ArticleCancers2023
MRI Radiomics and Machine Learning for the Prediction of Oncotype Dx Recurrence Score in Invasive Breast Cancer.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Are There Socio-Demographic Inequalities in the Utilisation of Tumour and ctDNA Somatic Mutation Testing in Solid Tumours? A Systematic Review.Cancer medicine · 2026Pooled it
- MRI-based quantification of intratumoral heterogeneity for predicting recurrence risk in ER+/HER2- breast cancer.Insights into imaging · 2026Article
- Ultrasound-based multiregional radiomics nomogram for predicting recurrence in HER2-positive breast cancer.BMC medical imaging · 2026Article
- Predictions of Oncotype DXBioengineering (Basel, Switzerland) · 2026Article
- MRI-Based peritumoral radiomics for predicting recurrence risk in ER+/HER2- breast cancer.BMC medical imaging · 2026Article
- Radiomics Analysis of QUS Spectral Parametric Images for Predicting the Risk of Breast Cancer Recurrence.Cancers · 2025Article
- Application of Machine Learning to Breast MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- Role of radiomics in predicting early disease recurrence in locally advanced breast cancer patients: integration of radiomic features and RECIST criteria.La Radiologia medica · 2025Article
- Radiomics Analysis of Breast MRI to Predict Oncotype Dx Recurrence Score: Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Harnessing artificial intelligence for predicting breast cancer recurrence: a systematic review of clinical and imaging data.Discover oncology · 2025Review
- A Multiparametric MRI-based Radiomics Model for Stratifying Postoperative Recurrence in Luminal B Breast Cancer.Journal of imaging informatics in medicine · 2024Article
- Development and validation of a clinical breast cancer tool for accurate prediction of recurrence.NPJ breast cancer · 2024Article
- Article
- Machine learning-based models for the prediction of breast cancer recurrence risk.BMC medical informatics and decision making · 2023Article
- AI-Enhanced PET and MR Imaging for Patients with Breast Cancer.PET clinics · 2023Review
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo non-invasively predict Oncotype DX recurrence scores (ODXRS) in patients with MATERIALS AND
methodsPre-operative DCE-MRI of patients with IBC, no history of neoadjuvant therapy prior to MRI, and for which the ODXRS was available, were retrospectively selected from a public dataset. ODXRS was obtained on histological tumor samples and considered as positive if greater than 16 and 26 in patients aged under and over 50 years, respectively. Tumor lesions were manually annotated by three independent operators on DCE-MRI images through 3D ROIs positioning. Radiomic features were therefore extracted and selected using multistep feature selection process. A logistic regression ML classifier was then employed for the prediction of ODXRS.
results248 patients were included, of which 87 with positive ODXRS. 166 (66%) patients were grouped in the training set, while 82 (33%) in the test set. A total of 1288 features was extracted. Of these, 1244 were excluded as 771, 82 and 391 were excluded as not stable (
conclusionsRadiomics and ML applied to pre-operative DCE-MRI in patients with IBC showed promises for the non-invasive prediction of ODXRS, aiding in selecting patients who will benefit from NAC.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.