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Background: Whether the 7th edition of American Joint Committee on Cancer (AJCC) TNM staging system (AJCC-7) is a successful revision remains debatable. We aimed to compare the predictive capacity of the AJCC-7 for colorectal cancer with the 6th edition of the AJCC TNM staging system (AJCC-6)., Methods: The National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) dataset consisting of 158,483 records was used in this study. We evaluated the predictive capacity of the two editions of the staging system using Harrell's C index and Bayesian Information Criterion (BIC)., Results: There was a significant prognostic difference between patients at stage IIB and IIC (P < 0.001). Stage III patients with similar prognoses were adequately sub-grouped in the same stage according to AJCC-7. The Harrell's C index revealed a value of 0.7692 for AJCC-7, which was significantly better than 0.7663 for AJCC-6 (P < 0.001). BIC analysis provided consistent results (P < 0.001)., Conclusions: This study demonstrates that AJCC-7 is superior to the AJCC-6 staging system in predictive capacity.

Objective: To assess the rationality of the seventh edition of TNM staging system on tumor deposits (TDs) and propose a novel subclassification., Summary Background Data: The TDs had been debated for many years. The seventh edition of TNM staging system proposed a "pN1c" concept. However, the value of the modification is still debated., Methods: A total of 1541 patients with colorectal cancer were reviewed. Overall survival rates were compared between patients without LNM but TD (+), and those who were TD (-). The TDs were stratified into the "any T + any N" category. Two-step multivariate analysis was performed to identify significant prognostic factors. Univariate analysis was used to determine whether a correlation existed between the number of TDs and prognosis., Results: There was a significant prognostic difference between patients without LNM or TDs compared with those with positive TDs. Only in T3N2bM0 there was a significant prognostic difference between LNM (+), TD (+) patients and TD (-) patients. The seventh edition of TNM staging system was substituted by the novel TNM staging system in 2-step multivariate analysis. Only in T3N1cM0 there was a significant prognostic difference between patients with only 1 TD and those with more than 1 TD., Conclusion: The seventh edition of TNM staging system on TDs satisfactorily predicts patients' outcome for those without LNM. Patients who categorized as T3N2bM0TD (+) and T4N2bM0TD (-/+) should be reclassified as stage IV. Number of TDs was not an independent prognostic parameter in the TNM staging system.

Objective: Over the past decades, many studies have used data mining technology to predict the 5-year survival rate of colorectal cancer, but there have been few reports that compared multiple data mining algorithms to the TNM classification of malignant tumors (TNM) staging system using a dataset in which the training and testing data were from different sources. Here we compared nine data mining algorithms to the TNM staging system for colorectal survival analysis., Methods: Two different datasets were used: 1) the National Cancer Institute's Surveillance, Epidemiology, and End Results dataset; and 2) the dataset from a single Chinese institution. An optimization and prediction system based on nine data mining algorithms as well as two variable selection methods was implemented. The TNM staging system was based on the 7(th) edition of the American Joint Committee on Cancer TNM staging system., Results: When the training and testing data were from the same sources, all algorithms had slight advantages over the TNM staging system in predictive accuracy. When the data were from different sources, only four algorithms (logistic regression, general regression neural network, bayesian networks, and Naïve Bayes) had slight advantages over the TNM staging system. Also, there was no significant differences among all the algorithms (p>0.05)., Conclusions: The TNM staging system is simple and practical at present, and data mining methods are not accurate enough to replace the TNM staging system for colorectal cancer survival prediction. Furthermore, there were no significant differences in the predictive accuracy of all the algorithms when the data were from different sources. Building a larger dataset that includes more variables may be important for furthering predictive accuracy.