AJNR. American journal of neuroradiology最新文献

{"title":"Diagnostic Performance of Fast Brain MRI Compared with Routine Clinical MRI in Patients with Glioma Grades 3 and 4: A Pilot Study.","authors":"Francesca De Luca, Annika Suneson, Annika Kits, Emilia Palmér, Stefan Skare, Anna Falk Delgado","doi":"10.3174/ajnr.A8558","DOIUrl":"10.3174/ajnr.A8558","url":null,"abstract":"<p><strong>Background and purpose: </strong>EPIMix is a fast brain MRI technique not previously investigated in patients with grade 3 and 4 gliomas. This pilot study aimed to investigate the diagnostic performance of EPIMix in the radiological treatment evaluation of adult patients with grade 3 and 4 gliomas compared with routine clinical MRI (rcMRI).</p><p><strong>Materials and methods: </strong>Patients with grade 3 and 4 gliomas investigated with rcMRI and EPIMix were retrospectively included in the study. Three readers (R1-R3) participated in the radiological assessment applying the Response Assessment for Neuro-Oncology (RANO 2.0) criteria, of whom two (R1 and R2) independently evaluated EPIMix and later rcMRI by measuring contrast-enhancing and non-contrast-enhancing tumor regions at each follow-up. For cases with discrepant evaluations, an unblinded side-by-side (EPIMix and rcMRI) reading was performed together with a third reader (R3). Comparisons between methods (EPIMix versus rcMRI) were performed using the weighted Cohen κ. The sensitivity and specificity to progressive disease (PD) on a follow-up scan were calculated for EPIMix compared with rcMRI with receiver operating characteristic curves (ROC) to assess the area under the curve (AUC).</p><p><strong>Results: </strong>Of 35 patients (mean age, 53 years; 31% women), a total of 93 MRIs encompassing 58 follow-up investigations showed PD at a blinded reading in 33% of EPIMix (19/58, R1-2), while in 31% (18/58 exams, R1), and 34% (20/58 exams, R2) of rcMRI. An almost perfect agreement for tumor category assessment was found between EPIMix and rcMRI (EPIMixR1 versus rcMRIR1 κ = 0.96; EPIMixR2 versus rcMRIR2 κ = 0.89). The sensitivity for EPIMix to detect PD was 1.00 (0.81-1.00) for R1 and 0.90 (0.68-0.99) for R2, while the specificity was 0.97 (0.86-1.00) for R1 and R2. The AUC for PD was 0.99 for R1 (EPIMixR1 versus rcMRIR1) and 0.94 for R2 (EPIMixR2 versus rcMRIR2). The <i>P</i> value of the DeLong test AUCR1 versus AUCR2 was <i>P</i> = .20 (R1-R2).</p><p><strong>Conclusions: </strong>In this pilot study, EPIMix was used as a fast MRI alternative for treatment evaluation of patients with glioma grades 3 and 4, with high but slightly lower diagnostic performance than rcMRI.</p>","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":" ","pages":"983-989"},"PeriodicalIF":0.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12091972/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Postprocedural Brachial Neuritis: Clinical, Electrodiagnostic, and Neuroimaging Features.","authors":"Vardhaan S Ambati, Neha Madugala, Noriko Anderson, Ann N Poncelet, Bradley R Bedell, Reshma P Kolala, Praveen V Mummaneni, Vinil N Shah","doi":"10.3174/ajnr.A8561","DOIUrl":"10.3174/ajnr.A8561","url":null,"abstract":"<p><strong>Background and purpose: </strong>Brachial neuritis is a monophasic condition affecting the brachial plexus and its branches, manifesting as acute shoulder and upper arm pain, followed by weakness and paresthesias. It can be triggered by antecedent events, including procedures such as surgery. Misdiagnosis and delay in diagnosis are common. Imaging is important to confirm the diagnosis of postprocedural brachial neuritis and exclude other etiologies.</p><p><strong>Materials and methods: </strong>Clinical, electrodiagnostic, and neuroimaging features of patients with postprocedural brachial neuritis from a single quaternary care institution were identified and analyzed.</p><p><strong>Results: </strong>Six patients (2 women) were identified with a median age of 62 (range 49-70) years. Antecedent procedures included 4 cervical spine surgeries, 1 rotator cuff repair, and 1 central venous catheter placement. Time to symptom onset ranged from 1 day to 2 weeks. The initial symptom for 5 of the 6 patients was severe upper extremity pain followed by weakness. All patients had electrodiagnostic tests and MR neurography consistent with brachial neuritis. MR neurogram showed plexus and/or terminal branch abnormalities with associated muscular denervation edema. The C5 or C6 root and/or upper trunk were always involved. The most common branches affected were the suprascapular, long thoracic, and axillary nerves. Hourglass constrictions (HGCs) of these nerves were seen in 3 of 6 patients. The average time to diagnosis was 3.4 (range 1.5-5) months.</p><p><strong>Conclusions: </strong>Postprocedural brachial neuritis is an under-recognized cause of acute upper extremity pain and weakness. MR neurography can exclude iatrogenic causes and document the presence of HGCs in affected nerves. Diagnostic neuroradiologists should be aware of this clinical entity and associated neuroimaging findings.</p>","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":" ","pages":"1050-1055"},"PeriodicalIF":0.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12091966/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142585038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Empowering Data Sharing in Neuroscience: A Deep Learning Deidentification Method for Pediatric Brain MRIs.","authors":"Ariana M Familiar, Neda Khalili, Nastaran Khalili, Cassidy Schuman, Evan Grove, Karthik Viswanathan, Jakob Seidlitz, Aaron Alexander-Bloch, Anna Zapaishchykova, Benjamin H Kann, Arastoo Vossough, Phillip B Storm, Adam C Resnick, Anahita Fathi Kazerooni, Ali Nabavizadeh","doi":"10.3174/ajnr.A8581","DOIUrl":"10.3174/ajnr.A8581","url":null,"abstract":"<p><strong>Background and purpose: </strong>Privacy concerns, such as identifiable facial features within brain scans, have hindered the availability of pediatric neuroimaging data sets for research. Consequently, pediatric neuroscience research lags adult counterparts, particularly in rare disease and under-represented populations. The removal of face regions (image defacing) can mitigate this; however, existing defacing tools often fail with pediatric cases and diverse image types, leaving a critical gap in data accessibility. Given recent National Institutes of Health data sharing mandates, novel solutions are a critical need.</p><p><strong>Materials and methods: </strong>To develop an artificial intelligence (AI)-powered tool for automatic defacing of pediatric brain MRIs, deep learning methodologies (nnU-Net) were used by using a large, diverse multi-institutional data set of clinical radiology images. This included multiparametric MRIs (T1-weighted [T1W], T1W-contrast-enhanced, T2-weighted [T2W], T2W-FLAIR) with 976 total images from 208 patients with brain tumor (Children's Brain Tumor Network, CBTN) and 36 clinical control patients (Scans with Limited Imaging Pathology, SLIP) ranging in age from 7 days to 21 years old.</p><p><strong>Results: </strong>Face and ear removal accuracy for withheld testing data were the primary measure of model performance. Potential influences of defacing on downstream research usage were evaluated with standard image processing and AI-based pipelines. Group-level statistical trends were compared between original (nondefaced) and defaced images. Across image types, the model had high accuracy for removing face regions (mean accuracy, 98%; <i>n</i>=98 subjects/392 images), with lower performance for removal of ears (73%). Analysis of global and regional brain measures (SLIP cohort) showed minimal differences between original and defaced outputs (mean <i>r</i> _S = 0.93, all <i>P</i> < .0001). AI-generated whole brain and tumor volumes (CBTN cohort) and temporalis muscle metrics (volume, cross-sectional area, centile scores; SLIP cohort) were not significantly affected by image defacing (all <i>r</i> _S > 0.9, <i>P</i> < .0001).</p><p><strong>Conclusions: </strong>The defacing model demonstrates efficacy in removing facial regions across multiple MRI types and exhibits minimal impact on downstream research usage. A software package with the trained model is freely provided for wider use and further development (pediatric-auto-defacer; https://github.com/d3b-center/pediatric-auto-defacer-public). By offering a solution tailored to pediatric cases and multiple MRI sequences, this defacing tool will expedite research efforts and promote broader adoption of data sharing practices within the neuroscience community.</p>","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":" ","pages":"964-972"},"PeriodicalIF":0.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12091965/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142634321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Based Algorithm for Automatic Quantification of Nigrosome-1 and Parkinsonism Classification Using Susceptibility Map-Weighted MRI.","authors":"Pae Sun Suh, Hwan Heo, Chong Hyun Suh, MyeongOh Lee, Soohwa Song, Donghoon Shin, Sungyang Jo, Sun Ju Chung, Hwon Heo, Woo Hyun Shim, Ho Sung Kim, Sang Joon Kim, Eung Yeop Kim","doi":"10.3174/ajnr.A8585","DOIUrl":"10.3174/ajnr.A8585","url":null,"abstract":"<p><strong>Background and purpose: </strong>The diagnostic performance of deep learning model that simultaneously detecting and quantifying nigrosome-1 abnormality by using susceptibility map-weighted imaging (SMwI) remains unexplored. This study aimed to develop and validate a deep learning-based automatic quantification for nigral hyperintensity and a classification algorithm for neurodegenerative parkinsonism.</p><p><strong>Materials and methods: </strong>We retrospectively collected 450 participants (210 with idiopathic Parkinson disease [IPD] and 240 individuals in the control group) for training data between November 2022 and May 2023, and 237 participants (168 with IPD, 58 with essential tremor, and 11 with drug-induced parkinsonism) for validation data between July 2021 and January 2022. SMwI data were reconstructed from multiecho gradient echo. Diagnostic performance for diagnosing IPD was assessed by using deep learning-based automatic quantification (Heuron NI) and classification (Heuron IPD) models. Reference standard for IPD was based on N-3-fluoropropyl-2-β-carbomethoxy-3-β-(4-iodophenyl) nortropane PET finding. Additionally, the correlation between the Hoehn and Yahr (H&Y) stage and volume of nigral hyperintensity in patients with IPD was assessed.</p><p><strong>Results: </strong>Quantification of nigral hyperintensity by using Heuron NI showed an area under the curve (AUC) of 0.915 (95% CI, 0.872-0.947) and 0.928 (95% CI, 0.887-0.957) on the left and right, respectively. Classification of nigral hyperintensity abnormality by using Heuron IPD showed area under the curve of 0.967 (95% CI, 0.936-0.986) and 0.976 (95% CI, 0.948-0.992) on the left and right, respectively. H&Y score ≥3 showed smaller nigral hyperintensity volume (1.43 ± 1.19 mm³) compared with H&Y score 1-2.5 (1.98 ± 1.63 mm³; <i>P</i> = .008).</p><p><strong>Conclusions: </strong>Our deep learning-based model proves rapid, accurate automatic quantification of nigral hyperintensity, facilitating IPD diagnosis, symptom severity prediction, and patient stratification for personalized therapy. Further study is warranted to validate the findings across various clinical settings.</p>","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":" ","pages":"999-1006"},"PeriodicalIF":0.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12091983/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142640405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accuracy of an nnUNet Neural Network for the Automatic Segmentation of Intracranial Aneurysms, Their Parent Vessels, and Major Cerebral Arteries from MRI-TOF.","authors":"Elisa Colombo, Mathijs de Boer, Lambertus Bartels, Luca Regli, Tristan van Doormaal","doi":"10.3174/ajnr.A8607","DOIUrl":"10.3174/ajnr.A8607","url":null,"abstract":"<p><strong>Background and purpose: </strong>The automatic recognition of intracraial aneurysms by means of machine-learning algorithms represents a new frontier for diagnostic and therapeutic goals. Yet, the current algorithms focus solely on the aneurysms and not on the recognition of their parent vessels. The purpose of the present study is the development of a new machine-learning algorithm for fully automatic identification of cerebral arteries and intracranial aneurysms (IAs) based on a manually segmented MRA-TOF data set.</p><p><strong>Materials and methods: </strong>In this retrospective single-center study, 62 MRA-TOF scans of a total of 73 untreated, unruptured IAs were manually color-labeled in 21 classes. A nnUNet architecture was trained on MRA-TOF images. The performance of the automatic segmentation was compared with the manual segmentation by using the Dice Similarity Coefficient (DSC), Centerline Dice (ClDice), and 95th percentile Hausdorff Distance (HD95). Sensitivity was computed for aneurysm detection.</p><p><strong>Results: </strong>Across all 21 classes, the median DSC was 0.86 [95% CI: 0.81-0.89], the median ClDice was 0.91 [0.85, 0.94], and the median HD95 was 2.9 [1.0, 14.9] mm. Sensitivity of the model for aneurysm detection was 0.8. For this class specifically, a median DSC of 0.88 [0.13, 0.92], median ClDice of 0.89 [0.06, 1.0], and median HD95 of 1.8 [0.58, 81] mm was achieved. The volume of the labeled anatomic structure was the most relevant determinant of accuracy in this model. Median time to predict was 130.6 [60.9, 284.1] seconds.</p><p><strong>Conclusions: </strong>The nnUNet MRA-TOF-based algorithm provided a fast and adequate automatic extraction of unruptured IAs, their parent vessels, and the most relevant cerebral arteries. Future steps involve the expansion of the training set with the inclusion of more MRA-TOF studies with and without IAs and its incorporation in 3D imaging viewers and treatment prediction.</p>","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":" ","pages":"956-963"},"PeriodicalIF":0.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12091975/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142694064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stent Angioplasty for the Treatment of Cerebral Vasospasm: A Systematic Review and Meta-Analysis.","authors":"Jonathan Cortese, Esref Alperen Bayraktar, Sherief Ghozy, Armin Zarrintan, Cem Bilgin, Waleed Brinjikji, Ramanathan Kadirvel, Pervinder Bhogal, David F Kallmes","doi":"10.3174/ajnr.A8617","DOIUrl":"10.3174/ajnr.A8617","url":null,"abstract":"<p><strong>Background: </strong>Several case series and prospective cohorts have reported the use of stent retrievers (SR) and specifically designed expanding stents (ES) to perform in situ mechanical stent angioplasty to treat cerebral vasospasm in subarachnoid vasospasm.</p><p><strong>Purpose: </strong>The aim of this study was to review and conduct a meta-analysis to evaluate the safety and efficacy of this novel technique.</p><p><strong>Data sources: </strong>A systematic review and meta-analysis was conducted according to established protocols. Searches were conducted in PubMed, Scopus, Web of Science, and EMBASE databases up to June 2024, including variations of \"stent,\" \"expanding device,\" \"vasospasm,\" \"subarachnoid hemorrhage.\" Original studies reporting treatment outcomes for vasospasm by using SR/ES in more than 5 patients were included.</p><p><strong>Study selection: </strong>Pooled data from 8 studies, comprising 156 patients and 428 targeted vessels treated with stent angioplasty for vasospasm were analyzed.</p><p><strong>Data analysis: </strong>We evaluated rates of angiographic success, complications, recurrence, and neurologic improvement. Meta-analysis was performed by using a random-effects model.</p><p><strong>Data synthesis: </strong>The angiographic success rate was 81.8% (95% CI: 70.6-89.3). Subgroup analysis showed a success rate of 86.5% (95% CI: 62.6-96.1) with ES and 80.5% (95%CI: 62.6-93.1) with SR. Overall complication rate was 1.1% (95% CI: 0.0-3.6), due to clot formation or hemorrhage. Recurrence of vasospasm was noted in 12.8% (95% CI: 5.2-28.1) while neurologic improvement was seen in 65.9% (95% CI: 51.1-78.1) of the cases. Finally, it should be noted that all included studies used stent angioplasty in combination with intra-arterial vasodilators.</p><p><strong>Limitations: </strong>Our meta-analysis is limited by selection and reporting biases, as well as high heterogeneity. Moreover, the overall low quality of available evidence is the main limitation of our results.</p><p><strong>Conclusions: </strong>Combination of stent angioplasty and intra-arterial vasodilators was found to have high rates of angiographic success and low incidences of adverse events. Randomized controlled trials are needed to confirm their efficacy and safety compared with medical and balloon angioplasty treatments.</p>","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":" ","pages":"943-949"},"PeriodicalIF":0.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12091979/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143671981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association of Pretreatment Perfusion Imaging Parameters with 90-Day Excellent Functional Outcomes in Anterior Circulation Distal Medium Vessel Occlusion Stroke.","authors":"Hamza Adel Salim, Vaibhav Vagal, Dhairya A Lakhani, Janet Mei, Licia Luna, Yasmin Aziz, Aneri Balar, Adam A Dmytriw, Adrien Guenego, Basel Musmar, Nimer Adeeb, Victor C Urrutia, Elisabeth B Marsh, Raf Llinas, Argye E Hillis, Hanzhang Lu, Risheng Xu, Dylan Wolman, Benjamin Pulli, Kambiz Nael, Max Wintermark, Jeremy J Heit, Gregory W Albers, Tobias D Faizy, Vivek Yedavalli","doi":"10.3174/ajnr.A8584","DOIUrl":"10.3174/ajnr.A8584","url":null,"abstract":"<p><strong>Background and purpose: </strong>Acute ischemic strokes caused by distal medium vessel occlusions (DMVOs) represent a remarkable proportion of all stroke cases, yet the predictors of excellent functional outcomes in these patients remain poorly understood. This study aims to identify pretreatment CTP parameters associated with excellent functional outcomes, defined as an mRS score of 0-1 at 90 days, in patients with anterior circulation DMVO.</p><p><strong>Materials and methods: </strong>We conducted a retrospective multicenter study involving patients with anterior DMVO, from 2 stroke centers within the Johns Hopkins Medical Enterprise. Baseline demographic, clinical, and imaging data were collected, with CTP parameters analyzed by using RapidAI software. Univariable and multivariable logistic regression models were used to identify predictors of excellent outcomes. Receiver operating characteristic (ROC) curves were constructed to assess the predictive accuracy of CTP parameters.</p><p><strong>Results: </strong>Among the 82 patients (median age, 71 years; 57% women), occlusions were located in the M2 segment in 89%, M3 in 8.5%, and A2 in 2.4%. Intravenous thrombolysis (IVT) was administered to 37% of patients, and endovascular therapy (EVT) was attempted in 59%. Excellent outcomes at 90 days were achieved in 45% of patients. In univariate analysis, white race (OR, 4.14; 95% CI, 1.66-10.9; <i>P</i> = .003), higher CBV index (OR per 0.1-unit change, 1.45; 95% CI, 1.08-2.05; <i>P</i> = .022), and lower relative CBF (rCBF <20%) volumes (OR, 0.91; 95% CI, 0.81-0.98; <i>P</i> = .038) were significantly associated with excellent outcomes. In multivariate analysis adjusting for age, sex, race, IVT administration, EVT attempted, dyslipidemia, and premorbid mRS, higher CBV index remained a significant independent predictor (OR per 0.1-unit change, 1.72; 95% CI, 1.14-2.81; <i>P</i> = .017), and lower rCBF <20% volume was associated with better outcomes (OR, 0.91; 95% CI, 0.80-0.98; <i>P</i> = .05). The multivariate model demonstrated good predictive performance (area under the ROC curve, 80%; 95% CI, 70%-90%; <i>P</i> < .001).</p><p><strong>Conclusions: </strong>In patients with anterior circulation DMVO, a higher CBV index on pretreatment CTP is an independent predictor of excellent functional outcomes at 90 days. These findings suggest that CTP parameters, particularly the CBV index, may be useful in prognostic assessment for this stroke population. Further studies are needed to validate these results and optimize therapeutic approaches.</p>","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":" ","pages":"892-899"},"PeriodicalIF":0.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12091971/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142640303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coronal Clival Cleft in CHARGE Syndrome: Fetal MRI Series.","authors":"Sara Reis Teixeira, Carmen Cerron-Vela, Nahla Khalek, Renee Wright, Matthew T Whitehead","doi":"10.3174/ajnr.A8609","DOIUrl":"10.3174/ajnr.A8609","url":null,"abstract":"<p><strong>Background and purpose: </strong>CHARGE is a syndrome that affects the brain, eyes, ears, heart, face, and genitourinary system. Prenatal diagnosis could optimize counseling, delivery planning, and therapeutic interventions; however, reports of associated fetal neuroimaging features are scarce. While some findings are nonspecific, olfactory, inner ear, and skull base anomalies are commonly present and may be observable at the time of fetal imaging. We sought to determine the scope of prenatal CNS MRI findings in CHARGE syndrome with emphasis on findings not included in the diagnostic criteria for CHARGE syndrome, such as coronal clival cleft.</p><p><strong>Materials and methods: </strong>Retrospective review of fetal +/- postnatal neuroimaging from patients with genetic diagnosis of CHARGE syndrome was conducted. Brain, ear, eye, face, and skull base bone abnormalities were documented. Descriptive statistics were employed to characterize the findings.</p><p><strong>Results: </strong>Eighteen fetal-maternal dyads were included. Median gestational age at time of prenatal MRI was 26.25 weeks. Thirteen (72%) subjects were born alive, of which 10 (55.6%) were males. One died on the first day of life (5.6%) and 4/18 (22.2%) underwent termination of pregnancy. Twelve (66.7%) had postnatal brain MRI and/or temporal bone CT. On prenatal MRI, coronal clival cleft was seen in 72% of the cases and confirmed in all patients with postnatal imaging. Inner ear dysplasia was universally seen in all prenatal MRI, except for 1 case, in which this was not evaluable, and confirmed in all cases postnatally. On prenatal imaging, olfactory apparatus hypoplasia/absence was detected in 83% of the cases, followed by globe dysmorphia and/or colobomas in 44% of the cases, atresia of choanae (39%), facial clefts (22%), and cerebellar malformation (16.7%). Of the 10 cases with postnatal brain MRI available, 4 (40%) demonstrated cerebellar gray matter heterotopia.</p><p><strong>Conclusions: </strong>The most common fetal neuroimaging findings supporting the diagnosis of CHARGE syndrome are olfactory deficiency and inner ear dysplasia. Coronal clival clefts are often present and are frequently visible on prenatal MR imaging. The presence of a clival coronal cleft should raise the possibility of CHARGE syndrome, particularly when associated with other known cardinal findings, such as cerebellar dysgenesis, olfactory apparatus deficiency, and inner ear dysplasia.</p>","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":"46 5","pages":"1022-1028"},"PeriodicalIF":0.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12091968/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144055366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Evaluation of Automated Artificial Intelligence-Based Brain Tumor Response Assessment in Patients with Glioblastoma.","authors":"Jikai Zhang, Dominic LaBella, Dylan Zhang, Jessica L Houk, Jeffrey D Rudie, Haotian Zou, Pranav Warman, Maciej A Mazurowski, Evan Calabrese","doi":"10.3174/ajnr.A8580","DOIUrl":"10.3174/ajnr.A8580","url":null,"abstract":"<p><p>This project aimed to develop and evaluate an automated, AI-based, volumetric brain tumor MRI response assessment algorithm on a large cohort of patients treated at a high-volume brain tumor center. We retrospectively analyzed data from 634 patients treated for glioblastoma at a single brain tumor center over a 5-year period (2017-2021). The mean age was 56 ± 13 years. 372/634 (59%) patients were male, and 262/634 (41%) patients were female. Study data consisted of 3,403 brain MRI exams and corresponding standardized, radiologist-based brain tumor response assessments (BT-RADS). An artificial intelligence (AI)-based brain tumor response assessment (AI-VTRA) algorithm was developed using automated, volumetric tumor segmentation. AI-VTRA results were evaluated for agreement with radiologist-based response assessments and ability to stratify patients by overall survival. Metrics were computed to assess the agreement using BT-RADS as the ground-truth, fixed-time point survival analysis was conducted to evaluate the survival stratification, and associated P-values were calculated. For all BT-RADS categories, AI-VTRA showed moderate agreement with radiologist response assessments (F1 = 0.587-0.755). Kaplan-Meier survival analysis revealed statistically worse overall fixed time point survival for patients assessed as image worsening equivalent to RANO progression by human alone compared to by AI alone (log-rank <i>P</i> = .007). Cox proportional hazard model analysis showed a disadvantage to AI-based assessments for overall survival prediction (<i>P</i> = .012). In summary, our proposed AI-VTRA, following BT-RADS criteria, yielded moderate agreement for replicating human response assessments and slightly worse stratification by overall survival.</p>","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":" ","pages":"990-998"},"PeriodicalIF":0.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12091993/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142634272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prolonged Venous Transit on Perfusion Imaging Is Associated with Longer Lengths of Stay in Acute Large Vessel Occlusions.","authors":"Manisha Koneru, Janet Y Mei, Dhairya A Lakhani, Hamza A Salim, Mona Shahriari, Adam A Dmytriw, Adrien Guenego, Jeremy J Heit, Gregory W Albers, Dylan Wolman, Tobias D Faizy, Benjamin Pulli, Vaibhav Vagal, Aakanksha Sriwastwa, Yasmin Aziz, Risheng Xu, Hanzhang Lu, Victor C Urrutia, Elisabeth B Marsh, Richard Leigh, Mona Bahouth, Rafael H Llinas, Kambiz Nael, Argye E Hillis, Vivek S Yedavalli","doi":"10.3174/ajnr.A8611","DOIUrl":"10.3174/ajnr.A8611","url":null,"abstract":"<p><strong>Background and purpose: </strong>Prolonged venous transit (PVT+) is a marker of venous outflow; it is defined as the presence or absence of time-to-maximum ≥10 seconds timing in either the superior sagittal sinus or torcula. This novel perfusion imaging-based metric has been associated with higher odds of mortality and lower odds of functional recovery. This study aims to assess the relationship between PVT on admission perfusion imaging and length of hospital stay in large vessel occlusion strokes successfully reperfused with mechanical thrombectomy.</p><p><strong>Materials and methods: </strong>Patients with acute ischemic stroke with large vessel occlusions in the anterior circulation successfully treated with thrombectomy between January 2017 and September 2022 were retrospectively reviewed. The primary outcome was length of stay in the hospital due to the acute stroke event. Univariable and forward stepwise multivariable linear regressions were performed for the primary outcome.</p><p><strong>Results: </strong>Of 109 patients meeting inclusion, median age was 71 (interquartile range [IQR] 62-80) years. Median hospital length of stay was significantly greater in PVT+ patients (9 [IQR 6-18] days) compared with PVT- patients (6 [IQR 4-12] days, <i>P</i> = .03). In multivariable regression, PVT+ was significantly associated with length of stay, and PVT+ was associated with approximately 2 additional days of hospital stay compared with PVT- (<i>P</i> = .03).</p><p><strong>Conclusions: </strong>In successfully reperfused large vessel occlusion strokes, PVT+ was associated with an additional 2 days of hospital stay on average compared with PVT- patients, when adjusting for other clinical covariables. This simple, novel imaging metric is robust in correlating with a range of short- and long-term clinical outcomes.</p>","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":" ","pages":"887-891"},"PeriodicalIF":0.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12091976/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142735296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}