| 蔡俊辉,段绍峰,袁虎,吕燕,吴雅蔚,许晴,叶靖.机器学习鉴别表现为肺纯磨玻璃结节的浸润性腺癌与非浸润性腺癌[J].中国医学影像技术,2020,36(3):405~410 |
| 机器学习鉴别表现为肺纯磨玻璃结节的浸润性腺癌与非浸润性腺癌 |
| Machine learning in differentiating pulmonary invasive adenocarcinoma from non-invasive adenocarcinoma manifested as pure ground-glass nodule |
| 投稿时间：2019-08-09 修订日期：2019-12-05 |
| DOI：10.13929/j.issn.1003-3289.2020.03.021 |
| 中文关键词: 肺肿瘤 腺癌 体层摄影术,X线计算机 机器学习 |
| 英文关键词:lung neoplasms adenocarcinoma tomography,X-ray computed machine learning |
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| 中文摘要: |
| 目的 探讨基于影像组学特征构建的机器学习模型鉴别表现为肺纯磨玻璃结节的浸润性腺癌与非浸润性腺癌的可行性。方法 回顾性分析经手术病理证实的87例CT表现为纯磨玻璃结节的肺腺癌患者，其中浸润性腺癌32例，非浸润性腺癌55例（原位癌17例，微浸润性腺癌38例）。应用ITK-SNAP软件勾画ROI，A.K.软件提取影像组学特征。筛选有意义的特征参数，以Spearman相关性分析和Lasso回归分析进行特征降维。选取降维后的特征参数，分别构建支持向量机（SVM）、随机森林（RF）及逻辑回归（LR）3种机器学习模型，采用十折交叉验证法得到最优模型，绘制ROC曲线，评价3种模型的性能。结果 共提取396个影像组学特征，通过特征筛选后最终得到19个影像组学特征。SVM、RF、LR 3种机器学习模型可有效鉴别浸润性腺癌与非浸润性腺癌，准确率分别为93.30%、86.70%和83.30%，AUC分别为0.94、0.92和0.83。结论 基于影像组学特征构建的机器学习模型有较好的分类性能，可于术前有效鉴别肺浸润性腺癌与非浸润性腺癌。 |
| 英文摘要: |
| Objective To investigate the value of machine learning model based on radiomic features in differentiating pulmonary invasive adenocarcinoma from non-invasive adenocarcinoma manifested as pure ground-glass nodule (pGGN). Methods A total of 87 lung adenocarcinomas CT presented as pGGN were analyzed retrospectively, including 32 cases with invasive adenocarcinoma (IAC) and 55 cases with non-IAC (17 adenocarcinomas in-situ[AIS] and 38 minimally invasive adenocarcinomas[MIA]). The software ITK-SNAP was used to draw ROI, and the radiomic features were extracted using A.K. analysis software. After screening the significant characteristic parameters, the feature dimensionality reduction was conducted with Spearman analysis and Lasso regression. The final feature parameters were selected to construct three machine learning models, including support vector machine (SVM), random forest (RF) and logistics regression (LR). Then 10-fold cross validation was used to get the optimal model, and ROC curve was drawn to evaluate the performance of 3 models.Results A total of 396 radiomic features were extracted, and 19 features were finally obtained after feature screening. Machine learning models SVM, RF and LR could effectively distinguish IAC from non-IAC, with the accuracies of 93.30%, 86.70% and 83.30%, and AUC of 0.94, 0.92 and 0.83 respectively.Conclusion Machine learning models based on radiomic features have good classified performances, which can effectively distinguish IAC from non-IAC manifested as pGGN preoperation. |
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