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      A CT-based radiomics nomogram for differentiation of renal oncocytoma and chromophobe renal cell carcinoma with a central scar-matched study

      1 , 2 , 1 , 3 , 1 , 1
      The British Journal of Radiology
      British Institute of Radiology

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          Abstract

          Objective:

          Pre-operative differentiation between renal oncocytoma (RO) and chromophobe renal cell carcinoma (chRCC) is critical due to their different clinical behavior and different clinical treatment decisions. The aim of this study was to develop and validate a CT-based radiomics nomogram for the pre-operative differentiation of RO from chRCC.

          Methods:

          A total of 141 patients (84 in training data set and 57 in external validation data set) with ROs (n = 47) or chRCCs (n = 94) were included. Radiomics features were extracted from tri-phasic enhanced-CT images. A clinical model was developed based on significant patient characteristics and CT imaging features. A radiomics signature model was developed and a radiomics score (Rad-score) was calculated. A radiomics nomogram model incorporating the Rad-score and independent clinical factors was developed by multivariate logistic regression analysis. The diagnostic performance was evaluated and validated in three models using ROC curves.

          Results:

          Twelve features from CT images were selected to develop the radiomics signature. The radiomics nomogram combining a clinical factor (segmental enhancement inversion) and radiomics signature showed an AUC value of 0.988 in the validation set. Decision curve analysis revealed that the diagnostic performance of the radiomics nomogram was better than the clinical model and the radiomics signature.

          Conclusions:

          The radiomics nomogram combining clinical factors and radiomics signature performed well for distinguishing RO from chRCC.

          Advances in knowledge:

          Differential diagnosis between renal oncocytoma (RO) and chromophobe renal cell carcinoma (chRCC) is rather difficult by conventional imaging modalities when a central scar was present. A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of RO from chRCC with improved diagnostic efficacy. The CT-based radiomics nomogram might spare unnecessary surgery for RO.

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          Most cited references28

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          European Association of Urology Guidelines on Renal Cell Carcinoma: The 2019 Update

          The European Association of Urology Renal Cell Carcinoma (RCC) Guideline Panel has prepared evidence-based guidelines and recommendations for the management of RCC.
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            CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma.

            Radiomics provides opportunities to quantify the tumor phenotype non-invasively by applying a large number of quantitative imaging features. This study evaluates computed-tomography (CT) radiomic features for their capability to predict distant metastasis (DM) for lung adenocarcinoma patients.
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              • Record: found
              • Abstract: not found
              • Article: not found

              The Bayesian adaptive lasso regression

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                Author and article information

                Journal
                The British Journal of Radiology
                BJR
                British Institute of Radiology
                0007-1285
                1748-880X
                January 01 2022
                January 01 2022
                : 95
                : 1129
                Affiliations
                [1 ]Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
                [2 ]Department of Radiology, Qingdao Municipal Hospital, Qingdao, Shandong, China
                [3 ]Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao Shandong, China
                Article
                10.1259/bjr.20210534
                34735296
                019b9e53-0376-4cbe-8463-7b0e3d7aae5a
                © 2022
                History

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