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Development and validation of prognostic nomograms for early-onset locally advanced colon cancer
Background: The incidence of colorectal cancer in patients younger than 50 years has been increasing in recent years. Objective: Develop and validate prognostic nomograms predicting overall survival (OS) and cancer-specific survival (CSS) for early-onset locally advanced colon cancer (EOLACC) based...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Impact Journals
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7834989/ https://www.ncbi.nlm.nih.gov/pubmed/33289705 http://dx.doi.org/10.18632/aging.202157 |
Sumario: | Background: The incidence of colorectal cancer in patients younger than 50 years has been increasing in recent years. Objective: Develop and validate prognostic nomograms predicting overall survival (OS) and cancer-specific survival (CSS) for early-onset locally advanced colon cancer (EOLACC) based on the Surveillance, Epidemiology, and End Results (SEER) database. Results: The entire cohort comprised 13,755 patients with EOLACC. The nomogram predicting OS for EOLACC displayed that T stage contributed the most to prognosis, followed by N stage, regional nodes examined (RNE) and surgery. The nomogram predicting CSS for EOLACC demonstrated similar results. Various methods identified the discriminating superiority of the nomograms. X-tile software was used to classify patients into high-risk, medium-risk, and low-risk according to the risk score of the nomograms. The risk stratification effectively avoided the survival paradox. Conclusions: We established and validated nomograms for predicting OS and CSS based on a national cohort of almost 13,000 EOLACC patients. The nomograms could effectively solve the issue of survival paradox of the AJCC staging system and be an excellent tool to integrate the clinical characteristics to guide the therapeutic choice for EOLACC patients. Methods: Nomograms were constructed based on the SEER database and the Cox regression model. |
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