Evaluation of Apparent Diffusion Coefficient to Predict Grade, Microinvasion, and Invasion in Ductal Carcinoma In Situ of the Breast

Heba Hussein; Caroline Chung; Hadas Moshonov; Naomi Miller; Supriya R Kulkarni; Anabel M Scaranelo

doi:10.1016/j.acra.2015.08.004

Evaluation of Apparent Diffusion Coefficient to Predict Grade, Microinvasion, and Invasion in Ductal Carcinoma In Situ of the Breast

Acad Radiol. 2015 Dec;22(12):1483-8. doi: 10.1016/j.acra.2015.08.004. Epub 2015 Sep 26.

Authors

Heba Hussein¹, Caroline Chung², Hadas Moshonov³, Naomi Miller⁴, Supriya R Kulkarni⁵, Anabel M Scaranelo⁵

Affiliations

¹ Joint Department of Medical Imaging, Mount Sinai Hospital, Women's College Hospital, University Health Network, University of Toronto, 600 University Avenue, Toronto, ON, M5G 2M9, Canada. Electronic address: hebamostafa.hussein@sunnybrook.ca.
² Department of Radiation Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada.
³ Department of Biostatistics/Joint Department of Medical Imaging, Mount Sinai Hospital, Women's College Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada.
⁴ Department of Pathology, University Health Network, University of Toronto, Toronto, Ontario, Canada.
⁵ Joint Department of Medical Imaging, Mount Sinai Hospital, Women's College Hospital, University Health Network, University of Toronto, 600 University Avenue, Toronto, ON, M5G 2M9, Canada.

PMID: 26391856
DOI: 10.1016/j.acra.2015.08.004

Abstract

Rationale and objectives: To evaluate the role of apparent diffusion coefficient (ADC) in distinguishing ductal carcinoma in situ (DCIS) grades and identifying microinvasive and/or invasive disease in the preoperative evaluation of patients with core biopsy-proven DCIS.

Materials and methods: Research Ethics Board-approved study with informed consent from 81 women (age, 36-84 years) scheduled for core-biopsy with results of 82 noninvasive breast carcinomas. All patients were assessed preoperatively by diffusion sequence in addition to contrast magnetic resonance imaging (MRI). Lesion morphology and ADC values were recorded. The Kruskal-Wallis or one-way analysis of variance test and Pearson correlation coefficient were used to study the association between ADC and MRI lesion characteristics. Logistic regression analysis was used to evaluate the ability of ADC to predict the presence of invasion.

Results: Surgical pathology demonstrated associated invasive cancer in 26.8%, microinvasion in 14.6%, and pure DCIS in 58.5%. The minimum regions of interest (ROI)-based ADC was significantly different among the following three groups (P < .001, Kruskal-Wallis test): 0.98 × 10(-3) mm(2)/s ± 0.25 for pure DCIS, 0.82 × 10(-3) mm(2)/s ± 0.20 for DCIS with microinvasion, and 0.71 × 10(-3) mm(2)/s ± 0.27 for DCIS with invasive disease. Based on logistic regression analysis, the minimum ROI-based ADC of 0.56 × 10(-3) mm(2)/s was a significant predictor for invasive disease (odds ratio = 0.02, 95% confidence interval [0.002, 0.207], P = .001). Regardless of the field strength (1.5 vs. 3.0 T) ADC values of high-grade and non-high-grade DCIS were not significantly different.

Conclusions: Pure DCIS had the highest "ROI-based" ADC measured using 1.5 T or 3.0 T. The ADC was able to identify microinvasion or invasive cancer in biopsy-proven DCIS lesions but not to distinguish the DCIS grades.

Keywords: Breast cancer; DCIS; apparent diffusion coefficient; diffusion-weighted imaging.

MeSH terms

Adult
Aged
Aged, 80 and over
Breast Neoplasms / diagnostic imaging*
Breast Neoplasms / pathology*
Breast Neoplasms / surgery
Carcinoma, Intraductal, Noninfiltrating / diagnostic imaging*
Carcinoma, Intraductal, Noninfiltrating / pathology*
Carcinoma, Intraductal, Noninfiltrating / surgery
Diffusion Magnetic Resonance Imaging / methods*
Female
Humans
Image Interpretation, Computer-Assisted*
Middle Aged
Neoplasm Grading / methods
Retrospective Studies