Ultrasound-based radiomics model for predicting molecular biomarkers in breast cancer

Abstract

BackgroundBreast cancer (BC) is the most common cancer in women and is highly heterogeneous. BC can be classified into four molecular subtypes based on the status of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and proliferation marker protein Ki-67. However, they can only be obtained by biopsy or surgery, which is invasive. Radiomics can noninvasively predict molecular expression via extracting the image features. Nevertheless, there is a scarcity of data available regarding the prediction of molecular biomarker expression using ultrasound (US) images in BC.ObjectivesTo investigate the prediction performance of US radiomics for the assessment of molecular profiling in BC.MethodsA total of 342 patients with BC who underwent preoperative US examination between January 2013 and December 2021 were retrospectively included. They were confirmed by pathology and molecular subtype analysis of ER, PR, HER2 and Ki-67. The radiomics features were extracted and four molecular models were constructed through support vector machine (SVM). Pearson correlation coefficient heatmaps are employed to analyze the relationship between selected features and their predictive power on molecular expression. The receiver operating characteristic curve was used for the prediction performance of US radiomics in the assessment of molecular profiling.Results359 lesions with 129 ER- and 230 ER+, 163 PR- and 196 PR+, 265 HER2- and 94 HER2+, 114 Ki-67- and 245 Ki-67+ expression were included. 1314 features were extracted from each ultrasound image. And there was a significant difference of some specific radiomics features between the molecule positive and negative groups. Multiple features demonstrated significant association with molecular biomarkers. The area under curves (AUCs) were 0.917, 0.835, 0.771, and 0.896 in the training set, while 0.868, 0.811, 0.722, and 0.706 in the validation set to predict ER, PR, HER2, and Ki-67 expression respectively.ConclusionUltrasound-based radiomics provides a promising method for predicting molecular biomarker expression of ER, PR, HER2, and Ki-67 in BC.

Diagnostic performance of ultrasound-based artificial intelligence for predicting key molecular markers in breast cancer: A systematic review and meta-analysis

“…Six studies (6/23) predicted the ER and PR status in BC patients, of which only two studies (2/ 6) had adequate data [22,33], which is not enough to pool the effect sizes for quantitative analysis. As mentioned, Bo-Yang Zhou et al [15] designed a CNN model of US features with high accuracy (AUC: 0.89-0.96) in predicting BC molecular subtypes' four-classification (HER2, ER, PR, Ki67).…”

“…Only one study [31] did not specify the segmentation method. The open-source ITK-SNAP software was the most commonly used tool for the segmentation process (5/14), and it was utilized in five studies [22,28,34,36,37]. Two studies used deep learning (2/14) for the segmentation process.…”

Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography

“…For instance, Ren et al [184] and Fan et al [185] were able to predict EGFR mutation status using radiomics analysis on vertebral metastases from lung cancer, eliminating the need for actual testing. Xu R. et al [186] were able to develop a radiomics model to predict molecular biomarkers such as estrogen receptors (ERs), progesterone receptors (PRs), and human epidermal growth factor receptor 2 (HER2) status using ultrasound images of the breast, which otherwise can only be obtained by biopsy or surgery. Similarly, AI models could be trained to predict molecular markers (such as BTMs) and genetic markers from CT images without the need for formal testing.…”