Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] · 2025
Development and validation of a novel chronic pancreatitis pathological grade based on artificial intelligence.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Chen Chengwei, Zhou Jian, Mo Shaojia, Li Jing, Fang Xu, Liu Fang, Wang Tiegong, Wang Li, Lu Jianping, Jiang Hui, Shao Chengwei, Bian Yun
Аннотация
BACKGROUND: Effective chronic pancreatitis (CP) treatment requires accurate severity evaluation, but no histopathology grading system exists. This study aimed to develop and validate a novel CP pathological grade (Histopathology-derived CPpG) using quantified pathological and radiological characteristics through deep learning.
METHODS: Patients with pathologically/clinically confirmed CP or recurrent acute pancreatitis were retrospectively enrolled (2011-2023). Whole-slide CP images were automatically segmented and quantified via DeeplabV3+, followed by latent class analysis to develop Histopathology-derived CPpG. A deep learning radiomics score (DLRS) was created to predict Histopathology-derived CPpG using preoperative CT scans of patients with pathologically confirmed CP. CT-predicted CPpG was then validated in an independent group of patients with clinically confirmed CP and recurrent acute pancreatitis.
RESULTS: The study included 2054 patients with CP and recurrent acute pancreatitis, with 181 cases of pathologically confirmed CP. Histopathology-derived CPpG I had a higher proportion of acini, acinus-to-stroma ratio, acinus-to-islet ratio, islet-to-stroma ratio, and (acinus + islet)-to-stroma ratio, and a lower proportion of stroma and lymphocytes compared to CPpG II. The DLRS demonstrated high performance in the validation (AUC, 0.84; 95 % CI: 0.75-0.92) and test (AUC, 0.76; 95 % CI: 0.65-0.87) sets. In a large-scale clinical validation, CT-predicted grades were significantly associated with endocrine and exocrine function, as well as prognosis (P < .05).
CONCLUSION: This study developed a novel pathological classification, Histopathology-derived CPpG, which accurately reflects disease severity. Additionally, the non-invasive DLRS shows great potential for dynamically monitoring CP severity and evaluating pancreatic endocrine and exocrine function, as well as prognosis.
METHODS: Patients with pathologically/clinically confirmed CP or recurrent acute pancreatitis were retrospectively enrolled (2011-2023). Whole-slide CP images were automatically segmented and quantified via DeeplabV3+, followed by latent class analysis to develop Histopathology-derived CPpG. A deep learning radiomics score (DLRS) was created to predict Histopathology-derived CPpG using preoperative CT scans of patients with pathologically confirmed CP. CT-predicted CPpG was then validated in an independent group of patients with clinically confirmed CP and recurrent acute pancreatitis.
RESULTS: The study included 2054 patients with CP and recurrent acute pancreatitis, with 181 cases of pathologically confirmed CP. Histopathology-derived CPpG I had a higher proportion of acini, acinus-to-stroma ratio, acinus-to-islet ratio, islet-to-stroma ratio, and (acinus + islet)-to-stroma ratio, and a lower proportion of stroma and lymphocytes compared to CPpG II. The DLRS demonstrated high performance in the validation (AUC, 0.84; 95 % CI: 0.75-0.92) and test (AUC, 0.76; 95 % CI: 0.65-0.87) sets. In a large-scale clinical validation, CT-predicted grades were significantly associated with endocrine and exocrine function, as well as prognosis (P < .05).
CONCLUSION: This study developed a novel pathological classification, Histopathology-derived CPpG, which accurately reflects disease severity. Additionally, the non-invasive DLRS shows great potential for dynamically monitoring CP severity and evaluating pancreatic endocrine and exocrine function, as well as prognosis.