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. 2019 Sep 12;21(1):106.
doi: 10.1186/s13058-019-1187-z.

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

Doris Leithner  1   2 , Joao V Horvat  1 , Maria Adele Marino  1   3 , Blanca Bernard-Davila  4 , Maxine S Jochelson  1 , R Elena Ochoa-Albiztegui  1 , Danny F Martinez  1 , Elizabeth A Morris  1 , Sunitha Thakur  5 , Katja Pinker  6   7
Affiliations

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

Doris Leithner et al. Breast Cancer Res. .

Abstract

Background: 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.

Methods: One 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.

Results: In 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%).

Conclusions: In 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.

Keywords: Breast cancer; Contrast-enhanced; Magnetic resonance imaging; Molecular subtype; Radiomics.

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Conflict of interest statement

Elizabeth A. Morris and Katja Pinker were in part funded by the project: Use Of ctNA To Distinguish Between Benign and Malignant BIRADS 4 Radiographic Lesions. Maxine S Jochelson has received speaker honoraria from GE Healthcare. The other authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Manual region of interest placement for radiomic analysis in a 56-year-old patient with a HER2-enriched invasive ductal carcinoma in the right breast
Fig. 2
Fig. 2
Original CE-MRI images and corresponding color-coded sum entropy feature map as overlay of the tumor area of triple-negative (TN) and HER2-enriched (HER2) breast cancer. TN shows a clearly lower sum entropy than HER2
Fig. 3
Fig. 3
Top: contrast-enhanced fat-saturated T1-weighted image of a 50-year-old patient with a HER2-enriched cancer in the left breast. Bottom: contrast-enhanced T1-weighted image of a 59-year-old patient with a triple-negative cancer in the right breast. Both lesions are irregularly shaped and margined, with heterogeneous contrast enhancement and central necrosis. Radiomic signatures derived from contrast-enhanced MRI (CE-MRI) accurately differentiated HER2-enriched from triple-negative breast cancer with an overall accuracy of 73.5% in our patient collective
See this image and copyright information in PMC

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