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Breast Cancer Research and Treatment

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Open Access 01-04-2020 | Magnetic Resonance Imaging | Clinical trial

Multiparametric radiomics methods for breast cancer tissue characterization using radiological imaging

Authors: Vishwa S. Parekh, Michael A. Jacobs

Published in: Breast Cancer Research and Treatment | Issue 2/2020

Abstract

Background and purpose

Multiparametric radiological imaging is vital for detection, characterization, and diagnosis of many different diseases. Radiomics provide quantitative metrics from radiological imaging that may infer potential biological meaning of the underlying tissue. However, current methods are limited to regions of interest extracted from a single imaging parameter or modality, which limits the amount of information available within the data. This limitation can directly affect the integration and applicable scope of radiomics into different clinical settings, since single image radiomics are not capable of capturing the true underlying tissue characteristics in the multiparametric radiological imaging space. To that end, we developed a multiparametric imaging radiomic (mpRad) framework for extraction of first and second order radiomic features from multiparametric radiological datasets.

Methods

We developed five different radiomic techniques that extract different aspects of the inter-voxel and inter-parametric relationships within the high-dimensional multiparametric magnetic resonance imaging breast datasets. Our patient cohort consisted of 138 breast patients, where, 97 patients had malignant lesions and 41 patients had benign lesions. Sensitivity, specificity, receiver operating characteristic (ROC) and areas under the curve (AUC) analysis were performed to assess diagnostic performance of the mpRad parameters. Statistical significance was set at p < 0.05.

Results

The mpRad features successfully classified malignant from benign breast lesions with excellent sensitivity and specificity of 82.5% and 80.5%, respectively, with Area Under the receiver operating characteristic Curve (AUC) of 0.87 (0.81–0.93). mpRad provided a 9–28% increase in AUC metrics over single radiomic parameters.

Conclusions

We have introduced the mpRad framework that extends radiomic analysis from single images to multiparametric datasets for better characterization of the underlying tissue biology.

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Title: Multiparametric radiomics methods for breast cancer tissue characterization using radiological imaging
Authors: Vishwa S. Parekh
Michael A. Jacobs
Publication date: 01-04-2020
Publisher: Springer US
Keywords: Magnetic Resonance Imaging
Magnetic Resonance Imaging
Breast Cancer
Breast Cancer
Published in: Breast Cancer Research and Treatment / Issue 2/2020
Print ISSN: 0167-6806
Electronic ISSN: 1573-7217
DOI: https://doi.org/10.1007/s10549-020-05533-5

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Background and purpose

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