aceMultimodal Gene Expression Deep Learning for Predicting Sentinel Lymph Node Macro-metastasis in Early Breast Cancer: Development and Validation in the SCAN-B Cohort
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…
Adding tumor gene expression to preoperative clinical data in a deep learning model improved prediction of sentinel lymph node macro-metastasis in early breast cancer over clinical data alone.
The study was retrospective, and the biopsy-based preoperative application is described only as a future possibility, so clinical benefit has not been demonstrated.
Evidence
- Peer-reviewedClinical Cancer Research2026-10-08
How should this claim be treated?
Truvace Impact Record TRV-2026-1718, v1: “Multimodal Gene Expression Deep Learning for Predicting Sentinel Lymph Node Macro-metastasis in Early Breast Cancer: Development and Validation in the SCAN-B Cohort.” Truvace, 2026-10-17. /record/TRV-2026-1718 (accessed at citation time). sha256 169b451ffe2e16d0…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1718 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.