ArticleEuropean radiology2023
A multiparametric approach to predict triple-negative breast cancer including parameters derived from ultrafast dynamic contrast-enhanced MRI.
Article in European radiology, 2023. 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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13 citing papers in PubMed, 10 citations in OpenAlex.
- Performance of MRI-based deep learning models in differentiation of triple negative breast cancer from other breast cancer subtypes: A systematic review and meta-analysis.European journal of radiology open · 2026Article
- Peritumoral fat content using iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL) is associated with triple-negative breast cancer.Japanese journal of radiology · 2026Article
- Diagnostic performance of kinetic parameters of ultrafast breast MRI and their associations with immunohistopathological findings of breast carcinoma.Scientific reports · 2026Article
- A multiparametric magnetic resonance imaging model incorporating the relative apparent diffusion coefficient for preoperative discrimination of triple-negative breast cancer.American journal of translational research · 2026Article
- Ultrafast MRI and diffusion-weighted imaging: a review of morphological evaluation and image quality in breast MRI.Japanese journal of radiology · 2025Review
- Development of a prediction model for HER2 low breast cancer using quantitative intra- and peri-tumoral heterogeneity and MRI features on high-spatial resolution ultrafast DCE-MRI.Quantitative imaging in medicine and surgery · 2025Article
- Establishment of an interpretable MRI radiomics-based machine learning model capable of predicting axillary lymph node metastasis in invasive breast cancer.Scientific reports · 2025Article
- Abbreviated and Ultrafast Dynamic Contrast-enhanced (DCE) MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- Superior performance in classification of breast cancer molecular subtype and histological factors by radiomics based on ultrafast MRI over standard MRI: evidence from a prospective study.La Radiologia medica · 2025Observational
- Asymmetric background parenchymal enhancement on contrast-enhanced mammography: associated factors, diagnostic workup, and clinical outcome.European radiology · 2025Article
- Analysis of dynamic contrast-enhanced T1-weighted imaging parameters in type II TIC breast lesions.Frontiers in oncology · 2025Article
- The potential role of breast MRI in evaluation of triple-negative breast cancer and fibroadenoma of less than 3 cm.Translational cancer research · 2024Article
- Preoperative prediction of extensive intraductal component in invasive breast cancer based on intra- and peri-tumoral heterogeneity in high-resolution ultrafast DCE-MRI.Scientific reports · 2024Article
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10 authors at 5 institutions in 3 countries.
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Abstract
objectiveTriple-negative breast cancer (TNBC) is a highly proliferative breast cancer subtype. We aimed to identify TNBC among invasive cancers presenting as masses using maximum slope (MS) and time to enhancement (TTE) measured on ultrafast (UF) DCE-MRI, ADC measured on DWI, and rim enhancement on UF DCE-MRI and early-phase DCE-MRI.
methodsThis retrospective single-center study, between December 2015 and May 2020, included patients with breast cancer presenting as masses. Early-phase DCE-MRI was performed immediately after UF DCE-MRI. Interrater agreements were evaluated using the intraclass correlation coefficient (ICC) and Cohen's kappa. Univariate and multivariate logistic regression analyses of the MRI parameters, lesion size, and patient age were performed to predict TNBC and create a prediction model. The programmed death-ligand 1 (PD-L1) expression statuses of the patients with TNBCs were also evaluated.
resultsIn total, 187 women (mean age, 58 years ± 12.9 [standard deviation]) with 191 lesions (33 TNBCs) were evaluated. The ICC for MS, TTE, ADC, and lesion size were 0.95, 0.97, 0.83, and 0.99, respectively. The kappa values of rim enhancements on UF and early-phase DCE-MRI were 0.88 and 0.84, respectively. MS on UF DCE-MRI and rim enhancement on early-phase DCE-MRI remained significant parameters after multivariate analyses. The prediction model created using these significant parameters yielded an area under the curve of 0.74 (95% CI, 0.65, 0.84). The PD-L1-expressing TNBCs tended to have higher rim enhancement rates than the non-PD-L1-expressing TNBCs.
conclusionA multiparametric model using UF and early-phase DCE-MRI parameters may be a potential imaging biomarker to identify TNBCs. CLINICAL RELEVANCE STATEMENT: Prediction of TNBC or non-TNBC at an early point of diagnosis is crucial for appropriate management. This study offers the potential of UF and early-phase DCE-MRI to offer a solution to this clinical issue. KEY POINTS: • It is crucial to predict TNBC at an early clinical period. • Parameters on UF DCE-MRI and early-phase conventional DCE-MRI help in predicting TNBC. • Prediction of TNBC by MRI may be useful in determining appropriate clinical management.
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