Investigation of diagnostic value of artificial intelligence systems in the diagnosis of breast cancer based on histopathological images using Meta-MUMS DTA tool

Abstract

ORIGINAL ARTICLES Epidemiology Biostatistics and Public Health - 2020, Volume 17, Number 2Investigation of diagnostic value of artificial intelligence systems in the diagnosis of breast cancer based on histopathological images using Meta-MUMS DTA tool
Investigation of diagnostic value of artificial
intelligence systems in the diagnosis of breast
cancer based on histopathological images
using Meta-MUMS DTA tool
ABSTRACT
Background: Various artificial intelligence systems are available for diagnosing breast cancer based on
histopathological images. Assessing the performance of existing methodologies for breast cancer diagnosis is vital.
Methods: The SCOPUS database has been searched for studies up to December 15, 2018. We extracted the data,
including "true positive," "true negative," "false positive," and "false negative". The pooled sensitivity, pooled specificity,
positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, area under the curve of summary receiver
operating characteristic curve were useful in assessing the diagnostic accuracy. Egger's test, Deeks' funnel plot, SVE
(Smoothed Variance regression model based on Egger’s test), SVT (Smoothed Variance regression model based on
Thompson’s method), and trim and fill methodologies were essential tests for publication bias identification.
Results: Three studies with eight approaches from thirty-seven articles were found eligible for further analysis. A
sensitivity of 0.95, a specificity of 0.78, a PLR of 7525, an NLR of 0.06, a DOR of 88.15, and an AUC of 0.953
showed high significant heterogeneity; however, the reason was not the threshold effect. The publication bias was
detected by SVE, SVT, and trim and fill analysis.
Conclusion: The artificial intelligent (AI) systems play a pivotal role in the diagnosis of breast cancer using
histopathological cell images and are important decision-makers for pathologists. The analyses revealed that the
overall accuracy of AI systems is promising for breast cancer; however, the pooled specificity is lower than pooled
sensitivity. Moreover, the approval of the results awaits conducting randomized clinical trials with sufficient data