ArticleJournal of clinical imaging science2022
Quantitative classification of invasive and noninvasive breast cancer using dynamic magnetic resonance imaging of the mammary gland.
Article in Journal of clinical imaging science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
The trial behind it
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
3 citing papers in PubMed, 7 citations in OpenAlex.
- Development of a prognostic model for preoperative stage I-III breast cancer using machine learning with integrated cone-beam breast computed tomography data.La Radiologia medica · 2026Article
- Advancing Breast Cancer Treatment: The Role of Immunotherapy and Cancer Vaccines in Overcoming Therapeutic Challenges.Vaccines · 2025Review
- Diagnostic efficacy of ultrasound elastography and dynamic contrast-enhanced MR in benign and malignant breast masses.American journal of translational research · 2023Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Breast cancers are classified as invasive or noninvasive based on histopathological findings. Although time-intensity curve (TIC) analysis using magnetic resonance imaging (MRI) can differentiate benign from malignant disease, its diagnostic ability to quantitatively distinguish between invasive and noninvasive breast cancers has not been determined. In this study, we evaluated the ability of TIC analysis of dynamic MRI data (MRI-TIC) to distinguish between invasive and noninvasive breast cancers. Material and Methods: We collected and analyzed data for 429 cases of epithelial invasive and noninvasive breast carcinomas. TIC features were extracted in washout areas suggestive of malignancy. Results: The graph determining the positive diagnosis rate for invasive and noninvasive cases revealed that the cut-off θ Conclusion: MRI-TIC is effective for the classification of invasive vs. noninvasive breast cancer.
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Registered trials
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