Multimodality deep learning radiomics predicts pathological response after neoadjuvant chemoradiotherapy for esophageal squamous cell carcinoma

Multimodality deep learning radiomics predicts pathological response after neoadjuvant chemoradiotherapy for esophageal squamous cell carcinoma

Abstract Objectives This study aimed to develop and validate a deep-learning radiomics model using CT, T2, and DWI images for predicting pathological complete response (pCR) in patients with esophageal squamous cell carcinoma (ESCC) undergoing neoadjuvant chemoradiotherapy (nCRT). Materials and meth...

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Main Authors: Yunsong Liu, Yi Wang, Xinyang Hu, Xin Wang, Liyan Xue, Qingsong Pang, Huan Zhang, Zeliang Ma, Heping Deng, Zhaoyang Yang, Xujie Sun, Yu Men, Feng Ye, Kuo Men, Jianjun Qin, Nan Bi, Jing Zhang, Qifeng Wang, Zhouguang Hui
Format: Article
Language:English
Published: SpringerOpen 2024-11-01
Series:Insights into Imaging
Subjects:
Esophageal neoplasms
Multimodal imaging
Deep learning
Treatment outcome
Neoadjuvant chemoradiotherapy
Online Access:https://doi.org/10.1186/s13244-024-01851-0
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https://doi.org/10.1186/s13244-024-01851-0

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