Hereditary and sporadic cerebellar ataxias represent a vast and still growing group of diseases whose diagnosis and differentiation cannot only rely on clinical evaluation. Brain imaging including magnetic resonance (MR) and nuclear medicine techniques allows for characterization of structural and functional abnormalities underlying symptomatic ataxias. These methods thus constitute a potential source of radiological biomarkers, which could be used to identify these diseases and differentiate subgroups of them, and to assess their severity and their evolution. Such biomarkers mainly comprise qualitative and quantitative data obtained from MR including proton spectroscopy, diffusion imaging, tractography, voxel-based morphometry, functional imaging during task execution or in a resting state, and from SPETC and PET with several radiotracers. In the current article, we aim to illustrate briefly some applications of these neuroimaging tools to evaluation of cerebellar disorders such as inherited cerebellar ataxia, fetal developmental malformations, and immune-mediated cerebellar diseases and of neurodegenerative or early-developing diseases, such as dementia and autism in which cerebellar involvement is an emerging feature. Although these radiological biomarkers appear promising and helpful to better understand ataxia-related anatomical and physiological impairments, to date, very few of them have turned out to be specific for a given ataxia with atrophy of the cerebellar system being the main and the most usual alteration being observed. Consequently, much remains to be done to establish sensitivity, specificity, and reproducibility of available MR and nuclear medicine features as diagnostic, progression and surrogate biomarkers in clinical routine.
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http://dx.doi.org/10.1007/s12311-014-0610-3 | DOI Listing |
Sci Rep
January 2025
Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, Moorenstr. 5, 40225, Dusseldorf, Germany.
Aim of this study was to proof the concept of optimizing the contrast between prostate cancer (PC) and healthy tissue by DWI post-processing using a quadrature method. DWI post-processing was performed on 30 patients (median age 67 years, prostate specific antigen 8.0 ng/ml) with PC and clear MRI findings (PI-RADS 4 and 5).
View Article and Find Full Text PDFAcad Radiol
January 2025
Medical Image Processing Group, 602 Goddard building, 3710 Hamilton Walk, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 (M.L., M.A., J.K.U., Y.T., C.W., N.P., S.M., D.A.T.). Electronic address:
Rationale And Objectives: Cardiovascular toxicity is a well-known complication of thoracic radiation therapy (RT), leading to increased morbidity and mortality, but existing techniques to predict cardiovascular toxicity have limitations. Predictive biomarkers of cardiovascular toxicity may help to maximize patient outcomes.
Methods: The machine learning optimal biomarker (OBM) method was employed to predict development of cardiotoxicity (based on serial echocardiographic measurements of left ventricular ejection fraction and longitudinal strain) from computed tomography (CT) images in patients with thoracic malignancy undergoing RT.
J Immunother Cancer
January 2025
Department of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan
Background: Cholangiocarcinoma is a challenging malignancy with limited responses to conventional therapies, particularly immune checkpoint inhibitor therapy. Tumor-infiltrating lymphocytes (TILs) and tertiary lymphoid structures (TLSs) are key components of the tumor microenvironment (TME) and have been implicated in the immune response to cancer. However, the role and difference of TLSs and TILs in patients with cholangiocarcinoma remains unclear.
View Article and Find Full Text PDFBackground: Investigators and funding organizations desire knowledge on topics and trends in publicly funded research but current efforts for manual categorization have been limited in breadth and depth of understanding.
Purpose: We present a semi-automated analysis of 21 years of R-type National Cancer Institute (NCI) grants to departments of radiation oncology and radiology using natural language processing (NLP).
Methods: We selected all non-education R-type NCI grants from 2000 to 2020 awarded to departments of radiation oncology/radiology with affiliated schools of medicine.
J Urol
January 2025
Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO.
Purpose: Conventional prostate magnetic resonance imaging has limited accuracy for clinically significant prostate cancer (csPCa). We performed diffusion basis spectrum imaging (DBSI) prior to biopsy and applied artificial intelligence models to these DBSI metrics to predict csPCa.
Materials And Methods: Between February 2020 and March 2024, 241 patients underwent prostate MRI that included conventional and DBSI-specific sequences prior to prostate biopsy.
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