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Radiomics and Machine Learning with Multiparametric Breast MRI for Improved Diagnostic Accuracy in Breast Cancer Diagnosis

Authors :
Sunitha B. Thakur
Elizabeth A. Morris
Isaac Daimiel Naranjo
Roberto Lo Gullo
Caleb Sooknanan
Varadan Sevilimedu
Katja Pinker
Maxine S. Jochelson
Pascal A. T. Baltzer
Thomas H. Helbich
Jeffrey S. Reiner
Peter Gibbs
Source :
Diagnostics, Diagnostics; Volume 11; Issue 6; Pages: 919, Diagnostics, Vol 11, Iss 919, p 919 (2021)
Publication Year :
2021

Abstract

The purpose of this multicenter retrospective study was to evaluate radiomics analysis coupled with machine learning (ML) of dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) radiomics models separately and combined as multiparametric MRI for improved breast cancer detection. Consecutive patients (Memorial Sloan Kettering Cancer Center, January 2018–March 2020; Medical University Vienna, from January 2011–August 2014) with a suspicious enhancing breast tumor on breast MRI categorized as BI-RADS 4 and who subsequently underwent image-guided biopsy were included. In 93 patients (mean age: 49 years ± 12 years; 100% women), there were 104 lesions (mean size: 22.8 mm; range: 7–99 mm), 46 malignant and 58 benign. Radiomics features were calculated. Subsequently, the five most significant features were fitted into multivariable modeling to produce a robust ML model for discriminating between benign and malignant lesions. A medium Gaussian support vector machine (SVM) model with five-fold cross validation was developed for each modality. A model based on DWI-extracted features achieved an AUC of 0.79 (95% CI: 0.70–0.88), whereas a model based on DCE-extracted features yielded an AUC of 0.83 (95% CI: 0.75–0.91). A multiparametric radiomics model combining DCE- and DWI-extracted features showed the best AUC (0.85; 95% CI: 0.77–0.92) and diagnostic accuracy (81.7%; 95% CI: 73.0–88.6). In conclusion, radiomics analysis coupled with ML of multiparametric MRI allows an improved evaluation of suspicious enhancing breast tumors recommended for biopsy on clinical breast MRI, facilitating accurate breast cancer diagnosis while reducing unnecessary benign breast biopsies.

Details

ISSN :
20754418
Volume :
11
Issue :
6
Database :
OpenAIRE
Journal :
Diagnostics (Basel, Switzerland)
Accession number :
edsair.doi.dedup.....2743650e3d7be74befc3668b1953c708