Accurate prediction of epidermal growth factor receptor mutation status in early‐stage lung adenocarcinoma, using radiomics and clinical features

• Published in: Asia-Pacific journal of clinical oncology 2022-12, Vol.18 (6), p.586-594
• Main Authors: Zhu, Huiyuan, Song, Yueqiang, Huang, Zike, Zhang, Lian, Chen, Yanqing, Tao, Guangyu, She, Yunlang, Sun, Xiwen, Yu, Hong
• Format: Article
• Language: English
• Subjects: Adenocarcinoma; Cancer; Emphysema; Epidermal growth factor; epidermal growth factor receptor; Epidermal growth factor receptors; lung adenocarcinoma; Lungs; Mutation; Prediction models; Radiomics; Regression analysis; spiral computed; tomography
• ISSN: 1743-7555; 1743-7563
• DOI: 10.1111/ajco.13641

Objectives To develop a nomogram based on CT radiomics and clinical features to predict the epidermal growth factor receptor (EGFR) mutations in early‐stage lung adenocarcinomas. Methods A retrospective analysis of postoperative patients with pathologically confirmed lung adenocarcinoma, which had been tested for EGFR mutations was performed from January 2015 to December 2015. Patients were randomly assigned to training and validation cohorts. A total of 1,078 radiomics features were extracted. least absolute shrinkage and selection operator (LASSO) regression analysis was applied to select clinical and radiomics features, and to establish predictive models. The radiomics score (rad‐score) of each patient was calculated. The discrimination of the model was evaluated with area under the curve. Results 1092 patients (444 men and 648 women; mean age: 59.59±9.6) were enrolled. The radiomics signature consisted of 28 radiomics features and emphysema. The mean validation cohort result of the rad‐score for patients with EGFR mutations (0.814±0.988) was significantly higher than those with EGFR wild‐type (0.315±1.237; p = 0.001). When combined with clinical features, LASSO regression analysis revealed four radiomics features, emphysema, and three clinical features including sex, age, and histologic subtype as associated with to EGFR mutation status. The nomogram that combined radiomics and clinical features significantly improved the predictive discrimination (AUC: 0.723), which is better than that of the radiomics signature alone (AUC: 0.646). Conclusion A relationship between selected radiomics features and EGFR mutant lung adenocarcinomas is demonstrated. A nomogram, combining radiomics features and clinical features for EGFR prediction in early‐stage lung adenocarcinomas, has shown a moderate discriminatory efficiency and high sensitivity, providing additional information for clinicians. We develop a nomogram for predicting epidermal growth factor receptor mutations. Locate the value each variable axis and draw a vertical line to the “Points” axis to determine points, locate the sum of points for all variables on the “Total Points” line to obtain the individual probability of EGFR mutation.