| 叶钉利,姜雯,吴佳妮,黄志成.基于CT影像组学模型预测肺原位腺癌及微浸润腺癌与浸润性腺癌[J].中国医学影像技术,2020,36(9):1345~1349 |
| 基于CT影像组学模型预测肺原位腺癌及微浸润腺癌与浸润性腺癌 |
| CT radiomics model for evaluation on pathologic types of lung adenocarcinoma in situ combined with minimally invasive adenocarcinoma and invasive adenocarcinoma |
| 投稿时间：2019-11-21 修订日期：2020-05-13 |
| DOI：10.13929/j.issn.1003-3289.2020.09.017 |
| 中文关键词: 肺肿瘤 体层摄影术,X线计算机 影像组学 病理学 |
| 英文关键词:lung neoplasms tomography, X-ray computed radiomics pathology |
| 基金项目:吉林省卫生与健康技术创新项目（2018J026）。 |
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| 摘要点击次数: 1832 |
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| 中文摘要: |
| 目的 探讨基于CT影像组学预测肺腺癌中的原位癌（AIS）和微浸润腺癌（MIA）以及浸润性腺癌（IAC）的价值。方法 回顾性分析542例经手术病理确诊且病理亚型明确的肺腺癌患者，将AIS及MIA归为第1组，IAC为第2组。比较2组患者性别和年龄差异。采用特征提取软件提取病灶三维纹理特征参数，分析组间差异明显的影像组学特征，筛选最佳影像组学特征构建预测模型。按2：1比例将数据分为训练集和验证集，采用6种机器学习算法对5倍交叉验证数据集进行分类，选择最佳分类器；以之分析5倍交叉验证数据集、训练集和验证集，获得模型预测肺腺癌病理分型的ROC曲线及相应AUC、特异度、敏感度及准确率。结果 第1组235例，第2组307例，组间性别和年龄差异均无统计学意义（χ2=0.56、t=-0.19，P=0.63、0.98）。共提取病灶1 766个三维纹理特征参数，其中988个影像组学特征存在明显组间差异，最终以10个最佳影像组学特征构建预测模型。以Perceptron分类器为最佳分类器。模型预测验证集病理分型的AUC为0.95，准确率、特异度、敏感度分别为0.88、0.87、0.84。结论 基于CT影像组学模型能有效预测肺腺癌中的AIS及MIA与IAC。 |
| 英文摘要: |
| Objective To investigate the value of CT radiomics model for predicting pathologic types of adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) among lung adenocarinoma. Methods Data of 542 patients with pathologically confirmed lung adenocarcinoma and clear subtypes were retrospective analyzed. AIS and MIA were classified as group 1 while IAC as group 2. The gender and age were compared between 2 groups. Feature extraction software was used to extract three-dimensional texture feature parameters of each lesion, and the imaging omics features obviously different between 2 groups were retained, then the optimal imaging omics features were selected to build a predictive model. All the data were divided into training set and validation set in a ratio of 2:1. Six machine learning algorithms were used to classify the five-fold cross-training sets to select the best classifier. Then, the five-fold cross-training data set, training set and validation set were analyzed with the prediction model to obtain the ROC curves of the model in predicting pathological subtypes of lung adenocarcinoma as well as the relative AUC, accuracy, specificity and sensitivity. Results There were 235 patients in group 1 and 307 in group 2. No statistical difference of gender nor age was found between 2 groups (χ2=0.56, t=-0.19, P=0.63, 0.98). A total of 1 766 three-dimensional texture feature parameters were extracted from the lesions, including 988 imaging omics features significantly different between 2 groups. Finally, 10 optimal imaging omics features were retained to construct the prediction model. Perceptron classifier was the best classifier. AUC of the predictive model in predicting pathological subtypes of validation set was 0.95, and the relative accuracy, specificity and sensitivity was 0.88, 0.87 and 0.84, respectively. Conclusion CT radiomics medel could effectively predict pathological subtypes of AIS, MIA and IAC among lung adenocarcinoma. |
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