A multidimensional deep ensemble learning model predicts pathological response and outcomes in esophageal squamous cell carcinoma treated with neoadjuvant chemoradiotherapy from pretreatment CT imaging: A multicenter study
Yunsong Liu , Yang Su , Jun Peng , Wencheng Zhang , Fangdong Zhao , Yue Li , Xinyun Song , Zeliang Ma , Wanting Zhang , Jianrui Ji , Ye Chen , Yu Men , Feng Ye , Kuo Men , Jianjun Qin , Wenyang Liu , Xin Wang , Nan Bi , Liyan Xue , Wen Yu , Zhouguang Hui
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引用次数: 0
Abstract
Purpose
Neoadjuvant chemoradiotherapy (nCRT) followed by esophagectomy remains standard for locally advanced esophageal squamous cell carcinoma (ESCC). However, accurately predicting pathological complete response (pCR) and treatment outcomes remains challenging. This study aimed to develop and validate a multidimensional deep ensemble learning model (DELRN) using pretreatment CT imaging to predict pCR and stratify prognostic risk in ESCC patients undergoing nCRT.
Methods
In this multicenter, retrospective cohort study, 485 ESCC patients were enrolled from four hospitals (May 2009–August 2023, December 2017–September 2021, May 2014–September 2019, and March 2013–July 2019). Patients were divided into a discovery cohort (n = 194), an internal cohort (n = 49), and three external validation cohorts (n = 242). A multidimensional deep ensemble learning model (DELRN) integrating radiomics and 3D convolutional neural networks was developed based on pretreatment CT images to predict pCR and clinical outcomes. The model’s performance was evaluated by discrimination, calibration, and clinical utility. Kaplan-Meier analysis assessed overall survival (OS) and disease-free survival (DFS) at two follow-up centers.
Results
The DELRN model demonstrated robust predictive performance for pCR across the discovery, internal, and external validation cohorts, with area under the curve (AUC) values of 0.943 (95 % CI: 0.912–0.973), 0.796 (95 % CI: 0.661–0.930), 0.767 (95 % CI: 0.646–0.887), 0.829 (95 % CI: 0.715–0.942), and 0.782 (95 % CI: 0.664–0.900), respectively, surpassing single-domain radiomics or deep learning models. DELRN effectively stratified patients into high-risk and low-risk groups for OS (log-rank P = 0.018 and 0.0053) and DFS (log-rank P = 0.00042 and 0.035). Multivariate analysis confirmed DELRN as an independent prognostic factor for OS and DFS.
Conclusion
The DELRN model demonstrated promising clinical potential as an effective, non-invasive tool for predicting nCRT response and treatment outcome in ESCC patients, enabling personalized treatment strategies and improving clinical decision-making with future prospective multicenter validation.
期刊介绍:
Radiotherapy and Oncology publishes papers describing original research as well as review articles. It covers areas of interest relating to radiation oncology. This includes: clinical radiotherapy, combined modality treatment, translational studies, epidemiological outcomes, imaging, dosimetry, and radiation therapy planning, experimental work in radiobiology, chemobiology, hyperthermia and tumour biology, as well as data science in radiation oncology and physics aspects relevant to oncology.Papers on more general aspects of interest to the radiation oncologist including chemotherapy, surgery and immunology are also published.