| Hu Jiang,He Ruimin,Cheng Pinjing,Liu Xiaomin,Wu Haibiao,Liu Linfei,Wang Baiqi,Cheng Hao,Yang Junhui.Prediction of EGFR mutant subtypes in patients with non-small cell lung cancer by pre-treatment CT radiomics and machine learning[J].Chinese Journal of Radiological Medicine and Protection,2023,43(5):386-392 |
| Prediction of EGFR mutant subtypes in patients with non-small cell lung cancer by pre-treatment CT radiomics and machine learning |
| Received:December 23, 2022 |
| DOI：10.3760/cma.j.cn112271-20221223-00495 |
| KeyWords:Non-small cell lung cancer Epidermal growth factor receptor Computed tomography Radiomics Machine learning |
| FundProject:湖南省高校创新平台开放基金项目(20K110) |
| Author Name | Affiliation | E-mail | | Hu Jiang | School of Nuclear Science and Technology, University of South China, Hengyang 421001, China | | | He Ruimin | Department of Radiation Oncology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang 421001, China | | | Cheng Pinjing | School of Nuclear Science and Technology, University of South China, Hengyang 421001, China | nhuchpj@aliyun.com | | Liu Xiaomin | Hengyang Medical School, University of South China, Hengyang 421001, China | | | Wu Haibiao | The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang 421001, China | | | Liu Linfei | School of Nuclear Science and Technology, University of South China, Hengyang 421001, China | | | Wang Baiqi | Department of Radiation Oncology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang 421001, China | | | Cheng Hao | Department of Radiation Oncology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang 421001, China | | | Yang Junhui | School of Nuclear Science and Technology, University of South China, Hengyang 421001, China | |
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| Abstract:: |
| Objective To evaluate the feasibility and clinical value of pre-treatment non-enhanced chest CT radiomics features and machine learning algorithm to predict the mutation status and subtype (19Del/21L858R) of epidermal growth factor receptor (EGFR) for patients with non-small cell lung cancer (NSCLC). Methods This retrospective study enrolled 280 NSCLC patients from first and second affiliated hospital of University of South China who were confirmed by biopsy pathology, gene examination, and have pre-treatment non-enhanced CT scans. There are 136 patients were confirmed EGFR mutation. Primary lung gross tumor volume was contoured by two experienced radiologists and oncologists, and 851 radiomics features were subsequently extracted. Then, spearman correlation analysis and RELIEFF algorithm were used to screen predictive features. The two hospitals were training and validation cohort, respectively. Clinical-radiomics model was constructed using selected radiomics and clinical features, and compared with models built by radiomics features or clinical features respectively. In this study, machine learning models were established using support vector machine (SVM) and a sequential modeling procedure to predict the mutation status and subtype of EGFR. The area under receiver operating curve (AUC-ROC) was employed to evaluate the performances of established models. Results After feature selection, 21 radiomics features were found to be efffective in predicting EGFR mutation status and subtype and were used to establish radiomics models. Three types models were established, including clinical model, radiomics model, and clinical-radiomics model. The clinical-radiomics model showed the best predictive efficacy, AUCs of predicting EGFR mutation status for training dataset and validation dataset were 0.956 (95%CI: 0.952-1.000) and 0.961 (95%CI: 0.924-0.998), respectively. The AUCs of predicting 19Del/L858R mutation subtype for training dataset and validation dataset were 0.926 (95%CI: 0.893-0.959), 0.938 (95%CI: 0.876-1.000), respectively. Conclusions The constructed sequential models based on integration of CT radiomics, clinical features and machine learning can accurately predict the mutation status and subtype of EGFR. |
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