Testicular torsion is a medical emergency that requires an immediate and multidisciplinary approach from emergency, surgical, and radiological services. In this article, we discuss the current knowledge and growing value of ultrasound (US) for intravaginal testicular torsion diagnosis and our experience with manual testicular detorsion with US assistance. Testicular torsion requires prompt and accurate diagnosis and quick therapeutic action. Technological advances in US equipment and knowledge of this pathology place the radiologist in an excellent position for its diagnosis and management. During the same medical procedure, the radiologist can both confirm the intravaginal testicular torsion and attempt manual testicular detorsion. US-assisted manual testicular detorsion is a non-invasive, simple, quick, safe, and effective manoeuvre that can rapidly restore testicular blood flow, maximising testicular salvage, relieving the patient\'s symptoms, and facilitating surgery.

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UNASSIGNED: Diffusion-weighted imaging (DWI) is widely used in neuroradiology or abdominal imaging but not yet implemented in the diagnosis of musculoskeletal tumors.
UNASSIGNED: This study aimed to evaluate how including diffusion imaging in the MRI protocol for patients with musculoskeletal tumors affects the agreement between radiologists and non-radiologist.
UNASSIGNED: Thirty-nine patients with musculoskeletal tumors (Ewing sarcoma, osteosarcoma, and benign tumors) consulted at our institution were included. Three raters with different experience levels evaluated examinations blinded to all clinical data. The final diagnosis was determined by consensus. MRI examinations were split into 1) conventional sequences and 2) conventional sequences combined with DWI. We evaluated the presence or absence of diffusion restriction, solid nature, necrosis, deep localization, and diameter >4 cm as known radiological markers of malignancy. Agreement between raters was evaluated using Gwet\'s AC1 coefficients and interpreted according to Landis and Koch.
UNASSIGNED: The lowest agreement was for diffusion restriction in both groups of raters. Agreement among all raters ranged from 0.51 to 0.945, indicating moderate to almost perfect agreement, and 0.772-0.965 among only radiologists indicating substantial to almost perfect agreement.
UNASSIGNED: The agreement in evaluating diffusion-weighted MRI sequences was lower than that for conventional MRI sequences, both among radiologists and non-radiologist and among radiologists alone. This indicates that assessing diffusion imaging is more challenging, and experience may impact the agreement.

OBJECTIVE: To evaluate the frequency and content of media coverage pertaining to artificial intelligence (AI) and radiology in the United States from 1998 to 2023.
METHODS: The ProQuest US Newsstream database was queried for print and online articles mentioning AI and radiology published between January 1, 1998, and March 30, 2023. A Boolean search using terms related to radiology and AI was used to retrieve full text and publication information. One of 9 readers with radiology expertise independently reviewed randomly assigned articles using a standardized scoring system.
RESULTS: 379 articles met inclusion criteria, of which 290 were unique and 89 were syndicated articles. Most had a positive sentiment (74 %) towards AI, while negative sentiment was far less common (9 %). Frequency of positive sentiment was highest in articles with a focus on AI and radiology (86 %) and lowest in articles focusing on AI and non-medical topics (55 %). The net impact of AI on radiology was most commonly presented as positive (60 %). Benefits of AI were more frequently mentioned (76 %) than potential harms (46 %). Radiologists were interviewed or quoted in less than one-third of all articles.
CONCLUSIONS: Portrayal of the impact of AI on radiology in US media coverage was mostly positive, and advantages of AI were more frequently discussed than potential risks. However, articles with a general non-medical focus were more likely to have a negative sentiment regarding the impact of AI on radiology than articles with a more specific focus on medicine and radiology. Radiologists were infrequently interviewed or quoted in media coverage.

BACKGROUND: The rise of transformer-based large language models (LLMs), such as ChatGPT, has captured global attention with recent advancements in artificial intelligence (AI). ChatGPT demonstrates growing potential in structured radiology reporting-a field where AI has traditionally focused on image analysis.
METHODS: A comprehensive search of MEDLINE and Embase was conducted from inception through May 2024, and primary studies discussing ChatGPT\'s role in structured radiology reporting were selected based on their content.
RESULTS: Of the 268 articles screened, eight were ultimately included in this review. These articles explored various applications of ChatGPT, such as generating structured reports from unstructured reports, extracting data from free text, generating impressions from radiology findings and creating structured reports from imaging data. All studies demonstrated optimism regarding ChatGPT\'s potential to aid radiologists, though common critiques included data privacy concerns, reliability, medical errors, and lack of medical-specific training.
CONCLUSIONS: ChatGPT and assistive AI have significant potential to transform radiology reporting, enhancing accuracy and standardization while optimizing healthcare resources. Future developments may involve integrating dynamic few-shot prompting, ChatGPT, and Retrieval Augmented Generation (RAG) into diagnostic workflows. Continued research, development, and ethical oversight are crucial to fully realize AI\'s potential in radiology.

Improving the status of women in radiology is crucial to better work environments. There is strong evidence in the business world that women leaders improve the workplace by making it more financially viable and by increasing collaboration, job satisfaction, and engagement. Diverse leadership fosters innovation, and women approach problem-solving with unique insights and collaborative styles. Gender diversity in leadership correlates with improved patient outcomes because women leaders prioritize patient-centered care and communication. Women create sustainable, productive work and improve radiology. Women serve as powerful role models, inspiring the next generation of women in radiology and addressing gender disparities. Increasing women leaders in radiology is essential to increase the number of women in radiology. This article summarizes many challenges women face when taking leadership roles: organizational biases prioritizing male viewpoints and marginalizing women\'s voices and contributions, a lack of role models, a lack of time (\"second shift\"), a lack of confidence, a lack of interest or perceived benefit, a lack of support, burnout, and previous poor experiences. While systemic issues are difficult to overcome, this article assists in the training and development of women radiologists by offering strategies to enhance job satisfaction and bring new and valuable perspectives to leadership.

UNASSIGNED: This study aimed to survey general practitioners\' (GPs) and radiologists\' perspectives on referrals, imaging justification, and unnecessary imaging in Norway.
UNASSIGNED: The survey covered access to imaging, responsibilities, attitudes toward justification assessment, referral process, and demographics using multiple choice questions, statements to report agreement with using the Likert scale and one open question.
UNASSIGNED: Forty radiologists and 58 GPs attending national conferences completed a web-based survey, with a 20/15% response rate, respectively. Both radiologists (97%) and GPs (100%) considered avoiding unnecessary examinations essential to their role in the healthcare service. Still, 91% of GPs admitted that they referred to imaging they thought was not helpful, while about 60% of the radiologists agreed that unnecessary imaging was conducted in their workplace. GPs reported pressure from patients and patients having private insurance as the most common reasons for doing unnecessary examinations. In contrast, radiologists reported a lack of clinical information and the inability to discuss patient cases with the GPs as the most common reasons.
UNASSIGNED: This study adds to our understanding of radiologists\' and GPs\' perspectives on unnecessary imaging and referrals. Better guidelines and, even more importantly, better communication between the referrer and the radiologist are needed. Addressing these issues can reduce unnecessary imaging and improve the quality and safety of care.

BACKGROUND: Pancreatic cystic lesions (PCLs) are frequent and underreported incidental findings on computed tomography (CT) scans and can evolve to pancreatic cancer-the most lethal cancer, with less than 5 months of life expectancy.
OBJECTIVE: The aim of this study was to develop and validate an artificial deep neural network (attention gate U-Net, also named \"AGNet\") for automated detection of PCLs. This kind of technology can help radiologists to cope with an increasing demand of cross-sectional imaging tests and increase the number of PCLs incidentally detected, thus increasing the early detection of pancreatic cancer.
METHODS: We adapted and evaluated an algorithm based on an attention gate U-Net architecture for automated detection of PCL on CTs. A total of 335 abdominal CTs with PCLs and control cases were manually segmented in 3D by 2 radiologists with over 10 years of experience in consensus with a board-certified radiologist specialized in abdominal radiology. This information was used to train a neural network for segmentation followed by a postprocessing pipeline that filtered the results of the network and applied some physical constraints, such as the expected position of the pancreas, to minimize the number of false positives.
RESULTS: Of 335 studies included in this study, 297 had a PCL, including serous cystadenoma, intraductal pseudopapillary mucinous neoplasia, mucinous cystic neoplasm, and pseudocysts . The Shannon Index of the chosen data set was 0.991 with an evenness of 0.902. The mean sensitivity obtained in the detection of these lesions was 93.1% (SD 0.1%), and the specificity was 81.8% (SD 0.1%).
CONCLUSIONS: This study shows a good performance of an automated artificial deep neural network in the detection of PCL on both noncontrast- and contrast-enhanced abdominal CT scans.

OBJECTIVE: The number of international medical graduates (IMGs) entering radiology residencies and neuroradiology fellowships averaged 9.7% and 20.9% from 2021 to 2023, respectively. We aimed to determine whether IMG graduates are populating leadership roles at a proportionate rate in diagnostic radiology (DR) and neuroradiology.
METHODS: We surveyed 191 DR program directors, 94 neuroradiology program directors (PDs), 192 chairs of radiology, and 91 directors of neuroradiology inquiring about their original citizenship and medical school (American Medical Graduates [AMG] vs IMG). We reviewed institutional websites to obtain missing data and recorded H indices for each person using Scopus.
RESULTS: We confirmed the original citizenship and medical school location in 61-75% and 93-98% of each leadership group. We found that 16.2% of DR program directors, 43.7% of neuroradiology PDs, 28.5% of Chairs, and 40.6% of neuroradiology directors were not originally US citizens. The IMG rate was 18/188 (9.6%), 20/90 (22.2%), 26/186 (14.0%), and 19/85 (22.4%) for the same groups respectively. The most common country of origin and medical school cited was India for all leadership groups. IMGs had a median H index of 14 while AMG 10, significantly different (p = 0.021) CONCLUSION: Compared to the rate of diagnostic and neuroradiology trainees entering from 2021 to 2023, IMGs are proportionately represented at the leadership positions studied. The H index of the IMGs was higher than AMG. We conclude that IMGs have made substantial and proportionate inroads in radiology and neuroradiology leadership.

OBJECTIVE: To noninvasively detect prostate cancer and predict the Gleason grade using single-modality T2-weighted imaging with a deep-learning approach.
METHODS: Patients with prostate cancer, confirmed by histopathology, who underwent magnetic resonance imaging examinations at our hospital during September 2015-June 2022 were retrospectively included in an internal dataset. An external dataset from another medical center and a public challenge dataset were used for external validation. A deep-learning approach was designed for prostate cancer detection and Gleason grade prediction. The area under the curve (AUC) was calculated to compare the model performance.
RESULTS: For prostate cancer detection, the internal datasets comprised data from 195 healthy individuals (age: 57.27 ± 14.45 years) and 302 patients (age: 72.20 ± 8.34 years) diagnosed with prostate cancer. The AUC of our model for prostate cancer detection in the validation set (n = 96, 19.7%) was 0.918. For Gleason grade prediction, datasets comprising data from 283 of 302 patients with prostate cancer were used, with 227 (age: 72.06 ± 7.98 years) and 56 (age: 72.78 ± 9.49 years) patients being used for training and testing, respectively. The external and public challenge datasets comprised data from 48 (age: 72.19 ± 7.81 years) and 91 patients (unavailable information on age), respectively. The AUC of our model for Gleason grade prediction in the training set (n = 227) was 0.902, whereas those of the validation (n = 56), external validation (n = 48), and public challenge validation sets (n = 91) were 0.854, 0.776, and 0.838, respectively.
CONCLUSIONS: Through multicenter dataset validation, our proposed deep-learning method could detect prostate cancer and predict the Gleason grade better than human experts.
UNASSIGNED: Precise prostate cancer detection and Gleason grade prediction have great significance for clinical treatment and decision making.
CONCLUSIONS: Prostate segmentation is easier to annotate than prostate cancer lesions for radiologists. Our deep-learning method detected prostate cancer and predicted the Gleason grade, outperforming human experts. Non-invasive Gleason grade prediction can reduce the number of unnecessary biopsies.