Open AccessA predictive model to estimate the pretest probability of metastasis in patients with osteosarcoma
Author(s) -
Sisheng Wang,
Shaoluan Zheng,
KaiMing Hu,
Heyan Sun,
Jinling Zhang,
Genxiang Rong,
Jie Gao,
Nan Ding,
Binjie Gui
Publication year - 2017
Publication title -
medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 148
eISSN - 1536-5964
pISSN - 0025-7974
DOI - 10.1097/md.0000000000005909
Subject(s) - medicine , receiver operating characteristic , logistic regression , stepwise regression , oncology , odds ratio , pre and post test probability , metastasis , cancer
Abstract Osteosarcomas (OSs) represent a huge challenge to improve the overall survival, especially in metastatic patients. Increasing evidence indicates that both tumor-associated elements but also on host-associated elements are under a remarkable effect on the prognosis of cancer patients, especially systemic inflammatory response. By analyzing a series prognosis of factors, including age, gender, primary tumor size, tumor location, tumor grade, and histological classification, monocyte ratio, and NLR ratio, a clinical predictive model was established by using stepwise logistic regression involved circulating leukocyte to compute the estimated probabilities of metastases for OS patients. The clinical predictive model was described by the following equations: probability of developing metastases = ex/(1 + ex), x = −2.150 + (1.680 × monocyte ratio) + (1.533 × NLR ratio), where is the base of the natural logarithm, the assignment to each of the 2 variables is 1 if the ratio >1 (otherwise 0). The calculated AUC of the receiver-operating characteristic curve as 0.793 revealed well accuracy of this model (95% CI, 0.740–0.845). The predicted probabilities that we generated with the cross-validation procedure had a similar AUC (0.743; 95% CI, 0.684–0.803). The present model could be used to improve the outcomes of the metastases by developing a predictive model considering circulating leukocyte influence to estimate the pretest probability of developing metastases in patients with OS.