Interpretable multimodal radiopathomics model predicting pathological complete response to neoadjuvant chemoimmunotherapy in esophageal squamous cell carcinoma.
Authors
Affiliations (8)
Affiliations (8)
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China.
- Department of Radiation Oncology (Maastro), GROW Research Institute of Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands.
- School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
- Department of Radiation Oncology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China.
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
- Data Science Institute (DSI), Hasselt University, Hasselt, Belgium.
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China [email protected] [email protected].
Abstract
Accurate preoperative prediction of pathological complete response (pCR) following neoadjuvant chemoimmunotherapy (nCIT) could help individualize treatment for patients with esophageal squamous cell carcinoma (ESCC). This study aimed to develop and externally validate an interpretable multimodal machine learning framework that integrates CT radiomics and H&E-stained whole-slide images pathomics to predict pCR. In this multicenter, retrospective study, 335 patients with ESCC who received nCIT followed by esophagectomy were enrolled from three institutions. Patients from one center were divided into a training set (181 patients) and an internal test set (115 patients), while data from the other two centers comprised an external test set (39 patients). We developed unimodal radiomics and pathomics models, and two multimodal fusion models-an intermediate fusion model (MIFM) and a late fusion model (MLFM). Model performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, specificity, and F1 score, with exploratory survival stratification by observed and model-predicted pCR status. Interpretability was treated as a design constraint and operationalized at both the feature and model levels. The MIFM outperformed unimodal models and the MLFM across all cohorts, achieving AUC/accuracy/sensitivity/specificity/F1 score of 0.97/0.93/0.84/0.96/0.86 (training set), 0.78/0.87/0.62/0.93/0.63 (internal test set), and 0.76/0.77/0.54/0.88/0.61 (external test set). Both observed and predicted pCR status showed exploratory prognostic stratification for overall survival. Feature definitions were mathematically or morphologically explicit, and case-level/cohort-level explanations together with decision-pathway views provided insights into model reasoning. We additionally provide a user-friendly Graphical User Interface to facilitate clinical practice. We developed and externally validated an interpretable radiopathomics fusion framework that predicts pCR after nCIT in ESCC using standard-of-care data. This model holds promise as an effective tool for guiding individualized decisions between surveillance and timely surgery.