ArticleTherapeutics and clinical risk management2022
Assessing Metabolic Risk Factors for LVSI in Endometrial Cancer: A Cross-Sectional Study.
Article in Therapeutics and clinical risk management, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 6 citations in OpenAlex.
- Development of an interpretable machine learning model for lymphovascular space invasion prediction in patients with endometrioid endometrial carcinoma: A prospective study.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Fats and Facts: A Meta-Analysis of Lipid Biomarkers in Endometrial Cancer.Life (Basel, Switzerland) · 2026Review
- A Nomogram for Preoperative Prediction of Tumor Aggressiveness and Lymphovascular Space Involvement in Patients with Endometrial Cancer.Journal of clinical medicine · 2025Article
- A high-risk prediction model for endometrial cancer: exploring the synergistic interaction between polycystic ovary syndrome and metabolic syndrome.American journal of cancer research · 2025Article
- Construction and validation of a clinical risk model based on machine learning for screening characteristic factors of lymphovascular space invasion in endometrial cancer.Scientific reports · 2024Article
- Review
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7 authors at 2 institutions in 1 country.
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Abstract
Objective: This study analyzed metabolic factors associated with lymphovascular space invasion (LVSI) and compared the difference between type 1 and type 2 endometrial cancer (EC). Methods: Four hundred patients primarily diagnosed with EC who underwent hysterectomy with pathological results at Fujian Medical University Cancer Hospital from January 2019 to January 2021 were included. Demographic variable data were collected as well as pathological results. Laboratory evaluations included fasting blood glucose (FBG), serum cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), apolipoprotein A (Apo A) and apolipoprotein B (Apo B). Characterization of binary logistic regression models was used to test the odds ratios (ORs) between LVSI and its metabolic parameters with different subtypes of EC. Results: The results indicated that CA125, ROMA, Ki67 score, FBG and TC were higher in EC patients with LVSI (all p<0.05). Negative ER and PR expression was positively associated with LVSI (P<0.05). CA125, ROMA, FBG, TC and ER were found to be independent risk factors for LVSI. CA125, ROMA and FBG were significantly elevated in type 1 EC patients with LVSI compared with those without LVSI (all p<0.05). TC and Ki67 scores were much higher in type 2 EC patients with vs without LVSI (all p<0.05). Negative PR expression was positively related to both type 1 and type 2 EC patients with LVSI. Consequently, CA125, ROMA, FBG and Apo B were found to be independent risk factors for LVSI in type 1 EC, and TC was found to be an independent risk factor for LVSI in type 2 EC. Conclusion: FBG and TC were both independent risk factors for LVSI in EC. FBG and Apo B were independent risk factors for LVSI in type 1 EC. TC was an independent risk factor for LVSI in type 2 EC.
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