Gao Huan, He Zhi-Yi, Du Xing-Li, Wang Zheng-Gang, Xiang Li
School of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, China.
Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Front Oncol. 2022 May 13;12:817372. doi: 10.3389/fonc.2022.817372. eCollection 2022.
This study aimed to develop an artificial neural network (ANN) model for predicting synchronous organ-specific metastasis in lung cancer (LC) patients.
A total of 62,151 patients who diagnosed as LC without data missing between 2010 and 2015 were identified from Surveillance, Epidemiology, and End Results (SEER) program. The ANN model was trained and tested on an 75/25 split of the dataset. The receiver operating characteristic (ROC) curves, area under the curve (AUC) and sensitivity were used to evaluate and compare the ANN model with the random forest model.
For distant metastasis in the whole cohort, the ANN model had metrics AUC = 0.759, accuracy = 0.669, sensitivity = 0.906, and specificity = 0.613, which was better than the random forest model. For organ-specific metastasis in the cohort with distant metastasis, the sensitivity in bone metastasis, brain metastasis and liver metastasis were 0.913, 0.906 and 0.925, respectively. The most important variable was separate tumor nodules with 100% importance. The second important variable was visceral pleural invasion for distant metastasis, while histology for organ-specific metastasis.
Our study developed a "two-step" ANN model for predicting synchronous organ-specific metastasis in LC patients. This ANN model may provide clinicians with more personalized clinical decisions, contribute to rationalize metastasis screening, and reduce the burden on patients and the health care system.
本研究旨在开发一种人工神经网络（ANN）模型，用于预测肺癌（LC）患者的同步器官特异性转移。
从监测、流行病学和最终结果（SEER）计划中识别出2010年至2015年间共62151例诊断为LC且无数据缺失的患者。ANN模型在数据集的75/25分割上进行训练和测试。使用受试者工作特征（ROC）曲线、曲线下面积（AUC）和敏感性来评估ANN模型并与随机森林模型进行比较。
对于整个队列中的远处转移，ANN模型的指标为AUC = 0.759，准确率 = 0.669，敏感性 = 0.906，特异性 = 0.613，优于随机森林模型。对于有远处转移的队列中的器官特异性转移，骨转移、脑转移和肝转移的敏感性分别为0.913、0.906和0.925。最重要的变量是独立肿瘤结节，重要性为100%。第二个重要变量是远处转移的脏层胸膜侵犯，而器官特异性转移的是组织学。
我们的研究开发了一种“两步”ANN模型，用于预测LC患者的同步器官特异性转移。这种ANN模型可能为临床医生提供更个性化的临床决策，有助于使转移筛查合理化，并减轻患者和医疗保健系统的负担。
