Abstract
Background
The study of poorly differentiated adenocarcinoma of the rectosigmoid junction (RSJ) remains underexplored. This study aims to develop a postoperative nomogram to accurately predict cancer-specific survival (CSS) in patients afflicted with this form of cancer.
Methods
The study utilized data from the Surveillance, Epidemiology, and End Results database, focusing on patients diagnosed with RSJ cancer between 2004 and 2017. Participants were divided into a training cohort and a validation cohort at a 7:3 ratio. Initially, the training cohort was analyzed using Cox univariate analysis to identify significantly impactful factors. These factors were then examined through Cox multivariate analysis to isolate the best predictors for CSS, which were used to construct the nomogram. The validity of this nomogram was subsequently tested using the validation cohort.
Results
The study enrolled a total of 2668 patients, with 1867 in the training cohort and 801 in the validation cohort. The 1-, 3-, and 5-year CSS rates were 86.5%, 67.9%, and 57.8%, respectively. Significant predictors identified included race, age, and stage. The constructed nomogram was validated through receiver operating characteristic analysis, calibration, and decision curve analysis, confirming its reliability and accuracy in predicting CSS.
Conclusion
Race, age, and staging have been affirmed as significant prognostic indicators for CSS. This study has successfully developed a postoperative nomogram that effectively predicts the 1-, 3-, and 5-year CSS for these patients. This predictive model holds substantial clinical value, providing essential guidance for therapeutic decision-making and patient counseling.
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