Expert Radiologists Research Articles

Artificial Intelligence Analysis Using MRI and PET Imaging in Gliomas: A Narrative Review.

The lack of early detection and a high rate of recurrence/progression after surgery are defined as the most common causes of a very poor prognosis of Gliomas. The developments of quantification systems with special regards to artificial intelligence (AI) on medical images (CT, MRI, PET) are under evaluation in the clinical and research context in view of several applications providing different information related to the reconstruction of imaging, the segmentation of tissues acquired, the selection of features, and the proper data analyses. Different approaches of AI have been proposed as the machine and deep learning, which utilize artificial neural networks inspired by neuronal architectures. In addition, new systems have been developed using AI techniques to offer suggestions or make decisions in medical diagnosis, emulating the judgment of radiologist experts. The potential clinical role of AI focuses on the prediction of disease progression in more aggressive forms in gliomas, differential diagnosis (pseudoprogression vs. proper progression), and the follow-up of aggressive gliomas. This narrative Review will focus on the available applications of AI in brain tumor diagnosis, mainly related to malignant gliomas, with particular attention to the postoperative application of MRI and PET imaging, considering the current state of technical approach and the evaluation after treatment (including surgery, radiotherapy/chemotherapy, and prognostic stratification).

Imaging of pulmonary fibrosis in children: A review, with proposed diagnostic criteria.

Computed tomography (CT) imaging findings of pulmonary fibrosis are well established for adults and have been shown to correlate with prognosis and outcome. Recognition of fibrotic CT findings in children is more limited. With approved treatments for adult pulmonary fibrosis, it has become critical to define CT criteria for fibrosis in children, to identify patients in need of treatment and those eligible for clinical trials. Understanding how pediatric fibrosis compares with idiopathic pulmonary fibrosis and other causes of fibrosis in adults is increasingly important as these patients transition to adult care teams. Here, we review what is known regarding the features of pulmonary fibrosis in children compared with adults. Pulmonary fibrosis in children may be associated with genetic surfactant dysfunction disorders, autoimmune systemic disorders, and complications after radiation, chemotherapy, transplantation, and other exposures. Rather than a basal-predominant usual interstitial pneumonia pattern with honeycombing, pediatric fibrosis is primarily characterized by reticulation, traction bronchiectasis, architectural distortion, or cystic lucencies/abnormalities. Ground-glass opacities are more frequent in children with fibrotic interstitial lung disease than adults, and disease distribution appears more diffuse, without clearly defined axial or craniocaudal predominance. Following discussion and consensus amongst a panel of expert radiologists, pathologists and physicians, distinctive disease features were integrated to develop criteria for the first global Phase III trial in children with pulmonary fibrosis.

Behind the screen: underreported contribution of the expert radiologist in inflammatory bowel disease conferences and patient care

Behind the screen: underreported contribution of the expert radiologist in inflammatory bowel disease conferences and patient care

Evaluating Recalibrating AI Models for Breast Cancer Diagnosis in a New Context: Insights from Transfer Learning, Image Enhancement and High-Quality Training Data Integration.

This paper investigates the adaptability of four state-of-the-art artificial intelligence (AI) models to the Australian mammographic context through transfer learning, explores the impact of image enhancement on model performance and analyses the relationship between AI outputs and histopathological features for clinical relevance and accuracy assessment. A total of 1712 screening mammograms (n = 856 cancer cases and n = 856 matched normal cases) were used in this study. The 856 cases with cancer lesions were annotated by two expert radiologists and the level of concordance between their annotations was used to establish two sets: a 'high-concordances subset' with 99% agreement of cancer location and an 'entire dataset' with all cases included. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of Globally aware Multiple Instance Classifier (GMIC), Global-Local Activation Maps (GLAM), I&H and End2End AI models, both in the pretrained and transfer learning modes, with and without applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. The four AI models with and without transfer learning in the high-concordance subset outperformed those in the entire dataset. Applying the CLAHE algorithm to mammograms improved the performance of the AI models. In the high-concordance subset with the transfer learning and CLAHE algorithm applied, the AUC of the GMIC model was highest (0.912), followed by the GLAM model (0.909), I&H (0.893) and End2End (0.875). There were significant differences (p < 0.05) in the performances of the four AI models between the high-concordance subset and the entire dataset. The AI models demonstrated significant differences in malignancy probability concerning different tumour size categories in mammograms. The performance of AI models was affected by several factors such as concordance classification, image enhancement and transfer learning. Mammograms with a strong concordance with radiologists' annotations, applying image enhancement and transfer learning could enhance the accuracy of AI models.

Detection of Bowel and Mesenteric Injuries Using Deep Learning Computer Vision Models

Background/Objective:While only seen in 1-5% of patients who undergo a CT (computed tomography) scan, blunt bowel and mesenteric injuries (BMI) are associated with significantly increased morbidity and mortality. A significant cause of the increased morbidity of BMI is due to the difficulty of diagnosis from clinical and imaging information which leads to delay in diagnosis. Accurate and timely diagnosis is vital to reduce the morbidity of BMI.&#x0D; Methods:For this project, our primary objective is to create a binary prediction model that determines if a patient has BMI based on their abdominal CT scans. Due to the importance of the early and definitive diagnosis of BMI in trauma patients, an extension of this project will seek to introduce explainability into the model to highlight which features on the CT scan caused the model to make its prediction. The patients with BMI were sourced from a trauma registry that recorded trauma cases from IU Health with relevant diagnosis codes. The images from our search will be reduced to the relevant slices for diagnosis of BMI and then used to train an ML model to makea yes/no prediction from the image. Once the model is trained, testing data will be evaluated on the model and the gradient vectors from the model during inference will be used to create a heatmap with GRAD-CAM that illustrates what portions of the image were relevant for the decision made by the algorithm.&#x0D; Future Directions:Using the collected abdominal CTs, we can train our machine learning pipeline to detect BMI. Based on the performance of the model, we will determine if we need to collect more data. Then, we can evaluate the explainability of the model using GRAD-CAM and compareperformance of the ML model to the performance of expert and trainee radiologists.

Assessing the impact of structured reporting on learning how to report lung cancer staging CT: A triple cohort study on inexperienced readers

PurposeTo assess the clinical utility of chest computed tomography (CT) reports for non-small-cell lung cancer (NSCLC) staging generated by inexperienced readers using structured reporting (SR) templates from the Royal College of Radiologists (RCR-SR) and the Italian Society of Medical and Interventional Radiology (SIRM-SR), compared to traditional non-systematic reports (NSR). MethodsIn a cohort of 30 NSCLC patients, six third-year radiology residents reported CT examinations in two 2-month-apart separate sessions using NSR in the first and NSR, RCR-SR, or SIRM-SR in the second. Couples of expert radiologists and thoracic oncologists in consensus evaluated completeness, accuracy, and clarity. All the quality indicators were expressed on a 100-point scale. The Wilcoxon signed ranks, and Wilcoxon-Mann Whitney tests were used for statistical analyses. ResultsResults showed significantly higher completeness for RCR-SR (90 %) and SIRM-SR (100 %) compared to NSR (70 %) in the second session (all p < 0.001). SIRM-SR demonstrated superior accuracy (70 % vs. 55 %, p < 0.001) over NSR, while RCR-SR and NSR accuracy did not significantly differ (60 % vs. 62.5 %, p = 0.06). In the second session, RCR-SR and SIRM-SR surpassed NSR in completeness, accuracy, and clarity (all p < 0.001, except p = 0.04 for accuracy between RCR-SR and NSR). SIRM-SR outperformed RCR-SR in completeness (100 % vs. 90 %, p < 0.001) and accuracy (70 % vs. 62.5 %, p = 0.002), with equivalent clarity (90 % for both, p = 0.27). ConclusionsInexperienced readers using RCR-SR and SIRM-SR demonstrated high-quality reporting, indicating their potential in radiology residency programs to enhance reporting skills for NSCLC staging and effective interaction with all the physicians involved in managing NSCLC patients.

Is the diagnostic model based on convolutional neural network superior to pediatric radiologists in the ultrasonic diagnosis of biliary atresia?

Many screening and diagnostic methods are currently available for biliary atresia (BA), but the early and accurate diagnosis of BA remains a challenge with existing methods. This study aimed to use deep learning algorithms to intelligently analyze the ultrasound image data, build a BA ultrasound intelligent diagnostic model based on the convolutional neural network, and realize an intelligent diagnosis of BA. A total of 4,887 gallbladder ultrasound images of infants with BA, non-BA hyperbilirubinemia, and healthy infants were collected. Two mask region convolutional neural network (Mask R-CNN) models based on different backbone feature extraction networks were constructed. The diagnostic performance between the two models was compared through good-quality images at the image level and the patient level. The diagnostic performance between the two models was compared through poor-quality images. The diagnostic performance of BA between the model and four pediatric radiologists was compared at the image level and the patient level. The classification performance of BA in model 2 was slightly higher than that in model 1 in the test set, both at the image level and at the patient level, with a significant difference of p = 0.0365 and p = 0.0459, respectively. The classification accuracy of model 2 was slightly higher than that of model 1 in poor-quality images (88.3% vs. 86.4%), and the difference was not statistically significant (p = 0.560). The diagnostic performance of model 2 was similar to that of the two radiology experts at the image level, and the differences were not statistically significant. The diagnostic performance of model 2 in the test set was higher than that of the two radiology experts at the patient level (all p < 0.05). The performance of model 2 based on Mask R-CNN in the diagnosis of BA reached or even exceeded the level of pediatric radiology experts.

An automated cervical cancer diagnosis using genetic algorithm and CANFIS approaches.

Cervical malignancy is considered among the most perilous cancers affecting women in numerous East African and South Asian nations, both in terms of its prevalence and fatality rates. This research aims to propose an efficient automated system for the segmentation of cancerous regions in cervical images. The proposed techniques encompass preprocessing, feature extraction with an optimized feature set, classification, and segmentation. The original cervical image undergoes smoothing using the Gaussian Filter technique, followed by the extraction of Local Binary Pattern (LBP) and Grey Level Co-occurrence Matrix (GLCM) features from the enhanced cervical images. LBP features capture pixel relationships within a mask window, while GLCM features quantify energy metrics across all pixels in the images. These features serve to distinguish normal cervical images from abnormal ones. The extracted features are optimized using Genetic Algorithm (GA) as an optimization method, and the optimized sets of features are classified using the Co-Active Adaptive Neuro-Fuzzy Inference System (CANFIS) classification method. Subsequently, a morphological segmentation technique is employed to categorize irregular cervical images, identifying and segmenting malignant regions within them. The proposed approach achieved a sensitivity of 99.09%, specificity of 99.39%, and accuracy of 99.36%. The proposed approach demonstrated superior performance compared to state-of-the-art techniques, and the results have been validated by expert radiologists.

Manual prostate MRI segmentation by readers with different experience: a study of the learning progress

ObjectiveTo evaluate the learning progress of less experienced readers in prostate MRI segmentation.Materials and methodsOne hundred bi-parametric prostate MRI scans were retrospectively selected from the Göteborg Prostate Cancer Screening 2 Trial (single center). Nine readers with varying degrees of segmentation experience were involved: one expert radiologist, two experienced radiology residents, two inexperienced radiology residents, and four novices. The task was to segment the whole prostate gland. The expert’s segmentations were used as reference. For all other readers except three novices, the 100 MRI scans were divided into five rounds (cases 1–10, 11–25, 26–50, 51–76, 76–100). Three novices segmented only 50 cases (three rounds). After each round, a one-on-one feedback session between the expert and the reader was held, with feedback on systematic errors and potential improvements for the next round. Dice similarity coefficient (DSC) > 0.8 was considered accurate.ResultsUsing DSC > 0.8 as the threshold, the novices had a total of 194 accurate segmentations out of 250 (77.6%). The residents had a total of 397/400 (99.2%) accurate segmentations. In round 1, the novices had 19/40 (47.5%) accurate segmentations, in round 2 41/60 (68.3%), and in round 3 84/100 (84.0%) indicating learning progress.ConclusionsRadiology residents, regardless of prior experience, showed high segmentation accuracy. Novices showed larger interindividual variation and lower segmentation accuracy than radiology residents. To prepare datasets for artificial intelligence (AI) development, employing radiology residents seems safe and provides a good balance between cost-effectiveness and segmentation accuracy. Employing novices should only be considered on an individual basis.Clinical relevance statementEmploying radiology residents for prostate MRI segmentation seems safe and can potentially reduce the workload of expert radiologists. Employing novices should only be considered on an individual basis.Key Points• Using less experienced readers for prostate MRI segmentation is cost-effective but may reduce quality. • Radiology residents provided high accuracy segmentations while novices showed large inter-reader variability. • To prepare datasets for AI development, employing radiology residents seems safe and might provide a good balance between cost-effectiveness and segmentation accuracy while novices should only be employed on an individual basis. Graphical abstract

Local Ablation for Hepatocellular Carcinoma: 2024 Expert Consensus-Based Practical Recommendations of the Korean Liver Cancer Association.

Local ablation for hepatocellular carcinoma (HCC), a non-surgical option that directly targets and destroys tumor cells, has advanced significantly since the 1990s. Therapies with different energy sources, such as radiofrequency ablation, microwave ablation, and cryoablation, employ different mechanisms to induce tumor necrosis. The precision, safety, and effectiveness of these therapies have increased with advances in guiding technologies and device improvements. Consequently, local ablation has become the first-line treatment for early-stage HCC. The lack of organized evidence and expert opinions regarding patient selection, pre-procedure preparation, procedural methods, swift post-treatment evaluation, and follow-up has resulted in clinicians following varied practices. Therefore, an expert consensus-based practical recommendation for local ablation was developed by a group of experts in radiology and hepatology from the Research Committee of the Korean Liver Cancer Association in collaboration with the Korean Society of Image-guided Tumor Ablation to provide useful information and guidance for performing local ablation and for the pre- and post-treatment management of patients.

THYROID DISEASE PREDICTION USING CNN

The thyroid gland, one of the largest endocrine organs, plays a key role in regulating metabolism. Early detection of thyroid diseases significantly lowers mortality rates.Diagnosing these diseases traditionally relies on the expertise of radiologists and pathologists, but this paper demonstrates that deep learning techniques offer a promising solution for automatic detection.The study introduces a novel approach that uses two pre-operative medical imaging techniques to classify different thyroid conditions, including normal, thyroiditis, cystic, and cancer. Utilizing a cutting -edge CNN, the model achieved high accuracy-0.972 for ultrasound images and 0.942 for CT scans. These result show the potential of CNN models in medical imaging, suggesting they could be used more broadly in clinical setting.

A convolution neural network for rapid and accurate staging of breast cancer based on mammography

IntroductionBreast cancer (BC) has been one of the main reasons for women's deaths in the recent decade. This study hypothesizes that the deep features resulting from mammography staging can be used to determine tumor staging and lymph nodes of the sentinel lymph node (SLN) before surgery in BC patients. MethodsA retrospective study of female BC patients being treated. Pathological variables were collected from medical records, including age, tumor location, staging based on the American Joint Committee on Cancer (AJCC), pathological type, human epidermal growth factor receptor 2 (HER2) status, Progesterone Receptor (PR) status, and Estrogen Receptor (ER) status. The contrast enhancement technique with Contrast-Limited Adaptive Histogram Equalization (CLAHE) was applied to the studied data using MATLAB software. The images were first changed from color to gray in the preprocessing process, and then the images were changed from gray to color again. The regions of interest (ROI) of the primary tumor were isolated from other areas of breast tissue by two expert radiologists. Therefore, a proposed convolution neural network (CNN) model was applied. ResultsIn this study, 390 patients' information was used to analyze the data. In total, out of 390 patients, 300 had metastatic SLN involvement, and 390 had a tumor size greater than 1 mm. Performance evaluation of the proposed CNN staging was AUC 0.919 (95 % CI: 0.852,0.971) for the training group and AUC 0.877 (95 % CI: 0.805,0.949) for the validation group. ConclusionsThis study proposes a deep learning model based on CNN based on mammographic images, which has a suitable function for determining tumor staging and SLN metastasis.

COVLIAS 3.0: cloud-based quantized hybrid UNet3+ deep learning for COVID-19 lesion detection in lung computed tomography.

When RT-PCR is ineffective in early diagnosis and understanding of COVID-19 severity, Computed Tomography (CT) scans are needed for COVID diagnosis, especially in patients having high ground-glass opacities, consolidations, and crazy paving. Radiologists find the manual method for lesion detection in CT very challenging and tedious. Previously solo deep learning (SDL) was tried but they had low to moderate-level performance. This study presents two new cloud-based quantized deep learning UNet3+ hybrid (HDL) models, which incorporated full-scale skip connections to enhance and improve the detections. Annotations from expert radiologists were used to train one SDL (UNet3+), and two HDL models, namely, VGG-UNet3+ and ResNet-UNet3+. For accuracy, 5-fold cross-validation protocols, training on 3,500 CT scans, and testing on unseen 500 CT scans were adopted in the cloud framework. Two kinds of loss functions were used: Dice Similarity (DS) and binary cross-entropy (BCE). Performance was evaluated using (i) Area error, (ii) DS, (iii) Jaccard Index, (iii) Bland-Altman, and (iv) Correlation plots. Among the two HDL models, ResNet-UNet3+ was superior to UNet3+ by 17 and 10% for Dice and BCE loss. The models were further compressed using quantization showing a percentage size reduction of 66.76, 36.64, and 46.23%, respectively, for UNet3+, VGG-UNet3+, and ResNet-UNet3+. Its stability and reliability were proved by statistical tests such as the Mann-Whitney, Paired t-Test, Wilcoxon test, and Friedman test all of which had a p < 0.001. Full-scale skip connections of UNet3+ with VGG and ResNet in HDL framework proved the hypothesis showing powerful results improving the detection accuracy of COVID-19.

Accelerating biomedical image segmentation using equilibrium optimization with a deep learning approach

&lt;abstract&gt; &lt;p&gt;Biomedical image segmentation is a vital task in the analysis of medical imaging, including the detection and delineation of pathological regions or anatomical structures within medical images. It has played a pivotal role in a variety of medical applications, involving diagnoses, monitoring of diseases, and treatment planning. Conventionally, clinicians or expert radiologists have manually conducted biomedical image segmentation, which is prone to human error, subjective, and time-consuming. With the advancement in computer vision and deep learning (DL) algorithms, automated and semi-automated segmentation techniques have attracted much research interest. DL approaches, particularly convolutional neural networks (CNN), have revolutionized biomedical image segmentation. With this motivation, we developed a novel equilibrium optimization algorithm with a deep learning-based biomedical image segmentation (EOADL-BIS) technique. The purpose of the EOADL-BIS technique is to integrate EOA with the Faster RCNN model for an accurate and efficient biomedical image segmentation process. To accomplish this, the EOADL-BIS technique involves Faster R-CNN architecture with ResNeXt as a backbone network for image segmentation. The region proposal network (RPN) proficiently creates a collection of a set of region proposals, which are then fed into the ResNeXt for classification and precise localization. During the training process of the Faster RCNN algorithm, the EOA was utilized to optimize the hyperparameter of the ResNeXt model which increased the segmentation results and reduced the loss function. The experimental outcome of the EOADL-BIS algorithm was tested on distinct benchmark medical image databases. The experimental results stated the greater efficiency of the EOADL-BIS algorithm compared to other DL-based segmentation approaches.&lt;/p&gt; &lt;/abstract&gt;

IOTA simple rules: An efficient tool for evaluation of ovarian tumors by non-experienced but trained examiners - A prospective study

ObjectivesA simple and efficient tool for evaluating ovarian tumors in general hospitals where radiologists without experience in gynecological ultrasound is necessary. This study aims to evaluate the diagnostic performance of IOTA simple rules in initial classification of ovarian tumors by non-experienced examiners who have received simple training. Materials and methodA prospective single-center study was conducted at Hanoi Obstetrics and Gynecology Hospital. Three resident gynecologists trained themselves for two weeks and then received hands-on practice under the supervision of experts for another two weeks. The examiners performed ultrasound on 424 eligible women scheduled for surgery for ovarian tumors and classified the tumors based on IOTA simple rules. The postoperative pathology of ovarian tumors was used as the gold standard. Results90.8 % (385/424) of the tumors were benign. Simple rules were applicable in 399/424 (94.1 %) tumors, with a sensitivity of 84.8 % (95 % CI, 70.2–94.3), specificity of 98.9 % (95 % CI, 97.5–99.7), positive predictive value of 87.5 % (95 % CI, 73.3–95.9), and negative predictive value of 98.6 % (95 % CI, 97.1–99.5). The sensitivity of IOTA simple rules was higher in postmenopausal women (91.7 % vs. 81.0 %), while the specificity was higher in premenopausal women (99.4 % vs. 95.8 %). Accuracy was 100 % in all ten pregnant women were assessed using these rules. ConclusionIn conclusion, in the hands of non-expert examiners who were trained thoroughly, IOTA simple rules are a simple and efficient tool for clinical practice in centers where expert radiologists in gynecology are not always available. The training program is simple and could be applied widely in other clinical centers. Further studies are necessary to evaluate the effectiveness of the IOTA simple rules in assessing ovarian tumors among pregnant women.

Non-alcoholic fatty liver disease (NAFLD) and obesity in inflammatory bowel disease (IBD) patients in Gorgan.

According to the significance of extraintestinal symptoms in inflammatory bowel disease (IBD) patients and their connection with obesity, we aimed to investigate the prevalence of fatty liver in IBD patients of Sayyad Shirazi Hospital in Gorgan, Iran, in relation to obesity, anthropometric indicators and body image in these patients. Forty patients with IBD were recruited from all registered patients at the Golestan Research Center of Gastroenterology and Hepatology, following the specified inclusion and exclusion criteria. After obtaining written informed consent and filling in the questionnaire, the demographic and anthropometric indicators, and variables related to the disease were measured. The liver sonography was performed on all patients and graded by an expert radiologist. Data were analyzed using SPSS Version 16.0 statistical software at the significance level of 0.05. We showed no significant difference between the distribution of demographic and anthropometric indicators in different groups of IBD patients. However, we demonstrated that the inappropriate values of HDL (0.004) and high values of LDL (0.015) were associated with fatty liver in IBD patients. Our findings also showed that NAFLD was significantly associated with overweight and obesity among IBD patients (P = 0.003). Our findings showed the epidemiological burden of NAFLD in IBD patients. Since fatty liver was associated with obesity, it is recommended that IBD patients be screened for risk factors associated with NAFLD to prevent liver disease.

Clinical value of contrast-enhanced ultrasound quantitative analysis for differentiating thyroid lesions in Hashimoto's thyroiditis patients.

The role of quantitative contrast-enhanced ultrasound (CEUS) in the evaluation of thyroid nodules with Hashimoto's thyroiditis (HT) has received little attention. This was a retrospective cohort study. We consecutively enrolled 242 patients (49 males, 193 females, average age 52 years) with a combined total of 248 thyroid nodules coexisting with HT who underwent biopsy/resection-proven pathology from December 2016 to June 2021. All patients underwent preoperative ultrasound (US) and CEUS examinations performed by 2 radiologists independently. Quantitative analysis of CEUS using time-intensity curves (TIC) was measured by an expert radiologist from the thyroid intra-nodule and the surrounding parenchyma and their ratios. Receiver operating characteristic (ROC) analysis was performed to evaluate their diagnostic performance. The patients were divided into the nodular HT (NHT) group (n=42), the papillary thyroid carcinoma (PTC) group (n=154), and the primary thyroid lymphoma (PTL) group (n=52) according to their pathological results. TIC parameters revealed that PTC and PTL showed faster time to peak (TTP) (P=0.044, P=0.049), lower peak intensity (PI) (both P<0.001), and smaller areas under the curve (both P<0.001) than those of NHT. The intra nodule of PTL showed an obviously slower perfusion (ratio =0.90, P<0.001) and lower PI (ratio =0.84, P<0.001) compared with the thyroid parenchyma. TIC improved performance in distinguishing PTL from NHT [area under the curve (AUC): 0.947, 95% confidence interval (CI): 0.903-0.991], but inferior performance in differentiating PTC and NHT (AUC: 0.838, 95% CI: 0.759-0.917). CEUS quantitative analysis could be valuable in differentiating thyroid malignancies in patients with HT.

TOWARDS NOVEL DIAGNOSTIC MARKER BASED ON BREAST ARTERIAL CALCIFICATIONS

In recent years, medical image analysis has advanced significantly, mainly due to the development of computer vision tools. With AI becoming more and more prevalent, these technologies offer tremendous potential for the field of radiology. Novel computer-aided detection (CAD) methods can aid radiologists by reducing their workload, decreasing costs, speeding up diagnoses, and providing supporting tools for more accurate diagnostic assessments. Our latest study on Breast Arterial Calcification (BAC) demonstrates how these cutting-edge CAD systems can seamlessly integrate into clinical practice, ultimately enhancing patient care efficiency and effectiveness. Recent literature [1-3] shows that BAC could become a cardiovascular disease (CVD) biomarker. In collaboration with Radboud UMC (Nijmegen,The Netherlands), we evaluated a novel BAC detector capable of identifying BAC occurrence in screening mammograms. It is particularly challenging since even well-trained radiologists often differ in BAC grade assessments. Detecting and quantifying early BAC stages, which are not apparent in the mammograms, is crucial for accurate CVD assessment. Using advanced imaging techniques and statistical modelling, our research aims to determine BAC prevalence and severity concerning CVD outcomes, including stroke and coronary artery disease. We achieved a 70% accuracy rate in BAC detection from 500 mammograms annotated bytworadiologists via consensus. It is a noteworthy achievement given the heterogeneous, imbalanced data and weak labelling provided byradiologists. However, we are still looking for a sufficient pool of annotated parallel BAC-CVD cases, which is essential for further study. Although annotation is costly, and quality varies with radiologist expertise, we aim to expand our dataset to include data from other institutions to provide more generalizable results, preferably independent of demographic biases.

Značaj ultrasonografije u praćenju multiplih fibroadenoma dojke

Introduction: Regular ultrasonographic check-ups help in early detection and follow-up of different diseases. Ultrasonography (US) is of the utmost diagnostic importance for mammographically "dense breasts", where glandular tissue is predominant, while in so-called "fat breasts" it is less reliable. It is used to determine the solid or cystic nature of a lesion, often tissue characterization, and the Doppler technique could reveal vascularization characteristics of the mass. Fibroadenomas are the most common benign breast tumors in women of all ages until menopause, with the highest incidence between 15 and 25 years of age. The US detects distinctly circumscribed, rarely round or lobular, hypoechoic masses with smooth and flat contours, uniform echoes with posterior augmentation, and marginal echo fading. Case report: We presented a female patient aged 21, who had multiple lesions in both breasts (25 lesions in total). Ultrasonographic exams were performed multiple times, in the last 5 years, and magnetic resonance (NMR) was done twice. Histopathologic verification diagnosed fibroadenomas. The biggest masses were surgically removed four times in this patient. What are the next steps? Are surgical interventions needed again, and if not how should she be followed up in the future? Is the implant insertion eligible for the sake of esthetic correction? Conclusion: Ultrasonography is a very significant and important method of follow-up of breast lesions. Mammography and magnetic resonance are used as complementary methods. According to a radiologist's expert opinions on breast diseases, ultrasonographic exams every six months and NMR may indicate the need for surgical intervention, especially if new lesions are detected or existing lesions are growing fast.

Intravascular ultrasound use in peripheral arterial and deep venous interventions: Multidisciplinary expert opinion from SCAI/AVF/AVLS/SIR/SVM/SVS

Percutaneous revascularization is the primary strategy for treating lower extremity venous and arterial disease. Angiography is limited by its ability to accurately size vessels, precisely determine the degree of stenosis and length of lesions, characterize lesion morphology, or correctly diagnose postintervention complications. These limitations are overcome with use of intravascular ultrasound (IVUS). IVUS has demonstrated the ability to improve outcomes following percutaneous coronary intervention, and there is increasing evidence to support its benefits in the setting of peripheral vascular intervention. At this stage in its evolution, there remains a need to standardize the use and approach to peripheral vascular IVUS imaging. This manuscript represents considerations and consensus perspectives that emerged from a roundtable discussion including 15 physicians with expertise in interventional cardiology, interventional radiology, and vascular surgery, representing 6 cardiovascular specialty societies, held on February 3, 2023. The roundtable’s aims were to assess the current state of lower extremity revascularization, identify knowledge gaps and need for evidence, and determine how IVUS can improve care and outcomes for patients with peripheral arterial and deep venous pathology.