Abstract 726: Machine learning prediction of financial toxicity in patients with resected lung cancer
Authors: Deboever Nathaniel , Al-Tashi Qasem , Eisenberg Michael , Hofstetter Wayne , Mehran Reza , Rice David , Roth Jack , Sepesi Boris , Swisher Stephen , Vaporciyan Ara , Walsh Garrett , Antonoff Mara , Wu Jia , Rajaram Ravi 
Published: April 2023

Abstract
Background: Financial toxicity (FT) refers to the financial stress and detrimental impact on quality of life experienced by patients due to cancer-related treatment costs. Patients with moderate/severe FT often use injurious coping mechanisms, such as skipping clinic visits, resulting in worse survivorship. We aimed to use machine learning (ML) techniques to predict FT in patients with resected lung cancer (LC).
Methods: Patients who underwent LC resection at our center between January 2016 and December 2021 were identified. Following consent, patients were surveyed to ascertain demographic and financial data and sent the COST questionnaire to evaluate the presence of moderate or severe FT. Clinicopathologic variables, including smoking status, stage, and type of surgery, were extracted from the medical record. Patients were randomly divided into training and test sets (70%/30%, respectively). First, we identified the most informative features. Then, four ML algorithms (decision tree, random forest [RF], gradient boosting, and extreme gradient boosting) were trained. We ensembled the four models’ predictions to optimize the model.
Results: There were 1477 patients identified, of whom 462 (31.3%) completed the survey. 46 (10.0%) patients experienced moderate/severe FT related to their LC diagnosis and surgery. The variables most influential in our models included demographic, economic, clinical, pathologic, and operative characteristics (Table). The RF model achieved the best prediction performance among all four models. The ensemble model further augmented performance and yielded an accuracy of 0.86, precision of 0.93, recall of 0.86, leading to an F1 score of 0.88, indicative of a reliable algorithm.
Conclusions: ML algorithms can accurately identify LC patients at risk of experiencing moderate/severe FT after surgery. Once identified, patients at high likelihood of developing FT may benefit from financial counseling and social work evaluation prior to lung resection.
The 12 most influential variables in our models, prompting their inclusion in the final algorithm. Demographic Economic Clinical Pathologic Operative Age Employment Status Smoking Status Histopathology Extent of Resection Race Household Income Body Mass Index Marital Status Credit Score Forced Expiratory Volume in 1 second (FEV1) Residence Size
Citation Format: Nathaniel Deboever, Qasem Al-Tashi, Michael Eisenberg, Wayne Hofstetter, Reza Mehran, David Rice, Jack Roth, Boris Sepesi, Stephen Swisher, Ara Vaporciyan, Garrett Walsh, Mara Antonoff, Jia Wu, Ravi Rajaram. Machine learning prediction of financial toxicity in patients with resected lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 726.