[1]戴倩,王梦,黄钢.基于18F-FDG PET CT影像组学的非小细胞肺癌病理亚型分类[J].中国医学物理学杂志,2023,40(4):416-422.[doi:DOI:10.3969/j.issn.1005-202X.2023.04.004]
　DAI Qian,WANG Meng,HUANG Gang.Classification of pathological subtypes of non-small-cell lung cancer based on 18F-FDG PET CT radiomics[J].Chinese Journal of Medical Physics,2023,40(4):416-422.[doi:DOI:10.3969/j.issn.1005-202X.2023.04.004]
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基于18F-FDG PET CT影像组学的非小细胞肺癌病理亚型分类()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
40卷
期数:
2023年第4期
页码:
416-422
栏目:
医学影像物理
出版日期:
2023-04-25

文章信息/Info

Title:
Classification of pathological subtypes of non-small-cell lung cancer based on 18F-FDG PET CT radiomics
文章编号:
1005-202X（2023）04-0416-07
作者:
戴倩1; 王梦1; 黄钢2
1.上海理工大学健康科学与工程学院， 上海 200093； 2.上海健康医学院上海分子影像学重点实验室， 上海 201318
Author(s):
DAI Qian1;  WANG Meng1;  HUANG Gang2
1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 2. Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
关键词:
影像组学; 非小细胞肺癌; 18F-FDG PET CT; 病理亚型; 机器学习
Keywords:
Keywords: radiomics non-small-cell lung cancer 18F-FDG PET/CT pathological subtype machine learning
分类号:
R318；R734.2
DOI:
DOI:10.3969/j.issn.1005-202X.2023.04.004
文献标志码:
A
摘要:
目的：旨在建立一种基于18F-FDG PET CT的临床—影像组学相结合的综合模型用于区分非小细胞肺癌中的腺癌和鳞癌。方法：回顾性收集上海交通大学附属胸科医院120例经病理学验证为腺癌（65例）和鳞癌（55例）的患者，从预处理的CT图像和PET图像中分别提取1 218、108个影像组学特征，并纳入10个临床特征因素；卡方检验和Wilcoxon检验用于对临床特征进行筛选，并使用Relief算法和最小绝对收缩和选择算子（LASSO）对影像组学特征进行筛选；通过6种机器学习分类器分别建立临床、影像组学、综合模型。通过受试者工作特征（ROC）曲线及曲线下面积（AUC）来评价模型的分类能力。结果：综合模型在训练集和测试集中均表现出最高的AUC值和准确率，其中随机森林（RF）和Bagging分类器表现出的分类效果最佳。经五折交叉验证后，训练集中RF和Bagging的AUC值和准确率分别为0.92±0.03、0.86±0.06和0.92±0.02、0.83±0.02；测试集中RF和Bagging的AUC值和准确率分别为0.92、0.81和0.91、0.86。结论：结合18F-FDG PET CT临床特征和影像组学特征的分类预测模型可以很好地区分腺癌、鳞癌。
Abstract:
Abstract: Objective To establish a comprehensive clinical-radiomics model based on 18F-FDG PET/CT for differentiating adenocarcinoma and squamous cell carcinoma in non-small-cell lung cancer. Methods A total of 120 cases of pathologically verified adenocarcinoma (n=65) and squamous cell carcinoma (n=55) from Shanghai Chest Hospital were collected retrospectively. In addition to 1 218 and 108 radiomics signatures extracted from the preprocessed CT images and PET images, 10 clinical features were included. Chi-square test and Wilcoxon test were used to screen clinical features, and radiomic signatures were screened using Relief algorithm and least absolute shrinkage and selection operator. Six machine learning classifiers were used to build clinical, radiomics, and comprehensive models. The classification ability of the model was evaluated using receiver operating characteristic (ROC) curve and area under curve (AUC). Results The comprehensive model exhibited the highest AUC and accuracy in both training and test sets, with random forest (RF) and Bagging classifiers showing the best classification results. After 5-fold cross-validation, the AUC and accuracy of RF in the training set were 0.92±0.03, 0.86±0.06, while those of Bagging were 0.92±0.02, 0.83±0.02. In the test set, RF and Bagging also had the optimal classification performances (RF: AUC=0.92, accuracy=0.81 Bagging: AUC=0.91, accuracy=0.86). Conclusion The classification prediction model combining 18F-FDG PET/CT clinical features and radiomics signatures can be well used to distinguish adenocarcinoma and squamous cell carcinoma.

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备注/Memo

备注/Memo:
【收稿日期】2022-11-08 【基金项目】国家自然科学基金（82127807）；上海市分子影像学重点实验室建设项目（18DZ2260400） 【作者简介】戴倩，硕士，研究方向：医学图像处理，E-mail: 763336113@qq.com 【通信作者】黄钢，博士，研究方向：核医学分子影像，E-mail: huanggang@sumhs.cn
更新日期/Last Update: 2023-04-25