Dataset Information

Development and validation of nomograms for predicting survival in patients with non-metastatic colorectal cancer.

ABSTRACT:

Background

This study aimed to develop nomograms for predicting survival in patients with non-metastatic colorectal cancer (CRC).

Results

On multivariate analyses of the derivation set, the nomograms for OS and CSS shared common significant prognostic factors: age, first-degree relative cancer history, differentiation grade, vessels/nerves invasion, TNM stage, CEA, CA19-9 and PNI. The nomograms displayed good accuracy in predicting OS and CSS, with C-indexes of 0.75 and 0.76, respectively. The calibration plots also showed an excellent agreement between the predicted and observed survival probabilities. Furthermore, the predictive accuracy of the nomograms was confirmed in the validation set, with C-indexes of 0.79 and 0.83 for OS and CSS, respectively.

Materials and methods

On the basis of data from 822 patients with resected non-metastatic CRC, nomograms for predicting overall survival (OS) and cancer-specific survival (CSS) were established using Cox regression model. The predictive performance of the nomograms was assessed by concordance index (C-index) and calibration plot. An independent external cohort of 171 patients was used to validate the nomograms.

Conclusions

We developed and validated two nomograms for patients with non-metastatic CRC, which could provide individual prediction of OS and CSS with high accuracy.

SUBMITTER: Jiang H

PROVIDER: S-EPMC5444709 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Publications

Development and validation of nomograms for predicting survival in patients with non-metastatic colorectal cancer.

Jiang Huihong H Tang Erjiang E Xu Dan D Chen Ying Y Zhang Yong Y Tang Min M Xiao Yihua Y Zhang Zhiyong Z Deng Xiaxing X Li Huaguang H Lin Moubin M

Oncotarget 20170501 18

<h4>Background</h4>This study aimed to develop nomograms for predicting survival in patients with non-metastatic colorectal cancer (CRC).<h4>Results</h4>On multivariate analyses of the derivation set, the nomograms for OS and CSS shared common significant prognostic factors: age, first-degree relative cancer history, differentiation grade, vessels/nerves invasion, TNM stage, CEA, CA19-9 and PNI. The nomograms displayed good accuracy in predicting OS and CSS, with C-indexes of 0.75 and 0.76, resp ...[more]

PMID: 28415740

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Project description:BackgroundTriple negative breast cancer (TNBC) is an aggressive and heterogeneous disease. Nomograms predicting outcomes of TNBC are needed for risk management.MethodsNomograms were based on an analysis of 296 non-metastatic TNBC patients treated at Sun Yat-sen Memorial Hospital from 2002 to 2014. The end points were disease-free survival (DFS) and overall survival (OS). Predictive accuracy and discriminative ability were evaluated by concordance index (C-index), area under the curve (AUC) and calibration curve, and compared with the American Joint Committee on Cancer (AJCC) staging system, PREDICT and CancerMath. Models were subjected to bootstrap internal validation and external validation using independent cohorts of 191 patients from the second Xiangya Hospital and Peking University Shenzhen Hospital between 2007 and 2012.ResultsOn multivariable analysis of training cohort, independent prognostic factors were stromal tumor-infiltrating lymphocytes (TILs), tumor size, node status, and Ki67 index, which were then selected into the nomograms. The calibration curves for probability of DFS and OS showed optimal agreement between nomogram prediction and actual observation. The C-index of nomograms was significantly higher than that of the seventh and eighth AJCC staging system for predicting DFS (training: 0.743 vs 0.666 (P = 0.003) and 0.664 (P = 0.024); validation: 0.784 vs 0.632 (P = 0.02) and 0.607 (P = 0.002)) and OS (training: 0.791 vs 0.683 (P = 0.004) and 0.677 (P < 0.001); validation: 0.783 vs 0.656 (P = 0.006) and 0.606 (P = 0.001)). Our nomograms had larger AUCs compared with PREDICT and CancerMath. In addition, the nomograms showed good performance in stratifying different risk groups of patients both in the training and validation cohorts.ConclusionWe have developed novel and practical nomograms that can provide individual prediction of DFS and OS for TNBC based on stromal TILs, tumor size, node status, and Ki67 index. Our nomograms may help clinicians in risk consulting and selection of long term survivors.

Project description:ObjectiveThis study aimed to establish and validate the nomograms for predicting overall survival (OS) probabilities in differentiated thyroid cancer (DTC) patients who received and did not receive radioiodine therapy (RAI), respectively.MethodsIn this study, 11, 099 patients diagnosed with DTC in the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2016 were selected. Whether they have RAI, they are divided into RAI (n=6427) and non-RAI (n=4672) groups. They were randomly assigned to either a training cohort (RAI: n=4498, non-RAI: n=3263) or a validation cohort (RAI: n=1929, non-RAI: n=1399) using R software to divide the patients in a 7-to-3 ratio randomly. Variables were selected using a backward stepwise method in a Cox regression model to determine the independent prognostic factors, which were then utilized to build two nomograms to predict the 5-, 8-, and 10-year OS probabilities in DTC patients with or without RAI. The concordance index (C-index), the area under the time-dependent receiver operating characteristics curve (AUC), the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), calibration plotting, and decision-curve analysis (DCA) were used to evaluate the performance of our models.ResultsThe multivariate analyses demonstrated that birth of the year, race, histological type, tumor size, grade, TNM stage, lymph node dissections, surgery, and chemotherapy were risk factors for OS. Compared to the AJCC stage, the C-index (RAI: training group: 0.911 vs. 0.810, validation group: 0.873 vs. 0.761; non-RAI: training group: 0.903 vs. 0.846, validation group: 0.892 vs. 0.808). The AUC values for the training cohort (RAI: 0.940, 0.933, and 0.942; non-RAI: 0.891, 0.884, and 0.852 for the 5-, 8-, and 10-year OS, respectively) and validation cohort (RAI: 0.855, 0.825, and 0.900, non-RAI: 0.867, 0.896, and 0.899), and the calibration plots of both two models all exhibited better performance. Additionally, the NRI and IDI further showed that they exhibited good 5-, 8-, and 10-year net benefits.ConclusionWe have established the prediction models of DTC patients with or without RAI respectively through various variables. The nomogram may be more targeted to guide clinical decisions in the future.

Dataset Information

Development and validation of nomograms for predicting survival in patients with non-metastatic colorectal cancer.

Background

Results

Materials and methods

Conclusions

Publications

Development and validation of nomograms for predicting survival in patients with non-metastatic colorectal cancer.

Similar Datasets

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