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

CT-Based Radiomics and Deep Learning for BRCA Mutation and Progression-Free Survival Prediction in Ovarian Cancer Using a Multicentric Dataset

31 May 2022

Abstract excerpt

PURPOSE: Build predictive radiomic models for early relapse and BRCA mutation based on a multicentric database of high-grade serous ovarian cancer (HGSOC) and validate them in a test set coming from different institutions. METHODS: Preoperative CTs of patients with HGSOC treated at four referral centers were retrospectively acquired and manually segmented. Hand-crafted features and deep radiomics features were...

Read the complete abstract on PubMed

Topics

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

Share to Ovarian cancer diagnosis and treatment
Share to PARP inhibition in cancer therapy
Share to Radiomics and Machine Learning in Medical Imaging

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Source

Identifiers

PMID: 35681720
DOI: 10.3390/cancers14112739

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 10 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

CT-Based Radiomics and Deep Learning for BRCA Mutation and Progression-Free Survival Prediction in Ovarian Cancer Using a Multicentric DatasetPMID 35681720Read ↗

End-to-End Integrative Segmentation and Radiomics Prognostic Models Improve Risk Stratification of High-Grade Serous Ovarian Cancer: A Retrospective Multi-Cohort StudySemantic proximity Read ↗

CT-based radiomics nomogram analysis for assessing BRCA mutation status in patients with high-grade serous ovarian cancer.Semantic proximity Read ↗

CT-Based Radiomics and Machine Learning for Differentiating Benign, Borderline, and Early-stage Malignant Ovarian Tumors: A Multi-Class Classification and Multi-Model Comparation StudySemantic proximity Read ↗

Prediction of BRCA Gene Mutation Status in Epithelial Ovarian Cancer by Radiomics Models based on 2D and 3D CT ImagesSemantic proximity Read ↗

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

Prediction of BRCA gene mutation status in epithelial ovarian cancer by radiomics models based on 2D and 3D CT images.Semantic proximity Read ↗

A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancerSemantic proximity Read ↗

The Use of Machine Learning Models and Radiomics for Segmentation and Classification of Adnexal Masses on Ultrasound: A multi-cohort retrospective studySemantic 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 ↗

CT-based machine learning radiomics predict CCR5 expression level and survival in ovarian cancerSemantic proximity Read ↗

Development and validation of ultrasound-based radiomics model to predict germline BRCA mutations in patients with breast cancer.Semantic proximity Read ↗

A CT-based deep learning for segmenting tumors and predicting microsatellite instability in patients with colorectal cancers: a multicenter cohort studySemantic proximity Read ↗

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