Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Development of model for identifying homologous recombination deficiency (HRD) status of ovarian cancer with deep learning on whole slide images

4 Mar 2025

Abstract excerpt

BACKGROUND: Homologous recombination deficiency (HRD) refers to the dysfunction of homologous recombination repair (HRR) at the cellular level. The assessment of HRD status has the important significance for the formulation of treatment plans, efficacy evaluation, and prognosis prediction of patients with ovarian cancer. OBJECTIVES: This study aimed to construct a deep learning-based classifier for identifying...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to AI in cancer detection
Share to PARP inhibition in cancer therapy

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 40038690
DOI: 10.1186/s12967-025-06234-7

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Development of model for identifying homologous recombination deficiency (HRD) status of ovarian cancer with deep learning on whole slide imagesPMID 40038690Read ↗

HRDPath: An Explainable Multi-Model Deep Learning Architecture for Predicting Homologous Recombination Deficiency from Histopathology ImagesSemantic proximity Read ↗

AI-Enabled Prediction of Homologous Recombination Deficiency from Histopathology ImagesSemantic proximity Read ↗

Deep learning predicts HRD and platinum response from histology slides in breast and ovarian cancerSemantic proximity Read ↗

Deep Learning for Detecting BRCA Mutations in High-Grade Ovarian Cancer Based on an Innovative Tumor Segmentation Method From Whole Slide Images.Semantic proximity Read ↗

Deep-Learning to Predict BRCA Mutation and Survival from Digital H&E Slides of Epithelial Ovarian Cancer.Semantic proximity Read ↗

Homologous recombination deficiency prediction from whole slide images using label refinement and foundation-model benchmarking in ovarian cancerSemantic proximity Read ↗

Direct prediction of Homologous Recombination Deficiency from routine histology in ten different tumor types with attention-based Multiple Instance Learning: a development and validation studySemantic proximity Read ↗

Deep learning identifies morphological patterns of homologous recombination deficiency in luminal breast cancers from whole slide imagesSemantic proximity Read ↗

Multi-Omics Deep-Learning Prediction of Homologous Recombination Deficiency-Like Phenotype Improved Risk Stratification and Guided Therapeutic Decisions in Gynecological CancersSemantic proximity Read ↗

Deep Learning Predicts Underlying Features on Pathology Images with Therapeutic Relevance for Breast and Gastric CancerSemantic proximity Read ↗

Validation of Genomic and Transcriptomic Models of Homologous Recombination Deficiency in a Real-World Pan-Cancer CohortSemantic proximity Read ↗

Histopathology and proteomics are synergistic for High-Grade Serous Ovarian Cancer platinum response predictionSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.