General lightweight framework for vision foundation model supporting multi-task and multi-center medical image analysis.

The foundation model, trained on extensive and diverse datasets, has shown strong performance across numerous downstream tasks. Nevertheless, its application in the medical domain is significantly hindered by issues such as data volume, heterogeneity, and privacy concerns. Therefore, we propose the Vision Foundation Model General Lightweight (VFMGL) framework, which facilitates the decentralized construction of expert clinical models for various medical tasks.

Preoperative discrimination of absence or presence of myometrial invasion in endometrial cancer with an MRI-based multimodal deep learning radiomics model.

Objective: Accurate preoperative evaluation of myometrial invasion (MI) is essential for treatment decisions in endometrial cancer (EC). However, the diagnostic accuracy of commonly utilized magnetic resonance imaging (MRI) techniques for this assessment exhibits considerable variability. This study aims to enhance preoperative discrimination of absence or presence of MI by developing and validating a multimodal deep learning radiomics (MDLR) model based on MRI.

Sorafenib Plus Hepatic Arterial Infusion of Oxaliplatin, Fluorouracil, and Leucovorin vs Sorafenib Alone for Hepatocellular Carcinoma With Portal Vein Invasion: A Randomized Clinical Trial.

Importance: Sorafenib is the first-line treatment for hepatocellular carcinoma with portal vein invasion; however, it has shown unsatisfactory survival benefit. Sorafenib plus hepatic arterial infusion chemotherapy (HAIC) of oxaliplatin, fluorouracil, and leucovorin (FOLFOX) has shown promising results for these patients in a previous phase 2 study.

Objective: To investigate the efficacy and safety of sorafenib plus HAIC compared with sorafenib for hepatocellular carcinoma with portal vein invasion.