ArticleJournal of magnetic resonance imaging : JMRI2019
Association of distant recurrence-free survival with algorithmically extracted MRI characteristics in breast cancer.
Article in Journal of magnetic resonance imaging : JMRI, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Personalized Breast Cancer Prognosis Using a Model Based on MRI and Clinicopathological Variables.Journal of imaging informatics in medicine · 2026Article
- Preliminary Evaluation of Radiomics in Contrast-Enhanced Mammography for Prognostic Prediction of Breast Cancer.Cancers · 2025Article
- Deciphering Breast Cancer Complexity: A Study on the Predictive Power of MRI Texture Analysis for Tumor Characterization and Treatment Response.Journal of the Belgian Society of Radiology · 2025Article
- Automatic segmentation-based multi-modal radiomics analysis of US and MRI for predicting disease-free survival of breast cancer: a multicenter study.Breast cancer research : BCR · 2024Article
- A glimpse into the future: Integrating artificial intelligence for precision HER2-positive breast cancer management.iMetaOmics · 2024Review
- Machine learning radiomics of magnetic resonance imaging predicts recurrence-free survival after surgery and correlation of LncRNAs in patients with breast cancer: a multicenter cohort study.Breast cancer research : BCR · 2023Article
- How Radiomics Can Improve Breast Cancer Diagnosis and Treatment.Journal of clinical medicine · 2023Review
- Radiomic signatures based on pretreatment 18F-FDG PET/CT, combined with clinicopathological characteristics, as early prognostic biomarkers among patients with invasive breast cancer.Frontiers in oncology · 2023Article
- Magnetic Resonance Imaging-Based Radiomics Features Associated with Depth of Invasion Predicted Lymph Node Metastasis and Prognosis in Tongue Cancer.Journal of magnetic resonance imaging : JMRI · 2022Article
- Predicting cancer outcomes with radiomics and artificial intelligence in radiology.Nature reviews. Clinical oncology · 2022Review
- Development and Validation of an MRI Radiomics-Based Signature to Predict Histological Grade in Patients with Invasive Breast Cancer.Breast cancer (Dove Medical Press) · 2022Article
- Prediction for Distant Metastasis of Breast Cancer Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging Images under Deep Learning.Computational intelligence and neuroscience · 2022Article
- Imaging in Locoregional Management of Breast Cancer.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2020Review
- Article
- The Impact of Preoperative Radiomics Signature on the Survival of Breast Cancer Patients With Residual Tumors After NAC.Frontiers in oncology · 2020Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundWhile important in diagnosis of breast cancer, the scientific assessment of the role of imaging in prognosis of outcomes and treatment planning is limited. PURPOSE: To evaluate the potential of using quantitative imaging variables for stratifying risk of distant recurrence in breast cancer patients. STUDY TYPE: Retrospective. POPULATION: In all, 892 female invasive breast cancer patients. SEQUENCE: Dynamic contrast-enhanced MRI with field strength 1.5 T and 3 T. ASSESSMENT: Computer vision algorithms were applied to extract a comprehensive set of 529 imaging features quantifying size, shape, enhancement patterns, and heterogeneity of the tumors and the surrounding tissue. Using a development set with 446 cases, we selected 20 imaging features with high prognostic value. STATISTICAL TESTS: We evaluated the imaging features using an independent test set with 446 cases. The principal statistical measure was a concordance index between individual imaging features and patient distant recurrence-free survival (DRFS).
resultsThe strongest association with DRFS that persisted after controlling for known prognostic clinical and pathology variables was found for signal enhancement ratio (SER) partial tumor volume (concordance index [C] = 0.768, 95% confidence interval [CI]: 0.679-0.856), tumor major axis length (C = 0.742, 95% CI: 0.650-0.834), kurtosis of the SER map within tumor (C = 0.640, 95% CI: 0.521-0.760), tumor cluster shade (C = 0.313, 95% CI: 0.216-0.410), and washin rate information measure of correlation (C = 0.702, 95% CI: 0.601-0.803). DATA
conclusionQuantitative assessment of breast cancer features seen in a routine breast MRI might be able to be used for assessment of risk of distant recurrence. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 6 J. Magn. Reson. Imaging 2019.
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