Optimal 18F-FDG PET/CT radiomics model development for predicting EGFR mutation status and prognosis in lung adenocarcinoma: a multicentric study

Frontiers in Oncology

Oncology

Nan Li¹  Panli Li¹  Shaoli Song¹  Yan Zuo¹  Qiufang Liu¹  Jianping Zhang² 

[1] Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China;Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China;Shanghai Key Laboratory of Bioactive Small Molecules, Fudan University, Shanghai, China;

关键词: lung adenocarcinoma;    PET/CT;    epidermal growth factor receptor;    radiomics;    machine learning;   

DOI  :  10.3389/fonc.2023.1173355

 received in 2023-02-24, accepted in 2023-04-24,  发布年份 2023

来源: Frontiers

【 摘 要 】

PurposeTo develop and interpret optimal predictive models to identify epidermal growth factor receptor (EGFR) mutation status and subtypes in patients with lung adenocarcinoma based on multicentric 18F-FDG PET/CT data, and further construct a prognostic model to predict their clinical outcome.MethodsThe 18F-FDG PET/CT imaging and clinical characters of 767 patients with lung adenocarcinoma from 4 cohorts were collected. Seventy-six radiomics candidates using cross-combination method to identity EGFR mutation status and subtypes were built. Further, Shapley additive explanations and local interpretable model-agnostic explanations were used for optimal models’ interpretation. Moreover, in order to predict the overall survival, a multivariate Cox proportional hazard model based on handcrafted radiomics features and clinical characteristics was constructed. The predictive performance and clinical net benefit of the models were evaluated via area under receiver operating characteristic (AUC), C-index and decision curve analysis. ResultsAmong the 76 radiomics candidates, light gradient boosting machine classifier (LGBM) combined with recursive feature elimination wrapped LGBM feature selection method achieved best performance in predicting EGFR mutation status (AUC reached 0.80, 0.61, 0.71 in the internal test cohort and two external test cohorts, respectively). And extreme gradient boosting classifier combined with support vector machine feature selection method achieved best performance in predicting EGFR subtypes (AUC reached 0.76, 0.63, 0.61 in the internal test cohort and two external test cohorts, respectively). The C-index of the Cox proportional hazard model achieved 0.863.ConclusionsThe integration of cross-combination method and the external validation from multi-center data achieved a good prediction and generalization performance in predicting EGFR mutation status and its subtypes. The combination of handcrafted radiomics features and clinical factors achieved good performance in predicting prognosis. With the urgent needs of multicentric 18F-FDG PET/CT trails, robust and explainable radiomics models have great potential in decision making and prognosis prediction of lung adenocarcinoma.

【 授权许可】

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Copyright © 2023 Zuo, Liu, Li, Li, Zhang and Song