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      A clinical-radiomics nomogram for the preoperative prediction of lymph node metastasis in colorectal cancer

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          Abstract

          Background

          Accurate lymph node metastasis (LNM) prediction in colorectal cancer (CRC) patients is of great significance for treatment decision making and prognostic evaluation. We aimed to develop and validate a clinical-radiomics nomogram for the individual preoperative prediction of LNM in CRC patients.

          Methods

          We enrolled 766 patients (458 in the training set and 308 in the validation set) with clinicopathologically confirmed CRC. We included nine significant clinical risk factors (age, sex, preoperative carbohydrate antigen 19-9 (CA19-9) level, preoperative carcinoembryonic antigen (CEA) level, tumor size, tumor location, histotype, differentiation and M stage) to build the clinical model. We used analysis of variance (ANOVA), relief and recursive feature elimination (RFE) for feature selection (including clinical risk factors and the imaging features of primary lesions and peripheral lymph nodes), established classification models with logistic regression analysis and selected the respective candidate models by fivefold cross-validation. Then, we combined the clinical risk factors, primary lesion radiomics features and peripheral lymph node radiomics features of the candidate models to establish combined predictive models. Model performance was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). Finally, decision curve analysis (DCA) and a nomogram were used to evaluate the clinical usefulness of the model.

          Results

          The clinical-primary lesion radiomics-peripheral lymph node radiomics model, with the highest AUC value (0.7606), was regarded as the candidate model and had good discrimination and calibration in both the training and validation sets. DCA demonstrated that the clinical-radiomics nomogram was useful for preoperative prediction in the clinical environment.

          Conclusion

          The present study proposed a clinical-radiomics nomogram with a combination of clinical risk factors and radiomics features that can potentially be applied in the individualized preoperative prediction of LNM in CRC patients.

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          Most cited references23

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          Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer.

          To develop and validate a radiomics nomogram for preoperative prediction of lymph node (LN) metastasis in patients with colorectal cancer (CRC).
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            • Record: found
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            • Article: not found

            Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

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              The Immunoscore: Colon Cancer and Beyond

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                Author and article information

                Contributors
                gyang@phy.ecnu.edu.cn
                t983352@126.com
                Journal
                J Transl Med
                J Transl Med
                Journal of Translational Medicine
                BioMed Central (London )
                1479-5876
                30 January 2020
                30 January 2020
                2020
                : 18
                : 46
                Affiliations
                [1 ]Department of Radiology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, 200032 People’s Republic of China
                [2 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, Department of Oncology, Shanghai Medical College, , Fudan University, ; Shanghai, 200032 People’s Republic of China
                [3 ]GRID grid.22069.3f, ISNI 0000 0004 0369 6365, Shanghai Key Laboratory of Magnetic Resonance, , East China Normal University, ; Shanghai, 200062 People’s Republic of China
                [4 ]Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, 200032 People’s Republic of China
                Article
                2215
                10.1186/s12967-020-02215-0
                6993349
                32000813
                7989c430-88fe-4bb2-867d-3f5d63ef2598
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 22 September 2019
                : 8 January 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 81971687
                Award ID: 61731009
                Award Recipient :
                Funded by: Shanghai Sailing Program
                Award ID: 19YF1409900
                Categories
                Research
                Custom metadata
                © The Author(s) 2020

                Medicine
                Medicine

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