A novel model for predicting postoperative liver metastasis in R0 resected pancreatic neuroendocrine tumors: integrating computational pathology and deep learning-radiomics

作者：Mengke Ma, Wenchao Gu, Yun Liang, Xue-ping Han, Meng Zhang, Midie Xu, Heli Gao, Wei Tang, Dan Huang · 发表于：Journal of Translational Medicine · 年份：2024 · DOI：10.1186/s12967-024-05449-4 · 被引用次数：21 · 研究领域：Neuroendocrine Tumor Research Advances、Pancreatic and Hepatic Oncology Research、Radiomics and Machine Learning in Medical Imaging

BACKGROUND: Postoperative liver metastasis significantly impacts the prognosis of pancreatic neuroendocrine tumor (panNET) patients after R0 resection. Combining computational pathology and deep learning radiomics can enhance the detection of postoperative liver metastasis in panNET patients. METHODS: Clinical data, pathology slides, and radiographic images were collected from 163 panNET patients post-R0 resection at Fudan University Shanghai Cancer Center (FUSCC) and FUSCC Pathology Consultation Center. Digital image analysis and deep learning identified liver metastasis-related features in Ki67-stained whole slide images (WSIs) and enhanced CT scans to create a nomogram. The model's performance was validated in both internal and external test cohorts. RESULTS: Multivariate logistic regression identified nerve infiltration as an independent risk factor for liver metastasis (p < 0.05). The Pathomics score, which was based on a hotspot and the heterogeneous distribution of Ki67 staining, showed improved predictive accuracy for liver metastasis (AUC = 0.799). The deep learning-radiomics (DLR) score achieved an AUC of 0.875. The integrated nomogram, which combines clinical, pathological, and imaging features, demonstrated outstanding performance, with an AUC of 0.985 in the training cohort and 0.961 in the validation cohort. High-risk group had a median recurrence-free survival of 28.5 months compared to 34.7 months for the low-risk group, showing significant correlation with pr...