Primary retroperitoneal neoplasms (PRNs) are a complex and diverse group of tumors arising in the retroperitoneal space, excluding those from retroperitoneal organs. These masses present significant diagnostic challenges due to their heterogeneous nature. PRNs primarily include sarcomas, neurogenic tumors, extragonadal germ cell tumors, and lymphomas, with the majority being malignant. This necessitates thorough evaluation by radiologists to assess resectability and the need for biopsy. Liposarcomas, the most common primary retroperitoneal sarcomas, and leiomyosarcomas, known for potential vessel involvement, exhibit distinct imaging patterns aiding differentiation. Neurogenic tumors, originating from nerve sheath, ganglionic, or paraganglionic cells, often appear in younger patients and have characteristic imaging features. Primary retroperitoneal extragonadal germ cell tumors are rare and are believed to originate from primordial germ cells that do not successfully migrate during embryonic development. Lymphomas are generally homogeneous on cross-sectional imaging; however, non-Hodgkin lymphomas can sometimes appear heterogeneous, complicating differentiation from other non-lipomatous retroperitoneal masses. Additionally, conditions like retroperitoneal fibrosis and Erdheim-Chester disease can mimic PRNs, complicating diagnosis and management. This review aims to provide radiologists with essential diagnostic points for identifying PRNs, emphasizing the importance of precise imaging interpretation. Understanding these distinctions is vital for guiding clinical management and optimizing patient outcomes.

Necrotizing Fasciitis (NF) is a rare, but highly lethal, rapidly progressive necrotic infection of the soft-tissue fascia. More common in immunocompromised patients, NF typically affects the extremities, perineal area, genital area (Fournier gangrene), and less commonly the torso. Although classically a clinical diagnosis, imaging is a powerful adjunct to facilitate early diagnosis in equivocal cases. The key features of NF of radiography, ultrasound, CT, and MRI are reviewed, as are the inherent limitations of NF diagnosis for each modality. With a high level of sensitivity, specificity, and soft tissue contrast, MRI remains the gold standard imaging method for evaluation of NF.

COVID-19 was declared as a pandemic by the World Health Organization (WHO) in March 2020. The COVID-19 pandemic became a major public health burden with a high morbidity and mortality rate. In response to the pandemic, several COVID-19 vaccines were introduced to prevent infection and control the transmission of the virus. These vaccines have proven to be effective and relatively safe causing mild side effects in most individuals. However, these vaccines have also been associated with rare but life-threatening complications involving multiple body systems including the pulmonary, cardiovascular, lymphatic, vascular, gastrointestinal, and the central and peripheral nervous systems. This article describes the various severe complications and highlights the role of radiologists and imaging in aiding the prompt recognition of vaccine associated complications allowing for improved patient management.

Sonographic placental elastography has recently been employed as a non-invasive tool to investigate the structural alterations associated with various conditions such as pre-eclampsia, gestational diabetes and fetal growth restriction (FGR). The study was conducted based on the hypothesis that the placental elasticity might differ with varying severity of FGR and with that of appropriate for gestational age (AGA) pregnancies.

Theranostics is emerging as a critical pillar of oncologic management, as exemplified by the success of Lu-177-PSMA-617 for the treatment of castration-resistant prostate cancer. The emergence of such theranostic agents represents an opportunity to reconsider facets of nuclear medicine practice that will enable its engagement in high-volume radioligand delivery. In this article, we aim to explore simple ethical principles that can guide the development of theranostics programs as radiopharmaceutical agents proliferate and the typical nuclear medicine physician transitions from a primarily diagnostic role to a mixed diagnostic and therapeutic role. Such a mixed role will demand all the attendant competencies of direct patient care. We argue that restructuring nuclear medicine practice to meet this challenge involves developing processes for promoting the principle of fairness in patient selection for theranostic agents and for promoting the principle of responsibility during the administration of theranostic agents. We further specify that this responsibility extends to the patient receiving the therapy, the local community of the patient, and the general community exposed to the population of patients receiving theranostic agents. PRéCIS: The expansion of radioligand therapy requires promoting the ethical principle of fairness in patient selection and the ethical principle of responsibility in the delivery of radioligand therapy.

Accurate image interpretation is essential in the field of radiology to the healthcare team in order to provide optimal patient care. This article discusses the use of artificial intelligence (AI) confidence levels to enhance the accuracy and dependability of its radiological diagnoses. The current advances in AI technologies have changed how radiologists and clinicians make the diagnoses of pathological conditions such as aneurysms, hemorrhages, pneumothorax, pneumoperitoneum, and particularly fractures. To enhance the utility of these AI models, radiologists need a more comprehensive understanding of the model's levels of confidence and certainty behind the results they produce. This allows radiologists to make more informed decisions that have the potential to drastically change a patient's clinical management. Several AI models, especially those utilizing deep learning models (DL) with convolutional neural networks (CNNs), have demonstrated significant potential in identifying subtle findings in medical imaging that are often missed by radiologists. It is necessary to create standardized levels of confidence metrics in order for AI systems to be relevant and reliable in the clinical setting. Incorporating AI into clinical practice does have certain obstacles like the need for clinical validation, concerns regarding the interpretability of AI system results, and addressing confusion and misunderstandings within the medical community. This study emphasizes the importance of AI systems to clearly convey their level of confidence in radiological diagnosis. This paper highlights the importance of conducting research to establish AI confidence level metrics that are limited to a specific anatomical region or lesion type. KEY POINT OF THE VIEW: Accurate fracture diagnosis relies on radiologic certainty, where Artificial intelligence (AI), especially convolutional neural networks (CNNs) and deep learning (DL), shows promise in enhancing X-ray interpretation amidst a shortage of radiologists. Overcoming integration challenges through improved AI interpretability and education is crucial for widespread acceptance and better patient outcomes.

Most physicians appreciate that practicing medicine is a commitment to continuous learning. However, "learning" can be mistakenly understood as simply the acquisition of facts and new knowledge. But learning also necessitates the constant re-examination and challenging of one's existing body of knowledge, as misinformation persists when one's beliefs are not challenged or questioned in the light of new information. One example is the pervasive belief that postoperative atelectasis causes fever despite ample evidence to the contrary. Herein we examine the imaging characteristics of atelectasis, and the means of differentiation of atelectasis from consolidation. We also explore the history of this persistent myth and review the existing literature on the actual causes of postoperative fever.

To study the diagnostic image quality of high b-value diffusion weighted images (DWI) derived from standard and variably reduced datasets reconstructed with a commercially available deep learning reconstruction (DLR) algorithm.

Pneumothorax (PTX) is a common clinical urgency, its diagnosis is usually performed on chest radiography (CXR), and it presents a setting where artificial intelligence (AI) methods could find terrain in aiding radiologists in facing increasing workloads. Hence, the purpose of our study was to test an AI system for the detection of PTX on CXR examinations, to review its diagnostic performance in such setting alongside that of reading radiologists.

Epicardial adipose tissue volume (EATv), is well correlated with coronary artery disease (CAD), however not reported clinically. Breast density, measured on mammography, has shown promise as a reflector of cardiometabolic risk, with less dense breasts indicating greater proportion of adipose tissue. We aimed to evaluate the association between breast density, EATv and CAD.

This study evaluates the accuracy of ChatGPT-4 and ChatGPT-4o in generating Breast Imaging Reporting and Data System (BI-RADS) scores from radiographic images. We tested both models using 77 breast cancer images from radiopaedia.org, including mammograms and ultrasounds. Images were analyzed in separate sessions to avoid bias. ChatGPT-4 and ChatGPT-4o achieved a 66.2 % accuracy across all BI-RADS cases. Performance was highest in BI-RADS 5 cases, with ChatGPT-4 and ChatGPT4o scoring 84.4 % and 88.9 %, respectively. However, both models struggled with BIRADS 1-3 cases, often assigning higher severity ratings. This study highlights the limitations of current LLMs in accurately grading these images and emphasizes the need for further research in these technologies before clinical integration.

Although many advancements have been made in imaging modalities that can be used to diagnose pulmonary embolism (PE), computed tomography pulmonary angiography (CTPA) is still the preferred gold standard for promptly diagnosing pulmonary embolism by looking for filling defects caused by the embolus lodged within the main pulmonary artery or its respective branches. The diagnosis is made by the radiologists in emergency settings where quick detection of a PE on CTPA helps the Pulmonary Embolism Response Team (PERT) in quick management. Thus, utmost care is needed to follow standard image acquisition protocols and optimal contrast administration techniques to achieve a contrast opacification of at least 210 Hounsfield units for the radiologists to easily pinpoint an embolus within the pulmonary arteries. Even following proper CTPA scan acquisition guidelines, a CTPA image is prone to several artifacts that can be mistaken for a PE, resulting in a false positive read. In addition to this, many incidental findings, that can be the etiology of chest pain in a PE-suspected patient, are often overlooked by emergency radiologists who try to be as quick as possible in their read so that timely management of PE can be ensued. Taking this into account, our review paper provides the audience with a comprehensive understanding of the clinical aspects of pulmonary embolism and the imaging modalities used for PE detection. The main focus is on CTPA, its acquisition protocols, and the various incidental findings and artifacts to look for while interpreting a CTPA scan. PRéCIS: Beyond the filling defects, a CTPA scan should also be assessed by the radiologists for any incidental findings while keeping in mind several associated pitfalls and artifacts of CTPA.

In this pilot study, a multidisciplinary group of educators describes their collaborative efforts to achieve successful integration of radiologist-led standardized interactive radiology teaching sessions into established Internal Medicine and Surgery clerkship curricula, with an overall improvement of student knowledge of radiology concepts, as well as a perceived improvement in understanding of the role of radiology in clinical care.

This study evaluates the prognostic significance of pleural effusion (PE) in COVID-19 patients across thirteen centers in Germany, aiming to clarify its role in predicting clinical outcomes.

The purpose of this study was to evaluate various morphologic features of axillary nodes on ultrasound (US) in predicting malignancy and estimate the incidence of malignancy in axillary nodes based on their imaging mode of detection.