Breast Tumor Classification Based on MRI-US Images by Disentangling Modality Features

Liu, Chengcheng; Li, Zeju; Zhou, Jin; Xiao, Qin; Zhou, Shichong; Chang, Cai; Gu, Yajia; Guo, Yi; Wang, Yuanyuan

doi:10.1109/jbhi.2022.3140236

Cited by 25 publications

(5 citation statements)

References 46 publications

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“…A multimodal classifier combining mammography and DCE-MRI achieved a better diagnostic performance than any single modality model and was in line with our result (52). Recently, Qiao et al built a MUM-Net classifier based on DCE-MRI and conventional US, which achieved AUCs of 0.858, 0.870 and 0.857 for predicting lymph node metastasis, histological grades, and Ki-67 expression levels, respectively (53). Although the tasks were different and could not be directly compared with our study, they can serve as a reference for the performance of our model.…”

Section: Discussionsupporting

confidence: 89%

Multi-modality radiomics nomogram based on DCE-MRI and ultrasound images for benign and malignant breast lesion classification

et al. 2022

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ObjectiveTo develop a multi-modality radiomics nomogram based on DCE-MRI, B-mode ultrasound (BMUS) and strain elastography (SE) images for classifying benign and malignant breast lesions.Material and MethodsIn this retrospective study, 345 breast lesions from 305 patients who underwent DCE-MRI, BMUS and SE examinations were randomly divided into training (n = 241) and testing (n = 104) datasets. Radiomics features were extracted from manually contoured images. The inter-class correlation coefficient (ICC), Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection and radiomics signature building. Multivariable logistic regression was used to develop a radiomics nomogram incorporating radiomics signature and clinical factors. The performance of the radiomics nomogram was evaluated by its discrimination, calibration, and clinical usefulness and was compared with BI-RADS classification evaluated by a senior breast radiologist.ResultsThe All-Combination radiomics signature derived from the combination of DCE-MRI, BMUS and SE images showed better diagnostic performance than signatures derived from single modality alone, with area under the curves (AUCs) of 0.953 and 0.941 in training and testing datasets, respectively. The multi-modality radiomics nomogram incorporating the All-Combination radiomics signature and age showed excellent discrimination with the highest AUCs of 0.964 and 0.951 in two datasets, respectively, which outperformed all single modality radiomics signatures and BI-RADS classification. Furthermore, the specificity of radiomics nomogram was significantly higher than BI-RADS classification (both p < 0.04) with the same sensitivity in both datasets.ConclusionThe proposed multi-modality radiomics nomogram based on DCE-MRI and ultrasound images has the potential to serve as a non-invasive tool for classifying benign and malignant breast lesions and reduce unnecessary biopsy.

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Section: Discussionsupporting

confidence: 89%

Multi-modality radiomics nomogram based on DCE-MRI and ultrasound images for benign and malignant breast lesion classification

et al. 2022

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“…The overall number of patients was 11.005, with a mean age of 52.7 years. However, two studies [20,23] did not provide the mean age of subjects. One study [36] included men BC; the remaining studies were from the female population.…”

Section: Plos Onementioning

confidence: 99%

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

Fu,

Zhou,

2024

PLoS ONE

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Background Breast cancer (BC) diagnosis and treatment rely heavily on molecular markers such as HER2, Ki67, PR, and ER. Currently, these markers are identified by invasive methods. Objective This meta-analysis investigates the diagnostic accuracy of ultrasound-based radiomics as a novel approach to predicting these markers. Methods A comprehensive search of PubMed, EMBASE, and Web of Science databases was conducted to identify studies evaluating ultrasound-based radiomics in BC. Inclusion criteria encompassed research on HER2, Ki67, PR, and ER as key molecular markers. Quality assessment using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS) was performed. The data extraction step was performed systematically. Results Our meta-analysis quantifies the diagnostic accuracy of ultrasound-based radiomics with a sensitivity and specificity of 0.76 and 0.78 for predicting HER2, 0.80, and 0.76 for Ki67 biomarkers. Studies did not provide sufficient data for quantitative PR and ER prediction analysis. The overall quality of studies based on the RQS tool was moderate. The QUADAS-2 evaluation showed that the studies had an unclear risk of bias regarding the flow and timing domain. Conclusion Our analysis indicated that AI models have a promising accuracy for predicting key molecular biomarkers’ status in BC patients. We performed the quantitative analysis for HER2 and Ki67 biomarkers which yielded a moderate to high accuracy. However, studies did not provide adequate data for meta-analysis of ER and PR prediction accuracy of developed models. The overall quality of the studies was acceptable. In future research, studies need to report the results thoroughly. Also, we suggest more prospective studies from different centers.

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“…The calculation of weight is based on the obtained feature parameters and is obtained by entropy method. After quantifying the obtained breast tumour ultrasound image features one by one, C1-C4 is reconstructed based on the weight of the super-pixel centre and the reconstructed pixel [21], to obtain new breast tumour ultrasound image pixels, to achieve the reconstruction of breast tumour ultrasound image.…”

Section: Figure 2 Ultrasound Image Reconstructionmentioning

confidence: 99%

Quantitative feature classification for breast ultrasound images using improved naive bayes

Sang

et al. 2022

IET Image Processing

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The quantitative feature classification of breast ultrasound images can explore the intrinsic connection between image features and lesions, and become an important basis for the diagnosis of tumor nature in clinical medicine. However, the tumor features extracted from breast ultrasound images are generally based on the overall situation, which has an impact on the classification of features in ultrasound images. Therefore, a quantitative feature classification algorithm for breast ultrasound images using improved Naive Bayes (NB) is proposed. First, histogram features, grayscale co‐generation matrix and other texture features are extracted from the ultrasound image. Second, NB is combined with decision tree calculation to improve the traditional NB algorithm. Finally, feature classification is optimized using improved NB algorithm to achieve high accuracy in the classification of ultrasound images. The results show that the contour of the lesion recognition results obtained by the proposed algorithm is the most complete, and the other regions can maximize the retention of the texture features of the original image. The classification accuracy is high, and the classification time of quantitative features of breast ultrasound images is short. It has application value in the field of breast ultrasound diagnosis.

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Breast Tumor Classification Based on MRI-US Images by Disentangling Modality Features

Cited by 25 publications

References 46 publications

Multi-modality radiomics nomogram based on DCE-MRI and ultrasound images for benign and malignant breast lesion classification

Multi-modality radiomics nomogram based on DCE-MRI and ultrasound images for benign and malignant breast lesion classification

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

Quantitative feature classification for breast ultrasound images using improved naive bayes

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