2019 ASCO Annual Meeting!
Session: Health Services Research, Clinical Informatics, and Quality of Care
Type: Poster Session
Time: Saturday June 1, 1:15 PM to 4:15 PM
Location: Hall A
Development of an artificial intelligence model to predict survival at specific time intervals for lung cancer patients.
Health Services Research, Clinical Informatics, and Quality of Care
2019 ASCO Annual Meeting
Poster Board Number:
Poster Session (Board #247)
J Clin Oncol 37, 2019 (suppl; abstr 6556)
Author(s): Smita Agrawal, Vivek Vaidya, Prajwal Chandrashekaraiah, Hemant Kulkarni, Li Chen, Karl Rudeen, Babu Narayanan, Orr Inbar, Brigham Hyde; Concerto HealthAI, Bengaluru, India; Concerto Health AI, Bengaluru, India; Concerto HealthAI, Bangalore, India; Concerto Health AI, Banglore , India; Concerto HealthAI, Boston, MA; Concerto Health AI, New York, NY; Concerto Health AI, Ra'anana, Israel
Background: Survival prediction models for lung cancer patients could help guide their care and therapy decisions. The objectives of this study were to predict probability of survival beyond 90, 180 and 360 days from any point in a lung cancer patient’s journey. Methods: We developed a Gradient Boosting model (XGBoost) using data from 55k lung cancer patients in the ASCO CancerLinQ database that used 3958 unique variables including Dx and Rx codes, biomarkers, surgeries and lab tests from ≤1 year prior to the prediction point, which was chosen at random for each patient. We used 40% data for training, 25% for hyper-parameter tuning, 20% for testing and 15% for holdout validation. Death date available in the Electronic Health Record was cross checked by linkage to death registries. Results: The model was validated on the holdout set of 8,468 patients. The Area Under the Curve (AUC) for the model was 0.79. The precision and recall for predicting survival beyond the three time points were between 0.7-0.8 and 0.8-0.9 respectively (see table). This compares favourably to other lung cancer survival models created using different machine learning techniques (Jochems 2017, Dekker 2009). A Cox-PH model created using the top 20 variables also had a significantly lower performance (see table). Analysis of input variables yielded distinctive patterns for patient subgroups and time points. Tumor status, medications, lab values and functional status were found to be significant in patient sub cohorts. Conclusions: An AI model to predict survival of lung cancer patients built using a large real world dataset yielded high accuracy. This general model can further be used to predict survival of sub cohorts stratified by variables such as stage or various treatment effects. Such a model could be useful for assessing patient risk and treatment options, evaluating cost and quality of care or determining clinical trial eligibility.
|Precision||Recall||F1 score||Support||AUC||Cox PH AUC|
|Death in 90D||0.61||0.44||0.51||2235||0.79||0.69|
|Alive after 90D||0.82||0.9||0.86||6233||0.79||0.69|
|Death in 180D||0.69||0.53||0.6||3151||0.79||0.7|
|Alive after 180D||0.76||0.86||0.8||5317||0.79||0.7|
|Death in 360D||0.74||0.67||0.7||4073||0.79||0.71|
|Alive after 360D||0.72||0.78||0.75||4395||0.79||0.71|