Published online Oct 6, 2020. doi: 10.12998/wjcc.v8.i19.4331
Peer-review started: May 29, 2020
First decision: July 25, 2020
Revised: August 8, 2020
Accepted: August 26, 2020
Article in press: August 26, 2020
Published online: October 6, 2020
Ovarian metastasis is a special type of distant metastasis unique to female patients with gastric cancer. A prediction model based on risk factors is needed to improve the rate of detection and diagnosis.
Gastric cancer with ovarian metastasis is rarely reported and no study has shown the relationship between the clinicopathologic features and the occurrence of ovarian metastasis. We attempted to structure a visual model to help us predict the risk of ovarian metastasis in gastric cancer.
The present study aimed to analyze risk factors of ovarian metastasis in women with gastric cancer and establish a nomogram to predict the probability of occurrence based on different clinicopathological features.
A total of 1696 female patients diagnosed with gastric cancer were included. 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.
Ovarian metastasis occurred in 83 of 1696 female patients. This study found that age ≤ 50 years, Lauren typing of non-intestinal, gastric cancer lesions containing signet-ring cell components, N stage > N2, positive expression of ER, serum CA125 > 35 U/mL, and a NLR > 2.16 were independent risk factors (all P < 0.05). A nomogram was constructed to quantitate the probability of the occurrence of ovarian metastasis which was internally validated.
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.
We will conduct a multi-center retrospective study and include more cases for analysis in the near future. External data from the SEER database will be used for further validation.