Deep learning model based on multi-lesion and time series CT images for predicting the benefits from anti-HER2 targeted therapy in stage IV gastric cancer

Deep learning model based on multi-lesion and time series CT images for predicting the benefits from anti-HER2 targeted therapy in stage IV gastric cancer

Abstract Objective To develop and validate a deep learning model based on multi-lesion and time series CT images in predicting overall survival (OS) in patients with stage IV gastric cancer (GC) receiving anti-HER2 targeted therapy. Methods A total of 207 patients were enrolled in this multicenter s...

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Bibliographic Details
Main Authors: Meng He, Zi-fan Chen, Song Liu, Yang Chen, Huan Zhang, Li Zhang, Jie Zhao, Jie Yang, Xiao-tian Zhang, Lin Shen, Jian-bo Gao, Bin Dong, Lei Tang
Format: Article
Language:English
Published: SpringerOpen 2024-02-01
Series:Insights into Imaging
Subjects:
Human epidermal growth factor receptor 2
Deep learning
Gastric cancer
Computed tomography (X-ray)
Treatment outcome
Online Access:https://doi.org/10.1186/s13244-024-01639-2

Internet

https://doi.org/10.1186/s13244-024-01639-2

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