Observational studyMedicine2019
Radiomics signature on 3T dynamic contrast-enhanced magnetic resonance imaging for estrogen receptor-positive invasive breast cancers: Preliminary results for correlation with Oncotype DX recurrence scores.
Observational study in Medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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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
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Is radiomic MRI a feasible alternative to OncotypeDX® recurrence score testing? A systematic review and meta-analysis.BJS open · 2021Pooled it
- Deep learning radiomics based on preoperative multiparametric MRI in predicting breast cancer recurrence risk.BMC medical imaging · 2026Article
- Radiomics Analysis of QUS Spectral Parametric Images for Predicting the Risk of Breast Cancer Recurrence.Cancers · 2025Article
- Radiomics Analysis of Breast MRI to Predict Oncotype Dx Recurrence Score: Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Shallow and deep learning classifiers in medical image analysis.European radiology experimental · 2024Review
- Association Between Ki-67 Proliferative Index and Oncotype-Dx Recurrence Score in Hormone Receptor-Positive, HER2-Negative Early Breast Cancers. A Systematic Review of the Literature.Breast cancer : basic and clinical research · 2024Review
- Machine Learning Predicts Pathologic Complete Response to Neoadjuvant Chemotherapy for ER+HER2- Breast Cancer: Integrating Tumoral and Peritumoral MRI Radiomic Features.Diagnostics (Basel, Switzerland) · 2023Article
- MRI-Based Radiomics Approach Predicts Tumor Recurrence in ER + /HER2 - Early Breast Cancer Patients.Journal of digital imaging · 2023Article
- A radiomic model to classify response to neoadjuvant chemotherapy in breast cancer.BMC medical imaging · 2022Article
- Precision Medicine in Breast Cancer: Do MRI Biomarkers Identify Patients Who Truly Benefit from the Oncotype DX Recurrence ScoreDiagnostics (Basel, Switzerland) · 2022Article
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- Utility of CT texture analysis to differentiate olfactory neuroblastoma from sinonasal squamous cell carcinoma.Scientific reports · 2021Article
- Radiomics derived from dynamic contrast-enhanced MRI pharmacokinetic protocol features: the value of precision diagnosis ovarian neoplasms.European radiology · 2021Article
- Predictive Ki-67 Proliferation Index of Cervical Squamous Cell Carcinoma Based on IVIM-DWI Combined with Texture Features.Contrast media & molecular imaging · 2021Article
- Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms.Journal of oncology · 2021Review
- Radiomic signatures derived from multiparametric MRI for the pretreatment prediction of response to neoadjuvant chemotherapy in breast cancer.The British journal of radiology · 2020Article
- Radiomics in Breast Imaging from Techniques to Clinical Applications: A Review.Korean journal of radiology · 2020Review
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Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To evaluate the ability of a radiomics signature based on 3T dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) to distinguish between low and non-low Oncotype DX (OD) risk groups in estrogen receptor (ER)-positive invasive breast cancers.Between May 2011 and March 2016, 67 women with ER-positive invasive breast cancer who performed preoperative 3T MRI and OD assay were included. We divided the patients into low (OD recurrence score [RS] <18) and non-low risk (RS ≥18) groups. Extracted radiomics features included 8 morphological, 76 histogram-based, and 72 higher-order texture features. A radiomics signature (Rad-score) was generated using the least absolute shrinkage and selection operator (LASSO). Univariate and multivariate logistic regression analyses were performed to investigate the association between clinicopathologic factors, MRI findings, and the Rad-score with OD risk groups, and the areas under the receiver operating characteristic curves (AUC) were used to assess classification performance of the Rad-score.The Rad-score was constructed for each tumor by extracting 10 (6.3%) from 158 radiomics features. A higher Rad-score (odds ratio [OR], 65.209; P <.001), Ki-67 expression (OR, 17.462; P = .007), and high p53 (OR = 8.449; P = .077) were associated with non-low OD risk. The Rad-score classified low and non-low OD risk with an AUC of 0.759.The Rad-score showed the potential for discrimination between low and non-low OD risk groups in patients with ER-positive invasive breast cancers.
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