Frontiers in radiology最新文献

{"title":"Imaging spectrum of amyloid-related imaging abnormalities associated with aducanumab immunotherapy","authors":"H. Sotoudeh, Mohammadreza Alizadeh, Ramin Shahidi, Parnian Shobeiri, Z. Saadatpour, C. A. Wheeler, Marissa Natelson Love, Manoj Tanwar","doi":"10.3389/fradi.2023.1305390","DOIUrl":"https://doi.org/10.3389/fradi.2023.1305390","url":null,"abstract":"Alzheimer's Disease (AD) is a leading cause of morbidity. Management of AD has traditionally been aimed at symptom relief rather than disease modification. Recently, AD research has begun to shift focus towards disease-modifying therapies that can alter the progression of AD. In this context, a class of immunotherapy agents known as monoclonal antibodies target diverse cerebral amyloid-beta (Aβ) epitopes to inhibit disease progression. Aducanumab was authorized by the US Food and Drug Administration (FDA) to treat AD on June 7, 2021. Aducanumab has shown promising clinical and biomarker efficacy but is associated with amyloid-related imaging abnormalities (ARIA). Neuroradiologists play a critical role in diagnosing ARIA, necessitating familiarity with this condition. This pictorial review will appraise the radiologic presentation of ARIA in patients on aducanumab.","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"101 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139383592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Empowering breast cancer diagnosis and radiology practice: advances in artificial intelligence for contrast-enhanced mammography","authors":"Ketki Kinkar, Brandon K. K. Fields, Mary W. Yamashita, Bino A. Varghese","doi":"10.3389/fradi.2023.1326831","DOIUrl":"https://doi.org/10.3389/fradi.2023.1326831","url":null,"abstract":"Artificial intelligence (AI) applications in breast imaging span a wide range of tasks including decision support, risk assessment, patient management, quality assessment, treatment response assessment and image enhancement. However, their integration into the clinical workflow has been slow due to the lack of a consensus on data quality, benchmarked robust implementation, and consensus-based guidelines to ensure standardization and generalization. Contrast-enhanced mammography (CEM) has improved sensitivity and specificity compared to current standards of breast cancer diagnostic imaging i.e., mammography (MG) and/or conventional ultrasound (US), with comparable accuracy to MRI (current diagnostic imaging benchmark), but at a much lower cost and higher throughput. This makes CEM an excellent tool for widespread breast lesion characterization for all women, including underserved and minority women. Underlining the critical need for early detection and accurate diagnosis of breast cancer, this review examines the limitations of conventional approaches and reveals how AI can help overcome them. The Methodical approaches, such as image processing, feature extraction, quantitative analysis, lesion classification, lesion segmentation, integration with clinical data, early detection, and screening support have been carefully analysed in recent studies addressing breast cancer detection and diagnosis. Recent guidelines described by Checklist for Artificial Intelligence in Medical Imaging (CLAIM) to establish a robust framework for rigorous evaluation and surveying has inspired the current review criteria.","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"11 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139382926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Rising stars in neuroradiology: 2022","authors":"Thomas C. Booth","doi":"10.3389/fradi.2023.1349600","DOIUrl":"https://doi.org/10.3389/fradi.2023.1349600","url":null,"abstract":"","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"13 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139384013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Radiomics and radiogenomics in genitourinary oncology: artificial intelligence and deep learning applications","authors":"Alessandro Stefano, Elena Bertelli, A. Comelli, Marco Gatti, A. Stanzione","doi":"10.3389/fradi.2023.1325594","DOIUrl":"https://doi.org/10.3389/fradi.2023.1325594","url":null,"abstract":"","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"10 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139173884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Standardized brain tumor imaging protocols for clinical trials: current recommendations and tips for integration","authors":"F. Sanvito, Timothy J. Kaufmann, T. Cloughesy, Patrick Y. Wen, B. Ellingson","doi":"10.3389/fradi.2023.1267615","DOIUrl":"https://doi.org/10.3389/fradi.2023.1267615","url":null,"abstract":"Standardized MRI acquisition protocols are crucial for reducing the measurement and interpretation variability associated with response assessment in brain tumor clinical trials. The main challenge is that standardized protocols should ensure high image quality while maximizing the number of institutions meeting the acquisition requirements. In recent years, extensive effort has been made by consensus groups to propose different “ideal” and “minimum requirements” brain tumor imaging protocols (BTIPs) for gliomas, brain metastases (BM), and primary central nervous system lymphomas (PCSNL). In clinical practice, BTIPs for clinical trials can be easily integrated with additional MRI sequences that may be desired for clinical patient management at individual sites. In this review, we summarize the general concepts behind the choice and timing of sequences included in the current recommended BTIPs, we provide a comparative overview, and discuss tips and caveats to integrate additional clinical or research sequences while preserving the recommended BTIPs. Finally, we also reflect on potential future directions for brain tumor imaging in clinical trials.","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"8 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139004949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility of four-dimensional similarity filter for radiation dose reduction in dynamic myocardial computed tomography perfusion imaging.","authors":"Yuta Yamamoto, Yuki Tanabe, Akira Kurata, Shuhei Yamamoto, Tomoyuki Kido, Teruyoshi Uetani, Shuntaro Ikeda, Shota Nakano, Osamu Yamaguchi, Teruhito Kido","doi":"10.3389/fradi.2023.1214521","DOIUrl":"https://doi.org/10.3389/fradi.2023.1214521","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>We aimed to evaluate the impact of four-dimensional noise reduction filtering using a four-dimensional similarity filter (4D-SF) on radiation dose reduction in dynamic myocardial computed tomography perfusion (CTP).</p><p><strong>Materials and methods: </strong>Forty-three patients who underwent dynamic myocardial CTP using 320-row computed tomography (CT) were included in the study. The original images were reconstructed using iterative reconstruction (IR). Three different CTP datasets with simulated noise, corresponding to 25%, 50%, and 75% reduction of the original dose (300 mA), were reconstructed using a combination of IR and 4D-SF. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were assessed, and CT-derived myocardial blood flow (CT-MBF) was quantified. The results were compared between the original and simulated images with radiation dose reduction.</p><p><strong>Results: </strong>The median SNR (first quartile-third quartile) at the original, 25%-, 50%-, and 75%-dose reduced-simulated images with 4D-SF was 8.3 (6.5-10.2), 16.5 (11.9-21.7), 15.6 (11.0-20.1), and 12.8 (8.8-18.1) and that of CNR was 4.4 (3.2-5.8), 6.7 (4.6-10.3), 6.6 (4.3-10.1), and 5.5 (3.5-9.1), respectively. All the dose-reduced-simulated CTPs with 4D-SF had significantly higher image quality scores in SNR and CNR than the original ones (25%-, 50%-, and 75%-dose reduced vs. original images, <i>p</i> < 0.05, in each). The CT-MBF in 75%-dose reduced-simulated CTP was significantly lower than 25%-, 50%- dose-reduced-simulated, and original CTPs (vs. 75%-dose reduced-simulated images, <i>p</i> < 0.05, in each).</p><p><strong>Conclusion: </strong>4D-SF has the potential to reduce the radiation dose associated with dynamic myocardial CTP imaging by half, without impairing the robustness of MBF quantification.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"3 ","pages":"1214521"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10722229/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138814458","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":"Factors affecting the labelling accuracy of brain MRI studies relevant for deep learning abnormality detection.","authors":"Matthew Benger, David A Wood, Sina Kafiabadi, Aisha Al Busaidi, Emily Guilhem, Jeremy Lynch, Matthew Townend, Antanas Montvila, Juveria Siddiqui, Naveen Gadapa, Gareth Barker, Sebastian Ourselin, James H Cole, Thomas C Booth","doi":"10.3389/fradi.2023.1251825","DOIUrl":"10.3389/fradi.2023.1251825","url":null,"abstract":"<p><p>Unlocking the vast potential of deep learning-based computer vision classification systems necessitates large data sets for model training. Natural Language Processing (NLP)-involving automation of dataset labelling-represents a potential avenue to achieve this. However, many aspects of NLP for dataset labelling remain unvalidated. Expert radiologists manually labelled over 5,000 MRI head reports in order to develop a deep learning-based neuroradiology NLP report classifier. Our results demonstrate that binary labels (normal vs. abnormal) showed high rates of accuracy, even when only two MRI sequences (T2-weighted and those based on diffusion weighted imaging) were employed as opposed to all sequences in an examination. Meanwhile, the accuracy of more specific labelling for multiple disease categories was variable and dependent on the category. Finally, resultant model performance was shown to be dependent on the expertise of the original labeller, with worse performance seen with non-expert vs. expert labellers.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"3 ","pages":"1251825"},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10711054/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138814423","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":"Radiomic analysis of the proximal femur in osteoporosis women using 3T MRI.","authors":"Dimitri Martel, Anmol Monga, Gregory Chang","doi":"10.3389/fradi.2023.1293865","DOIUrl":"10.3389/fradi.2023.1293865","url":null,"abstract":"<p><strong>Introduction: </strong>Osteoporosis (OP) results in weak bone and can ultimately lead to fracture. MRI assessment of bone structure and microarchitecture has been proposed as method to assess bone quality and fracture risk <i>in vivo</i>. Radiomics provides a framework to analyze the textural information of MR images. The purpose of this study was to analyze the radiomic features and its abilityto differentiate between subjects with and without prior fragility fracture.</p><p><strong>Methods: </strong>MRI acquisition was performed on <i>n </i>= 45 female OP subjects: 15 with fracture history (Fx) and 30 without fracture history (nFx) using a high-resolution 3D Fast Low Angle Shot (FLASH) sequence at 3T. Second and first order radiomic features were calculated in the trabecular region of the proximal femur on T1-weighted MRI signal of a matched dataset. Significance of the feature's predictive ability was measured using Wilcoxon test and Area Under the ROC (AUROC) curve analysis. The features were correlated DXA and FRAX score.</p><p><strong>Result: </strong>A set of three independent radiomic features (Dependence Non-Uniformity (DNU), Low Gray Level Emphasis (LGLE) and Kurtosis) showed significant ability to predict fragility fracture (AUROC DNU = 0.751, <i>p</i> < 0.05; AUROC LGLE = 0.729, <i>p</i> < 0.05; AUROC Kurtosis = 0.718, <i>p</i> < 0.05) with low to moderate correlation with FRAX and DXA.</p><p><strong>Conclusion: </strong>Radiomic features can measure bone health in MRI of proximal femur and has the potential to predict fracture.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"3 ","pages":"1293865"},"PeriodicalIF":0.0,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10702560/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138814547","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":"Retraction: CT-based risk factors for mortality of patients with COVID-19 pneumonia in Wuhan, China: a retrospective study","authors":"","doi":"10.3389/fradi.2023.1330251","DOIUrl":"https://doi.org/10.3389/fradi.2023.1330251","url":null,"abstract":"","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":" 26","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135292754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Role of computed tomography in the evaluation of regional metastasis in well-differentiated thyroid cancer","authors":"Richa Vaish, Abhishek Mahajan, Nilesh Sable, Rohit Dusane, Anuja Deshmukh, Munita Bal, Anil K. D’cruz","doi":"10.3389/fradi.2023.1243000","DOIUrl":"https://doi.org/10.3389/fradi.2023.1243000","url":null,"abstract":"Background Accurate neck staging is essential for performing appropriate surgery and avoiding undue morbidity in thyroid cancer. The modality of choice for evaluation is ultrasonography (US), which has limitations, particularly in the central compartment, that can be overcome by adding a computed tomography (CT). Methods A total of 314 nodal levels were analyzed in 43 patients with CT, and US; evaluations were done between January 2013 and November 2015. The images were reviewed by two radiologists independently who were blinded to histopathological outcomes. The sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and accuracy of US, CT, and US + CT were calculated using histology as the gold standard. Results The overall sensitivity, specificity, PPV, and NPV for US, CT, and US + CT were 53.9%, 88.8%, 74.1%, and 76.4%; 81.2%, 68.0%, 60.1%, and 85.9%; and 84.6%, 66.0%, 59.6%, and 87.8%, respectively. The overall accuracy of the US was 75.80%, the CT scan was 72.93%, and the US + CT scan was 72.93%. For the lateral compartment, the sensitivity, specificity, PPV, and NPV for the US, CT, and US + CT were 56.6%, 91.4%, 77.1%, and 80.5%; 80.7%, 70.6%, 58.3%, and 87.8%; and 84.3%, 68.7%, 57.9%, and 89.6%, respectively. The accuracy of the US was 79.67%, the CT scan was 73.98%, and the US + CT scan was 73.98% for the lateral compartment. For the central compartment, the sensitivity, specificity, PPV, and NPV for the US, CT, and US + CT were 47.1%, 76.5%, 66.7%, and 59.1%; 82.4%, 55.9%, 65.1%, and 76.0%; and 85.3%, 52.9%, 64.4%, and 78.3%, respectively. The accuracy of the US was 61.76%, the CT scan was 69.12%, and the US + CT scan was 69.12% for the central compartment. Conclusions This study demonstrated that CT has higher sensitivity in detecting nodal metastasis; however, its role is complementary to US due to low specificity.","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"316 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135871629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}