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      Computed Tomography Urography: State of the Art and Beyond

      , , , , , ,
      Tomography
      MDPI AG

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          Abstract

          Computed Tomography Urography (CTU) is a multiphase CT examination optimized for imaging kidneys, ureters, and bladder, complemented by post-contrast excretory phase imaging. Different protocols are available for contrast administration and image acquisition and timing, with different strengths and limits, mainly related to kidney enhancement, ureters distension and opacification, and radiation exposure. The availability of new reconstruction algorithms, such as iterative and deep-learning-based reconstruction has dramatically improved the image quality and reducing radiation exposure at the same time. Dual-Energy Computed Tomography also has an important role in this type of examination, with the possibility of renal stone characterization, the availability of synthetic unenhanced phases to reduce radiation dose, and the availability of iodine maps for a better interpretation of renal masses. We also describe the new artificial intelligence applications for CTU, focusing on radiomics to predict tumor grading and patients’ outcome for a personalized therapeutic approach. In this narrative review, we provide a comprehensive overview of CTU from the traditional to the newest acquisition techniques and reconstruction algorithms, and the possibility of advanced imaging interpretation to provide an up-to-date guide for radiologists who want to better comprehend this technique.

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          • Record: found
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          Radiomics: extracting more information from medical images using advanced feature analysis.

          Solid cancers are spatially and temporally heterogeneous. This limits the use of invasive biopsy based molecular assays but gives huge potential for medical imaging, which has the ability to capture intra-tumoural heterogeneity in a non-invasive way. During the past decades, medical imaging innovations with new hardware, new imaging agents and standardised protocols, allows the field to move towards quantitative imaging. Therefore, also the development of automated and reproducible analysis methodologies to extract more information from image-based features is a requirement. Radiomics--the high-throughput extraction of large amounts of image features from radiographic images--addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory. Copyright © 2011 Elsevier Ltd. All rights reserved.
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            • Record: found
            • Abstract: found
            • Article: not found

            Prognostic significance of morphologic parameters in renal cell carcinoma.

            The prognostic significance of morphologic parameters was evaluated in 103 patients with renal cell carcinoma diagnosed during 1961--1974. Pathologic material was classified as to pathologic stage, tumor size, cell arrangement, cell type and nuclear grade. Four nuclear grades (1--4) were defined in order of increasing nuclear size, irregularity and nucleolar prominence. Nuclear grade was more effective than each of the other parameters in predicting development of distant metastasis following nephrectomy. Among 45 patients who presented in Stage I, tumors classified as nuclear grade 1 did not metastasize for at least 5 years, whereas 50% of the higher grade tumors did so. Moreover, among Stage I tumors there was a significant difference in subsequent metastatic rate between nuclear grades 1 and 2. There was an apparent positive relationship between cell type and metastatic rate; clear cell tumors were less aggressive than predominantly granular cell tumors (metastatic rate 38% versus 71%). This relationship in part a function of the nuclear grade: only 5% of grade 3 and 4 tumors consisted of clear cells, whereas such high grades were seen in 57% of granular cell tumors. The size of the primary correlated well with the stage at the time of surgery. However, with the exception of extremely large and small tumors, the size was not useful in predicting the subsequent course of patients treated for Stage I tumors. Nuclear grade was the most significant prognostic criterion for the outcome of Stage I renal cell carcinoma.
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              • Record: found
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              • Article: not found

              Texture analysis of medical images.

              The analysis of texture parameters is a useful way of increasing the information obtainable from medical images. It is an ongoing field of research, with applications ranging from the segmentation of specific anatomical structures and the detection of lesions, to differentiation between pathological and healthy tissue in different organs. Texture analysis uses radiological images obtained in routine diagnostic practice, but involves an ensemble of mathematical computations performed with the data contained within the images. In this article we clarify the principles of texture analysis and give examples of its applications, reviewing studies of the technique.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Tomography
                Tomography
                MDPI AG
                2379-139X
                June 2023
                April 30 2023
                : 9
                : 3
                : 909-930
                Article
                10.3390/tomography9030075
                10204399
                37218935
                d1053bef-48d4-48d4-87a6-b50f6166b2f1
                © 2023

                https://creativecommons.org/licenses/by/4.0/

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