Application of machine learning based on habitat imaging and vision transformer to predict treatment response of locally advanced esophageal squamous cell carcinoma following neoadjuvant chemoimmunotherapy: a multi-center study

Application of machine learning based on habitat imaging and vision transformer to predict treatment response of locally advanced esophageal squamous cell carcinoma following neoadjuvant chemoimmunotherapy: a multi-center study

ObjectiveCurrent medical examinations and biomarkers struggle to assess the efficacy of chemoimmunotherapy (nICT) for locally advanced esophageal squamous cell carcinoma (ESCC). This study aimed to develop a machine learning model integrating habitat imaging and deep learning (DL) to predict the tre...

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Bibliographic Details
Main Authors: Shu-Han Xie, Hui Xu, Hai Zhang, Jin-Xin Xu, Shi-Jie Huang, Wen-Yi Liu, Zi-Lu Tang, Rong-Yu Xu, Sun-Kui Ke, Jin-Biao Xie, Qing-Yi Feng, Ming-Qiang Kang
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-08-01
Series:Frontiers in Immunology
Subjects:
neoadjuvant chemoimmunotherapy
treatment response
habitat imaging
vision transformer
machine learning
tumor subregions
Online Access:https://www.frontiersin.org/articles/10.3389/fimmu.2025.1603249/full
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