Radiomic signatures with contrast-enhanced magnetic resonance imaging for the assessment of breast cancer receptor status and molecular subtypes: initial results.
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...
| Published in: | Breast Cancer Research Vol. 21; no. 1 |
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| Main Authors: | , , , , , , , , , |
| Format: | research Journal Article |
| Published: |
BioMed Central
9/12/2019
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=138589917&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138589917 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14655411 8UYJ jtl: Breast Cancer Research issn: 14655411 maglogo: N pubinfo: dt: 9/12/2019 vid: 21 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 138589917 138589917 NLM31514736 138589917 10.1186/s13058-019-1187-z NLM31514736 138589917 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Radiomic signatures with contrast-enhanced magnetic resonance imaging for the assessment of breast cancer receptor status and molecular subtypes: initial results. aug: au: Leithner, Doris Horvat, Joao V. Marino, Maria Adele Bernard-Davila, Blanca Jochelson, Maxine S. Ochoa-Albiztegui, R. Elena Martinez, Danny F. Morris, Elizabeth A. Thakur, Sunitha Pinker, Katja affil: Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, 10065, New York, NY, USA sug: subj: Breast Neoplasms Breast Neoplasms Pathology Radiographic Image Interpretation, Computer-Assisted Magnetic Resonance Imaging Reproducibility of Results Aged Human Middle Age Receptors, Cell Surface Metabolism Breast Neoplasms Classification Female Adult Retrospective Design Validation Studies Comparative Studies Evaluation Research Multicenter Studies Aged: 65+ years Middle Aged: 45-64 years Adult: 19-44 years Female ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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