Assessing Authorship Rates over Time in Original Radiologic Research Publications.

Background Previous studies have shown an increase in the number of authors on radiologic articles between 1950 and 2013, but the cause is unclear. Purpose To determine whether authorship rate in radiologic and general medical literature has continued to increase and to assess study variables associated with increased author numbers. Materials and Methods PubMed/Medline was searched for articles published between January 1998 and October 2022 in general radiology and general medical journals with the top five highest current impact factors. Generalized linear regression analysis was used to calculate adjusted incidence rate ratios (IRRs) for the numbers of authors. Wald tests assessed the associations between study variables and the numbers of authors per article. Combined mixed-effects regression analysis was performed to compare general medicine and radiology journals. Results There were 3381 original radiologic research articles that were analyzed. Authorship rate increased between 1998 (median, six authors; IQR, 4) and 2022 (median, 11 authors; IQR, 8). Later publication year was associated with more authors per article (IRR, 1.02; 95% CI: 1.01, 1.02; P < .001) after adjusting for publishing journal, continent of origin of first author, number of countries involved, PubMed/Medline original article type, study design, number of disciplines involved, multicenter or single-center study, reporting of a priori power calculation, reporting of obtaining informed consent, study sample size, and number of article pages. There were 1250 general medicine original research articles that were analyzed. Later publication year was also associated with more authors after adjustment for the study variables (IRR, 1.04; 95% CI: 1.03, 1.05; P < .001). There was a stronger increase in authorship by publication year for general medicine journals compared with radiology journals (IRR, 1.02; 95% CI: 1.01, 1.02; P < .001). Conclusion An increase in authorship rate was observed in the radiologic and general medical literature between 1998 and 2022, and the number of authors per article was independently associated with later year of publication. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Arrivé in this issue.

Is work overload associated with diagnostic errors on 18F-FDG-PET/CT?

PURPOSE: To determine the association between workload and diagnostic errors on 18F-FDG-PET/CT. MATERIALS AND METHODS: This study included 103 18F-FDG-PET/CT scans with a diagnostic error that was corrected with an addendum between March 2018 and July 2023. All scans were performed at a tertiary care center. The workload of each nuclear medicine physician or radiologist who authorized the 18F-FDG-PET/CT report was determined on the day the diagnostic error was made and normalized for his or her own average daily production (workloadnormalized). A workloadnormalized of more than 100% indicates that the nuclear medicine physician or radiologist had a relative work overload, while a value of less than 100% indicates a relative work underload on the day the diagnostic error was made. The time of the day the diagnostic error was made was also recorded. Workloadnormalized was compared to 100% using a signed rank sum test, with the hypothesis that it would significantly exceed 100%. A Mann-Kendall test was performed to test the hypothesis that diagnostic errors would increase over the course of the day. RESULTS: Workloadnormalized (median of 121%, interquartile range: 71 to 146%) on the days the diagnostic errors were made was significantly higher than 100% (P = 0.014). There was no significant upward trend in the frequency of diagnostic errors over the course of the day (Mann-Kendall tau = 0.05, P = 0.7294). CONCLUSION: Work overload seems to be associated with diagnostic errors on 18F-FDG-PET/CT. Diagnostic errors were encountered throughout the entire working day, without any upward trend towards the end of the day.

Diagnostic imaging of the diabetic foot: an EANM evidence-based guidance.

PURPOSE: Consensus on the choice of the most accurate imaging strategy in diabetic foot infective and non-infective complications is still lacking. This document provides evidence-based recommendations, aiming at defining which imaging modality should be preferred in different clinical settings. METHODS: This working group includes 8 nuclear medicine physicians appointed by the European Association of Nuclear Medicine (EANM), 3 radiologists and 3 clinicians (one diabetologist, one podiatrist and one infectious diseases specialist) selected for their expertise in diabetic foot. The latter members formulated some clinical questions that are not completely covered by current guidelines. These questions were converted into statements and addressed through a systematic analysis of available literature by using the PICO (Population/Problem-Intervention/Indicator-Comparator-Outcome) strategy. Each consensus statement was scored for level of evidence and for recommendation grade, according to the Oxford Centre for Evidence-Based Medicine (OCEBM) criteria. RESULTS: Nine clinical questions were formulated by clinicians and used to provide 7 evidence-based recommendations: (1) A patient with a positive probe-to-bone test, positive plain X-rays and elevated ESR should be treated for presumptive osteomyelitis (OM). (2) Advanced imaging with MRI and WBC scintigraphy, or [18F]FDG PET/CT, should be considered when it is needed to better evaluate the location, extent or severity of the infection, in order to plan more tailored treatment. (3) In a patient with suspected OM, positive PTB test but negative plain X-rays, advanced imaging with MRI or WBC scintigraphy + SPECT/CT, or with [18F]FDG PET/CT, is needed to accurately assess the extent of the infection. (4) There are no evidence-based data to definitively prefer one imaging modality over the others for detecting OM or STI in fore- mid- and hind-foot. MRI is generally the first advanced imaging modality to be performed. In case of equivocal results, radiolabelled WBC imaging or [18F]FDG PET/CT should be used to detect OM or STI. (5) MRI is the method of choice for diagnosing or excluding Charcot neuro-osteoarthropathy; [18F]FDG PET/CT can be used as an alternative. (6) If assessing whether a patient with a Charcot foot has a superimposed infection, however, WBC scintigraphy may be more accurate than [18F]FDG PET/CT in differentiating OM from Charcot arthropathy. (7) Whenever possible, microbiological or histological assessment should be performed to confirm the diagnosis. (8) Consider appealing to an additional imaging modality in a patient with persisting clinical suspicion of infection, but negative imaging. CONCLUSION: These practical recommendations highlight, and should assist clinicians in understanding, the role of imaging in the diagnostic workup of diabetic foot complications.

The Effect of Image Resampling on the Performance of Radiomics-Based Artificial Intelligence in Multicenter Prostate MRI.

BACKGROUND: Single center MRI radiomics models are sensitive to data heterogeneity, limiting the diagnostic capabilities of current prostate cancer (PCa) radiomics models. PURPOSE: To study the impact of image resampling on the diagnostic performance of radiomics in a multicenter prostate MRI setting. STUDY TYPE: Retrospective. POPULATION: Nine hundred thirty patients (nine centers, two vendors) with 737 eligible PCa lesions, randomly split into training (70%, N = 500), validation (10%, N = 89), and a held-out test set (20%, N = 148). FIELD STRENGTH/SEQUENCE: 1.5T and 3T scanners/T2-weighted imaging (T2W), diffusion-weighted imaging (DWI), and apparent diffusion coefficient maps. ASSESSMENT: A total of 48 normalized radiomics datasets were created using various resampling methods, including different target resolutions (T2W: 0.35, 0.5, and 0.8 mm; DWI: 1.37, 2, and 2.5 mm), dimensionalities (2D/3D) and interpolation techniques (nearest neighbor, linear, Bspline and Blackman windowed-sinc). Each of the datasets was used to train a radiomics model to detect clinically relevant PCa (International Society of Urological Pathology grade ≥ 2). Baseline models were constructed using 2D and 3D datasets without image resampling. The resampling configurations with highest validation performance were evaluated in the test dataset and compared to the baseline models. STATISTICAL TESTS: Area under the curve (AUC), DeLong test. The significance level used was 0.05. RESULTS: The best 2D resampling model (T2W: Bspline and 0.5 mm resolution, DWI: nearest neighbor and 2 mm resolution) significantly outperformed the 2D baseline (AUC: 0.77 vs. 0.64). The best 3D resampling model (T2W: linear and 0.8 mm resolution, DWI: nearest neighbor and 2.5 mm resolution) significantly outperformed the 3D baseline (AUC: 0.79 vs. 0.67). DATA CONCLUSION: Image resampling has a significant effect on the performance of multicenter radiomics artificial intelligence in prostate MRI. The recommended 2D resampling configuration is isotropic resampling with T2W at 0.5 mm (Bspline interpolation) and DWI at 2 mm (nearest neighbor interpolation). For the 3D radiomics, this work recommends isotropic resampling with T2W at 0.8 mm (linear interpolation) and DWI at 2.5 mm (nearest neighbor interpolation). EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

The radiology job market in the Netherlands: which subspecialties and other skills are in demand?

OBJECTIVES: To evaluate the current job market for medical specialists in radiology and nuclear medicine (NM) in the Netherlands. METHODS: Vacancies posted for radiologists and nuclear medicine physicians in the Netherlands between December 2020 and February 2022 were collected and analyzed. RESULTS: A total of 157 vacancies (146 for radiologist and 11 for nuclear medicine physicians) were included. The most sought-after subspecialties were all-round (22%), abdominal (19%), and interventional radiology (14%), and 30% of vacancies preferred applicants with additional non-clinical skills (research, teaching, management, information and communications technology (ICT)/artificial intelligence (AI)). Non-academic hospitals significantly more frequently requested all-round radiologists (n = 31) than academic hospitals (n = 1) (p = 0.001), while the distribution of other requested subspecialties was not significantly different between non-academic and academic vacancies. Non-academic hospitals also significantly more frequently requested additional research tasks in their vacancies (n = 35) compared to academic hospitals (n = 4) (p = 0.011). There were non-significant trends for non-academic hospitals more frequently requesting teaching tasks in their vacancies (n =18) than academic hospitals (n = 1) (p = 0.051), and for non-academic hospitals more frequently asking for management skills (n = 11) than academic hospitals (n = 0) (p = 0.075). CONCLUSION: All-round, abdominal, and interventional radiologists are most in demand on the job market in the Netherlands. All-round radiologists are particularly sought after by non-academic hospitals, whereas nuclear radiologists who completed the Dutch integrated NM and radiology residency seem to be welcomed by hospitals searching for a nuclear medicine specialist. Finally, non-clinical skills (research, teaching, management, ICT/AI) are commonly requested. These data can be useful for residents and developers of training curricula. CLINICAL RELEVANCE STATEMENT: An overview of the radiology job market and the requested skills is important for residents, for those who seek work as a radiologist, and for those who are involved in the design and revision of residency programs. KEY POINTS: Review of job vacancies over an extended period of time provides valuable information to residents and feedback to potentially improve radiology and nuclear medicine (NM) residency programs. All-round radiologists are wanted in non-academic hospitals and nuclear radiologists (those who have completed an integrated NM-radiology curriculum) are welcomed by hospitals searching for nuclear medicine specialists in the Netherlands. There is a need to train residents in important non-clinical skills, such as research and teaching, but also management and communications technology/artificial intelligence.

What is the added value of specialist radiology review of multidisciplinary team meeting cases in a tertiary care center?

PURPOSE: Multidisciplinary team meetings (MDTMs) are an important component of the workload of radiologists. This study investigated how often subspecialized radiologists change patient management in MDTMs at a tertiary care institution. MATERIALS AND METHODS: Over 2 years, six subspecialty radiologists documented their contributions to MDTMs at a tertiary care center. Both in-house and external imaging examinations were discussed at the MDTMs. All imaging examinations (whether primary or second opinion) were interpreted and reported by subspecialty radiologist prior to the MDTMs. The management change ratio (MCratio) of the radiologist was defined as the number of cases in which the radiologist's input in the MDTM changed patient management beyond the information that was already provided by the in-house (primary or second opinion) radiology report, as a proportion of the total number of cases whose imaging examinations were prepared for demonstration in the MDTM. RESULTS: Sixty-eight MDTMs were included. The time required for preparing and attending all MDTMs (excluding imaging examinations that had not been reported yet) was 11,000 min, with a median of 172 min (IQR 113-200 min) per MDTM, and a median of 9 min (IQR 8-13 min) per patient. The radiologists' input changed patient management in 113 out of 1138 cases, corresponding to an MCratio of 8.4%. The median MCratio per MDTM was 6% (IQR 0-17%). CONCLUSION: Radiologists' time investment in MDTMs is considerable relative to the small proportion of cases in which they influence patient management in the MDTM. The use of radiologists for MDTMs should therefore be improved. CLINICAL RELEVANCE STATEMENT: The use of radiologists for MDTMs (multidisciplinary team meetings) should be improved, because their time investment in MDTMs is considerable relative to the small proportion of cases in which they influence patient management in the MDTM. KEY POINTS: • Multidisciplinary team meetings (MDTMs) are an important component of the workload of radiologists. • In a tertiary care center in which all imaging examinations have already been interpreted and reported by subspecialized radiologists before the MDTM takes place, the median time investment of a radiologist for preparing and demonstrating one MDTM patient is 9 min. • In this setting, the radiologist changes patient management in only a minority of cases in the MDTM.

Assessing deep learning reconstruction for faster prostate MRI: visual vs. diagnostic performance metrics.

OBJECTIVE: Deep learning (DL) MRI reconstruction enables fast scan acquisition with good visual quality, but the diagnostic impact is often not assessed because of large reader study requirements. This study used existing diagnostic DL to assess the diagnostic quality of reconstructed images. MATERIALS AND METHODS: A retrospective multisite study of 1535 patients assessed biparametric prostate MRI between 2016 and 2020. Likely clinically significant prostate cancer (csPCa) lesions (PI-RADS ≥ 4) were delineated by expert radiologists. T2-weighted scans were retrospectively undersampled, simulating accelerated protocols. DL reconstruction (DLRecon) and diagnostic DL detection (DLDetect) were developed. The effect on the partial area under (pAUC), the Free-Response Operating Characteristic (FROC) curve, and the structural similarity (SSIM) were compared as metrics for diagnostic and visual quality, respectively. DLDetect was validated with a reader concordance analysis. Statistical analysis included Wilcoxon, permutation, and Cohen's kappa tests for visual quality, diagnostic performance, and reader concordance. RESULTS: DLRecon improved visual quality at 4- and 8-fold (R4, R8) subsampling rates, with SSIM (range: -1 to 1) improved to 0.78 ± 0.02 (p < 0.001) and 0.67 ± 0.03 (p < 0.001) from 0.68 ± 0.03 and 0.51 ± 0.03, respectively. However, diagnostic performance at R4 showed a pAUC FROC of 1.33 (CI 1.28-1.39) for DL and 1.29 (CI 1.23-1.35) for naive reconstructions, both significantly lower than fully sampled pAUC of 1.58 (DL: p = 0.024, naïve: p = 0.02). Similar trends were noted for R8. CONCLUSION: DL reconstruction produces visually appealing images but may reduce diagnostic accuracy. Incorporating diagnostic AI into the assessment framework offers a clinically relevant metric essential for adopting reconstruction models into clinical practice. CLINICAL RELEVANCE STATEMENT: In clinical settings, caution is warranted when using DL reconstruction for MRI scans. While it recovered visual quality, it failed to match the prostate cancer detection rates observed in scans not subjected to acceleration and DL reconstruction.

No need for fasting prior to doppler ultrasound of pediatric liver transplants: A self-controlled study.

BACKGROUND: Children frequently undergo routine Doppler-ultrasound (DUS) after liver transplantation (LT) for which they are fasted, but this may cause hunger and discomfort. OBJECTIVE: To determine if DUS measurements, with focus on the portal vein (PV), are affected by prandial changes, and if this affects distress and feasibility of the DUS. MATERIALS AND METHODS: Children were prospectively included to undergo a pre- and postprandial DUS on the same day at 6 months after LT. Pre- and anastomotic PV peak systolic velocity (PSV), and hepatic artery and hepatic vein DUS measurements were obtained. Pre- and postprandial measurements, and relative postprandial change of PV velocity ratio (VR) compared to PV anastomotic PSV, were compared using paired-sample t-tests and intraclass correlation coefficients (ICC). Obscuration by bowel gas, difficulty of DUS, and impact of fasting were assessed using 5-point rating scales. RESULTS: Twenty-eight children (median age 3.5 years, IQR 1.6-10.8) were included; four were subsequently excluded because they were not fasted (N = 2) or withdrew consent for the second DUS (N = 2). Measurements between pre- and postprandial DUS, and relative postprandial change of VR compared to PV anastomotic PSV, were not significantly different (p > .05). Test consistency was good (ICC = 0.69, 95% CI = 0.29-0.67) for PV anastomotic PSV, and excellent (95% CI = 0.61-0.93) for PV VR. Obscuration by bowel gas or ease of DUS did not change after eating (p > .05). The majority (16/28, 57.2%) found fasting difficult, and several (13/28, 46.4%) got upset when fasted. CONCLUSION: Children with an LT do not need to be fasted for routine DUS, which may decrease the burden of the examination.

Focused view CT angiography for selective visualization of stroke related arteries: technical feasibility.

OBJECTIVES: This study investigated the technical feasibility of focused view CTA for the selective visualization of stroke related arteries. METHODS: A total of 141 CTA examinations for acute ischemic stroke evaluation were divided into a set of 100 cases to train a deep learning algorithm (dubbed "focused view CTA") that selectively extracts brain (including intracranial arteries) and extracranial arteries, and a test set of 41 cases. The visibility of anatomic structures at focused view and unmodified CTA was assessed using the following scoring system: 5 = completely visible, diagnostically sufficient; 4 = nearly completely visible, diagnostically sufficient; 3 = incompletely visible, barely diagnostically sufficient; 2 = hardly visible, diagnostically insufficient; 1 = not visible, diagnostically insufficient. RESULTS: At focused view CTA, median scores for the aortic arch, subclavian arteries, common carotid arteries, C1, C6, and C7 segments of the internal carotid arteries, V4 segment of the vertebral arteries, basilar artery, cerebellum including cerebellar arteries, cerebrum including cerebral arteries, and dural venous sinuses, were all 4. Median scores for the C2 to C5 segments of the internal carotid arteries, and V1 to V3 segments of the vertebral arteries ranged between 3 and 2. At unmodified CTA, median score for all above-mentioned anatomic structures was 5, which was significantly higher (p < 0.0001) than that at focused view CTA. CONCLUSION: Focused view CTA shows promise for the selective visualization of stroke-related arteries. Further improvements should focus on more accurately visualizing the smaller and tortuous internal carotid and vertebral artery segments close to bone. CLINICAL RELEVANCE: Focused view CTA may speed up image interpretation time for LVO detection and may potentially be used as a tool to study the clinical relevance of incidental findings in future prospective long-term follow-up studies. KEY POINTS: • A deep learning-based algorithm ("focused view CTA") was developed to selectively visualize relevant structures for acute ischemic stroke evaluation at CTA. • The elimination of unrequested anatomic background information was complete in all cases. • Focused view CTA may be used to study the clinical relevance of incidental findings.

Diagnostic radiology and its future: what do clinicians need and think?

OBJECTIVE: To investigate the view of clinicians on diagnostic radiology and its future. METHODS: Corresponding authors who published in the New England Journal of Medicine and the Lancet between 2010 and 2022 were asked to participate in a survey about diagnostic radiology and its future. RESULTS: The 331 participating clinicians gave a median score of 9 on a 0-10 point scale to the value of medical imaging in improving patient-relevant outcomes. 40.6%, 15.1%, 18.9%, and 9.5% of clinicians indicated to interpret more than half of radiography, ultrasonography, CT, and MRI examinations completely by themselves, without consulting a radiologist or reading the radiology report. Two hundred eighty-nine clinicians (87.3%) expected an increase in medical imaging utilization in the coming 10 years, whereas 9 clinicians (2.7%) expected a decrease. The need for diagnostic radiologists in the coming 10 years was expected to increase by 162 clinicians (48.9%), to remain stable by 85 clinicians (25.7%), and to decrease by 47 clinicians (14.2%). Two hundred clinicians (60.4%) expected that artificial intelligence (AI) will not make diagnostic radiologists redundant in the coming 10 years, whereas 54 clinicians (16.3%) thought the opposite. CONCLUSION: Clinicians who published in the New England Journal of Medicine or the Lancet attribute high value to medical imaging. They generally need radiologists for cross-sectional imaging interpretation, but for a considerable proportion of radiographs, their service is not required. Most expect medical imaging utilization and the need for diagnostic radiologists to increase in the foreseeable future, and do not expect AI to make radiologists redundant. CLINICAL RELEVANCE STATEMENT: The views of clinicians on radiology and its future may be used to determine how radiology should be practiced and be further developed. KEY POINTS: • Clinicians generally regard medical imaging as high-value care and expect to use more medical imaging in the future. • Clinicians mainly need radiologists for cross-sectional imaging interpretation while they interpret a substantial proportion of radiographs completely by themselves. • The majority of clinicians expects that the need for diagnostic radiologists will not decrease (half of them even expect that we need more) and does not believe that AI will replace radiologists.

Did medical doctors who order abdominal CT scans during on-call hours truly become worse at clinical reasoning? Yes, they did.

OBJECTIVE: To investigate temporal changes in clinical reasoning quality of physicians who requested abdominal CT scans at a tertiary care center during on-call hours within a 15-year period. METHODS: This retrospective study included 531 patients who underwent abdominal CT at a tertiary care center during on-call hours on 36 randomly sampled unique calendar days in each of the years between 2005 and 2019. Clinical reasoning quality was expressed as a percentage (0-100%), taking into account the degree by which the differential diagnoses on the CT request form matched the CT diagnosis. Temporal changes in the quality of clinical reasoning and number of CT scans were assessed using Mann-Kendall tests. Associations between the quality of clinical reasoning with patient age and gender, requesting department, and time of CT scanning were determined with linear regression analyses. RESULTS: The median annual clinical reasoning score was 0.4% (interquartile range: 0.3 to 0.6%; range: 0.1 to 1.9%). The quality of clinical reasoning significantly decreased between 2005 and 2019 (Mann-Kendall Tau of -0.829, p < 0.001), while the number of abdominal CT scans significantly increased (Mann-Kendall tau of 0.790, p < 0.001). There was a significant association between the quality of clinical reasoning and patient age (ß coefficient of 0.210, p = 0.002). The quality of clinical reasoning was not significantly associated with patient gender, requesting department, or time of CT scanning. CONCLUSION: The clinical reasoning quality of physicians who request abdominal CT scans during on-call hours has deteriorated over time. Clinical reasoning appears to be worse in younger patients. KEY POINTS: • In patients with suspected acute abdominal pathology who are scheduled to undergo CT scanning, referring physicians generally have difficulties in making an accurate pretest (differential) diagnosis. • Clinical reasoning quality of physicians who request acute abdominal CT scans has deteriorated over the years, while the number of CT scans has shown a significant increase. • Clinical reasoning quality appears to be worse in younger patients in this setting.

Optimal radiological gallbladder lesion characterization by combining visual assessment with CT-based radiomics.

OBJECTIVES: Differentiating benign gallbladder diseases from gallbladder cancer (GBC) remains a radiological challenge because they can appear very similar on imaging. This study aimed at investigating whether CT-based radiomic features of suspicious gallbladder lesions analyzed by machine learning algorithms could adequately discriminate benign gallbladder disease from GBC. In addition, the added value of machine learning models to radiological visual CT-scan interpretation was assessed. METHODS: Patients were retrospectively selected based on confirmed histopathological diagnosis and available contrast-enhanced portal venous phase CT-scan. The radiomic features were extracted from the entire gallbladder, then further analyzed by machine learning classifiers based on Lasso regression, Ridge regression, and XG Boosting. The results of the best-performing classifier were combined with radiological visual CT diagnosis and then compared with radiological visual CT assessment alone. RESULTS: In total, 127 patients were included: 83 patients with benign gallbladder lesions and 44 patients with GBC. Among all machine learning classifiers, XG boosting achieved the best AUC of 0.81 (95% CI 0.72-0.91) and the highest accuracy rate of 73% (95% CI 65-80%). When combining radiological visual interpretation and predictions of the XG boosting classifier, the highest diagnostic performance was achieved with an AUC of 0.98 (95% CI 0.96-1.00), a sensitivity of 91% (95% CI 86-100%), a specificity of 93% (95% CI 90-100%), and an accuracy of 92% (95% CI 90-100%). CONCLUSIONS: Machine learning analysis of CT-based radiomic features shows promising results in discriminating benign from malignant gallbladder disease. Combining CT-based radiomic analysis and radiological visual interpretation provided the most optimal strategy for GBC and benign gallbladder disease differentiation. KEY POINTS: Radiomic-based machine learning algorithms are able to differentiate benign gallbladder disease from gallbladder cancer. Combining machine learning algorithms with a radiological visual interpretation of gallbladder lesions at CT increases the specificity, compared to visual interpretation alone, from 73 to 93% and the accuracy from 85 to 92%. Combined use of machine learning algorithms and radiological visual assessment seems the most optimal strategy for GBC and benign gallbladder disease differentiation.

Doppler-ultrasound reference values after pediatric liver transplantation: a consecutive cohort study.

OBJECTIVES: Doppler ultrasound (DUS) is the main imaging modality to evaluate vascular complications of pediatric liver transplants (LT). The current study aimed to determine reference values and their change over time. METHODS: A consecutive cohort of pediatric patients undergoing an LT were retrospectively included between 2015 and 2020. Timepoints for standardized DUS were intra-operative and postoperative (day 0), days 1-7, months 1 and 3, and years 1 and 2. DUS measurements of the hepatic artery (HA), portal vein (PV), and hepatic vein(s) (HV) were included if there were no complications during 2 years follow-up. Measurements consisted of: peak systolic velocity (PSV) and resistive index (RI) for the HA, PSV for the PV, and venous pulsatility index (VPI) for the HV. Generalized estimating equations were used to analyze change over time. RESULTS: One hundred twelve pediatric patients with 123 LTs were included (median age 3.3 years, interquartile range 0.7-10.1). Ninety-five HAs, 100 PVs, and 115 HVs without complications were included. Reference values for HA PSV and RI, PV PSV, and HV VPI were obtained for all timepoints (4043 included data points in total) and presented using 5th-95th percentiles and threshold values. All reference values changed significantly over time (p = 0.032 to p < 0.001). CONCLUSIONS: DUS reference values of hepatic vessels in children after LT are presented, reference values change over time with specific vessel-dependent patterns. Timepoint-specific reference values improve the interpretation of DUS values and may help to better weigh their clinical significance. KEY POINTS: • Doppler ultrasound reference values of pediatric liver transplantations are not static but change over time. Applying the correct reference values for the specific timepoint may further improve the interpretation of the measurements. • The pattern of change over time of Doppler ultrasound measurements differs between the hepatic vessel and measurement; knowledge of these patterns may help radiologists to better understand normal postoperative hemodynamic changes.

A new medical imaging postprocessing and interpretation concept to investigate the clinical relevance of incidentalomas: can we keep Pandora's box closed?

BACKGROUND: Incidental imaging findings (incidentalomas) are common, but there is currently no effective means to investigate their clinical relevance. PURPOSE: To introduce a new concept to postprocess a medical imaging examination in a way that incidentalomas are concealed while its diagnostic potential is maintained to answer the referring physician's clinical questions. MATERIAL AND METHODS: A deep learning algorithm was developed to automatically eliminate liver, gallbladder, pancreas, spleen, adrenal glands, lungs, and bone from unenhanced computed tomography (CT). This deep learning algorithm was applied to a separately held set of unenhanced CT scans of 27 patients who underwent CT to evaluate for urolithiasis, and who had a total of 32 incidentalomas in one of the aforementioned organs. RESULTS: Median visual scores for organ elimination on modified CT were 100% for the liver, gallbladder, spleen, and right adrenal gland, 90%-99% for the pancreas, lungs, and bones, and 80%-89% for the left adrenal gland. In 26 out of 27 cases (96.3%), the renal calyces and pelves, ureters, and urinary bladder were completely visible on modified CT. In one case, a short (<1 cm) trajectory of the left ureter was not clearly visible due to adjacent atherosclerosis that was mistaken for bone by the algorithm. Of 32 incidentalomas, 28 (87.5%) were completely concealed on modified CT. CONCLUSION: This preliminary technical report demonstrated the feasibility of a new approach to postprocess and evaluate medical imaging examinations that can be used by future prospective research studies with long-term follow-up to investigate the clinical relevance of incidentalomas.

Early increase in single-kidney glomerular filtration rate after living kidney donation predicts long-term kidney function.

Single-kidney glomerular filtration rate (GFR) increases after living kidney donation due to compensatory hyperfiltration and structural changes. The implications of inter-individual variability in this increase in single-kidney GFR are unknown. Here, we aimed to identify determinants of the increase in single-kidney GFR at three-month postdonation, and to investigate its relationship with long-term kidney function. In a cohort study in 1024 donors, we found considerable inter-individual variability of the early increase in remaining single-kidney estimated GFR (eGFR) (median [25th-75th percentile]) 12 [8-18] mL/min/1.73m2. Predonation eGFR, age, and cortical kidney volume measured by CT were the main determinants of the early postdonation increase in single-kidney eGFR. Individuals with a stronger early increase in single-kidney eGFR had a significantly higher five-year postdonation eGFR, independent of predonation eGFR and age. Addition of the postdonation increase in single-kidney eGFR to a model including predonation eGFR and age significantly improved prediction of a five-year postdonation eGFR under 50 mL/min/1.73m2. Results at ten-year follow-up were comparable, while accounting for left-right differences in kidney volume did not materially change the results. Internal validation using 125I-iothalamate-based measured GFR in 529 donors and external validation using eGFR data in 647 donors yielded highly similar results. Thus, individuals with a more pronounced increase in single-kidney GFR had better long-term kidney function, independent of predonation GFR and age. Hence, the early postdonation increase in single-kidney GFR, considered indicative for kidney reserve capacity, may have additional value to eGFR and age to personalize follow-up intensity after living kidney donation.

Health Care Industry Payments to Editorial Board Members of Imaging-related Journals.

Background Editorial board members may be biased due to conflicts of interest (COIs). Purpose To investigate the frequency and amount of payments from industry to editorial board members of imaging-related journals and whether they are in agreement with the disclosure status as provided by the journal. Materials and Methods Editorial board members of 15 U.S.-based imaging-related journals who were listed in the Open Payments database (OPD) were included. Payments from industry to editorial board members in the year 2020 were extracted from the OPD and compared with publicly available COI disclosure data as provided by the journals. The Kruskal-Wallis test was used for statistical analysis. Results A total of 519 editorial board members were included, of whom 214 (41%) received industry payment and 305 (59%) did not. Payments to editorial board members by the industry ranged from $12.63 to $404 625.47 (median, $2397.48). Most payments from industry (59%) were ascribed to consulting. Editorial board members of the journals JACC: Cardiovascular Imaging and Journal of Vascular and Interventional Radiology received significantly higher amounts of individual payments from industry than editorial board members of most other journals. Financial COI disclosures were not publicly listed for 413 of the 519 (80%) editorial board members, 169 of whom received payments from industry according to the OPD. Of the 106 editorial board members whose financial COI disclosures were publicly listed, 36 (34%) were discordant with the OPD. Conclusion Payments from industry to Open Payments database-listed editorial board members of imaging-related journals are prevalent. Imaging-related journals often do not report or do not accurately report payments from industry to their editorial board members © RSNA, 2022.

The integrated nuclear medicine and radiology residency program in the Netherlands: strengths and potential areas for improvement according to nuclear medicine physicians and radiologists.

PURPOSE: To evaluate the Dutch integrated nuclear medicine and radiology residency program from the perspective of nuclear medicine physicians and radiologists. METHODS: A survey was distributed among nuclear medicine physicians and radiologists in hospitals that participate in the Dutch integrated nuclear medicine and radiology training program. RESULTS: A total of 139 completed questionnaires were included. Nuclear medicine physicians (n = 36) assigned a mean score of 5.7 ± 2.0, and radiologists (n = 103) assigned a mean score of 6.5 ± 2.8 (on a 1-10 scale) to the success of the integrated training program in their hospital. On multiple regression, female gender of the survey participant (B = 2.22, P = 0.034), musculoskeletal radiology as subspecialty of the survey participant (B = 3.36, P = 0.032), and the survey participant's expectancy of resident's ability to handle workload after completion of residency were significantly associated with perceived success of the integrated training program (B = 1.16, P = 0.023). Perceived strengths of the integrated training program included broadening of expertise, a better preparation of future imaging specialists for hybrid imaging, increased efficiency in training residents, and increased efficiency in multidisciplinary meetings. Perceived weaknesses of the integrated training program included reduced exposure to nuclear medicine, less time for research and innovation, and concerns about its international recognition. CONCLUSION: This study provided insights into the experiences of nuclear medicine physicians and radiologists with the Dutch integrated nuclear medicine and radiology residency program, which may be helpful to improve the program and similar residency programs in other countries.

Diagnostic performance of MRI in detecting locally recurrent soft tissue sarcoma: systematic review and meta-analysis.

OBJECTIVE: To systematically review the diagnostic criteria and performance of MRI in detecting locally recurrent soft tissue sarcoma. METHODS: Medline and Embase were searched for original studies on the diagnostic performance of MRI detecting locally recurrent soft tissue sarcoma. Study quality was assessed using QUADAS-2. Sensitivity and specificity were pooled using a bivariate random-effects model. RESULTS: Ten studies were included. There was a high risk of bias with respect to patient selection in 2 studies and a high risk of bias with respect to flow and timing in 8 studies. The presence of a mass yielded a pooled sensitivity of 80.9% and a pooled specificity of 77.0%. Hyperintensity at T2-weighted imaging yielded a pooled sensitivity of 82.4% and a pooled specificity of 11.0%. Hypo- or isointensity at T1-weighted imaging yielded a pooled sensitivity of 82.0% and a pooled specificity of 14.3%. Contrast enhancement images yielded a pooled sensitivity of 95.9% and a pooled specificity of 12.3%. Low signal mass on the apparent diffusion coefficient (ADC) map yielded a pooled sensitivity of 67.5% and a pooled specificity of 95.3%. Early and rapid arterial phase enhancement at dynamic contrast-enhanced (DCE) MRI yielded a pooled sensitivity of 91.3% and a pooled specificity of 84.7%. CONCLUSION: The presence of a mass appears a useful criterion to diagnose locally recurrent soft tissue sarcoma. Signal characteristics at standard T2- and T1-weighted imaging and contrast enhancement seem less useful because they lack specificity. Functional MRI techniques, including DWI with ADC mapping and DCE, may help to make a correct diagnosis. KEY POINTS: • The presence of a mass at MRI appears useful to diagnose locally recurrent soft tissue sarcoma, because both sensitivity and specificity are fairly high. • Signal characteristics at standard T2- and T1-weighted sequences and contrast enhancement suffer from poor specificity. • DWI with ADC mapping and DCE may help to make a correct diagnosis, but further research is needed to better understand the value of these functional MRI techniques.

Value-based radiology cannot thrive without reforms and research.

KEY POINTS: • A value-based system aims to achieve improved patient-relevant outcomes without increasing costs.• Value-based radiology cannot thrive as long as volume dominates as the most important metric to reward clinical performance.• Reforms and research are needed to enable radiologists to practice value-based healthcare.

A deep learning masked segmentation alternative to manual segmentation in biparametric MRI prostate cancer radiomics.

OBJECTIVES: To determine the value of a deep learning masked (DLM) auto-fixed volume of interest (VOI) segmentation method as an alternative to manual segmentation for radiomics-based diagnosis of clinically significant (CS) prostate cancer (PCa) on biparametric magnetic resonance imaging (bpMRI). MATERIALS AND METHODS: This study included a retrospective multi-center dataset of 524 PCa lesions (of which 204 are CS PCa) on bpMRI. All lesions were both semi-automatically segmented with a DLM auto-fixed VOI method (averaging < 10 s per lesion) and manually segmented by an expert uroradiologist (averaging 5 min per lesion). The DLM auto-fixed VOI method uses a spherical VOI (with its center at the location of the lowest apparent diffusion coefficient of the prostate lesion as indicated with a single mouse click) from which non-prostate voxels are removed using a deep learning-based prostate segmentation algorithm. Thirteen different DLM auto-fixed VOI diameters (ranging from 6 to 30 mm) were explored. Extracted radiomics data were split into training and test sets (4:1 ratio). Performance was assessed with receiver operating characteristic (ROC) analysis. RESULTS: In the test set, the area under the ROC curve (AUCs) of the DLM auto-fixed VOI method with a VOI diameter of 18 mm (0.76 [95% CI: 0.66-0.85]) was significantly higher (p = 0.0198) than that of the manual segmentation method (0.62 [95% CI: 0.52-0.73]). CONCLUSIONS: A DLM auto-fixed VOI segmentation can provide a potentially more accurate radiomics diagnosis of CS PCa than expert manual segmentation while also reducing expert time investment by more than 97%. KEY POINTS: • Compared to traditional expert-based segmentation, a deep learning mask (DLM) auto-fixed VOI placement is more accurate at detecting CS PCa. • Compared to traditional expert-based segmentation, a DLM auto-fixed VOI placement is faster and can result in a 97% time reduction. • Applying deep learning to an auto-fixed VOI radiomics approach can be valuable.