Artificial intelligence model to predict slow progression for advanced non-small cell lung cancer (aNSCLC) patients receiving second-line therapies

• Published in: Journal of clinical oncology Vol. 38; no. 15_suppl; p. e21596
• Main Authors: Charest, Francois, Heilbroner, Samuel P, Rudeen, Karl, Chawla, Jitesh, Suryadevara, Somasekhar, Narayanan, Babu
• Format: Journal Article
• Language: English
• Published: 20.05.2020
• ISSN: 0732-183X; 1527-7755
• DOI: 10.1200/JCO.2020.38.15_suppl.e21596

Abstract only e21596 Background: There are ongoing efforts to understand and predict exceptional response to existing cancer therapies, but few clinical characteristics of these patients are known. We trained a machine learning model using the Concerto HealthAI database of oncology EMR data that includes clinical data from CancerLinQ Discovery to predict slow progression, a proxy for exceptional response, in aNSCLC in the second line setting. Methods: We trained an XGBoost model to predict patients with a progression free survival (PFS) greater than 180 days from the start of second line therapy (index date). This cutoff approximately determines the top 20% of PFS values in our database (median PFS = 86 days). Patients were included from the study if they (1) were pathologically confirmed aNSCLC without other primary cancer diagnoses and (2) started their second-line therapy between 2013 and 2017. Patients were labeled as slow progressors if they (1) had no evidence of progression or death within 180 days of index and (2) were evaluated for progression for at least 180 days post-index. The model considered data up to 120 days prior to index date. Risk factors in the model included demographics, vitals, common labs, common medical conditions, ECOG performance status, stage, histology, prior cancer treatment patterns, prior progression/response assessments, and medication history. Feature importance was evaluated using SHapley Additive exPlanations (SHAP). Results: 2205 patients met selection criteria of the study. Of these, 420 were labeled as slow progressors. 1776 patients were used for model training and 429 were set aside for model validation. The final model was able to predict slow progression with an AUCROC of 0.75 (F-score 0.48, precision 0.39, recall 0.6). The performance compares favorably to that of a logistic regression model (0.66 AUCROC). Top features that indicated slow progression included a low number of prior progression events or regimens, absence of metastatic disease, lower stage/t-stage/ECOG, absence of COPD, previous treatment with an EGFR inhibitor, normal Alk-Phos/WBC (versus elevated), absence of tachycardia, and a normal BMI (versus low). Conclusions: Machine learning and real world-data provided promising results in predicting slow progression in aNSCLC and may be useful in discovering novel drivers of favorable response.