[1]丛玉林,徐小虎,沈春林,等.基于CT特征构建预测肺结节良恶性的机器学习模型[J].中国医学物理学杂志,2024,41(10):1315-1320.[doi:DOI:10.3969/j.issn.1005-202X.2024.10.017]
　CONG Yulin,XU Xiaohu,SHEN Chunlin,et al.Machine learning model predicts benign and malignant pulmonary nodules based on CT features[J].Chinese Journal of Medical Physics,2024,41(10):1315-1320.[doi:DOI:10.3969/j.issn.1005-202X.2024.10.017]
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基于CT特征构建预测肺结节良恶性的机器学习模型()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
41卷
期数:
2024年第10期
页码:
1315-1320
栏目:
医学人工智能
出版日期:
2024-10-25

文章信息/Info

Title:
Machine learning model predicts benign and malignant pulmonary nodules based on CT features
文章编号:
1005-202X（2024）10-1315-06
作者:
丛玉林; 徐小虎; 沈春林; 许亚春
海安市人民医院影像科， 江苏 海安 226600
Author(s):
CONG Yulin;  XU Xiaohu;  SHEN Chunlin;  XU Yachun
Department of Imaging, Haian Peoples Hospital, Haian 226600, China
关键词:
CT特征; 模型构建; 肺结节; 良恶性; 机器学习模型
Keywords:
Keywords: CT feature model construction pulmonary nodule benign and malignance machine learning model
分类号:
R318；R734.2
DOI:
DOI:10.3969/j.issn.1005-202X.2024.10.017
文献标志码:
A
摘要:
目的：基于CT特征构建预测肺结节良恶性的机器学习模型。方法：选取海安市人民医院于2021年1月至2023年1月间CT上表现单发亚实性结节的患者129例，所有病例均行胸部CT扫描，记录病灶定量参数、形态学和影像组学特征。根据相关诊断标准进行肺结节良恶性分型，病例用于划分训练集、内部测试集。模型包括组学标签、形态学模型、CT模型、综合模型。结果：研究纳入训练集98例（恶性27例，良性71例），内部测试集31例（恶性7例，良性24例）。单因素分析显示，年龄、病灶直径、平均密度、毛刺征、胸膜凹陷征、空泡征、空气支气管征在恶性组和良性组间差异有统计学意义（P<0.05），恶性组平均密度、毛刺征、胸膜凹陷征、空泡征、空气支气管征、病灶直径大于良性组（P<0.05）；LinkDocAI-肺结节智能诊断系统勾画感兴趣区域并提取其中的1 000个组学特征，从98例病例里面予以相应的特征挑选，特征剔除缺失、标准化处理、低度重要特征值、高度相关特征、相关性检验结束后挑选特征共20个。利用十折交叉验证及LASSO回归，以λ1se为最优的λ构建组学标签，最终将Gradient_Shape_MinorAxis、LBP_Glszm_ZoneEntropy两个最有意义特征纳入；本研究选CT模型为最优模型，该模型在训练集、内部测试集的受试者特征曲线下面积分别为0.912、0.889。结论：基于CT特征构建预测肺结节良恶性的机器学习模型具有较好的预测效能，能对肺结节良恶性分型进行鉴别诊断。
Abstract:
Abstract: Objective To construct a machine learning model for predicting benign and malignant pulmonary nodules based on CT features. Methods A total of 129 patients with single solid nodules on CT from January 2021 to January 2023 in Haian Peoples Hospital were selected. All of them underwent chest CT scan, and the quantitative parameters, morphological features and radiomics features were recorded. The differentiation of benign and malignant pulmonary nodules was carried out according to relevant diagnostic criteria. The cases were divided into the training set and the internal test set. The constructed models included radiomics labels, morphological model, CT model and combined model. Results There were 98 cases in the training set (27 malignance and 71 benign) and 31 cases in the internal test set (7 malignance and 24 benign). Univariate analysis showed that there were significant differences in age, lesion diameter, mean density, burr sign, pleural depression sign, vacuole sign and air bronchial sign between malignant group and benign group (P<0.05). Compared with benign group, malignant group had higher proportions of burr sign, pleural depression sign, vacuole sign, air bronchial sign, and larger lesion diameter and mean density (P<0.05). LinkDocAI intelligent diagnosis system for pulmonary nodules was used to outline regions of interest and from which 1 000 radiomics features were extracted. The feature selection was performed in 98 cases, and 20 features were screened out after standardized treatment and correlation testing, excluding missing features, low importance feature values and highly correlated features. Through LASSO regression and 10-fold cross validation, λ1se was selected as the optimal λ to construct radiomics labels, and the two most meaningful features (LBP_Glszm_ZoneEntropy and Gradient_Shape_MinorAxis) were enrolled. CT model was considered as the optimal model in this study, and it had an area under receiver operating characteristic curve of 0.912 and 0.889 in the training set and the internal testing set, respectively. Conclusion The machine learning model to predict benign and malignant lung nodules based on CT features has good predictive efficiency, and it can realize the differential diagnosis of benign and malignant pulmonary nodules.

相似文献/References:

[1]许郭婷,吴爱荣,林嘉希,等.基于深度卷积神经网络的上消化道内镜解剖分类模型构建[J].中国医学物理学杂志,2023,40(8):1051.[doi:DOI:10.3969/j.issn.1005-202X.2023.08.021]
　XU Guoting,WU Airong,et al.Development of anatomical classification models for upper gastrointestinal endoscopy based on deep convolutional neural networks[J].Chinese Journal of Medical Physics,2023,40(10):1051.[doi:DOI:10.3969/j.issn.1005-202X.2023.08.021]

备注/Memo

备注/Memo:
【收稿日期】2024-06-11 【基金项目】南通市卫健委指令性面上课题（MS2022100） 【作者简介】丛玉林，住院医师，研究方向：影像诊断，E-mail: cyl_yulin@163.com 【通信作者】许亚春，主任医师，研究方向：胸部影像诊断，E-mail: 398461468@qq.com
更新日期/Last Update: 2024-10-29