基于DCE-MRI影像组学非负矩阵分解的乳腺癌病理信息缺失填充研究

doi:10.3969/j.issn.0258-8021.2021.04.03

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Abstract Breast cancer pathology report is the main basis for the diagnosis and treatment of breast cancer. However, sometimes there may loss of histological information in the clinical practices. In this study, imaging features of the lesion area of the dynamic enhanced magnetic resonance imaging (DCE-MRI) were combined with the histological information of the corresponding breast cancer patients to establish a non-negative matrix factorization based radiomics model to achieve the imputation of missing molecular subtypes and Cytokeratin 5/6 gene expression. A total of 139 cases of breast cancer patients were collected before surgery or before chemotherapy and were randomly divided into 89 cases as training set and 50 cases as test set. Breast tumor areas were segmented and the morphological and texture features were extracted from the lesion area and statistically analyzed. The cross-validated support vector machine recursive feature elimination (SVM-RFECV) method was used for the feature selection, and the image features were further filtered through a union-based method. Combining the clinical pathological information of breast cancer, a non-negative matrix factorization (NMF) imputation model and a collaborative filtering (CF) imputation model were established, and the AUC was calculated to evaluate the imputation performance of the model. When the clinical pathological information missing rate was different, the AUC value of the NMF model was higher than that of the CF model, the highest AUC was 0.772, and the NMF imputation effect was significantly better (P<0.05) than the CF method when the missing rate was between 20% and 40%. In the case of quantitative image features, the AUC value of the NMF model was higher than that of the CF model, the highest AUC was 0.780, and the difference between the two was statistically significant (P<0.05) when 140 image features were used. These experimental results showed that DCE-MRI radiomics combined with non-negative matrix factorization effectively filled the missing molecular subtypes and CK5/6 clinical indicators.

Key words： breast cancer histological information dynamic contrast enhanced magnetic resonance imaging non-negative matrix factorization matrix completion

Received: 02 December 2020

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R318

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	Articles by authors
	Fu Zhenyu
	Fan Ming
	Li Lihua

Cite this article:

Fu Zhenyu,Fan Ming,Li Lihua. DCE-MRI Radiomics Based Non-negative Matrix Factorization for Imputation of Missing Histological Information of Breast Cancer[J]. Chinese Journal of Biomedical Engineering, 2021, 40(4): 401-409.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2021.04.03 OR http://cjbme.csbme.org/EN/Y2021/V40/I4/401

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