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Academic Journal
Recursive partitioning staging system based on the log odds of the negative lymph node/T stage ratio in colon mucinous adenocarcinoma
Huajun Cai, Jintao Zeng, Ye Wang, Jinfu Zhuang, Xing Liu, Guoxian Guan
Frontiers in Immunology, Vol 15 (2024)
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Title | Recursive partitioning staging system based on the log odds of the negative lymph node/T stage ratio in colon mucinous adenocarcinoma |
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Authors | Huajun Cai, Jintao Zeng, Ye Wang, Jinfu Zhuang, Xing Liu, Guoxian Guan |
Publication Year |
2024
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Source |
Frontiers in Immunology, Vol 15 (2024)
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Description |
BackgroundThis study aimed to investigate the prognostic significance of the log odds of negative lymph nodes/T stage ratio (LONT) and develop an efficient prognostic staging system using LONT in patients with colon mucinous adenocarcinoma (MAC).MethodsThis study included 5,236 patients diagnosed with colon MAC obtained from the Surveillance, Epidemiology, and End Results database. The Kaplan–Meier method, subgroup analysis, receiver operating characteristic (ROC) curve, and Cox proportional hazard regression model were used to determine the clinical outcomes. Recursive partitioning analysis (RPA) was used to develop a novel prognostic system.ResultsThe 1-, 3-, and 5-year ROC curves, used to predict cancer-specific survival (CSS) and overall survival (OS), demonstrated that the areas under the ROC curve for LONT were superior to those of pT, pN, and pTNM stages. Additionally, a lower LONT was correlated with worse clinical outcomes. The LONT classification efficiently differentiated the prognosis of patients in terms of OS and CSS. Multivariate Cox analyses revealed that LONT was an independent prognostic factor for both CSS and OS. Based on the pT stage and LONT, a novel prognostic staging system was developed using RPA, demonstrating a good prognostic predictive performance.ConclusionA lower LONT was associated with worse survival in patients with colon MAC. The pT stage and LONT-based prognostic staging system facilitated risk stratification in these patients.
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Document Type |
article
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Language |
English
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Publisher Information |
Frontiers Media S.A., 2024.
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Subject Terms | |