Contrast-enhanced mammography and radiomics analysis for noninvasive breast cancer characterization: Initial results Journal Article
| Authors: | Marino, M. A.; Pinker, K.; Leithner, D.; Sung, J.; Avendano, D.; Morris, E. A.; Jochelson, M. |
| Article Title: | Contrast-enhanced mammography and radiomics analysis for noninvasive breast cancer characterization: Initial results |
| Abstract: | Purpose: To investigate the potential of contrast-enhanced mammography (CEM) and radiomics analysis for the noninvasive differentiation of breast cancer invasiveness, hormone receptor status, and tumor grade. Procedures: This retrospective study included 100 patients with 103 breast cancers who underwent pretreatment CEM. Radiomics analysis was performed using MAZDA software. Lesions were manually segmented. Radiomic features were derived from first-order histogram (HIS), co-occurrence matrix (COM), run length matrix (RLM), absolute gradient, autoregressive model, the discrete Haar wavelet transform (WAV), and lesion geometry. Fisher, probability of error and average correlation (POE+ACC), and mutual information (MI) coefficients informed feature selection. Linear discriminant analysis followed by k-nearest neighbor classification (with leave-one-out cross-validation) was used for pairwise texture-based separation of tumor invasiveness and hormone receptor status using histopathology as the standard of reference. Results: Radiomics analysis achieved the highest accuracies of 87.4 % for differentiating invasive from noninvasive cancers based on COM+HIS/MI, 78.4 % for differentiating HR positive from HR negative cancers based on COM+HIS/Fisher, 97.2 % for differentiating human epidermal growth factor receptor 2 (HER2)-positive/HR-negative from HER2-negative/HR-positive cancers based on RLM+WAV/MI, 100 % for differentiating triple-negative from triple-positive breast cancers mainly based on COM+WAV+HIS/POE+ACC, and 82.1 % for differentiating triple-negative from HR-positive cancers mainly based on WAV+HIS/Fisher. Accuracies for differentiating grade 1 vs. grades 2 and 3 cancers were 90 % for invasive cancers (based on COM/MI) and 100 % for noninvasive cancers (almost entirely based on COM/MI). Conclusions: Radiomics analysis with CEM has potential for noninvasive differentiation of tumors with different degrees of invasiveness, hormone receptor status, and tumor grade. © 2019, World Molecular Imaging Society. |
| Keywords: | adult; aged; middle aged; major clinical study; cancer grading; diagnostic accuracy; biomarkers; breast cancer; tumor differentiation; retrospective study; mammography; tumors; discriminant analysis; contrast enhancement; carcinoma in situ; contrast media; hormone receptor; non invasive procedure; iodinated contrast medium; triple negative breast cancer; diagnostic test accuracy study; tumor invasion; human epidermal growth factor receptor 2 positive breast cancer; human; female; priority journal; article; feature selection; radiomics; haar transform; k nearest neighbor |
| Journal Title: | Molecular Imaging and Biology |
| Volume: | 22 |
| Issue: | 3 |
| ISSN: | 1536-1632 |
| Publisher: | Springer |
| Date Published: | 2020-06-01 |
| Start Page: | 780 |
| End Page: | 787 |
| Language: | English |
| DOI: | 10.1007/s11307-019-01423-5 |
| PUBMED: | 31463822 |
| PROVIDER: | scopus |
| PMCID: | PMC7047570 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 1 July 2020 -- Source: Scopus |
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