Journal of cancer research and clinical oncology · 2024
Integrative radiopathomics model for predicting progression-free survival in patients with nonmetastatic nasopharyngeal carcinoma.
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Hou Jing, Yi Xiaochun, Li Handong, Lu Qiang, Lin Huashan, Li Junjun, Zeng Biao, Yu Xiaoping
Аннотация
PURPOSE: To construct an integrative radiopathomics model for predicting progression-free survival (PFS) in nonmetastatic nasopharyngeal carcinoma (NPC) patients.
METHODS: 357 NPC patients who underwent pretreatment MRI and pathological whole-slide imaging (WSI) were included in this study and randomly divided into two groups: a training set (n = 250) and validation set (n = 107). Radiomic features extracted from MRI were selected using the minimum redundancy maximum relevance and least absolute shrinkage and selection operator methods. The pathomics signature based on WSI was constructed using a deep learning architecture, the Swin Transformer. The radiopathomics model was constructed by incorporating three feature sets: the radiomics signature, pathomics signature, and independent clinical factors. The prognostic efficacy of the model was assessed using the concordance index (C-index). Kaplan-Meier curves for the stratified risk groups were tested by the log-rank test.
RESULTS: The radiopathomics model exhibited superior predictive performance with C-indexes of 0.791 (95% confidence interval [CI]: 0.724-0.871) in the training set and 0.785 (95% CI: 0.716-0.875) in the validation set compared to any single-modality model (radiomics: 0.619, 95% CI: 0.553-0.706; pathomics: 0.732, 95% CI: 0.662-0.802; clinical model: 0.655, 95% CI: 0.581-0.728) (all, P < 0.05). The radiopathomics model effectively stratified patients into high- and low-risk groups in both the training and validation sets (P < 0.001).
CONCLUSION: The developed radiopathomics model demonstrated its reliability in predicting PFS for NPC patients. It effectively stratified individual patients into distinct risk groups, providing valuable insights for prognostic assessment.
METHODS: 357 NPC patients who underwent pretreatment MRI and pathological whole-slide imaging (WSI) were included in this study and randomly divided into two groups: a training set (n = 250) and validation set (n = 107). Radiomic features extracted from MRI were selected using the minimum redundancy maximum relevance and least absolute shrinkage and selection operator methods. The pathomics signature based on WSI was constructed using a deep learning architecture, the Swin Transformer. The radiopathomics model was constructed by incorporating three feature sets: the radiomics signature, pathomics signature, and independent clinical factors. The prognostic efficacy of the model was assessed using the concordance index (C-index). Kaplan-Meier curves for the stratified risk groups were tested by the log-rank test.
RESULTS: The radiopathomics model exhibited superior predictive performance with C-indexes of 0.791 (95% confidence interval [CI]: 0.724-0.871) in the training set and 0.785 (95% CI: 0.716-0.875) in the validation set compared to any single-modality model (radiomics: 0.619, 95% CI: 0.553-0.706; pathomics: 0.732, 95% CI: 0.662-0.802; clinical model: 0.655, 95% CI: 0.581-0.728) (all, P < 0.05). The radiopathomics model effectively stratified patients into high- and low-risk groups in both the training and validation sets (P < 0.001).
CONCLUSION: The developed radiopathomics model demonstrated its reliability in predicting PFS for NPC patients. It effectively stratified individual patients into distinct risk groups, providing valuable insights for prognostic assessment.