Pancreas · 2025
A Novel Deep Learning-based Pathomics Score for Prognostic Stratification in Pancreatic Ductal Adenocarcinoma.
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.
Liu Wenbin, Li Jing, Yuan Xiaohan, Chen Chengwei, Shen Yixuan, Zhang Xinyue, Yu Jieyu, Zhu Mengmeng, Fang Xu, Liu Fang, Wang Tiegong, Wang Li, Fan Jie, Jiang Hui, Lu Jianping, Shao Chengwei, Bian Yun
Аннотация
BACKGROUND AND OBJECTIVES: Accurate survival prediction for pancreatic ductal adenocarcinoma (PDAC) is crucial for personalized treatment strategies. This study aims to construct a novel pathomics indicator using hematoxylin and eosin-stained whole slide images and deep learning to enhance PDAC prognosis prediction.
METHODS: A retrospective, 2-center study analyzed 864 PDAC patients diagnosed between January 2015 and March 2022. Using weakly supervised and multiple instance learning, pathologic features predicting 2-year survival were extracted. Pathomics features, including probability histograms and TF-IDF, were selected through random survival forests. Survival analysis was conducted using Kaplan-Meier curves, log-rank tests, and Cox regression, with AUROC and C-index used to assess model discrimination.
RESULTS: The study cohort comprised 489 patients for training, 211 for validation, and 164 in the neoadjuvant therapy (NAT) group. A pathomics score was developed using 7 features, dividing patients into high-risk and low-risk groups based on the median score of 131.11. Significant survival differences were observed between groups (P<0.0001). The pathomics score was a robust independent prognostic factor [Training: hazard ratio (HR)=3.90; Validation: HR=3.49; NAT: HR=4.82; all P<0.001]. Subgroup analyses revealed higher survival rates for early-stage low-risk patients and NAT responders compared to high-risk counterparts (both P<0.05 and P<0.0001). The pathomics model surpassed clinical models in predicting 1-, 2-, and 3-year survival.
CONCLUSIONS: The pathomics score serves as a cost-effective and precise prognostic tool, functioning as an independent prognostic indicator that enables precise stratification and enhances the prediction of prognosis when combined with traditional pathologic features. This advancement has the potential to significantly impact PDAC treatment planning and improve patient outcomes.
METHODS: A retrospective, 2-center study analyzed 864 PDAC patients diagnosed between January 2015 and March 2022. Using weakly supervised and multiple instance learning, pathologic features predicting 2-year survival were extracted. Pathomics features, including probability histograms and TF-IDF, were selected through random survival forests. Survival analysis was conducted using Kaplan-Meier curves, log-rank tests, and Cox regression, with AUROC and C-index used to assess model discrimination.
RESULTS: The study cohort comprised 489 patients for training, 211 for validation, and 164 in the neoadjuvant therapy (NAT) group. A pathomics score was developed using 7 features, dividing patients into high-risk and low-risk groups based on the median score of 131.11. Significant survival differences were observed between groups (P<0.0001). The pathomics score was a robust independent prognostic factor [Training: hazard ratio (HR)=3.90; Validation: HR=3.49; NAT: HR=4.82; all P<0.001]. Subgroup analyses revealed higher survival rates for early-stage low-risk patients and NAT responders compared to high-risk counterparts (both P<0.05 and P<0.0001). The pathomics model surpassed clinical models in predicting 1-, 2-, and 3-year survival.
CONCLUSIONS: The pathomics score serves as a cost-effective and precise prognostic tool, functioning as an independent prognostic indicator that enables precise stratification and enhances the prediction of prognosis when combined with traditional pathologic features. This advancement has the potential to significantly impact PDAC treatment planning and improve patient outcomes.