TabSurv: Tabular foundation model for breast cancer prognosis using gene expression data

Objective: Accurate and robust survival prediction is essential for personalised breast cancer prognosis and treatment decision-making. However, existing machine learning–based survival models often lack stability under cohort heterogeneity and distribution shift, while tabular foundation models typically do not support censored time-to-event data. This study aims to develop a foundation-model-based survival framework that explicitly handles right censoring and enables stable deployment across heterogeneous cohorts without task-specific retraining. Methods: We propose TabSurv, a foundation-model-based framework for survival analysis that adapts pretrained tabular foundation models to censored time-to-event prediction. Built on TabPFN, TabSurv operates via in-context learning without gradient-based training or hyperparameter tuning. To address censoring, TabSurv introduces a two-stage, censoring-aware strategy. In the first stage, the model learns from patients with observed events and generates multiple plausible survival times for each censored patient, with all imputed times occurring after the observed censoring time. In the second stage, the model uses each imputed dataset as context and makes predictions through in-context learning. These predictions are then combined to produce the final result while accounting for uncertainty in the imputed survival times. For treatment recommendation, TabSurv fits a TabPFN regressor in context to risk scores from a random survival forest, which handles censoring, and estimates each patient’s predicted risk under hypothetical treatment reassignment. Results: TabSurv was evaluated on twelve breast cancer gene expression cohorts under in-distribution and out-of-distribution settings. It achieved competitive or superior prognostic performance compared with eight established survival models and demonstrated the highest predictive stability under cohort heterogeneity. In exploratory treatment-recommendation experiments on METABRIC, patients whose treatment matched TabSurv’s recommendation showed the largest average restricted mean survival time advantage among the compared methods. Conclusion: TabSurv provides an efficient and stable foundation-model-based approach to censored survival analysis, supporting robust prognosis and personalised treatment recommendation in heterogeneous breast cancer cohorts.