In this IRB approved retrospective study 41 women with biopsy-proven invasive breast cancers (IBC) were imaged using contrast-enhanced mammography (CEM), prior to any treatment. Size-matched regions of interest (ROIs) were manually contoured by an experienced breast radiologist on the CEM capturing the breast lesion and breast parenchymal enhancement (BPE), respectively. Radiomics analysis was performed using LifEx software and 109 radiomics metrics spanning 6 different texture families were extracted from each ROI. Predictive models of lesion malignancy were developed using multiple classifiers and used to subclassify breast cancers based on their hormone receptor status. The 10- fold cross validation was used to construct the decision classifier and performance was assessed. CEM radiomics models based on Random Forest, Real Adaboost, and ElasticNet classifiers achieved an AUC of 0.83, 0.82 and 0.74, respectively in discriminating malignant breast lesions from varying amounts of BPE. Accounting for the varying levels of BPE, revealed a reduction in AUC-based prediction of lesion vs. BPE as the qualitative assessment of BPE increased from minimal to moderate (AUCs of 0.89 vs 0.74). Further analyses of the IBC based on their hormone receptor status showed that triple negative breast lesions showed statistically significant differences in multiple radiomics metrics compared to ER+ PR+ HER2- and HER2+. The predicted probability of the radiomics model was significantly different across three receptor-based subtypes and between high and low nuclear grade breast cancers. CEM Radiomics demonstrated good discrimination (AUC>0.8) of malignant breast lesions despite varying BPE levels and supports breast lesion subtyping.
In this Institutional Review Board (IRB) approved, Health Insurance Portability and Accountability Act (HIPAA) compliant, prospective study, uncompressed envelope data (RF data) were collected from 100 patients with focal renal masses using an RS80A ultrasound scanner with B-mode and CEUS. By summing and averaging the Nakagami images formed using sliding windows, we use the average ‘m’ to stratify manually segmented masses, using data from both the B-mode and CEUS scans. Wilcoxon rank sum test using an alpha value of 0.05 was used detect differences between the groups. Logistic regression was used for classification and the area under the receiver operator curve (AUC) was used to assess performance. Among the 100 masses, 40 were benign, 37 were malignant based on histopathology, and 23 were radiologically and clinically presumed malignant but with no pathological proof at the time of data analysis. Univariate analyses showed significant (p<0.01) differences between the benign and non-benign masses on both B-mode and CEUS, with non-benign masses having smaller ‘m’. Predictive models constructed using Nakagami parameters extracted from Bmode and CEUS-based RF scans showed an AUC of 0.67 95% CI: (0.56, 0.78) and 0.61 95% CI: (0.5, 0.73), respectively for discriminating benign from non-benign renal masses. The concordance between the two assessments was 95%. We present a framework for characterizing images using speckle textural properties, for example Nakagami analysis, to aid in objective tissue characterization using ultrasound.
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