Automated detection of steno-occlusive lesion on time-of-flight magnetic resonance angiography: an observer performance study.

BACKGROUND AND PURPOSE: Intracranial steno-occlusive lesions are responsible for acute ischemic stroke. However, the clinical benefits of artificial intelligence-based methods for detecting pathologic lesions in intracranial arteries have not been evaluated. We aimed to validate the clinical utility of an artificial intelligence model for detecting steno-occlusive lesions in the intracranial arteries. MATERIALS AND METHODS: Overall, 138 TOF-MRA images were collected from two institutions, which served as internal (n = 62) and external (n = 76) test sets, respectively. Each study was reviewed by five radiologists (two neuroradiologists and three radiology residents) to compare the usage and non-usage of our proposed artificial intelligence model for TOF-MRA interpretation. They identified the steno-occlusive lesions and recorded their reading time. Observer performance was assessed using the area under the Jackknife free-response receiver operating characteristic curve and reading time for comparison. RESULTS: The average area under the Jackknife free-response receiver operating characteristic curve for the five radiologists demonstrated an improvement from 0.70 without artificial intelligence to 0.76 with artificial intelligence (P = .027). Notably, this improvement was most pronounced among the three radiology residents, whose performance metrics increased from 0.68 to 0.76 (P = .002). Despite an increased reading time upon using artificial intelligence, there was no significant change among the readings by radiology residents. Moreover, the use of artificial intelligence resulted in improved inter-observer agreement among the reviewers (the intraclass correlation coefficient increased from 0.734 to 0.752). CONCLUSIONS: Our proposed artificial intelligence model offers a supportive tool for radiologists, potentially enhancing the accuracy of detecting intracranial steno-occlusion lesions on TOF-MRA. Less-experienced readers may benefit the most from this model.ABBREVIATIONS: AI = Artificial intelligence; AUC = Area under the receiver operating characteristic curve; AUFROC = Area under the Jackknife free-response receiver operating characteristic curve; DL = Deep learning; ICC = Intraclass correlation coefficient; IRB = Institutional Review Boards; JAFROC = Jackknife free-response receiver operating characteristic.

Glymphatic function assessment in Parkinson's disease using diffusion tensor image analysis along the perivascular space.

INTRODUCTION: Glymphatic dysfunction can contribute to α-synucleinopathies. We examined glymphatic function in idiopathic Parkinson's disease (PD) utilizing Diffusion Tensor Image Analysis aLong the Perivascular Space (DTI-ALPS). METHODS: This study enrolled consecutive patients diagnosed with de novo PD between June 2017 and March 2019 who underwent brain DTI with concurrent 123I-2ß-carbomethoxy-3ß-(4-iodophenyl)-N-(3-fluoropropyl)-nortropane (123I-FP-CIT) SPECT, and age- and sex-matched controls. From DTI-ALPS, the ALPS-index was calculated as a ratio of diffusivities along the x-axis in the region of neural fibers passing vertically to the diffusivities perpendicular to them, which reflected perivascular water motion at the lateral ventricular body level. The ALPS-index of the PD and control groups was compared using Student's t-test; its correlations with clinical scores for motor and cognition (UPDRS-III, MMSE, and MoCA) and striatal dopamine transporter uptake measured by 123I-FP-CIT specific binding ratios (SBRs) were examined using a correlation coefficient. RESULTS: In all, 54 patients in the de novo PD group (31 women, 23 men; mean age, 68.9 ± 9.4 years) and 54 in the control group (mean age, 69.0 ± 10.5 years) were included. The ALPS-index was lower in the PD group than in the controls (1.51 ± 0.22 versus 1.66 ± 0.20; P < 0.001). In the PD group, the ALPS-index negatively correlated with the UPDRS-III score (r = -0.526), and positively correlated with the MMSE (r = 0.377) and MoCA scores (r = 0.382) (all, P < 0.05). No correlation was observed between the ALPS-index and striatal 123I-FP-CIT SBRs (P > 0.05). CONCLUSIONS: DTI-ALPS can reveal glymphatic dysfunction in patients with PD, whose severity correlated with motor and cognitive dysfunction, but not striatal dopamine transporter uptake.

Altered Brain Glymphatic Flow at Diffusion-Tensor MRI in Rapid Eye Movement Sleep Behavior Disorder.

Background Brain glymphatic dysfunction may contribute to the development of α-synucleinopathies. Yet, noninvasive imaging and quantification remain lacking. Purpose To examine glymphatic function of the brain in isolated rapid eye movement sleep behavior disorder (RBD) and its relevance to phenoconversion with use of diffusion-tensor imaging (DTI) analysis along the perivascular space (ALPS). Materials and Methods This prospective study included consecutive participants diagnosed with RBD, age- and sex-matched control participants, and participants with Parkinson disease (PD) who were enrolled and examined between May 2017 and April 2020. All study participants underwent 3.0-T brain MRI including DTI, susceptibility-weighted and susceptibility map-weighted imaging, and/or dopamine transporter imaging using iodine 123-2ß-carbomethoxy-3ß-(4-iodophenyl)-N-(3-fluoropropyl)-nortropane SPECT at the time of participation. Phenoconversion status to α-synucleinopathies was unknown at the time of MRI. Participants were regularly followed up and monitored for any signs of α-synucleinopathies. The ALPS index reflecting glymphatic activity was calculated by a ratio of the diffusivities along the x-axis in the projection and association neural fibers to the diffusivities perpendicular to them and compared according to the groups with use of the Kruskal-Wallis and Mann-Whitney U tests. The phenoconversion risk in participants with RBD was evaluated according to the ALPS index with use of a Cox proportional hazards model. Results Twenty participants diagnosed with RBD (12 men; median age, 73 years [IQR, 66-76 years]), 20 control participants, and 20 participants with PD were included. The median ALPS index was lower in the group with RBD versus controls (1.53 vs 1.72; P = .001) but showed no evidence of a difference compared with the group with PD (1.49; P = .68). The conversion risk decreased with an increasing ALPS index (hazard ratio, 0.57 per 0.1 increase in the ALPS index [95% CI: 0.35, 0.93]; P = .03). Conclusion DTI-ALPS in RBD demonstrated a more severe reduction of glymphatic activity in individuals with phenoconversion to α-synucleinopathies. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Filippi and Balestrino in this issue.

A Nationwide Web-Based Survey of Neuroradiologists' Perceptions of Artificial Intelligence Software for Neuro-Applications in Korea.

OBJECTIVE: We aimed to investigate current expectations and clinical adoption of artificial intelligence (AI) software among neuroradiologists in Korea. MATERIALS AND METHODS: In April 2022, a 30-item online survey was conducted by neuroradiologists from the Korean Society of Neuroradiology (KSNR) to assess current user experiences, perceptions, attitudes, and future expectations regarding AI for neuro-applications. Respondents with experience in AI software were further investigated in terms of the number and type of software used, period of use, clinical usefulness, and future scope. Results were compared between respondents with and without experience with AI software through multivariable logistic regression and mediation analyses. RESULTS: The survey was completed by 73 respondents, accounting for 21.9% (73/334) of the KSNR members; 72.6% (53/73) were familiar with AI and 58.9% (43/73) had used AI software, with approximately 86% (37/43) using 1-3 AI software programs and 51.2% (22/43) having up to one year of experience with AI software. Among AI software types, brain volumetry software was the most common (62.8% [27/43]). Although 52.1% (38/73) assumed that AI is currently useful in practice, 86.3% (63/73) expected it to be useful for clinical practice within 10 years. The main expected benefits were reducing the time spent on repetitive tasks (91.8% [67/73]) and improving reading accuracy and reducing errors (72.6% [53/73]). Those who experienced AI software were more familiar with AI (adjusted odds ratio, 7.1 [95% confidence interval, 1.81-27.81]; P = 0.005). More than half of the respondents with AI software experience (55.8% [24/43]) agreed that AI should be included in training curriculums, while almost all (95.3% [41/43]) believed that radiologists should coordinate to improve its performance. CONCLUSION: A majority of respondents experienced AI software and showed a proactive attitude toward adopting AI in clinical practice, suggesting that AI should be incorporated into training and active participation in AI development should be encouraged.

Deep learning regressor model based on nigrosome MRI in Parkinson syndrome effectively predicts striatal dopamine transporter-SPECT uptake.

PURPOSE: Nigrosome imaging using susceptibility-weighted imaging (SWI) and dopamine transporter imaging using 123I-2ß-carbomethoxy-3ß-(4-iodophenyl)-N-(3-fluoropropyl)-nortropane (123I-FP-CIT) single-photon emission computerized tomography (SPECT) can evaluate Parkinsonism. Nigral hyperintensity from nigrosome-1 and striatal dopamine transporter uptake are reduced in Parkinsonism; however, quantification is only possible with SPECT. Here, we aimed to develop a deep-learning-based regressor model that can predict striatal 123I-FP-CIT uptake on nigrosome magnetic resonance imaging (MRI) as a biomarker for Parkinsonism. METHODS: Between February 2017 and December 2018, participants who underwent 3 T brain MRI including SWI and 123I-FP-CIT SPECT based on suspected Parkinsonism were included. Two neuroradiologists evaluated the nigral hyperintensity and annotated the centroids of nigrosome-1 structures. We used a convolutional neural network-based regression model to predict striatal specific binding ratios (SBRs) measured via SPECT using the cropped nigrosome images. The correlation between measured and predicted SBRs was evaluated. RESULTS: We included 367 participants (203 women (55.3%); age, 69.0 ± 9.2 [range, 39-88] years). Random data from 293 participants (80%) were used for training. In the test set (74 participants [20%]), the measured and predicted 123I-FP-CIT SBRs were significantly lower with the loss of nigral hyperintensity (2.31 ± 0.85 vs. 2.44 ± 0.90) than with intact nigral hyperintensity (4.16 ± 1.24 vs. 4.21 ± 1.35, P < 0.01). The sorted measured 123I-FP-CIT SBRs and the corresponding predicted values were significantly and positively correlated (ρc = 0.7443; 95% confidence interval, 0.6216-0.8314; P < 0.01). CONCLUSION: A deep learning-based regressor model effectively predicted striatal 123I-FP-CIT SBRs based on nigrosome MRI with high correlation using manually-measured values, enabling nigrosome MRI as a biomarker for nigrostriatal dopaminergic degeneration in Parkinsonism.

Fully Automated Segmentation Models of Supratentorial Meningiomas Assisted by Inclusion of Normal Brain Images.

To train an automatic brain tumor segmentation model, a large amount of data is required. In this paper, we proposed a strategy to overcome the limited amount of clinically collected magnetic resonance image (MRI) data regarding meningiomas by pre-training a model using a larger public dataset of MRIs of gliomas and augmenting our meningioma training set with normal brain MRIs. Pre-operative MRIs of 91 meningioma patients (171 MRIs) and 10 non-meningioma patients (normal brains) were collected between 2016 and 2019. Three-dimensional (3D) U-Net was used as the base architecture. The model was pre-trained with BraTS 2019 data, then fine-tuned with our datasets consisting of 154 meningioma MRIs and 10 normal brain MRIs. To increase the utility of the normal brain MRIs, a novel balanced Dice loss (BDL) function was used instead of the conventional soft Dice loss function. The model performance was evaluated using the Dice scores across the remaining 17 meningioma MRIs. The segmentation performance of the model was sequentially improved via the pre-training and inclusion of normal brain images. The Dice scores improved from 0.72 to 0.76 when the model was pre-trained. The inclusion of normal brain MRIs to fine-tune the model improved the Dice score; it increased to 0.79. When employing BDL as the loss function, the Dice score reached 0.84. The proposed learning strategy for U-net showed potential for use in segmenting meningioma lesions.

[MRI Findings in Parkinson's Disease: Radiologic Assessment of Nigrostriatal Degeneration]. / 파킨슨병의 ìžê¸°ê³µëª ì˜ìƒ 소견: í‘ì§ˆì„ ì¡°ì²´ 변성의 ì˜ìƒí•™ì  평가.

Parkinson's disease (PD) is a movement disorder that develops due to degenerative loss of dopaminergic cells in the substantia nigra of the midbrain. Recent advances in MRI techniques have demonstrated various imaging findings that can reflect the underlying pathophysiological processes occurring in Parkinson's disease. Many imaging studies have shown that such findings can assist in the diagnosis of Parkinson's disease and its differentiation from atypical parkinsonism. In this review, we present MRI techniques that can be used in clinical assessment, such as nigrosome imaging and neuromelanin imaging, and we provide the detailed imaging features of Parkinson's disease reflecting nigrostriatal degeneration.

Deep learning-based diagnosis of Alzheimer's disease using brain magnetic resonance images: an empirical study.

The limited accessibility of medical specialists for Alzheimer's disease (AD) can make obtaining an accurate diagnosis in a timely manner challenging and may influence prognosis. We investigated whether VUNO Med-DeepBrain AD (DBAD) using a deep learning algorithm can be employed as a decision support service for the diagnosis of AD. This study included 98 elderly participants aged 60 years or older who visited the Seoul Asan Medical Center and the Korea Veterans Health Service. We administered a standard diagnostic assessment for diagnosing AD. DBAD and three panels of medical experts (ME) diagnosed participants with normal cognition (NC) or AD using T1-weighted magnetic resonance imaging. The accuracy (87.1% for DBAD and 84.3% for ME), sensitivity (93.3% for DBAD and 80.0% for ME), and specificity (85.5% for DBAD and 85.5% for ME) of both DBAD and ME for diagnosing AD were comparable; however, DBAD showed a higher trend in every analysis than ME diagnosis. DBAD may support the clinical decisions of physicians who are not specialized in AD; this may enhance the accessibility of AD diagnosis and treatment.

MRI-Based Demonstration of the Normal Glymphatic System in a Human Population: A Systematic Review.

Background: The glymphatic system has been described as one that facilitates the exchange between the cerebrospinal fluid (CSF) and interstitial fluid, and many recent studies have demonstrated glymphatic flow based on magnetic resonance imaging (MRI). We aim to systematically review the studies demonstrating a normal glymphatic flow in a human population using MRI and to propose a detailed glymphatic imaging protocol. Methods: We searched the MEDLINE and EMBASE databases to identify studies with human participants involving MRI-based demonstrations of the normal glymphatic flow. We extracted data on the imaging sequence, imaging protocol, and the targeted anatomical structures on each study. Results: According to contrast-enhanced MRI studies, peak enhancement was sequentially detected first in the CSF space, followed by the brain parenchyma, the meningeal lymphatic vessel (MLV), and, finally, the cervical lymph nodes, corresponding with glymphatic flow and explaining the drainage into the MLV. Non-contrast flow-sensitive MRI studies revealed similar glymphatic inflow from the CSF space to the brain parenchyma and efflux of exchanged fluid from the brain parenchyma to the MLV. Conclusion: We may recommend T1-weighted contrast-enhanced MRI for visualizing glymphatic flow. Our result can increase understanding of the glymphatic system and may lay the groundwork for establishing central nervous system fluid dynamic theories and developing standardized imaging protocols.

Predictive accuracy of T2-FLAIR mismatch sign for the IDH-mutant, 1p/19q noncodeleted low-grade glioma: An updated systematic review and meta-analysis.

BACKGROUND: The T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign, has been considered a highly specific imaging biomarker of IDH-mutant, 1p/19q noncodeleted low-grade glioma. This systematic review and meta-analysis aimed to evaluate the diagnostic performance of T2-FLAIR mismatch sign for prediction of a patient with IDH-mutant, 1p/19q noncodeleted low-grade glioma, and identify the causes responsible for the heterogeneity across the included studies. METHODS: A systematic literature search in the Ovid-MEDLINE and EMBASE databases was performed for studies reporting the relevant topic before November 17, 2020. The pooled sensitivity and specificity values with their 95% confidence intervals were calculated using bivariate random-effects modeling. Meta-regression analyses were also performed to determine factors influencing heterogeneity. RESULTS: For all the 10 included cohorts from 8 studies, the pooled sensitivity was 40% (95% confidence interval [CI] 28-53%), and the pooled specificity was 100% (95% CI 95-100%). In the hierarchic summary receiver operating characteristic curve, the difference between the 95% confidence and prediction regions was relatively large, indicating heterogeneity among the studies. Higgins I2 statistics demonstrated considerable heterogeneity in sensitivity (I2 = 83.5%) and considerable heterogeneity in specificity (I2 = 95.83%). Among the potential covariates, it seemed that none of factors was significantly associated with study heterogeneity in the joint model. However, the specificity was increased in studies with all the factors based on the differences in the composition of the detailed tumors. CONCLUSIONS: The T2-FLAIR mismatch sign is near-perfect specific marker of IDH mutation and 1p/19q noncodeletion.

Diagnostic assessment of magnetic resonance imaging for patients with intralabyrinthine schwannoma: A systematic review.

OBJECTIVES: Recent advancements in high-resolution imaging have improved the diagnostic assessment of magnetic resonance imaging (MRI) for intralabyrinthine schwannoma (ILS). This systematic review aimed to evaluate the diagnostic performance of MRI for patients with ILS. METHODS: Ovid-MEDLINE and EMBASE databases were searched for related studies on the diagnostic performance of MRI for patients with ILS published up to February 10, 2020. The primary endpoint was the diagnostic performance of MRI for ILS. The quality of the enrolled studies was assessed using tailored questionnaires and the Quality Assessment of Diagnostic Accuracy Studies-2 criteria. RESULTS: Overall, 6 retrospective studies that included 122 patients with ILS from a parent population of 364 were included. The sample size, parent population and its composition, reference standard, detailed parameters of MRI, and even the diagnostic methods varied between the studies. The studies had moderate quality. The sensitivity of combination of T2WI and CE-T1WI was over 90%. Relative sensitivity of T2WI comparative to CE-T1WI ranged from 62% to 100%, and the specificity were 100%. CONCLUSIONS: MRI has acceptable diagnostic performance for ILS. There is a need for well-organized research to reduce the factors causing heterogeneity.

Radiologic Differentiation between Granulomatosis with Polyangiitis and Its Mimics Involving the Skull Base in Humans Using High-Resolution Magnetic Resonance Imaging.

Granulomatosis with polyangiitis (GPA) can involve the skull base or the Eustachian tubes. GPA is diagnosed on the basis of clinical manifestations and serological tests, although it is challenging to discriminate GPA from infectious processes driving skull base osteomyelitis (SBO) and malignant processes such as nasopharyngeal carcinoma (NPC). Moreover, current serological tests have a low sensitivity and cannot distinguish GPA from these other conditions. We hypothesized that certain MRI characteristics would differ significantly among conditions and aimed to evaluate whether the features could differentiate between GPA, SBO, and NPC involving the skull base. We retrospectively evaluated the MRI findings of patients with GPA, SBO, and NPC. We performed univariable logistic regression analyses to identify the predictive variables for differentiating between conditions and evaluated their diagnostic values. We showed, for the first time, that certain MRI findings significantly differed between patients with GPA and those with SBO or NPC, including the lesion morphology and extent, the apparent diffusion coefficient (ADC) values, the contrast enhancement patterns, the presence or absence of necrosis, and retropharyngeal lymphadenopathy. In conclusion, utilizing certain MRI features can improve the diagnostic performance of MRI by differentiating GPA with skull base involvement from other conditions with similar radiologic findings, including SBO and NPC, facilitating treatment plans and, thus, improving patient outcomes.

Deep Learning-Based Computer-Aided Detection System for Automated Treatment Response Assessment of Brain Metastases on 3D MRI.

BACKGROUND: Although accurate treatment response assessment for brain metastases (BMs) is crucial, it is highly labor intensive. This retrospective study aimed to develop a computer-aided detection (CAD) system for automated BM detection and treatment response evaluation using deep learning. METHODS: We included 214 consecutive MRI examinations of 147 patients with BM obtained between January 2015 and August 2016. These were divided into the training (174 MR images from 127 patients) and test datasets according to temporal separation (temporal test set #1; 40 MR images from 20 patients). For external validation, 24 patients with BM and 11 patients without BM from other institutions were included (geographic test set). In addition, we included 12 MRIs from BM patients obtained between August 2017 and March 2020 (temporal test set #2). Detection sensitivity, dice similarity coefficient (DSC) for segmentation, and agreements in one-dimensional and volumetric Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria between CAD and radiologists were assessed. RESULTS: In the temporal test set #1, the sensitivity was 75.1% (95% confidence interval [CI]: 69.6%, 79.9%), mean DSC was 0.69 ± 0.22, and false-positive (FP) rate per scan was 0.8 for BM ≥ 5 mm. Agreements in the RANO-BM criteria were moderate (κ, 0.52) and substantial (κ, 0.68) for one-dimensional and volumetric, respectively. In the geographic test set, sensitivity was 87.7% (95% CI: 77.2%, 94.5%), mean DSC was 0.68 ± 0.20, and FP rate per scan was 1.9 for BM ≥ 5 mm. In the temporal test set #2, sensitivity was 94.7% (95% CI: 74.0%, 99.9%), mean DSC was 0.82 ± 0.20, and FP per scan was 0.5 (6/12) for BM ≥ 5 mm. CONCLUSIONS: Our CAD showed potential for automated treatment response assessment of BM ≥ 5 mm.

Comparison of Thermal Ablation and Surgery for Low-Risk Papillary Thyroid Microcarcinoma: A Systematic Review and Meta-Analysis.

OBJECTIVE: Although thermal ablation is effective in treating low-risk papillary thyroid microcarcinomas (PTMCs), comparison of treatment outcomes between thermal ablation and surgery has not yet been systematically evaluated. This study aimed to compare the efficacy and safety of thermal ablation and surgery for the treatment of low-risk PTMCs. MATERIALS AND METHODS: Ovid-MEDLINE and EMBASE databases were searched for studies reporting comparisons of treatment results between thermal ablation and surgery for patients with low-risk PTMC published up to April 6, 2020. The analysis evaluated the efficacy (local tumor recurrence, occurrence of new tumor, metastasis, and rescue surgery) and safety (complication rate) of thermal ablation and surgery. RESULTS: This systematic review included four studies with a total of 339 PTMCs in 339 patients who underwent thermal ablation and 320 PTMCs in 314 patients who underwent surgery. There was no local tumor recurrence or distant metastasis in either group. There was no significant difference in the pooled proportion of lymph node metastasis (2.6% with thermal ablation vs. 3.3% with surgery, p = 0.65), occurrence of new tumors (1.4% with thermal ablation vs. 1.3% with surgery, p = 0.85), or rescue surgery (2.6% with thermal ablation vs. 1.6% with surgery, p = 0.62). However, the pooled complication rate was significantly higher in the surgery group than in the ablation group (3.3% with thermal ablation vs. 7.8% with surgery, p = 0.03). CONCLUSION: Both thermal ablation and surgery are effective and safe options for the management of low-risk PTMCs, with thermal ablation achieving a lower complication rate. Therefore, thermal ablation may be considered as an alternative treatment option for low-risk PTMC in patients who refuse surgery and active surveillance or are ineligible for surgery.

Classification of true progression after radiotherapy of brain metastasis on MRI using artificial intelligence: a systematic review and meta-analysis.

BACKGROUND: Classification of true progression from nonprogression (eg, radiation-necrosis) after stereotactic radiotherapy/radiosurgery of brain metastasis is known to be a challenging diagnostic task on conventional magnetic resonance imaging (MRI). The scope and status of research using artificial intelligence (AI) on classifying true progression are yet unknown. METHODS: We performed a systematic literature search of MEDLINE and EMBASE databases to identify studies that investigated the performance of AI-assisted MRI in classifying true progression after stereotactic radiotherapy/radiosurgery of brain metastasis, published before November 11, 2020. Pooled sensitivity and specificity were calculated using bivariate random-effects modeling. Meta-regression was performed for the identification of factors contributing to the heterogeneity among the studies. We assessed the quality of the studies using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria and a modified version of the radiomics quality score (RQS). RESULTS: Seven studies were included, with a total of 485 patients and 907 tumors. The pooled sensitivity and specificity were 77% (95% CI, 70-83%) and 74% (64-82%), respectively. All 7 studies used radiomics, and none used deep learning. Several covariates including the proportion of lung cancer as the primary site, MR field strength, and radiomics segmentation slice showed a statistically significant association with the heterogeneity. Study quality was overall favorable in terms of the QUADAS-2 criteria, but not in terms of the RQS. CONCLUSION: The diagnostic performance of AI-assisted MRI seems yet inadequate to be used reliably in clinical practice. Future studies with improved methodologies and a larger training set are needed.

Imaging the Substantia Nigra in Parkinson Disease and Other Parkinsonian Syndromes.

Parkinson disease is characterized by dopaminergic cell loss in the substantia nigra of the midbrain. There are various imaging markers for Parkinson disease. Recent advances in MRI have enabled elucidation of the underlying pathophysiologic changes in the nigral structure. This has contributed to accurate and early diagnosis and has improved disease progression monitoring. This article aims to review recent developments in nigral imaging for Parkinson disease and other parkinsonian syndromes, including nigrosome imaging, neuromelanin imaging, quantitative iron mapping, and diffusion-tensor imaging. In particular, this article examines nigrosome imaging using 7-T MRI and 3-T susceptibility-weighted imaging. Finally, this article discusses volumetry and its clinical importance related to symptom manifestation. This review will improve understanding of recent advancements in nigral imaging of Parkinson disease. Published under a CC BY 4.0 license.

Image Findings of Acute to Subacute Craniocervical Arterial Dissection on Magnetic Resonance Vessel Wall Imaging: A Systematic Review and Proportion Meta-Analysis.

Background and Purpose: This systematic review and meta-analysis aimed to evaluate the pooled proportion of image findings of acute to subacute craniocervical arterial dissection (AD) direct signs on magnetic resonance vessel wall imaging (MR-VWI) and to identify factors responsible for the heterogeneity across the included studies. Methods: A systematic literature search in the Ovid-MEDLINE and EMBASE databases was performed for studies published on the relevant topic before April 14, 2020. Pooled sensitivity and specificity values and their 95% confidence intervals (CIs) were calculated using bivariate random-effects modeling. Meta-regression analyses were also performed to determine factors influencing heterogeneity. Results: Eleven articles with data for 209 patients with acute to subacute craniocervical AD who underwent MR-VWI were included in this systematic review and meta-analysis. The most common findings on MR-VWI were wall hematoma (84%; 95% CI, 71%-92%), abnormal enhancement (72%; 95% CI, 49%-88%), aneurysmal dilatation (71%, 95% CI, 53%-84%), and intimal flap or double lumen signs (49%; 95% CI, 29%-71%). Among the potential covariates of heterogeneity, the presence of contrast-enhanced T1-weighted imaging (CE-T1WI) within the MR-VWI sequence combination significantly affected the pooled proportion of the intimal flap or double lumen signs. Conclusion: Wall hematoma and intimal flap or double lumen signs were the most common and least common direct sign image findings, respectively, on MR-VWI in patients with acute to subacute craniocervical AD. Furthermore, the absence of CE-T1WI in MR-VWI protocol was the cause of heterogeneity for the detection of the intimal flap or double lumen signs. This data may help improve MR-VWI interpretation and enhance the understanding of the radiologic diagnosis of craniocervical AD.

Iron-sensitive magnetic resonance imaging in Parkinson's disease: a systematic review and meta-analysis.

OBJECTIVE: To evaluate the diagnostic performance of iron-sensitive sequences targeting the substantia nigra for distinguishing patients with Parkinson's disease from control participants and to identify factors causing heterogeneity. METHODS: A systematic literature search in the Ovid-MEDLINE and EMBASE databases was performed for studies reporting the relevant topic before March 6, 2020. The pooled sensitivity and specificity values with their 95% confidence intervals were calculated using bivariate random-effects modeling. Subgroup and meta-regression analyses were also performed to determine factors influencing heterogeneity affecting the diagnostic performance among the clinical, MRI, and analytic characteristics. RESULTS: A total of 22 articles including 1126 patients with Parkinson's disease and 933 control participants were enrolled in this systematic review and meta-analysis. Of those, 12 studies used objective analyses of quantitative susceptibility measurements, and 10 visually assessed the nigrosome-1 in subjective analyses. Iron-sensitive nigral magnetic resonance imaging showed a pooled sensitivity of 92% (95% confidence interval 88-95%) and a pooled specificity of 90% (95% confidence interval 81-95%). According to subgroup and meta-regression analyses, a longer mean disease duration in patients with Parkinson's disease (≥ 5 years), subjective analysis, a smaller size of pixel (< 0.6 mm2), a larger flip angle (> 15°), a smaller slice thickness (≤ 1 mm), and specific targeting of the substantia nigra pars compacta improved the diagnostic performance. CONCLUSION: Iron-sensitive nigral magnetic resonance imaging had a favorable diagnostic performance in discriminating patients with Parkinson's disease from control participants. Subjective analytic methods remain superior to objective approaches. Further improvements of the spatial resolution and contrast-to-noise ratio to specifically target the nigrosome-1 with objective analytic methods will be needed.

Five-year follow-up results of thermal ablation for low-risk papillary thyroid microcarcinomas: systematic review and meta-analysis.

OBJECTIVES: Confidence in long-term treatment results of thermal ablation for papillary thyroid microcarcinoma (PTMC) is required in comparison with active surveillance. The objective of this meta-analysis is to report 5-year follow-up results of thermal ablation for PTMC. METHODS: Ovid MEDLINE and EMBASE databases were searched through May 30, 2020, for studies reporting outcomes in patients with PTMC treated with thermal ablation and followed up for at least 5 years. Data were extracted and methodological quality was assessed independently by two radiologists according to the PRISMA guidelines. RESULTS: Three studies, involving 207 patients with 219 PTMCs, met the inclusion criteria through database searches. None of these patients experienced local tumor recurrence, lymph node metastasis, or distant metastasis or underwent delayed surgery during a mean pooled 67.8-month follow-up. Five new tumors appeared in the remaining thyroid gland of four patients, with four of these tumors successfully treated by repeat thermal ablation. The pooled mean major complication rate was 1.2%, with no patient experiencing life-threatening or delayed complications. CONCLUSIONS: Thermal ablation is an excellent local tumor control method in patients with low-risk PTMC, with low major complication rates at 5 years. KEY POINTS: • No local tumor recurrence, lymph node metastasis, or distant metastasis was noted by thermal ablation during follow-up of 5 years and none underwent delayed surgery. • The pooled mean major complication rate was 1.2%.