The association between molecular biomarkers and ultrasonographic radiomics features for triple negative invasive breast carcinoma
10.3760/cma.j.issn.1004-4477.2019.02.010
- VernacularTitle:浸润性三阴性乳腺癌超声影像组学特征与肿瘤生物学特性的关系研究
- Author:
Jiawei LI
1
;
Zhou FANG
;
Jin ZHOU
;
Yuyang TONG
;
Zhaoting SHI
;
Cai CHANG
;
Yi GUO
;
Jinhua YU
;
Yuanyuan WANG
Author Information
1. 复旦大学附属肿瘤医院超声科 复旦大学上海医学院肿瘤学系
- Keywords:
Ultrasonography;
Breast neoplasms;
Pathology;
Radiomics;
Immunohistochemistry
- From:
Chinese Journal of Ultrasonography
2019;28(2):137-143
- CountryChina
- Language:Chinese
-
Abstract:
Objective To evaluate the association between quantitative ultrasonographic features and clinical ,pathological and immunohistochemical features of triple negative invasive breast carcinoma( TNBC) . Methods With the ethical approval , 96 patients who were pathologically confirmed as TNBC were retrospectively reviewed . All patients were sub-grouped according to age ,tumor size ,pathological grade , Ki67 expression level and human epidermal growth factor receptor 2 ( HER-2) score .Ultrasound images were segmented for the breast carcinoma mass using a phase-based active contour model . The high-throughput radiomics features were extracted based on the two-dimensional sonographic features . There were 460 features extracted from each ultrasound image . A series of computer aided algorithms including K-svd algorithm ,sparse representation ,support vector machine ( SVM ) and radial basis function were used to determine the high-throughput sonographic features that were highly correlated to clinical ,pathological and immunohistochemical features of TNBC . The performance efficacy was expressed by accuracy and area under curve ( AUC) of the ROC curve . Results The high-throughput ultrasonographic features of invasive TNBC could predict its pathological grade ,Ki67 level and HER-2 score with the accuracy 92 .2％ -96 .9％and AUC 98 .7％ -99 .9％ . There were 82 radiomics features selected for predicting the pathological grade of TNBC , the feature with the maximum weight was the elliptic-normalized eccentricity based on morphological features . There were 100 features selected for predicting the Ki67 expression level ,the feature with the maximum weight was the standard deviation of the annular region based on the boundary texture features . There were 85 features selected for the prediction of HER-2 score ,the most powerful parameter was the intensity based on NGTDM texture features . Conclusions Quantitative high-throughput ultrasonographic features are correlated with the pathological and immunohistochemical characteristics of invasive TNBC . High-throughput ultrasonographic features are valuable in predicting biological behavior of TNBC .
