Predictive factors of lymph node metastasis and pattern of repartition in patients with epithelial ovarian cancer.
David AtallahWissam ArabBruno DagherNour KhalilElsa RawadiBachir AtallahWadih GhanameNadine El KassisGeorges ChahineMalak MoubarakPublished in: Future oncology (London, England) (2021)
Aim: To determine the rate, repartition and risk factors of lymph node (LN) metastasis in patients with epithelial ovarian cancer. Methods: We reviewed retrospectively the pathological and clinical data of 184 patients with epithelial ovarian cancer at a tertiary care center in Beirut, Lebanon. Results: 88% of patients received a pelvic and para-aortic lymphadenectomy. 70% of patients presented LN metastases at both pelvic and para-aortic levels, while isolated pelvic or para-aortic LN metastases were seen in 16 and 14% of cases, respectively. In a univariate analysis, the rate of positive LNs was higher in patients with serous histology (65 vs 33%; p < 0.001), high-grade tumors (68 vs 26%; p < 0.001), bilateral adnexal involvement (74 vs 27%; p < 0.001), advanced clinical stage (p < 0.001), interval debulking surgery (63.2 vs 36.8%; p = 0.003) and positive peritoneal cytology (79 vs 26%; p < 0.001). In a multivariate analysis, the rate of LN involvement was significantly higher in patients with higher grade, advanced clinical stage and positive peritoneal cytology. Conclusion: Serous histology, grade 3 tumors, positive peritoneal cytology, advanced clinical stage, interval surgery and bilateral adnexal involvement can predict LN metastasis in patients with epithelial ovarian cancer.
Keyphrases
- high grade
- lymph node metastasis
- lymph node
- end stage renal disease
- low grade
- ejection fraction
- risk factors
- newly diagnosed
- minimally invasive
- chronic kidney disease
- aortic valve
- tertiary care
- squamous cell carcinoma
- peritoneal dialysis
- rectal cancer
- heart failure
- early stage
- prognostic factors
- neoadjuvant chemotherapy
- pulmonary artery
- machine learning
- data analysis
- ultrasound guided
- big data
- artificial intelligence
- coronary artery disease
- patient reported
- pulmonary hypertension
- papillary thyroid