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.