Establishment and validation of a nomogram to predict the risk of ovarian metastasis in gastric cancer: Based on a large cohort.

Background: Ovarian metastasis is a special type of distant metastasis unique to female patients with gastric cancer. The pathogenesis of ovarian metastasis is incompletely understood, and the treatment options are controversial. Few studies have predicted the risk of ovarian metastasis. It is not clear which type of gastric cancer is more likely to metastasize to the ovary. A prediction model based on risk factors is needed to improve the rate of detection and diagnosis.

Aim: To analyze risk factors of ovarian metastasis in female patients with gastric cancer and establish a nomogram to predict the probability of occurrence based on different clinicopathological features.

Methods: A retrospective cohort of 1696 female patients with gastric cancer between January 2006 and December 2017 were included in a single center, and patients with distant metastasis other than ovary and peritoneum metastasis were excluded. Potential risk factors for ovarian metastasis were analyzed using univariate and multivariable logistic regression. Independent risk factors were chosen to construct a nomogram which received internal validation.

Results: Ovarian metastasis occurred in 83 of 1696 female patients. Univariate analysis showed that age, Lauren type, whether the primary lesion contained signet-ring cells, vascular tumor emboli, T stage, N stage, the expression of estrogen receptor, the expression of progesterone receptor, serum carbohydrate antigen 125 and the neutrophil-to-lymphocyte ratio were risk factors for ovarian metastasis of gastric cancer (all < 0.05). Multivariate analysis showed that age ≤ 50 years, Lauren typing of non-intestinal, gastric cancer lesions containing signet-ring cell components, N stage > N2, positive expression of estrogen receptor, serum carbohydrate antigen 125 > 35 U/mL, and a neutrophil-to-lymphocyte ratio > 2.16 were independent risk factors (all < 0.05). The independent risk factors were constructed into a nomogram model using R language software. The consistency index after continuous correction was 0.840 [95% confidence interval: (0.774-0.906)]. After the internal self-sampling (Bootstrap) test, the calibration curve of the model was obtained with an average absolute error of 0.007. The receiver operating characteristic curve of the obtained model was drawn. The area under the curve was 0.867, the maximal Youden index was 0.613, the corresponding sensitivity was 0.794, and the specificity was 0.819.

Conclusion: The nomogram model performed well in the prediction of ovarian metastasis. Attention should be paid to the possibility of ovarian metastasis in high-risk populations during re-examination, to ensure early detection and treatment.