Machine learning-based algorithm demonstrates differences in del19 and L858R EGFR subgroups in non-small cell lung cancer: a single center experience.

Background: Exon del19 and L858R mutations account for 90% of EGFR mutant non-small cell lung cancer (NSCLC). LUX lung 3 and 6 initially reported a survival difference between these two. However, other studies did not demonstrate the same. By using machine learning (ML), it is possible to discover novel patterns for cancer susceptibility, recurrence, prognostication, and therapy. We evaluate the effect of these two molecular subtypes on overall survival/progression-free survival (OS/PFS).

Methods: 413 patients with stage IV EGFR mutant NSCLC were analyzed for clinicopathologic features, treatment details, and survival outcome. PFS prediction models were built using ensemble decision trees, and random forest. Ensemble decision trees were built and validation was performed using survival analysis. Clustering regression techniques were then applied to train and test the prediction of the 1 PFS of patients.

Results: The median age of the cohort was 59 years comprising 53% males and 47% females. 275 (66.5%) patients showed a del19 mutation type and 138 (33.5%) harbored L858R. After clustering, the most important variables were age (P<0.05), ECOG performance status (PS) (P<0.04), PDL1 (P<0.09), smoking status (P<0.01) and to a lesser extent, number of extrathoracic metastasis (ETM) sites (median 1.2, P<0.06), brain metastasis (P<0.06) and gender (P<0.08). The prediction for 1 PFS for del19 showed mean absolute error of 2.6 months and 4.72 months for L858R. The accuracy was 79.8% with 82% sensitivity, 79% specificity and AUC: 0.72. The precision was 92% with a Mathews correlation coefficient of 0.59.

Conclusion: This study used machine learning modeling with fair accuracy to demonstrate that ECOG PS, age at diagnosis, and smoking status are the three main predictive factors of PFS in these patients.