Dataset Information

Development and validation of a web-based calculator to predict individualized conditional risk of site-specific recurrence in nasopharyngeal carcinoma: Analysis of 10,058 endemic cases.

ABSTRACT:

Background

Conditional survival (CS) provides dynamic prognostic estimates by considering the patients existing survival time. Since CS for endemic nasopharyngeal carcinoma (NPC) is lacking, we aimed to assess the CS of endemic NPC and establish a web-based calculator to predict individualized, conditional site-specific recurrence risk.

Methods

Using an NPC-specific database with a big-data intelligence platform, 10,058 endemic patients with non-metastatic stage I-IVA NPC receiving intensity-modulated radiotherapy with or without chemotherapy between April 2009 and December 2015 were investigated. Crude CS estimates of conditional overall survival (COS), conditional disease-free survival (CDFS), conditional locoregional relapse-free survival (CLRRFS), conditional distant metastasis-free survival (CDMFS), and conditional NPC-specific survival (CNPC-SS) were calculated. Covariate-adjusted CS estimates were generated using inverse probability weighting. A prediction model was established using competing risk models and was externally validated with an independent, non-metastatic stage I-IVA NPC cohort undergoing intensity-modulated radiotherapy with or without chemotherapy (n = 601) at another institution.

Results

The median follow-up of the primary cohort was 67.2 months. The 5-year COS, CDFS, CLRRFS, CDMFS, and CNPC-SS increased from 86.2%, 78.1%, 89.8%, 87.3%, and 87.6% at diagnosis to 87.3%, 87.7%, 94.4%, 96.0%, and 90.1%, respectively, for an existing survival time of 3 years since diagnosis. Differences in CS estimates between prognostic factor subgroups of each endpoint were noticeable at diagnosis but diminished with time, whereas an ever-increasing disparity in CS between different age subgroups was observed over time. Notably, the prognoses of patients that were poor at diagnosis improved greatly as patients survived longer. For individualized CS predictions, we developed a web-based model to estimate the conditional risk of local (C-index, 0.656), regional (0.667), bone (0.742), lung (0.681), and liver (0.711) recurrence, which significantly outperformed the current staging system (P < 0.001). The performance of this web-based model was further validated using an external validation cohort (median follow-up, 61.3 months), with C-indices of 0.672, 0.736, 0.754, 0.663, and 0.721, respectively.

Conclusions

We characterized the CS of endemic NPC in the largest cohort to date. Moreover, we established a web-based calculator to predict the CS of site-specific recurrence, which may help to tailor individualized, risk-based, time-adapted follow-up strategies.

SUBMITTER: Wu CF

PROVIDER: S-EPMC7819551 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

Development and validation of a web-based calculator to predict individualized conditional risk of site-specific recurrence in nasopharyngeal carcinoma: Analysis of 10,058 endemic cases.

Wu Chen-Fei CF Lv Jia-Wei JW Lin Li L Mao Yan-Ping YP Deng Bin B Zheng Wei-Hong WH Wen Dan-Wan DW Chen Yue Y Kou Jia J Chen Fo-Ping FP Yang Xing-Li XL Zheng Zi-Qi ZQ Li Zhi-Xuan ZX Xu Si-Si SS Ma Jun J Sun Ying Y

Cancer communications (London, England) 20201203 1

<h4>Background</h4>Conditional survival (CS) provides dynamic prognostic estimates by considering the patients existing survival time. Since CS for endemic nasopharyngeal carcinoma (NPC) is lacking, we aimed to assess the CS of endemic NPC and establish a web-based calculator to predict individualized, conditional site-specific recurrence risk.<h4>Methods</h4>Using an NPC-specific database with a big-data intelligence platform, 10,058 endemic patients with non-metastatic stage I-IVA NPC receivin ...[more]

PMID: 33270990

Similar Datasets

Project description:Background: Recurrence remains one of the key reasons of relapse after the radical radiation for locally advanced nasopharyngeal carcinoma (NPC). Here, the multiple molecular and clinical variables integrated decision tree algorithms were designed to predict individual recurrence patterns (with VS without recurrence) for locally advanced NPC. Methods: A total of 136 locally advanced NPC patients retrieved from a randomized controlled phase III trial, were included. For each patient, the expression levels of 33 clinicopathological biomarkers in tumor specimen, 3 Epstein-Barr virus related serological antibody titer and 5 clinicopathological variables, were detected and collected to construct the decision tree algorithm. The expression level of 33 clinicopathological biomarkers in tumor specimen was evaluated by immunohistochemistry staining. Results: Three algorithm classifiers, augmented by the adaptive boosting algorithm for variable selection and classification, were designed to predict individual recurrence pattern. The classifiers were trained in the training subset and further tested using a 10-fold cross-validation scheme in the validation subset. In total, 13 molecules expression level in tumor specimen, including AKT1, Aurora-A, Bax, Bcl-2, N-Cadherin, CENP-H, HIF-1α, LMP-1, C-Met, MMP-2, MMP-9, Pontin and Stathmin, and N stage were selected to construct three 10-fold cross-validation decision tree classifiers. These classifiers showed high predictive sensitivity (87.2-93.3%), specificity (69.0-100.0%), and overall accuracy (84.5-95.2%) to predict recurrence pattern individually. Multivariate analyses confirmed the decision tree classifier was an independent prognostic factor to predict individual recurrence (algorithm 1: hazard ration (HR) 0.07, 95% confidence interval (CI) 0.03-0.16, P < 0.01; algorithm 2: HR 0.13, 95% CI 0.04-0.44, P < 0.01; algorithm 3: HR 0.13, 95% CI 0.03-0.68, P = 0.02). Conclusion: Multiple molecular and clinicopathological variables integrated decision tree algorithms may individually predict the recurrence pattern for locally advanced NPC. This decision tree algorism provides a potential tool to select patients with high recurrence risk for intensive follow-up, and to diagnose recurrence at an earlier stage for salvage treatment in the NPC endemic region.

Dataset Information

Development and validation of a web-based calculator to predict individualized conditional risk of site-specific recurrence in nasopharyngeal carcinoma: Analysis of 10,058 endemic cases.

Background

Methods

Results

Conclusions

Publications

Development and validation of a web-based calculator to predict individualized conditional risk of site-specific recurrence in nasopharyngeal carcinoma: Analysis of 10,058 endemic cases.

Similar Datasets

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