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

Autor: Mengke Ma, Wenchao Gu, Yun Liang, Xueping Han, Meng Zhang, Midie Xu, Heli Gao, Wei Tang, Dan Huang
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Journal of Translational Medicine, Vol 22, Iss 1, Pp 1-14 (2024)
Druh dokumentu: article
ISSN: 1479-5876
DOI: 10.1186/s12967-024-05449-4
Popis: Abstract 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
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