Omental metastasis as a predictive risk factor for unfavorable prognosis in patients with stage III-IV epithelial ovarian cancer.

BACKGROUND: Epithelial ovarian cancer has a clear predilection for the omentum as the site of metastasis; however, its contribution to clinical outcomes remains unresolved. This study aimed to evaluate the prognostic significance and efficacy of chemotherapy in the presence of omental metastasis. METHODS: A retrospective cohort study was performed in 56 patients with stage III-IV ovarian cancer who underwent primary debulking surgery between 2004 and 2018 at Kumamoto University Hospital. RESULTS: Thirty-six (64.3%) patients were categorized into the omental metastasis-positive group, whereas 20 (35.7%) patients were in the omental metastasis-negative group. The 5-year overall survival rates were 43.4% in the omental metastasis-positive group and 93.8% in the omental metastasis-negative group. Statistically significant differences were observed in overall survival (p = 0.002) and progression-free survival (p = 0.036) between the omental metastasis-positive and metastasis-negative groups. Notably, multivariate analysis demonstrated that the existence of omental metastasis is an independent risk factor for overall survival in patients with stage III-IV ovarian cancer (hazard ratio 8.90, 95% confidence interval 1.16-69.77; p = 0.038). Furthermore, the omental metastasis-positive group had significantly lower overall response rates to chemotherapy for recurrent disease, compared to the omental metastasis-negative group (31.6% vs. 85.7%, p = 0.026). CONCLUSION: Our present data demonstrated that omental metastasis is closely associated with an unfavorable prognosis due to increased chemoresistance in patients with stage III-IV ovarian cancer. Elucidating the biological mechanism of omental metastasis will shed light on novel therapeutic approaches for the management of advanced ovarian cancer patients.

Fake review identification and utility evaluation model using machine learning.

Due to the structural growth of e-commerce platforms, the frequency of exchange of opinions and the number of online reviews of platform participants related to products are increasing. However, given the growth of fake reviews, the corresponding growth in the quality of online reviews seems to be slow, at best. The number of cases of harm to retailers and customers caused by malicious false reviews is steadily increasing every year. In this context, it is becoming difficult for users to determine useful reviews amid a flood of information. As a result, the intrinsic value of online reviews that reduce uncertainty in pre-purchase decisions is blurred, and e-commerce platforms are on the verge of losing credibility and traffic. Through this study, we intend to present solutions related to review filtering and classification by constructing a model for judging the authenticity and usefulness of online reviews using machine learning.