ArticleEuropean radiology2019
Differentiation of triple-negative breast cancer from other subtypes through whole-tumor histogram analysis on multiparametric MR imaging.
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51 citing papers in PubMed, 2 syntheses or guidelines pooled it, 88 citations in OpenAlex.
- Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group.European radiology · 2020Guideline
- Can apparent diffusion coefficient (ADC) distinguish breast cancer from benign breast findings? A meta-analysis based on 13 847 lesions.BMC cancer · 2019Pooled it
- Biparametric MRI in prostate cancer: utility of whole-prostate and whole-lesion histogram and texture analysis for clinically significant prostate cancer.Japanese journal of radiology · 2026Article
- Integrating time-dependent diffusion MRI and intravoxel incoherent motion for predicting NPI and molecular subtypes in breast cancer.BMC medical imaging · 2026Article
- The role of magnetic resonance imaging (MRI) in breast cancer molecular subtypes.Polish journal of radiology · 2026Review
- Improved diagnostic value of whole-lesion histogram and texture analyses on multiparametric breast MRI for papillary neoplasms with non-mass enhancement.Gland surgery · 2025Article
- Artificial intelligence in precision medicine: transforming disease subtyping, medical imaging, and pharmacogenomics.Emerging topics in life sciences · 2025Article
- The impact of radiogenomics on breast cancer.American journal of cancer research · 2025Review
- Enhancing preoperative HER2 status classification of invasive breast cancers using machine learning models based on clinicopathological and MRI features: a multicenter study.Frontiers in cell and developmental biology · 2025Article
- MRI-Derived Radiomics For Classifying Breast Cancer Molecular Subtypes: a Modeling Approach.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2025Article
- Correlation analysis of multiparametric magnetic resonance imaging features and molecular subtypes of breast cancer.Journal of clinical imaging science · 2025Article
- Dynamic contrast-enhanced MRI-based radiomics model of intra-tumoral kinetic heterogeneity for predicting breast cancer molecular subtypes.Frontiers in molecular biosciences · 2025Article
- Prediction of tumor response to neoadjuvant chemotherapy in high-grade osteosarcoma using clustering-based analysis of magnetic resonance imaging: an exploratory study.La Radiologia medica · 2025Article
- Multiparametric MRI-based radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer.Scientific reports · 2024Article
- Histogram analysis comparison of readout-segmented and single-shot echo-planar imaging for differentiating luminal from non-luminal breast cancer.Scientific reports · 2024Article
- MTFN: multi-temporal feature fusing network with co-attention for DCE-MRI synthesis.BMC medical imaging · 2024Article
- Voxel-wise mapping of DCE-MRI time-intensity-curve profiles enables visualizing and quantifying hemodynamic heterogeneity in breast lesions.European radiology · 2024Article
- The use of radiomics in magnetic resonance imaging for the pre-treatment characterisation of breast cancers: A scoping review.Journal of medical radiation sciences · 2023Article
- Quantitative apparent diffusion coefficient metrics for MRI-only suspicious breast lesions: any added clinical value?Quantitative imaging in medicine and surgery · 2023Article
- Radiogenomics: a key component of precision cancer medicine.British journal of cancer · 2023Review
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
Abstract
purposeTo identify triple-negative (TN) breast cancer imaging biomarkers in comparison to other molecular subtypes using multiparametric MR imaging maps and whole-tumor histogram analysis. MATERIALS AND
methodsThis retrospective study included 134 patients with invasive ductal carcinoma. Whole-tumor histogram-based texture features were extracted from a quantitative ADC map and DCE semi-quantitative maps (washin and washout). Univariate analysis using the Student's t test or Mann-Whitney U test was performed to identify significant variables for differentiating TN cancer from other subtypes. The ROC curves were generated based on the significant variables identified from the univariate analysis. The AUC, sensitivity, and specificity for subtype differentiation were reported.
resultsThe significant parameters on the univariate analysis achieved an AUC of 0.710 (95% confidence interval [CI] 0.562, 0.858) with a sensitivity of 63.6% and a specificity of 73.1% at the best cutoff point for differentiating TN cancers from Luminal A cancers. An AUC of 0.763 (95% CI 0.608, 0.917) with a sensitivity of 86.4% and a specificity of 72.2% was achieved for differentiating TN cancers from human epidermal growth factor receptor 2 (HER2) positive cancers. Also, an AUC of 0.683 (95% CI 0.556, 0.809) with a sensitivity of 54.5% and a specificity of 83.9% was achieved for differentiating TN cancers from non-TN cancers. There was no significant feature on the univariate analysis for TN cancers versus Luminal B cancers.
conclusionsWhole-tumor histogram-based imaging features derived from ADC, along with washin and washout maps, provide a non-invasive analytical approach for discriminating TN cancers from other subtypes. KEY POINTS: • Whole-tumor histogram-based features on MR multiparametric maps can help to assess biological characterization of breast cancer. • Histogram-based texture analysis may predict the molecular subtypes of breast cancer. • Combined DWI and DCE evaluation helps to identify triple-negative breast cancer.
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