Computer-Aided Diagnosis Parameters of Invasive Carcinoma of No Special Type on 3T MRI:Correlation with Pathologic Immunohistochemical Markers
- Author:
Jinho JEONG
1
;
Chang Suk PARK
;
Jung Whee LEE
;
Kijun KIM
;
Hyeon Sook KIM
;
Sun-Young JUN
;
Se-Jeong OH
Author Information
- Publication Type:Original Article
- From:Journal of the Korean Radiological Society 2022;83(1):149-161
- CountryRepublic of Korea
- Language:English
-
Abstract:
Purpose:To investigate the correlation between computer-aided diagnosis (CAD) parameters in 3-tesla (T) MRI and pathologic immunohistochemical (IHC) markers in invasive carcinoma of no special type (NST).
Materials and Methods:A total of 94 female who were diagnosed with NST carcinoma and underwent 3T MRI using CAD, from January 2018 to April 2019, were included. The relationship between angiovolume, curve peak, and early and late profiles of dynamic enhancement from CAD with pathologic IHC markers and molecular subtypes were retrospectively investigated using Dwass, Steel, Critchlow-Fligner multiple comparison analysis, and univariate binary logistic regression analysis.
Results:In NST carcinoma, a higher angiovolume was observed in tumors of higher nuclear and histologic grades and in lymph node (LN) (+), estrogen receptor (ER) (-), progesterone receptor (PR) (-), human epidermal growth factor 2 (HER2) (+), and Ki-67 (+) tumors. A high rate of delayed washout and a low rate of delayed persistence were observed in Ki-67 (+) tumors. In the binary logistic regression analysis of NST carcinoma, a high angiovolume was significantly associated with a high nuclear and histologic grade, LN (+), ER (-), PR (-), HER2 (+) status, and non-luminal subtypes. A high rate of washout and a low rate of persistence were also significantly correlated with the Ki-67 (+) status.
Conclusion:Angiovolume and delayed washout/persistent rate from CAD parameters in contrast enhanced breast MRI correlated with predictive IHC markers. These results suggest that CAD parameters could be used as clinical prognostic, predictive factors.
