An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer

An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer

Abstract Background Programmed death ligand 1 (PD-L1) expression status, closely related to immunotherapy outcomes, is a reliable biomarker for screening patients who may benefit from immunotherapy. Here, we developed and validated an interpretable machine learning (ML) model based on contrast-enhan...

Full description

Saved in:
Bibliographic Details
Main Authors: Lihuan Dai, Jinxue Yin, Xin Xin, Chun Yao, Yongfang Tang, Xiaohong Xia, Yuanlin Chen, Shuying Lai, Guoliang Lu, Jie Huang, Purong Zhang, Jiansheng Li, Xiangguang Chen, Xi Zhong
Format: Article
Language:English
Published: BMC 2025-03-01
Series:Cancer Imaging
Subjects:
Gastric cancer
Programmed death ligand 1
Computed tomography
Radiomics
Machine learning
SHapley additive explanations
Online Access:https://doi.org/10.1186/s40644-025-00855-3
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1186/s40644-025-00855-3

Similar Items