Enhancing Osteosarcoma Survival Predictions: A Comparative Study of a Multicomponent-Model Machine Learning Approach Integrating SEER and NCDB Data Sets Versus Conventional Single-Data-Set Modeling.

Abstract

Osteosarcoma treatment decisions require accurate prognostic assessment, yet utilization of machine learning (ML) models is problematic because performance degrades consistently when models are applied across data sets. Current single-data-set models (single models) learn population-specific patterns rather than generalizable disease characteristics, limiting their clinical implementation. We developed a multicomponent-model (multi-model) ML framework using domain-adversarial training across 2 national registries, integrating structured clinical variables with text-based patient data to learn generalizable disease patterns and achieve reliable survival prediction across diverse populations. We conducted a retrospective study using data from 2 national cancer registries: SEER (Surveillance, Epidemiology, and End Results; n = 4,278 patients, 2004 to 2015) and NCDB (National Cancer Database; n = 4,049 patients, 2004 to 2018). We compared the cross-data-set performance of single models versus the multi-models. Primary outcomes were performance metrics for 2-year and 5-year overall survival predictions, measured by the area under the receiver operating characteristic curve (AUC), precision, recall, F1-score, and Brier score. Single models achieved strong internal validation performance (AUC, 0.898 to 0.927) but performance declined substantially in cross-data-set validation (AUC, 0.563 to 0.665). The multi-model approach achieved cross-data-set AUCs of 0.708 to 0.843 for 2-year survival and 0.648 to 0.798 for 5-year survival, with improvements of 0.085 to 0.199 over single models across all evaluation metrics. The multi-model ML approach demonstrated improved osteosarcoma prognostic ability across health-care data sets, addressing the generalizability challenges of models based on a single data set. Enhanced cross-data-set performance suggests the potential for consistent risk stratification to guide surgical planning, adjuvant therapy selection, and patient counseling. Prospective validation is needed to evaluate clinical impact. Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.