术后肝转移显着影响 R0 切除后胰腺神经内分泌肿瘤 (panNET) 患者的预后。结合计算病理学和深度学习放射组学可以增强 panNET 患者术后肝转移的检测。收集了复旦大学附属肿瘤医院 (FUSCC) 和 FUSCC 病理学中心 163 例 R0 切除术后 panNET 患者的临床数据、病理切片和影像学图像咨询中心。数字图像分析和深度学习在 Ki67 染色的全切片图像 (WSI) 和增强 CT 扫描中识别出与肝转移相关的特征，以创建列线图。该模型的性能在内部和外部测试队列中得到了验证。多变量逻辑回归将神经浸润确定为肝转移的独立危险因素（p<<0.05）。基于热点和 Ki67 染色的异质分布的病理组学评分显示，肝转移的预测准确性有所提高 (AUC = 0.799)。深度学习放射组学 (DLR) 评分的 AUC 为 0.875。综合列线图结合了临床、病理和影像学特征，表现出出色的性能，训练队列中的 AUC 为 0.985，验证队列中的 AUC 为 0.961。高风险组的中位无复发生存期为 28.5 个月，而低风险组为 34.7 个月，与预后显着相关 (p< 0.05)。一种整合计算病理评分和深度学习-放射组学的新预测模型可以更好地预测 panNET 患者术后肝转移，帮助临床医生开发个性化治疗。© 2024。作者。

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.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.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 prognosis (p < 0.05).A new predictive model that integrates computational pathologic scores and deep learning-radiomics can better predict postoperative liver metastasis in panNET patients, aiding clinicians in developing personalized treatments.© 2024. The Author(s).