Implementation of machine learning into clinical breast MRI: Potential for objective and accurate decision-making in suspicious breast masses

Stephan Ellmann, Evelyn Wenkel, Matthias Dietzel, Christian Bielowski, Sulaiman Vesal, Andreas Maier, Matthias Hammon, Rolf Janka, Peter A Fasching, Matthias W Beckmann, Rüdiger Schulz Wendtland, Michael Uder and Tobias Bäuerle

PLOS ONE, 2020, vol. 15, issue 1, 1-15

Abstract: We investigated whether the integration of machine learning (ML) into MRI interpretation can provide accurate decision rules for the management of suspicious breast masses. A total of 173 consecutive patients with suspicious breast masses upon complementary assessment (BI-RADS IV/V: n = 100/76) received standardized breast MRI prior to histological verification. MRI findings were independently assessed by two observers (R1/R2: 5 years of experience/no experience in breast MRI) using six (semi-)quantitative imaging parameters. Interobserver variability was studied by ICC (intraclass correlation coefficient). A polynomial kernel function support vector machine was trained to differentiate between benign and malignant lesions based on the six imaging parameters and patient age. Ten-fold cross-validation was applied to prevent overfitting. Overall diagnostic accuracy and decision rules (rule-out criteria) to accurately exclude malignancy were evaluated. Results were integrated into a web application and published online. Malignant lesions were present in 107 patients (60.8%). Imaging features showed excellent interobserver variability (ICC: 0.81–0.98) with variable diagnostic accuracy (AUC: 0.65–0.82). Overall performance of the ML algorithm was high (AUC = 90.1%; BI-RADS IV: AUC = 91.6%). The ML algorithm provided decision rules to accurately rule-out malignancy with a false negative rate

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0228446

DOI: 10.1371/journal.pone.0228446

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