A Statiscal Analysis of the Role Various of Co-variables in Detemining, Number of Nodes to be Dissected in Endometrial Cancer by Cart Model, Ahpgic

Abstract

Aim and Objective: The main objective of this paper is to predict the role of covariables in determining the number of nodes to be dissected in endometrial cancer, using the best regression model. Also, to compare the accuracy of the cart model with the traditional regression model, to accurately find and predict the co-variable in determining the number of nodes to be dissected. Material Method: Data on 170 endometrial cancer patients with their covariates were collected from the institute ahpgic and used for the said objectives. The data for this paper was collected wherein dependent variable is total number of lymph node involved and 10 co-variates (independent variables) age, Postmenopausal Bleeding, Obstetrics History, Nodal Status, Tumor Size, Histology, Grade, Myometrial Invasion, Lymphovascular Space Invasion and Cervical Extension. Methods: multiple regression and cart model. Results: Average number of lymph node dissection among patients having tumor size less than 1.9 cm is 3.73 (approx4) and the patients having a tumor size 1.9 cm is 12.4 (approx13). Average nos of nodes dissection among the patients having prior dissected nodal staus as b/l pelvic lymphdenectomy 10.9 (approx 11) and patients having prior dissected b/l paraortic + b/l pelvic lymhadenectomy is 14.1 (approx14). Thus CART MODEL can predict with more accuracy of 95.9% than the multiple regression model which is of 88.3%, based on the selected covariates and validated by receiver operating characteristics (ROC) curve. Conclusion: Thus we conclude that if tumor size >1.9cm (approx> 2cm) the 12 nodes and if less than 1.9cm (approx< 2cm) then 3.73 i.e approx 4 nodes should be dissected We found that classification and regression tree (CART) model is able to predict the role of the co variate i.e tumor size in deciding the number of lymph node dissection for the EC patients with an accuracy of 95.9% based on the selected covariates and validated by receiver operating characteristics (ROC) curve. Thus CART MODEL can predict with more accuracy of 95.9% than the multiple regression model which is of 88.3%, based on the selected covariates and validated by receiver operating characteristics (ROC) curve. Purpose: The above study will be of help in deciding the nos of nodes to be dissected in endometrial cancer so, that unnecessary morbidities like bleeding and lymphoedem, and waste of resources i.e money and time.