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This study demonstrates that combining clinical variables with computed tomography radiomics features improves the prediction of recurrence-free survival in high-grade serous ovarian cancer patients undergoing neoadjuvant chemotherapy and s
Radiomic analysis of epicardial adipose tissue from routine cardiac CT scans enables robust, noninvasive early prediction of heart failure risk, enhancing conventional models and supporting precision prevention strategies.
A multi-institutional AI model identified pre-diagnostic pancreatic cancer on routine CT with meaningful lead time, higher sensitivity than radiologists, longitudinal stability, and externally validated specificity in a low-prevalence setti
This review evaluates how deep learning-assisted delta radiomics on MRI, combined with AFP response, provides a non-invasive, high-accuracy prediction of pathological complete response in unresectable HCC undergoing conversion immunotherapy
A single-center study demonstrates that an AI pipeline for CT-based lymph node segmentation and extranodal extension classification significantly improves prognostic accuracy for HPV-positive oropharyngeal cancer compared to traditional rad
This article explores how novel automated imaging tools, combined with clinical biomarkers, enhance risk stratification in mCRPC, while also reviewing the pivotal OS data from abiraterone and enzalutamide trials that inform contemporary tre
Integrated radiomic-clinical machine learning models outperform traditional clinical biomarkers in predicting survival and treatment response in unresectable hepatocellular carcinoma patients undergoing atezolizumab plus bevacizumab therapy
Coronary CTA, guided by clinical risk models and advanced radiomic plaque analysis, improves prediction and prevention of myocardial infarction in patients with new-onset chest pain, particularly in those with low clinical likelihood of cor
