Radiology Training Research Articles

Grading lumbar foraminal stenosis - Interrater agreement of radiologists and radiology trainees before and after education of a standardised grading scale.

Lumbar foraminal stenosis is a key contributor to low back pain. Imaging, particularly MRI, is commonly used in the assessment of foraminal stenosis, contributing to treatment planning. The adoption of a standardised grading system to try and improve inter-rater agreement is thought to be of importance. Our study aims to assess the variability of grading lumbar foraminal stenosis amongst reporting doctors, determine whether education about a validated grading scale increases agreement, and determine if these changes persist over time. A single-site study involving MRI reporting registrars/radiologists was performed. Participants were shown select MRI images and asked to grade the degree of stenosis in each on a 4-point scale. Subsequently, they were educated about Lee etal's grading system and asked to re-grade the cases 1 and 6 weeks later. The level of agreement was calculated using Gwet's AC1 coefficient and Krippendorff's Alpha. The baseline level of agreement was substantial (AC1 = 0.71). This decreased to a moderate level of agreement post-intervention (AC1 = 0.575 at 1-week, P-value 0.033 and AC1 = 0.598 at 6 weeks, P-value 0.012). A grading of severe stenosis was 21% more likely 6 weeks post-education. The baseline agreement at our institution was substantial, thought to be due to the single-centre nature of the study. Moderate agreement was achieved after education regarding the Lee etal.'s scale, in-line with other studies, with changes maintained at 6 weeks, showing retention of the scale parameters. Grading of severe stenosis was more common post intervention.

A teleradiology network for the improvement of healthcare and patient management in the developing countries of the African continent

IntroductionAfrica is the second-largest continent on Earth in terms of both size and population. However, inaccessibility and shortfall of trained radiologists impede the delivery of adequate healthcare to such a large population. Teleradiology holds considerable potential in improving patient outcomes and healthcare delivery in African nations by furnishing timely interpretation of radiological examinations, particularly in those areas where there is a particular scarcity of radiologists. The aim of the present study was to assess the impact of teleradiology in the improvement of healthcare and patient management in the developing countries of the African continent. MethodsIn this retrospective study, from January 2017 and December 2022, the scans of a cohort of patients from eight African countries were uploaded to the teleradiology cloud server and interpreted by board certified radiologists empanelled by a teleradiology service provider. ResultsThe telehealth model proposed in the study was seen to provide timely and quality reporting of 58,223 scans of 39,513 patients with a mean turn-around-time (TAT) of 2.46 h 95 % CI (2.44–2.48). DiscussionA dedicated teleradiology model designed in this study allowed the interpretation and analysis of the scans of the cohort of patients from hospitals in African countries by teleradiologists via high quality DICOM-image transfer over a cloud-based platform. The outcomes of our investigation reflect that teleradiology provides an effective solution for early diagnosis/interpretation of examinations performed in Africa. Further, the currently proposed teleradiology model may be used for other developing countries across the world to improve quality of care.

Deep learning for automatic segmentation of vestibular schwannoma: a retrospective study from multi-center routine MRI.

Automatic segmentation of vestibular schwannoma (VS) from routine clinical MRI has potential to improve clinical workflow, facilitate treatment decisions, and assist patient management. Previous work demonstrated reliable automatic segmentation performance on datasets of standardized MRI images acquired for stereotactic surgery planning. However, diagnostic clinical datasets are generally more diverse and pose a larger challenge to automatic segmentation algorithms, especially when post-operative images are included. In this work, we show for the first time that automatic segmentation of VS on routine MRI datasets is also possible with high accuracy. We acquired and publicly release a curated multi-center routine clinical (MC-RC) dataset of 160 patients with a single sporadic VS. For each patient up to three longitudinal MRI exams with contrast-enhanced T1-weighted (ceT1w) (n = 124) and T2-weighted (T2w) (n = 363) images were included and the VS manually annotated. Segmentations were produced and verified in an iterative process: (1) initial segmentations by a specialized company; (2) review by one of three trained radiologists; and (3) validation by an expert team. Inter- and intra-observer reliability experiments were performed on a subset of the dataset. A state-of-the-art deep learning framework was used to train segmentation models for VS. Model performance was evaluated on a MC-RC hold-out testing set, another public VS datasets, and a partially public dataset. The generalizability and robustness of the VS deep learning segmentation models increased significantly when trained on the MC-RC dataset. Dice similarity coefficients (DSC) achieved by our model are comparable to those achieved by trained radiologists in the inter-observer experiment. On the MC-RC testing set, median DSCs were 86.2(9.5) for ceT1w, 89.4(7.0) for T2w, and 86.4(8.6) for combined ceT1w+T2w input images. On another public dataset acquired for Gamma Knife stereotactic radiosurgery our model achieved median DSCs of 95.3(2.9), 92.8(3.8), and 95.5(3.3), respectively. In contrast, models trained on the Gamma Knife dataset did not generalize well as illustrated by significant underperformance on the MC-RC routine MRI dataset, highlighting the importance of data variability in the development of robust VS segmentation models. The MC-RC dataset and all trained deep learning models were made available online.

The ASNR International Collaborations Committee: Cultivating a Global Learning Community.

The American Society of Neuroradiology has expanded its global presence, driven by the efforts of the International Collaborations Committee. This committee is actively involved in training radiologists and fostering collaborations worldwide in the fields of education, research, and community service. This article explores key initiatives of the committee, such as the Anne G. Osborn ASNR International Outreach Professor Program, the International Imaging Series, and Virtual Reading Rooms. Additionally, we provide insight into recent developments related to the pandemic and outline future opportunities.

International cooperation in radiology training: A multipronged approach

IntroductionMedical imaging has undergone significant technical advancements in recent years, posing a considerable challenge for radiologists to stay up-to-date with emerging modalities and their applications in daily practice. This challenge is even more daunting in developing countries with limited resources compared to the US and other developed nations with greater economic assets. The collaboration between the United States and other advanced nations with radiological institutions in Latin America has been a significant achievement in the pursuit of new opportunities for continuous medical education. The aim of this study was to evaluate the effectiveness of international collaborations among Spanish-speaking Latin American institutions and radiologists through a survey. Materials and MethodsA group of radiologists and institutions from various countries, including the USA, Spain, and Argentina, who have been working together for several years to improve Radiology education across Latin America, were selected. An online survey was conducted. The survey included questions about interest in the activities, participation, and impact of radiologic education during these educational efforts. ResultsThe survey received responses from 166 participants, all of whom reported knowledge of at least one type of educational activity. The most well-known activity was ALAT Webinars. The primary motivators for participation were the quality of the content and the opportunity to learn new information. Additionally, improving local education in radiology and receiving expert advice on radiology issues were identified as priorities for participation in international collaborations. The Cronbach alpha coefficient was calculated for individual and global Likert questions, resulting in a global score of 0.96. ConclusionThe study confirms the significance of a multifaceted approach to address gaps in radiology education. While traditional models have relied on hosting international visitors or sending US teachers abroad, the results suggest that using a variety of methods will have a greater impact than relying on a single technique for those who benefit most from collaborative efforts.

Chest Xray Medical Diagnosis with Deep Learning

Detecting Disease in Chest X-Rays with Deep Learning Chest X-rays (CXRs) is a common and vital diagnostic tool, but interpreting them accurately requires trained radiologists who can be scarce in many areas. This research explores the potential of deep learning (DL), a type of artificial intelligence (AI), to automatically detect diseases in CXRs. The review explains the basics of DL for CXR analysis, including how deep neural networks (DNNs) work and how transfer learning and data augmentation techniques can improve their performance. It then examines recent studies on applying DNNs to identify common CXR abnormalities like lung nodules, pneumonia, and pneumothorax. This includes exploring multi-class classification, where the model can detect multiple diseases simultaneously. The abstract compares the performance of these techniques with human observers and discusses the challenges of implementing DNN models in clinical practice, particularly their relationship with radiologists. Overall, the research investigates how DL can potentially improve disease detection using CXRs, addressing the need for accurate and accessible diagnostic tools. Key Words: Chest X-rays, Deep Learning, Disease Detection, Artificial Intelligence, Neural Networks

Comment on: ChatGPT: Chasing the Storm in Radiology Training and Education

Comment on: ChatGPT: Chasing the Storm in Radiology Training and Education

Female and underrepresented minority representation in radiology

Rational and objectiveDiversity, equity, inclusion, and representation in various sectors have garnered increasing attention in the past two decades, including healthcare. In this report we investigate representation of females and underrepresented minorities (URM) in the field of radiology and asses for significant growth trends in representation in residency training programs in the United States. Materials and methodsDe-identified trainee demographic information for active radiology trainees from 2016 to 2021 was queried using the Accreditation Council for Graduate Medical Education (ACGME), and new radiology trainees using the National Resident Matching Program (NRMP)’s Main Residency Match Data and Reports databooks. ResultsIn 2021 females represented 26.7% of DR residency trainees and 22% of IR integrated trainees. In the same year URM trainees represented 11.3% of trainees and 8.7% of IR integrated trainees. From 2017 to 2021, diagnostic radiology had a compound average growth rate (CAGR) 1% (p <0.01) of female representation and 1.12% (p<0.01) of URM representation. ConclusionThis study quantifies female and underrepresented minority representation among radiology trainees for diagnostic radiology and radiology subspecialities, identifying modest uptrends in representation within both demographics.

Role of Lung Ultrasonography in Acute Respiratory Distress in Pediatric Age Group: A Prospective Single-Centre Study.

Introduction Lung diseases are the most frequently encountered formof diseases primarily affecting infants under one year of age. Although the chest X-ray is the first modality of choice, ultrasonography (USG) has emerged as an alternative. Lung ultrasound (LUS) finds its application in the evaluation of several pediatric lung diseases. Objective To assess the use of LUS in acute lower respiratory infections and assess the correlation between etiological diagnosis and radiological diagnosis. Methods This was a hospital-based prospective observational study conducted with children presenting with upper respiratory infections. Around 97 children were included in the study. Clinical diagnosis was made by the pediatrician. LUS was performed by a trained radiologist, using the two-dimensional (2D) ultrasound mode and motion mode (M mode) to assess the LUS in the respective areas of the chest, thereby assessing bilateral lung fields for these patients. Results The majority of our study participants were under one year old (87%), and more than half were male (55%). Bronchiolitis and lower respiratory tract infections (LRIs) were the most commonly seen clinical diagnoses. The distribution of USGfindings was statistically significant across the clinical diagnosis (p-value< 0.05). Conclusion Our study found that LUS can serve as an important tool for diagnosing several acute respiratory diseases. It also showed that LUS can replace X-rays in cases of children diagnosed with acute respiratory diseases.

Interventional Radiology Training in Singapore

AbstractThis short review chronicles the training process of interventional radiologists in Singapore over the decades since the introduction of angiography as a technique back in the 1950s to the current state today.

Deep multiscale convolutional feature learning for intracranial hemorrhage classification and weakly supervised localization

ObjectiveThis study evaluated the performance of attentional fusion model-based multiscale features in classifying intracerebral hemorrhage and the localization of bleeding focus based on weakly supervised target localization. MethodsA publicly available dataset provided by the American College of Neuroradiology (ASNR) was used, consisting of 750,000 computed tomography (CT) scans of the brain, manually marked by radiologists for intracranial hemorrhage and five hemorrhage subtypes. A multiscale feature classification and weakly supervised localization framework based on an attentional fusion mechanism were applied, which could be annotated at the slice level and provided intracranial hemorrhage classification and hemorrhage focus localization. ResultsThe designed framework achieved excellent performance for classification and localization. The area under the curve (AUC) for predicting bleeding was 0.973. High AUC values were observed for the five hemorrhage subtypes (epidural AUC = 0.891, subdural AUC = 0.991, subarachnoid AUC = 0.983, intraventricular AUC = 0.995, intraparenchymal AUC = 0.990). This model outperformed the average entry-level radiology trainee compared to previously reported data. ConclusionThe designed method quickly and accurately detected intracerebral hemorrhage, classifying hemorrhage subtypes and locating bleeding points with image-level annotation alone. The results indicate that this framework can significantly reduce diagnostic time while improving the detection of intracerebral hemorrhage in emergencies. It can thus be integrated into the diagnostic radiology workflow in the future.

Artificial Intelligence for Quantifying Cumulative Small Bowel Disease Severity on CT-Enterography in Crohn's Disease.

Assessing the cumulative degree of bowel injury in ileal Crohn's disease (CD) is difficult. We aimed to develop machine learning (ML) methodologies for automated estimation of cumulative ileal injury on computed tomography-enterography (CTE) to help predict future bowel surgery. Adults with ileal CD using biologic therapy at a tertiary care center underwent ML analysis of CTE scans. Two fellowship-trained radiologists graded bowel injury severity at granular spatial increments along the ileum (1 cm), called mini-segments. ML segmentation methods were trained on radiologist grading with predicted severity and then spatially mapped to the ileum. Cumulative injury was calculated as the sum (S-CIDSS) and mean of severity grades along the ileum. Multivariate models of future small bowel resection were compared with cumulative ileum injury metrics and traditional bowel measures, adjusting for laboratory values, medications, and prior surgery at the time of CTE. In 229 CTE scans, 8,424 mini-segments underwent analysis. Agreement between ML and radiologists injury grading was strong (κ = 0.80, 95% confidence interval 0.79-0.81) and similar to inter-radiologist agreement (κ = 0.87, 95% confidence interval 0.85-0.88). S-CIDSS (46.6 vs 30.4, P = 0.0007) and mean cumulative injury grade scores (1.80 vs 1.42, P < 0.0001) were greater in CD biologic users that went to future surgery. Models using cumulative spatial metrics (area under the curve = 0.76) outperformed models using conventional bowel measures, laboratory values, and medical history (area under the curve = 0.62) for predicting future surgery in biologic users. Automated cumulative ileal injury scores show promise for improving prediction of outcomes in small bowel CD. Beyond replicating expert judgment, spatial enterography analysis can augment the personalization of bowel assessment in CD.

Gender diversity in radiology: Shedding light on the 256 shades of grey.

Despite medical school cohorts being composed of approximately 50% men and women, trainee positions in Clinical Radiology remain predominantly occupied by men. This study aims to identify reasons behind the gender gap, explore why more women are not pursuing a career in Clinical Radiology and to assess if there are gender differences in these reasons. Prospective multi-centre study using a voluntary, anonymous questionnaire aimed at senior Medical Students and Junior Doctors. A total of 318 participants responded to the questionnaire - 197 women (61.9%), 114 men (35.8%), 4 non-binary (1.3%), 2 preferred not to answer gender (0.6%), 1 I/they (0.3%). The most common reasons for not considering Clinical Radiology as a specialty include 'perceived limited patient contact' (62%), 'never/rarely exposed to it' (59%) and 'too lonely' (49%). Reasons with the largest gender discrepancies were 'too technology heavy' and 'limited patient contact', both cited more frequently by women. Most respondents indicated that their opinions of Clinical Radiology may change with more exposure during medical school, more patient contact and more mentorship. This study has revealed multiple reasons why women may not pursue specialty training in Clinical Radiology. Notable differences were found in the reasonings provided by men and women. Limited Clinical Radiology exposure and mentorship in medical school result in a lack of interest in this specialty as a career option. A proposed solution is to offer more medical school lectures and more junior doctor rotations to debunk stereotypes that may be negatively impacting opinions on Clinical Radiology. More mentorship of Medical Students and Junior Doctors, particularly from women, may also help decrease the gender gap.

Enhancing radiation protection at Sirius: methodology and results of beamline training

This work is about radiation protection training at Sirius, a unique radiation facility in Brazil for being a synchrotron-type particle accelerator. Research in several areas takes place at the facility, attracting researchers from all over the world. The radiological protection group at Sirius works continuously to ensure everyone's safety, in addition to shielding and monitoring, there are several security elements and one of them is the application of radiological safety training, where the researcher is presented the “Search” procedure. At the end of the training, the researcher answers a feedback form, which is used as a satisfaction survey. In the first semester for 2022, 46 people were trained of which 43.47% responded to the feedback form. Of the total responses, 95% were satisfied. Due to the large number of people attending Sirius, the radiation safety training is in the process of moving to an online format, which brings new challenges.

Cross-Sectional Imaging Characteristics of Pancreatic Intraductal Oncocytic Papillary Neoplasms.

Pancreatic intraductal oncocytic papillary neoplasms (IOPN) are rare precursors to pancreatic ductal adenocarcinoma. We report cross-sectional computed tomography and magnetic resonance imaging (where available) findings of pancreatic IOPNs. Consecutive cases of pancreatic IOPNs identified on pathology between 2008 and 2020 at University of Pittsburgh and Johns Hopkins University were included in the study. Cross-sectional imaging of all patients was reviewed by two subspecialty trained abdominal radiologists. Patient demographics, cross-sectional imaging appearances and growth characteristics were evaluated. In this dual-center study, 14 patients with IOPNs were included. Median age was 64 years, and 64% were male. The median size of the lesions was 5.4 cm (range, 1.4-12.3 cm). All patients had either an enhancing mural nodule (93% of patients) and/or thick internal septations (29%). Thin/imperceptible outer wall was seen in 93%. Main duct was involved in 64% of the cases. Only 14% of the cases did not demonstrate abutment of the main duct. Histologic evaluation of surgical specimen showed high-grade dysplasia without invasive carcinoma in 57% and invasive carcinoma in 43% of cases. Lesions with invasive carcinoma were larger (7.1 cm vs 4.3 cm, P = 0.05) and tended to have larger mural nodule (3.7 cm vs 1.8 cm) compared with those without invasive carcinoma. Pancreatic IOPNs are rare cystic premalignant lesions, which among resected cases, are predominantly seen in middle aged men, are often large, have enhancing mural nodules and frequently harbor invasive carcinoma.

Usar una herramienta comercial de inteligencia artificial no entrenada para COVID-19 mejora ligeramente la interpretación de las radiografías con neumonía COVID-19, especialmente entre lectores inexpertos

IntroductionOur objective is to evaluate how useful an artificial intelligence (AI) tool is to chest radiograph readers with various levels of expertise for the diagnosis of COVID-19 pneumonia when the tool has been trained on a non-COVID-19 pneumonia pathology. MethodsData was collected for patients who had previously undergone a chest radiograph and digital tomosynthesis due to suspected COVID-19 pneumonia. The gold standard consisted of the readings of two expert radiologists who assessed the presence and distribution of COVID-19 pneumonia on the images. Six medical students, two radiology trainees, and two other expert thoracic radiologists participated as additional readers. Two radiograph readings and a third supported by the AI Thoracic Care Suite tool were performed. COVID-19 pneumonia distribution and probability were assessed along with the contribution made by AI. Agreement and diagnostic performance were analysed. ResultsThe sample consisted of 113 cases, of which 56 displayed lung opacities, 52.2% were female, and the mean age was 50.70±14.9. Agreement with the gold standard differed between students, trainees, and radiologists. There was a non-significant improvement for four of the six students when AI was used. The use of AI by students did not improve the COVID-19 pneumonia diagnostic performance but it did reduce the difference in diagnostic performance with the more expert radiologists. Furthermore, it had more influence on the interpretation of mild pneumonia than severe pneumonia and normal radiograph findings. AI resolved more doubts than it generated, especially among students (31.30 vs 8.32%), followed by trainees (14.45 vs 5.7%) and radiologists (10.05% vs 6.15%). ConclusionFor expert and lesser experienced radiologists, this commercial AI tool has shown no impact on chest radiograph readings of patients with suspected COVID-19 pneumonia. However, it aided the assessment of inexperienced readers and in cases of mild pneumonia.

Impact of COVID-19 Pandemic on Radiology Department Employees and Trainees in Al-Qassim, 2021.

Objectives This study aimed to assess the impact of the COVID-19 pandemic on radiology department employees and trainees. It also compared the impact of COVID-19 to the pre-COVID-19 era in the Al-Qassim region. Methods This was a quantitative observational analytical cross-sectional study conducted in the largest government hospitals under the Ministry of Health (MOH) in Al-Qassim. A pre-determined questionnaire was distributed among radiology staff that included demographic characteristics, the impact of the COVID-19 pandemic among radiology staff, the behavior of staff related to COVID-19 infection, and the assessment of mental health using the patient health questionnaire (PHQ-9). Results Eighty-four radiology staff were recruited (64.3% males vs 35.7% females). Of these, 66.7% were trainees and the rest were employees (33.3%). Of the trainees,32.1% and 42.9% thought that elective imaging, procedures, and outpatient/clinic exposures were reduced during the pandemic, and 37.5% indicated that their training had been affected negatively. The prevalence of depression among radiology staff was 36.9%. The prevalence of depression was substantially higher among radiology trainees (p=0.038), those who were not infected with COVID-16 (p=0.041), and those who indicated that their studying time increased at the time of the pandemic (p=0.047). However, after conducting multivariate regression analysis, these variables did not seem to have significantly affected depression (p>0.05). Conclusion Training and medical education have been affected negatively because of the outbreak. Studying time and research activities of employees and trainees slowed down, which could be critical to their careers. Trainees complained about the significant reduction in their exposure to clinics and imaging procedures. Therefore, a method to safeguard the well-being of employees and trainees in the radiology department is necessary to limit the impact of such pandemics.

Prognostic significance of MRI-detected extramural venous invasion according to grade and response to neo-adjuvant treatment in locally advanced rectal cancer A national cohort study after radiologic training and reassessment

BackgroundDetection of grade 3–4 extra mural venous invasion (mrEMVI) on magnetic resonance imaging (MRI) is associated with an increased distant metastases (DM)-rate. This study aimed to determine the impact of different grades of mrEMVI and their disappearance after neoadjuvant therapy. MethodsA Dutch national retrospective cross-sectional study was conducted, including patients who underwent resection for rectal cancer in 2016 from 60/69 hospitals performing rectal surgery. Patients with a cT3-4 tumour ≤8 cm from the anorectal junction were selected and their MRI-scans were reassessed by trained abdominal radiologists. Positive mrEMVI grades (3 and 4) were analyzed in regard to 4-year local recurrence (LR), DM, disease-free survival (DFS) and overall survival (OS). ResultsThe 1213 included patients had a median follow-up of 48 months (IQR 30–54). Positive mrEMVI was present in 324 patients (27%); 161 had grade 3 and 163 had grade 4. A higher mrEMVI stage (grade 4 vs grade 3 vs no mrEMVI) increased LR-risk (21% vs 18% vs 7%, <0.001) and DM-risk (49% vs 30% vs 21%, p < 0.001) and decreased DFS (42% vs 55% vs 69%, p < 0.001) and OS (62% vs 76% vs 81%, p < 0.001), which remained independently associated in multivariable analysis. When mrEMVI had disappeared on restaging MRI, DM-rate was comparable to initial absence of mrEMVI (both 26%), whereas LR-rate remained high (22% vs 9%, p = 0.006). ConclusionThe negative oncological impact of mrEMVI on recurrence and survival rates was dependent on grading. Disappearance of mrEMVI on restaging MRI decreased the risk of DM, but not of LR.

Qualitative and quantitative spermatic cord abnormalities at CT predict symptomatic scrotal pathology

PurposeTo evaluate quantitative and qualitative spermatic cord CT abnormalities and presence of unilateral or bilateral symptomatic scrotal pathology (SSP) at ultrasound.MethodsThis retrospective study included 122 male patients (mean age 47.8 years) undergoing scrotal ultrasound within 24 h of contrast-enhanced CT (n = 85), non-contrast CT (NECT, n = 32) or CT-Urogram (n = 5). CECT quantitative analysis assessed differential cord enhancement using maximum Hounsfield unit measurements. Three fellowship trained body radiologists independently assessed qualitative cord abnormalities for both CECT and NECT. Qualitative and quantitative findings were compared with the presence of SSP. Reader performance, interobserver agreement and reader confidence were assessed for NECT and CECT. Quantitative cutoff points were identified which maximized accuracy, specificity, negative predictive value, and other measures.ResultsSSP was present in 36/122 patients (29.5%). Positive cases were unilateral in 30 (83.3%) and bilateral in 6 (16.6%). At quantitative assessment, 25% differential cord enhancement had the highest diagnostic accuracy (88.9%), with 90.5% positive predictive value, 88.4% negative predictive value, 96.8% specificity, and 70.4% sensitivity. At qualitative evaluation, CECT reader performance was excellent (aggregate AUC = 0.86; P < .001); NECT was poorly discriminatory, although remained significant (aggregate AUC = 0.67; P = .002). Readers had significantly higher confidence levels with CECT (P < .001). Qualitative inter-observer agreement was high for both CECT and NECT (ICC = 0.981 and 0.963, respectively).ConclusionSimple quantitative assessment of differential cord enhancement is highly accurate and specific for SSP at CECT. Qualitative abnormalities at CECT and NECT are also both predictors of SSP, however, CECT significantly out-performs non-contrast exams.Graphical abstract

Creating a Sustainable Foundation for IR Services and Training in Sub-Saharan Africa: 5-Year Update on the Road2IR Initiative

PurposeTo evaluate the growth and quality of an interventional radiology (IR) training model designed for resource-constrained settings and implemented in Tanzania as well as its overall potential to increase access to minimally invasive procedures across the region. Materials and MethodsIR training in Tanzania began in October 2018 through monthly deployment of visiting teaching teams for hands-on training combined with in-person and remote lectures. A competency-based 2-year Master of Science in IR curriculum was inaugurated at the nation’s main teaching hospital in October 2019, with the first 2 classes graduating in 2021 and 2022. Procedural data, demographics, and clinical outcomes were collected and analyzed throughout the duration of this program. ResultsFrom October 2018 to July 2022, 1,595 procedures were performed in Tanzania: 1,236 nonvascular and 359 vascular, all with local fellows as primary interventional radiologists. Of these, 97.2% were technically successful, 95.2% were without adverse events, and 28.9% were performed independently by Tanzanian fellows and faculty with no difference in adverse event and technical success rates (P = .63 and P = .90, respectively), irrespective of procedural class. Ten IR physicians graduated from this program during the study period, followed by another 3 per year going forward. Partner training programs in Uganda and Rwanda mirroring this model commenced in 2023 and 2024, respectively. ConclusionsThe reported training model offers a practical and effective solution to meet many of the challenges associated with the lack of access to IR in sub-Saharan Africa.