Nomogram for prediction of peritoneal metastasis risk in colorectal cancer

Clinical significance of the modified Naples prognostic score in patients with stage II-III colon cancer undergoing curative resection: a retrospective study from the real world

Background

The Naples prognostic score (NPS) determined by the nutritional and inflammatory condition of an individual is attracting growing attention for predicting postoperative outcomes in a variety of malignancies. The study aimed to assess the clinical significance of a modified NPS (M-NPS) and establish and validate nomograms incorporating M-NPS in curative stage II-III colon cancer patients.

Methods

We retrospectively analyzed 328 stage II-III colon cancer patients receiving radical surgical resection at our hospital from January 2011 to December 2016. Kaplan-Meier (KM) survival analysis and Cox regression analysis were executed for overall survival (OS) and cancer-specific survival (CSS). Independent predictive indicators were applied to develop nomograms. The model's performance was evaluated using many different methods.

Results

Of a total of 328 cases, 153 cases were in group 0, 145 in group 1, and 30 in group 2. In terms of OS or CSS, there were obvious differences between groups 0 and 1, and between groups 0 and 2. Age, obstruction, N stage, gross tumor type, and M-NPS group were independent prognostic indicators for OS, while obstruction, gross tumor type, M-NPS group, and N stage were independent predictive parameters for CSS. Furthermore, the training and validation sets were randomly allocated among a cohort of 328 patients. OS and CSS prediction nomograms were developed. In the training and validation cohort, the C-index and ROC analysis showed good discrimination, calibration curves exhibited an excellent level of consistency between model-predicted survival and actual survival outcomes, and DCA curves demonstrated good clinical performance.

Conclusion

M-NPS is a reliable survival predictor in patients with curative stage II-III colon cancer. Nomograms incorporating M-NPS for OS and CSS have good predictive performance and clinical utility.

A novel risk model for predicting peritoneal metastasis in colorectal cancer based on the SEER database

BACKGROUND

Early detection and intervention could significantly improve the prognosis of patients with peritoneal metastasis (PM). Our main purpose was to develop a model to predict the risk of PM in patients with colorectal cancer (CRC).

METHODS

Patients from the Surveillance, Epidemiology, and End Results (SEER) database with CRC classified according to the AJCC 8th TNM staging system were selected for the study. After data pre-processing, the dataset was divided into a training set and a validation set. In the training set, univariate logistic analysis and stepwise multivariate logistic regression analysis were utilized to screen clinical features and construct a risk prediction model. Then, we validated the model using the confusion matrix, receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves to examine its performance.

RESULTS

The model constructed using stepwise multivariate logistic regression analysis incorporated the following eight clinical features: age, tumor location, histological type, T stage, carcinoembryonic antigen (CEA) level, tumor deposits (TDs), log odds (LODDS) of metastatic lymph nodes, and extraperitoneal metastasis (EM). The areas under the curve (AUCs) of the model in the training and validation sets were 0.924 and 0.912, respectively. The accuracy and the recall ratio were higher than 0.8 in both cohorts. DCA and the calibration curves also confirmed its excellent predictive power.

CONCLUSIONS

Our model can effectively predict the risk of PM in CRC patients, which is of great significance for the timely identification of patients at high risk of PM and further clinical decision-making.