Predictors for lymph nodes involvement in low risk endometrial cancer

Role of Systemic Inflammatory Reaction in Female Genital Organ Malignancies - State of the Art

Systemic inflammatory reaction (SIR) is an unfavorable prognostic factor in many malignancies and has a role in all stages of the neoplastic process: initiation, promotion, and disease progression. Analysis of SIR can be performed by assessing indicators (eg, lymphocyte-to-neutrophil, platelet-to-lymphocyte, and monocyte-to-neutrophil ratios) and products of neutrophils and lymphocytes (ie, the systemic immune-inflammation index), or by examining the relationship between levels of C-reactive protein and albumin (based on the Glasgow Prognostic Score, modified Glasgow Prognostic Score, and C-reactive protein-to-albumin ratio). Risk stratification is essential in the clinical management of cancer; hence, the evaluation of these factors has potential applications in the clinical management of patients with cancer and in the development of new therapeutic targets. This review summarizes the current knowledge on SIR indicators and presents their clinical utility in malignancies of the female genital organs.

Cervical invasion, lymphovascular space invasion, and ovarian metastasis as predictors of lymph node metastasis and poor outcome on stages I to III endometrial cancers: a single-center retrospective study

BACKGROUND

The aim of this study is to determine pathological factors that increase the risk of LNM and indicate poor survival of patients diagnosed with endometrial cancer and treated with surgical staging.

METHOD

Between January 2010 and November 2018, we enrolled 874 eligible patients who received staging surgery in the First Affiliated Hospital of Anhui Medical University. The roles of prognostic risk factors, such as age, histological subtype, tumor grade, myometrial infiltration, tumor diameter, cervical infiltration, lymphopoiesis space invasion (LVSI), CA125, and ascites, were evaluated. Multivariable logistic regression models were used to identify the predictors of LNM. Kaplan-Meier and COX regression models were utilized to study the overall survival.

RESULTS

Multivariable regression analysis confirmed cervical stromal invasion (OR 3.412, 95% CI 1.631-7.141; P < 0.01), LVSI (OR 2.542, 95% CI 1.061-6.004; P = 0.04) and ovarian metastasis (OR 6.236, 95% CI 1.561-24.904; P = 0.01) as significant predictors of nodal dissemination. Furthermore, pathological pattern (P = 0.03), myometrial invasion (OR 2.70, 95% CI 1.139-6.40; P = 0.01), and lymph node metastasis (OR 9.675, 95% CI 3.708-25.245; P < 0.01) were independent predictors of decreased overall survival.

CONCLUSIONS

Cervical invasion, lymphopoiesis space invasion, and ovarian metastasis significantly convey the risk of LNM. Pathological type, myometrial invasion, and lymph node metastasis are all important predictors of survival and should be scheduled for completion when possible in the surgical staging procedure.

Pretreatment Neutrophil-to-Lymphocyte Ratio Was a Predictor of Lymph Node Metastasis in Endometrial Cancer Patients

OBJECTIVE

The pretreatment neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) have been reported to be useful as markers for prognostic factors and metastasis in several cancers. The aim of this study was to identify the predictor of lymph node (LN) metastasis by pretreatment NLR and PLR in patients with endometrial cancer.

METHODS

Medical charts of the patients with endometrial cancers that received primary surgery at our hospital between 2007 and 2013 were retrospectively analyzed. The cutoff value was calculated from the receiver operating characteristics (ROC) curve. Clinicopathological parameters including inflammatory markers were evaluated for LN metastasis using multiple logistic regression analysis.

RESULTS

Among 197 patients enrolled in the study, LN metastasis was observed in 25 patients (13%). ROC curves demonstrated that the best cutoff value of NLR for predicting LN metastasis was 2.18 and that of PLR was 206. In univariate analysis, several pathological factors, NLR, and PLR were identified as predictors of LN metastasis. In multiple logistic regression analysis, lymphovascular invasion and NLR were found to be significantly correlated with LN metastasis (p = 0.002, 0.039).

CONCLUSION

A higher pretreatment NLR was identified as a predictor of LN metastasis in endometrial cancers. Although further study is needed to confirm the results, NLR could be a candidate clinical marker for detection of LN metastasis.

A novel prediction method for lymph node involvement in endometrial cancer: machine learning

OBJECTIVE

The necessity of lymphadenectomy and the prediction of lymph node involvement (LNI) in endometrial cancer (EC) have been hotly-debated questions in recent years. Machine learning is a broad field that can produce results and estimations. In this study we constructed prediction models for EC patients using the Naïve Bayes machine learning algorithm for LNI prediction.

METHODS

The study assessed 762 patients with EC. Algorithm models were based on the following histopathological factors: V1: final histology; V2: presence of lymphovascular space invasion (LVSI); V3: grade; V4: tumor diameter; V5: depth of myometrial invasion (MI); V6: cervical glandular stromal invasion (CGSI); V7: tubal or ovarian involvement; and V8: pelvic LNI. Logistic regression analysis was also used to evaluate the independent factors affecting LNI.

RESULTS

The mean age of patients was 59.1 years. LNI was detected in 102 (13.4%) patients. Para-aortic LNI (PaLNI) was detected in 54 (7.1%) patients, of which four patients had isolated PaLNI. The accuracy rate of the algorithm models was found to be between 84.2% and 88.9% and 85.0% and 97.6% for LNI and PaLNI, respectively. In multivariate analysis, the histologic type, LVSI, depth of MI, and CGSI were independently and significantly associated with LNI (p<0.001 for all).

CONCLUSIONS

Machine learning may have a place in the decision tree for the management of EC. This is a preliminary report about the use of a new statistical technique. Larger studies with the addition of sentinel lymph node status, laboratory findings, or imaging results with machine learning algorithms may herald a new era in the management of EC.