Representation of International Medical Graduates Among Diagnostic Radiology Chairs, Neuroradiology Chiefs, and Program Directors.

RATIONALE AND OBJECTIVES: 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. MATERIALS AND 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.

Imaging of Neuroendocrine Neoplasms; Principles of Treatment Strategies. What Referring Clinicians Want to Know.

ABSTRACT: Neuroendocrine neoplasms (NENs) are a diverse group of tumors that express neuroendocrine markers and primarily affect the lungs and digestive system. The incidence of NENs has increased over time due to advancements in imaging and diagnostic techniques. Effective management of NENs requires a multidisciplinary approach, considering factors such as tumor location, grade, stage, symptoms, and imaging findings. Treatment strategies vary depending on the specific subtype of NEN. In this review, we will focus on treatment strategies and therapies including the information relevant to clinicians in order to undertake optimal management and treatment decisions, the implications of different therapies on imaging, and how to ascertain their possible complications and treatment effects.

Diagnostic Anatomic Imaging for Neuroendocrine Neoplasms: Maximizing Strengths and Mitigating Weaknesses.

ABSTRACT: Neuroendocrine neoplasms are a heterogeneous group of gastrointestinal and lung tumors. Their diverse clinical manifestations, variable locations, and heterogeneity present notable diagnostic challenges. This article delves into the imaging modalities vital for their detection and characterization. Computed tomography is essential for initial assessment and staging. At the same time, magnetic resonance imaging (MRI) is particularly adept for liver, pancreatic, osseous, and rectal imaging, offering superior soft tissue contrast. The article also highlights the limitations of these imaging techniques, such as MRI's inability to effectively evaluate the cortical bone and the questioned cost-effectiveness of computed tomography and MRI for detecting specific gastric lesions. By emphasizing the strengths and weaknesses of these imaging techniques, the review offers insights into optimizing their utilization for improved diagnosis, staging, and therapeutic management of neuroendocrine neoplasms.

An Illustrated Review of the Recent 2019 World Health Organization Classification of Neuroendocrine Neoplasms: A Radiologic and Pathologic Correlation.

ABSTRACT: Recent advances in molecular pathology and an improved understanding of the etiology of neuroendocrine neoplasms (NENs) have given rise to an updated World Health Organization classification. Since gastroenteropancreatic NENs (GEP-NENs) are the most common forms of NENs and their incidence has been increasing constantly, they will be the focus of our attention. Here, we review the findings at the foundation of the new classification system, discuss how it impacts imaging research and radiological practice, and illustrate typical and atypical imaging and pathological findings. Gastroenteropancreatic NENs have a highly variable clinical course, which existing classification schemes based on proliferation rate were unable to fully capture. While well- and poorly differentiated NENs both express neuroendocrine markers, they are fundamentally different diseases, which may show similar proliferation rates. Genetic alterations specific to well-differentiated neuroendocrine tumors graded 1 to 3 and poorly differentiated neuroendocrine cancers of small cell and large-cell subtype have been identified. The new tumor classification places new demands and creates opportunities for radiologists to continue providing the clinically most relevant report and on researchers to design projects, which continue to be clinically applicable.

State-of-the-Art Hybrid Imaging of Neuroendocrine Neoplasms.

ABSTRACT: Neuroendocrine neoplasms (NENs) may be challenging to diagnose due to their small size and diverse anatomical locations. Hybrid imaging techniques, specifically positron emission tomography/computed tomography (PET/CT) and positron emission tomography/magnetic resonance imaging (PET/MRI), represent the current state-of-the-art for evaluating NENs. The preferred radiopharmaceuticals for NEN PET imaging are gallium-68 (68Ga) DOTA-peptides, which target somatostatin receptors (SSTR) overexpressed on NEN cells. Clinical applications of [68Ga]Ga-DOTA-peptides PET/CT include diagnosis, staging, prognosis assessment, treatment selection, and response evaluation. Fluorodeoxyglucose-18 (18F-FDG) PET/CT aids in detecting low-SSTR-expressing lesions and helps in patient stratification and treatment planning, particularly in grade 3 neuroendocrine tumors (NETs). New radiopharmaceuticals such as fluorine-labeled SSTR agonists and SSTR antagonists are emerging as alternatives to 68Ga-labeled peptides, offering improved detection rates and favorable biodistribution. The maturing of PET/MRI brings advantages to NEN imaging, including simultaneous acquisition of PET and MRI images, superior soft tissue contrast resolution, and motion correction capabilities. The PET/MRI with [68Ga]Ga-DOTA-peptides has demonstrated higher lesion detection rates and more accurate lesion classification compared to PET/CT. Overall, hybrid imaging offers valuable insights in the diagnosis, staging, and treatment planning of NENs. Further research is needed to refine response assessment criteria and standardize reporting guidelines.

Radiology trainee retirement programs: Options and participation.

BACKGROUND: Financial security and retirement planning are essential for all medical professionals including radiology residents. Given the available discretionary income during residency it is crucial to have insights into investment. We aimed to assess the prevalence of retirement plans offered to radiology residents, the available choices, and their degree of participation. METHODS: Two surveys were created using Qualtrics platform, one targeting program directors and another one oriented to radiology residents with items focused on demographic, financial status, offered plans, and participation. Analysis was performed using chi square whenever appropriate using Qualtrics platform. P values less than 0.05 were considered statistically significant. RESULTS: 199 radiology residents (72.6% attending university-based programs) responded to the survey. 83.7% participated in retirement plans; university-based programs had the highest rate of offering plans; 82.8% vs 70% and 53.8% for university, community and hybrid programs respectively. The most frequently offered retirement plan was a 403(b). Most of the residents (94.3%) started investment in the retirement program as early as PGY1 and PGY2. A considerable proportion of programs lacked formal training on retirement plans; however, residents emphasized the significance of financial education during their residency. CONCLUSION: Radiology residents are more likely to participate in a retirement plan if it is offered by their program. Providing proper guidance by incorporating financial education into radiology residency curricula can optimize residents' decision making and economical planning, leading to a more secure financial future.

External validation of a machine learning based algorithm to differentiate hepatic mucinous cystic neoplasms from benign hepatic cysts.

PURPOSE: To externally validate an algorithm for non-invasive differentiation of hepatic mucinous cystic neoplasms (MCN) from benign hepatic cysts (BHC), which differ in management. METHODS: Patients with cystic liver lesions pathologically confirmed as MCN or BHC between January 2005 and March 2022 from multiple institutions were retrospectively included. Five readers (2 radiologists, 3 non-radiologist physicians) independently reviewed contrast-enhanced CT or MRI examinations before tissue sampling and applied the 3-feature classification algorithm described by Hardie et al. to differentiate between MCN and BHC, which had a reported accuracy of 93.5%. The classification was then compared to the pathology results. Interreader agreement between readers across different levels of experience was evaluated with Fleiss' Kappa. RESULTS: The final cohort included 159 patients, median age of 62 years (IQR [52.0, 70.0]), 66.7% female (106). Of all patients, 89.3% (142) had BHC, and the remaining 10.7% (17) had MCN on pathology. Agreement for class designation between the radiologists was almost perfect (Fleiss' Kappa 0.840, p < 0.001). The algorithm had an accuracy of 98.1% (95% CI [94.6%, 99.6%]), a positive predictive value of 100.0% (95% CI [76.8%, 100.0%]), a negative predictive value of 97.9% (95% CI [94.1%, 99.6%]), and an area under the receiver operator characteristic curve (AUC) of 0.911 (95% CI [0.818, 1.000]). CONCLUSION: The evaluated algorithm showed similarly high diagnostic accuracy in our external, multi-institutional validation cohort. This 3-feature algorithm is easily and rapidly applied and its features are reproducible among radiologists, showing promise as a clinical decision support tool.