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Cancer Imaging
Published in: Cancer Imaging 1/2019

Open Access 01-12-2019 | Magnetic Resonance Imaging | Research article

Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution

Authors: Yu Ji, Hui Li, Alexandra V. Edwards, John Papaioannou, Wenjuan Ma, Peifang Liu, Maryellen L. Giger

Published in: Cancer Imaging | Issue 1/2019

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Abstract

Background

As artificial intelligence methods for the diagnosis of disease advance, we aimed to evaluate machine learning in the predictive task of distinguishing between malignant and benign breast lesions on an independent clinical magnetic resonance imaging (MRI) dataset within a single institution for subsequent use as a computer aid for radiologists.

Methods

Computer analysis was conducted on consecutive dynamic contrast-enhanced MRI (DCE-MRI) studies from 1483 breast cancer and 496 benign patients who underwent MRI examinations between February 2015 and October 2017; with the age ranges of the cancer and benign patients being 19 to 77 and 16 to 76 years old, respectively. Cases were separated into a training dataset (years 2015 & 2016; 1444 cases) and an independent testing dataset (year 2017; 535 cases) based solely on MRI examination date. After radiologist indication of the lesion, the computer automatically segmented and extracted radiomic features, which were subsequently merged with a support-vector machine (SVM) to yield a lesion signature. Area under the receiving operating characteristic (ROC) curve (AUC) with 95% confidence intervals (CI) served as the primary figure of merit in the statistical evaluation for this clinical classification task.

Results

In the task of distinguishing malignant and benign breast lesions DCE-MRI, the trained predictive model yielded an AUC value of 0.89 (95% CI: 0.858, 0.922) on the independent image set. AUC values of 0.88 (95% CI: 0.845, 0.926) and 0.90 (95% CI: 0.837, 0.940) were obtained for mass lesions only and non-mass lesions only, respectively. Compared with actual clinical management decisions, the predictive model achieved 99.5% sensitivity with 9.6% fewer recommended biopsies.

Conclusion

On an independent, consecutive clinical dataset within a single institution, a trained machine learning system yielded promising performance in distinguishing between malignant and benign breast lesions.
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Metadata
Title
Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution
Authors
Yu Ji
Hui Li
Alexandra V. Edwards
John Papaioannou
Wenjuan Ma
Peifang Liu
Maryellen L. Giger
Publication date
01-12-2019
Publisher
BioMed Central
Published in
Cancer Imaging / Issue 1/2019
Electronic ISSN: 1470-7330
DOI
https://doi.org/10.1186/s40644-019-0252-2

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