European journal of radiology · 2025
Interpretable fusion deep learning on super-resolution MRI for perineural invasion prediction in pancreatic ductal adenocarcinoma: a multicenter study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Li Jianbo, Xu Jie, Shen Tingting, Zhou Jianjun, Wang Peng, Lu Tianyu, Li Dalong, Zeng Mengsu, Tang Qiying, Zhu Qiuyang, Sun Haitao
Аннотация
PURPOSE: To develop an interpretable fusion deep learning model based on super-resolution (SR) MRI for predicting preoperative perineural invasion (PNI) in pancreatic ductal adenocarcinoma (PDAC) and to evaluate its role in guiding postoperative prognostic and therapeutic decision-making.
MATERIALS AND METHODS: In this multicenter retrospective study, we enrolled 714 eligible patients, allocating 608 to a development/internal validation set and 106 from three external centers to an external validation set. The fusion clinical-radiomics-deep transfer learning (FCDR) was developed to predict PNI by integrating imaging signatures derived from deep learning and radiomics on SR-MRI with clinical risk factors and was optimized by selecting the best-performing among seven machine learning algorithms. The final model was subsequently validated for its incremental value in predicting postoperative prognosis and guiding adjuvant therapy.
RESULTS: The FCDR model achieved superior performance of PNI prediction with AUCs of 0.929, 0.886, and 0.832 across development, internal and external validation sets, significantly outperforming single clinical, DL, or radiomics models. The FCDR model-stratified high-risk group was associated with significantly worse postoperative OS and RFS (p < 0.05). Moreover, the high-risk PNI subgroup stratified by this model derived a significant OS and RFS benefit from adjuvant therapy. Model interpretability was affirmed by SHAP analysis.
CONCLUSION: The proposed interpretable fusion model serves as an effective tool for PNI evaluation, prognostic stratification, and tailoring of adjuvant therapy in PDAC, holding significant promise for personalized precision medicine.
CRITICAL RELEVANCE STATEMENT: The constructed fusion model offers a robust, non-invasive tool for identifying PNI in pancreatic ductal adenocarcinoma, showing significant potential to guide personalized treatment strategies and improve patient outcomes.
MATERIALS AND METHODS: In this multicenter retrospective study, we enrolled 714 eligible patients, allocating 608 to a development/internal validation set and 106 from three external centers to an external validation set. The fusion clinical-radiomics-deep transfer learning (FCDR) was developed to predict PNI by integrating imaging signatures derived from deep learning and radiomics on SR-MRI with clinical risk factors and was optimized by selecting the best-performing among seven machine learning algorithms. The final model was subsequently validated for its incremental value in predicting postoperative prognosis and guiding adjuvant therapy.
RESULTS: The FCDR model achieved superior performance of PNI prediction with AUCs of 0.929, 0.886, and 0.832 across development, internal and external validation sets, significantly outperforming single clinical, DL, or radiomics models. The FCDR model-stratified high-risk group was associated with significantly worse postoperative OS and RFS (p < 0.05). Moreover, the high-risk PNI subgroup stratified by this model derived a significant OS and RFS benefit from adjuvant therapy. Model interpretability was affirmed by SHAP analysis.
CONCLUSION: The proposed interpretable fusion model serves as an effective tool for PNI evaluation, prognostic stratification, and tailoring of adjuvant therapy in PDAC, holding significant promise for personalized precision medicine.
CRITICAL RELEVANCE STATEMENT: The constructed fusion model offers a robust, non-invasive tool for identifying PNI in pancreatic ductal adenocarcinoma, showing significant potential to guide personalized treatment strategies and improve patient outcomes.