Article

Predicting MammaPrint Recurrence Risk from Breast Cancer Pathological Images Using a Weakly Supervised Transformer

2025-04-29

Abstract excerpt

Recurrence related to poor prognosis is a leading cause of mortality in patients with breast cancer (BC). The MammaPrint (MP) genomic assay is designed to stratify recurrence risk and evaluate chemotherapy benefits for early-stage HR+/HER2- BC patients. However, MP fails to reveal spatial tumor morphology and is limited by high costs. In this study, we establish a BC MP cohort and develop CPMP, a weakly supervised...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: ab5a7b01-807f-5fc4-9eb9-0588521c89b8
DOI: 10.1101/2025.04.26.648504

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Predicting MammaPrint Recurrence Risk from Breast Cancer Pathological Images Using a Weakly Supervised TransformerDOI 10.1101/2025.04.26.648504Read

Deep Learning on Histopathological Images to Predict Breast Cancer Recurrence Risk and Chemotherapy BenefitSemantic proximity Read

Multimodal histopathologic models stratify hormone receptor-positive early breast cancerSemantic proximity Read

Development and Validation of a Multimodal Clinical, Pathologic, and Genomic Model for Breast Cancer RecurrenceSemantic proximity Read

Assessing the risk of recurrence in early-stage breast cancer through H&E stained whole slide imagesSemantic proximity Read

Clinical Validation of RlapsRisk BC in an international multi-cohorts settingSemantic proximity Read

Breast cancer histopathology image-based gene expression prediction using spatial transcriptomics data and deep learningSemantic proximity Read

Decoding Tumor Phenotypes: A Radiologist-Inspired Deep Learning Framework for Breast Cancer Recurrence PredictionSemantic proximity Read

Deep-learning and transfer learning identify new breast cancer survival subtypes from single-cell imaging dataSemantic proximity Read

Deep-learning and transfer learning identify new breast cancer survival subtypes from single-cell imaging dataSemantic proximity Read

Generative AI Enables Breast Cancer Genomic Subtype Prediction from Histology ImagesSemantic proximity Read

Weakly supervised contrastive learning infers molecular subtypes and recurrence of breast cancer from unannotated pathology imagesSemantic proximity Read

Deep learning from HE slides to predict the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancerSemantic proximity Read