Multichannel deep learning prediction of major pathological response after neoadjuvant immunochemotherapy in lung cancer: a multicenter diagnostic study.
Authors
Affiliations (7)
Affiliations (7)
- Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China.
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430000, China.
- School of Computer Science and Engineering, Sun Yat-sen University, Guangdong Province Key Laboratory of Computational Science, Guangzhou 510000, China.
- Department of Oncology, Yichang Central People's Hospital (The First Clinical Medical School of China Three Gorges University), Yichang 443000, Hubei Province, China.
- Department of Thoracic Surgery, Anyang Tumor Hospital, The Fourth Affiliated Hospital of Henan University of Science and Technology, Anyang 455000, China.
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China.
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, Henan, China.
Abstract
This study aimed to develop a pretreatment CT-based multichannel predictor integrating deep learning features encoded by Transformer models for preoperative diagnosis of major pathological response (MPR) in non-small cell lung cancer (NSCLC) patients receiving neoadjuvant immunochemotherapy. This multicenter diagnostic study retrospectively included 332 NSCLC patients from four centers. Pretreatment computed tomography images were preprocessed and segmented into region of interest cubes for radiomics modeling. These cubes were cropped into four groups of 2 dimensional image modules. GoogLeNet architecture was trained independently on each group within a multichannel framework, with gradient-weighted class activation mapping and SHapley Additive exPlanations value for visualization. Deep learning features were carefully extracted and fused across the four image groups using the Transformer fusion model. After models training, model performance was evaluated via the area under the curve (AUC), sensitivity, specificity, F1 score, confusion matrices, calibration curves, decision curve analysis, integrated discrimination improvement, net reclassification improvement, and DeLong test. The dataset was allocated into training (n = 172, Center 1), internal validation (n = 44, Center 1), and external test (n = 116, Centers 2-4) cohorts. Four optimal deep learning models and the best Transformer fusion model were developed. In the external test cohort, traditional radiomics model exhibited an AUC of 0.736 [95% confidence interval (CI): 0.645-0.826]. The optimal deep learning imaging module showed superior AUC of 0.855 (95% CI: 0.777-0.934). The fusion model named Transformer_GoogLeNet further improved classification accuracy (AUC = 0.924, 95% CI: 0.875-0.973). The new method of fusing multichannel deep learning with the Transformer Encoder can accurately diagnose whether NSCLC patients receiving neoadjuvant immunochemotherapy will achieve MPR. Our findings may support improved surgical planning and contribute to better treatment outcomes through more accurate preoperative assessment.