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

Yan Zuo; Qiufang Liu; Nan Li; Panli Li; Jianping Zhang; Shaoli Song

doi:10.3389/fonc.2023.1173355

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

Front Oncol. 2023 May 8:13:1173355. doi: 10.3389/fonc.2023.1173355. eCollection 2023.

Authors

Yan Zuo¹, Qiufang Liu¹, Nan Li¹, Panli Li¹, Jianping Zhang^{1

2}, Shaoli Song¹

Affiliations

¹ Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China.
² Shanghai Key Laboratory of Bioactive Small Molecules, Fudan University, Shanghai, China.

Abstract

Purpose: To 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.

Methods: The 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.

Results: Among 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.

Conclusions: The 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.

Keywords: PET/CT; epidermal growth factor receptor; lung adenocarcinoma; machine learning; radiomics.

Grants and funding

This work was supported by National Natural Science Foundation of China (Grant numbers: 81971648, 82001866), Innovative research team of high-level local universities in Shanghai and a key laboratory program of the Education Commission of Shanghai Municipality (Grant numbers: ZDSYS14005), Medical-engineering integration program of Fudan university (Grant numbers: YG2021-001).