A radio-pathological fusion model for predicting PD-L1 expression and immunotherapy response in non-small cell lung cancer.
Authors
Affiliations (12)
Affiliations (12)
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- Medical Imaging International Scientific and Technological Cooperation Base of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- DUT-RU International School of Information Science and Engineering, Dalian University of Technology, Dalian, Liaoning, China.
- DUT-RU Co-Research Center of Advanced ICT for Active Life, Dalian University of Technology, Dalian, Liaoning, China.
- Department of Radiology, Shaoxing Second Hospital, Shaoxing, China.
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China. [email protected].
- DUT-RU International School of Information Science and Engineering, Dalian University of Technology, Dalian, Liaoning, China. [email protected].
- DUT-RU Co-Research Center of Advanced ICT for Active Life, Dalian University of Technology, Dalian, Liaoning, China. [email protected].
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China. [email protected].
- Medical Imaging International Scientific and Technological Cooperation Base of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China. [email protected].
Abstract
This study aims to construct a multimodal fusion model (FM) based on CT and hematoxylin and eosin (H&E) stained slices to predict the PD-L1 expression in non-small cell lung cancer (NSCLC) and to explore its additional value in predicting the prognosis of immunotherapy. A retrospective analysis was conducted of 328 NSCLC patients with available PD-L1 immunohistochemical results. They were randomly divided into a training set, a validation set, and a test set in a 4:1:1 ratio. Radiomics and pathological models were constructed based on CT images and H&E slides, respectively, to predict PD-L1 expression, and then a radio-pathological FM was established. Then, the radio-pathological FM was used to generate predictive scores for an independent NSCLC immunotherapy survival validation cohort. A total of 55.5% (182/328) of patients were PD-L1 positive and included in the PD-L1 prediction cohort. Compared to the single-modality model, the radio-pathological FM achieved the highest predictive performance, with AUCs of 0.90, 0.80, and 0.73 across the three subsets, respectively. In the survival validation cohort, patients in the high-score group had significantly better progression-free survival (PFS) and overall survival than those in the low-score group. Furthermore, the FM score was an independent predictor of PFS. When combined with clinical factors, its C-index for predicting PFS was 0.74 (95% CI: 0.665-0.809). For the first time, a radio-pathological FM was constructed to predict PD-L1 expression in NSCLC. The study also demonstrated the model's potential for predicting patient prognosis under immunotherapy. This first fusion model combining CT radiomics and hematoxylin and eosin (H&E) deep learning non-invasively predicts programmed death-ligand 1 (PD-L1) and immunotherapy response in non-small cell lung cancer (NSCLC). The fusion model can accurately predict programmed death-ligand 1 (PD-L1) and immunotherapy outcomes in non-small cell lung cancer (NSCLC). The fusion model outperformed either single-modality model in distinguishing PD-L1-positive. Potential to reduce PD-L1 immunohistochemical testing and support precision immunotherapy decisions.