Contrast-Enhanced Mammography: Reviewing the Past and Looking to the Future

Validation of Contrast-Enhanced Mammography as Breast Imaging Modality Compared to Standard Mammography and Digital Breast Tomosynthesis

Contrast-enhanced mammography (CEM) is a relatively new imaging technique that allows morphologic, anatomic and functional imaging of the breast. The aim of our study was to validate contrast-enhanced mammography (CEM) compared to mammography (MMG) and digital breast tomosynthesis (DBT) in daily clinical practice. This retrospective study included 316 consecutive patients who underwent MMG, DBT and CEM at the Centre for Prevention and Diagnosis of Chronic Diseases of Primorsko-goranska County. Two breast radiologists independently analyzed the image data, without available anamnestic information and without the possibility of comparison with previous images, to determine the presence of suspicious lesions and their morphological features according to the established criteria of the Breast Imaging Reporting and Data System (BI-RADS) lexicon. The diagnostic value of MMG, DBT and CEM was assessed by ROC analysis. The interobserver agreement was excellent. CEM showed higher diagnostic accuracy in terms of sensitivity and specificity compared to MMG and DBT, the reporting time for CEM was significantly shorter, and CEM findings resulted in a significantly lower proportion of equivocal findings (BI-RADS 0), suggesting fewer additional procedures. In conclusion, CEM achieves high diagnostic accuracy while maintaining simplicity, reproducibility and applicability in complex clinical settings.

Analysis of dual-energy mammography subtraction technique for the dose and image quality evaluation using 3D-printed breast phantom

This study aimed to quantitatively assess the radiation dose using XR-QA2 and the image quality of the dual-energy subtraction mammography technique on an in-house phantom. The analysis was carried out to investigate the effect of targets/filters on dose value and image quality using an in-house phantom made of PLA + as an object representing compressed breasts. All irradiation parameters were performed in the craniocaudal position with manual mode. Mean glandular dose (MGD) was recorded, followed by the calculation of the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and modulation transfer function (MTF) for image quality assessment parameters. The results showed that the image quality was accepted at dose levels within the IAEA and BAPETEN tolerance limit for 60 mm equivalent compressed breast using dual-energy mammography. Furthermore, the target/filter (W/Rh) reduced the dose by 1.03 mGy compared to the Mo/Mo and Mo/Rh with an enhancement in image quality. This indicated that the target/filter (W/Rh) combination was optimal due to the image quality improvement obtained with lower MGD.

Clinical Analysis of 137 Cases of Ovarian Tumors in Pregnancy

Ovarian tumors do not really typically occur in association with pregnant; however, once they do, the treatment is critical. It is important to note that around 6% of ovarian tumors in pregnancies are cancerous. The problems induced by ovarian tumors in pregnancy particularly necessitate rapid medical intervention and are much more frequent than cancer. Medication choices and survival of ovary tumor patients could be influenced by varied diagnoses of ovarian masses. So, we present an upgraded logistic regression (ULR) approach in this paper. Initially, the collection of 137 patient datasets was employed in screening test to identify the ovarian tumor as benign-tumor and malignant-tumor by using contrast-enhanced ultrasonography (CEU) method. Then, the screening test images are preprocessed using wavelet transform (WT) approach. The preprocessed data are extracted by using local binary pattern (LBP) and laws' texture energy (LTE) techniques. Finally, the clinical analysis of the ovarian tumor can be obtained by the proposed ULR approach. The performances were examined and compared with existing approaches to achieve the proposed approach with greatest correctness. The findings are depicted by utilizing the MATLAB tool.