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PubMed2025

Habitat radiomics predicts occult lymph node metastasis and uncovers immune microenvironment of head and neck cancer.

BACKGROUND: Occult lymph node metastasis (LNM) is a key prognostic factor for patients with head and neck squamous cell carcinoma (HNSCC). This study was to establish radiomics models derived from intratumoral, peritumoral, and habitat regions for identifying occult LNM in HNSCC. METHODS: Patients with pathologically confirmed HNSCC from three medical Centers (from March 2014 to April 2024) and The Cancer Genome Atlas (TCGA) were enrolled. Center 1 was split into training (n = 330) and internal test sets (n = 154), while Center 2 and Center 3 served as the external test set (n = 183). Genomic set (n = 50) from TCGA and single-cell RNA sequencing set (n = 6) from Center 1 were used for biological analysis. We used the intratumoral, peritumoral, and habitat volumes of interest (VOIs) to extract radiomics features, respectively. Based on Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) classifiers, nine radiomics models were built to confirm the optimal predictive performance. The best-performing model, along with clinical-radiologic data, was combined to develop a hybrid model. The log-rank test was used to evaluate the model's prognostic performance. Additionally, bulk and single-cell RNA sequencing were applied for investigating the biological mechanisms underlying the optimal model. RESULTS: The RF-habitat radiomics model showed the best performance, achieving AUCs of 0.835-0.919 across all datasets. Survival analysis further confirmed the prognostic value of the RF-habitat radiomics model. The RF-habitat radiomics model and the hybrid model notably surpassed the clinical model in predictive performance. Moreover, the RF-habitat radiomics model was associated with the abundance level of exhaustion-associated CD8 + T cells, uncovering the immune microenvironment characteristics contributing to occult LNM in HNSCC. CONCLUSIONS: The RF-habitat radiomics model demonstrated excellent performance for predicting occult LNM in HNSCC across three cohorts, providing a non-invasive solution for occult LNM. Furthermore, radiogenomic analysis further revealed the biological associations of the model, primarily related to T cell dysfunction.

Humans↗

PubMed2026

Toward precision prognosis: Predicting recurrence-free survival in high-grade serous ovarian cancer patients using multi-time point clinical and computed tomography radiomics data.

OBJECTIVE: To evaluate the predictive value of clinical, genomic, and radiomics features in estimating recurrence-free survival (RFS) in patients with high-grade serous ovarian carcinoma (HGSOC) treated with neoadjuvant chemotherapy (NACT). METHODS: This single-center, retrospective study included 91 patients with HGSOC who underwent treatment with NACT followed by surgery, and who had portal venous phase contrast enhanced CT imaging at baseline and after NACT. First-order texture features based on 2D segmentation were extracted from baseline and post-NACT CT images for selected disease sites using commercially available texture software. Multivariate Cox models assessed the prognostic significance of features at baseline, after NACT, and post-surgery time points, and model performance in predicting RFS was evaluated using C-statistics. RESULTS: A model including only baseline clinical data had C-statistic 0.53, while a model including both clinical and radiomics features at baseline had C-statistic 0.63. After NACT, a model including all baseline data plus the change in radiomics features between baseline and post-NACT had C-statistic 0.63. Post-surgery, a model including all baseline data plus surgical outcome had C-statistic 0.69. Incorporating changes in radiomic features between time points did not measurably enhance model performance in the post-surgery data set (C-statistic 0.7). Age, residual disease at surgery, and kurtosis were individually associated with shorter RFS. CONCLUSIONS: Radiomic features extracted from CT imaging may offer additive prognostic value for predicting RFS in HGSOC when integrated with clinical and genetic data. Our results support the potential integration of radiomic analysis with clinical data to improve outcome prediction in HGSOC.

Humans↗

PubMed2026

Spinal meningiomas: histopathological grading using a benchmark radiomics model with notes on disease control.

OBJECTIVE: Spinal meningiomas (SMs) are common primary spinal tumors for which surgery is considered the first-line treatment when safe and feasible. The ability to extrapolate the tumor grade from preoperative imaging may significantly inform early patient expectation-setting regarding recurrence. Building on radiomics studies in cranial meningiomas, the authors aimed to construct a benchmark radiomics model to preoperatively identify the histological grade of SMs. METHODS: Institutional surgical records from May 2012 to November 2025 were queried for pathology-confirmed meningiomas below the foramen magnum, with preoperative contrast-enhanced imaging available for segmentation. SMs were classified as low-grade (WHO grade 1) and high-grade (WHO grade 2 tumors and grade 1 tumors with atypia). Tumors were manually segmented, and features were extracted using the PyRadiomics software package. An ensemble model of k-nearest neighbors, random forest, and support vector machine classifiers was trained using nested cross-validation on a subset of 10 features to differentiate tumor grades. Clinical data for the cohort were also extracted, and disease control in an adjunctive clinical series was assessed. RESULTS: Seventy-four patients were included in radiomics analysis, with an area under the receiver operating characteristic curve of 0.879 and a mean F1 score of 0.748. The model's top 5 features were all texture features that differed significantly (p < 0.05) across low- and high-grade SMs. These included measures of tumor textural and contrast-enhancement heterogeneity, with overlap with features reported in radiomics models for histological grading of intracranial meningiomas. Fifty-five patients with a median radiographic follow-up of 22.2 (range 1.9-86.4) months remained for clinical analysis after exclusion of patients with less than 1 month of follow-up and syndromic meningiomas. Four recurrences occurred at a median of 20.8 (range 1.8-41.8) months. High-grade tumor pathology did not significantly impact progression-free survival (p = 0.682, log-rank test; Cox regression high vs low grade hazard ratio [HR] 0.62, 95% CI 0.06-6.11, p = 0.685). Subtotal resection was associated with poorer progression-free survival than gross-total resection (p = 0.004, log-rank test; Cox regression subtotal vs gross-total resection HR 10.62, 95% CI 1.46-77.05, p = 0.019). These findings remain contextualized within a relatively limited follow-up window and small recurrence event count, suggesting a need to characterize the interplay between tumor grade and extent of resection as drivers of local disease control in SMs. CONCLUSIONS: A preoperative radiomics model can stratify high-grade SMs using open-source tools applied to single-institution data.

Humans↗

PubMed2026

Linking MRI radiomics to transcriptomics-based radiosensitivity in lower-grade glioma: A radiogenomic framework.

BACKGROUND: RSI is a transcriptomics-based biomarker associated with radiotherapy outcomes, but its clinical application is constrained by the requirement for tumor tissue and RNA sequencing. This study investigates whether MRI-derived radiomic features can reflect RSI-defined intrinsic radiosensitivity in lower-grade glioma.This addresses a critical gap arising from the limited availability of matched imaging and genomic data in routine clinical practice. METHODS: MRI-derived radiomic features were extracted from FLAIR images of lower-grade glioma patients obtained from TCIA and matched with transcriptomic data from TCGA. A total of 107 patients with both MRI and RNA sequencing data were included in the radiogenomic analysis. Radiomic features were ranked using a Borda-based ensemble feature selection strategy. Five supervised machine-learning classifiers were trained to predict RSI-based radiosensitivity classification, and model interpretability was assessed using SHAP within radiogenomic framework. RESULTS: Classification performance increased with feature number and stabilized at compact subset of 13 radiomic features. Logistic regression showed stable performance with an AUC of 0.82 (95 % CI: 0.71-0.93). SHAP analysis indicated that heterogeneity-related texture features were dominant contributors to model predictions, with many associated with the RR phenotype, while others were linked to the RS phenotype. CONCLUSION: An MRI-based radiomic signature enables non-invasive prediction of RSI-defined radiosensitivity in lower-grade glioma. Rather than offering an immediately deployable clinical tool, this study establishes a proof-of-concept radiogenomic framework demonstrating that intrinsic radiosensitivity, traditionally assessed through invasive molecular assays, can be approximated using quantitative imaging features. These findings highlight the potential of imaging-based radiosensitivity assessment and provide a foundation for future radiogenomic investigations.

Lower-grade glioma↗

PubMed2026

Radiomics as a spatial context for treatment decision-making in head and neck cancer.

Radiomics has been widely explored as a non-invasive biomarker in head and neck squamous cell carcinoma (HNSCC), yet its clinical role remains unclear. Tissue-based biomarkers differ in their susceptibility to spatial sampling. Biomarkers such as PD-L1 expression, immune-cell infiltration, necrosis, and immune exclusion may exhibit substantial spatial heterogeneity, whereas HPV/p16 status and some genomic alterations are generally more stable across the tumor. Nevertheless, localized sampling may incompletely capture heterogeneity in selected clinical contexts. This mismatch becomes clinically relevant when treatment decisions, particularly for chemoradiotherapy, immunotherapy, or de-escalation, are based on potentially non-representative biopsy findings. In this narrative review, we argue that the role of radiomics is not to outperform established biomarkers, but to contextualize them by capturing spatial heterogeneity related to hypoxia, necrosis, stromal architecture, and immune exclusion. We synthesize current evidence linking radiomic features to these biological processes and map them to specific clinical decision points, including larynx preservation, immunotherapy stratification, and recurrence assessment. Rather than serving as a standalone predictor, radiomics may provide complementary spatial information that helps identify situations in which biopsy-derived biomarkers should be interpreted with caution. Although current evidence is largely retrospective, radiomics offers a pragmatic framework for integrating spatial information into biomarker-guided clinical workflows.

Journal Article↗

PubMed2026

Radiomics-based gradient boosting model on contrast-enhanced MRI for non-invasive prediction of epidermal growth factor receptor expression and therapeutic response to EGFR-targeted antibody-drug conjugates in high-grade glioma organoid models.

BACKGROUND: Epidermal growth factor (EGF) and its receptor EGF(EGFR) play crucial roles in glioblastoma (GBM) prognosis. However, non-invasive assessment of their expression remains challenging. This study aimed to determine whether radiomics features extracted from contrast-enhanced MRI could predict EGFR expression in high-grade gliomas (HGG) and to explore their associations with immune infiltration and therapeutic response of EGFR-Targeted antibody drug conjugates(EGFR-ADCs). METHODS: We extracted radiomic features from contrast-enhanced MRI of 298 GBM patients from The Cancer Imaging Archive (TCIA) and matched them with RNA-seq data from The Cancer Genome Atlas (TCGA). Feature selection was performed using minimum redundancy maximum relevance (mRMR) and recursive feature elimination (RFE). Machine learning models were built to predict EGF/EGFR expression. Radiogenomic associations were validated by immune infiltration analysis. Patient-Derived Tumor-Like Cell Clusters (PTC) were used to compare the antitumor efficacy of EGFR- ADCs and temozolomide. RESULTS: Elevated EGF/EGFR expression correlated with poor prognosis and increased infiltration of M2 macrophages, regulatory T cells, and CD4⁺ memory T cells. Pathway analysis demonstrated significant enrichment of the mechanistic target of rapamycin (mTOR) and Mitogen-Activated Protein Kinase (MAPK) signaling cascades. Radiomics-based prediction models achieved robust performance (AUC > 0.85) in stratifying EGFR expression status. In EGFR-positive tumor tissues, EGFR-ADCs exerted antitumor efficacy similar to that of temozolomide. CONCLUSIONS: EGF/EGFR expression is associated with immunosuppressive microenvironments and adverse outcomes in HGG. Radiomics may provide a non-invasive approach for estimating EGFR expression, although model performance requires external validation and EGFR-ADCs showed partial inhibitory activity within the tested range, though potency remains to be defined.These findings suggest a framework into radiogenomic stratification and targeted therapy in GBM.

Radiomics↗

PubMed2026

A Comprehensive Review of Radiomics in Pulmonary Nodule Management: Clinical Applications and Standardization Dilemmas.

Lung cancer is the most common and fatal malignant tumour. Early detection and treatment are likely to reduce mortality, but most pulmonary nodules identified during routine health checks are harmless. Consequently, a clear distinction between benign and malignant nodules is vital to improve early detection and reduce unnecessary interventions. Radiomics, a new omics technology, can be used to extract high-dimensional quantitative features from medical images, providing a profound understanding of tumour pathophysiology. Radiomics has attracted the attention of medical researchers since its formal definition by the Dutch researcher Lambin et al. in 2012. The number of research papers on radiomics has grown tremendously over the past few years. At present, it is used to predict pulmonary nodule malignancy, for noninvasive risk stratification, for integration with genomics to identify genetic mutations associated with lung cancer, and for evaluation of therapeutic responses. With this review, we summarise the literature on radiomics of pulmonary nodules, discuss how it could be used in nodule management, and address the current challenges and future directions for improving precision oncology.

Humans↗

PubMed2026

MRI-based radiomics model for predicting VEGFA expression and prognosis in lower-grade glioma.

BACKGROUND: Gliomas are the most common primary tumors of the central nervous system. Their treatment remains highly challenging, with high rates of associated disability and mortality. Conventional prognostic indicators no longer adequately satisfy the clinical demands of precision medicine. Therefore, it is essential to further explore novel prognostic biomarkers to enable accurate risk stratification and to provide new reference indicators for personalized precision therapy. PURPOSES: This study aimed to investigate the prognostic significance of vascular endothelial growth factor A (VEGFA) in patients diag nosed with lower-grade gliomas (LGGs) using an MRI based radiomics model. METHODS: Data regarding VEGFA expression and clinical records of LGG patients were retrieved from The Cancer Genome Atlas (TCGA). Corresponding preoperative MRI data were obtained from The Cancer Imaging Archive (TCIA) for radiomic feature extraction. Patients were stratified into high- and low- VEGFA expression groups based on survival information from the current cohort using the survminer package. The overall survival (OS) was assessed using Kaplan-Meier analysis and Cox proportional hazards regression. Predictive models were developed using logistic regression (LR), and model performance was evaluated via receiver operating characteristic (ROC) curve analysis, with area under the curve (AUC) values reported. An optimized model incorporating the Akaike information criterion (AIC) was also constructed (AIC-LR). RESULTS: VEGFA expression was significantly associated with OS (P = 0.002). Multivariate Cox regression confirmed VEGFA as an independent prognostic factor (hazard ratio [HR] = 2.545, 95% confidence interval: 1.422-4.555). Furthermore, VEGFA expression correlated with immune infiltration levels, particularly of M1 and M2 macrophages and T follicular helper cells, and was associated with enrichment in Wnt signaling and B cell receptor signaling pathways. The LR and AIC-LR models demonstrated acceptable predictive performance, with AUCs of 0.728 (95% CI: 0.612-0.843) and 0.725(95% CI: 0.612-0.839) in the training cohort, and 0.704 (95% CI: 0.562-0.847) and 0.718(95% CI: 0.576-0.861) in the validation cohort, respectively. CONCLUSIONS: The MRI based radiomics model showed potential for noninvasive assessment of VEGFA expression and may provide auxiliary information for prognostic evaluation in LGG. Further validation in larger samples and independent external cohorts is required before clinical application.

Radiomics↗

PubMed2026

Spatial habitat radiomics predicts tertiary lymphoid structure status and identifies an IDO1+ migratory dendritic cell axis in breast cancer.

BACKGROUND: Tertiary lymphoid structures (TLS) are spatially organized immune niches associated with therapeutic response and favorable outcomes in breast cancer (BC). However, TLS assessment currently relies on invasive tissue-based analyses, and the biological mechanisms underlying imaging-based TLS prediction remain poorly understood. METHODS: We developed and validated a spatial heterogeneity-based radiomic TLS signature (shTLS) using dynamic contrast-enhanced MRI to non-invasively predict TLS status across multicenter BC cohorts. Spatial habitat radiomics were used to capture intratumoral and peritumoral immune-related heterogeneity. Integrated multi-omics analyses, including transcriptomics, pathomics, genomics, single-cell RNA sequencing, immunohistochemistry, and multiplex immunofluorescence, were performed to biologically interpret shTLS-defined subgroups. Functional drug-sensitivity assays were conducted to assess therapeutic implications. RESULTS: The shTLS model achieved robust predictive performance across independent cohorts and molecular subtypes. High shTLS scores were associated with immune-inflamed tumors characterized by spatially clustered activated T cells and dendritic cells (DCs). In contrast, shTLS-low tumors exhibited an immunosuppressive spatial niche with peripheral accumulation of CD4+ PD-1+ T cells and plasma cells, increased immune-tumor separation, and enhanced inflammatory and immunoregulatory signaling. An indoleamine 2,3-dioxygenase 1 (IDO1)-associated immunoregulatory program was observed in the shTLS-low tumors, which appeared to be preferentially expressed by LAMP3+CCR7+ migratory DCs. Pharmacologic inhibition of IDO1 enhanced chemotherapy and CDK4/6 inhibitor sensitivity in vitro. CONCLUSION: This study establishes spatial radiomics as a non-invasive approach to decode TLS-associated immune ecosystems and supports the presence of an IDO1-associated immunosuppressive phenotype, providing biological insight and translational rationale for patient stratification and future combination strategies.

Humans↗

PubMed2026

Diffusion MRI radiomics in meningiomas: imaging correlates of tumor grade and intraoperative consistency.

OBJECTIVE: Despite advancements in imaging studies, the preoperative prediction of the biological behavior and intraoperative consistency of intracranial meningiomas remains limited. This study evaluated the association of volumetric diffusion-based and texture-derived radiomic features extracted from routine MRI with histopathological aggressiveness and intraoperative tumor consistency. METHODS: Ninety-seven intracranial meningiomas resected at two tertiary centers were retrospectively analyzed. Volumetric segmentation was performed on contrast-enhanced T1-weighted MRI and coregistered to apparent diffusion coefficient (ADC) maps. Data on first-order diffusion metrics and selected texture features were collected. The associations between World Health Organization (WHO) grade and Ki-67 index were assessed using nonparametric tests and Spearman correlation analysis. Independent factors associated with intraoperative tumor consistency (Zada grades 1-5) were evaluated via multivariate ordinal logistic regression analysis that adjusted for tumor volume, skull base location, calcification status, and WHO grade. Secondary receiver operating characteristic (ROC) curve analyses were performed to differentiate solid (Zada grades 4-5) from soft (Zada grades 1-2) tumors. ROC analyses were performed within the study cohort and were intended as exploratory assessments of discriminative performance. RESULTS: The mean ADC (ADCmean) and the 10th percentile of the ADC decreased significantly with increasing WHO grade (p < 0.001). ADCmean had a moderate inverse correlation with the Ki-67 index (r = -0.42, p < 0.001) and intraoperative tumor consistency (r = -0.45, p < 0.001). In the multivariate analysis, the ADCmean remained independently associated with increasing tumor firmness. Each 0.1 × 10-3 mm2/sec increase corresponded to a 38% reduction in the odds of belonging to a higher consistency category (OR 0.62, 95% CI 0.51-0.74, p < 0.001). The ROC analysis showed good discrimination for solid tumors (area under the curve 0.847, 95% CI 0.742-0.953) and soft tumors (area under the curve 0.824, 95% CI 0.714-0.935). Texture features had weaker associations with intraoperative tumor consistency. CONCLUSIONS: Volumetric diffusion-derived metrics, particularly ADCmean, are associated with both histopathological aggressiveness and intraoperative tumor firmness in meningiomas. Diffusion imaging may reflect a graded microstructural continuum rather than a purely dichotomous property, providing complementary preoperative insights into surgical complexity.

Humans↗

PubMed2025

Radiogenomic MRI biomarkers for noninvasive prediction of GPC3 expression and tumor microenvironment in hepatocellular carcinoma.

BACKGROUND: Glypican-3 (GPC3) is frequently overexpressed in hepatocellular carcinoma (HCC) and plays a key role in immune and metabolic remodeling of the tumor microenvironment. Reliable noninvasive biomarkers for predicting GPC3 status could improve patient stratification and support precision immunotherapy. METHODS: This multicenter retrospective study included 274 patients with pathologically confirmed hepatocellular carcinoma from three institutions, 34 external cases with MRI from The Cancer Imaging Archive, and 363 transcriptomic profiles from The Cancer Genome Atlas. Contrast-enhanced T1-weighted imaging and diffusion-weighted imaging were analyzed. Tumor and peritumoral regions were segmented manually and radiomic features extracted using PyRadiomics. Feature selection was performed with correlation filtering and least absolute shrinkage and selection operator regression. Machine learning classifiers including logistic regression, random forest, support vector machine, k-nearest neighbor, and decision tree were trained with 10-fold cross-validation and tested on independent external cohorts. A radiomics score was calculated for each patient. Radiogenomic analysis correlated radiomics scores with transcriptomic data using weighted gene co-expression network analysis. Hub genes and enriched pathways were identified, and immune infiltration and predicted immunotherapy response were assessed using computational methods. RESULTS: The random forest model using contrast-enhanced T1-weighted imaging achieved an area under the curve of 0.966 in training and 0.935 in internal validation. The integrated contrast-enhanced T1-weighted imaging plus diffusion-weighted imaging model reached an internal validation area under the curve of 0.979. In external testing, the best performance was obtained with a support vector machine model (area under the curve 0.756). Radiomics scores were significantly correlated with GPC3 expression (R = 0.78, p < 0.05). Transcriptomic analysis identified a 10-gene signature enriched in hypoxia and lipid metabolism pathways that stratified patients into prognostic subgroups (concordance index 0.720, hazard ratio 4.07, p < 0.0001). High-risk patients had greater immune infiltration and a lower predicted immune evasion score, suggesting a potential benefit from immunotherapy. CONCLUSIONS: MRI-based radiomics models can noninvasively predict GPC3 expression in hepatocellular carcinoma. Radiomics scores reflect underlying hypoxia and lipid metabolism pathways and stratify patients by prognosis and predicted immunotherapy response. These findings support radiogenomics as a translational approach to imaging-guided precision treatment in hepatocellular carcinoma.

Humans↗

PubMed2026

Multimodal features and prognostic risk assessment in locally advanced gastric cancer patients following neoadjuvant therapy based on machine learning algorithms: a multicenter study.

BACKGROUND: Neoadjuvant therapy (NAT) is recommended for locally advanced gastric cancer (LAGC), but some patients respond poorly. We aimed to construct a multimodal model integrating CT images, transcriptomic sequencing, and clinicopathological data to assess prognosis in LAGC patients receiving NAT. MATERIALS AND METHODS: This multicenter study included 505 LAGC patients who underwent NAT. Radiomic features were extracted from preoperative CT images of 505 patients. RNA-seq was performed on 277 post-NAT specimens, with additional data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (n = 804). Patients were divided into training (168 cases), internal validation (72 cases), and external validation cohorts. Machine learning algorithms identified key radiomic, molecular, and clinical features associated with NAT response, which were then integrated into a multimodal model to predict overall survival (OS) and disease-free survival (DFS). RESULTS: Six radiomic and three molecular features significantly associated with NAT response were selected. Radiomic risk (hazard ratio [HR]: 4.0, P < 0.001) and molecular risk (HR: 7.1, P < 0.001) were independent prognostic factors. By integrating radiomic risk, molecular risk, and clinical characteristics, a multimodal model (MuMo) was constructed.The C-index results (OS, C-index = 0.855; DFS, C-index = 0.786) demonstrated that MuMo outperformed the single-modality models and ypTNM staging.Mechanistic analysis suggested that the efficacy of neoadjuvant therapy was significantly enriched in immune-inflammatory pathways. CONCLUSIONS: MuMo can effectively predict postoperative survival risk in LAGC patients receiving NAT, serving as a powerful tool for optimizing prognostic assessment.

Humans↗

PubMed2026

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

Humans↗

PubMed2026

Leveraging Interradiomic Feature Relationships for Enhanced Prediction of Distant Metastasis and Characterization of Heterogeneity in Head and Neck Cancer.

PURPOSE: Distant metastasis remains a major cause of treatment failure in head and neck (HN) cancer, highlighting the need for more accurate early risk stratification. This study developed and validated a deep radiomics framework to characterize tumor heterogeneity from pretreatment computed tomography (CT) images and improve prediction of distant metastasis-free survival (DMFS). METHODS AND MATERIALS: This multicenter study included 3421 patients with HN cancer from 4 cohorts across 12 institutions. Radiomics features were extracted from primary tumors and transformed into OmicsMaps, a structured representation that spatially organizes interfeature relationships to facilitate learning of complex prognostic patterns. A convolutional neural network was trained to derive prognostic signatures, which were integrated with key clinical variables to construct an OmicsMap-clinical fusion model for patient risk stratification. Model performance was assessed using the concordance index (C-index) and time-dependent area under the receiver operating characteristic curve (AUC) in the CT Images from Large Head and Neck Cohort (RADCURE), HEAD-NECK-RADIOMICS-HN1 (HN1), and Head-Neck-Positron Emission Tomography-Computed Tomography (HN-PET-CT) cohorts. Radiogenomic analyses using RNA-seq data were conducted in the Cancer Genome Atlas Head-Neck Squamous Cell Carcinoma (TCGA-HNSC) cohort to investigate biological characteristics associated with the imaging-defined risk groups. RESULTS: The OmicsMap achieved C-index values of 0.742, 0.768, and 0.671 in the RADCURE, HN1, and HN-PET-CT cohorts, outperforming the conventional radiomics approach by 5.40%-6.37%. Incorporating clinical variables further improved generalizability, yielding a C-index of 0.864 (HN1) and 0.730 (HN-PET-CT), with time-dependent AUC of 0.727-0.895. The fusion model consistently stratified patients into distinct high- and low-risk groups for both DMFS and overall survival across cohorts (P <.01). Radiogenomic analyses revealed enrichment of immune-related pathways in the low-risk group, whereas the high-risk group exhibited a more aggressive phenotype enriched for proliferation, hypoxia, and epithelial-mesenchymal transition pathways, along with a fibrosis-prone tumor microenvironment characterized by extracellular matrix remodeling. CONCLUSIONS: Modeling interradiomic feature relationships using the OmicsMap representation substantially improves CT-based prediction of DMFS and characterization of tumor heterogeneity in HN cancer, supporting precision risk stratification in clinical oncology.

Journal Article↗

PubMed2026

Recent Advances in Surveillance Strategies for Nasopharyngeal Carcinoma.

PURPOSE OF REVIEW: Nasopharyngeal carcinoma (NPC) is a malignant tumor characterized by a distinct geographical distribution. Effective surveillance is crucial for the early detection of recurrence or metastasis and for improving patient prognosis.This review systematically examines current NPC follow-up protocols and recent developments to inform individualized precision surveillance. RECENT FINDINGS: This review focuses on two main aspects. 1) We compare and analyze current major NPC follow-up guidelines, with key discussions covering follow-up frequency, imaging modalities (including magnetic resonance imaging [MRI] and positron emission tomography [PET]), plasma Epstein-Barr virus DNA (EBV-DNA) monitoring, and functional assessments. 2)We elaborate on the application prospects and research progress of genomics, radiomics, and artificial intelligence in NPC surveillance. Studies suggest that risk-stratified, individualized follow-up strategies, such as those based on conditional survival models, can enhance the cost-effectiveness of surveillance. Additionally, emerging technologies, including radiomics and artificial intelligence, show promise for improving recurrence risk assessment, prognostic stratification, and individualized surveillance in NPC. Concurrently, advances in genomics and radiomics offer new opportunities for predicting complications and guiding treatment adjustments. Future efforts should focus on integrating multidisciplinary expertise to develop dynamic monitoring systems that enable precise follow-up and ultimately improve patient survival outcomes.

Humans↗

PubMed2026

Artificial intelligence in kidney cancer: a review of clinical applications across the disease spectrum.

PURPOSE OF REVIEW: This review examines recent advances (2024-2025) in the application of artificial intelligence (AI) to kidney cancer diagnosis, prognosis, and treatment planning. It categorizes studies across 13 clinical scenarios to assess where AI offers the most clinical utility. RECENT FINDINGS: AI models have demonstrated strong performance in a range of tasks including tumor grading, subtype classification, survival prediction, and risk stratification. Integration of radiomics, genomics, and histopathology has enabled personalized, noninvasive, and timely decision-making. The highest-performing models used CT-based radiomics, particularly for predicting progression-free and recurrence-free survival. However, performance varies across tasks and tumor subtypes, with lower accuracy in detecting oncocytomas or benign vs. malignant differentiation. AI applications in metastatic and nonresected cases remain underexplored, and ultrasound remains a largely under researched modality. While some models improve diagnostic accuracy and workflow efficiency, broader validation across diverse populations is still needed. SUMMARY: AI is transforming kidney cancer care across multiple clinical stages. Although promising, real-world implementation demands ongoing validation and postdeployment monitoring to prevent performance degradation due to distributional drift. AI's integration with multimodal data offers substantial potential to improve outcomes and reduce overtreatment.

Humans↗

PubMed2026

Breast Cancer Recurrence Status Assessment in 5 Years Using Multimodal Integrated Learning: A Feasibility Study.

Despite advances in breast cancer detection and treatment, recurrence after curative therapy continues to impact long-term survival and quality of life. Therefore, early identification of high-risk patients is crucial to guide personalized treatment and follow-up strategies. Although genomic assays provide valuable prognostic insights, their high cost and limited accessibility hinder widespread adoption in clinical practice. Recent machine learning or deep learning approaches leveraging clinical, imaging, or multimodal data have shown promise but do not reflect real-world clinical scenarios. This study proposes a deep learning-based multimodal framework for predicting 5-year breast cancer recurrence using routinely collected clinical data. The framework consists of three main components. First, we adopted automated tumor segmentation with MedSAM to extract the tumor region from ultrasound images. The radiomics features are extracted from those tumor regions. Second, report features are extracted using a Med-Contrastive Pre-trained Transformers (MedCPT)-based approach incorporating predefined, clinically informed queries. Third, a multimodal integration model jointly processes image, radiomics, clinical features, and report features through modality-specific branches. The image branch employs the Ultrasound Foundation Model (USFM) as the backbone, while structured tabular data is processed using the FT-Transformer architecture. The features of all branches are fused using a mixture-of-experts (MoE)-based classifier, and the entire model is trained using a progressive fusion training strategy. Experimental results confirm the feasibility of using ultrasound images with tumor mask integration for recurrence prediction and demonstrate the additive value of integrating multiple data modalities through the proposed multimodal integration model. The final model for recurrence prediction achieved an AUC of 0.7540, accuracy of 74.61%, sensitivity of 70.41%, and specificity of 76.44%. This feasibility study's findings underscore the potential of the proposed multimodal deep learning framework to provide accessible, accurate, and generalizable recurrence risk prediction using routinely available clinical data, potentially supporting more informed treatment decisions and personalized post-treatment monitoring in real-world clinical practice.

Breast cancer recurrence↗

PubMed2026

Machine learning-driven spleen imaging and genomics uncover a splenic connection to coronary artery disease.

Despite advances in managing traditional risk factors, coronary artery disease (CAD) remains the leading cause of mortality. Circulating hematopoietic cells influence risk for CAD separately from traditional risk factors, but the role of a key regulating organ, the spleen, is unknown. The understudied spleen is a representation of the hematopoietic system optimally suited for unbiased radiologic investigations toward mechanistic insights. Here, we leveraged deep learning to extract 107 splenic radiomic features from abdominal magnetic resonance imaging (MRI) scans of 42,059 UK Biobank participants and of 2745 Mass General Brigham Biobank (MGBB) participants. Of these, 10 features from UK Biobank were associated with CAD. Genome-wide association analysis of CAD-associated features identified 219 loci, including 9p21. Variants at 9p21, the strongest yet mechanistically elusive CAD locus, were associated with splenic features such as run-length nonuniformity, reflecting heterogeneity of continuous texture regions. Research MRI findings were consistent internally, but external clinical validation highlighted challenges in translating analyses of abdominal MRI scans to routine clinical practice because of variability in imaging protocols and greater clinical heterogeneity among patients. Our study, combining deep learning with genomics, presents a framework to uncover potential splenic involvement in CAD and emphasizes translational gaps between research and clinical radiomics.

Humans↗