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20.10.2018 | Clinical trial | Ausgabe 2/2019

Breast Cancer Research and Treatment 2/2019

Machine learning for diagnostic ultrasound of triple-negative breast cancer

Zeitschrift:
Breast Cancer Research and Treatment > Ausgabe 2/2019
Autoren:
Tong Wu, Laith R. Sultan, Jiawei Tian, Theodore W. Cary, Chandra M. Sehgal
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1007/​s10549-018-4984-7) contains supplementary material, which is available to authorized users.

Abstract

Purpose

Early diagnosis of triple-negative (TN) breast cancer is important due to its aggressive biological characteristics, poor clinical outcomes, and limited options for therapy. The goal of this study is to evaluate the potential of machine learning with quantitative ultrasound image features for the diagnosis of TN breast cancer.

Methods

Ultrasonic and clinical data of 140 surgically confirmed breast cancer cases were analyzed retrospectively for the diagnosis of TN and non-TN (NTN) subtypes. The subtypes were classified based on the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Ultrasound image features were measured from the grayscale and color Doppler images and used with logistic regression for classification by machine learning. Leave-one-out cross validation was used to train and test the differentiation. Diagnostic performance was measured by the area under receiver operating characteristic (ROC) curve, and sensitivity and specificity determined at the Youdons index.

Results

Of the twelve grayscale and Doppler features measured, eight were found to be statistically different for the TN and NTN subtypes (p < 0.05). The area under the ROC curve (AUC) of the statistically significant grayscale (GS) and color Doppler (CD) features was 0.85 and 0.65, respectively. The AUC increased to 0.88 when the GS and CD features were used together, with sensitivity of 86.96% and specificity of 82.91%. Consideration of patient age in the analysis did not improve discrimination of TN and NTN.

Conclusions

The analysis of breast ultrasound images by machine learning achieves high level of differentiation between the TN and NTN subtypes, exceeding the diagnostic performance by standard visual assessments of the images.

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Zusatzmaterial
Supplementary material 1 (DOCX 24 KB)
10549_2018_4984_MOESM1_ESM.docx
Literatur
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