American Journal of Neuroradiology最新文献

{"title":"CTA and CTP for Detecting Distal Medium Vessel Occlusions: A Systematic Review and Meta-analysis","authors":"J. Sousa, Anton Sondermann, Sara Bernardo-Castro, Ricardo Varela, Helena Donato, J. Sargento-Freitas","doi":"10.3174/ajnr.a8080","DOIUrl":"https://doi.org/10.3174/ajnr.a8080","url":null,"abstract":"","PeriodicalId":7875,"journal":{"name":"American Journal of Neuroradiology","volume":"44 3","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138952525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disconnection-Based Prediction of Poststroke Dysphagia","authors":"Kyung Jae Yoon, Chul-Hyun Park, Myung-Ho Rho, Minchul Kim","doi":"10.3174/ajnr.a8074","DOIUrl":"https://doi.org/10.3174/ajnr.a8074","url":null,"abstract":"","PeriodicalId":7875,"journal":{"name":"American Journal of Neuroradiology","volume":"143 7","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138953291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cortical Thin Patch Fraction Reflects Disease Burden in MS: The Mosaic Approach","authors":"Marlene Tahedl, Tun Wiltgen, Cuici Voon, Achim Berthele, J. Kirschke, B. Hemmer, Mark Mühlau, Claus Zimmer, B. Wiestler","doi":"10.3174/ajnr.a8064","DOIUrl":"https://doi.org/10.3174/ajnr.a8064","url":null,"abstract":"","PeriodicalId":7875,"journal":{"name":"American Journal of Neuroradiology","volume":"28 13","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138950349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response to Letter Regarding the Article “Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model”","authors":"K. Zhu, B.K. Menon, W. Qiu","doi":"10.3174/ajnr.a8075","DOIUrl":"https://doi.org/10.3174/ajnr.a8075","url":null,"abstract":"","PeriodicalId":7875,"journal":{"name":"American Journal of Neuroradiology","volume":"65 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138951374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monoclonal Antibodies: What the Diagnostic Neuroradiologist Needs to Know.","authors":"R Alsufayan, C Hess, T Krings","doi":"10.3174/ajnr.A7974","DOIUrl":"10.3174/ajnr.A7974","url":null,"abstract":"<p><p>Monoclonal antibodies have become increasingly popular as novel therapeutics against a variety of diseases due to their specificity, affinity, and serum stability. Due to the nearly infinite repertoire of monoclonal antibodies, their therapeutic use is rapidly expanding, revolutionizing disease course and management, and what is now considered experimental therapy may soon become approved practice. Therefore, it is important for radiologists, neuroradiologists, and neurologists to be aware of these drugs and their possible different imaging-related manifestations, including expected and adverse effects of these novel drugs. Herein, we review the most commonly used monoclonal antibody-targeted therapeutic agents, their mechanism of action, clinical applications, and major adverse events with a focus on neurologic and neurographic effects and discuss differential considerations, to assist in the diagnosis of these conditions.</p>","PeriodicalId":7875,"journal":{"name":"American Journal of Neuroradiology","volume":" ","pages":"1358-1366"},"PeriodicalIF":3.5,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10714862/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10021538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal Characteristics of CSF Venous Fistulas on Dynamic Decubitus CT Myelography: A Retrospective Multi-Institution Cohort Study","authors":"A. Callen, Mo Fakhri, V. Timpone, A. Thaker, W. P. Dillon, Vinil N. Shah","doi":"10.3174/ajnr.a8078","DOIUrl":"https://doi.org/10.3174/ajnr.a8078","url":null,"abstract":"","PeriodicalId":7875,"journal":{"name":"American Journal of Neuroradiology","volume":"97 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138590522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding Bias in Artificial Intelligence: A Practice Perspective","authors":"Melissa A. Davis, Ona Wu, Ichiro Ikuta, J. Jordan, Michele H. Johnson, Edward Quigley","doi":"10.3174/ajnr.a8070","DOIUrl":"https://doi.org/10.3174/ajnr.a8070","url":null,"abstract":"","PeriodicalId":7875,"journal":{"name":"American Journal of Neuroradiology","volume":"30 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138593505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Antiseizure Medication Treatment in Children with Rare Tuberous Sclerosis Complex-Related Epilepsy Using Deep Learning","authors":"Haifeng Wang, Zhanqi Hu, Dian Jiang, Rongbo Lin, Cailei Zhao, Xia Zhao, Yihang Zhou, Yanjie Zhu, Hongwu Zeng, Dong Liang, Jianxiang Liao, Zhicheng Li","doi":"10.3174/ajnr.a8053","DOIUrl":"https://doi.org/10.3174/ajnr.a8053","url":null,"abstract":"<sec><st>BACKGROUND AND PURPOSE:</st>\u0000<p>Tuberous sclerosis complex disease is a rare, multisystem genetic disease, but appropriate drug treatment allows many pediatric patients to have positive outcomes. The purpose of this study was to predict the effectiveness of antiseizure medication treatment in children with tuberous sclerosis complex&ndash;related epilepsy.</p>\u0000</sec>\u0000<sec><st>MATERIALS AND METHODS:</st>\u0000<p>We conducted a retrospective study involving 300 children with tuberous sclerosis complex&ndash;related epilepsy. The study included the analysis of clinical data and T2WI and FLAIR images. The clinical data consisted of sex, age of onset, age at imaging, infantile spasms, and antiseizure medication numbers. To forecast antiseizure medication treatment, we developed a multitechnique deep learning method called WAE-Net. This method used multicontrast MR imaging and clinical data. The T2WI and FLAIR images were combined as FLAIR3 to enhance the contrast between tuberous sclerosis complex lesions and normal brain tissues. We trained a clinical data-based model using a fully connected network with the above-mentioned variables. After that, a weighted-average ensemble network built from the ResNet3D architecture was created as the final model.</p>\u0000</sec>\u0000<sec><st>RESULTS:</st>\u0000<p>The experiments had shown that age of onset, age at imaging, infantile spasms, and antiseizure medication numbers were significantly different between the 2 drug-treatment outcomes (<I>P</I> &lt; .05). The hybrid technique of FLAIR3 could accurately localize tuberous sclerosis complex lesions, and the proposed method achieved the best performance (area under the curve = 0.908 and accuracy of 0.847) in the testing cohort among the compared methods.</p>\u0000</sec>\u0000<sec><st>CONCLUSIONS:</st>\u0000<p>The proposed method could predict antiseizure medication treatment of children with rare tuberous sclerosis complex&ndash;related epilepsy and could be a strong baseline for future studies.</p>\u0000</sec>","PeriodicalId":7875,"journal":{"name":"American Journal of Neuroradiology","volume":"107 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138573915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Determination of the H3 K27-Altered Status in Spinal Cord Diffuse Midline Glioma by Radiomics Based on T2-Weighted MR Images","authors":"Junjie Li, YongZhi Wang, Jinyuan Weng, Liying Qu, Minghao Wu, Min Guo, Jun Sun, Geli Hu, Xiaodong Gong, Xing Liu, Yunyun Duan, Zhizheng Zhuo, Wenqing Jia, Yaou Liu","doi":"10.3174/ajnr.a8056","DOIUrl":"https://doi.org/10.3174/ajnr.a8056","url":null,"abstract":"<sec><st>BACKGROUND AND PURPOSE:</st>\u0000<p>Conventional MR imaging is not sufficient to discern the H3 K27-altered status of spinal cord diffuse midline glioma. This study aimed to develop a radiomics-based model based on preoperative T2WI to determine the H3 K27-altered status of spinal cord diffuse midline glioma.</p>\u0000</sec>\u0000<sec><st>MATERIALS AND METHODS:</st>\u0000<p>Ninety-seven patients with confirmed spinal cord diffuse midline gliomas were retrospectively recruited and randomly assigned to the training (<I>n</I> = 67) and test (<I>n</I> = 30) sets. One hundred seven radiomics features were initially extracted from automatically-segmented tumors on T2WI, then 11 features selected by the Pearson correlation coefficient and the Kruskal-Wallis test were used to train and test a logistic regression model for predicting the H3 K27-altered status. Sensitivity analysis was performed using additional random splits of the training and test sets, as well as applying other classifiers for comparison. The performance of the model was evaluated through its accuracy, sensitivity, specificity, and area under the curve. Finally, a prospective set including 28 patients with spinal cord diffuse midline gliomas was used to validate the logistic regression model independently.</p>\u0000</sec>\u0000<sec><st>RESULTS:</st>\u0000<p>The logistic regression model accurately predicted the H3 K27-altered status with accuracies of 0.833 and 0.786, sensitivities of 0.813 and 0.750, specificities of 0.857 and 0.833, and areas under the curve of 0.839 and 0.818 in the test and prospective sets, respectively. Sensitivity analysis confirmed the robustness of the model, with predictive accuracies of 0.767&ndash;0.833.</p>\u0000</sec>\u0000<sec><st>CONCLUSIONS:</st>\u0000<p>Radiomics signatures based on preoperative T2WI could accurately predict the H3 K27-altered status of spinal cord diffuse midline glioma, providing potential benefits for clinical management.</p>\u0000</sec>","PeriodicalId":7875,"journal":{"name":"American Journal of Neuroradiology","volume":"22 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138573905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ethical Considerations and Fairness in the Use of Artificial Intelligence for Neuroradiology.","authors":"C G Filippi, J M Stein, Z Wang, S Bakas, Y Liu, P D Chang, Y Lui, C Hess, D P Barboriak, A E Flanders, M Wintermark, G Zaharchuk, O Wu","doi":"10.3174/ajnr.A7963","DOIUrl":"10.3174/ajnr.A7963","url":null,"abstract":"<p><p>In this review, concepts of algorithmic bias and fairness are defined qualitatively and mathematically. Illustrative examples are given of what can go wrong when unintended bias or unfairness in algorithmic development occurs. The importance of explainability, accountability, and transparency with respect to artificial intelligence algorithm development and clinical deployment is discussed. These are grounded in the concept of \"primum no nocere\" (first, do no harm). Steps to mitigate unfairness and bias in task definition, data collection, model definition, training, testing, deployment, and feedback are provided. Discussions on the implementation of fairness criteria that maximize benefit and minimize unfairness and harm to neuroradiology patients will be provided, including suggestions for neuroradiologists to consider as artificial intelligence algorithms gain acceptance into neuroradiology practice and become incorporated into routine clinical workflow.</p>","PeriodicalId":7875,"journal":{"name":"American Journal of Neuroradiology","volume":" ","pages":"1242-1248"},"PeriodicalIF":3.1,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10631523/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10129371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}