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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 7th 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. [ABSTRACT FROM AUTHOR]

Objective: At present, only the number of metastatic lymph nodes (LNs+) is used for the pN category of AJCC TNM system for colon cancer. Recently, the ratio of metastatic to examined lymph nodes (LNR) has been reported to represent powerful independent predictive capacity in colon cancer. We sought to propose a novel category (nLN) which intergrades LNR and LNs+ into the AJCC staging system for colon cancer. Design: 34476 patients from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) dataset with stage III colon cancer were reviewed. Harrell's C statistic was used to evaluate the predictive capacity. The Cox proportional hazards model was used to construct a novel category. Results: The LNR category had more predictive capacity than the pN category in whole groups of patients (Harrell's C index: 0.6194 vs 0.6113, p = 0.003). Subgroup analysis showed that the LNR category was not better than pN category in predictive capacity if the number of lymph nodes examined was more than 13. We also found that there was significant survival heterogeneity among different pN categories at the same LNR category (P<0.001). The Harrell's C index for our nLN category which intergrades LNR and LNs+ was 0.6228, which was significant higher than that of the pN category (Harrell's C index: 0.6113, P<0.001) or LNR category (Harrell's C index: 0.6194, P = 0.005), respectively. Conclusion: To evaluate the prognosis of colon cancer, our nLN category which intergrades LNR with LNs+ is more accurate than the pN category or LNR category, respectively. [ABSTRACT FROM AUTHOR]

Objective: The aim of the current study was to investigate which is the most suitable classification for colorectal cancer, log odds of positive lymph nodes (LODDS) classification or the classifications based on the number of positive lymph nodes (pN) and positive lymph node ratio(LNR) in a Chinese single institutional population. Design: Clinicopathologic and prognostic data of 1297 patients with colorectal cancer were retrospectively studied. The log-rank statistics, Cox's proportional hazards model, the Nagelkerke R2 index and a Harrell's C statistic were used. Results: Univariate and three-step multivariate analyses identified that LNR was a significant prognostic factor and LNR classification was superior to both the pN and LODDS classifications. Moreover, the results of the Nagelkerke R2 index (0.130) and a Harrell's C statistic (0.707) of LNR showed that LNR and LODDS classifications were similar and LNR was a little better than the other two classifications. Furthermore, for patients in each LNR classification, prognosis was homologous between those in different pN or LODDS classifications. However, for patients in pN1a, pN1b, LODDS2 and LODDS3 classifications, significant differences in survival were observed among patients in different LNR classifications. Conclusions: For patients with colorectal cancer, the LNR classification is more suitable than pN and LODDS classifications for prognostic assessment in a Chinese single institutional population. [ABSTRACT FROM AUTHOR]