ArticleJournal of experimental & clinical cancer research : CR2015
Quantitative discrimination between invasive ductal carcinomas and benign lesions based on semi-automatic analysis of time intensity curves from breast dynamic contrast enhanced MRI.
Article in Journal of experimental & clinical cancer research : CR, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Fat necrosis in the Breast: A systematic review of clinical.Lipids in health and disease · 2019Pooled it
- Improving diagnostic performance of O-RADS MRI: semi-quantitative intensity curves vs visual assessment of enhancement.European radiology · 2026Article
- Radiomics Nomogram Based on Radiomics Score from Multiregional Diffusion-Weighted MRI and Clinical Factors for Evaluating HER-2 2+ Status of Breast Cancer.Diagnostics (Basel, Switzerland) · 2021Article
- A comprehensive hierarchical classification based on multi-features of breast DCE-MRI for cancer diagnosis.Medical & biological engineering & computing · 2020Article
- Article
- Discrimination between benign and malignant breast lesions using volumetric quantitative dynamic contrast-enhanced MR imaging.European radiology · 2018Article
- Magnetic Resonance Imaging after Breast Oncoplastic Surgery: An Update.Breast care (Basel, Switzerland) · 2017Review
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundTraditional subjective method for the analysis of time-intensity curves (TICs) from breast dynamic contrast enhanced MRI (DCE-MRI) presented a low specificity. Hence, a semi-automatic quantitative method was proposed and evaluated for distinguishing invasive ductal carcinomas from benign lesions. MATERIALS AND
methodsIn the traditional method, the lesion was extracted by placing a region of interest (ROI) manually. The mean curve of the TICs from the ROI was subjectively classified as one of three patterns. Only one quantitative parameter, the mean value of maximum slope of increase (MSI), was provided. In the new method, the lesion was identified semi-automatically, and the mean curve was classified quantitatively. Some additional parameters, the signal intensity slope (SIslope), initial percentage of enhancement (Einitial), percentage of peak enhancement (Epeak), early signal enhancement ratio (ESER), and second enhancement percentage (SEP) were derived from the mean curves as well as the lesion areas. Wilcoxon's test and receiver operating characteristic (ROC) analyses were performed, and P < 0.05 was considered significant.
resultsAccording to the TIC classification results, the accuracies were 59.16% for the traditional manual method and 76.05% for the new method (P < 0.05). For the mean MSI values from the manual method, the accuracy was 63.35%. For the mean TICs derived from the semi-automatic method, the accuracies were 77.47% for SIslope, 65.24% for MSI, 58.45% for Einitial, 66.20% for Epeak, 71.83% for ESER, and 54.93% for SEP, respectively. For the lesion regions identified by the semi-automatic method, the accuracies were 73.24%, 72.54%, 58.45%, 62.68%, 64.09%, and 55.64%, respectively.
conclusionCompared with traditional subjective method, the semi-automatic quantitative method proposed in this study showed a higher performance, and should be used as a supplementary tool to aid radiologist's subjective interpretation.
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