ArticleBMC medical imaging2017
MRI texture analysis in differentiating luminal A and luminal B breast cancer molecular subtypes - a feasibility study.
Article in BMC medical imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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Who cites it
55 citing papers in PubMed, 95 citations in OpenAlex.
- MRI-based texture analysis for breast cancer subtype classification in a multi-ethnic population.Magma (New York, N.Y.) · 2026Article
- Exploiting machine-learning models with DCE-MRI radiomics for breast cancer molecular subtyping.Frontiers in oncology · 2026Article
- Emerging non-invasive imaging biomarkers of Ki-67 in pancreatic cancer: Toward predictive precision oncology.World journal of gastrointestinal oncology · 2025Article
- Intratumoral and peritumoral radiomics signature based on DCE-MRI can distinguish between luminal and non-luminal breast cancer molecular subtypes.Scientific reports · 2025Article
- Associations between dynamic-contrast enhanced MRI with histopathological features in atypical HCC using spatial co-registration with biopsy.American journal of translational research · 2025Article
- The impact of radiogenomics on breast cancer.American journal of cancer research · 2025Review
- 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
- Tumor Heterogeneity of Breast Cancer Assessed with Computed Tomography Texture Analysis: Association with Disease-Free Survival and Clinicopathological Prognostic Factor.Diagnostics (Basel, Switzerland) · 2023Article
- A quantitative heterogeneity analysis approach to molecular subtype recognition of breast cancer in dynamic contrast-enhanced magnetic imaging images from radiomics data.Quantitative imaging in medicine and surgery · 2023Article
- Influence of maternal psychological distress during COVID-19 pandemic on placental morphometry and texture.Scientific reports · 2023Article
- Whole-Tumor ADC Texture Analysis Is Able to Predict Breast Cancer Receptor Status.Diagnostics (Basel, Switzerland) · 2023Article
- Observational
- The application of radiomics in predicting gene mutations in cancer.European radiology · 2022Review
- A Complex Radiomic Signature in Luminal Breast Cancer from a Weighted Statistical Framework: A Pilot Study.Diagnostics (Basel, Switzerland) · 2022Article
- Characterization of breast cancer subtypes based on quantitative assessment of intratumoral heterogeneity using dynamic contrast-enhanced and diffusion-weighted magnetic resonance imaging.European radiology · 2022Article
- Machine learning with multiparametric breast MRI for prediction of Ki-67 and histologic grade in early-stage luminal breast cancer.European radiology · 2022Article
- Association between breast cancer's prognostic factors and 3D textural features of non-contrast-enhancedThe British journal of radiology · 2022Article
- Potential of the Non-Contrast-Enhanced Chest CT Radiomics to Distinguish Molecular Subtypes of Breast Cancer: A Retrospective Study.Frontiers in oncology · 2022Article
- Texture Analysis of Enhanced MRI and Pathological Slides Predicts EGFR Mutation Status in Breast Cancer.BioMed research international · 2022Article
- Texture analysis of low-flow vascular malformations in the oral and maxillofacial region: venous malformation vs. lymphatic malformation.Polish journal of radiology · 2022Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe aim of this study was to use texture analysis (TA) of breast magnetic resonance (MR) images to assist in differentiating estrogen receptor (ER) positive breast cancer molecular subtypes.
methodsTwenty-seven patients with histopathologically proven invasive ductal breast cancer were selected in preliminary study. Tumors were classified into molecular subtypes: luminal A (ER-positive and/or progesterone receptor (PR)-positive, human epidermal growth factor receptor type 2 (HER2) -negative, proliferation marker Ki-67 < 20 and low grade (I)) and luminal B (ER-positive and/or PR-positive, HER2-positive or HER2-negative with high Ki-67 ≥ 20 and higher grade (II or III)). Co-occurrence matrix -based texture features were extracted from each tumor on T1-weighted non fat saturated pre- and postcontrast MR images using TA software MaZda. Texture parameters and tumour volumes were correlated with tumour prognostic factors.
resultsTextural differences were observed mainly in precontrast images. The two most discriminative texture parameters to differentiate luminal A and luminal B subtypes were sum entropy and sum variance (p = 0.003). The AUCs were 0.828 for sum entropy (p = 0.004), and 0.833 for sum variance (p = 0.003), and 0.878 for the model combining texture features sum entropy, sum variance (p = 0.001). In the LOOCV, the AUC for model combining features sum entropy and sum variance was 0.876. Sum entropy and sum variance showed positive correlation with higher Ki-67 index. Luminal B types were larger in volume and moderate correlation between larger tumour volume and higher Ki-67 index was also observed (r = 0.499, p = 0.008).
conclusionsTexture features which measure randomness, heterogeneity or smoothness and homogeneity may either directly or indirectly reflect underlying growth patterns of breast tumours. TA and volumetric analysis may provide a way to evaluate the biologic aggressiveness of breast tumours and provide aid in decisions regarding therapeutic efficacy.
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