Development and validation of nomograms for predicting survival probability of patients with advanced adenocarcinoma in different EGFR mutation status
Journal
PLoS ONE
Journal Volume
14
Journal Issue
8
Date Issued
2019
Author(s)
Abstract
Introduction Molecular markers are important variables in the selection of treatment for cancer patients and highly associated with their survival. Therefore, a nomogram that can predict survival probability by incorporating epidermal growth factor receptor mutation status and treatments for patients with advanced adenocarcinoma would be highly valuable. The aim of the study is to develop and validate a novel nomogram, incorporating epidermal growth factor receptor mutation status and treatments, for predicting 1-year and 2-year survival probability of patients with advanced adenocarcinoma. Material and methods Data on 13,043 patients between June 1, 2011, and December 31, 2014 were collected. Seventy percent of them were randomly assigned to the training cohort for nomogram development, and the remaining 30% assigned to the validation cohort. The most important factors for constructing the nomogram were identified using multivariable Cox regression analysis. The discriminative ability and calibration of the nomograms were tested using C-statistics, calibration plots, and Kaplan-Meier curves. Results In the training cohort, 1-year and 2-year OS were 52.8% and 28.5% in EGFR(-) patients, and 73.9% and 44.1% in EGFR(+) patients, respectively. In EGFR(+) group, factors selected were age, gender, congestive heart failure, renal disease, number of lymph node examined, tumor stage, surgical intervention, radiotherapy, first-line chemotherapy, ECOG performance status, malignant pleural effusion, and smoking. In EGFR(-) group, factors selected were age, gender, myocardial infarction, cerebrovascular disease, chronic pulmonary disease, number of lymph node examined, tumor stage, surgical intervention, radiotherapy, ECOG performance status, malignant pleural effusion, and a history of smoking. Two nomograms show good accuracy in predicting OS, with a concordance index of 0.83 in EGFR(+) and of 0.88 in EGFR(-). Conclusions The survival prediction models can be used to make individualized predictions with different EGFR mutation status and a useful tool for selecting regimens for treating advanced adenocarcinoma. ? 2019 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
SDGs
Other Subjects
adult; advanced cancer; age; Article; cancer chemotherapy; cancer patient; cancer radiotherapy; cancer staging; cancer surgery; cerebrovascular disease; chronic lung disease; cohort analysis; congestive heart failure; EGFR gene; female; gene mutation; heart infarction; human; kidney disease; lung adenocarcinoma; lymph node biopsy; major clinical study; male; malignant pleura effusion; measurement accuracy; middle aged; nomogram; non small cell lung cancer; overall survival; prediction; probability; receptor gene; sex factor; smoking; validation study; aged; genetics; Kaplan Meier method; lung adenocarcinoma; lung tumor; mutation; nomogram; proportional hazards model; very elderly; EGFR protein, human; epidermal growth factor receptor; Adenocarcinoma of Lung; Aged; Aged, 80 and over; ErbB Receptors; Female; Humans; Kaplan-Meier Estimate; Lung Neoplasms; Male; Middle Aged; Mutation; Nomograms; Probability; Proportional Hazards Models
Publisher
Public Library of Science
Type
journal article