Joint AI-driven event prediction and longitudinal modeling in newly diagnosed and relapsed multiple myeloma

• Published in: NPJ digital medicine Vol. 7; no. 1; pp. 200 - 13
• Main Authors: Hussain, Zeshan, De Brouwer, Edward, Boiarsky, Rebecca, Setty, Sama, Gupta, Neeraj, Liu, Guohui, Li, Cong, Srimani, Jaydeep, Zhang, Jacob, Labotka, Rich, Sontag, David
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 29-07-2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705/117; 692/699/67; 692/700; Architecture; Biomarkers; Biomedicine; Biotechnology; Cancer therapies; Decision making; Machine learning; Medical prognosis; Medicine; Medicine & Public Health; Multiple myeloma; Patients; Survival analysis
• ISSN: 2398-6352; 2398-6352
• DOI: 10.1038/s41746-024-01189-3

Multiple myeloma management requires a balance between maximizing survival, minimizing adverse events to therapy, and monitoring disease progression. While previous work has proposed data-driven models for individual tasks, these approaches fail to provide a holistic view of a patient’s disease state, limiting their utility to assist physician decision-making. To address this limitation, we developed a transformer-based machine learning model that jointly (1) predicts progression-free survival (PFS), overall survival (OS), and adverse events (AE), (2) forecasts key disease biomarkers, and (3) assesses the effect of different treatment strategies, e.g., ixazomib, lenalidomide, dexamethasone (IRd) vs lenalidomide, dexamethasone (Rd). Using TOURMALINE trial data, we trained and internally validated our model on newly diagnosed myeloma patients (
N
 = 703) and externally validated it on relapsed and refractory myeloma patients (
N
 = 720). Our model achieved superior performance to a risk model based on the multiple myeloma international staging system (ISS) (
p
 < 0.001,
Bonferroni corrected
) and comparable performance to survival models trained separately on each task, but unable to forecast biomarkers. Our approach outperformed state-of-the-art deep learning models, tailored towards forecasting, on predicting key disease biomarkers (
p
 < 0.001,
Bonferroni corrected
). Finally, leveraging our model’s capacity to estimate individual-level treatment effects, we found that patients with IgA kappa myeloma appear to benefit the most from IRd. Our study suggests that a holistic assessment of a patient’s myeloma course is possible, potentially serving as the foundation for a personalized decision support system.