Value of multi‐center 18F‐FDG PET/CT radiomics in predicting EGFR mutation status in lung adenocarcinoma

Author:

Zuo Yan12,Liu Liu3,Chang Cheng3,Yan Hui3,Wang Lihua3,Sun Dazhen4,Ruan Maomei3,Lei Bei3,Xia Xunpeng5,Xie Wenhui3,Song Shaoli2,Huang Gang1ORCID

Affiliation:

1. Shanghai Key Laboratory of Molecular Imaging Shanghai University of Medicine and Health Sciences Shanghai China

2. Department of nuclear medicine Fudan University Shanghai Cancer Center Shanghai China

3. Department of nuclear medicine Shanghai Chest Hospital School of Medicine Shanghai Jiao Tong University Shanghai China

4. Department of Automation Shanghai Jiaotong University Shanghai China

5. School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China

Abstract

AbstractBackgroundAccurate, noninvasive, and reliable assessment of epidermal growth factor receptor (EGFR) mutation status and EGFR molecular subtypes is essential for treatment plan selection and individualized therapy in lung adenocarcinoma (LUAD). Radiomics models based on 18F‐FDG PET/CT have great potential in identifying EGFR mutation status and EGFR subtypes in patients with LUAD. The validation of multi‐center data, model visualization, and interpretation are significantly important for the management, application and trust of machine learning predictive models. However, few EGFR‐related research involved model visualization and interpretation, and multi‐center trial.PurposeTo develop explainable optimal predictive models based on handcrafted radiomics features (HRFs) extracted from multi‐center 18F‐FDG PET/CT to predict EGFR mutation status and molecular subtypes in LUAD.MethodsBaseline 18F‐FDG PET/CT images of 383 LUAD patients from three hospitals and one public data set were collected. Further, 1808 HRFs were extracted from the primary tumor regions using Pyradiomics. Predictive models were built based on cross‐combination of seven feature selection methods and seven machine learning algorithms. Yellowbrick and explainable artificial intelligence technology were used for model visualization and interpretation. Receiver operating characteristic curve, classification report and confusion matrix were used for model performance evaluation. Clinical applicability of the optimal models was assessed by decision curve analysis.ResultsSTACK feature selection method combined with light gradient boosting machine (LGBM) reached optimal performance in identifying EGFR mutation status ([area under the curve] AUC = 0.81 in the internal test cohort; AUC = 0.62 in the external test cohort). Random forest feature selection method combined with LGBM reached optimal performance in predicting EGFR mutation molecular subtypes (AUC = 0.89 in the internal test cohort; AUC = 0.61 in the external test cohort).ConclusionsExplainable machine learning models combined with radiomics features extracted from multi‐center/scanner 18F‐FDG PET/CT have certain potential to identify EGFR mutation status and subtypes in LUAD, which might be helpful to the treatment of LUAD.

Funder

National Basic Research Program of China

National Natural Science Foundation of China

Natural Science Foundation of Shanghai Municipality

Publisher

Wiley

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