Clinical impact and potential utility of non-enhanced computed tomography performed immediately after transarterial chemoembolization for hepatocellular carcinoma.

Background: Intratumoral lipiodol deposition following transarterial chemoembolization (TACE) is associated with the prognosis of hepatocellular carcinoma (HCC) patients. However, there is insufficient evidence regarding the actual clinical significance of the imaging tests conducted to evaluate the lipiodol uptake after TACE. This study evaluates the clinical impact and potential utility of performing immediate post-TACE non-enhanced computed tomography (NECT) on the treatment of HCC. Methods: This retrospective study at a tertiary referral center included patients undergoing their first session of conventional TACE for initial treatment of HCC from November 2021 to December 2022 with available immediate post-TACE NECT. Patients were categorized based on lipiodol uptake into Cohorts A (incomplete uptake with additional treatment before the first follow-up 1 month after TACE), B incomplete uptake without additional treatment before first follow-up), and C (complete uptake). Survival curves for the time to progression (TTP) were estimated using the Kaplan-Meier method and were compared by using the log-rank test. Results: Out of 189 patients, 58 (29.6%) showed incomplete lipiodol uptake; 2 in Cohort A and 56 in Cohort B. Cohort C included 131 patients (69.3%). Cohort B had the highest rate of residual viable tumor (48.2%) 1 month after TACE, compared to the other cohorts (0% in Cohort A and 32.1% in Cohort C). The median TTP of Cohort B was 7.9 months [95% confidence interval (CI): 4.6-15.7 months], significantly shorter than the 15.4 months (95% CI: 10.9-20.9 months) for Cohort C (P=0.03). During follow-up, no progression occurred in Cohort A. Conclusions: Assessment of lipiodol uptake by performing immediate post-TACE NECT can stratify HCC patients and facilitate early prediction of therapeutic response. Identifying suboptimal lipiodol uptake immediately after TACE can aid future treatment adjustments and potentially improving oncologic outcomes.

Impact of white matter hyperintensity volumes estimated by automated methods using deep learning on stroke outcomes in small vessel occlusion stroke.

Introduction: Although white matter hyperintensity (WMH) shares similar vascular risk and pathology with small vessel occlusion (SVO) stroke, there were few studies to evaluate the impact of the burden of WMH volume on early and delayed stroke outcomes in SVO stroke. Materials and methods: Using a multicenter registry database, we enrolled SVO stroke patients between August 2013 and November 2022. The WMH volume was estimated by automated methods using deep learning (VUNO Med-DeepBrain, Seoul, South Korea), which was a commercially available segmentation model. After propensity score matching (PSM), we evaluated the impact of WMH volume on early neurological deterioration (END) and poor functional outcomes at 3-month modified Ranking Scale (mRS), defined as mRS score >2 at 3 months, after an SVO stroke. Results: Among 1,718 SVO stroke cases, the prevalence of subjects with severe WMH (Fazekas score ≥ 3) was 68.9%. After PSM, END and poor functional outcomes at 3-month mRS (mRS > 2) were higher in the severe WMH group (END: 6.9 vs. 13.5%, p < 0.001; 3-month mRS > 2: 11.4 vs. 24.7%, p < 0.001). The logistic regression analysis using the PSM cohort showed that total WMH volume increased the risk of END [odd ratio [OR], 95% confidence interval [CI]; 1.01, 1.00-1.02, p = 0.048] and 3-month mRS > 2 (OR, 95% CI; 1.02, 1.01-1.03, p < 0.001). Deep WMH was associated with both END and 3-month mRS > 2, but periventricular WMH was associated with 3-month mRS > 2 only. Conclusion: This study used automated methods using a deep learning segmentation model to assess the impact of WMH burden on outcomes in SVO stroke. Our findings emphasize the significance of WMH burden in SVO stroke prognosis, encouraging tailored interventions for better patient care.

Comparing Diagnostic Accuracy of Radiologists versus GPT-4V and Gemini Pro Vision Using Image Inputs from Diagnosis Please Cases.

Background The diagnostic abilities of multimodal large language models (LLMs) using direct image inputs and the impact of the temperature parameter of LLMs remain unexplored. Purpose To investigate the ability of GPT-4V and Gemini Pro Vision in generating differential diagnoses at different temperatures compared with radiologists using Radiology Diagnosis Please cases. Materials and Methods This retrospective study included Diagnosis Please cases published from January 2008 to October 2023. Input images included original images and captures of the textual patient history and figure legends (without imaging findings) from PDF files of each case. The LLMs were tasked with providing three differential diagnoses, repeated five times at temperatures 0, 0.5, and 1. Eight subspecialty-trained radiologists solved cases. An experienced radiologist compared generated and final diagnoses, considering the result correct if the generated diagnoses included the final diagnosis after five repetitions. Accuracy was assessed across models, temperatures, and radiology subspecialties, with statistical significance set at P < .007 after Bonferroni correction for multiple comparisons across the LLMs at the three temperatures and with radiologists. Results A total of 190 cases were included in neuroradiology (n = 53), multisystem (n = 27), gastrointestinal (n = 25), genitourinary (n = 23), musculoskeletal (n = 17), chest (n = 16), cardiovascular (n = 12), pediatric (n = 12), and breast (n = 5) subspecialties. Overall accuracy improved with increasing temperature settings (0, 0.5, 1) for both GPT-4V (41% [78 of 190 cases], 45% [86 of 190 cases], 49% [93 of 190 cases], respectively) and Gemini Pro Vision (29% [55 of 190 cases], 36% [69 of 190 cases], 39% [74 of 190 cases], respectively), although there was no evidence of a statistically significant difference after Bonferroni adjustment (GPT-4V, P = .12; Gemini Pro Vision, P = .04). The overall accuracy of radiologists (61% [115 of 190 cases]) was higher than that of Gemini Pro Vision at temperature 1 (T1) (P < .001), while no statistically significant difference was observed between radiologists and GPT-4V at T1 after Bonferroni adjustment (P = .02). Radiologists (range, 45%-88%) outperformed the LLMs at T1 (range, 24%-75%) in most subspecialties. Conclusion Using direct radiologic image inputs, GPT-4V and Gemini Pro Vision showed improved diagnostic accuracy with increasing temperature settings. Although GPT-4V slightly underperformed compared with radiologists, it nonetheless demonstrated promising potential as a supportive tool in diagnostic decision-making. © RSNA, 2024 See also the editorial by Nishino and Ballard in this issue.

N-(2,3-dihydroxypropyl) carbamoyl side chain: a potentially significant factor for recurrent iodinated contrast medium-related adverse drug reactions.

PURPOSE: To determine whether switching to contrast media based on the sharing of N-(2,3-dihydroxypropyl) carbamoyl side chain reduces the recurrence of iodinated contrast media (ICM)-associated adverse drug reactions (ADRs). MATERIALS AND METHODS: This single-center retrospective study included 2133 consecutive patients (mean age ± SD, 56.1 ± 11.4 years; male, 1052 [49.3%]) who had a history of ICM-associated ADRs and underwent contrast-enhanced CT examinations. The per-patient and per-exam-based recurrence ADR rates were compared between cases of switching and non-switching the ICM from ICMs that caused the previous ADRs, and between cases that used ICMs with common and different carbamoyl side chains from ICMs that caused the previous ADRs. Downgrade rates (no recurrence or the occurrence of ADR less severe than index ADRs) were also compared. Propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) analysis were additionally performed. RESULTS: In per-patient analysis, switching of ICM showed a lower recurrence rate (switching, 10.4% [100/965] vs. non-switching, 28.4% [332/1168]), with the adjusted odds ratio (OR) of 0.27 (95% CI: 0.21, 0.34; p < 0.001). The result was consistent in PSM (OR, 0.29 [95% CI: 0.22, 0.39]; p < 0.001), IPTW (OR, 0.28 [95% CI: 0.22, 0.36]; p < 0.001), and in per-exam analysis (5.5% vs. 13.8%; OR, 0.32 [95% CI: 0.27, 0.37]; p < 0.001). There was lower per-exam recurrence (5.0% [195/3938] vs. 7.8% [79/1017]; OR, 0.63 [95% CI: 0.47, 0.83]; p = 0.001) and higher downgrade rates (95.6% [3764/3938] vs. 93.3% [949/1017]; OR, 1.51 [95% CI: 1.12, 2.03]; p = 0.006) when using different side chain groups. CONCLUSION: Switching to an ICM with a different carbamoyl side chain reduced the recurrent ADRs and their severity during subsequent examinations. CLINICAL RELEVANCE STATEMENT: Switching to an iodinated contrast media with a different carbamoyl side chain reduced the recurrent adverse drug reactions and their severity during subsequent examinations.

Factors to predict recurrence after epidural blood patch in patients with spontaneous intracranial hypotension.

OBJECTIVES: This study aimed to identify predictors for the recurrence of spontaneous intracranial hypotension (SIH) after epidural blood patch (EBP). BACKGROUND: Epidural blood patch is the main treatment option for SIH; however, the characteristics of patients who experience relapse after successful EBP treatment for SIH remain understudied. METHODS: In this exploratory, retrospective, case-control study, we included 19 patients with SIH recurrence after EBP and 36 age- and sex-matched patients without recurrence from a single tertiary medical institution. We analyzed clinical characteristics, neuroimaging findings, and volume changes in intracranial structures after EBP treatment. Machine learning methods were utilized to predict the recurrence of SIH after EBP treatment. RESULTS: There were no significant differences in clinical features between the recurrence and no-recurrence groups. Among brain magnetic resonance imaging signs, diffuse pachymeningeal enhancement and cerebral venous dilatation were more prominent in the recurrence group than no-recurrence group after EBP (14/19 [73%] vs. eight of 36 [22%] patients, p = 0.001; 11/19 [57%] vs. seven of 36 [19%] patients, p = 0.010, respectively). The midbrain-pons angle decreased in the recurrence group compared to the no-recurrence group after EBP, at a mean (standard deviation [SD]) of -12.0 [16.7] vs. +1.8[18.3]° (p = 0.048). In volumetric analysis, volume changes after EBP were smaller in the recurrence group than in the no-recurrence group in intracranial cerebrospinal fluid (mean [SD] -11.6 [15.3] vs. +4.8 [17.1] mL, p = 0.001) and ventricles (mean [SD] +1.0 [2.0] vs. +2.0 [2.5] mL, p = 0.003). Notably, the random forest classifier indicated that the model constructed with brain volumetry was more accurate in discriminating SIH recurrence (area under the curve = 0.80 vs. 0.52). CONCLUSION: Our study suggests that volumetric analysis of intracranial structures may aid in predicting recurrence after EBP treatment in patients with SIH.

Development of statistical auto-segmentation method for diffusion restriction gray matter lesions in patients with newly diagnosed sporadic Creutzfeldt-Jakob disease.

Quantification of diffusion restriction lesions in sporadic Creutzfeldt-Jakob disease (sCJD) may provide information of the disease burden. We aim to develop an automatic segmentation model for sCJD and to evaluate the volume of disease extent as a prognostic marker for overall survival. Fifty-six patients (mean age ± SD, 61.2 ± 9.9 years) were included from February 2000 to July 2020. A threshold-based segmentation was used to obtain abnormal signal intensity masks. Segmented volumes were compared with the visual grade. The Dice similarity coefficient was calculated to measure the similarity between the automatic vs. manual segmentation. Cox proportional hazards regression analysis was performed to evaluate the volume of disease extent as a prognostic marker. The automatic segmentation showed good correlation with the visual grading. The cortical lesion volumes significantly increased as the visual grade aggravated (extensive: 112.9 ± 73.2; moderate: 45.4 ± 30.4; minimal involvement: 29.6 ± 18.1 mm3) (P < 0.001). The deep gray matter lesion volumes were significantly higher for positive than for negative involvement of the deep gray matter (5.6 ± 4.6 mm3 vs. 1.0 ± 1.3 mm3, P < 0.001). The mean Dice similarity coefficients were 0.90 and 0.94 for cortical and deep gray matter lesions, respectively. However, the volume of disease extent was not associated with worse overall survival (cortical extent: P = 0.07; deep gray matter extent: P = 0.12).

Comparative Performance of Susceptibility Map-Weighted MRI According to the Acquisition Planes in the Diagnosis of Neurodegenerative Parkinsonism.

OBJECTIVE: To evaluate the diagnostic performance of susceptibility map-weighted imaging (SMwI) taken in different acquisition planes for discriminating patients with neurodegenerative parkinsonism from those without. MATERIALS AND METHODS: This retrospective, observational, single-institution study enrolled consecutive patients who visited movement disorder clinics and underwent brain MRI and 18F-FP-CIT PET between September 2021 and December 2021. SMwI images were acquired in both the oblique (perpendicular to the midbrain) and the anterior commissure-posterior commissure (AC-PC) planes. Hyperintensity in the substantia nigra was determined by two neuroradiologists. 18F-FP-CIT PET was used as the reference standard. Inter-rater agreement was assessed using Cohen's kappa coefficient. The diagnostic performance of SMwI in the two planes was analyzed separately for the right and left substantia nigra. Multivariable logistic regression analysis with generalized estimating equations was applied to compare the diagnostic performance of the two planes. RESULTS: In total, 194 patients were included, of whom 105 and 103 had positive results on 18F-FP-CIT PET in the left and right substantia nigra, respectively. Good inter-rater agreement in the oblique (κ = 0.772/0.658 for left/right) and AC-PC planes (0.730/0.741 for left/right) was confirmed. The pooled sensitivities for two readers were 86.4% (178/206, left) and 83.3% (175/210, right) in the oblique plane and 87.4% (180/206, left) and 87.6% (184/210, right) in the AC-PC plane. The pooled specificities for two readers were 83.5% (152/182, left) and 82.0% (146/178, right) in the oblique plane, and 83.5% (152/182, left) and 86.0% (153/178, right) in the AC-PC plane. There were no significant differences in the diagnostic performance between the two planes (P > 0.05). CONCLUSION: There are no significant difference in the diagnostic performance of SMwI performed in the oblique and AC-PC plane in discriminating patients with parkinsonism from those without. This finding affirms that each institution may choose the imaging plane for SMwI according to their clinical settings.

Corrigendum: Development and validation of a deep learning-based automatic segmentation model for assessing intracranial volume: comparison with NeuroQuant, FreeSurfer, and SynthSeg.

[This corrects the article DOI: 10.3389/fneur.2023.1221892.].

Reporting Quality of Research Studies on AI Applications in Medical Images According to the CLAIM Guidelines in a Radiology Journal With a Strong Prominence in Asia.

OBJECTIVE: We aimed to evaluate the reporting quality of research articles that applied deep learning to medical imaging. Using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines and a journal with prominence in Asia as a sample, we intended to provide an insight into reporting quality in the Asian region and establish a journal-specific audit. MATERIALS AND METHODS: A total of 38 articles published in the Korean Journal of Radiology between June 2018 and January 2023 were analyzed. The analysis included calculating the percentage of studies that adhered to each CLAIM item and identifying items that were met by ≤ 50% of the studies. The article review was initially conducted independently by two reviewers, and the consensus results were used for the final analysis. We also compared adherence rates to CLAIM before and after December 2020. RESULTS: Of the 42 items in the CLAIM guidelines, 12 items (29%) were satisfied by ≤ 50% of the included articles. None of the studies reported handling missing data (item #13). Only one study respectively presented the use of de-identification methods (#12), intended sample size (#19), robustness or sensitivity analysis (#30), and full study protocol (#41). Of the studies, 35% reported the selection of data subsets (#10), 40% reported registration information (#40), and 50% measured inter and intrarater variability (#18). No significant changes were observed in the rates of adherence to these 12 items before and after December 2020. CONCLUSION: The reporting quality of artificial intelligence studies according to CLAIM guidelines, in our study sample, showed room for improvement. We recommend that the authors and reviewers have a solid understanding of the relevant reporting guidelines and ensure that the essential elements are adequately reported when writing and reviewing the manuscripts for publication.

Detection rate of brain MR and MR angiography for neuroimaging abnormality in patients with newly diagnosed left-sided infective endocarditis.

We aimed to investigate the detection rate of brain MR and MR angiography for neuroimaging abnormality in newly diagnosed left-sided infective endocarditis patients with/without neurological symptoms. This retrospective study included consecutive patients with definite or possible left-sided infective endocarditis according to the modified Duke criteria who underwent brain MRI and MR angiography between March 2015 and October 2020. The detection rate for neuroimaging abnormality on MRI was defined as the number of patients with positive brain MRI findings divided by the number of patients with left-sided infective endocarditis. Positive imaging findings included acute ischemic lesions, cerebral microbleeds, hemorrhagic lesions, and infectious aneurysms. In addition, aneurysm rupture rate and median period to aneurysm rupture were evaluated on follow-up studies. A total 115 patients (mean age: 55 years ± 19; 65 men) were included. The detection rate for neuroimaging abnormality was 77% (89/115). The detection rate in patients without neurological symptoms was 70% (56/80). Acute ischemic lesions, cerebral microbleeds, and hemorrhagic lesions including superficial siderosis and intracranial hemorrhage were detected on MRI in 56% (64/115), 57% (66/115), and 20% (23/115) of patients, respectively. In particular, infectious aneurysms were detected on MR angiography in 3% of patients (4/115), but MR angiography in 5 patients (4.3%) was insignificant for infectious aneurysm, which were detected using CT angiography (n = 3) and digital subtraction angiography (n = 2) during follow-up. Among the 9 infectious aneurysm patients, aneurysm rupture occurred in 4 (44%), with a median period of aneurysm rupture of 5 days. The detection rate of brain MRI for neuroimaging abnormality in newly diagnosed left-sided infective endocarditis patients was high (77%), even without neurological symptoms (70%).

Development and validation of a deep learning-based automatic segmentation model for assessing intracranial volume: comparison with NeuroQuant, FreeSurfer, and SynthSeg.

Background and purpose: To develop and validate a deep learning-based automatic segmentation model for assessing intracranial volume (ICV) and to compare the accuracy determined by NeuroQuant (NQ), FreeSurfer (FS), and SynthSeg. Materials and methods: This retrospective study included 60 subjects [30 Alzheimer's disease (AD), 21 mild cognitive impairment (MCI), 9 cognitively normal (CN)] from a single tertiary hospital for the training and validation group (50:10). The test group included 40 subjects (20 AD, 10 MCI, 10 CN) from the ADNI dataset. We propose a robust ICV segmentation model based on the foundational 2D UNet architecture trained with four types of input images (both single and multimodality using scaled or unscaled T1-weighted and T2-FLAIR MR images). To compare with our model, NQ, FS, and SynthSeg were also utilized in the test group. We evaluated the model performance by measuring the Dice similarity coefficient (DSC) and average volume difference. Results: The single-modality model trained with scaled T1-weighted images showed excellent performance with a DSC of 0.989 ± 0.002 and an average volume difference of 0.46% ± 0.38%. Our multimodality model trained with both unscaled T1-weighted and T2-FLAIR images showed similar performance with a DSC of 0.988 ± 0.002 and an average volume difference of 0.47% ± 0.35%. The overall average volume difference with our model showed relatively higher accuracy than NQ (2.15% ± 1.72%), FS (3.69% ± 2.93%), and SynthSeg (1.88% ± 1.18%). Furthermore, our model outperformed the three others in each subgroup of patients with AD, MCI, and CN subjects. Conclusion: Our deep learning-based automatic ICV segmentation model showed excellent performance for the automatic evaluation of ICV.

Detection rate of contrast-enhanced brain magnetic resonance imaging in patients with cognitive impairment.

INTRODUCTION: The number of brain MRI with contrast media performed in patients with cognitive impairment has increased without universal agreement. We aimed to evaluate the detection rate of contrast-enhanced brain MRI in patients with cognitive impairment. MATERIALS AND METHODS: This single-institution, retrospective study included 4,838 patients who attended outpatient clinics for cognitive impairment evaluation and underwent brain MRI with or without contrast enhancement from December 2015 to February 2020. Patients who tested positive for cognitive impairment were followed-up to confirm whether the result was true-positive and provide follow-up management. Detection rate was defined as the proportion of patients with true-positive results and was compared between groups with and without contrast enhancement. Individual matching in a 1:2 ratio according to age, sex, and year of test was performed. RESULTS: The overall detection rates of brain MRI with and without contrast media were 4.7% (57/1,203; 95% CI: 3.6%-6.1%) and 1.8% (65/3,635; 95% CI: 1.4%-2.3%), respectively (P<0.001); individual matching demonstrated similar results (4.7% and 1.9%). Among 1,203 patients with contrast media, 3.6% was only detectable with the aid of contrast media. The proportion of patients who underwent follow-up imaging or treatment for the detected lesions were significantly higher in the group with contrast media (2.0% and 0.6%, P < .001). CONCLUSIONS: Detection rate of brain MRI for lesions only detectable with contrast media in patients with cognitive impairment was not high enough and further study is needed to identify whom would truly benefit with contrast media.

Do radiomics or diffusion-tensor images provide additional information to predict brain amyloid-beta positivity?

The aim of the present study was to predict amyloid-beta positivity using a conventional T1-weighted image, radiomics, and a diffusion-tensor image obtained by magnetic resonance imaging (MRI). We included 186 patients with mild cognitive impairment (MCI) who underwent Florbetaben positron emission tomography (PET), MRI (three-dimensional T1-weighted and diffusion-tensor images), and neuropsychological tests at the Asan Medical Center. We developed a stepwise machine learning algorithm using demographics, T1 MRI features (volume, cortical thickness and radiomics), and diffusion-tensor image to distinguish amyloid-beta positivity on Florbetaben PET. We compared the performance of each algorithm based on the MRI features used. The study population included 72 patients with MCI in the amyloid-beta-negative group and 114 patients with MCI in the amyloid-beta-positive group. The machine learning algorithm using T1 volume performed better than that using only clinical information (mean area under the curve [AUC]: 0.73 vs. 0.69, p < 0.001). The machine learning algorithm using T1 volume showed better performance than that using cortical thickness (mean AUC: 0.73 vs. 0.68, p < 0.001) or texture (mean AUC: 0.73 vs. 0.71, p = 0.002). The performance of the machine learning algorithm using fractional anisotropy in addition to T1 volume was not better than that using T1 volume alone (mean AUC: 0.73 vs. 0.73, p = 0.60). Among MRI features, T1 volume was the best predictor of amyloid PET positivity. Radiomics or diffusion-tensor images did not provide additional benefits.

Development and validation of an automatic classification algorithm for the diagnosis of Alzheimer's disease using a high-performance interpretable deep learning network.

OBJECTIVES: To develop and validate an automatic classification algorithm for diagnosing Alzheimer's disease (AD) or mild cognitive impairment (MCI). METHODS AND MATERIALS: This study evaluated a high-performance interpretable network algorithm (TabNet) and compared its performance with that of XGBoost, a widely used classifier. Brain segmentation was performed using a commercially approved software. TabNet and XGBoost were trained on the volumes or radiomics features of 102 segmented regions for classifying subjects into AD, MCI, or cognitively normal (CN) groups. The diagnostic performances of the two algorithms were compared using areas under the curves (AUCs). Additionally, 20 deep learning-based AD signature areas were investigated. RESULTS: Between December 2014 and March 2017, 161 AD, 153 MCI, and 306 CN cases were enrolled. Another 120 AD, 90 MCI, and 141 CN cases were included for the internal validation. Public datasets were used for external validation. TabNet with volume features had an AUC of 0.951 (95% confidence interval [CI], 0.947-0.955) for AD vs CN, which was similar to that of XGBoost (0.953 [95% CI, 0.951-0.955], p = 0.41). External validation revealed the similar performances of two classifiers using volume features (0.871 vs. 0.871, p = 0.86). Likewise, two algorithms showed similar performances with one another in classifying MCI. The addition of radiomics data did not improve the performance of TabNet. TabNet and XGBoost focused on the same 13/20 regions of interest, including the hippocampus, inferior lateral ventricle, and entorhinal cortex. CONCLUSIONS: TabNet shows high performance in AD classification and detailed interpretation of the selected regions. CLINICAL RELEVANCE STATEMENT: Using a high-performance interpretable deep learning network, the automatic classification algorithm assisted in accurate Alzheimer's disease detection using 3D T1-weighted brain MRI and detailed interpretation of the selected regions. KEY POINTS: • MR volumetry data revealed that TabNet had a high diagnostic performance in differentiating Alzheimer's disease (AD) from cognitive normal cases, which was comparable with that of XGBoost. • The addition of radiomics data to the volume data did not improve the diagnostic performance of TabNet. • Both TabNet and XGBoost selected the clinically meaningful regions of interest in AD, including the hippocampus, inferior lateral ventricle, and entorhinal cortex.

Incidence and severity of nonionic low-osmolar iodinated contrast medium-related adverse drug reactions in the Republic of Korea: Comparison by generic.

We aimed to report the incidence and severity of nonionic low-osmolar iodine contrast medium (ICM)-related adverse drug reactions (ADRs) in the Republic of Korea, by analyzing data from our single tertiary institution and published Korean reports, and to determine whether there is a difference in the incidence of ICM-related ADR by ICM generics. A total of 1,161,419 consecutive contrast-enhanced computed tomography (CT) examinations between January 2016 and December 2021 at Asan Medical Center were included. A systematic search of the literature investigating the incidence of ICM-related ADR in the Republic of Korea published up to December 31, 2021 was performed. We pooled these outcomes with those of our study using a binomial-normal model, and the pooled incidences of ADRs were compared among ICM generics using chi-square tests. Seven studies with a total of 2,570,986 contrast-enhanced CT examinations from 12 institutions were included. The pooled incidences of overall, mild, moderate, and severe ICM-related ADRs in the Republic of Korea were 0.82% (95% CI: 0.61%-1.10%), 0.72% (95% CI: 0.50%-1.04%), 0.11% (95% CI: 0.08%-0.15%), and 0.013% (95% CI: 0.010%-0.018%), respectively. In multiple pairwise comparisons, there were no significant differences in the overall incidence of ADRs between ICM generics, except iomeprol versus iobitridol and iomeprol versus iohexol. For moderate and severe ADRs, there were no significant differences in ADR incidence between ICM generics. The incidence of moderate and severe ICM-related ADRs did not differ among ICM generics. Our results suggest that no restriction is required for selection among nonionic low-osmolar ICMs.

Risk estimation for idiopathic normal-pressure hydrocephalus: development and validation of a brain morphometry-based nomogram.

OBJECTIVES: To develop and validate a nomogram based on MRI features for predicting iNPH. METHODS: Patients aged ≥ 60 years (clinically diagnosed with iNPH, Parkinson's disease, or Alzheimer's disease or healthy controls) who underwent MRI including three-dimensional T1-weighted volumetric MRI were retrospectively identified from two tertiary referral hospitals (one hospital for derivation set and the other for validation set). Clinical and imaging features for iNPH were assessed. Deep learning-based brain segmentation software was used for 3D volumetry. A prediction model was developed using logistic regression and transformed into a nomogram. The performance of the nomogram was assessed with respect to discrimination and calibration abilities. The nomogram was internally and externally validated. RESULTS: A total of 452 patients (mean age ± SD, 73.2 ± 6.5 years; 200 men) were evaluated as the derivation set. One hundred eleven and 341 patients were categorized into the iNPH and non-iNPH groups, respectively. In multivariable analysis, high-convexity tightness (odds ratio [OR], 35.1; 95% CI: 4.5, 275.5), callosal angle < 90° (OR, 12.5; 95% CI: 3.1, 50.0), and normalized lateral ventricle volume (OR, 4.2; 95% CI: 2.7, 6.7) were associated with iNPH. The nomogram combining these three variables showed an area under the curve of 0.995 (95% CI: 0.991, 0.999) in the study sample, 0.994 (95% CI: 0.990, 0.998) in the internal validation sample, and 0.969 (95% CI: 0.940, 0.997) in the external validation sample. CONCLUSION: A brain morphometry-based nomogram including high-convexity tightness, callosal angle < 90°, and normalized lateral ventricle volume can help accurately estimate the probability of iNPH. KEY POINTS: • The nomogram with MRI findings (high-convexity tightness, callosal angle, and normalized lateral ventricle volume) helped in predicting the probability of idiopathic normal-pressure hydrocephalus. • The nomogram may facilitate the prediction of idiopathic normal-pressure hydrocephalus and consequently avoid unnecessary invasive procedures such as the cerebrospinal fluid tap test, drainage test, and cerebrospinal fluid shunt surgery.