Differentiation of Triple-Negative Phenotypes of Breast Cancer from Others Using Multiparametric Magnetic Resonance Imaging: A Prospective Imaging Study

Abstract To predict and differentiate triple-negative (TNBC) from non-triple-negative breast cancer (NTNBC) phenotypes using multiparametric magnetic resonance imaging (MpMRI). One hundred and two patients with BI-RADS 5/6 lesions who underwent MpMRI (3 Tesla) and were diagnosed with invasive ductal carcinoma on histopathological analysis after ultrasound-guided biopsy were categorized into TNBC and NTNBC groups based on immunohistochemistry. MRI included T1-weighted imaging, T2-weighted imaging, dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted imaging, and magnetic resonance spectroscopy (MRS). Morphological, semiquantitative, and quantitative DCE parameters, apparent diffusion coefficient (ADC) values, and MRS (tCho) were studied. Nonparametric categorical variables were analyzed using the chi-square test, continuous nonparametric variables using the Kruskal–Wallis, and continuous parametric variables using the ANOVA test to determine the association of parameters with NTNBC/TNBC subtypes. Receiver operating characteristic and logistic regression analyses were done to determine the diagnostic performance and independent predictors. Seventy-two cases were NTNBCs, and 30 cases were TNBCs. TNBCs showed more circumscribed margins (51.7%), rim enhancement (65.5%), perilesional edema (80%), unifocal lesions (76.7%), and axillary lymphadenopathy (73.3%). NTNBCs showed more irregular shapes (81.8%) and spiculated margins (56.1%). Choline peaks on MRS were more frequent in TNBC (60%). TNBCs had higher mean Ktrans (>0.53 min−1), higher signal enhancement ratio (>72.95), lower mean Ve (<0.34), and lower ADC values (<0.83 × 10−3 mm2/s). Multivariate regression analysis identified rim enhancement (odds ratio [OR]: 25.11), high mean Ktrans (OR: 17.25), and low Ve (OR: 0.004 for high Ve) as independent predictors of the TNBC subtype. MpMRI parameters could differentiate TNBC from NTNBC, offering potential noninvasive biomarkers, enhancing diagnostic precision, indicating revision of histopathological evaluation reports in doubtful cases, prognostication, and personalized patient management.

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Journal
Indian journal of radiology and imaging - new series/Indian journal of radiology and imaging/Indian Journal of Radiology & Imaging
Published
2026-02-05
DOI
https://doi.org/10.1055/s-0045-1812492
Primary Topic
MRI in cancer diagnosis
Type
article
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article

Differentiation of Triple-Negative Phenotypes of Breast Cancer from Others Using Multiparametric Magnetic Resonance Imaging: A Prospective Imaging Study

RanjanKumar Patel, Pankaj Kumar, Phanindra Kumar Swain, Arunprakash Pitchaimuthu et al.
Indian journal of radiology and imaging - new series/Indian journal of radiology and imaging/Indian Journal of Radiology & Imaging
MRI in cancer diagnosis
article

Differentiation of Triple-Negative Phenotypes of Breast Cancer from Others Using Multiparametric Magnetic Resonance Imaging: A Prospective Imaging Study

RanjanKumar Patel, Pankaj Kumar, Phanindra Kumar Swain, Arunprakash Pitchaimuthu, Suprava Naik, Pavithra Santhanam, Sourav Bhaduri, Taraprasad Tripathy, Saroj Kumar Das Majumdar, Pritinanda Mishra, Dillip Kumar Muduly, Sudipta Mohakud
article en

Abstract

Abstract To predict and differentiate triple-negative (TNBC) from non-triple-negative breast cancer (NTNBC) phenotypes using multiparametric magnetic resonance imaging (MpMRI). One hundred and two patients with BI-RADS 5/6 lesions who underwent MpMRI (3 Tesla) and were diagnosed with invasive ductal carcinoma on histopathological analysis after ultrasound-guided biopsy were categorized into TNBC and NTNBC groups based on immunohistochemistry. MRI included T1-weighted imaging, T2-weighted imaging, dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted imaging, and magnetic resonance spectroscopy (MRS). Morphological, semiquantitative, and quantitative DCE parameters, apparent diffusion coefficient (ADC) values, and MRS (tCho) were studied. Nonparametric categorical variables were analyzed using the chi-square test, continuous nonparametric variables using the Kruskal–Wallis, and continuous parametric variables using the ANOVA test to determine the association of parameters with NTNBC/TNBC subtypes. Receiver operating characteristic and logistic regression analyses were done to determine the diagnostic performance and independent predictors. Seventy-two cases were NTNBCs, and 30 cases were TNBCs. TNBCs showed more circumscribed margins (51.7%), rim enhancement (65.5%), perilesional edema (80%), unifocal lesions (76.7%), and axillary lymphadenopathy (73.3%). NTNBCs showed more irregular shapes (81.8%) and spiculated margins (56.1%). Choline peaks on MRS were more frequent in TNBC (60%). TNBCs had higher mean Ktrans (>0.53 min−1), higher signal enhancement ratio (>72.95), lower mean Ve (<0.34), and lower ADC values (<0.83 × 10−3 mm2/s). Multivariate regression analysis identified rim enhancement (odds ratio [OR]: 25.11), high mean Ktrans (OR: 17.25), and low Ve (OR: 0.004 for high Ve) as independent predictors of the TNBC subtype. MpMRI parameters could differentiate TNBC from NTNBC, offering potential noninvasive biomarkers, enhancing diagnostic precision, indicating revision of histopathological evaluation reports in doubtful cases, prognostication, and personalized patient management.

Indian journal of radiology and imaging - new series/Indian journal of radiology and imaging/Indian Journal of Radiology & Imaging
All India Institute of Medical Sciences Bhubaneswar (IN), TCG Crest (IN)
Good health and well-being
Openalex Percentile: Top 66%
MRI in cancer diagnosis
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