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Prognostic Nomograms for Nonelderly Adults with Gastric Signet Ring Cell Carcinoma
Author(s) -
Hui Wang,
Yao Peng,
Qi Huang,
Jingjing Wu,
Mingjun Zhang
Publication year - 2021
Publication title -
biomed research international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.772
H-Index - 126
eISSN - 2314-6141
pISSN - 2314-6133
DOI - 10.1155/2021/1274527
Subject(s) - nomogram , medicine , oncology , receiver operating characteristic , univariate , signet ring cell carcinoma , proportional hazards model , multivariate analysis , stage (stratigraphy) , concordance , cohort , cancer , multivariate statistics , adenocarcinoma , statistics , paleontology , mathematics , biology
Background Nomograms were established to predict the survival for gastric signet ring cell carcinoma (GSRC) in young and middle-aged adults. Material and Methods . Eligible patients with GSRC from 2004 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database and then divided into a training and a testing cohort in proportion. Independent prognostic factors were picked by univariate and multivariate Cox regression analysis to set up nomograms. The predictive effect and clinical value of nomograms were evaluated by the concordance index (C-index), calibration curves, and receiver operating characteristic curve (ROC).Results A total of 1686 GSRC patients were subsumed into this case for analysis, including a training ( n = 1180) and a testing cohort ( n = 506). Independent risk factors related to overall survival (OS) and cancer-specific survival (CSS) comprised of race, TNM stage, tumor size, number of positive lymph nodes (PLNE), and chemotherapy. For OS, the C-indexes of the training and testing cohorts were 0.737 and 0.752, while for CSS, C-indexes were, respectively, 0.749 and 0.751. These revealed that nomograms accurately predicted OS and CSS. Calibration curves and ROC demonstrated the apparent superiority of nomograms.Conclusion We built a well-understood and comprehensive prognostic assessment model for GSRC, which provided an individualized survival prediction in the form of a quantitative score that can be considered for clinical practice.

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