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      Survival nomograms for stage III colorectal cancer

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          Abstract

          The postoperative survival of patients with stage III colorectal cancer (CRC) various obviously. We sought to develop novel nomograms for predicting the survival of these patients after radical surgery and postoperative chemotherapy.

          A total of 620 consecutive patients with stage III CRC who underwent curative resection and postoperative chemotherapy between January 2009 and December 2015 were retrospectively collected and randomly allocated to the training (n = 372) or validation cohort (n = 248). Clinicopathological factors were collected and analyzed. On the basis of data from 372 patients in the training set, predictive factors for overall survival (OS) and disease-free survival (DFS) were identified using multivariate Cox regression and used to construct nomograms. The predictive performance of the nomograms was assessed by concordance index (C-index) and calibration plots. An external cohort of 248 patients was used to validate the nomograms. Furthermore, nomogram performance was compared with the performance of T and N stage stratification.

          Tumor differentiation grade, lymph node metastasis ratio, intravascular emboli (IVE), preoperative serum carcinoembryonic antigen (CEA) level, albumin to globulin ratio (AGR), T stage and N stage were significant prognostic factors for OS on multivariate analysis; whereas, Tumor differentiation grade, lymph node metastasis ratio, IVE, AGR and N stage were significant for DFS. Nomograms to predict 3- and 5-year OS and DFS were established that performed well (C-indexes of 0.734 [95% CI, 0.691–0.779] for OS and 0.699 [95% CI, 0.657–0.740] for DFS prediction), and nomogram accuracy was confirmed in the validation cohort. Furthermore, model comparison proved that the nomograms were superior to risk stratification by T and N stage for stage III CRC.

          We propose 2 practical nomograms for stage III CRC patients that provide more accurate prognostic predictions and should be helpful for guiding individualized treatment and postoperative surveillance.

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          NCCN Guidelines Insights: Colon Cancer, Version 2.2018

          The NCCN Guidelines for Colon Cancer provide recommendations regarding diagnosis, pathologic staging, surgical management, perioperative treatment, surveillance, management of recurrent and metastatic disease, and survivorship. These NCCN Guidelines Insights summarize the NCCN Colon Cancer Panel discussions for the 2018 update of the guidelines regarding risk stratification and adjuvant treatment for patients with stage III colon cancer, and treatment of BRAF V600E mutation-positive metastatic colorectal cancer with regimens containing vemurafenib.
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            Nutrition, inflammation and cancer

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              Prognostic modeling with logistic regression analysis: in search of a sensible strategy in small data sets.

              Clinical decision making often requires estimates of the likelihood of a dichotomous outcome in individual patients. When empirical data are available, these estimates may well be obtained from a logistic regression model. Several strategies may be followed in the development of such a model. In this study, the authors compare alternative strategies in 23 small subsamples from a large data set of patients with an acute myocardial infarction, where they developed predictive models for 30-day mortality. Evaluations were performed in an independent part of the data set. Specifically, the authors studied the effect of coding of covariables and stepwise selection on discriminative ability of the resulting model, and the effect of statistical "shrinkage" techniques on calibration. As expected, dichotomization of continuous covariables implied a loss of information. Remarkably, stepwise selection resulted in less discriminating models compared to full models including all available covariables, even when more than half of these were randomly associated with the outcome. Using qualitative information on the sign of the effect of predictors slightly improved the predictive ability. Calibration improved when shrinkage was applied on the standard maximum likelihood estimates of the regression coefficients. In conclusion, a sensible strategy in small data sets is to apply shrinkage methods in full models that include well-coded predictors that are selected based on external information.
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                Author and article information

                Journal
                Medicine (Baltimore)
                Medicine (Baltimore)
                MEDI
                Medicine
                Wolters Kluwer Health
                0025-7974
                1536-5964
                December 2018
                10 December 2018
                : 97
                : 49
                : e13239
                Affiliations
                [a ]Department of Gastrointestinal Surgery
                [b ]Department of Oncology, Xiangya Hospital, Central South University, Changsha, China.
                Author notes
                []Correspondence: Haiping Pei, Department of Gastrointestinal Surgery, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, P.R. China (e-mail: peihaiping1966@ 123456hotmail.com ).
                Article
                MD-D-18-05063 13239
                10.1097/MD.0000000000013239
                6310595
                30544384
                0ad901f5-6791-4b0f-bbd7-065926e01258
                Copyright © 2018 the Author(s). Published by Wolters Kluwer Health, Inc.

                This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc/4.0

                History
                : 24 July 2018
                : 21 October 2018
                Categories
                4500
                Research Article
                Observational Study
                Custom metadata
                TRUE

                colorectal cancer,disease-free survival,nomogram,overall survival,prognosis

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