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A nomogram to predict prognosis for gastric cancer with peritoneal dissemination
Shi Chen1,2, Xijie Chen1,2, Runcong Nie3
1Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, the Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou 510655, China.
Objective:
To identify independent prognostic factors to be included in a nomogram to predict the prognosis of gastric cancer patients with peritoneal dissemination.
Methods:
This is a retrospective study on 684 patients with a histological diagnosis of gastric cancer with peritoneal dissemination from the Sun Yat-sen University Cancer Center as the development set, and 62 gastric cancer patients from the Sixth Affiliated Hospital of Sun Yat-sen University as the validation group. Chi-square test and Cox regression analysis were used to compare the clinicopathological variables and the prognosis of gastric cancer patients with peritoneal dissemination. The Harrell's concordance index (C-index) and calibration curve were determined for comparisons of predictive ability of the nomogram.
Results:
Univariate and multivariate analyses showed that serum carcinoembryonic antigen (CEA) level (P=0.032), ascites grading (P=0.008), presence of extraperitoneal metastasis (P<0.001), seeding status (P=0.016) and performance status (P=0.009) were independent prognostic factors for gastric cancer patients with peritoneal dissemination in the development set. The nomogram model was constructed using these five factors. Internal validation showed that the C-index of the model was 0.641. For the external validation, the C-index of this model was 0.709.
Conclusions:
We developed and validated a nomogram to predict the prognosis for gastric cancer patients with peritoneal dissemination. This nomogram may play an important clinical role in guiding palliative therapy for these types of patients, although it may need more data for optimization.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.