Development and validation of a multivariable predictive model for EGFR gene mutation status in patients with lung adenocarcinoma

Yiping Shi, Qiongfang Zha, Qing Ye, Shan Xue, Huawei Wu, Daoqiang Tang,  Hui Qin,  Jing Zou

Abstract:

Detection of epidermal growth factor receptor (EGFR) is one real dilemma owing to the non-sufficient tissue for testing EGFR mutations in lung adenocarcinoma. A model for predicting EGFR mutations would be helpful for clinical decisions in those patients. A retrospective cohort of 1,196 patients diagnosed with lung adenocarcinoma was investigated between December 1, 2017, and December 31, 2019, in Renji Hospital, Shanghai, China. All patients were tested for EGFR mutations (amplification refractory mutation system, n=1,144; next-generation sequencing, n=52). Of 1,196 patients with lung adenocarcinoma, 944 met the inclusion criteria. A nomogram model was developed based on 567 patients and validated in 377 patients. Variables associated with EGFR mutations were age, sex, smoking history, lepidic predominant subtype, solid predominant subtype, mucinous adenocarcinoma, Ki67 expression, lobulation, solid texture in radiology, and pleural retraction. The nomogram based on the model performed well in the development group (c-index 0.789, 95% CI: 0.751–0.827), and the validation group (c-index 0.809, 95% CI: 0.771–0.847). At the probability cut-point of 0.7, the diagnostic efficiency was 82.7% in patients with NGS liquid biopsy. Decision curve analysis further confirmed the clinical usefulness of the nomogram, which showed that predicting the EGFR mutations probability applying this nomogram would be better than having all patients or none patients use this nomogram. A high probability group (>0.7) by nomogram model may suggest a high possibility of EGFR mutation, if tissue is limited, NGS-based ctDNA with liquid biopsy could be implemented effectively.