Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data.

Autor:	Lippenszky L; Science and Technology Organization-Artificial Intelligence & Machine Learning, GE HealthCare, Budapest, Hungary/San Ramon, CA., Mittendorf KF; Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN., Kiss Z; Science and Technology Organization-Artificial Intelligence & Machine Learning, GE HealthCare, Budapest, Hungary/San Ramon, CA., LeNoue-Newton ML; Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN., Napan-Molina P; Science and Technology Organization-Artificial Intelligence & Machine Learning, GE HealthCare, Budapest, Hungary/San Ramon, CA., Rahman P; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN.; Health Outcomes and Biomedical Informatics, University of Florida, Tallahassee, FL., Ye C; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN., Laczi B; Science and Technology Organization-Artificial Intelligence & Machine Learning, GE HealthCare, Budapest, Hungary/San Ramon, CA., Csernai E; Science and Technology Organization-Artificial Intelligence & Machine Learning, GE HealthCare, Budapest, Hungary/San Ramon, CA., Jain NM; Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.; OneOncology, Nashville, TN., Holt ME; Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.; Sarah Cannon Research Institute, Nashville, TN., Maxwell CN; Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN., Ball M; Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.; Vanderbilt University School of Medicine, Nashville, TN., Ma Y; Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.; Department of Pharmaceutical Services, Vanderbilt University Medical Center, Nashville, TN., Mitchell MB; Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.; Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Boston, MA., Johnson DB; Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.; Division of Hematology/Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN., Smith DS; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN., Park BH; Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.; Division of Hematology/Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN., Micheel CM; Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.; Division of Hematology/Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN., Fabbri D; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN., Wolber J; Pharmaceutical Diagnostics, GE HealthCare, Chalfont St Giles, United Kingdom., Osterman TJ; Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN.; Division of Hematology/Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN.
Jazyk:	angličtina
Zdroj:	JCO clinical cancer informatics [JCO Clin Cancer Inform] 2024 Feb; Vol. 8, pp. e2300207.
DOI:	10.1200/CCI.23.00207
Abstrakt:	Purpose: Although immune checkpoint inhibitors (ICIs) have improved outcomes in certain patients with cancer, they can also cause life-threatening immunotoxicities. Predicting immunotoxicity risks alongside response could provide a personalized risk-benefit profile, inform therapeutic decision making, and improve clinical trial cohort selection. We aimed to build a machine learning (ML) framework using routine electronic health record (EHR) data to predict hepatitis, colitis, pneumonitis, and 1-year overall survival. Methods: Real-world EHR data of more than 2,200 patients treated with ICI through December 31, 2018, were used to develop predictive models. Using a prediction time point of ICI initiation, a 1-year prediction time window was applied to create binary labels for the four outcomes for each patient. Feature engineering involved aggregating laboratory measurements over appropriate time windows (60-365 days). Patients were randomly partitioned into training (80%) and test (20%) sets. Random forest classifiers were developed using a rigorous model development framework. Results: The patient cohort had a median age of 63 years and was 61.8% male. Patients predominantly had melanoma (37.8%), lung cancer (27.3%), or genitourinary cancer (16.4%). They were treated with PD-1 (60.4%), PD-L1 (9.0%), and CTLA-4 (19.7%) ICIs. Our models demonstrate reasonably strong performance, with AUCs of 0.739, 0.729, 0.755, and 0.752 for the pneumonitis, hepatitis, colitis, and 1-year overall survival models, respectively. Each model relies on an outcome-specific feature set, though some features are shared among models. Conclusion: To our knowledge, this is the first ML solution that assesses individual ICI risk-benefit profiles based predominantly on routine structured EHR data. As such, use of our ML solution will not require additional data collection or documentation in the clinic.
Databáze:	MEDLINE
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