Breast Region Research Articles

Scatter correction for contrast-enhanced digital breast tomosynthesis with a dual-layer detector.

Contrast-enhanced digital breast tomosynthesis (CEDBT) highlights breast tumors with neo-angiogenesis. A recently proposed CEDBT system with a dual-layer (DL) flat-panel detector enables simultaneous acquisition of high-energy (HE) and low-energy (LE) projection images with a single exposure, which reduces acquisition time and eliminates motion artifacts. However, x-ray scatter degrades image quality and lesion detectability. We propose a practical method for accurate and robust scatter correction (SC) for DL-CEDBT. The proposed hybrid SC method combines the advantages of a two-kernel iterative convolution method and an empirical interpolation strategy, which accounts for the reduced scatter from the peripheral breast region due to thickness roll-off and the scatter contribution from the region outside the breast. Scatter point spread functions were generated using Monte Carlo simulations with different breast glandular fractions, compressed thicknesses, and projection angles. Projection images and ground truth scatter maps of anthropomorphic digital breast phantoms were simulated to evaluate the performance of the proposed SC method and three other kernel- and interpolation-based methods. The mean absolute relative error (MARE) between scatter estimates and ground truth was used as the metric for SC accuracy. DL-CEDBT shows scatter characteristics different from dual-shot, primarily due to the two energy peaks of the incident spectrum and the structure of the DL detector. Compared with the other methods investigated, the proposed hybrid SC method showed superior accuracy and robustness, with MARE of for all LE and HE projection images of different phantoms in both cranial-caudal and mediolateral-oblique views. After SC, cupping artifacts in the dual-energy image were removed, and the signal difference-to-noise ratio was improved by 82.0% for 8mm iodine objects. A practical SC method was developed, which provided accurate and robust scatter estimates to improve image quality and lesion detectability for DL-CEDBT.

Pectoralis Major Muscle 3D Volumetric Reconstruction in the Tuberous Breast: Anatomical and Radiomics Differences With Potential Surgical Impact.

Tuberous breast (TB) is a congenital anomaly characterised by morphological, structural and breast region alterations. The pectoralis major muscle (PMM) is crucial for correction when using breast implants, alterations at this level have received little attention in the literature. This study aims to define the anatomical characteristics of the PMM in TB patients using radiomic data extracted from MRI sequences. The PMM of 30 TB patients and 30 controls were analyzed by manual segmentation using 3D-Slicer v.5.4.0 software using radiomic techniques. A total of 46 features were extracted, both first-order statistical features and shape features (e.g. median, mean, standard deviation, volume, area, centroid, number of voxels, flatness, roundness, elongation, principal axis length x, y, z, diameter). A statistically significant difference was found in favor of the TB group for PMM roundness; PMM elongation was greater in the TB group. Principal axis 1, 2, and 3 were analyzed in the three planes x, y and z: in the TB group, principal x axis 3 was lower than in the control group. Also between the axes, principal z axis 2 is on average higher in the TB group than in the NON-TB group. These results provide the first quantitative evidence of alterations affecting the PMM in TB patients. These differences were found in different planes and volumetric shape features with potential surgical implications.

Factors Affecting the Complete Response in Breast and Axillary Regions Following Neoadjuvant Chemotherapy for Breast Cancer

Factors Affecting the Complete Response in Breast and Axillary Regions Following Neoadjuvant Chemotherapy for Breast Cancer

Discordance between peritumoral and subareolar injections for mapping sentinel lymph nodes in the breast.

Sentinel node biopsy (SNB) is a common staging tool for breast cancer. Initially, peritumoral (PT) injections were used, however subareolar (SA) injections were later introduced to simplify the technique. Controversy remains regarding whether PT and SA injections map the same sentinel lymph nodes (SLNs). This study aimed to determine whether the regional location of breast SLNs differs when using PT versus SA injections using a large dataset from a single institution. A total of 1035 patients who underwent breast SNB (PT injections: n = 858 and SA injections: n = 177) with lymphoscintigraphy and SPECT/CT were included. The identified SLN locations using SA injections were compared with those using PT injections. Differences in drainage proportions and odds ratios (ORs) for each clockface breast region and the whole breast were calculated using a two-proportion z-test and Fisher's Exact Test. A higher proportion of internal mammary SLNs were identified using PT injections for the whole breast (0.30 versus 0.09) and for all breast regions, with all regions showing statistical significance except the upper outer quadrant. Similarly, ORs showed identification of internal mammary SLNs was significantly higher when using PT injections (4.35, 95% CI 2.53 to 7.95). There were no significant differences in identifying axillary SLNs between injection sites. This is the largest cohort study to compare the regional location of breast SLNs identified using PT injections versus SA injections. Discordance was shown in the SLNs identified between injection techniques, with PT injections more frequently identifying internal mammary SLNs.

Diabetic mastopathy in an adult female patient with type 2 diabetes in Indonesia: a case report and literature review

Background: Diabetic mastopathy (DMP) is a rare pathological condition characterized by the formation of tumor-like fibroepithelial lesions in the breast region, typically observed in individuals afflicted with type 1 diabetes mellitus. This study presents an atypical occurrence of diabetic mastopathy found in a 29-year-old female with type 2 diabetes mellitus of the non-insulin-dependent subtype. Case Presentation: The physical examination revealed the presence of a firm and palpable lump near the nipple of the right breast. An ultrasound (US) examination was conducted, illustrating the presence of tissue distortion and nipple retraction of the breast. Lymphadenopathy in the right axilla and infraclavicular regions was also found. Histological findings demonstrated typical DMP with fibrosis without evidence of malignancy. Conclusion: Diabetic mastopathy is a rare disorder that may pose a challenge. However, meticulous monitoring and assertive management, along with timely identification and effective administration of medical care, can ultimately result in a positive prognosis.

A comprehensive approach for evaluating lymphovascular invasion in invasive breast cancer: Leveraging multimodal MRI findings, radiomics, and deep learning analysis of intra- and peritumoral regions

PurposeTo evaluate lymphovascular invasion (LVI) in breast cancer by comparing the diagnostic performance of preoperative multimodal magnetic resonance imaging (MRI)-based radiomics and deep-learning (DL) models. MethodsThis retrospective study included 262 patients with breast cancer—183 in the training cohort (144 LVI-negative and 39 LVI-positive cases) and 79 in the validation cohort (59 LVI-negative and 20 LVI-positive cases). Radiomics features were extracted from the intra- and peritumoral breast regions using multimodal MRI to generate gross tumor volume (GTV)_radiomics and gross tumor volume plus peritumoral volume (GPTV)_radiomics. Subsequently, DL models (GTV_DL and GPTV_DL) were constructed based on the GTV and GPTV to determine the LVI status. Finally, the most effective radiomics and DL models were integrated with imaging findings to establish a hybrid model, which was converted into a nomogram to quantify the LVI risk. ResultsThe diagnostic efficiency of GPTV_DL was superior to that of GTV_DL (areas under the curve [AUCs], 0.771 and 0.720, respectively). Similarly, GPTV_radiomics outperformed GTV_radiomics (AUC, 0.685 and 0.636, respectively). Univariate and multivariate logistic regression analyses revealed an association between imaging findings, such as MRI-axillary lymph nodes and peritumoral edema (AUC, 0.665). The hybrid model achieved the highest accuracy by combining GPTV_DL, GPTV_radiomics, and imaging findings (AUC, 0.872). ConclusionThe diagnostic efficiency of the GPTV-derived radiomics and DL models surpassed that of the GTV-derived models. Furthermore, the hybrid model, which incorporated GPTV_DL, GPTV_radiomics, and imaging findings, demonstrated the effective determination of LVI status prior to surgery in patients with breast cancer.

Recommendations for using analytical anisotropic algorithm and AcurosXB for epidermal dose calculations in breast radiotherapy from an in vivo Gafchromic film study.

This study recommends clinical epidermal dose calculation methods based on in-vivo film measurements and registered skin dose distributions with the Eclipse (Varian Medical Systems) treatment planning system's Analytical Anisotropic Algorithm (AAA) and Acuros XB (AXB) dose calculation algorithms. Eighteen AAA V13.6 breast plans were recalculated using AXB (dose to medium) V13.5 with the same beam parameters and monitor units as in the original plans. These are compared against in-vivo Gafchromic film measurements from the lateral and inferior breast regions. Three skin structures in the treatment planning system are evaluated: a surface layer of voxels of the body contour, a 0.2cm internal skin rind, and a 0.5cm internal skin rind. Systematic shifts are demonstrated between the film measurements of skin dose and the Eclipse dose calculations. On average, the dose to the surface layer of pixels is underestimated by AAA by 8% and overestimated by AXB by 3%. A 5mm skin rind extended into the body can increase epidermal dose calculations on average by 8% for AAA and 4% for AXB. This is the first study to register in-vivo skin dose distributions in the breast to the treatment planning system for comparison. Based on the results from this study it is recommended that epidermal dose is calculated with a 0.5cm skin rind for the AAA algorithm and with rind thickness up to 0.2cm for the AXB algorithm.

MRI-based Deep Learning Models for Preoperative Breast Volume and Density Assessment Assisting Breast Reconstruction.

The volume of the implant is the most critical element of breast reconstruction, so it is necessary to accurately assess the preoperative volume of the healthy and affected breasts and select the appropriate implant for placement. Accurate and automated methods for quantitative assessment of breast volume can optimize breast reconstruction surgery and assist physicians in clinical decision making. The aim of this study was to develop an artificial intelligence model for automated segmentation of the breast and measurement of volume. A total of 249 subjects undergoing breast reconstruction surgery were enrolled in this study. Subjects underwent preoperative breast MRI, and the breast region manually outlined by the imaging physician served as the gold standard for volume measurement by the automated segmentation model. In this study, we developed three automated algorithms for automatic segmentation of breast regions, including a simple alignment model, an alignment dynamic encoding model, and a deep learning model. The volumetric agreement between the three automated segmentation algorithms and the breast regions manually segmented by imaging physicians was evaluated by calculating the mean square error (MSE) and intragroup correlation coefficient (ICC), and the reproducibility of the automated segmentation of the breast regions was assessed by the test-retest step. The three breast automated segmentation models developed in this study (simple registration model, dynamic programming model, and deep learning model) showed strong ICC with manual segmentation of the breast region, with MSEs of 1.124, 0.693, and 0.781, and ICCs of 0.975 (95% CI, 0.869-0.991), 0.986 (95% CI, 0.967-0.996), and 0.983 (95% CI, 0.961-0.992), respectively. Regarding the test-retest results of breast volume, the dynamic programming model performed the best with an MSE of 0.370 and an ICC of 0.993 (95% CI, 0.982-0.997), followed by the deep learning algorithm with an MSE of 0.741 and an ICC of 0.983 (95% CI, 0.956-0.993), and the simple registration algorithm with an MSE of 0.763 and an ICC of 0.982 (95% CI, 0.949-0.993). The reproducibility of the breast region segmented by the three automated algorithms was higher than that of manual segmentation by different radiologists. The three automated breast segmentation algorithms developed in this study generate accurate and reliable breast regions, enable highly reproducible breast region segmentation and automated volume measurements, and provide a valuable tool for surgical selection of appropriate prostheses. This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Role of Ultrasound in Diagnosis of Fibrocystic Breast Disease

Background: Fibrocystic breast disease is the most typical benign breast disease, which is seen in women worldwide with features of pain and feeling of nodules. Its diagnosis is based on clinical symptoms, ultrasound, mammography, and in doubt cases, biopsy is indicated. For assessment of the breast, we clinically examined the breast, axilla, and both supra and infraclavicular regions for lymph node assessment, followed by ultrasound examination. Detection of lesions in any breast quadrant assessed by BIRADs, Breast Imaging Reporting, and Data System grouped in five brackets. Scanning by sonography using B-mode and Doppler study was improved for detecting and characterizing benign and malignant lesions. Objective: To assess a breast lesion by ultrasound with features suggestive of a benign or malignant nature. Method: This study included 210 women aged 20 - 45 who visited a clinic from October 2020 to April 2022 and were examined by ultrasound machine Samsung HS50 (KOREA) with an LA3-14AD probe. B-mode images obtained detection of the lesion, and we also used Doppler ultrasound for assessment of the vascularity of the lesion. The BI-RADS system was used for the categorization of all findings. Result: In 210 women, complaints of lumps were involved in our study. Imaging by B-mode ultrasound using the Doppler study assesses the vascularity of the lesion and provides more characterization of benign and malignant lesions. By B–mode imaging, 99 patients had regular ultrasound study, and 79 patients had simple cysts that appeared well defined, had familiar outlines, and had an anechoic rounded or oval shape with posterior enhancement. Twenty-eight patients with fibroadenoma visualized as round or oval-shaped hypoechoic lesions horizontally oriented to the planes of the breast with lateral shadowing. 2 patients had complex cysts that appear as well-defined, irregular outlines, rounded shape, a thick wall with internal echoes with posterior enhancement. 2 patients have a malignant lesion that appears ill-defined rough outline vertically oriented with breast planes and show posterior shadowing. Conclusion: Ultrasound was established as the most suitable imaging modality for the categorization of breast lesions and exclusion of malignancy, which is helpful for evading unnecessary biopsies.

Breast Delineation in Full-Field Digital Mammography Using the Segment Anything Model

Breast cancer is a major health concern worldwide. Mammography, a cost-effective and accurate tool, is crucial in combating this issue. However, low contrast, noise, and artifacts can limit the diagnostic capabilities of radiologists. Computer-Aided Diagnosis (CAD) systems have been developed to overcome these challenges, with the accurate outlining of the breast being a critical step for further analysis. This study introduces the SAM-breast model, an adaptation of the Segment Anything Model (SAM) for segmenting the breast region in mammograms. This method enhances the delineation of the breast and the exclusion of the pectoral muscle in both medio lateral-oblique (MLO) and cranio-caudal (CC) views. We trained the models using a large, multi-center proprietary dataset of 2492 mammograms. The proposed SAM-breast model achieved the highest overall Dice Similarity Coefficient (DSC) of 99.22% ± 1.13 and Intersection over Union (IoU) 98.48% ± 2.10 over independent test images from five different datasets (two proprietary and three publicly available). The results are consistent across the different datasets, regardless of the vendor or image resolution. Compared with other baseline and deep learning-based methods, the proposed method exhibits enhanced performance. The SAM-breast model demonstrates the power of the SAM to adapt when it is tailored to specific tasks, in this case, the delineation of the breast in mammograms. Comprehensive evaluations across diverse datasets—both private and public—attest to the method’s robustness, flexibility, and generalization capabilities.

Seed distribution stability in permanent breast seed implant brachytherapy

PURPOSESIn this study we aim to quantitatively evaluate the stability of implanted seeds in permanent breast seed implant (PBSI) brachytherapy and assess any impact on treatment quality. METHODS AND MATERIALSSixty-seven consecutive patients who received PBSI treatment at BC Cancer Kelowna from 2013 to 2021 with post-implant CT images available were included in this study. For each patient, two sets of post-implant CT scans were retrospectively analyzed: Day0, obtained immediately after implant, and Day30, obtained approximately one month following implant. Seed distributions were quantified using the 90% isodose contour, outlier seed maximum spread, and number of seeds located in the seroma as well as seroma quadrants. These were then compared between Day0 and Day30. Post-implant dosimetry of target volumes as well as critical structures were compared. RESULTSThe 90% isodose volume was found to decrease over time. All seeds remained in the breast region however the maximum spread of seeds increased in all directions from Day0 to Day30. All recorded target volume dosimetric parameters were, on average, lower on Day30 compared to Day0 but mean dosimetry levels still met clinical goals. Dose in critical structures was overall similar. CONCLUSIONSIn this study, we quantitatively described the changes in seed distributions as well as dosimetry from Day0 to Day30 post PBSI procedure. We addressed concerns related to seed stability in breast tissue and provided clinical evidence on dosimetric efficacy of the PBSI technique.

Computerized assessment of background parenchymal enhancement on breast dynamic contrast-enhanced-MRI including electronic lesion removal.

Current clinical assessment qualitatively describes background parenchymal enhancement (BPE) as minimal, mild, moderate, or marked based on the visually perceived volume and intensity of enhancement in normal fibroglandular breast tissue in dynamic contrast-enhanced (DCE)-MRI. Tumor enhancement may be included within the visual assessment of BPE, thus inflating BPE estimation due to angiogenesis within the tumor. Using a dataset of 426 MRIs, we developed an automated method to segment breasts, electronically remove lesions, and calculate scores to estimate BPE levels. A U-Net was trained for breast segmentation from DCE-MRI maximum intensity projection (MIP) images. Fuzzy -means clustering was used to segment lesions; the lesion volume was removed prior to creating projections. U-Net outputs were applied to create projection images of both, affected, and unaffected breasts before and after lesion removal. BPE scores were calculated from various projection images, including MIPs or average intensity projections of first- or second postcontrast subtraction MRIs, to evaluate the effect of varying image parameters on automatic BPE assessment. Receiver operating characteristic analysis was performed to determine the predictive value of computed scores in BPE level classification tasks relative to radiologist ratings. Statistically significant trends were found between radiologist BPE ratings and calculated BPE scores for all breast regions (Kendall correlation, ). Scores from all breast regions performed significantly better than guessing ( from the -test). Results failed to show a statistically significant difference in performance with and without lesion removal. BPE scores of the affected breast in the second postcontrast subtraction MIP after lesion removal performed statistically greater than random guessing across various viewing projections and DCE time points. Results demonstrate the potential for automatic BPE scoring to serve as a quantitative value for objective BPE level classification from breast DCE-MR without the influence of lesion enhancement.

Mycotic Granuloma on the Skin of a Desi Fowl: A Case Report

Background: A Desi fowl with a nodular growth on the skin of the left lateral aspect of the breast region was presented to VCC, NTR CVSC, Gannavaram. The mass was excised surgically and collected for histopathology and ultrastructural studies. Grossly, the tissue appeared as a white nodular growth, but was cheesy on the cut section. Methods: Fixation and processing of tissue growth for histopathology was done as per routine procedures. The special stains like PAS was carried out to demonstrate the fungal hyphae. Scanning electron microscopy was used to determine the morphology of fungal hyphae. Result: Histopathological sections revealed caseous necrotic centres with numerous fungal hyphae surrounded by polymorphonuclears, macrophages and Langerhans giant cells. Fungal hyphae in tissue sections measured 9-10 µm on micrometry indicating zygomycetes phylum. These pyogranulomatous lesions revealed pinkish red, broad, rarely septate fungal hyphae on PAS staining. Branched fungal hyphae along with attached terminal spores to few hyphae were evident on scanning electron microscopy (SEM).

Gan-based data augmentation to improve breast ultrasound and mammography mass classification

Data imbalance is a common problem in breast cancer diagnosis, to address this challenge, the research explores the use of Generative Adversarial Networks (GANs) to generate synthetic medical data. Various GAN methods, including Wasserstein GAN with Gradient Penalty (WGAN-GP), Cycle GAN, Conditional GAN, and Spectral Normalization GAN (SNGAN), were tested for data augmentation in breast regions of interest (ROIs) using mammography and ultrasound databases. The study employed real, synthetic, and hybrid ROIs (128x128 pixels) to train a Resnet network for classifying as benign (B) or malignant (M) classes. The quality and diversity of the synthetic data were assessed using several metrics: Fréchet Inception Distance (FID), Kernel Inception Distance (KID), Structural Similarity Index (SSIM), Multi-Scale SSIM (MS-SSIM), Blind Reference Image Spatial Quality Evaluator (BRISQUE), Naturalness Image Quality Evaluator (NIQE), and Perception-based Image Quality Evaluator (PIQE).Results revealed that the SNGAN model (FID = 52.89) was most effective for augmenting mammography data, while CGAN (FID = 116.03) excelled with ultrasound data. Cycle GAN and WGAN-GP, though demonstrating lower KID values, did not perform better than SNGAN and CGAN. The lower average MS-SSIM values suggested that SNGAN and CGAN produced a high diversity of synthetic images. However, lower SSIM, BRISQUE, NIQE, and PIQE values indicated poor quality in both real and synthetic images. Classification results showed high accuracy without data augmentation in both US (93.1 %B/94.9 %M) and mammography (80.9 %B/76.9 %M). The research concludes that preprocessing and characterizing ROIs by abnormality type is crucial to generate diverse synthetic data and improve accuracy in the classification process using combined GANs and CNN models.

Bridging the Gap: A Pilot Study on the Efficacy of Nerve Allografts in Autologous Breast Reconstruction

Abstract Background Breast anesthesia is commonly reported after mastectomy and reconstruction. During deep inferior epigastric perforator (DIEP) flap reconstruction, we coapt at least one of the T10 to T12 thoracoabdominal nerves within the flap to the anterior cutaneous branch of the third intercostal nerve using a nerve allograft. We aim to evaluate the efficacy of nerve grafting in improving sensory recovery following neurotized DIEP flap reconstruction. Methods Thirty patients (54 breasts) underwent immediate neurotized DIEP flap reconstruction using nerve grafts. Sensitivity evaluation was performed in nine breast regions. For each patient, sensation was compared between two time points: 3 to 6 months postoperatively versus 12 to 24 months postoperatively. The reconstructive BREAST-Q was used to survey patients' satisfaction of their breasts, physical well-being, psychosocial well-being, and sexual well-being. Results At 3 to 6 months postoperatively, patients had a mean sensitivity measurement of 52.1 g/mm2. At 12 to 24 months postoperatively, patients had a mean sensitivity measurement of 40.3 g/mm2. There was a significant decrease in the mean cutaneous threshold required for patients to perceive sensation between the two time points (–29.1%, p = 0.041). On the reconstructive BREAST-Q, patients scored significantly higher in breast satisfaction (56.7/100 vs. 75.1/100, 32.5%, p = 0.032) and physical well-being (66.0/100 vs. 85.5/100, 20.2%, p = 0.022) between the two time points. Conclusion Patients who undergo nerve graft-based DIEP flap reconstruction can expect significant improvements in sensation to pressure over time. This improvement found on sensory testing correlates with significant improvement in patients' BREAST-Q scores.

Comparison of Histological Skin Changes After Massive Weight Loss in Post-bariatric and Non-bariatric Patients.

Changes in the skin structure, including the collagen and elastin content, have been reported with massive weight loss (MWL) following bariatric metabolic surgery (BMS) and have been correlated to a higher risk of complications after body-contouring surgery (BCS). This study aimed at comparing the histological characteristics of the skin of patients having surgical MWL (SMWL) post-BMS to those with non-surgical massive weight loss (NSMWL). This prospective study compared the epidermal thickness, and collagen and elastin fibers content in 80 skin biopsies obtained from BCS procedures performed to patients who experienced MWL defined more than 50% of excess weight loss (%EWL) either SMWL (40 biopsies) or NSMWL (40 biopsies). Twenty biopsies in each group were obtained from abdominoplasties and 20 from breast reductions. Epidermal thickness was measured in H&E-stained sections, collagen fibers were assessed using Masson trichrome-stained sections, and elastin fibers were assessed using Modified Verhoeff's stained sections. Image analysis software was used to calculate the fractions of collagen and elastin fibers. This study included 77 patients, 38 SMWL patients, and 39 NSMWL patients. The SMWL group had a significantly higher age (p < 0.001), a longer time interval from intervention (p < 0.001), higher initial weight (p < 0.001), higher initial BMI (p < 0.001), lower current weight (p = 0.005), lower current BMI (p < 0.001), and significantly higher %EWL than NSMWL group (p < 0.001). No significant differences were detected between the two groups regarding complications after abdominoplasty (p = 1.000). The elastic fibers content in the dermis was significantly higher in the abdominal region of the NSMWL group than SMWL (p = 0.029). All other parameters showed non-significant differences between NSMWL and SMWL in the skin of abdomen and breast. The SMWL group had a significant reduction in elastic fiber content in the skin of the abdomen compared to the NSMWL group. The collagen content was equally reduced in both groups with non-significant differences in both breast and abdomen regions in both groups.

Attenuation correction and truncation completion for breast PET/MR imaging using deep learning.

Objective. Simultaneous PET/MR scanners combine the high sensitivity of MR imaging with the functional imaging of PET. However, attenuation correction of breast PET/MR imaging is technically challenging. The purpose of this study is to establish a robust attenuation correction algorithm for breast PET/MR images that relies on deep learning (DL) to recreate the missing portions of the patient's anatomy (truncation completion), as well as to provide bone information for attenuation correction from only the PET data.Approach. Data acquired from 23 female subjects with invasive breast cancer scanned with18F-fluorodeoxyglucose PET/CT and PET/MR localized to the breast region were used for this study. Three DL models, U-Net with mean absolute error loss (DLMAE) model, U-Net with mean squared error loss (DLMSE) model, and U-Net with perceptual loss (DLPerceptual) model, were trained to predict synthetic CT images (sCT) for PET attenuation correction (AC) given non-attenuation corrected (NAC) PETPET/MRimages as inputs. The DL and Dixon-based sCT reconstructed PET images were compared against those reconstructed from CT images by calculating the percent error of the standardized uptake value (SUV) and conducting Wilcoxon signed rank statistical tests.Main results. sCT images from the DLMAEmodel, the DLMSEmodel, and the DLPerceptualmodel were similar in mean absolute error (MAE), peak-signal-to-noise ratio, and normalized cross-correlation. No significant difference in SUV was found between the PET images reconstructed using the DLMSEand DLPerceptualsCTs compared to the reference CT for AC in all tissue regions. All DL methods performed better than the Dixon-based method according to SUV analysis.Significance. A 3D U-Net with MSE or perceptual loss model can be implemented into a reconstruction workflow, and the derived sCT images allow successful truncation completion and attenuation correction for breast PET/MR images.

Microneedling in Abdominal Scarring after DIEP-flap Breast Reconstruction to Improve Scar Quality: A Randomized Controlled Split Scar Trial.

Deep inferior epigastric perforator (DIEP) flap breast reconstruction leads to large scars in the breast and abdominal region. Common symptoms related to abdominal scarring include changes in color, stiffness, thickness, and irregularity of the skin. The aim of this study was to examine whether microneedling improves the abdominal scar quality after DIEP-flap breast reconstruction. A prospective randomized controlled within subject comparison design (N = 30) was used to study the effect of microneedling treatments on the abdominal scar, versus no treatment. Three electrically powered microneedling sessions were performed every 4 weeks on one side of the abdominal scar. Abdominal scar assessment was performed at baseline and 3-, 6-, and 9-month follow-up using the Patient and Observer Scar Assessment Scale (POSAS). Treated and untreated sides were compared using a t test or Wilcoxon signed-rank test in case the data were not normally distributed. Twenty-seven women completed the study. Microneedling treatment led to lower POSAS scores compared with the untreated scar side after the 3- and 9-month follow-up. At the 9-month follow-up, the POSAS total score (Mdn = 17, interquartile range = 18.3 versus Mdn = 21.4, interquartile range = 17.5) was statistically lower for the treated side compared with the untreated side, implying a better-appraised scar quality. The observer POSAS total score was statistically lower for the treated side compared with the untreated side. Based on patient and observer ratings, overall abdominal scars after DIEP-flap surgery improve significantly after microneedling treatment. Scar symptoms reduce faster under the influence of microneedling treatment compared with no treatment.

A Comparative Study of Breast Mass Detection Using YOLOv8 Deep Learning Model in Various Data Scenarios on Multi-View Digital Mammograms

Breast cancer is one of the most common types of cancer among women worldwide. It typically begins with abnormal cell growth in the breast glands or milk ducts and can spread to other tissues. Many breast cancer cases start with the presence of a mass and should be carefully examined. Masses can be monitored using X-ray-based digital mammography images, including right craniocaudal, left craniocaudal, right mediolateral oblique, and left mediolateral oblique views. In this study, automatic mass detection and localization were performed on mammography images taken from the full-field digital mammography VinDr-Mammo dataset using the YOLOv8 deep learning model. Three different scenarios were tested: raw data, data with preprocessing to crop breast regions, and data with only mass regions cropped to a 1.2x ratio. The data were divided into 80% for training and 10% each for validation and testing. The results were evaluated using performance metrics such as precision, recall, F1-score, mAP, and training graphs. At the end of the study, it is demonstrated that the YOLOv8 deep learning model provides successful results in mass detection and localization, indicating its potential use as a computer-based decision support system.

Reduction of Radiation Risk to Cardiologists and Patients during Coronary Angiography: Effect of Exposure Angulation and Composite Shields

Purpose: This study aimed to design an improved form of a composite shield with different materials and shapes and simultaneously reduce the radiation dose to both the patient and operator.&#x0D; Materials and Methods: A female phantom study was performed with and without bismuth belt-shaped composite shields on the breast region at different beam projections used in coronary angiography. Dose measurements were conducted using GR-200 thermo-luminescence dosimeters, dose area product (DAP), and air kerma (AK) over regular and large breast locations, with and without using bismuth shields. An electronic personal dosimeter was used for operator dose assessment. Patients received doses between 2.27 mSv and 3.38 mSv, depending on the size and strength of beam projections.&#x0D; Results: The use of the developed shields caused a dose reduction of 18%–25% of sensitive breast tissue due to breast size and shield type. During coronary angiography, the mean values of DAP and AK were 2.02 (1.24-2.80) mGy.m2 and 314.1 (202.8-500) mGy, respectively. The highest recorded dose was at the LAO/CRA and LAO/CAU beam projections for both the patient and operator. After applying a belt shield, the operator's radiation dose was decreased by approximately 32%. We found a statistically significant correlation between the radiation dose received by the operator and the patient's breast radiation exposure dose (p&lt;0.001, r2=0.93).&#x0D; Conclusion: The designed belt shield can be a potentially promising protective device for decreasing the radiation risk to the patient's breast and the operator during coronary angiography. However, further studies will be considered before the application of this shield in standard clinical practice.