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Wang, J., Kato, F., Oyama-Manabe, N.O., Li, R., Cui, Y., Tha, K.K., Yamashita, H., Kudo, K. and Shirato, H. (2015) Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study. PLoS ONE, 10, e0143308.
https://doi.org/10.1371/journal.pone.0143308

has been cited by the following article:

• TITLE: Analysis of the Relationship between Image and Blood Examinations in an Artificial Intelligence System for the Molecular Diagnosis of Breast Cancer

  AUTHORS: Natsumi Wada, Maoko Nakashima, Yoshikazu Uchiyama

  KEYWORDS: Radiomic Feature, microRNA, Breast Cancer, Artificial Intelligence, Characterization

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.11 No.9, September 17, 2021

  ABSTRACT: Molecular subtype classification based on tumor genotype has recently been used for differential diagnosis of breast cancer. The shift from conventional tissue classification to molecular genetics-based classification is primarily because objective genetic information can ensure a biologically clear classification system and patient groups may be created for a given set of diagnoses and suitable treatments. Given the stressful nature of biopsy, radiomic studies are conducted to determine breast cancer subtypes using non-invasive imaging tests. Minimally invasive blood tests using microRNAs (miRNAs) contained in exosomes have been developed. We investigated the usefulness of radiomic features and miRNAs in distinguishing triple-negative breast cancer (TNBC) from other cancer types. Fat suppression T2-weighted magnetic resonance images and miRNAs of 60 cases (9 TNBC and 51 others) were retrieved from the Cancer Genome Atlas Breast Invasive Carcinoma. Six radiomic features and six miRNAs were selected by least absolute shrinkage and selection operator. Linear discriminant analysis was employed to distinguish between TNBC and others. With miRNAs, TNBC and others were completely separated, whereas with radiomic features, TNBC overlapped with other types of breast cancer. Receiver operating characteristic curve analysis results showed that the area under the curve of radiomic features and miRNAs was 0.85 and 1.0, respectively. miRNAs showed a higher discrimination performance than radiomic features. Although gene analysis is expensive and facilities for performing it are limited, miRNAs for blood tests may be useful in artificial intelligence systems for the molecular diagnosis of breast cancer.