A machine learning model integrating clinical-radiomics-deep learning features accurately predicts postoperative recurrence and metastasis of primary gastrointestinal stromal tumors

A machine learning model integrating clinical-radiomics-deep learning features accurately predicts postoperative recurrence and metastasis of primary gastrointestinal stromal tumors

Abstract Objectives Post-surgical prediction of recurrence or metastasis for primary gastrointestinal stromal tumors (GISTs) remains challenging. We aim to develop individualized clinical follow-up strategies for primary GIST patients, such as shortening follow-up time or extending drug administrati...

Full description

Saved in:
Bibliographic Details
Main Authors: WenJie Xie, Zhen Zhang, Zhao Sun, XiaoChen Wan, JieHan Li, JianWu Jiang, Qi Liu, Ge Yang, Yang Fu
Format: Article
Language:English
Published: SpringerOpen 2025-06-01
Series:Insights into Imaging
Subjects:
Gastrointestinal stromal tumors
Radiomics
Machine learning
Deep learning
Recurrence or metastasis
Online Access:https://doi.org/10.1186/s13244-025-02011-8
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items