AI and Cancer Research ── Breast ── 2026-10-10
Breast cancer: treatment, trials and AI
- Abstract
Background: This study evaluates DL using GEX and preoperatively available clinical data (PreopClinic) to predict SLNM, and explores their potential for guiding axillary surgery and prognostic assessment.
Methods: We retrospectively included 6,836 clinically node-negative T1-T2 patients with invasive breast cancer who underwent primary surgery from the SCAN-B. Three DL models—a multilayer perceptron, a pathway-informed sparse neural network, and a transformer—were developed using the development set (n=4,625) and evaluated against XGBoost in the independent test set (n=2,211).
Results: The Transformer outperformed other methods for GEX modeling and minimized prior gene selection. In the independent test set, the combined Pre-opClinic+GEX model significantly improved SLNM prediction compared to Pre-opClinic alone (ROC AUC 0.693 vs 0.596, P < 0.001) and identified low-risk patients who might avoid unnecessary SLNB (reduction rate 27.2% at 92.1% sen-sitivity). However, the combined model did not significantly outperform GEX alone. While GEX provided the dominant predictive signal, PreopClinic contributed complementary information with modest numerical gains in clinical utility. Across-subtype training outperformed within-subtype training, particularly in TNBC, where the combined model achieved AUC 0.734 (95% CI: 0.644-0.837). The derived SLNM predictor also provided prognostic information beyond the estab-lished prognostic factors. Although the models were developed primarily using surgical specimen–derived GEX, paired biopsy and surgical-specimen analyses (n=116) demonstrated substantial concordance of transcriptomic patterns and nodal predictions.
Conclusion: These findings highlight the Transformer’s robustness against noise and effectiveness in capturing informative transcriptomic features for SLNM pre-diction. The agreement observed between paired biopsy and surgical specimens supports the feasibility of future biopsy-based preoperative applications.
Journal IF-equivalent: 2.2 (OpenAlex 2-year mean citedness, value as of 2026-10-09, retrieved 2026-10-10; not the official Clarivate IF)Reference: Zhang D, Staaf J, Bendahl PO, Dihge L, Ohlsson M, Sjöström M, et al. Multimodal Gene Expression Deep Learning for Predicting Sentinel Lymph Node Macro-metastasis in Early Breast Cancer: Development and Validation in the SCAN-B Cohort. Clinical Cancer Research. 2026 Oct 8 [Epub ahead of print]. doi:10.1158/1078-0432.ccr-26-1134.Checked: Abstract only - Abstract
Background: Breast cancer patients receiving neoadjuvant treatment may benefit from a presurgical assessment of complete response to avoid unnecessary surgeries. We aimed to develop a machine learning model that predicts pathological complete response (pCR) based on results from different image modalities as well as tumor and patient characteristics.
Methods: Clinical data from 388 cases of invasive breast cancer receiving neoadjuvant treatment were extracted in this retrospective, monocentric study. Findings from ultrasound, mammography and magnetic resonance imaging combined with patient and histopathological information were included. A Pearson Product-Moment correlation and a LASSO regularization were performed to preselect the most relevant features for the outcome. Based on the preselected variables, we developed and validated a machine learning algorithm (logistic regression with elastic net penalty (GLM)) to predict pCR. The model’s performance was evaluated via area under the curve (AUROC), false positive rate (FPR) and positive predictive value (PPV).
Results: In the total cohort, pCR was detected in 158 out of 388 cases, resulting in a pCR rate of 40.7%. From 111 collected variables, 8 variables were removed due to a high correlation with other features. After the LASSO regularization, 14 variables were included as predictors for the final model. In the validation set, the GLM achieved an AUROC of 0.76 with a FPR of 8.7% and a PPV of 75%.
Conclusions: Through the combination of imaging and clinicopathological features, our machine learning model shows a promising approach to improve the prediction of pCR in breast cancer patients receiving neoadjuvant treatment. In combination with the LASSO regularization, it allows interesting insights into the predictive potential of different variables, which are available as part of standard clinical practice. Trial registration Not applicable.
Journal IF-equivalent: 3.9 (OpenAlex 2-year mean citedness, value as of 2026-10-09, retrieved 2026-10-10; not the official Clarivate IF)Reference: Reisig E, Cai L, Müller M, Schäfgen B, Pfob A. Machine learning model for the prediction of pathological complete response after neoadjuvant chemotherapy in breast cancer. BMC Cancer. 2026 Oct 8 [Epub ahead of print]. doi:10.1186/s12885-026-17115-7.Checked: Abstract only