Background: There is a large difference in postoperative survival in patients with nonmetastatic colorectal cancer. We aimed to develop nomograms incorporating both hematological biomarkers and clinical characteristics to predict overall survival (OS) in patients with radical surgery for non-metastatic colorectal cancer. Methods: A retrospective analysis was performed on date from 508 patients who underwent radical resection of colorectal cancer at the Affiliated Tumor Hospital of Guangxi Medical University from December 2011 to December 2015. Simple random sampling was performed by dividing these patients into a training set (n=355) and validation set(n=153), which yielded a 7:3 ratio in the sample sizes between these groups. Based on COX regression analysis of the results from the training cohort, a nomogram was developed to predict the three-year and five-year overall survival rate, and internal verification was also performed. The nomogram prediction accuracy and discriminating ability were evaluated by Harrell's C-index (C-index), calibration curves and were compared with the colorectal cancer TNM staging system. Results: We found that age, degree of differentiation, T stage, N stage, neurological invasion, neutrophils, monocytes, HGB, and LDH were independent risk factors for predicting OS in patients with colorectal cancer. In the training cohort, the C index was 0.796 (95% CI: 0.761-0.831). In the validation cohort, the C index was 0.671 (95% CI: 0.656-0.686). The nomogram showed a stronger predictive ability than did TNM staging. Decision curve analysis showed that the nomogram had value in terms of clinical application. Conclusion: Our nomogram combined hematological biomarkers and clinical characteristics and was highly effective in predicting OS in patients with non-metastatic colorectal cancer. Hence, our nomogram may provide a reference tool for clinicians to guide individualized treatment and follow-ups for patients with colorectal cancer.

Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. The existing staging system has a limited budget capacity for HCC recurrence. The authors aimed to establish and verify two nomogram models to predict disease-free survival (DFS) and overall survival (OS) in patients with HCC. Methods: Patients diagnosed with HCC between August 2011 and March 2016 were recruited. Data were randomly divided into a training cohort and a validation cohort. Based on univariate and multivariate Cox regression analysis, independent risk factors for DFS and OS were identified, and two nomogram models were established to predict patient survival. Results: Sex, tumor size, Barcelona Clinic Liver Cancer (BCLC) stage, tumor capsule, macrovascular invasion, AST-to-platelet ratio index, AST-to-lymphocyte ratio index, neutrophil–lymphocyte ratio and alpha-fetoprotein (AFP) were used to build the nomogram for DFS, while age, tumor size, BCLC stage, tumor capsule, macrovascular invasion, systemic immune-inflammation index, AST, total bilirubin and AFP were used to build the nomogram for OS. Calibration curves showed good agreement between the nomogram prediction and actual observation. C-indices in both nomograms were significantly higher than BCLC. Conclusion: The two nomograms improved the accuracy of individualized prediction of DFS and OS, which may help doctors screen patients with a high risk of recurrence to formulate individualized treatment plans.

Background: This work was designed to establish and verify our nomograms integrating clinicopathological characteristics with hematological biomarkers to predict both disease-free survival (DFS) and overall survival (OS) in solitary hepatocellular carcinoma (HCC) patients following hepatectomy.Methods: We scrutinized the data retrospectively from 414 patients with a clinicopathological diagnosis of solitary HCC from Guangxi Medical University Cancer Hospital (Nanning, China) between January 2004 and December 2012. Following the random separation of the samples in a 7:3 ratio into the training set and validation set, the former set was assessed by Cox regression analysis to develop two nomograms to predict the 1-year and 3-year DFS and OS (3-years and 5-years). This was followed by discrimination and calibration estimation employing Harrell’s C-index (C-index) and calibration curves, while the internal validation was also assessed.Results: In the training cohort, the tumor diameter, tumor capsule, macrovascular invasion, and alpha-fetoprotein (AFP) were included in the DFS nomogram. Age, tumor diameter, tumor capsule, macrovascular invasion, microvascular invasion, and aspartate aminotransferase (AST) were included in the OS nomogram. The C-index was 0.691 (95% CI: 0.644-0.738) for the DFS-nomogram and 0.713 (95% CI: 0.670-0.756) for the OS-nomogram. The survival probability calibration curves displayed a fine agreement between the predicted and observed ranges in both data sets. Conclusion: Our nomograms combined clinicopathological features with hematological biomarkers to emerge effective in predicting the DFS and OS in solitary HCC patients following curative liver resection. Therefore, the potential utility of our nomograms for guiding individualized treatment clinically and monitor the recurrence monitoring in these patients.