Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions.

Purpose:The purpose of this study was to discriminate between benign and malignant breast lesions through several classifiers using, as predictors, radiomic metrics extracted from CEM and DCE-MRI images. In order to optimize the analysis, balancing and feature selection procedures were performed. Methods: Fifty-four patients with 79 histo-pathologically proven breast lesions (48 malignant lesions and 31 benign lesions) underwent both CEM and DCE-MRI. The lesions were retrospectively analyzed with radiomic and artificial intelligence approaches. Forty-eight textural metrics were extracted, and univariate and multivariate analyses were performed: non-parametric statistical test, receiver operating characteristic (ROC) and machine learning classifiers. Results: Considering the single metrics extracted from CEM, the best predictors were KURTOSIS (area under ROC curve (AUC) = 0.71) and SKEWNESS (AUC = 0.71) calculated on late MLO view. Considering the features calculated from DCE-MRI, the best predictors were RANGE (AUC = 0.72), ENERGY (AUC = 0.72), ENTROPY (AUC = 0.70) and GLN (gray-level nonuniformity) of the gray-level run-length matrix (AUC = 0.72). Considering the analysis with classifiers and an unbalanced dataset, no significant results were obtained. After the balancing and feature selection procedures, higher values of accuracy, specificity and AUC were reached. The best performance was obtained considering 18 robust features among all metrics derived from CEM and DCE-MRI, using a linear discriminant analysis (accuracy of 0.84 and AUC = 0.88). Conclusions: Classifiers, adjusted with adaptive synthetic sampling and feature selection, allowed for increased diagnostic performance of CEM and DCE-MRI in the differentiation between benign and malignant lesions.

Evaluation of average glandular dose and investigation of the relationship with compressed breast thickness in dual energy contrast enhanced digital mammography and digital breast tomosynthesis.

PURPOSE: To quantitatively assess the dose of Dual energy contrast enhanced digital mammography (CEDM) and digital breast tomosynthesis (DBT) and to investigate the relationship between average absorbed glandular dose (AGD), compressed breast thickness (CBT) and compression force (CF). MATERIALS AND METHODS: All CEDM and DBT examinations were performed in cranio-caudal (CC) and medio-lateral oblique (MLO) view. Exposure parameters of 135 mammographic procedures that using AEC (automatic exposure control) mode were recorded. AGDs were calculated. Kruskal Wallis test was performed. RESULTS: CBT population ranged from 23 to 94 mm with a thickness median value of 52 mm in CC view and of 57 mm in MLO views. CEDM AGD median value was significatively lower than DBT AGD in each views (p << 0.01). AGD showed a positive correlation and linear regression with CBT for both CEDM and DBT while CF did not show a correlation and linear regression with AGD. The highest values were found for MLO view: R2 of 0.74 for CEDM and R2 of 0.61 for DBT. Kruskal Wallis test shows that there was a difference statistically significant between AGD values of CEDM and DBT in CC view respect to MLO views (p < 0.01). CONCLUSIONS: Dose values of both techniques meet the recommendations for maximum dose in mammography. The results of the present study indicated that there was significant difference between AGD for CEDM and DBT exposure in different views (AGD in CC views had the lowest value) and that CBT could influence the AGD while CF was not correlated to AGD.

Digital breast tomosynthesis and contrast-enhanced dual-energy digital mammography alone and in combination compared to 2D digital synthetized mammography and MR imaging in breast cancer detection and classification.

To compare diagnostic performance of contrast-enhanced dual-energy digital mammography (CEDM) and digital breast tomosynthesis (DBT) alone and in combination compared to 2D digital mammography (MX) and dynamic contrast-enhanced MRI (DCE-MRI) in women with breast lesions. We enrolled 100 consecutive patients with breast lesions (BIRADS 3-5 at imaging or clinically suspicious). CEDM, DBT, and DCE-MRI 2D were acquired. Synthetized MX was obtained by DBT. A total of 134 lesions were investigated on 111 breasts of 100 enrolled patients: 53 were histopathologically proven as benign and 81 as malignant. Nonparametric statistics and receiver operating characteristic (ROC) curve were performed. Two-dimensional synthetized MX showed an area under ROC curve (AUC) of 0.764 (sensitivity 65%, specificity 80%), while AUC was of 0.845 (sensitivity 80%, specificity 82%) for DBT, of 0.879 (sensitivity 82%, specificity 80%) for CEDM, and of 0.892 (sensitivity 91%, specificity 84%) for CE-MRI. DCE-MRI determined an AUC of 0.934 (sensitivity 96%, specificity 88%). Combined CEDM with DBT findings, we obtained an AUC of 0.890 (sensitivity 89%, specificity 74%). A difference statistically significant was observed only between DCE-MRI and CEDM (P = .03). DBT, CEDM, CEDM combined to tomosynthesis, and DCE-MRI had a high ability to identify multifocal and bilateral lesions with a detection rate of 77%, 85%, 91%, and 95% respectively, while 2D synthetized MX had a detection rate for multifocal lesions of 56%. DBT and CEDM have superior diagnostic accuracy of 2D synthetized MX to identify and classify breast lesions, and CEDM combined with DBT has better diagnostic performance compared with DBT alone. The best results in terms of diagnostic performance were obtained by DCE-MRI. Dynamic information obtained by time-intensity curve including entire phase of contrast agent uptake allows a better detection and classification of breast lesions.

Optical imaging of the breast: evaluation of deoxyhemoglobin concentration alteration in 166 patients with suspicious breast lesions.

BACKGROUND: We investigated the performance of optical imaging evaluating deoxyhemoglobin concentration alteration (DeHCA) in breast tissues. METHODS: We enrolled all consecutive patients from January 2015 to October 2016 with clinically suspicious and/or BI-RADS grade 3-5 lesions at mammography or ultrasound (US). Patients underwent optical imaging (ComfortScan) to evaluate for DeHCA. The reference standard was pathology from a surgical specimen for malignant lesions, pathology from a surgical specimen or core needle biopsy for benign lesions, and negative follow-up for contralateral negative breasts. Non-parametric statistics, receiver operating characteristic, and linear discrimination analyses were performed. RESULTS: Of 334 enrolled patients, 168 (50%) were excluded for technical problems and 166 (50%) (median age 52 years) were analyzed totaling 331 breasts and 176 lesions. Of these, 75 were benign (median size 19 mm) and 101 malignant (median size 20 mm). The median DeHCA score in malignant lesions (0.95, interquartile range [IQR] 1.00-0.87) was higher (p < 0.001) than in benign lesions (0.80, IQR 0.95-0.70). Using the optimal cutoff (0.85), DeHCA score was less accurate than mammography, US, and their combination, with 78% sensitivity, 52% specificity, 40% positive predictive value (PPV), and 85% negative predictive value (NPV); using a 0.8 cutoff, sensitivity reached 93% and NPV 91%, but specificity fell to 32% and PPV to 37%. The accuracy of DeHCA score linearly combined with mammography or US was higher than that of DeHCA score alone (p < 0.001) and not significantly higher than that of mammography or US alone. CONCLUSIONS: DeHCA score was significantly higher in malignant than in benign lesions, but its accuracy was significantly lower than that of mammography or US. Future refinements are needed.