A Nomogram for Predicting Cancer-Specific Survival of TNM 8th Edition Stage I Non-small-cell Lung Cancer.

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2019
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Abstract
Models for predicting the survival outcomes of stage I non-small-cell lung cancer (NSCLC) defined by the newly released 8th edition TNM staging system are scarce. This study aimed to develop a nomogram for predicting the cancer-specific survival (CSS) of these patients and identifying individuals with a higher risk for CSS.A total of 30,475 NSCLC cases were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. We identified and integrated the risk factors to build a nomogram. The model was subjected to bootstrap internal validation with the SEER database, and external validation with a multicenter cohort of 1133 patients from China. The difference in the impact of adjuvant chemotherapy on model-defined high- and low-risk patients was examined using the National Cancer Database (NCDB).Eight independent prognostic factors were identified and integrated into the model. The calibration curves showed good agreement. The concordance index (C-index) of the nomogram was higher than that of the staging system (IA1, IA2, IA3, and IB) (internal validation set 0.63 vs. 0.56; external validation set 0.66 vs. 0.55; both p < 0.01). Specifically, 21.7% of stage IB patients (7.5% of all stage I) were categorized into the high-risk group (score > 30). There was a significant interaction effect between the adjuvant chemotherapy and risk groups in the NCDB cohort (p = 0.003).We established a practical nomogram to predict CSS for 8th edition stage I NSCLC. A prospective study is warranted to determine its role in identifying adjuvant chemotherapy candidates.
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Authors Zeng, Yuan;Mayne, Nicholas;Yang, Chi-Fu Jeffrey;D'Amico, Thomas A;Ng, Calvin S H;Liu, Chia-Chuan;Petersen, René Horsleben;Rocco, Gaetano;Brunelli, Alessandro;Liu, Jun;Liu, Yang;Huang, Weizhe;He, Jiaxi;Wang, Wei;Jiang, Long;Cui, Fei;Wang, Wenjun;Liang, Wenhua;He, Jianxing;, ;
Journal annals of surgical oncology
Year 2019
DOI 10.1245/s10434-019-07318-7
URL
Keywords Keywords not found

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