AI and Cancer Research ── Breast ── 2026-11-01
Breast cancer: treatment, trials and AI
- Abstract
Background: Breast cancer remains a leading cause of cancer-related mortality worldwide, with pronounced regional inequalities in middle-income countries such as Brazil. In Mato Grosso, structural disparities and limited access to specialized care pose additional challenges for accurate prognostic assessment. This study aimed to develop and internally validate Machine Learning (ML) models and prognostic nomograms to predict overall survival (OS) and breast cancer-specific survival (CSS), as well as to stratify patient risk in Mato Grosso, Brazil.
Methods: A retrospective population-based cohort study was performed using data from the Population-Based Cancer Registry (RCBP) (2001-2018), deterministically linked to the Mortality Information System (SIM) (2001-2022). Five survival prediction models were developed and internally validated, including four ML survival models, Random Survival Forest (RSF), Gradient Boosting Survival Analysis (GBS), Survival Support Vector Machine (SSVM), and ComponentWise Gradient Boosting (CWGB), and the conventional Cox proportional hazards (CPH) model. The dataset was split into training and test sets (80:20, stratified). Model performance was assessed using the concordance index (C-index), time-dependent area under the curve (AUCt), and integrated Brier score (IBS), with bootstrap confidence intervals. The highest-performing model was interpreted using SHAP values and permutation-based feature importance. Cox-based nomograms were then constructed and internally validated using 1,000 bootstrap resamples, followed by risk stratification and decision curve analysis to evaluate clinical utility.
Results: GBS demonstrated the highest overall performance for both OS and breast CSS, with strong discrimination and good calibration. For OS, the C-index reached 0.781 (95% CI 0.776-0.786), with an AUCt of 0.843 and an IBS of 0.129; for CSS, the C-index reached 0.790 (0.784-0.796), with an AUCt of 0.852 and an IBS of 0.120. CPH showed comparable discrimination but weaker calibration overall. The nomograms maintained stable discrimination at 1-, 3-, 5-, and 10-year time points and demonstrated good agreement between predicted and observed outcomes. Both ML models and Cox-derived nomograms achieved significant risk stratification (log-rank P< 0.001). SHAP analyses identified diagnostic method, tumor morphology, marital status, education level, and age as key predictors.
Conclusions: In this study, ML models, particularly GBS, and Cox-based nomograms demonstrated individualized prognostic capability for OS and breast CSS. These approaches may support effective risk stratification, facilitating the identification of patients at higher mortality risk and providing a scalable framework for risk-adapted management in real-world public health settings.
Journal IF-equivalent: 6.6 (OpenAlex 2-year mean citedness, value as of 2026-10-04, retrieved 2026-10-05; not the official Clarivate IF)Reference: Xavier SP, Victor A, Gotine AREM, das Neves MAB, Cardoso LB, de Albuquerque Maia FH, et al. Predicting overall and breast cancer-specific survival: machine learning models and prognostic nomograms from a population-based registry. BMC Med Inform Decis Mak. 2026 Oct 30 [Epub ahead of print]. doi:10.1186/s12911-026-03931-1.Checked: Abstract only - Abstract
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.
Journal IF-equivalent: 6.3 (OpenAlex 2-year mean citedness, value as of 2026-10-31, retrieved 2026-11-01; not the official Clarivate IF)Reference: Vu T, Tran HX, Li X, Pinero S, Liu L, Li J, et al. TabSurv: Tabular foundation model for breast cancer prognosis using gene expression data. Comput Methods Programs Biomed. 2027 Jan;288:109693. doi:10.1016/j.cmpb.2026.109693. PMID: 42962386.Checked: Abstract only