What does this research mean for the field?

Quantitative DCE-MRI parameters and apparent diffusion coefficient (ADC) correlate with breast cancer prognostic factors and molecular subtypes, and a multivariable model combining Ktrans, Kep, and inverted Ve can accurately identify triple-negative breast cancer preoperatively. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To evaluate the correlation between quantitative DCE-MRI parameters, ADC, and breast cancer prognostic factors or molecular subtypes, and to develop a multivariable model for identifying triple-negative breast cancer.

September 14, 2026BMC CancerOpen Access

Correlation between quantitative parameters and apparent diffusion coefficient of 3.0T dynamic contrast-enhanced MRI and prognostic factors and molecular classification of breast cancer

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Authors

JFJieting Fu QJQiaosheng Jiang CSChen Sun

Discussion

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Overview

Retrospective study demonstrates DCE-MRI and ADC parameters correlate with molecular subtypes in breast cancer, suggesting multivariable imaging can identify triple-negative disease preoperatively.

Key Points

To evaluate the correlation between quantitative DCE-MRI parameters, ADC, and breast cancer prognostic factors or molecular subtypes, and to develop a multivariable model for identifying triple-negative breast cancer.
Retrospective analysis of 746 patients with surgically and pathologically confirmed breast cancer who underwent preoperative 3.0T DCE-MRI and DWI between January 2016 and January 2019.
Quantitative parameters (Ktrans, Kep, Ve) were calculated using the extended Tofts model alongside ADC values, with inter-reader reproducibility assessed in 100 lesions.
Multivariable logistic regression was used to model preoperative triple-negative breast cancer (TNBC) identification, evaluated by area under the ROC curve (AUC).
After Bonferroni correction, ER and PR status negatively correlated with Ktrans, Kep, and ADC (all P < 0.001), while Ki-67 positively correlated with Ktrans and Kep (all P < 0.001).
TNBC lesions showed the highest mean Ktrans (2.45 ± 0.45 min⁻¹), highest Kep (6.40 ± 0.71 min⁻¹), and lowest Ve (0.42 ± 0.15), with Kep yielding the highest single-parameter AUC for TNBC (0.826, 95% CI 0.789–0.863).
A multivariable logistic regression model combining Ktrans, Kep, and inverted Ve achieved an AUC of 0.891 (95% CI 0.857–0.926), significantly outperforming any individual parameter (DeLong P < 0.001).

Cite This Study

Fu et al. (2026) studied this question.

synapsesocial.com/papers/6aa7b2f70926e14a848b18a2 https://doi.org/10.1186/s12885-026-16485-2

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September 14, 2026BMC CancerOpen Access

Correlation between quantitative parameters and apparent diffusion coefficient of 3.0T dynamic contrast-enhanced MRI and prognostic factors and molecular classification of breast cancer

View Full Paper

Ask AI

Bookmark

Authors

JFJieting Fu QJQiaosheng Jiang CSChen Sun

Discussion

Member takes

Overview

Retrospective study demonstrates DCE-MRI and ADC parameters correlate with molecular subtypes in breast cancer, suggesting multivariable imaging can identify triple-negative disease preoperatively.

Key Points

To evaluate the correlation between quantitative DCE-MRI parameters, ADC, and breast cancer prognostic factors or molecular subtypes, and to develop a multivariable model for identifying triple-negative breast cancer.
Retrospective analysis of 746 patients with surgically and pathologically confirmed breast cancer who underwent preoperative 3.0T DCE-MRI and DWI between January 2016 and January 2019.
Quantitative parameters (Ktrans, Kep, Ve) were calculated using the extended Tofts model alongside ADC values, with inter-reader reproducibility assessed in 100 lesions.
Multivariable logistic regression was used to model preoperative triple-negative breast cancer (TNBC) identification, evaluated by area under the ROC curve (AUC).
After Bonferroni correction, ER and PR status negatively correlated with Ktrans, Kep, and ADC (all P < 0.001), while Ki-67 positively correlated with Ktrans and Kep (all P < 0.001).
TNBC lesions showed the highest mean Ktrans (2.45 ± 0.45 min⁻¹), highest Kep (6.40 ± 0.71 min⁻¹), and lowest Ve (0.42 ± 0.15), with Kep yielding the highest single-parameter AUC for TNBC (0.826, 95% CI 0.789–0.863).
A multivariable logistic regression model combining Ktrans, Kep, and inverted Ve achieved an AUC of 0.891 (95% CI 0.857–0.926), significantly outperforming any individual parameter (DeLong P < 0.001).

Cite This Study

Fu et al. (2026) studied this question.

synapsesocial.com/papers/6aa7b2f70926e14a848b18a2 https://doi.org/10.1186/s12885-026-16485-2

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