Improved Cancer Detection Using Artificial Intelligence: a Retrospective Evaluation of Missed Cancers on Mammography.

Content Provider	Europe PMC
Author	Watanabe, Alyssa T. Lim, Vivian Vu, Hoanh X. Chim, Richard Weise, Eric Liu, Jenna Bradley, William G. Comstock, Christopher E.
Abstract	To determine whether cmAssist™, an artificial intelligence-based computer-aided detection (AI-CAD) algorithm, can be used to improve radiologists’ sensitivity in breast cancer screening and detection. A blinded retrospective study was performed with a panel of seven radiologists using a cancer-enriched data set from 122 patients that included 90 false-negative mammograms obtained up to 5.8 years prior to diagnosis and 32 BIRADS 1 and 2 patients with a 2-year follow-up of negative diagnosis. The mammograms were performed between February 7, 2008 (earliest) and January 8, 2016 (latest), and were all originally interpreted as negative in conjunction with R2 ImageChecker CAD, version 10.0. In this study, the readers analyzed the 122 studies before and after review of cmAssist™, an AI-CAD software for mammography. The statistical significance of our findings was evaluated using Student’s t test and bootstrap statistical analysis. There was a substantial and significant improvement in radiologist accuracy with use of cmAssist, as demonstrated in the 7.2% increase in the area-under-the-curve (AUC) of the receiver operating characteristic (ROC) curve with two-sided p value < 0.01 for the reader group. All radiologists showed a significant improvement in their cancer detection rate (CDR) with the use of cmAssist (two-sided p value = 0.030, confidence interval = 95%). The readers detected between 25 and 71% (mean 51%) of the early cancers without assistance. With cmAssist, the overall reader CDR was 41 to 76% (mean 62%). The percentage increase in CDR for the reader panel was significant, ranging from 6 to 64% (mean 27%) with the use of cmAssist. There was less than 1% increase in the readers’ false-positive recalls with use of cmAssist. With the use of cmAssist TM, there was a substantial and statistically significant improvement in radiologists’ accuracy and sensitivity for detection of cancers that were originally missed. The percentage increase in CDR for the radiologists in the reader panel ranged from 6 to 64% (mean 27%) with the use of cmAssist, with negligible increase in false-positive recalls.Electronic supplementary materialThe online version of this article (10.1007/s10278-019-00192-5) contains supplementary material, which is available to authorized users.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC6646649&blobtype=pdf
ISSN	08971889
Journal	Journal of Digital Imaging [J Digit Imaging]
Volume Number	32
DOI	10.1007/s10278-019-00192-5
PubMed Central reference number	PMC6646649
Issue Number	4
PubMed reference number	31011956
e-ISSN	1618727X
Language	English
Publisher	Springer International Publishing
Publisher Date	2019-08-01
Publisher Place	Gewerbestrasse 11, Cham, Ch 6330, Switzerland
Access Restriction	Open
Rights License	Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. © The Author(s) 2019
Subject Keyword	Mammography Artificial intelligence Computer-aided detection Cancer detection Breast cancer Deep learning
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Computer Science Applications Radiological and Ultrasound Technology