Prediction of Recurrence Pattern of Pancreatic Cancer Post-Pancreatic Surgery Using Histology-Based Supervised Machine Learning Algorithms: A Single-Center Retrospective Study
Prediction of Recurrence Pattern of Pancreatic Cancer Post-Pancreatic Surgery Using Histology-Based Supervised Machine Learning Algorithms: A Single-Center Retrospective Study
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DOI：
10.1245/s10434-022-11471-x
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发表时间：
2022-03-01
影响因子：
3.7
通讯作者：
Takahashi, Yu
中科院分区：
文献类型：
--
作者：
Hayashi, Koki;Ono, Yoshihiro;Takahashi, Yu
背景胰腺癌（PC）患者预后差，复发率高。由于进一步的治疗适用于特定的复发事件，因此预测手术后的复发模式很重要。本研究旨在使用基于组织学的机器学习模型来识别和预测 PC 的早期和晚期复发模式。患者和方法 2001 年至 2014 年间接受前期 PC 根治性手术的患者均纳入其中。检查每个首次复发部位的复发时间和预后。基于组织学的监督机器学习方法结合了卷积神经网络和随机森林，用于预测复发和各自的转移部位。使用受试者工作特征曲线下面积 (AUC) 评估准确性。结果 总共纳入 524 名患者。术后第一年，肝脏复发占所有复发事件的 47.8%。与此同时，肺部复发发生得较晚，并且可能在术后 5 年以上才变得明显，并有进一步手术的指征。就实质性远处器官转移而言，肝和肺转移被确定为代表性的早期和晚期复发事件。机器学习模型对训练和测试数据的预测 AUC 分别为 1.000 和 0.861，预测不复发的预测 AUC 均为 1.000。结论 我们将肝脏和肺部确定为早期和晚期复发部位，使用机器学习模型可以高概率地区分。使用该模型预测复发部位可能有助于 PC 患者的进一步治疗。
Background Patients with pancreatic cancer (PC) have poor prognosis and a high incidence of recurrence. Since further treatment is applicable for specific recurrent events, it is important to predict recurrence patterns after surgery. This study aimed to identify and predict early and late recurrence patterns of PC using a histology-based machine learning model. Patients and Methods Patients who underwent upfront curative surgery for PC between 2001 and 2014 were included. The timing of recurrence and prognosis of each first recurrence site were examined. A histology-based supervised machine learning method, which combined convolutional neural networks and random forest, was used to predict the recurrence and respective sites of metastasis. Accuracy was evaluated using area under the receiver operating characteristic curve (AUC). Results In total, 524 patients were included. Recurrence in the liver accounted for 47.8% of all recurrence events in the first year after surgery. Meanwhile, recurrence in the lung occurred later and could become apparent more than 5 years post-surgery, with indications for further surgery. In terms of substantial distant organ metastases, liver and lung metastases were identified as representative early and late recurrence events. The predictive AUCs of the machine learning model for training and test data were 1.000 and 0.861, respectively, and for predicting nonrecurrence were 1.000 for both. Conclusions We identified the liver and lung as early and late recurrence sites, which could be distinguished with high probability using a machine learning model. Prediction of recurrence sites using this model may be useful for further treatment of patients with PC.