Added value of mean and entropy of apparent diffusion coefficient values for evaluating histologic phenotypes of invasive ductal breast cancer with MR imaging
- 181 Downloads
To study the added value of mean and entropy of apparent diffusion coefficient (ADC) values at standard (800 s/mm2) and high (1500 s/mm2) b-values obtained with diffusion-weighted imaging in identifying histologic phenotypes of invasive ductal breast cancer (IDC) with MR imaging.
One hundred thirty-four IDC patients underwent diffusion-weighted imaging with b-values of 800 and 1500 s/mm2, and corresponding ADC800 and ADC1500 maps were generated. Mean and entropy of volumetric ADC values were compared with molecular markers (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor 2 [HER2], and Ki-67). Associations among morphologic features, ADC metrics, and phenotypes (luminal A, luminal B [HER2 negative], luminal B [HER2 positive], HER2 positive, and triple negative) were evaluated.
Mean ADC values were significantly decreased in ER-positive, PR-positive, and HER2-negative tumors (p < 0.01). Ki-67 ≥ 20% tumors demonstrated significantly higher ADC entropy values compared with Ki-67 < 20% tumors (p < 0.001). Luminal A subtype tended to display lower ADC entropy values compared with other subtypes, while HER2-positive subtype tended to display higher mean ADC values. ADC1500 entropy provided superior diagnostic performance over ADC800 entropy (p = 0.04). Independent risk factors were ADC1500 entropy (p = 0.002) associated with luminal A, irregular mass shape (p = 0.018) and ADC1500 entropy (p = 0.022) with luminal B (HER2 positive), mean ADC1500 (p = 0.018) with HER2 positive, and smooth mass margin (p = 0.012) and rim enhancement (p = 0.003) with triple negative.
Mean and entropy of ADC values provided complementary information and added value for evaluating IDC histologic phenotypes. High-b-value ADC1500 may facilitate better phenotype discrimination.
• ADC metrics are associated with molecular marker status in IDC.
• ADC 1500 improves differentiation of histologic phenotypes compared with ADC 800 .
• ADC metrics add value to morphologic features in IDC phenotyping.
KeywordsDiffusion magnetic resonance imaging Breast cancer Phenotype Immunohistochemistry Prognosis
Apparent diffusion coefficient
Dynamic contrast enhanced
Human epidermal growth factor receptor 2
Invasive ductal carcinoma
Region of interest
Spectral adiabatic inversion recovery
T1-weighted high resolution isotropic volume examination
This study has received funding from the National Natural Science Foundation of China (nos. 81501458 and 81701642) and Medical Engineering Cross Research Foundation of Shanghai Jiao Tong University (nos. YG2015QN37 and YG2014ZD05).
Compliance with Ethical Standards
The scientific guarantor of this publication is Jianrong Xu.
Conflict of Interest
The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
Statistics and Biometry
No complex statistical methods were necessary for this paper.
Written informed consent was waived by the Institutional Review Board.
Institutional Review Board approval was obtained.
Study subjects or cohorts overlap
Some study subjects or cohorts have been previously reported in:
Suo S, Cheng F, Cao M et al (2017) Multiparametric diffusion-weighted imaging in breast lesions: Association with pathologic diagnosis and prognostic factors. J Magn Reson Imaging. DOI: 10.1002/jmri.25612
• diagnostic or prognostic study
• performed at one institution
- 3.Marusyk A, Polyak K (2010) Tumor heterogeneity: causes and consequences. Biochim Biophys Acta 1805:105–117Google Scholar
- 10.Karan B, Pourbagher A, Torun N (2016) Diffusion-weighted imaging and (18) F-fluorodeoxyglucose positron emission tomography/computed tomography in breast cancer: Correlation of the apparent diffusion coefficient and maximum standardized uptake values with prognostic factors. J Magn Reson Imaging 43:1434–1444CrossRefGoogle Scholar
- 14.American College of Radiology (2013) Breast Imaging Reporting and Data System (BI-RADS), 5th edn. American College of Radiology, Reston, VAGoogle Scholar
- 29.Surov A, Meyer HJ, Wienke A (2017) Associations between apparent diffusion coefficient (ADC) and KI 67 in different tumors: a meta-analysis. Part 1: ADCmean. Oncotarget 8:75434–75444Google Scholar
- 33.Tamura T, Murakami S, Naito K, Yamada T, Fujimoto T, Kikkawa T (2014) Investigation of the optimal b-value to detect breast tumors with diffusion weighted imaging by 1.5-T MRI. Cancer Imaging 14:11Google Scholar