Radiotherapy plays a critical role in treating esophageal cancer, but individual responses vary significantly, impacting patient outcomes. This study integrates machine learning-driven multimodal radiomics and transcriptomics to develop predictive models for radiotherapy sensitivity and prognosis in esophageal cancer. We applied the SEResNet101 deep learning model to imaging and transcriptomic data from the UCSC Xena and TCGA databases, identifying prognosis-associated genes such as STUB1, PEX12, and HEXIM2. Using Lasso regression and Cox analysis, we constructed a prognostic risk model that accurately stratifies patients based on survival probability. Notably, STUB1, an E3 ubiquitin ligase, enhances radiotherapy sensitivity by promoting the ubiquitination and degradation of SRC, a key oncogenic protein. In vitro and in vivo experiments confirmed that STUB1 overexpression or SRC silencing significantly improves radiotherapy response in esophageal cancer models. These findings highlight the predictive power of multimodal data integration for individualized radiotherapy planning and underscore STUB1 as a promising therapeutic target for enhancing radiotherapy efficacy in esophageal cancer.

Biochemical recurrence (BCR) after radical prostatectomy exhibits heterogeneous prognostic implications. Recent advancements in imaging and biomarkers have high potential for personalizing care. Prostate-specific membrane antigen imaging (PSMA)-PET/CT has revolutionized the BCR management in prostate cancer by detecting microscopic lesions earlier than conventional staging, leading to improved cancer control outcomes and changes in treatment plans in approximately two-thirds of cases. Salvage radiotherapy, often combined with androgen deprivation therapy, remains the standard treatment for high-risk BCR postprostatectomy, with PSMA-PET/CT guiding treatment adjustments, such as the radiation field, and improving progression-free survival. Advancements in biomarkers, genomic classifiers, and artificial intelligence-based models have enhanced risk stratification and personalized treatment planning, resulting in both treatment intensification and de-escalation. While conventional risk grouping relying on Gleason score and PSA level and kinetics remain the foundation for BCR management, PSMA-PET/CT, novel biomarkers, and artificial intelligence may enable more personalized treatment strategies.

Radiogenomics holds promise in identifying molecular alterations in nonsmall cell lung cancer (NSCLC) using imaging features. Previously, we developed a radiogenomics model to predict epidermal growth factor receptor (EGFR) mutations based on contrast-enhanced computed tomography (CECT) in NSCLC patients. The current study aimed to externally validate this model using a publicly available National Institutes of Health (NIH)-based NSCLC dataset and assess the effect of EGFR mutation prevalence on model performance through synthetic minority oversampling technique (SMOTE). The original radiogenomics model was validated on an independent NIH cohort (n=140). For assessing the influence of disease prevalence, six SMOTE-augmented datasets were created, simulating EGFR mutation prevalence from 25% to 50%. Seven models were developed (one from original data, six SMOTE-augmented), each undergoing rigorous cross-validation, feature selection, and logistic regression modeling. Models were tested against the NIH cohort. Performance was compared using area under the receiver operating characteristic curve (Area Under the Curve [AUC]), and differences between radiomic-only, clinical-only, and combined models were statistically assessed. External validation revealed poor diagnostic performance for both our model and a previously published EGFR radiomics model (AUC ∼0.5). The clinical model alone achieved higher diagnostic accuracy (AUC 0.74). SMOTE-augmented models showed increased sensitivity but did not improve overall AUC compared to the clinical-only model. Changing EGFR mutation prevalence had minimal impact on AUC, challenging previous assumptions about the influence of sample imbalance on model performance. External validation failed to reproduce prior radiogenomics model performance, while clinical variables alone retained strong predictive value. SMOTE-based oversampling did not improve diagnostic accuracy, suggesting that, in EGFR prediction, radiomics may offer limited value beyond clinical data. Emphasis on robust external validation and data-sharing is essential for future clinical implementation of radiogenomic models.

Optical Coherence Tomography (OCT) has become an indispensable imaging modality in ophthalmology, providing high-resolution cross-sectional images of the retina. Accurate classification of OCT images is crucial for diagnosing retinal diseases such as Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME). This study explores the efficacy of various deep learning models, including convolutional neural networks (CNNs) and Vision Transformers (ViTs), in classifying OCT images. We also investigate the impact of integrating metadata (patient age, sex, eye laterality, and year) into the classification process, even when a significant portion of metadata is missing. Our results demonstrate that multimodal models leveraging both image and metadata inputs, such as the Multimodal ResNet18, can achieve competitive performance compared to image-only models, such as DenseNet121. Notably, DenseNet121 and Multimodal ResNet18 achieved the highest accuracy of 95.16%, with DenseNet121 showing a slightly higher F1-score of 0.9313. The multimodal ViT-based model also demonstrated promising results, achieving an accuracy of 93.22%, indicating the potential of Vision Transformers (ViTs) in medical image analysis, especially for handling complex multimodal data.

Ovarian cancer, a leading cause of cancer-related deaths among women, comprises distinct subtypes each requiring different treatment approaches. This paper presents a deep-learning framework for classifying ovarian cancer subtypes using Whole Slide Imaging (WSI). Our method contains three stages: image tiling, feature extraction, and multi-instance learning. Our approach is trained and validated on a public dataset from 80 distinct patients, achieving up to 89,8% accuracy with a notable improvement in computational efficiency. The results demonstrate the potential of our framework to augment diagnostic precision in clinical settings, offering a scalable solution for the accurate classification of ovarian cancer subtypes.

Employing a whole-brain (WB) mask as a region of interest for extracting radiomic features is a feasible, albeit less common, approach in neuro-oncology research. This study aims to evaluate the relationship between WB radiomic features, derived from various neuroimaging modalities in patients with gliomas, and some key baseline characteristics of patients and tumors such as sex, histological tumor type, WHO Grade (2021), IDH1 mutation status, necrosis lesions, contrast enhancement, T/N peak value and metabolic tumor volume. Forty-one patients (average age 50 ± 15 years, 21 females and 20 males) with supratentorial glial tumors were enrolled in this study. A total of 38,720 radiomic features were extracted. Cluster analysis revealed that whole-brain images of biologically different tumors could be distinguished to a certain extent based on their imaging biomarkers. Machine learning capabilities to detect image properties like contrast-enhanced or necrotic zones validated radiomic features in objectifying image semantics. Furthermore, the predictive capability of imaging biomarkers in determining tumor histology, grade and mutation type underscores their diagnostic potential. Whole-brain radiomics using multimodal neuroimaging data appeared to be informative in neuro-oncology, making research in this area well justified.

Artificial intelligence (AI) has transformed medical diagnostics by enhancing the accuracy of disease detection, particularly through deep learning models to analyze medical imaging data. However, the energy demands of training these models, such as ResNet and MobileNet, are substantial and often overlooked; however, researchers mainly focus on improving model accuracy. This study compares the energy use of these two models for classifying thoracic diseases using the well-known CheXpert dataset. We calculate power and energy consumption during training using the EnergyEfficientAI library. Results demonstrate that MobileNet outperforms ResNet by consuming less power and completing training faster, resulting in lower overall energy costs. This study highlights the importance of prioritizing energy efficiency in AI model development, promoting sustainable, eco-friendly approaches to advance medical diagnosis.

In this study, we attempted to identify the subtypes of autism spectrum disorder (ASD) with the help of anatomical alterations found in structural magnetic resonance imaging (sMRI) data of the ASD brain and machine learning tools. Initially, the sMRI data was preprocessed using the FreeSurfer toolbox. Further, the brain regions were segmented into 148 regions of interest using the Destrieux atlas. Features such as volume, thickness, surface area, and mean curvature were extracted for each brain region. We performed principal component analysis independently on the volume, thickness, surface area, and mean curvature features and identified the top 10 features. Further, we applied k-means clustering on these top 10 features and validated the number of clusters using Elbow and Silhouette method. Our study identified two clusters in the dataset which significantly shows the existence of two subtypes in ASD. We identified the features such as volume of scaled lh_G_front middle, thickness of scaled rh_S_temporal transverse, area of scaled lh_S_temporal sup, and mean curvature of scaled lh_G_precentral as the significant features discriminating the two clusters with statistically significant p-value (p<0.05). Thus, our proposed method is effective for the identification of ASD subtypes and can also be useful for the screening of other similar neurological disorders.

Glioblastoma (GBM) is the most aggressive adult primary brain cancer, characterized by significant heterogeneity, posing challenges for patient management, treatment planning, and clinical trial stratification. We developed a highly reproducible, personalized prognostication, and clinical subgrouping system using machine learning (ML) on routine clinical data, magnetic resonance imaging (MRI), and molecular measures from 2838 demographically diverse patients across 22 institutions and 3 continents. Patients were stratified into favorable, intermediate, and poor prognostic subgroups (I, II, and III) using Kaplan-Meier analysis (Cox proportional model and hazard ratios [HR]). The ML model stratified patients into distinct prognostic subgroups with HRs between subgroups I-II and I-III of 1.62 (95% CI: 1.43-1.84, P < .001) and 3.48 (95% CI: 2.94-4.11, P < .001), respectively. Analysis of imaging features revealed several tumor properties contributing unique prognostic value, supporting the feasibility of a generalizable prognostic classification system in a diverse cohort. Our ML model demonstrates extensive reproducibility and online accessibility, utilizing routine imaging data rather than complex imaging protocols. This platform offers a unique approach to personalized patient management and clinical trial stratification in GBM.

Deep learning models hold significant promise for disease diagnosis but often lack transparency in their decision-making processes, limiting trust and hindering clinical adoption. This study introduces a novel multi-task learning framework to enhance the medical explainability of deep learning models for diagnosing age-related macular degeneration (AMD) using fundus images. The framework simultaneously performs AMD classification and lesion segmentation, allowing the model to support its diagnoses with AMD-associated lesions identified through segmentation. In addition, we perform an in-depth interpretability analysis of the model, proposing the Medical Explainability Index (MXI), a novel metric that quantifies the medical relevance of the generated heatmaps by comparing them with the model's lesion segmentation output. This metric provides a measurable basis to evaluate whether the model's decisions are grounded in clinically meaningful information. The proposed method was trained and evaluated on the Automatic Detection Challenge on Age-Related Macular Degeneration (ADAM) dataset. Experimental results demonstrate robust performance, achieving an area under the curve (AUC) of 0.96 for classification and a Dice similarity coefficient (DSC) of 0.59 for segmentation, outperforming single-task models. By offering interpretable and clinically relevant insights, our approach aims to foster greater trust in AI-driven disease diagnosis and facilitate its adoption in clinical practice.