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Updated: Jul 15, 2026
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
CT-based deep learning predicts immunotherapy response in esophageal squamous cell cancer patients
Tongxin Li1,2, Rutao Fan2,3, Zhili Liu4
1Department of Digital Medicine, College of Biomedical Engineering and Medical Imaging, Army Medical University (Third Military Medical University), Chongqing, China.
Background:
Immunotherapy has emerged as a promising addition to neoadjuvant therapy for locally advanced esophageal squamous cell carcinoma (LA-ESCC), yet treatment response varies substantially among patients. Reliable biomarkers for predicting response to immunochemotherapy (ICT) remain lacking. We aimed to develop and externally validate a deep learning model for predicting immune Response Evaluation Criteria in Solid Tumors (iRECIST) response to ICT in LA-ESCC.
Methods:
This retrospective, dual-center study included 290 patients with pathologically confirmed LA-ESCC. Of these, 264 patients from The First Affiliated Hospital of Army Medical University were randomly divided into training (n=185) and internal testing cohorts (n=79), and 26 patients from Banan District People's Hospital of Chongqing served as an external testing cohort. A deep learning-based model was developed using pretreatment contrast-enhanced computed tomography (CT) images to predict short-term iRECIST-defined therapeutic response. Model discrimination, calibration, and clinical utility were assessed using the area under the receiver operating characteristic curve (AUC), bootstrap 95% confidence intervals (CIs), Brier scores, calibration intercept and slope, and decision curve analysis (DCA).
Results:
The model achieved AUCs of 0.853 (95% CI: 0.795-0.906), 0.794 (95% CI: 0.683-0.886), and 0.759 (95% CI: 0.549-0.925) in the training, internal testing, and external testing cohorts, respectively. Calibration was acceptable in the training and internal testing cohorts but weakened in the small external cohort (slope =0.36; 95% CI: -0.02 to 0.75), and DCA showed greater net benefit than "treat-all" and "treat-none" strategies across a range of threshold probabilities, with wider variability in the external cohort.
Conclusions:
In this dual-center retrospective cohort, a deep-learning model based on pretreatment contrast-enhanced CT showed exploratory predictive value for short-term radiological response to ICT in LA-ESCC. Given the small external cohort and single-region design, the present results should be regarded as hypothesis-generating, and prospective multi-regional validation is required before any clinical use.
