A multicenter retrospective study of 309 patients found that integrating multimodal ultrasound radiomics with clinical predictors improved preoperative prediction of ...
A multicenter study demonstrates that combining clinical variables with CT radiomics features enables machine learning models to accurately distinguish peripheral ...
A dual-center study demonstrates that an MRI fusion model integrating habitat and peritumoral radiomics accurately predicts p53-abnormal endometrial cancer before ...
A multicenter retrospective study of 96 Ewing sarcoma patients found that MRI radiomics models moderately outperformed expert radiological readings in ...
A retrospective study demonstrated that an interpretable machine learning model combining novel PET distance metrics and habitat radiomics moderately predicts ...
Researchers from Vellore Institute of Technology in Chennai, India, have developed a multimodal machine learning pipeline that classifies brain lesions ...
A multicenter retrospective study of 875 clear cell renal cell carcinoma patients showed that an explainable multimodal artificial intelligence system ...
A Matters Arising commentary highlights external validation, potential data leakage, and model selection considerations in a PET/CT radiomics model predicting ...
A comprehensive review of 36 contemporary studies examines artificial intelligence models across thyroid cancer diagnosis, risk stratification, and treatment planning ...
A two-center retrospective study demonstrates that volumetric deep learning from single-sequence contrast-enhanced MRI can accurately predict glioma IDH mutation status ...
Embark on a thrilling journey of discovery with Scienmag.com—your ultimate source for cutting-edge breakthroughs. Immerse yourself in a world where curiosity knows no limits and tomorrow’s possibilities become today’s reality!
