Value of machine learning and 18F-FDG PET/CT radiomics features in lung adenocarcinoma EGFR mutation subtypes prediction
10.3760/cma.j.cn321828-20201105-00401
- VernacularTitle:机器学习结合 18F-FDG PET/CT影像组学特征对肺腺癌EGFR突变亚型的预测价值
- Author:
Ziyang WANG
1
;
Guotao YIN
;
Xiaofeng LI
;
Wengui XU
Author Information
1. 天津医科大学肿瘤医院分子影像及核医学诊疗科、国家肿瘤临床医学研究中心、天津市肿瘤防治重点实验室、天津市恶性肿瘤临床研究中心 300060
- Keywords:
Lung neoplasms;
Adenocarcinoma;
Receptor, epidermal growth factor;
Mutation;
Positron-emission tomography;
Tomography, X-ray computed;
Deoxyglucose;
Forecast
- From:
Chinese Journal of Nuclear Medicine and Molecular Imaging
2021;41(8):479-485
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the predictive values for mutation subtypes of epidermal growth factor receptor (EGFR) in patients with lung adenocarcinoma (LUAD) based on machine learning and 18F-fluorodeoxyglucose (FDG) PET/CT images. Methods:18F-FDG PET/CT images and pathological data of 238 patients with LUAD (126 patients (54 males, 72 females, median age 62 years) with EGFR mutation; 112 patients (68 males, 44 females, median age 61 years) with wild-type EGFR)) were retrospectively collected at Tianjin Medical University Cancer Institute and Hospital between April 2016 and May 2020. Volumes of interest (VOI) of PET and CT images were delineated respectively and three-dimensional-based and two-dimensional-based radiomics features were extracted from VOIs. Three machine learning classifiers of K-nearest neighbor (KNN), support vector machine (SVM) and Adaboost were trained in training set with CT, PET and fusion PET/CT radiomics features respectively. Well trained classifiers were tested in test set. Each predictive model was evaluated by using the receiver operating characteristic (ROC) curve. Results:A total of 126 patients were EGFR mutation including 3 patients with 18 exon mutation, 6 patients with 20 exon mutation, 42 patients with 19 exon mutation, and 75 patients with 21 exon mutation. Finally, patients with 18 exon mutation and 20 exon mutation were removed due to the scale was too small to be trained adequately by machine learning classifiers. Predictive performance of mean PET/CT feature-based model (Adaboost: area under curve (AUC)=0.87, 95% CI: 0.75-0.99) in EGFR mutation subtypes was better than PET feature-based model (Adaboost: AUC=0.64, 95% CI: 0.46-0.83; z=2.04, P<0.05) and CT feature-based model (Adaboost: AUC=0.64, 95% CI: 0.45-0.83; z=2.06, P<0.05). There was no statistical difference between predictive performance of mean PET/CT feature-based model (SVM: AUC=0.76, 95% CI: 0.56-0.96) and PET/CT concatenation feature-based model (SVM: AUC=0.75, 95% CI: 0.59-0.92; z=1.14, P>0.05). Conclusion:Machine learning and 18F-FDG PET/CT radiomics features can provide predictive value for EGFR mutation subtypes in patients with LUAD.
