Moving beyond the traditional two-step approach for prognosis prediction: The BayeSarc model.
Gabriele Tinè, Dario Callegaro, Sandro Pasquali, Jay Steven Wunder, Peter Charles Ferguson, Anthony Michael Griffin, D. Strauß, Andrew J. Hayes, Sylvie Bonvalot, Dimitri Tzanis, Toufik Bouhadiba, Paolo Giovanni Casali, Alessandro Gronchi, Rosalba Miceli · Journal of Clinical Oncology · 2025
11572 Background: Extremity Soft tissue sarcomas (eSTS) are rare and heterogeneous, limiting the collection of large datasets for robust predictive modeling. Sarculator, a Cox model-based tool for overall survival (OS) prediction, was built using the traditional two-step paradigm (1) model building and (2) external validation. However, this method can underperform on external cohorts, often yielding low predictive accuracy and limited generalizability. We introduced a Bayesian Sequential Learning strategy to iteratively refine Sarculator, incorporating new data while preserving prior properties. Methods: The initial model was built on the Italian Sarculator development cohort, with age, tumor size, tumor grade, and histology as covariates. Sequential updates were then performed with the three original Sarculator external validation cohorts , and a more recent Italian cohort. Each step used the results from the previous update as prior information for the next. Performance was assessed as discriminative ability (C-index) and calibration. Key differences from the original Sarculator were Bayesian Cox modelling, and a piecewise-constant hazard. Results: The two-step approach yields separate performance metrics for each cohort, making generalizability unclear when performance drops (e.g. French cohort, Table). Conversely, the sequential approach progressively increases the total information (number of patients and follow-up), without discarding previous evidence, and readjusts performance metrics at each step. Occasional declines in the C-index reflect cohort-specific divergences but can be reversed in subsequent updates if newer cohorts share similar features. Ultimately, the final BayeSarc outperformed the initial model in discriminative ability, calibration, and reduced uncertainty in predictions. Conclusions: BayeSarc is an accurate, generalizable OS prediction model for eSTS, preserving external validation properties while moving beyond the conventional two-step approach. By building on prior evidence, the model dynamically adapts over time, ultimately relying on 4713 patients, with results independent of cohort order. BayeSarc sets a benchmark for future rare-disease prognostic research, paving the way for incorporating new cohorts and/or prognostic variables (e.g. emerging biomarkers). Cohorts Istituto Nazionale Tumori, Milan, Italy1994-2013 Mount Sinai Hospital, Toronto, Canada 1994-2013 Royal Marsden Hospital,London, UK2006-2013 Institut Gustave Roussy, Villejuif, France 1996-2012 Istituto Nazionale Tumori, Milan, Italy2014-2021 Two-step procedure Dev N=1452 Val 1 N=1436 Val 2N=440 Val 3 N=420 Val 4N=965 C-index 0.767 0.775 0.762 0.698 0.765 Bayesian updating Dev N=1452 Upd 1 N=2888 Upd 2 N=3228 Upd 3 N=3748 Upd 4 N=4713 C-index 0.761 0.775 0.771 0.707 0.796