Added value of histogram analysis of apparent diffusion coefficient maps for differentiating triple-negative breast cancer from other subtypes of breast cancer on standard MRI
Authors Liu HL, Zong M, Wei H, Wang C, Lou JJ, Wang SQ, Zou QG, Jiang YN
Received 30 March 2019
Accepted for publication 11 July 2019
Published 6 September 2019 Volume 2019:11 Pages 8239—8247
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 2
Editor who approved publication: Dr Sanjeev Srivastava
Hong-Li Liu,1,* Min Zong,1,* Han Wei,1 Cong Wang,2 Jian-Juan Lou,1 Si-Qi Wang,1 Qi-Gui Zou,1 Yan-Ni Jiang1
1Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, People’s Republic of China; 2Department of Pathology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Yan-Ni Jiang
Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Road, Nanjing 210029, People’s Republic of China
Tel +86 1 377 665 2465
Fax +86 258 372 4440
Background: Triple-negative breast cancers generally occur in young women with remarkable potential to be aggressive. It will be of great help to detect this subtype of tumor early. To retrospectively evaluate the performance of histogram analysis of apparent diffusion coefficient (ADC) maps in distinguishing triple-negative breast cancer (TNBC) from other subtypes of breast cancer (non-TNBC), when combined with magnetic resonance imaging (MRI) features.
Materials and methods: From February 2014 to December 2018, 192 patients were included in this study taking preoperative standard MRI (s-MRI) and DWI. Seventy-six of them were pathologically confirmed with TNBC and rest 116 with other subtypes. First, their clinical-pathological features and morphological characteristics on MRI were assessed, including tumor size, foci quantity, tumor shape, margin, internal enhancement, and time-signal intensity curve types, in addition to the signal intensity on T2-weighted images. Second, whole-lesion apparent diffusion coefficient (ADC) histogram analysis was executed. Finally, both univariate and multivariate regression analyses were applied to identify the most useful variables in separating TNBCs from non-TNBCs, and then their effects were evaluated following receiver operating characteristic curve analysis.
Result: Multivariate regression analysis indicated that circumscribed margin, rim enhancement, and ADC90 were important predictors for TNBC. Increased area under curve (AUC) and improved specificity can be obtained when combined s-MRI and DWI (circumscribed margin+rim enhancement+ADC90>1.47×10−3 mm2/s) is taken as the criterion, other than s-MRI (circumscribed margin+rim enhancement) alone (s-MRI+DWI vs s-MRI; AUC, 0.833 vs 0.797; specificity, 98.3% vs 89.7%; sensitivity, 68.4% vs 69.7%).
Conclusion: Circumscribed margin and rim enhancement on s-MRI and ADC90 are three important elements in detecting TNBC, while ADC histogram analysis can provide additional value in this detection.
Keywords: triple-negative breast cancer, magnetic resonance imaging, morphological features, diffusion-weighted imaging, histogram analysis
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