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Updated: May 15, 2025
Isolation and Characterization of Patient-derived Pancreatic Ductal Adenocarcinoma Organoid Models
Published on: January 14, 2020
Labor-Efficient Pathological Auxiliary Diagnostic Model for Primary and Metastatic Tumor Tissue Detection in
Xinyi Ke1, Moxuan Yang2, Jingci Chen1
1Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Abstract:
Accurate histopathological evaluation of pancreatic ductal adenocarcinoma (PDAC), including primary tumor lesions and lymph node metastases, is critical for prognostic evaluation and personalized therapeutic strategies. Distinct from other solid tumors, PDAC presents unique diagnostic challenges owing to its extensive desmoplasia, unclear tumor boundary, and difficulty in differentiating from chronic pancreatitis. These characteristics not only complicate pathological diagnosis but also hinder the acquisition of pixel-level annotations required for training computational pathology models. In this study, we present PANseg, a multiscale weakly supervised deep learning framework for PDAC segmentation, trained and tested on 368 whole-slide images (WSIs) from 208 patients across 2 independent centers. Using only image-level labels (2048 × 2048 pixels), PANseg achieved comparable performance with fully supervised baseline (FSB) across the internal test set 1 (17 patients/58 WSIs; PANseg area under the receiver operating characteristic curve [AUROC]: 0.969 vs FSB AUROC: 0.968), internal test set 2 (40 patients/44 WSIs; PANseg AUROC: 0.991 vs FSB AUROC: 0.980), and external test set (20 patients/20 WSIs; PANseg AUROC: 0.950 vs FSB AUROC: 0.958). Moreover, the model demonstrated considerable generalizability with previously unseen sample types, attaining AUROCs of 0.878 on fresh-frozen specimens (20 patients/20 WSIs) and 0.821 on biopsy sections (20 patients/20 WSIs). In lymph node metastasis detection, PANseg augmented the diagnostic accuracy of 6 pathologists from 0.888 to 0.961, while reducing the average diagnostic time by 32.6% (72.0 vs 48.5 minutes). This study demonstrates that our weakly supervised model can achieve expert-level segmentation performance and substantially reduce annotation burden. The clinical implementation of PANseg holds great potential in enhancing diagnostic precision and workflow efficiency in the routine histopathological assessment of PDAC.
