Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects.

Although artificial intelligence (AI) methods hold promise for medical imaging-based prediction tasks, their integration into medical practice may present a double-edged sword due to bias (i.e., systematic errors). AI algorithms have the potential to mitigate cognitive biases in human interpretation, but extensive research has highlighted the tendency of AI systems to internalize biases within their model. This fact, whether intentional or not, may ultimately lead to unintentional consequences in the clinical setting, potentially compromising patient outcomes. This concern is particularly important in medical imaging, where AI has been more progressively and widely embraced than any other medical field. A comprehensive understanding of bias at each stage of the AI pipeline is therefore essential to contribute to developing AI solutions that are not only less biased but also widely applicable. This international collaborative review effort aims to increase awareness within the medical imaging community about the importance of proactively identifying and addressing AI bias to prevent its negative consequences from being realized later. The authors began with the fundamentals of bias by explaining its different definitions and delineating various potential sources. Strategies for detecting and identifying bias were then outlined, followed by a review of techniques for its avoidance and mitigation. Moreover, ethical dimensions, challenges encountered, and prospects were discussed.

Evaluating the quality of radiomics-based studies for endometrial cancer using RQS and METRICS tools.

OBJECTIVE: To assess the methodological quality of radiomics-based models in endometrial cancer using the radiomics quality score (RQS) and METhodological radiomICs score (METRICS). METHODS: We systematically reviewed studies published by October 30th, 2023. Inclusion criteria were original radiomics studies on endometrial cancer using CT, MRI, PET, or ultrasound. Articles underwent a quality assessment by novice and expert radiologists using RQS and METRICS. The inter-rater reliability for RQS and METRICS among radiologists with varying expertise was determined. Subgroup analyses were performed to assess whether scores varied according to study topic, imaging technique, publication year, and journal quartile. RESULTS: Sixty-eight studies were analysed, with a median RQS of 11 (IQR, 9-14) and METRICS score of 67.6% (IQR, 58.8-76.0); two different articles reached maximum RQS of 19 and METRICS of 90.7%, respectively. Most studies utilised MRI (82.3%) and machine learning methods (88.2%). Characterisation and recurrence risk stratification were the most explored outcomes, featured in 35.3% and 19.1% of articles, respectively. High inter-rater reliability was observed for both RQS (ICC: 0.897; 95% CI: 0.821, 0.946) and METRICS (ICC: 0.959; 95% CI: 0.928, 0.979). Methodological limitations such as lack of external validation suggest areas for improvement. At subgroup analyses, no statistically significant difference was noted. CONCLUSIONS: Whilst using RQS, the quality of endometrial cancer radiomics research was apparently unsatisfactory, METRICS depicts a good overall quality. Our study highlights the need for strict compliance with quality metrics. Adhering to these quality measures can increase the consistency of radiomics towards clinical application in the pre-operative management of endometrial cancer. CLINICAL RELEVANCE STATEMENT: Both the RQS and METRICS can function as instrumental tools for identifying different methodological deficiencies in endometrial cancer radiomics research. However, METRICS also reflected a focus on the practical applicability and clarity of documentation. KEY POINTS: The topic of radiomics currently lacks standardisation, limiting clinical implementation. METRICS scores were generally higher than the RQS, reflecting differences in the development process and methodological content. A positive trend in METRICS score may suggest growing attention to methodological aspects in radiomics research.

Distinguishing Pituitary Metastasis and Pituitary Neuroendocrine Tumors through Conventional MR Imaging and Clinical Features.

BACKGROUND AND PURPOSE: Given their overlapping features, pituitary metastases frequently imitate pituitary neuroendocrine tumors in neuroimaging studies. This study aimed to distinguish pituitary metastases from pituitary neuroendocrine tumors on the basis of conventional MR imaging and clinical features as a practical approach. MATERIALS AND METHODS: In this 2-center retrospective study, backward from January 2024, preoperative pituitary MR imaging examinations of 22 pituitary metastases and 74 pituitary neuroendocrine tumors were analyzed. Exclusion criteria were as follows: absence of a definitive histopathologic diagnosis, history of pituitary surgery or radiation therapy before MR imaging, and pituitary neuroendocrine tumors treated with medical therapy. Two radiologists systematically evaluated 13 conventional MR imaging features that have been reported more commonly as indicative of pituitary metastases and pituitary neuroendocrine tumors in the literature. Age, sex, history of cancer, and maximum tumor size constituted the clinical/epidemiologic features. The primary cancer origin for this study was also noted. Univariable and multivariable logistic regression was used for the selection of variables, determining independent predictors, and modeling. Interobserver agreement was evaluated for all imaging parameters using the Cohen κ statistic or intraclass correlation coefficient. RESULTS: A total of 22 patients with pituitary metastases (8 women; mean age, 49.5 [SD, 13] years) and 74 patients with pituitary neuroendocrine tumors (36 women; mean age, 50.1 [SD, 11] years) were enrolled. There was no statistically significant distributional difference in age, sex, or maximum tumor size between the 2 groups. Lung cancer (9/22; 41%) was the most commonly reported primary tumor, followed by breast (3/22; 13.6%) and unknown cancer (3/22; 13.6%). Logistic regression revealed 3 independent predictors: rapid growth on control MR imaging, masslike or nodular expansion of the pituitary stalk, and a history of cancer. The model based on these 3 features achieved an area under the curve, accuracy, sensitivity, specificity, and Brier score of 0.987 (95% CI, 0.964-1), 97.9% (95% CI, 92.7%-99.8%), 95.5% (95% CI, 77.2%-99.9%), 98.6% (95% CI, 92.7%-100%), and 0.025, respectively. CONCLUSIONS: Two conventional features based on pituitary MR imaging with the clinical variable of history of cancer had satisfying predictive performance, making them potential discriminators between pituitary metastases and pituitary neuroendocrine tumors. In cases in which differentiation between pituitary metastases and pituitary neuroendocrine tumors poses a challenge, the results of this study may help with the diagnosis.

Utility of diffusion tensor imaging and generalized q-sampling imaging for predicting short-term clinical effect of deep brain stimulation in Parkinson's disease.

PURPOSE: To assess whether diffusion tensor imaging (DTI) and generalized q-sampling imaging (GQI) metrics could preoperatively predict the clinical outcome of deep brain stimulation (DBS) in patients with Parkinson's disease (PD). METHODS: In this single-center retrospective study, from September 2021 to March 2023, preoperative DTI and GQI examinations of 44 patients who underwent DBS surgery, were analyzed. To evaluate motor functions, the Unified Parkinson's Disease Rating Scale (UPDRS) during on- and off-medication and Parkinson's Disease Questionnaire-39 (PDQ-39) scales were used before and three months after DBS surgery. The study population was divided into two groups according to the improvement rate of scales: ≥ 50% and < 50%. Five target regions, reported to be affected in PD, were investigated. The parameters having statistically significant difference were subjected to a receiver operating characteristic (ROC) analysis. RESULTS: Quantitative anisotropy (qa) values from globus pallidus externus, globus pallidus internus (qa_Gpi), and substantia nigra exhibited significant distributional difference between groups in terms of the improvement rate of UPDRS-3 scale during on-medication (p = 0.003, p = 0.0003, and p = 0.0008, respectively). In ROC analysis, the best parameter in predicting DBS response included qa_Gpi with a cut-off value of 0.01370 achieved an area under the ROC curve, accuracy, sensitivity, and specificity of 0.810, 73%, 62.5%, and 85%, respectively. Optimal cut-off values of ≥ 0.01864 and ≤ 0.01162 yielded a sensitivity and specificity of 100%, respectively. CONCLUSION: The imaging parameters acquired from GQI, particularly qa_Gpi, may have the ability to non-invasively predict the clinical outcome of DBS surgery.

Explanation and Elaboration with Examples for CLEAR (CLEAR-E3): an EuSoMII Radiomics Auditing Group Initiative.

Overall quality of radiomics research has been reported as low in literature, which constitutes a major challenge to improve. Consistent, transparent, and accurate reporting is critical, which can be accomplished with systematic use of reporting guidelines. The CheckList for EvaluAtion of Radiomics research (CLEAR) was previously developed to assist authors in reporting their radiomic research and to assist reviewers in their evaluation. To take full advantage of CLEAR, further explanation and elaboration of each item, as well as literature examples, may be useful. The main goal of this work, Explanation and Elaboration with Examples for CLEAR (CLEAR-E3), is to improve CLEAR's usability and dissemination. In this international collaborative effort, members of the European Society of Medical Imaging Informatics-Radiomics Auditing Group searched radiomics literature to identify representative reporting examples for each CLEAR item. At least two examples, demonstrating optimal reporting, were presented for each item. All examples were selected from open-access articles, allowing users to easily consult the corresponding full-text articles. In addition to these, each CLEAR item's explanation was further expanded and elaborated. For easier access, the resulting document is available at https://radiomic.github.io/CLEAR-E3/ . As a complementary effort to CLEAR, we anticipate that this initiative will assist authors in reporting their radiomics research with greater ease and transparency, as well as editors and reviewers in reviewing manuscripts.Relevance statement Along with the original CLEAR checklist, CLEAR-E3 is expected to provide a more in-depth understanding of the CLEAR items, as well as concrete examples for reporting and evaluating radiomic research.Key points• As a complementary effort to CLEAR, this international collaborative effort aims to assist authors in reporting their radiomics research, as well as editors and reviewers in reviewing radiomics manuscripts.• Based on positive examples from the literature selected by the EuSoMII Radiomics Auditing Group, each CLEAR item explanation was further elaborated in CLEAR-E3.• The resulting explanation and elaboration document with examples can be accessed at  https://radiomic.github.io/CLEAR-E3/ .

Spontaneous intracranial hypotension: Exploring the viability of non-contrast FLAIR as a substitute for contrast-enhanced T1WI in assessing pachymeningeal thickening.

PURPOSE: To avoid contrast administration in spontaneous intracranial hypotension (SIH), some studies suggest accepting diffuse pachymeningeal hyperintensity (DPMH) on non-contrast fluid-attenuated inversion recovery (FLAIR) as an equivalent sign to diffuse pachymeningeal enhancement (DPME) on contrast-enhanced T1WI (T1ce), despite lacking thorough performance metrics. This study aimed to comprehensively explore its feasibility. METHODS: In this single-center retrospective study, between April 2021 and November 2023, brain MRI examinations of 43 patients clinically diagnosed with SIH were assessed using 1.5 and 3.0 Tesla MRI scanners. Two radiologists independently assessed the presence or absence of DPMH on FLAIR and DPME on T1ce, with T1ce serving as a gold-standard for pachymeningeal thickening. The contribution of the subdural fluid collections to DPMH was investigated with quantitative measurements. Using Cohen's kappa statistics, interobserver agreement was assessed. RESULTS: In 39 out of 43 patients (90.7%), pachymeningeal thickening was observed on T1ce. FLAIR sequence produced an accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 72.1%, 71.8%, 75.0%, 96.6%, and 21.4% respectively, for determining pachymeningeal thickening. FLAIR identified pachymeningeal thickening in 28 cases; however, among these, 21 cases (75%) revealed that the pachymeningeal hyperintense signal was influenced by subdural fluid collections. False-negative rate for FLAIR was 28.2% (11/39). CONCLUSION: The lack of complete correlation between FLAIR and T1ce in identifying pachymeningeal thickening highlights the need for caution in removing contrast agent administration from the MRI protocol of SIH patients, as it reveals a major criterion (i.e., pachymeningeal enhancement) of Bern score.

Ventral amygdalofugal pathway as an integrated surgically important network: microsurgical anatomy and segmentation based on fiber dissection.

OBJECTIVE: The ventral amygdalofugal pathway (VAFP) provides afferent and efferent connections to the amygdala and spans along some of the frequently traversed intra-axial surgical corridors as a dominant fiber bundle. This study aimed to reveal the frequently overlooked VAFP fibers by examining their courses and connections to the basal forebrain, septal region, hypothalamus, thalamus, tegmentum, and brainstem. METHODS: Ten postmortem human brains were used to display the characteristics of the VAFP, and fiber dissection results were compared with those of tractography. RESULTS: From anterior to posterior, the VAFP was separated into 5 different portions: 1) amygdala-substantia innominata; 2) amygdaloseptal (diagonal band of Broca); 3) amygdalo-thalamic; 4) amygdalo-hypothalamic, intermingling with the medial forebrain bundle and extending to the bed nucleus of stria terminalis; and 5) amygdalotegmental. The results of fiber dissections were confirmed with findings obtained from diffusion tensor tractography. CONCLUSIONS: This study supports the concept that interconnected forebrain, diencephalic, mesencephalic, and brainstem connections of the VAFP form an integrated surgically important network. The fiber dissection findings also provide the neuroanatomical basis for VAFP segmentation, which may help neurosurgeons better appreciate the complex microsurgical anatomy of the amygdalar connections. Amygdala-substantia innominata and amygdalotegmental connections are demonstrated for the first time and clarified within the structure of the VAFP.

Imlifidase in kidney transplantation.

Kidney transplantation, the gold-standard therapeutic approach for patients with end-stage kidney disease, offers improvement in patient survival and quality of life. However, broad sensitization against human leukocyte antigens often resulting in a positive crossmatch against the patient's living donor or the majority of potential deceased donors in the allocation system represents a major obstacle due to a high risk for antibody-mediated rejection, delayed graft function and allograft loss. Kidney-paired donation and desensitization protocols have been established to overcome this obstacle, with limited success. Imlifidase, a novel immunoglobulin G (IgG)-degrading enzyme derived from Streptococcus pyogenes and recombinantly produced in Escherichia coli, is a promising agent for recipients with a positive crossmatch against their organ donor with high specificity towards IgG, rapid action and high efficacy in early pre-clinical and clinical studies. However, the rebound of IgG after a few days can lead to antibody-mediated rejection, making the administration of potent immunosuppressive regimens in the early post-transplant phase necessary. There is currently no comparative study evaluating the efficiency of imlifidase therapy compared with conventional desensitization protocols along with the lack of randomized control trials, indicating the clear need for future large-scale clinical studies in this field. Besides providing a practical framework for the clinical use of the agent, our aim in this article is to evaluate the underlying mechanism of action, efficiency and safety of imlifidase therapy in immunologically high-risk kidney transplant recipients.

The role of anticomplement therapy in the management of the kidney allograft.

As the number of patients living with kidney failure grows, the need also grows for kidney transplantation, the gold standard kidney replacement therapy that provides a survival advantage. This may result in an increased rate of transplantation from HLA-mismatched donors that increases the rate of antibody-mediated rejection (AMR), which already is the leading cause of allograft failure. Plasmapheresis, intravenous immunoglobulin therapy, anti-CD20 therapies (i.e., rituximab), bortezomib and splenectomy have been used over the years to treat AMR as well as to prevent AMR in high-risk sensitized kidney transplant recipients. Eculizumab and ravulizumab are monoclonal antibodies targeting the C5 protein of the complement pathway and part of the expanding field of anticomplement therapies, which is not limited to kidney transplant recipients, and also includes complement-mediated microangiopathic hemolytic anemia, paroxysmal nocturnal hemoglobinuria, and ANCA-vasculitis. In this narrative review, we summarize the current knowledge concerning the pathophysiological background and use of anti-C5 strategies (eculizumab and ravulizumab) and C1-esterase inhibitor in AMR, either to prevent AMR in high-risk desensitized patients or to treat AMR as first-line or rescue therapy and also to treat de novo thrombotic microangiopathy in kidney transplant recipients.

Effects of erector spinae plane block on opioid consumption in patients undergoing hand-assisted laparoscopic donor nephrectomy: a randomized controlled trial.

BACKGROUND: The erector spinae plane block is a relatively new regional anesthesia technique that is expected to provide some benefits for postoperative analgesia. This study investigated the effects of erector spinae plane block on postoperative opioid consumption in kidney donors undergoing hand-assisted laparoscopic donor nephrectomy for renal transplantation. METHODS: Fifty-two donors scheduled for elective hand-assisted laparoscopic donor nephrectomy were randomly divided into the block (25 donors) and control (27 donors) groups. Donors in the block group received 30 mL of 0.25% bupivacaine under ultrasound guidance, whereas the control group received no block treatment. The primary outcome measure was the amount of fentanyl administered via patient-controlled analgesia at 24 h. Secondary outcomes included the duration of stay, opioid consumption in the post-anesthesia care unit, and pain scores during the recording hours. RESULTS: No significant differences were observed between the groups regarding total opioid consumption converted to intravenous morphine equivalent administered via patient-controlled analgesia (33.3±21.4 mg vs. 37.5±18.5 mg; P=0.27) and in the postanesthesia care unit (1.5±0.9 mg vs. 1.4±0.8 mg; P=0.55). The duration of stay in the postanesthesia care unit (86.3±32.6 min vs. 85.7±33.6 min; P=0.87) was similar between the groups. There was no significant difference between the groups in the postoperative donor-reported NRS pain scores (P>0.05 for all the time points). CONCLUSIONS: Preoperative erector spinae plane block is not an effective strategy for reducing postoperative pain or opioid consumption in patients undergoing hand-assisted laparoscopic donor nephrectomy. Different block combinations are needed for optimal pain management in hand-assisted laparoscopic donor nephrectomy.

Meta-research on reporting guidelines for artificial intelligence: are authors and reviewers encouraged enough in radiology, nuclear medicine, and medical imaging journals?

PURPOSE: To determine how radiology, nuclear medicine, and medical imaging journals encourage and mandate the use of reporting guidelines for artificial intelligence (AI) in their author and reviewer instructions. METHODS: The primary source of journal information and associated citation data used was the Journal Citation Reports (June 2023 release for 2022 citation data; Clarivate Analytics, UK). The first- and second-quartile journals indexed in the Science Citation Index Expanded and the Emerging Sources Citation Index were included. The author and reviewer instructions were evaluated by two independent readers, followed by an additional reader for consensus, with the assistance of automatic annotation. Encouragement and submission requirements were systematically analyzed. The reporting guidelines were grouped as AI-specific, related to modeling, and unrelated to modeling. RESULTS: Out of 102 journals, 98 were included in this study, and all of them had author instructions. Only five journals (5%) encouraged the authors to follow AI-specific reporting guidelines. Among these, three required a filled-out checklist. Reviewer instructions were found in 16 journals (16%), among which one journal (6%) encouraged the reviewers to follow AI-specific reporting guidelines without submission requirements. The proportions of author and reviewer encouragement for AI-specific reporting guidelines were statistically significantly lower compared with those for other types of guidelines (P < 0.05 for all). CONCLUSION: The findings indicate that AI-specific guidelines are not commonly encouraged and mandated (i.e., requiring a filled-out checklist) by these journals, compared with guidelines related to modeling and unrelated to modeling, leaving vast space for improvement. This meta-research study hopes to contribute to the awareness of the imaging community for AI reporting guidelines and ignite large-scale group efforts by all stakeholders, making AI research less wasteful. CLINICAL SIGNIFICANCE: This meta-research highlights the need for improved encouragement of AI-specific guidelines in radiology, nuclear medicine, and medical imaging journals. This can potentially foster greater awareness among the AI community and motivate various stakeholders to collaborate to promote more efficient and responsible AI research reporting practices.

Should Calcineurin Inhibitors/Sirolimus Be Ceased Completely In Posterior Reversible Encephalopathy Syndrome?

BACKGROUND: To investigate the relationship between immunosuppressive treatments and posterior reversible encephalopathy syndrome (PRES) in transplant patients. METHODS: We presented a retrospective study of 4 cases of PRES in transplant patients. Patient records were reviewed to identify potential risk factors, clinical presentations, radiological findings, and immunosuppressive treatments used. RESULTS: Our analysis revealed a potential association between immunosuppressive treatments and the development of PRES in transplant patients. Specifically, we found that adjusting or switching immunosuppressive treatments can improve outcomes and prevent the recurrence of PRES. CONCLUSION: Our findings highlight the importance of recognizing PRES as a potential complication of immunosuppressive treatments in transplant patients. Early detection and management, including a review of immunosuppressive treatments, may improve patient outcomes and prevent further complications.

Self-reported checklists and quality scoring tools in radiomics: a meta-research.

OBJECTIVE: To evaluate the use of reporting checklists and quality scoring tools for self-reporting purposes in radiomics literature. METHODS: Literature search was conducted in PubMed (date, April 23, 2023). The radiomics literature was sampled at random after a sample size calculation with a priori power analysis. A systematic assessment for self-reporting, including the use of documentation such as completed checklists or quality scoring tools, was conducted in original research papers. These eligible papers underwent independent evaluation by a panel of nine readers, with three readers assigned to each paper. Automatic annotation was used to assist in this process. Then, a detailed item-by-item confirmation analysis was carried out on papers with checklist documentation, with independent evaluation of two readers. RESULTS: The sample size calculation yielded 117 papers. Most of the included papers were retrospective (94%; 110/117), single-center (68%; 80/117), based on their private data (89%; 104/117), and lacked external validation (79%; 93/117). Only seven papers (6%) had at least one self-reported document (Radiomics Quality Score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD), or Checklist for Artificial Intelligence in Medical Imaging (CLAIM)), with a statistically significant binomial test (p < 0.001). Median rate of confirmed items for all three documents was 81% (interquartile range, 6). For quality scoring tools, documented scores were higher than suggested scores, with a mean difference of - 7.2 (standard deviation, 6.8). CONCLUSION: Radiomic publications often lack self-reported checklists or quality scoring tools. Even when such documents are provided, it is essential to be cautious, as the accuracy of the reported items or scores may be questionable. CLINICAL RELEVANCE STATEMENT: Current state of radiomic literature reveals a notable absence of self-reporting with documentation and inaccurate reporting practices. This critical observation may serve as a catalyst for motivating the radiomics community to adopt and utilize such tools appropriately, thereby fostering rigor, transparency, and reproducibility of their research, moving the field forward. KEY POINTS: • In radiomics literature, there has been a notable absence of self-reporting with documentation. • Even if such documents are provided, it is critical to exercise caution because the accuracy of the reported items or scores may be questionable. • Radiomics community needs to be motivated to adopt and appropriately utilize the reporting checklists and quality scoring tools.

METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII.

PURPOSE: To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies. METHODS: We conducted an online modified Delphi study with a group of international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members to identify the items to be voted; and Stage#3, four rounds of the modified Delphi exercise by panelists to determine the items eligible for the METRICS and their weights. The consensus threshold was 75%. Based on the median ranks derived from expert panel opinion and their rank-sum based conversion to importance scores, the category and item weights were calculated. RESULT: In total, 59 panelists from 19 countries participated in selection and ranking of the items and categories. Final METRICS tool included 30 items within 9 categories. According to their weights, the categories were in descending order of importance: study design, imaging data, image processing and feature extraction, metrics and comparison, testing, feature processing, preparation for modeling, segmentation, and open science. A web application and a repository were developed to streamline the calculation of the METRICS score and to collect feedback from the radiomics community. CONCLUSION: In this work, we developed a scoring tool for assessing the methodological quality of the radiomics research, with a large international panel and a modified Delphi protocol. With its conditional format to cover methodological variations, it provides a well-constructed framework for the key methodological concepts to assess the quality of radiomic research papers. CRITICAL RELEVANCE STATEMENT: A quality assessment tool, METhodological RadiomICs Score (METRICS), is made available by a large group of international domain experts, with transparent methodology, aiming at evaluating and improving research quality in radiomics and machine learning. KEY POINTS: • A methodological scoring tool, METRICS, was developed for assessing the quality of radiomics research, with a large international expert panel and a modified Delphi protocol. • The proposed scoring tool presents expert opinion-based importance weights of categories and items with a transparent methodology for the first time. • METRICS accounts for varying use cases, from handcrafted radiomics to entirely deep learning-based pipelines. • A web application has been developed to help with the calculation of the METRICS score ( https://metricsscore.github.io/metrics/METRICS.html ) and a repository created to collect feedback from the radiomics community ( https://github.com/metricsscore/metrics ).

Multiple sclerosis versus cerebral small vessel disease in MRI: a practical approach using qualitative and quantitative signal intensity differences in white matter lesions.

BACKGROUND: Multiple sclerosis (MS) and cerebral small vessel disease (CSVD) are relatively common radiological entities that occasionally necessitate differential diagnosis. PURPOSE: To investigate the differences in magnetic resonance imaging (MRI) signal intensity (SI) between MS and CSVD related white matter lesions. MATERIAL AND METHODS: On 1.5-T and 3-T MRI scanners, 50 patients with MS (380 lesions) and 50 patients with CSVD (395 lesions) were retrospectively evaluated. Visual inspection was used to conduct qualitative analysis on diffusion-weighted imaging (DWI)_b1000 to determine relative signal intensity. The thalamus served as the reference for quantitative analysis based on SI ratio (SIR). The statistical analysis utilized univariable and multivariable methods. There were analyses of patient and lesion datasets. On a dataset restricted by age (30-50 years), additional evaluations, including unsupervised fuzzy c-means clustering, were performed. RESULTS: Using both quantitative and qualitative features, the optimal model achieved a 100% accuracy, sensitivity, and specificity with an area under the curve (AUC) of 1 in patient-wise analysis. With an AUC of 0.984, the best model achieved a 94% accuracy, sensitivity, and specificity when using only quantitative features. The model's accuracy, sensitivity, and specificity were 91.9%, 84.6%, and 95.8%, respectively, when using the age-restricted dataset. Independent predictors were T2_SIR_max (optimal cutoff=2.1) and DWI_b1000_SIR_mean (optimal cutoff=1.1). Clustering also performed well with an accuracy, sensitivity, and specificity of 86.5%, 70.6%, and 100%, respectively, in the age-restricted dataset. CONCLUSION: SI characteristics derived from DWI_b1000 and T2-weighted-based MRI demonstrate excellent performance in differentiating white matter lesions caused by MS and CSVD.

Self-reporting with checklists in artificial intelligence research on medical imaging: a systematic review based on citations of CLAIM.

OBJECTIVE: To evaluate the usage of a well-known and widely adopted checklist, Checklist for Artificial Intelligence in Medical imaging (CLAIM), for self-reporting through a systematic analysis of its citations. METHODS: Google Scholar, Web of Science, and Scopus were used to search for citations (date, 29 April 2023). CLAIM's use for self-reporting with proof (i.e., filled-out checklist) and other potential use cases were systematically assessed in research papers. Eligible papers were evaluated independently by two readers, with the help of automatic annotation. Item-by-item confirmation analysis on papers with checklist proof was subsequently performed. RESULTS: A total of 391 unique citations were identified from three databases. Of the 118 papers included in this study, 12 (10%) provided a proof of self-reported CLAIM checklist. More than half (70; 59%) only mentioned some sort of adherence to CLAIM without providing any proof in the form of a checklist. Approximately one-third (36; 31%) cited the CLAIM for reasons unrelated to their reporting or methodological adherence. Overall, the claims on 57 to 93% of the items per publication were confirmed in the item-by-item analysis, with a mean and standard deviation of 81% and 10%, respectively. CONCLUSION: Only a small proportion of the publications used CLAIM as checklist and supplied filled-out documentation; however, the self-reported checklists may contain errors and should be approached cautiously. We hope that this systematic citation analysis would motivate artificial intelligence community about the importance of proper self-reporting, and encourage researchers, journals, editors, and reviewers to take action to ensure the proper usage of checklists. CLINICAL RELEVANCE STATEMENT: Only a small percentage of the publications used CLAIM for self-reporting with proof (i.e., filled-out checklist). However, the filled-out checklist proofs may contain errors, e.g., false claims of adherence, and should be approached cautiously. These may indicate inappropriate usage of checklists and necessitate further action by authorities. KEY POINTS: • Of 118 eligible papers, only 12 (10%) followed the CLAIM checklist for self-reporting with proof (i.e., filled-out checklist). More than half (70; 59%) only mentioned some kind of adherence without providing any proof. • Overall, claims on 57 to 93% of the items were valid in item-by-item confirmation analysis, with a mean and standard deviation of 81% and 10%, respectively. • Even with the checklist proof, the items declared may contain errors and should be approached cautiously.

Pancreatic steatosis is an independent risk factor for post-transplant diabetes mellitus in kidney transplant patients.

BACKGROUND AND AIM: Post-transplant diabetes mellitus (PTDM) is associated with an increased risk of post-transplant cardiovascular diseases, and several risk factors of PTDM have been shown in the literature. Yet, the relationship between hepatic and pancreatic steatosis with post-transplant diabetes mellitus remains vague. We aimed to evaluate pancreatic steatosis, a novel component of metabolic syndrome, and hepatic steatosis association with post-transplant diabetes mellitus in a single-center retrospective cohort study conducted on kidney transplant recipients. METHOD: We have performed a single-center retrospective cohort study involving all kidney transplant recipients. We have utilized pretransplant Fibrosis-4, nonalcoholic fatty liver disease fibrosis score, and abdominal computed tomography for the assessment of visceral steatosis status. RESULTS: We have included 373 kidney transplant recipients with a mean follow-up period of 32 months in our final analysis. Post-transplant diabetes mellitus risk is associated with older age (p < .001), higher body-mass index (p < .001), nonalcoholic fatty liver disease-fibrosis score (p = .002), hepatic (p < .001) or pancreatic (p < .001) steatosis on imaging and higher pre-transplant serum triglyceride (p = .003) and glucose levels (p = .001) after multivariate analysis. CONCLUSION: Our study illustrates that recipients' pancreatic steatosis is an independent predictive factor for post-transplant diabetes mellitus including in kidney transplant patients.

Large language models in radiology: fundamentals, applications, ethical considerations, risks, and future directions.

With the advent of large language models (LLMs), the artificial intelligence revolution in medicine and radiology is now more tangible than ever. Every day, an increasingly large number of articles are published that utilize LLMs in radiology. To adopt and safely implement this new technology in the field, radiologists should be familiar with its key concepts, understand at least the technical basics, and be aware of the potential risks and ethical considerations that come with it. In this review article, the authors provide an overview of the LLMs that might be relevant to the radiology community and include a brief discussion of their short history, technical basics, ChatGPT, prompt engineering, potential applications in medicine and radiology, advantages, disadvantages and risks, ethical and regulatory considerations, and future directions.

Reproducibility of rCBV in glioblastomas using T2*-weighted perfusion MRI: an evaluation of sampling, normalization, and experience.

PURPOSE: The reproducibility of relative cerebral blood volume (rCBV) measurements among readers with different levels of experience is a concern. This study aimed to investigate the inter-reader reproducibility of rCBV measurement of glioblastomas using the hotspot method in dynamic susceptibility contrast perfusion magnetic resonance imaging (DSC-MRI) with various strategies. METHODS: In this institutional review board-approved single-center study, 30 patients with glioblastoma were retrospectively evaluated with DSC-MRI at a 3.0 Tesla scanner. Three groups of reviewers, including neuroradiologists, general radiologists, and radiology residents, calculated the rCBV based on the number of regions of interest (ROIs) and reference areas. For statistical analysis of feature reproducibility, the intraclass correlation coefficient (ICC) and Bland-Altman plots were used. Analyses were made among individuals, reader groups, reader-group pooling, and a population that contained all of them. RESULTS: For individuals, the highest inter-reader reproducibility was observed between neuroradiologists [ICC: 0.527; 95% confidence interval (CI): 0.21-0.74] and between residents (ICC: 0.513; 95% CI: 0.20-0.73). There was poor reproducibility in the analyses of individuals with different levels of experience (ICC range: 0.296-0.335) and in reader-wise and group-wise pooling (ICC range: 0.296-0.335 and 0.397-0.427, respectively). However, an increase in ICC values was observed when five ROIs were used. In an analysis of all strategies, the ICC for the centrum semiovale was significantly higher than that for contralateral white matter (P < 0.001). CONCLUSION: The inter-reader reproducibility of rCBV measurement was poor to moderate regardless of whether it was calculated by neuroradiologists, general radiologists, or residents, which may indicate the need for automated methods. Choosing five ROIs and using the centrum semiovale as a reference area may increase reliability for all users.