (PIIV), global immune–nutrition-information index (GINI), nutritional risk index (NRI), and related biomarkers. Associations with pCR were examined using chi-square testing and univariate logistic regression, and diagnostic performance was assessed through receiver operating characteristic (ROC) analysis. Results: pCR was achieved in 116 patients (49.2%). Logistic regression identified the NRI (OR = 2.336), ΔGINI (OR = 2.323), ALI (OR = 1.318), PNI (OR = 1.365), HALP score (OR = 1.217), ΔSIRI (OR = 2.207), and ΔPIIV (OR = 2.001) as significant predictors. ROC analysis showed that the NRI (AUC = 0.840) and ΔGINI (AUC = 0.807) were the strongest discriminators of pCR. In aLASSO (Least Absolute Shrinkage and Selection Operator)-penalized logistic regression with 10-fold cross-validation, the NRI and ΔGINI emerged as independent predictors of pCR (OR = 1.28 and OR = 1.23, respectively), showing acceptable calibration particularly in the moderate-to-high probability range. Conclusions: Both baseline and Δ immune–nutritional biomarkers predict pCR following NACT in breast cancer. The NRI and ΔGINI demonstrated the best diagnostic performance, whereas ΔSIRI and ΔPIIV also showed meaningful associations. Easily obtainable, low-cost indices—particularly Δ markers—may support the early identification of responders and facilitate more personalized therapeutic decision-making in breast cancer management. 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