- 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.
MetadatenVerfasserangaben: | Doris Leithner, Joao V. Horvat, Maria Adele Marino, Blanca Bernard-Davila, Maxine S. Jochelson, R. Elena Ochoa-Albiztegui, Danny F. Martinez, Elizabeth A. Morris, Sunitha Thakur, Katja Pinker |
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URN: | urn:nbn:de:hebis:30:3-530808 |
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DOI: | https://doi.org/10.1186/s13058-019-1187-z |
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ISSN: | 1465-542X |
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ISSN: | 1465-5411 |
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Pubmed-Id: | https://pubmed.ncbi.nlm.nih.gov/31514736 |
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Titel des übergeordneten Werkes (Englisch): | Breast cancer research |
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Verlag: | BioMed Central |
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Verlagsort: | London |
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Sonstige beteiligte Person(en): | Joanne Chin |
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Dokumentart: | Wissenschaftlicher Artikel |
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Sprache: | Englisch |
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Jahr der Fertigstellung: | 2019 |
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Datum der Erstveröffentlichung: | 12.09.2019 |
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Veröffentlichende Institution: | Universitätsbibliothek Johann Christian Senckenberg |
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Datum der Freischaltung: | 11.03.2020 |
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Freies Schlagwort / Tag: | Breast cancer; Contrast-enhanced; Magnetic resonance imaging; Molecular subtype; Radiomics |
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Jahrgang: | 21 |
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Ausgabe / Heft: | 1, Art. 106 |
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Seitenzahl: | 11 |
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Erste Seite: | 1 |
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Letzte Seite: | 11 |
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Bemerkung: | Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
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HeBIS-PPN: | 464654882 |
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Institute: | Medizin / Medizin |
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DDC-Klassifikation: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
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Sammlungen: | Universitätspublikationen |
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Lizenz (Deutsch): | Creative Commons - Namensnennung 4.0 |
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