Establishment of prognostic nomograms for predicting the progression free survival of EGFR-sensitizing mutation, advanced lung cancer patients treated with EGFR-TKIs.

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BackgroundThere is a lack of clinically available predictive models for patients with epidermal growth factor receptor (EGFR) mutation positive, advanced non–small cell lung cancer (NSCLC) treated with EGFR‐tyrosine kinase inhibitors (TKIs).MethodsThe clinical data of patients at the Cancer Hospital, Chinese Academy of Medical Sciences between from January 2016 to January 2021 were retrospectively retrieved as training set. The patients from BENEFIT trial were for the validation cohort. The nomogram was built based on independent predictors identified by univariate and multivariate Cox regression analyses. The discrimination and calibration of the nomogram were evaluated by C‐index and calibration plots.ResultsA total of 502 patients with complete clinical data and follow‐up information were enrolled in this study. Five independent prognostic factors, including The Eastern Cooperative Oncology Group Performance Status scale (ECOG PS), EGFR mutation subtype, EGFR co‐mutation, liver metastasis and malignant pleural effusion (p < 0.05). The C‐indexes of the nomogram were 0.694 (95% confidence interval [CI], 0.663–0.725) for the training set and 0.653 (95% CI, 0.610–0.696) for the validation set. The calibration curves for the probabilities of 9‐, 12‐ and 18‐month progression‐free survival (PFS) revealed satisfactory consistency in both the internal and external validations. Additionally, the patients were divided into two groups according to risk (high‐risk, low‐risk), and significant differences in PFS were observed between the groups in the training and external validation cohorts (p < 0.001).ConclusionsWe constructed and validated a convenient nomogram that have the potential to become an accurate and reliable tool for patients with EGFR mutation positive, advanced NSCLC to individually predict their potential benefits from EGFR‐TKIs, and facilitate clinical decision‐making.