The association between molecular biomarkers and ultrasonographic radiomics features for triple negative invasive breast carcinoma

医学 病态的 免疫组织化学 三阴性乳腺癌 超声波 特征(语言学) 乳腺超声检查 乳腺癌 乳腺癌 无线电技术 接收机工作特性 放射科 病理 肿瘤科 人工智能 内科学 癌症 乳腺摄影术 计算机科学 哲学 语言学
作者
Jiawei Li,Zhou Fang,Jin Zhou,Yuyang Tong,Zhaoting Shi,Cai Chang,Yi Guo,Jinhua Yu,Yuanyuan Wang
出处
期刊:Chinese Journal of Ultrasonography [Chinese Medical Association]
卷期号:28 (2): 137-143 被引量:2
标识
DOI:10.3760/cma.j.issn.1004-4477.2019.02.010
摘要

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. Key words: Ultrasonography; Breast neoplasms; Pathology; Radiomics; Immunohistochemistry
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