Nomogram Based on Liver Function Test Indicators for Survival Prediction in Nasopharyngeal Carcinoma Patients Receiving PD-1 Inhibitor Therapy

PURPOSE

The aim of this study was to investigate the prognostic significance of PD-1 inhibitor therapy in nasopharyngeal carcinoma (NPC) and to develop a nomogram to estimate individual risks.

METHODS

We retrospectively analyzed 162 NPC patients who were administered the PD-1 inhibitor combined with radiotherapy and chemotherapy at the Sun Yat-Sen University Cancer Center. In total, 108 NPC patients were included in the training cohort and 54 NPC patients were included in the validation cohort. Univariate and multivariate Cox survival analyses were performed to determine the prognostic factors for 1-year and 2-year progression-free survival (PFS). In addition, a nomogram model was constructed to predict the survival probability of PFS. A consistency index (C-index), a decision curve, a clinical impact curve, and a standard curve were used to measure predictive accuracy, the clinical net benefit, and the consistency of prognostic factors.

RESULTS

Univariate and multivariate analyses indicated that the metastasis stage, the levels of ALT, the AST/ALT ratio, and the LDH were independent risk factors associated with the prognosis of PD-1 inhibitor therapy. A nomogram based on these four indicators was constructed and the Kaplan-Meier survival analysis showed that patients with a higher total score have a shorter PFS. The C-index of this model was 0.732 in the training cohort and 0.847 in the validation cohort, which are higher than those for the TNM stages (training cohort: 0.617; validation cohort: 0.727; p <0.05). Decision Curve Analysis (DCA), Net Reclassification Improvement (NRI), and Integrated Discrimination Improvement (IDI) showed that our model has better prediction accuracy than TNM staging.

CONCLUSIONS

Predicting PFS in NPC patients based on liver function-related indicators before PD-1 treatment may help clinicians predict the efficacy of PD-1 treatment in these patients.

Development and validation of a pretreatment nomogram to predict overall survival in gastric cancer

Background

Pretreatment clinical staging is essential to select therapy. However, there have been no published pretreatment gastric cancer nomograms constructed using pretreatment clinical prognostic factors, including in nonresection patients. We aimed to develop a new pretreatment gastric cancer nomogram for individualized prediction of overall survival (OS).

Methods

The nomogram was developed using data of 5231 Japanese gastric cancer patients, and it was created with a Cox regression model. Fifteen clinical variables, which were obtained at pretreatment, were collected and registered. Data of two independent cohorts of patients from Seoul St. Mary's Hospital (1001 patients), and the University of Verona (389 patients) formed the external validation cohorts. The model was validated internally and externally using measures of discrimination (Harrell's C‐index), calibration, and decision curve analysis.

Results

The developed nomogram showed good discrimination, with a C‐index of 0.855; that of the American Joint Committee on Cancer (AJCC) clinical stage was 0.819. In the external validation procedure, the C‐indexes were 0.856 (AJCC, 0.795) in the Seoul St. Mary's cohort and 0.714 (AJCC, 0.648) in the University of Verona cohort. The nomogram performed well in the calibration and decision curve analyses when applied to both the internal and external validation cohorts. A stage‐specific subset survival analysis of the three risk groups calculated using the nomogram also showed the superiority of nomogram‐prediction when compared to AJCC.

Conclusion

This new pretreatment model accurately predicts OS in gastric cancer and can be used for patient counseling in clinical practice and stratification in clinical trials.