Analysis and external validation of a nomogram to predict peritoneal dissemination in gastric cancer

Application of Circulating Tumor Cells and Interleukin-6 in Preoperative Prediction of Peritoneal Metastasis of Advanced Gastric Cancer

Background

The purpose of this study was to explore the clinical significance of circulating tumor cells (CTCs) and cytokines in peripheral blood in preoperative prediction of peritoneal metastasis (PM) in advanced gastric cancer (AGC).

Methods

The clinicopathological characteristics of 282 patients with AGC were retrospectively analyzed. The patients were divided into training and validation groups according to the time of receiving treatment. We used univariate analysis and multivariate logistic regression analysis to screen out the independent risk factors of PM in AGC. Then, we incorporated independent risk factors into the nomogram, and evaluated the discriminative ability.

Results

The levels of CTCs and interleukin-6 (IL-6) of AGC patients with PM were higher than those without PM (P<0.05). Moreover, the levels of CTCs and IL-6 in the occult peritoneal metastasis (OPM) group and the CT-positive PM group were higher than those in the negative PM (P<0.05). Multivariate logistic regression analysis showed that IL-6 > 12.22 pg/mL, CTCs > 4/5mL, CA724 > 6 IU/mL, CA125 > 35 U/mL and tumor size > 5 cm were independent risk factors for PM of AGC. The area under the ROC curve of the nomogram were 0.898 and 0.926 in the training and validation sets, respectively. The clinical decision curve showed that the nomogram had good clinical utility.

Conclusion

CTCs and IL-6 in peripheral blood are promising biomarkers for predicting the risk of PM in AGC. The nomogram constructed from five risk factors can effectively assess the risk of PM in AGC patients individually.

Development and validation of a novel nomogram for postoperative overall survival of patients with primary gastric signet-ring cell carcinoma: a population study based on SEER database

BACKGROUND

Gastric signet ring cell carcinoma (GSRCC) is a highly malignant subtype of gastric cancer. We tried to establish and validate a nomogram using common clinical variables to achieve more personalized management.

METHODS

We analyzed patients with GSRCC in the Surveillance, Epidemiology, and End Results database between 2004 and 2017. The survival curve was calculated by the Kaplan-Meier method, and the difference in survival curve was tested by log-rank test. We used the cox proportional hazard model to evaluate independent factors of prognosis, and established a nomogram to predict 1-, 3- and 5- overall survival (OS). Harrell's consistency index and calibration curve were used to measure the discrimination and calibration of the nomogram. In addition, we used decision curve analysis (DCA) to compare the net clinical benefits of the nomogram and American Joint Committee on Cancer (AJCC) staging system.

RESULTS

The prognosis nomogram predicting 1-, 3- and 5-years OS for patients with GSRCC is established for the first time. The C-index and AUC of nomogram were higher than that of the American Joint Committee on Cancer (AJCC) staging system in the training set. Our model also shows better performance than the AJCC staging system in the validation set, and importantly, DCA shows that our model has a better net benefit than the AJCC stage.

CONCLUSIONS

We have developed and validated a new nomogram and risk classification system, which is better than the AJCC staging system. It will help clinicians manage postoperative patients with GSRCC more accurately.

A clinical model to predict distant metastasis in patients with superficial gastric cancer with negative lymph node metastasis and a survival analysis for patients with metastasis

BACKGROUND

Distant metastasis (DM) is relatively rare in superficial gastric cancer (SGC), especially in patients without lymph node metastasis. This study aimed to explore the main clinical risk factors for DM in patients with superficial gastric cancer-no lymph node metastasis (SGC-NLNM) and the prognostic factors for patients with DM.

METHODS

Records of patients with SGC-NLNM between 2004 and 2015 were collected from the public Surveillance, Epidemiology, and End Results (SEER) database. Both univariate and multivariate logistic regressions were performed to analyze the clinical risk factors for DM. The Kaplan-Meier method and Cox regression model were used to identify prognostic factors for patients with DM. A nomogram was built based on multivariate logistic regression and evaluated by the C-index, the calibration, and the area under the receiver operating characteristic curve (AUC).

RESULTS

We developed and validated a nomogram to predict DM in patients with SGC-NLNM, showing that race, age, primary site, depth, size, and grade were independent risk factors. The built nomogram had a good discriminatory performance, with a C-index of 0.836 (95% confidence interval [CI]: 0.813-0.859). Calibration plots showed that the predicted DM probability was identical to the actual observations in both the training and validation sets. AUC was 0.846 (95% CI: 0.820-0.871) and 0.801 (95% CI: 0.751-0.850) in the training and validation sets, respectively. The results of the survival analysis revealed that surgery (hazard ratio [HR] = 0.249; 95% CI, 0.125-0.495), chemotherapy (HR = 0.473; 95% CI, 0.353-0.633), and grade (HR = 1.374; 95% CI, 1.018-1.854) were independent prognostic factors associated with cancer-specific survival (CSS), but radiotherapy was not (log-rank test, p = 0.676).

CONCLUSIONS

We constructed a sensitive and discriminative nomogram to identify high-risk patients with SGC-NLNM who may harbor dissemination at initial diagnosis. The tumor size and primary site were the largest contributors to DM prediction. Compared with radiotherapy, aggressive surgery, and chemotherapy may be better options for patients with DM.