Radiomics in Abdominopelvic Solid-Organ Oncologic Imaging: Current Status.

Radiomics is the process of extraction of high-throughput quantitative imaging features from medical images. These features represent noninvasive quantitative biomarkers that go beyond the traditional imaging features visible to the human eye. This article first reviews the steps of the radiomics pipeline, including image acquisition, ROI selection and image segmentation, image preprocessing, feature extraction, feature selection, and model development and application. Current evidence for the application of radiomics in abdominopelvic solid-organ cancers is then reviewed. Applications including diagnosis, subtype determination, treatment response assessment, and outcome prediction are explored within the context of hepatobiliary and pancreatic cancer, renal cell carcinoma, prostate cancer, gynecologic cancer, and adrenal masses. This literature review focuses on the strongest available evidence, including systematic reviews, meta-analyses, and large multicenter studies. Limitations of the available literature are highlighted, including marked heterogeneity in radiomics methodology, frequent use of small sample sizes with high risk of overfitting, and lack of prospective design, external validation, and standardized radiomics workflow. Thus, although studies have laid a foundation that supports continued investigation into radiomics models, stronger evidence is needed before clinical adoption.

Vascular Anomaly Syndromes in the ISSVA Classification System: Imaging Findings and Role of Interventional Radiology in Management.

Vascular anomalies encompass a spectrum of tumors and malformations that can cause significant morbidity and mortality in children and adults. Use of the International Society for the Study of Vascular Anomalies (ISSVA) classification system is strongly recommended for consistency. Vascular anomalies can occur in isolation or in association with clinical syndromes that involve complex multifocal lesions affecting different organ systems. Thus, it is critical to be familiar with the differences and similarities among vascular anomalies to guide selection of the appropriate imaging studies and possible interventions. Syndromes associated with simple vascular malformations include hereditary hemorrhagic telangiectasia, blue rubber bleb nevus syndrome, Gorham-Stout disease, and primary lymphedema. Syndromes categorized as vascular malformations associated with other anomalies include Klippel-Trenaunay-Weber syndrome, Parkes Weber syndrome, Servelle-Martorell syndrome, Maffucci syndrome, macrocephaly-capillary malformation, CLOVES (congenital lipomatous overgrowth, vascular malformations, epidermal nevi, and scoliosis, skeletal, and spinal anomalies) syndrome, Proteus syndrome, Bannayan-Riley-Ruvalcaba syndrome, and CLAPO (capillary malformations of the lower lip, lymphatic malformations of the face and neck, asymmetry of the face and limbs, and partial or generalized overgrowth) syndrome. With PHACES (posterior fossa malformations, hemangiomas, arterial anomalies, cardiac defects and/or coarctation of the aorta, eye abnormalities, and sternal clefting or supraumbilical raphe) syndrome, infantile hemangiomas associated with other lesions occur. Diagnostic and interventional radiologists have important roles in diagnosing these conditions and administering image-guided therapies-embolization and sclerotherapy, and different ablation procedures in particular. The key imaging features of vascular anomaly syndromes based on the 2018 ISSVA classification system and the role of interventional radiology in the management of these syndromes are reviewed. Online supplemental material is available for this article. ©RSNA, 2022.

Neurofibromatosis from Head to Toe: What the Radiologist Needs to Know.

Neurofibromatosis type 1 (NF1) and neurofibromatosis type 2 (NF2) are autosomal dominant inherited neurocutaneous disorders or phakomatoses secondary to mutations in the NF1 and NF2 tumor suppressor genes, respectively. Although they share a common name, NF1 and NF2 are distinct disorders with a wide range of multisystem manifestations that include benign and malignant tumors. Imaging plays an essential role in diagnosis, surveillance, and management of individuals with NF1 and NF2. Therefore, it is crucial for radiologists to be familiar with the imaging features of NF1 and NF2 to allow prompt diagnosis and appropriate management. Key manifestations of NF1 include café-au-lait macules, axillary or inguinal freckling, neurofibromas or plexiform neurofibromas, optic pathway gliomas, Lisch nodules, and osseous lesions such as sphenoid dysplasia, all of which are considered diagnostic features of NF1. Other manifestations include focal areas of signal intensity in the brain, low-grade gliomas, interstitial lung disease, various abdominopelvic neoplasms, scoliosis, and vascular dysplasia. The various NF1-associated abdominopelvic neoplasms can be categorized by their cellular origin: neurogenic neoplasms, interstitial cells of Cajal neoplasms, neuroendocrine neoplasms, and embryonal neoplasms. Malignant peripheral nerve sheath tumors and intracranial tumors are the leading contributors to mortality in NF1. Classic manifestations of NF2 include schwannomas, meningiomas, and ependymomas. However, NF2 may have shared cutaneous manifestations with NF1. Lifelong multidisciplinary management is critical for patients with either disease. The authors highlight the genetics and molecular pathogenesis, clinical and pathologic features, imaging manifestations, and multidisciplinary management and surveillance of NF1 and NF2. Online supplemental material is available for this article. ©RSNA, 2022.

Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities.

ABSTRACT: Artificial intelligence (AI) is the most revolutionizing development in the health care industry in the current decade, with diagnostic imaging having the greatest share in such development. Machine learning and deep learning (DL) are subclasses of AI that show breakthrough performance in image analysis. They have become the state of the art in the field of image classification and recognition. Machine learning deals with the extraction of the important characteristic features from images, whereas DL uses neural networks to solve such problems with better performance. In this review, we discuss the current applications of machine learning and DL in the field of diagnostic radiology.Deep learning applications can be divided into medical imaging analysis and applications beyond analysis. In the field of medical imaging analysis, deep convolutional neural networks are used for image classification, lesion detection, and segmentation. Also used are recurrent neural networks when extracting information from electronic medical records and to augment the use of convolutional neural networks in the field of image classification. Generative adversarial networks have been explicitly used in generating high-resolution computed tomography and magnetic resonance images and to map computed tomography images from the corresponding magnetic resonance imaging. Beyond image analysis, DL can be used for quality control, workflow organization, and reporting.In this article, we review the most current AI models used in medical imaging research, providing a brief explanation of the various models described in the literature within the past 5 years. Emphasis is placed on the various DL models, as they are the most state-of-art in imaging analysis.

Acing the Fundamentals of Radiology: An Online Series for Medical Students and Interns.

PURPOSE: The current undergraduate radiology education predominantly integrates radiology with other disciplines during preclerkship years and is often taught by nonradiologists. Early exposure to radiology and profound understanding of scientific fundamentals of imaging modalities and techniques are essential for a better understanding and interest in the specialty. Furthermore, the COVID-19 pandemic-related impact on in-person medical education aggravated the need for alternative virtual teaching initiatives to provide essential knowledge to medical students. METHODS: The authors designed an online 7-session course on the principles of imaging modalities for medical students and fresh graduates in the United States and abroad. The course was delivered online and taught by radiologists from different US institutions. Pretests and posttests were delivered before and after each session, respectively, to assess change in knowledge. At the end of the course, a survey was distributed among students to collect their assessment and feedback. RESULTS: A total of 162 students and interns initially enrolled in the program by completing a sign-up interest form. An average of 65 participants attended each live session, with the highest attendance being 93 live attendees. An average of 44 attendees completed both the pretest and posttest for each session. There was a statistically significant increase in posttest scores compared with pretest scores ( P < 0.01) for each session; on average, the posttest scores were 48% higher than the pretest scores. A total of 84 participants answered the end-of-course survey. A total of 11% of the respondents described themselves as first year, 17% as second year, 18% as third year, 21% as fourth year, and 33% as "other." Attendees were enrolled in medical schools across 21 different countries with 35% of the respondents studying medicine in the United States. More than 76% of the respondents stated that they "strongly agree" that the program increased their understanding of radiology, increased their interest in radiology, and would be useful in their clinical practice in the future. Eighty-three percent of the respondents stated that they "strongly agree" that "this course was a worthwhile experience." Particularly, more than 84% of the respondents stated that among the most important components in enhancing their understanding of radiology were "the interpretation of normal imaging" and "interpretation of clinical cases." Ninety-two percent of the respondents stated that "the amount of effort to complete the requirements for this program was just right." Participants were also asked to rate each of the 8 sessions using the following scale: poor = 1 point, fair = 2, good = 3, and excellent = 4. The average rating for all 8 sessions was 3.61 points (SD = 0.55), which translates to 96% of the sessions being rated good or excellent. Eighty percent of the participants reported that the topics presented in the program were "excellent and clinically important to learn," and 20% of the participants reported that the topics presented were "good and somewhat important to learn." The participants were asked to evaluate their confidence regarding basic radiology skills before and after the program using the following scale: not confident at all = 1 point, somewhat confident = 2, moderately confident = 3, and very confident = 4. Figure 2 summarizes the responses of the participants. CONCLUSIONS: An online course to teach the fundamentals of imaging modalities could be delivered through a webinar format to medical students and interns in several countries to address the potential gaps in radiology education, therefore increasing their understanding of the different imaging modalities and their proper use in medicine.

Knowing the ABCs: teaching the principles of radiology to medical students in Turkey.

BACKGROUND: Radiology education in Turkey is mainly taught during clinical years of medical school and often lacks main principles. Exposure to the fundamentals of radiology at an early stage of medical education may drastically help students generate a better understanding of radiology and expand their interest in the specialty. With the Principles of Radiology Course that we provided, pre- and post-session tests, and assessment survey at the end of the course, we aimed to evaluate the effectiveness of such an online course among Turkish medical students. METHODS: A total of nine online sessions on imaging modalities principles was developed by radiology professors. Each session was given through Zoom by radiologists from different U.S.-institutions to Turkish medical students from state (n = 33) and private (n = 8) universities. Pretests and post-tests were given to participants via Qualtrics before and after each session, respectively. Paired two-sample t-tests were conducted to detect the variance and p=-.05 was used as the significance level. An evaluation survey was distributed at the end of the course to collect their feedback through SurveyMonkey. RESULTS: A total of 1,438 predominantly Turkish (99.32%) medical students engaged with this course. An average of 506 students completed both pre-test and post-test. There was a statistically significant (p < .001) increase in the scores in post-test (mean[range]:7.58[5.21-8.53]) relative to pre-test (mean[range]:5.10[3.52-8.53]). Four hundred and thirty-nine participants (F/M:63.33%/35.54%) completed the end-of-course survey. A total of 71% and 69.70% of the participants strongly agreed that the course would be useful in their clinical practice and had increased their understanding of radiology. They also reported that their level of confidence in the subjects had increased 68% and reached a weighted average of 3.09/4. The survey revealed that 396 (90.21%) of the participants strongly or somewhat agree that introductory principles and concepts should be presented in earlier years of medical education. Compared to in-person education, 358 (81.55%) found the course extremely or very convenient. CONCLUSION: Online lecture series consisting of the principles of the radiological imaging modalities can be offered to Turkish medical students to enhance their grasp of the various imaging modalities and their correct clinical application.

Influences of the Social Media Continuum Model in Radiology: Theory and Reality.

Social media utilization has been growing exponentially worldwide and has created a thriving venue for radiologists and the profession of radiology to engage in on both the academic and social levels. The aim of this article is to conduct updated literature review and address a gap in the literature by introducing a simple classification for social media utilization and a new theoretical model to outline the role and potential value of social media in the realm of radiology. We propose classifying social media through usage-driven and access-driven indices. Furthermore, we discuss the interdependency of radiologists, other physicians and non-physician stakeholders, scientific journals, conferences/meetings and the general public in an integrated social media continuum model. With the ongoing sub-specialization of radiology, social media helps mitigate the physical barriers of making connections with peers and audiences which would have otherwise been unfeasible. The constant evolution and diversification of social media platforms necessitates a novel approach to better understand its role through a radiological lens. With the looming fear of 'ancillary service' labelling, social media could be the golden plate to halt the path towards commoditization of radiology.

Teaching Radiology to Medical Students in Canada; a Virtual, Integrative, Clinical Approach.

PURPOSE: To construct, apply, and evaluate a multidisciplinary approach in teaching radiology to Canadian medical students. METHODS: A multidisciplinary team of radiology and other disciplines experts designed an online 5-session course that was delivered to medical students. The topics of each session were clinical cases involving different systems. The target audience was medical students of Canadian schools. Pretests and post-tests were administered before and after each session respectively. An evaluation survey was distributed at the end of the course to gauge students' perceptions of this experience. RESULTS: An average of 425 medical students attended the live sessions. For each session, 405 students completed both the pre-tests and post-tests. In general, students scored an average of 56% higher on the post-test than on the pre-test. The final course survey was completed by 469 students. The survey results show that more than 98% of students found the course to meet or exceed their expectations. Over 80% of students agreed that the course increased their interest in radiology and about 81% agree that the topics presented were excellent and clinically important. The ratings in the final survey results also indicate that students increased their confidence in basic radiology skills after completing the course. CONCLUSIONS: The implementation of an integrative clinical approach to teaching radiology in a virtual setting is achievable. It provides efficient use of educational resources while being accessible by a large number of students across different medical schools.

Implications of the Gut Microbiome in Parkinson's Disease.

Parkinson's disease is a common neurodegenerative disorder that presents with nonmotor and motor symptoms. The nonmotor manifestations of Parkinson's disease often begin years before the motor symptoms. Autopsy studies, including both Parkinson's disease patients and matched controls, demonstrated that α-synuclein aggregates in Parkinson's disease patients can be found in both the substantia nigra and the enteric nervous system. Therefore, it has been hypothesized that the pathological process that leads eventually to Parkinson's disease might initially take place in the enteric nervous system years before the appearance of motor features. The gut microbiome plays essential roles in the development and maintenance of different body systems. Dysbiosis of the normal gut microbiome is thought to be associated with pathophysiologic changes not only in the gastrointestinal system itself but also in the enteric and central nervous systems. These changes are thought to ultimately cause loss of dopaminergic neurons via various mechanisms including the release of neurotoxins into the systemic circulation, decreased production of neuroprotective factors, and triggering inflammatory and autoimmune responses. In this review, we review the gut microbiome changes in Parkinson's disease and discuss the mechanisms by which gut microbiome dysbiosis may be a contributing factor to the pathophysiology of Parkinson's disease. © 2020 International Parkinson and Movement Disorder Society.

Burnout and the role of mentorship for radiology trainees and early career radiologists

Burnout is a widespread issue among physicians, including radiologists and radiology trainees. Long hours, isolation, and substantial stress levels contribute to healthcare workers experiencing a substantially higher rate of burnout compared with other professionals. Resident physicians, continuously exposed to stressors such as new clinical situations and performance feedback, are particularly susceptible. Mentorship has proven to be an effective strategy in mitigating burnout. Various mentorship delivery models exist, all aiming to have mentors serve as role models to mentees, thereby alleviating stress and anxiety. Physician groups and healthcare enterprises have actively implemented these programs, recognizing them as both successful and cost-effective. This article explores different mentorship models, their implementation processes, and the effectiveness of these programs as a standard component of academic departments.

Predictive Value of Quantitative Parameters of 18F-FDG PET/CT in Patients with Liposarcoma.

The purpose of this study was to evaluate the predictive features of baseline F-18-fluorodeoxy-D-glucose positron emission tomography (18F-FDG PET)/computed tomography (CT) parameters in patients with dedifferentiated liposarcomas (DDLPSs) and well-differentiated liposarcomas (WDLPSs) receiving systemic treatment. A total of 24 patients with liposarcoma who underwent longitudinal 18F-FDG PET/CT in systemic therapy were included. All volumetric segmentation of each tumor section and semiquantitative imaging parameters were extracted from the axial field of view from both PET and CT images. Maximum, mean, and minimum standardized uptake values (SUVmax, SUVmean, and SUVmin), Hounsfield units (HUs), and their respective changes from baseline and posttreatment were calculated. The voxel values from unenhanced CT images were correlated with PET-derived parameters. The 18F-FDG uptake decreased by more than 56% on average in responders for both SUVmax and SUVmean in DDLPS. There was a decrease in HUmax in DDLPS among responders. Using AUC > 0.8 as a reasonable predictor, we found that the ratios of SUVmaxD/HUmean, SUVmaxD/HUmedian, and SUVmeanD/HUmedian at baseline were significant indicators of the response to treatment in patients with liposarcoma. The changes in SUVmean and not just SUVmax parameters could be considered as accurate tumor response indicators. For the first time, we introduced baseline SUV/HU ratios as a valuable diagnostic tool in predicting liposarcoma treatment outcomes. This ability was not revealed by classic semiquantitative PET or CT parameters at baseline.

Longitudinal changes in the carotid arteries of head and neck cancer patients following radiation therapy: Results from a prospective serial imaging biomarker characterization study.

INTRODUCTION: We prospectively evaluated morphologic and functional changes in the carotid arteries of patients treated with unilateral neck radiation therapy (RT) for head and neck cancer. METHODS: Bilateral carotid artery duplex studies were performed at 0, 3, 6, 12, 18 months and 2, 3, 4, and 5 years following RT. Intima media thickness (IMT); global and regional circumferential, as well as radial strain, arterial elasticity, stiffness, and distensibility were calculated. RESULTS: Thirty-eight patients were included. A significant difference in the IMT from baseline between irradiated and unirradiated carotid arteries was detected at 18 months (median, 0.073 mm vs -0.003 mm; P = 0.014), which increased at 3 and 4 years (0.128 mm vs 0.013 mm, P = 0.016, and 0.177 mm vs 0.023 mm, P = 0.0002, respectively). A significant transient change was noted in global circumferential strain between the irradiated and unirradiated arteries at 6 months (median difference, -0.89, P = 0.023), which did not persist. No significant differences were detected in the other measures of elasticity, stiffness, and distensibility. CONCLUSIONS: Functional and morphologic changes of the carotid arteries detected by carotid ultrasound, such as changes in global circumferential strain at 6 months and carotid IMT at 18 months, may be useful for the early detection of radiation-induced carotid artery injury, can guide future research aiming to mitigate carotid artery stenosis, and should be considered for clinical surveillance survivorship recommendations after head and neck RT.

Multidisciplinary approach to diagnostic radiology education: a novel educational intervention for Turkish medical students.

Teleconferencing can facilitate a multidisciplinary approach to teaching radiology to medical students. This study aimed to determine whether an online learning approach enables students to appreciate the interrelated roles of radiology and other specialties during the management of different medical cases. Turkish medical students attended five 60-90-minute online lectures delivered by radiologists and other specialists from the United States and Canada through Zoom meetings between November 2020 and January 2021. Student ambassadors from their respective Turkish medical schools recruited their classmates with guidance from the course director. Students took a pretest and posttest to assess the knowledge imparted from each session and a final course survey to assess their confidence in radiology and the value of the course. A paired t-test was used to assess pretest and posttest score differences. A 4-point Likert-type scale was used to assess confidence rating differences before and after attending the course sessions. A total of 1,458 Turkish medical students registered for the course. An average of 437 completed both pre- and posttests when accounting for all five sessions. Posttest scores were significantly higher than pretest scores for each session (P < 0.001). A total of 546 medical students completed the final course survey evaluation. Students' rating of their confidence in their radiology knowledge increased after taking the course (P < 0.001). Students who took our course gained an appreciation for the interrelated roles of different specialties in approaching medical diagnoses and interpreting radiological findings. These students also reported an increased confidence in radiology topics and rated the course highly relevant and insightful. Overall, our findings indicated that multidisciplinary online education can be feasibly implemented for medical students by video teleconferencing.

Computed tomography radiomics-based cross-sectional detection of mandibular osteoradionecrosis in head and neck cancer survivors.

Purpose: This study aims to identify radiomic features extracted from contrast-enhanced CT scans that differentiate osteoradionecrosis (ORN) from normal mandibular bone in patients with head and neck cancer (HNC) treated with radiotherapy (RT). Materials and Methods: Contrast-enhanced CT (CECT) images were collected for 150 patients (80% train, 20% test) with confirmed ORN diagnosis at The University of Texas MD Anderson Cancer Center between 2008 and 2018. Using PyRadiomics, radiomic features were extracted from manually segmented ORN regions and the corresponding automated control regions, the later defined as the contralateral healthy mandible region. A subset of pre-selected features was obtained based on correlation analysis (r > 0.95) and used to train a Random Forest (RF) classifier with Recursive Feature Elimination. Model explainability SHapley Additive exPlanations (SHAP) analysis was performed on the 20 most important features identified by the trained RF classifier. Results: From a total of 1316 radiomic features extracted, 810 features were excluded due to high collinearity. From a set of 506 pre-selected radiomic features, the optimal subset resulting on the best discriminative accuracy of the RF classifier consisted of 67 features. The RF classifier was well calibrated (Log Loss 0.296, ECE 0.125) and achieved an accuracy of 88% and a ROC AUC of 0.96. The SHAP analysis revealed that higher values of Wavelet-LLH First-order Mean and Median were associated with ORN of the jaw (ORNJ). Conversely, higher Exponential GLDM Dependence Entropy and lower Square First-order Kurtosis were more characteristic of normal mandibular tissue. Conclusion: This study successfully developed a CECT-based radiomics model for differentiating ORNJ from healthy mandibular tissue in HNC patients after RT. Future work will focus on the detection of subclinical ORNJ regions to guide earlier interventions.

Associations Between Radiation Oncologist Demographic Factors and Segmentation Similarity Benchmarks: Insights From a Crowd-Sourced Challenge Using Bayesian Estimation.

PURPOSE: The quality of radiotherapy auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of clinician-derived segmentations are poorly understood; our study aims to quantify these factors. METHODS: Organ at risk (OAR) and tumor-related segmentations provided by radiation oncologists from the Contouring Collaborative for Consensus in Radiation Oncology data set were used. Segmentations were derived from five disease sites: breast, sarcoma, head and neck (H&N), gynecologic (GYN), and GI. Segmentation quality was determined on a structure-by-structure basis by comparing the observer segmentations with an expert-derived consensus, which served as a reference standard benchmark. The Dice similarity coefficient (DSC) was primarily used as a metric for the comparisons. DSC was stratified into binary groups on the basis of structure-specific expert-derived interobserver variability (IOV) cutoffs. Generalized linear mixed-effects models using Bayesian estimation were used to investigate the association between demographic variables and the binarized DSC for each disease site. Variables with a highest density interval excluding zero were considered to substantially affect the outcome measure. RESULTS: Five hundred seventy-four, 110, 452, 112, and 48 segmentations were used for the breast, sarcoma, H&N, GYN, and GI cases, respectively. The median percentage of segmentations that crossed the expert DSC IOV cutoff when stratified by structure type was 55% and 31% for OARs and tumors, respectively. Regression analysis revealed that the structure being tumor-related had a substantial negative impact on binarized DSC for the breast, sarcoma, H&N, and GI cases. There were no recurring relationships between segmentation quality and demographic variables across the cases, with most variables demonstrating large standard deviations. CONCLUSION: Our study highlights substantial uncertainty surrounding conventionally presumed factors influencing segmentation quality relative to benchmarks.

Mental imagery content is associated with disease severity and specific brain functional connectivity changes in patients with Parkinson's disease.

Mental imagery is the mental re-creation of perceptual experiences, events and scenarios, and motor acts. In our previous study, we assessed whether motor imagery (MI) training combined with functional magnetic resonance imaging-based neurofeedback could improve the motor function of nondemented subjects with mild Parkinson's disease (PD) (N = 22). We used visual imagery (VI) (e.g., of scenes or events, but not of self-movements) training without neurofeedback for the control group (N = 22). Notably, both groups showed significant and comparable improvement in motor function after four weeks of daily imagery practice. In this study, we further examined the neural correlates of the motor enhancement as a result of the VI training by analyzing the self-reported VI content during daily practice and relating its quality to the functional connectivity characteristics of the same subjects. We demonstrated that the VI practice encompassed multisensory, spatial, affective, and executive processes all of which are also important for motor function in real life. Subjects with worse global disease severity also showed poorer quality of the VI content. Finally, the quality of the VI content showed significant positive correlations with the functional connectivity changes during the VI tasks in brain areas supporting visuospatial and sensorimotor processes. Our findings suggest that mental imagery training combining VI and MI may enhance motor function in patients with mild PD, and more broadly, underline the importance of incorporating self-reports of thoughts and experiences in neuroimaging studies that examine the brain mechanisms of complex cognitive processes especially in neuropsychiatric patient populations.

Deep-learning CT reconstruction in clinical scans of the abdomen: a systematic review and meta-analysis.

OBJECTIVE: To perform a systematic literature review and meta-analysis of the two most common commercially available deep-learning algorithms for CT. METHODS: We used PubMed, Scopus, Embase, and Web of Science to conduct systematic searches for studies assessing the most common commercially available deep-learning CT reconstruction algorithms: True Fidelity (TF) and Advanced intelligent Clear-IQ Engine (AiCE) in the abdomen of human participants since only these two algorithms currently have adequate published data for robust systematic analysis. RESULTS: Forty-four articles fulfilled inclusion criteria. 32 studies evaluated TF and 12 studies assessed AiCE. DLR algorithms produced images with significantly less noise (22-57.3% less than IR) but preserved a desirable noise texture with increased contrast-to-noise ratios and improved lesion detectability on conventional CT. These improvements with DLR were similarly noted in dual-energy CT which was only assessed for a single vendor. Reported radiation reduction potential was 35.1-78.5%. Nine studies assessed observer performance with the two dedicated liver lesion studies being performed on the same vendor reconstruction (TF). These two studies indicate preserved low contrast liver lesion detection (> 5 mm) at CTDIvol 6.8 mGy (BMI 23.5 kg/m2) to 12.2 mGy (BMI 29 kg/m2). If smaller lesion detection and improved lesion characterization is needed, a CTDIvol of 13.6-34.9 mGy is needed in a normal weight to obese population. Mild signal loss and blurring have been reported at high DLR reconstruction strengths. CONCLUSION: Deep learning reconstructions significantly improve image quality in CT of the abdomen. Assessment of other dose levels and clinical indications is needed. Careful choice of radiation dose levels is necessary, particularly for small liver lesion assessment.

Seeing Radiology Curricula Through Turkish Medical Students' Eyes: A Survey of Turkish Medical Schools' Radiology Education.

INTRODUCTION: Radiology education is essential for nonradiologist specialists and practitioners as well as for radiologists. We conducted a survey to gather the opinions of Turkish medical students from first to sixth grade regarding their radiology curricula, radiology education content, and perceptions of various imaging modalities and to assess the amount, adequacy, and homogeneity of radiology education in various schools. METHODS: Turkish medical students were reached by student ambassadors from 10 different schools of medicine via social media and email. They were provided with a 20-question survey-via the SurveyMonkey platform-related to their radiology curriculum and their perceptions of the radiology education at their schools and of different imaging modalities. Subjective parameters were scaled by a 4-point Likert scale and the results are reported by percentages of students. RESULTS: A total of 988 medical students (F/M: 61%/39%) from 41 different medical schools participated in this survey. Of those, 57% were preclinical students (≤ third year of medical school), while 43% were clinical students (> third year). More than half of the students (51%) stated that the amount of radiology education included in their curriculum was too little, while 44% of them stated it was just right and only 5% stated it was too much. Only 31% of the participants stated that they were able to review radiology images on their own. When asked about their level of confidence in identifying the position of lines and tubes, pneumonia, pneumothorax, and pleural effusion on chest radiographs, 41%, 39%, 41%, and 41% of the participants, respectively, stated that they were not confident. Thirty-five percent of the participants had not received any training in comparing normal to abnormal imaging of bone fractures, pneumonia, pleural effusion, subdural hemorrhage, or pneumothorax. The majority of the Turkish medical students in this survey had never heard (57%) nor used (64%) the American College of Radiology Appropriateness Criteria. CONCLUSIONS: The radiology curriculum in Turkey differs among various schools and most students stated that preclinical radiology course content was inadequate. Further studies and improvements must be conducted to provide high-quality, equitable radiology education that begins during preclinical training with respect to the students' opinions.