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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Autori principali:	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
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	SpringerOpen 2024-11-01
Serie:	Insights into Imaging
Soggetti:	Esophageal neoplasms Multimodal imaging Deep learning Treatment outcome Neoadjuvant chemoradiotherapy
Accesso online:	https://doi.org/10.1186/s13244-024-01851-0
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