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.

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.

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.

Publication pressure in medical imaging.

PURPOSE: To investigate the degree of perceived publication pressure in medical imaging. METHOD: Corresponding authors who published an article in one of the top 12 general radiology journals were invited to complete a survey about publication pressure. The revised Publication Pressure Questionnaire (PPQr) was used. Higher PPQr scores (5-point Likert scale) indicate a more negative view towards the various domains of publication pressure. RESULTS: 203 corresponding authors participated. Median PPQr scores in the domains "publication stress", "publication attitude", and "publication resources" were 3.33, 3.50, and 3.67, respectively. Age 25-34 years (ß coefficient 0.366, P = 0.047), female gender (ß coefficient 0.293, P = 0.020), and 5-10 years of research experience (ß coefficient 0.370, P = 0.033) were associated with a higher level of perceived publication stress, whereas age ≥ 65 years was negatively associated with perceived publication stress (ß coefficient -0.846, P < 0.001). Age 55-64 years and age > 65 years were associated with a more positive view towards the publication climate (ß coefficients -0.391 and -0.663, P = 0.018 and P = 0.002, respectively). Age 45-54 years was associated with a perception of fewer factors available to alleviate publication pressure (ß coefficient 0.301, P = 0.014), whereas age 25-34 years was associated with a perception of more factors available to alleviate publication pressure (ß coefficient -0.352, P = 0.012). CONCLUSION: Perceived publication pressure among medical imaging researchers appears to be appreciable and is associated with several (academic) demographics.

Imaging overuse in the emergency department: The view of radiologists and emergency physicians.

PURPOSE: To identify the perceived factors contributing to imaging overuse in the emergency department, according to radiologists and emergency physicians. METHOD: A survey study on imaging overuse in the emergency department was conducted among 66 radiologists and 425 emergency physicians. Five-point Likert scales (not a problem at all/strongly disagree [score 1] to very serious problem/strongly agree [score 5]) were used to score the various aspects of overimaging. RESULTS: Both radiologists and emergency physicians gave a median score of 4 to the question if imaging overuse is a problem in their emergency department. CT accounts for the vast majority of imaging overuse, according to both radiologists (84.8%) and emergency physicians (75.3%). Defensive medicine/fear of malpractice, the presence of less experienced staff, and easy access to imaging all were given a median score of 4 as factors that influence imaging overuse, by both physician groups. Median ratings regarding the influence of pressure from patients and a lack of time to examine patients on imaging overuse varied between 3 and 4 for radiologists and emergency physicians. Pressure from consultants to perform imaging, the use of imaging to decrease turnaround time in the emergency department, a lack of space in the emergency department, a lack of proper medical education, and inability to access outside imaging studies, were also indicated to give rise to imaging overuse. CONCLUSIONS: Imaging overuse in the emergency department (particularly CT overuse) is a problem according to most radiologists and emergency physicians, and is driven by several factors.

Retrospective Radiology Research: Do We Need Informed Patient Consent?

While knowledge of the population's view on the need for informed consent for retrospective radiology research may provide valuable insight into how an optimal balance can be achieved between patient rights versus an expedited advancement of radiology science, this is a topic that has been ignored in the literature so far. To investigate the view of the general population, survey data were collected from 2407 people representative of the Dutch population. The results indicate that for non-commercial institutions, especially hospitals (97.4 per cent), respondents agree with the retrospective use of imaging data, although they generally indicate that their explicit consent is required. However, most respondents (63.5 per cent) would never allow commercial firms to retrospectively use their imaging data. When including only respondents who completed the minimally required reading time of 12.3 s to understand the description about retrospective radiology research given in the survey (n = 770), almost all (98.9 per cent) mentioned to have no objections for their imaging data to be used by hospitals for retrospective research, with 57.9 per cent indicating their consent to be required and 41.0 per cent indicating that explicit patient consent to be unnecessary. We conclude that the general population permits retrospective radiology research by hospitals, and a substantial proportion indicates explicit patient consent to be unnecessary when understanding what retrospective radiology research entails. However, the general population's support for the unrestricted retrospective use of imaging data for research purposes without patient consent decreases for universities not linked to hospitals, other non-commercial institutions, government agencies, and particularly commercial firms.

Does FDG-PET/CT for incidentally found pulmonary lesions lead to a cascade of more incidental findings?

OBJECTIVE: To determine the frequency, nature, and downstream healthcare costs of new incidental findings that are found on whole-body FDG-PET/CT in patients with a non-FDG-avid pulmonary lesion ≥10 mm that was incidentally found on previous imaging. MATERIALS AND METHODS: This retrospective study included a consecutive series of patients who underwent whole-body FDG-PET/CT because of an incidentally found pulmonary lesion ≥10 mm. RESULTS: Seventy patients were included, of whom 23 (32.9 %) had an incidentally found pulmonary lesion that proved to be non-FDG-avid. In 12 of these 23 cases (52.2 %) at least one new incidental finding was discovered on FDG-PET/CT. The total number of new incidental findings was 21, of which 7 turned out to be benign, 1 proved to be malignant (incurable metastasized cancer), and 13 whose nature remained unclear. One patient sustained permanent neurologic impairment of the left leg due to iatrogenic nerve damage during laparotomy for an incidental finding which turned out to be benign. The total costs of all additional investigations due to the detection of new incidental findings amounted to €9903.17, translating to an average of €141.47 per whole-body FDG-PET/CT scan performed for the evaluation of an incidentally found pulmonary lesion. CONCLUSION: In many patients in whom whole-body FDG-PET/CT was performed to evaluate an incidentally found pulmonary lesion that turned out to be non-FDG-avid and therefore very likely benign, FDG-PET/CT detected new incidental findings in our preliminary study. Whether the detection of these new incidental findings is cost-effective or not, requires further research with larger sample sizes.

Reading room assistants to reduce workload and interruptions of radiology residents during on-call hours: Initial evaluation.

PURPOSE: To determine how much timesaving and reduction of interruptions reading room assistants can provide by taking over non-image interpretation tasks (NITs) from radiology residents during on-call hours. METHODS: Reading room assistants are medical students who were trained to take over NITs from radiology residents (e.g. answering telephone calls, administrative tasks and logistics) to reduce residents' workload during on-call hours. Reading room assistants' and residents' activities were tracked during 6 weekend dayshifts in a tertiary care academic center (with more than 2.5 million inhabitants in its catchment area) between 10 a.m. and 5p.m. (7-hour shift, 420 min), and time spent on each activity was recorded. RESULTS: Reading room assistants spent the most time on the following timesaving activities for residents: answering incoming (41 min, 19%) and outgoing telephone calls (35 min, 16%), ultrasound machine related activities (19 min, 9%) and paramedical assistance such as supporting residents during ultrasound guided procedures and with patients (17 min, 8%). Reading room assistants saved 132 min of residents' time by taking over NITs while also spending circa 31 min consulting the resident, resulting in a net timesaving of 101 min (24%) during a 7-hour shift. The reading room assistants also prevented residents from being interrupted, at a mean of 18 times during the 7-hour shift. CONCLUSION: This study shows that the implementation of reading room assistants to radiology on-call hours could provide a timesaving for residents and also reduce the number of times residents are being interrupted during their work.

What makes a good scientific presentation on artificial intelligence in medical imaging?

PURPOSE: Adequate communication of scientific findings is crucial to enhance knowledge transfer. This study aimed to determine the key features of a good scientific oral presentation on artificial intelligence (AI) in medical imaging. METHODS: A total of 26 oral presentations dealing with original research on AI studies in medical imaging at the 2023 RSNA annual meeting were included and systematically assessed by three observers. The presentation quality of the research question, inclusion criteria, reference standard, method, results, clinical impact, presentation clarity, presenter engagement, and the presentation's quality of knowledge transfer were assessed using five-point Likert scales. The number of slides, the average number of words per slide, the number of interactive slides, the number of figures, and the number of tables were also determined for each presentation. Mixed-effects ordinal regression was used to assess the association between the above-mentioned variables and the quality of knowledge transfer of the presentation. RESULTS: A significant positive association was found between the quality of the presentation of the research question and the presentation's quality of knowledge transfer (odds ratio [OR]: 2.5, P = 0.005). The average number of words per slide was significantly negatively associated with the presentation's quality of knowledge transfer (OR: 0.9, P < 0.001). No other significant associations were found. CONCLUSION: Researchers who orally present their scientific findings in the field of AI and medical imaging should pay attention to clearly communicating their research question and minimizing the number of words per slide to maximize the value of their presentation.

Machine learning-based radiomic analysis and growth visualization for ablation site recurrence diagnosis in follow-up CT.

OBJECTIVES: Detecting ablation site recurrence (ASR) after thermal ablation remains a challenge for radiologists due to the similarity between tumor recurrence and post-ablative changes. Radiomic analysis and machine learning methods may show additional value in addressing this challenge. The present study primarily sought to determine the efficacy of radiomic analysis in detecting ASR on follow-up computed tomography (CT) scans. The second aim was to develop a visualization tool capable of emphasizing regions of ASR between follow-up scans in individual patients. MATERIALS AND METHODS: Lasso regression and Extreme Gradient Boosting (XGBoost) classifiers were employed for modeling radiomic features extracted from regions of interest delineated by two radiologists. A leave-one-out test (LOOT) was utilized for performance evaluation. A visualization method, creating difference heatmaps (diff-maps) between two follow-up scans, was developed to emphasize regions of growth and thereby highlighting potential ASR. RESULTS: A total of 55 patients, including 20 with and 35 without ASR, were included in the radiomic analysis. The best performing model was achieved by Lasso regression tested with the LOOT approach, reaching an area under the curve (AUC) of 0.97 and an accuracy of 92.73%. The XGBoost classifier demonstrated better performance when trained with all extracted radiomic features than without feature selection, achieving an AUC of 0.93 and an accuracy of 89.09%. The diff-maps correctly highlighted post-ablative liver tumor recurrence in all patients. CONCLUSIONS: Machine learning-based radiomic analysis and growth visualization proved effective in detecting ablation site recurrence on follow-up CT scans.

Using deep learning to optimize the prostate MRI protocol by assessing the diagnostic efficacy of MRI sequences.

PURPOSE: To explore diagnostic deep learning for optimizing the prostate MRI protocol by assessing the diagnostic efficacy of MRI sequences. METHOD: This retrospective study included 840 patients with a biparametric prostate MRI scan. The MRI protocol included a T2-weighted image, three DWI sequences (b50, b400, and b800 s/mm2), a calculated ADC map, and a calculated b1400 sequence. Two accelerated MRI protocols were simulated, using only two acquired b-values to calculate the ADC and b1400. Deep learning models were trained to detect prostate cancer lesions on accelerated and full protocols. The diagnostic performances of the protocols were compared on the patient-level with the area under the receiver operating characteristic (AUROC), using DeLong's test, and on the lesion-level with the partial area under the free response operating characteristic (pAUFROC), using a permutation test. Validation of the results was performed among expert radiologists. RESULTS: No significant differences in diagnostic performance were found between the accelerated protocols and the full bpMRI baseline. Omitting b800 reduced 53% DWI scan time, with a performance difference of + 0.01 AUROC (p = 0.20) and -0.03 pAUFROC (p = 0.45). Omitting b400 reduced 32% DWI scan time, with a performance difference of -0.01 AUROC (p = 0.65) and + 0.01 pAUFROC (p = 0.73). Multiple expert radiologists underlined the findings. CONCLUSIONS: This study shows that deep learning can assess the diagnostic efficacy of MRI sequences by comparing prostate MRI protocols on diagnostic accuracy. Omitting either the b400 or the b800 DWI sequence can optimize the prostate MRI protocol by reducing scan time without compromising diagnostic quality.

Multimodal AI Combining Clinical and Imaging Inputs Improves Prostate Cancer Detection.

OBJECTIVES: Deep learning (DL) studies for the detection of clinically significant prostate cancer (csPCa) on magnetic resonance imaging (MRI) often overlook potentially relevant clinical parameters such as prostate-specific antigen, prostate volume, and age. This study explored the integration of clinical parameters and MRI-based DL to enhance diagnostic accuracy for csPCa on MRI. MATERIALS AND METHODS: We retrospectively analyzed 932 biparametric prostate MRI examinations performed for suspected csPCa (ISUP ≥2) at 2 institutions. Each MRI scan was automatically analyzed by a previously developed DL model to detect and segment csPCa lesions. Three sets of features were extracted: DL lesion suspicion levels, clinical parameters (prostate-specific antigen, prostate volume, age), and MRI-based lesion volumes for all DL-detected lesions. Six multimodal artificial intelligence (AI) classifiers were trained for each combination of feature sets, employing both early (feature-level) and late (decision-level) information fusion methods. The diagnostic performance of each model was tested internally on 20% of center 1 data and externally on center 2 data (n = 529). Receiver operating characteristic comparisons determined the optimal feature combination and information fusion method and assessed the benefit of multimodal versus unimodal analysis. The optimal model performance was compared with a radiologist using PI-RADS. RESULTS: Internally, the multimodal AI integrating DL suspicion levels with clinical features via early fusion achieved the highest performance. Externally, it surpassed baselines using clinical parameters (0.77 vs 0.67 area under the curve [AUC], P < 0.001) and DL suspicion levels alone (AUC: 0.77 vs 0.70, P = 0.006). Early fusion outperformed late fusion in external data (0.77 vs 0.73 AUC, P = 0.005). No significant performance gaps were observed between multimodal AI and radiologist assessments (internal: 0.87 vs 0.88 AUC; external: 0.77 vs 0.75 AUC, both P > 0.05). CONCLUSIONS: Multimodal AI (combining DL suspicion levels and clinical parameters) outperforms clinical and MRI-only AI for csPCa detection. Early information fusion enhanced AI robustness in our multicenter setting. Incorporating lesion volumes did not enhance diagnostic efficacy.

Pictorial review of multiparametric MRI in bladder urothelial carcinoma with variant histology: pearls and pitfalls.

Bladder cancer (BC), predominantly comprising urothelial carcinomas (UCs), ranks as the tenth most common cancer worldwide. UCs with variant histology (variant UC), including squamous differentiation, glandular differentiation, plasmacytoid variant, micropapillary variant, sarcomatoid variant, and nested variant, accounting for 5-10% of cases, exhibit more aggressive and advanced tumor characteristics compared to pure UC. The Vesical Imaging-Reporting and Data System (VI-RADS), established in 2018, provides guidelines for the preoperative evaluation of muscle-invasive bladder cancer (MIBC) using multiparametric magnetic resonance imaging (mpMRI). This technique integrates T2-weighted imaging (T2WI), dynamic contrast-enhanced (DCE)-MRI, and diffusion-weighted imaging (DWI) to distinguish MIBC from non-muscle-invasive bladder cancer (NMIBC). VI-RADS has demonstrated high diagnostic performance in differentiating these two categories for pure UC. However, its accuracy in detecting muscle invasion in variant UCs is currently under investigation. These variant UCs are associated with a higher likelihood of disease recurrence and require precise preoperative assessment and immediate surgical intervention. This review highlights the potential value of mpMRI for different variant UCs and explores the clinical implications and prospects of VI-RADS in managing these patients, emphasizing the need for careful interpretation of mpMRI examinations including DCE-MRI, particularly given the heterogeneity and aggressive nature of variant UCs. Additionally, the review addresses the fundamental MRI reading procedures, discusses potential causes of diagnostic errors, and considers future directions in the use of artificial intelligence and radiomics to further optimize the bladder MRI protocol.