Abstract
Objective The study aimed to investigate the predictive classification accuracy of computer semiautomatic segmentation algorithm for the histological grade of breast tumors through the magnetic resonance imaging (MRI) examination. Methods Five dynamic contrast-enhanced (DCE) MRI regions of interest (ROIs) were captured using computer semiautomatic segmentation method, referring to the entire tumor area, tumor border area, proximal gland area, middle gland area, and distal gland area. According to the mutual information maximum protocol, the corresponding five ROIs were extracted from diffusion weighted imaging (DWI) combined with DCE-MRI images. To use the features in the nonoverlapping area of DWI image and DCE-MRI image as elements, a single-variable logistic regression model was established corresponding to element characteristics. After multiple training, the model was evaluated using the receiver operating characteristic (ROC) curve and area under curve (AUC). Results This DCE-MRI combined with DWI was superior to DCE-MRI and DW in the prediction of tumor area features. To use DCE-MRI or DWI alone was less effective than DCE-MRI combined with DWI. The DWI combined DCE-MRI demonstrated good regional segmentation effects in the tumour area, with luminal A value being 0.767 and the area under curve (AUC) value being 0.758. After optimization, the AUC value of the tumor area was 0.929, indicating that classification effects can be enhanced by combining the two imaging methods, which complemented each other. Conclusions The DWI combined DCE-MRI imaging has improved the early diagnosis effects of breast cancer by predicting the occurrence of breast cancer through the labeling of biomarkers.
Highlights
Under tremendous pressure for breast cancer treatment [1], it is desperately needed to prevent breast cancer through deep learning of a large amount of clinical individual data, together with the optimization of tumor detection methods according to tumour molecular characteristics [2]
This paper proposed a computer semiautomatic segmentation method to collect five dynamic enhanced magnetic resonance imaging (MRI) areas of interest (ROIs), including the whole tumor area, the marginal tumor area, the proximal glandular area, the middle glandular area, and the distal glandular area
Feature Analysis of the dynamic contrast-enhanced (DCE)-MRI Image of the Tumor Area. ere were a total of 87 features extracted from DCEMRI images, including 19 texture features, 10 statistical features, and 58 dynamic enhancement features. e singlevariable regression was performed with 87 features as independent variables and breast cancer grade and molecular typing labels as dependent variables
Summary
Under tremendous pressure for breast cancer treatment [1], it is desperately needed to prevent breast cancer through deep learning of a large amount of clinical individual data, together with the optimization of tumor detection methods according to tumour molecular characteristics [2]. MRI can detect concealed lesions, and drug treatment in the early stage can exempt the patient from the resection surgery in the advanced stage [3]. For patients who are diagnosed with breast cancer for the first time, the disease condition is classified through image detection. Based on different imaging parameters, there are DCE-MRI imaging and DWI imaging [4,5,6], displaying distinct tumour histological characteristics. DCE-MRI mainly presents the morphological characteristics of the lesion. DWI is able to perform detection in vivo using microscopic motion imaging of water molecules. Ey are usually used in combination to diagnose breast cancer [7] DWI is able to perform detection in vivo using microscopic motion imaging of water molecules. ey are usually used in combination to diagnose breast cancer [7]
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