School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China.
Phys Med Biol. 2020 Feb 12;65(4):045006. doi: 10.1088/1361-6560/ab6e51.
In order to assist doctors in arranging the postoperative treatments and re-examinations for non-small cell lung cancer (NSCLC) patients, this study was initiated to explore a prognostic analysis method for NSCLC based on computed tomography (CT) radiomics. The data of 173 NSCLC patients were collected retrospectively and the clinically meaningful 3-year survival was used as the predictive limit to predict the patient's prognosis survival time range. Firstly, lung tumors were segmented and the radiomics features were extracted. Secondly, the feature weighting algorithm was used to screen and optimize the extracted original feature data. Then, the selected feature data combining with the prognosis survival of patients were used to train machine learning classification models. Finally, a prognostic survival prediction model and radiomics prognostic factors were obtained to predict the prognosis survival time range of NSCLC patients. The classification accuracy rate under cross-validation was up to 88.7% in the prognosis survival analysis model. When verifying on an independent data set, the model also yielded a high prediction accuracy which is up to 79.6%. Inverse different moment, lobulation sign and angular second moment were NSCLC prognostic factors based on radiomics. This study proved that CT radiomics features could effectively assist doctors to make more accurate prognosis survival prediction for NSCLC patients, so as to help doctors to optimize treatment and re-examination for NSCLC patients to extend their survival time.
为协助医生为非小细胞肺癌（NSCLC）患者安排术后治疗和复查，本研究旨在探索一种基于计算机断层扫描（CT）放射组学的 NSCLC 预后分析方法。回顾性收集了 173 例 NSCLC 患者的数据，并将有临床意义的 3 年生存率作为预测极限，以预测患者的预后生存时间范围。首先，对肺肿瘤进行分割，并提取放射组学特征。其次，使用特征加权算法对提取的原始特征数据进行筛选和优化。然后，将选定的特征数据与患者的预后生存情况相结合，用于训练机器学习分类模型。最后，获得预后生存预测模型和放射组学预后因素，以预测 NSCLC 患者的预后生存时间范围。预后生存分析模型的交叉验证分类准确率高达 88.7%。在验证独立数据集时，该模型的预测准确率也高达 79.6%。反矩、分叶征和角二阶矩是基于放射组学的 NSCLC 预后因素。本研究证明 CT 放射组学特征可有效协助医生为 NSCLC 患者做出更准确的预后生存预测，从而帮助医生优化 NSCLC 患者的治疗和复查，延长其生存时间。
