Machine Learning Comparison and Combined Model Optimization Based on DCE‐MRI Radiomics for Preoperative Assessment of Lymphovascular Invasion in Breast Cancer: A Multicenter Study

Study of Adaptive Radiotherapy for High‐Grade Glioma Based on Interfraction MRI

Currently, there is no clear guidance on the appropriate population or timing for adaptive radiotherapy (ART) in high‐grade glioma (HGG) patients. This study aims to provide individualized ART for HGG patients.

Current Medical Imaging

MRI-Based Radiomics and Machine Learning for Predicting Pathological Tumor Invasion and Nodal Status in Rectal Cancer: A Retrospective Study

The clinico-radiological variables analyzed using ML showed a predictive performance similar to that of a radiologist in this setting, and the radiomic and combined models achieved a moderate and heterogeneous performance.

Performance of Machine Learning Models Based on Medical Imaging in Predicting Pathological Grade of Clear Cell Renal Cell Carcinoma

Predicting clear cell renal cell carcinoma (ccRCC) pathological grade preoperatively is critical for clinical management. This study aims to evaluate the diagnostic accuracy and clinical utility of machine learning (ML)‐based imaging models.

The use of biparametric magnetic resonance imaging in active surveillance of prostate cancer

This study aims to evaluate the performance of biparametric magnetic resonance imaging (bpMRI) compared to multiparametric MRI (mpMRI) for active surveillance (AS) in prostate cancer (PCa), focusing on lesion detection, PI‐RADS classification changes and grade reclassification (GR) rates.

Explainable MedAttn-ResNet50 for Automated Kidney Tumor Classification from CT Images With Grad-CAM Visualization

: Kidney tumor classification from CT imaging remains challenging due to CT-specific intensity variations, tumor heterogeneity