Laboratory investigation; a journal of technical methods and pathology · 2026
Prediction of Lymph Node Metastasis in Early Gastric Cancer Using Foundation Model Ensembles and Patch-Based Histopathological Interpretation.
СтатьяНазвание 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.
Chung Woojin, Park Yujun, Kwak Yoonjin, Lee Hae Dong, Kim Bohyoung, Shin Cheol Min, Nam Yoonho, Lee Hye Seung
Аннотация
PURPOSE: Accurate prediction of lymph node metastasis (LNM) remains challenging in early gastric cancer (EGC), particularly when determining the need for additional surgery after endoscopic resection. We developed and evaluated a deep learning framework using multiple pathology foundation models for LNM prediction and explored histologic features associated with model-predicted metastatic risk.
MATERIALS AND METHODS: Foundation model ensembles were developed using whole-slide images from surgical EGC cases and evaluated in internal and external surgical cohorts. The model was further tested in an endoscopic submucosal dissection cohort in which all patients subsequently underwent additional gastrectomy with lymph node dissection. Performance was assessed using the area under the curve (AUC) and compared with scratch-trained baseline models. At a fixed sensitivity of 1.00, the potential reduction in lymph node dissection among histologically node-negative patients was evaluated. Model-guided histologic analysis was performed to interpret features associated with predicted metastatic risk.
RESULTS: The final ensemble model achieved an AUC of 0.879 (95% CI, 0.799-0.944) in the internal and 0.801 (95% CI, 0.697-0.891) in the external cohort, representing improvements of 0.040 and 0.066, respectively, over the baseline model. In the endoscopic submucosal dissection cohort, the model achieved an AUC of 0.766 (95% CI, 0.635-0.882), representing an improvement of 0.189 over the baseline model. At a fixed sensitivity of 1.00, the model suggested avoiding lymph node dissection in ∼5% of histologically node-negative patients. Model-guided histologic analysis showed associations consistent with established pathological features, with exploratory observations including context-dependent associations between inflammatory infiltration and tumor differentiation, differences in predicted risk between well- and moderately differentiated tumors, and lower predicted risk in poorly cohesive carcinoma.
CONCLUSIONS: A multiple foundation model ensemble showed improved LNM risk prediction in EGC and provided interpretable histologic insights, supporting clinical understanding and risk stratification.
MATERIALS AND METHODS: Foundation model ensembles were developed using whole-slide images from surgical EGC cases and evaluated in internal and external surgical cohorts. The model was further tested in an endoscopic submucosal dissection cohort in which all patients subsequently underwent additional gastrectomy with lymph node dissection. Performance was assessed using the area under the curve (AUC) and compared with scratch-trained baseline models. At a fixed sensitivity of 1.00, the potential reduction in lymph node dissection among histologically node-negative patients was evaluated. Model-guided histologic analysis was performed to interpret features associated with predicted metastatic risk.
RESULTS: The final ensemble model achieved an AUC of 0.879 (95% CI, 0.799-0.944) in the internal and 0.801 (95% CI, 0.697-0.891) in the external cohort, representing improvements of 0.040 and 0.066, respectively, over the baseline model. In the endoscopic submucosal dissection cohort, the model achieved an AUC of 0.766 (95% CI, 0.635-0.882), representing an improvement of 0.189 over the baseline model. At a fixed sensitivity of 1.00, the model suggested avoiding lymph node dissection in ∼5% of histologically node-negative patients. Model-guided histologic analysis showed associations consistent with established pathological features, with exploratory observations including context-dependent associations between inflammatory infiltration and tumor differentiation, differences in predicted risk between well- and moderately differentiated tumors, and lower predicted risk in poorly cohesive carcinoma.
CONCLUSIONS: A multiple foundation model ensemble showed improved LNM risk prediction in EGC and provided interpretable histologic insights, supporting clinical understanding and risk stratification.