ArticleJournal of magnetic resonance imaging : JMRI2017
Can algorithmically assessed MRI features predict which patients with a preoperative diagnosis of ductal carcinoma in situ are upstaged to invasive breast cancer?
Article in Journal of magnetic resonance imaging : JMRI, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 30 citations in OpenAlex.
- Multicenter development and external validation of clinical-radiomics models to predict surgically confirmed upstaging in biopsy-proven DCIS using DCE-MRI.Journal of applied clinical medical physics · 2026Article
- Prediction of pathologic upstaging in ductal carcinoma in situ using MRI radiomics combined with clinicopathological features.Breast cancer research : BCR · 2026Article
- Super-Resolution Ultrasound Radiomics Can Predict the Upstaging of Ductal Carcinoma In Situ.Cancer medicine · 2025Article
- Predictive Performance of Radiomic Features Extracted from Breast MR Imaging in Postoperative Upgrading of Ductal Carcinoma in Situ to Invasive Carcinoma.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- Presurgical Upgrade Prediction of DCIS to Invasive Ductal Carcinoma Using Time-dependent Deep Learning Models with DCE MRI.Radiology. Artificial intelligence · 2024Article
- New Frontiers in Breast Cancer Imaging: The Rise of AI.Bioengineering (Basel, Switzerland) · 2024Review
- Predicting Underestimation of Invasive Cancer in Patients with Core-Needle-Biopsy-Diagnosed Ductal Carcinoma In Situ Using Deep Learning Algorithms.Tomography (Ann Arbor, Mich.) · 2022Article
- Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features.Radiology · 2022Article
- Ductal Carcinoma in Situ: State-of-the-Art Review.Radiology · 2022Review
- Prediction of the histologic upgrade of ductal carcinomaFrontiers in oncology · 2022Article
- Predicting Upstaging of DCIS to Invasive Disease: Radiologists's Predictive Performance.Academic radiology · 2020Article
- Machine learning in breast MRI.Journal of magnetic resonance imaging : JMRI · 2020Review
- Ductal Carcinoma in Situ: Current Concepts in Biology, Imaging, and Treatment.Journal of breast imaging · 2019Review
- Prediction of Surgical Upstaging Risk of Ductal Carcinoma In Situ Using Machine Learning Models.Journal of breast imagingArticle
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
purposeTo assess the ability of algorithmically assessed magnetic resonance imaging (MRI) features to predict the likelihood of upstaging to invasive cancer in newly diagnosed ductal carcinoma in situ (DCIS). MATERIALS AND
methodsWe identified 131 patients at our institution from 2000-2014 with a core needle biopsy-confirmed diagnosis of pure DCIS, a 1.5 or 3T preoperative bilateral breast MRI with nonfat-saturated T
resultsThe rate of upstaging of DCIS to invasive cancer in our study was 26.7% (35/131). Out of all imaging variables tested, the information measure of correlation 1, which quantifies spatial dependency in neighboring voxels of the tumor, showed the highest predictive value of upstaging with an area under the curve (AUC) = 0.719 (95% confidence interval [CI]: 0.609-0.829). This feature was statistically significant after adjusting for tumor size (P < 0.001).
conclusionAutomatically assessed MRI features may have a role in triaging which patients with a preoperative diagnosis of DCIS are at highest risk for occult invasive disease. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1332-1340.
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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.