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A combined prognostic factor for improved risk stratification of patients with oral cancer
Author(s) -
Kim KY,
Zhang X,
Kim SM,
Lee BD,
Cha IH
Publication year - 2017
Publication title -
oral diseases
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.953
H-Index - 87
eISSN - 1601-0825
pISSN - 1354-523X
DOI - 10.1111/odi.12579
Subject(s) - multivariate analysis , medicine , multivariate statistics , oncology , stage (stratigraphy) , pathological , univariate , risk factor , t stage , lymph node , univariate analysis , risk stratification , basal cell , resection margin , cancer , gastroenterology , surgery , resection , statistics , biology , paleontology , mathematics
Purpose We aimed to identify a combined prognostic factor for predicting better performance in risk stratification. Materials and Methods We reviewed the clinical and pathological variables of 316 patients with oral squamous cell carcinoma ( OSCC ) who underwent surgery. To identify a combined predictor, principal component analysis ( PCA ) was performed. Results Univariate analysis showed that the independent prognostic variables for overall survival ( OS ) were pathologic T stage (T1 vs T4, HR = 1.99, 95% CI : = 1.083–3.675, P = 0.026) and pathologic N stage (N0 vs N2, HR =1.90, 95% CI : = 1.17–3.08, P = 0.008). In the multivariate analysis, only pathologic T stage was significant ( P = 0.006 and P = 0.007); however, the multivariate model was not significant ( P = 0.191). The multivariate model became significant by including lymph node ratio ( LNR ) instead of pathologic N stage ( P = 0.0025 in numeric LNR , P = 0.0007 in categorized LNR ). Also, the performance of prediction model was improved by a combined prognostic factor ( P = 0.0002). Conclusions The newly identified combined prognostic factor included resection margin, differentiation, and LNR , and they were insignificant factors independently except for LNR . This combined prognostic factor showed a good performance although it did not include molecular markers; therefore, it may be used conveniently for risk stratification of patients with OSCC by combining only clinical information.