Radiomic signatures with contrast-enhanced magnetic resonance imaging for the assessment of breast cancer receptor status and molecular subtypes: initial results

Doris Leithner,Sunitha Thakur,Maxine S Jochelson,Danny F Martinez,Maria Adele Marino,Joao V Horvat,Blanca Bernard-Davila,Elizabeth A Morris,Katja Pinker,R Elena Ochoa-Albiztegui

doi:10.1186/s13058-019-1187-z

Abstract

BackgroundTo evaluate the diagnostic performance of radiomic signatures extracted from contrast-enhanced magnetic resonance imaging (CE-MRI) for the assessment of breast cancer receptor status and molecular subtypes.MethodsOne hundred and forty-three patients with biopsy-proven breast cancer who underwent CE-MRI at 3 T were included in this IRB-approved HIPAA-compliant retrospective study. The training dataset comprised 91 patients (luminal A, n = 49; luminal B, n = 8; HER2-enriched, n = 11; triple negative, n = 23), while the validation dataset comprised 52 patients from a second institution (luminal A, n = 17; luminal B, n = 17; triple negative, n = 18). Radiomic analysis of manually segmented tumors included calculation of features derived from the first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient (GRA), autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry (GEO). Fisher, probability of error and average correlation (POE + ACC), and mutual information coefficients were used for feature selection. Linear discriminant analysis followed by k-nearest neighbor classification (with leave-one-out cross-validation) was used for pairwise radiomic-based separation of receptor status and molecular subtypes. Histopathology served as the standard of reference.ResultsIn the training dataset, radiomic signatures yielded the following accuracies > 80%: luminal B vs. luminal A, 84.2% (mainly based on COM features); luminal B vs. triple negative, 83.9% (mainly based on GEO features); luminal B vs. all others, 89% (mainly based on COM features); and HER2-enriched vs. all others, 81.3% (mainly based on COM features). Radiomic signatures were successfully validated in the separate validation dataset for luminal A vs. luminal B (79.4%) and luminal B vs. triple negative (77.1%).ConclusionsIn this preliminary study, radiomic signatures with CE-MRI enable the assessment of breast cancer receptor status and molecular subtypes with high diagnostic accuracy. These results need to be confirmed in future larger studies.

Highlights

To evaluate the diagnostic performance of radiomic signatures extracted from contrast-enhanced magnetic resonance imaging (CE-MRI) for the assessment of breast cancer receptor status and molecular subtypes
This is a unique opportunity for medical imaging, and in this context, contrast-enhanced magnetic resonance imaging (CE-MRI) coupled with radiomic analyses has yielded initial encouraging results
In this study, we evaluated the diagnostic performance of CE-MRI coupled with radiomic analysis for the non-invasive differentiation of breast cancers with different receptor status and molecular subtypes

Summary

Introduction

To evaluate the diagnostic performance of radiomic signatures extracted from contrast-enhanced magnetic resonance imaging (CE-MRI) for the assessment of breast cancer receptor status and molecular subtypes. Traditional prognostic and predictive factors such as tumor size, grade, histopathologic type, and immunohistochemical receptor status are well established, it has become evident that these traditional classifications cannot fully capture the heterogeneity of breast cancer so that the classical approach of stratifying patients into treatment groups based on phenotypic biomarkers may be insufficient in some patients In this era of precision medicine, treatments are selected based on genetic tumor characteristics. There is a strong argument for the development of alternative means to derive tumor characteristics that are prognostic indicators, i.e., receptor status and molecular subtypes, from the tumor in its entirety and to spatio-longitudinal monitor tumor biology changes during treatment This is a unique opportunity for medical imaging, and in this context, contrast-enhanced magnetic resonance imaging (CE-MRI) coupled with radiomic analyses has yielded initial encouraging results. Prior studies have investigated radiomic signatures in the breast, but the generalization of these results is limited due to utilization of different MRI protocols and scanners [11, 16,17,18], results may be suboptimal due to inclusion of only a few radiomic features [11, 17,18,19,20], and only certain subgroups of breast cancers have been investigated [21]

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Results