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

Breast Cancer Research

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[1] 0000 0001 2171 9952, grid.51462.34, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA;0000 0001 2171 9952, grid.51462.34, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA;0000 0001 2171 9952, grid.51462.34, Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, 10065, New York, NY, USA;0000 0001 2171 9952, grid.51462.34, Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, 10065, New York, NY, USA;0000 0000 9259 8492, grid.22937.3d, Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria;0000 0001 2171 9952, grid.51462.34, Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, 10065, New York, NY, USA;0000 0001 2178 8421, grid.10438.3e, Department of Biomedical Sciences and Morphologic and Functional Imaging, University of Messina, Messina, Italy;0000 0001 2171 9952, grid.51462.34, Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, 10065, New York, NY, USA;0000 0004 0578 8220, grid.411088.4, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany;

关键词: Radiomics;    Contrast-enhanced;    Magnetic resonance imaging;    Breast cancer;    Molecular subtype;   

DOI  :  10.1186/s13058-019-1187-z

来源: publisher

【 摘 要 】

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.

【 授权许可】

CC BY