Abstract
The aim of our intra-individual comparison study was to investigate and compare the potential of radiomics analysis of contrast-enhanced mammography (CEM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast for the non-invasive assessment of tumor invasiveness, hormone receptor status, and tumor grade in patients with primary breast cancer. This retrospective study included 48 female patients with 49 biopsy-proven breast cancers who underwent pretreatment breast CEM and MRI. Radiomics analysis was performed by using MaZda software. Radiomics parameters were correlated with tumor histology (invasive vs. non-invasive), hormonal status (HR+ vs. HR−), and grading (low grade G1 + G2 vs. high grade G3). CEM radiomics analysis yielded classification accuracies of up to 92% for invasive vs. non-invasive breast cancers, 95.6% for HR+ vs. HR− breast cancers, and 77.8% for G1 + G2 vs. G3 invasive cancers. MRI radiomics analysis yielded classification accuracies of up to 90% for invasive vs. non-invasive breast cancers, 82.6% for HR+ vs. HR− breast cancers, and 77.8% for G1+G2 vs. G3 cancers. Preliminary results indicate a potential of both radiomics analysis of DCE-MRI and CEM for non-invasive assessment of tumor-invasiveness, hormone receptor status, and tumor grade. CEM may serve as an alternative to MRI if MRI is not available or contraindicated.
Highlights
Breast cancer is the most common cancer in women, accounting for 30% of all new cancer diagnoses in women [1]
The following accuracies were achieved for the differentiation of HR+ and HR− cancers: Fisher, 91.3% (WAV/RUN); POE, 95.6% (RUN); and mutual information (MI), 86.8% (WAV + co-occurrence matrix (COM))
The following accuracies were achieved for the differentiation of low grade (G1 + G2) and high grade G3 cancers: Fisher, 75.6% (WAV + RUN + COM); POE, 77.8% (RUN); and MI, 64.4% (WAV + COM) (Table 3)
Summary
Breast cancer is the most common cancer in women, accounting for 30% of all new cancer diagnoses in women [1]. Diagnostics 2020, 10, 492 subject to site selection bias and cannot always provide characterization of the entire tumor. Radiomics analysis can automatically extract, quantify, and mine high-dimensional features that are imperceptible to human eyes from medical imaging data in a non-invasive and cost-effective way [5,6,7]. The central premise of radiomics analysis is that the extracted imaging features are representative of the phenotypic and genotypic processes of the entire tumor and allow relevant insights into tumor biology [8,9]. Radiomics analysis can be coupled with the majority of clinically available medical imaging technologies, such as computed tomography, dynamic contrast enhanced magnetic resonance imaging (DCE-MRI), ultrasound, mammography, digital breast tomosynthesis, and positron emission tomography/computed tomography [10]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
