ArticleDiagnostics (Basel, Switzerland)2021
Differentiating Breast Tumors from Background Parenchymal Enhancement at Contrast-Enhanced Mammography: The Role of Radiomics-A Pilot Reader Study.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 15 citations in OpenAlex.
- Contrast-Enhanced Mammography: Advances, Challenges, and Case-Based Insights.Korean journal of radiology · 2025Review
- Improving the diagnostic performance of contrast-enhanced mammography through lesion conspicuity and enhancement quantification.European radiology · 2025Article
- Machine learning-based fusion model for predicting HER2 expression in breast cancer by Sonazoid-enhanced ultrasound: a multicenter study.Frontiers in medicine · 2025Article
- State-of-the-art for contrast-enhanced mammography.The British journal of radiology · 2024Review
- Identification and diagnosis of mammographic malignant architectural distortion using a deep learning based mask regional convolutional neural network.Frontiers in oncology · 2023Article
- Radiomics Nomogram: Prediction of 2-Year Disease-Free Survival in Young Age Breast Cancer.Cancers · 2022Article
- The Diagnostic Value of MRI-Based Radiomic Analysis of Lacrimal Glands in Patients with Sjögren's Syndrome.International journal of molecular sciences · 2022Article
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- A Complex Radiomic Signature in Luminal Breast Cancer from a Weighted Statistical Framework: A Pilot Study.Diagnostics (Basel, Switzerland) · 2022Article
- How Dual-Energy Contrast-Enhanced Spectral Mammography Can Provide Useful Clinical Information About Prognostic Factors in Breast Cancer Patients: A Systematic Review of Literature.Frontiers in oncology · 2022Review
- Comparison of Contrast-Enhanced Spectral Mammography and Contrast-Enhanced MRI in Screening Multifocal and Multicentric Lesions in Breast Cancer Patients.Contrast media & molecular imaging · 2022Article
- Architectural Distortion-Based Digital Mammograms Classification Using Depth Wise Convolutional Neural Network.Biology · 2021Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe purpose of this study was to assess the effectiveness of the radiomic analysis of contrast-enhanced spectral mammography (CESM) in discriminating between breast cancers and background parenchymal enhancement (BPE).
methodsThis retrospective study included 38 patients that underwent CESM examinations for clinical purposes between January 2019-December 2020. A total of 57 malignant breast lesions and 23 CESM examinations with 31 regions of BPE were assessed through radiomic analysis using MaZda software. The parameters that demonstrated to be independent predictors for breast malignancy were exported into the B11 program and a k-nearest neighbor classifier (k-NN) was trained on the initial groups of patients and was tested using a validation group. Histopathology results obtained after surgery were considered the gold standard.
resultsRadiomic analysis found WavEnLL_s_2 parameter as an independent predictor for breast malignancies with a sensitivity of 68.42% and a specificity of 83.87%. The prediction model that included CH1D6SumAverg, CN4D6Correlat, Kurtosis, Perc01, Perc10, Skewness, and WavEnLL_s_2 parameters had a sensitivity of 73.68% and a specificity of 80.65%. Higher values were obtained of WavEnLL_s_2 and the prediction model for tumors than for BPEs. The comparison between the ROC curves provided by the WaveEnLL_s_2 and the entire prediction model did not show statistically significant results (
conclusionRadiomic analysis has the potential to differentiate CESM between malignant lesions and BPE. Further quantitative insight into parenchymal enhancement patterns should be performed to facilitate the role of BPE in personalized clinical decision-making and risk assessment.
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