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Prognostic nomograms for predicting survival and distant metastases in locally advanced rectal cancers

Peng, J and Ding, Y and Tu, S and Shi, D and Sun, L and Li, X and Wu, H and Cai, S (2014) Prognostic nomograms for predicting survival and distant metastases in locally advanced rectal cancers. PLoS ONE, 9 (8).

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Aim: To develop prognostic nomograms for predicting outcomes in patients with locally advanced rectal cancers who do not receive preoperative treatment. Materials and Methods: A total of 883 patients with stage II-III rectal cancers were retrospectively collected from a single institution. Survival analyses were performed to assess each variable for overall survival (OS), local recurrence (LR) and distant metastases (DM). Cox models were performed to develop a predictive model for each endpoint. The performance of model prediction was validated by cross validation and on an independent group of patients. Results: The 5-year LR, DM and OS rates were 22.3%, 32.7% and 63.8%, respectively. Two prognostic nomograms were successfully developed to predict 5-year OS and DM-free survival rates, with c-index of 0.70 (95% CI = [0.66, 0.73]) and 0.68 (95% CI = [0.64, 0.72]) on the original dataset, and 0.76 (95% CI = [0.67, 0.86]) and 0.73 (95% CI = [0.63, 0.83]) on the validation dataset, respectively. Factors in our models included age, gender, carcinoembryonic antigen value, tumor location, T stage, N stage, metastatic lymph nodes ratio, adjuvant chemotherapy and chemoradiotherapy. Predicted by our nomogram, substantial variability in terms of 5-year OS and DM-free survival was observed within each TNM stage category. Conclusions: The prognostic nomograms integrated demographic and clinicopathological factors to account for tumor and patient heterogeneity, and thereby provided a more individualized outcome prognostication. Our individualized prediction nomograms could help patients with preoperatively under-staged rectal cancer about their postoperative treatment strategies and follow-up protocols. © 2014 Peng et al.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Peng, J
Tu, S
Shi, D
Sun, L
Li, X
Wu, H
Cai, S
ContributionContributors NameEmailPitt UsernameORCID
Date: 29 August 2014
Date Type: Publication
Journal or Publication Title: PLoS ONE
Volume: 9
Number: 8
DOI or Unique Handle: 10.1371/journal.pone.0106344
Schools and Programs: Dietrich School of Arts and Sciences > Statistics
School of Public Health > Biostatistics
Refereed: Yes
Date Deposited: 26 Sep 2014 14:29
Last Modified: 02 Feb 2019 13:55


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