A retrospective study demonstrates that a 2.5-dimensional Transformer fusion model using dynamic contrast-enhanced magnetic resonance imaging predicts breast cancer Ki-67 ...
An interpretable MRI-based machine learning nomogram predicted tumor budding grade in breast cancer with AUCs of 0.906, 0.845 and 0.825 ...
A multicenter retrospective study reports that a fusion model combining MRI habitat radiomics, Vision Transformer deep learning features and clinical ...
A single-center proof-of-concept study found that CT-based multi-station lymph-node radiomics significantly outperformed primary-tumor radiomics for preoperative prediction of lymph node ...
A two-center study combined multiphase DCE-MRI radiomics with conventional features to predict breast cancer TIL levels before surgery, reaching an ...
A retrospective single-center study of 487 patients developed interpretable machine learning models that predict gangrenous cholecystitis before surgery using five ...
A retrospective study of 115 surgically treated patients found that cross-phase MRI radiomics achieved an AUC of 0.850 for predicting ...
A fusion of CT-based habitat radiomics and 3D deep learning predicted the hidden STAS invasion pattern in early-stage lung adenocarcinoma ...
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!
