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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 < 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).
CONCLUSIONS: 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.
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 prognosis (p < 0.05).
CONCLUSIONS: 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.
摘要：
背景：胰腺神经内分泌肿瘤（panNET）患者R0切除术后肝转移对预后有显著影响。结合计算病理学和深度学习影像组学可以增强panNET患者术后肝转移的检测。
方法：临床数据，病理学幻灯片,收集了复旦大学上海肿瘤中心（FUSCC）和FUSCC病理咨询中心R0切除术后的163例panNET患者的X线图像。数字图像分析和深度学习在Ki67染色的整个载玻片图像（WSI）和增强CT扫描中识别出肝转移相关特征，以创建列线图。该模型的性能在内部和外部测试队列中都得到了验证。
结果：多因素logistic回归分析确定神经浸润是肝转移的独立危险因素（p<0.05）。Pathomics评分,这是基于热点和Ki67染色的异质性分布，显示肝转移的预测准确性提高（AUC=0.799）。深度学习-影像组学（DLR）评分的AUC为0.875。综合列线图，结合临床，病态,和成像功能，表现突出,训练队列的AUC为0.985，验证队列的AUC为0.961。高危组的中位无复发生存期为28.5个月，而低危组的中位无复发生存期为34.7个月。与预后显著相关(p<0.05)。
结论：整合了计算病理学评分和深度学习影像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。
方法：临床数据，病理学幻灯片,收集了复旦大学上海肿瘤中心（FUSCC）和FUSCC病理咨询中心R0切除术后的163例panNET患者的X线图像。数字图像分析和深度学习在Ki67染色的整个载玻片图像（WSI）和增强CT扫描中识别出肝转移相关特征，以创建列线图。该模型的性能在内部和外部测试队列中都得到了验证。
结果：多因素logistic回归分析确定神经浸润是肝转移的独立危险因素（p<0.05）。Pathomics评分,这是基于热点和Ki67染色的异质性分布，显示肝转移的预测准确性提高（AUC=0.799）。深度学习-影像组学（DLR）评分的AUC为0.875。综合列线图，结合临床，病态,和成像功能，表现突出,训练队列的AUC为0.985，验证队列的AUC为0.961。高危组的中位无复发生存期为28.5个月，而低危组的中位无复发生存期为34.7个月。与预后显著相关(p<0.05)。
结论：整合了计算病理学评分和深度学习影像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。
