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Portrait of Sophia Zackrisson. Photo

Sophia Zackrisson

Research group manager, Principal investigator, Professor, MD

Portrait of Sophia Zackrisson. Photo

A multiparametric approach to predict triple-negative breast cancer including parameters derived from ultrafast dynamic contrast-enhanced MRI

Author

  • Akane Ohashi
  • Masako Kataoka
  • Mami Iima
  • Maya Honda
  • Rie Ota
  • Yuta Urushibata
  • Marcel Dominik Nickel
  • Masakazu Toi
  • Sophia Zackrisson
  • Yuji Nakamoto

Summary, in English

Objective: Triple-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. Methods: This 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. Results: In 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. Conclusion: A 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.

Department/s

  • Radiology Diagnostics, Malmö
  • LUCC: Lund University Cancer Centre
  • LU Profile Area: Light and Materials
  • LTH Profile Area: Photon Science and Technology
  • EpiHealth: Epidemiology for Health

Publishing year

2023

Language

English

Pages

8132-8141

Publication/Series

European Radiology

Volume

33

Issue

11

Document type

Journal article

Publisher

Springer

Topic

  • Radiology, Nuclear Medicine and Medical Imaging

Keywords

  • Biomarkers
  • Breast
  • Breast neoplasms
  • Magnetic resonance imaging
  • Triple-negative breast neoplasms

Status

Published

Research group

  • Radiology Diagnostics, Malmö

ISBN/ISSN/Other

  • ISSN: 0938-7994