biostudies-literatureZhang LLthe China Postdoctoral Science Foundation Grant1758835920978132https://www.ebi.ac.uk/biostudies/studies/S-EPMC7758560biostudies-literatureUnknown12<h4>Background</h4>Early failure of cancer treatment generally indicates a poor prognosis. Here, we aim to develop and validate a pre-treatment nomogram to predict early metachronous metastasis (EMM) in nasopharyngeal carcinoma (NPC).<h4>Methods</h4>From 2009 to 2015, a total of 9461 patients with NPC (training cohort: <i>n</i> = 7096; validation cohort: <i>n</i> = 2365) were identified from an institutional big-data research platform. EMM was defined as time to metastasis within 2 years after treatment. Early metachronous distant metastasis-free survival (EM-DMFS) was the primary endpoint. A nomogram was established with the significant prognostic factors for EM-DMFS determined by multivariate Cox regression analyses in the training cohort. The Harrell Concordance Index (C-index), area under the receiver operator characteristic curve (AUC), and calibration curves were applied to evaluate this model.<h4>Results</h4>EMM account for 73.5% of the total metachronous metastasis rate and is associated with poor long-term survival in NPC. The final nomogram, which included six clinical variables, achieved satisfactory discriminative performance and significantly outperformed the traditional tumor-node-metastasis (TNM) classification for predicting EM-DMFS: C-index: 0.721 <i>versus</i> 0.638, <i>p</i> < 0.001; AUC: 0.730 <i>versus</i> 0.644, <i>p</i> < 0.001. The calibration curves showed excellent agreement between the predicted and actual EM-DMFS. The nomogram can stratify patients into three risk groups with distinct EM-DMFS (2-year DMFS: 96.8% <i>versus</i> 90.1% <i>versus</i> 80.3%, <i>p</i> < 0.001). A validation cohort supported the results. The three identified risk groups are correlated with the efficacy of different treatment regimens.<h4>Conclusion</h4>Our established nomogram can reliably predict EMM in patients with NPC and might aid in formulating risk-adapted treatment decisions and personalized patient follow-up strategies.Therapeutic advances in medical oncologyDevelopment and validation of a prognostic nomogram for the pre-treatment prediction of early metachronous metastasis in endemic nasopharyngeal carcinoma: a big-data intelligence platform-based analysis.PMC7758560No. 2019M663305Zhang LLHe WTWang TSong DXu FShao JYLi YYHuang YSHuang MYDeng QLfalseDevelopment and validation of a prognostic nomogram for the pre-treatment prediction of early metachronous metastasis in endemic nasopharyngeal carcinoma: a big-data intelligence platform-based analysis.<h4>Background</h4>Early failure of cancer treatment generally indicates a poor prognosis. Here, we aim to develop and validate a pre-treatment nomogram to predict early metachronous metastasis (EMM) in nasopharyngeal carcinoma (NPC).<h4>Methods</h4>From 2009 to 2015, a total of 9461 patients with NPC (training cohort: <i>n</i> = 7096; validation cohort: <i>n</i> = 2365) were identified from an institutional big-data research platform. EMM was defined as time to metastasis within 2 years after treatment. Early metachronous distant metastasis-free survival (EM-DMFS) was the primary endpoint. A nomogram was established with the significant prognostic factors for EM-DMFS determined by multivariate Cox regression analyses in the training cohort. The Harrell Concordance Index (C-index), area under the receiver operator characteristic curve (AUC), and calibration curves were applied to evaluate this model.<h4>Results</h4>EMM account for 73.5% of the total metachronous metastasis rate and is associated with poor long-term survival in NPC. The final nomogram, which included six clinical variables, achieved satisfactory discriminative performance and significantly outperformed the traditional tumor-node-metastasis (TNM) classification for predicting EM-DMFS: C-index: 0.721 <i>versus</i> 0.638, <i>p</i> < 0.001; AUC: 0.730 <i>versus</i> 0.644, <i>p</i> < 0.001. The calibration curves showed excellent agreement between the predicted and actual EM-DMFS. The nomogram can stratify patients into three risk groups with distinct EM-DMFS (2-year DMFS: 96.8% <i>versus</i> 90.1% <i>versus</i> 80.3%, <i>p</i> < 0.001). A validation cohort supported the results. The three identified risk groups are correlated with the efficacy of different treatment regimens.<h4>Conclusion</h4>Our established nomogram can reliably predict EMM in patients with NPC and might aid in formulating risk-adapted treatment decisions and personalized patient follow-up strategies.2020-01-01T00:00:00Z20202024-02-15T18:00:18.932Z2021-02-20T20:48:57ZS-EPMC77585603342502710.1177/1758835920978132