Development and validation of nomograms for predicting survival probability of patients with advanced adenocarcinoma in different EGFR mutation status.

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.

• Authors: Hsi-Chieh Chen, Elise Chia-Hui Tan, Chih-Hsien Liao, Zhong-Zhe Lin, Ming-Chin Yang
• Journal: PloS one (PLoS One) Vol. 14 Issue 8 Pg. e0220730 ( 2019) ISSN: 1932-6203 [Electronic] United States
• PMID: 31419239 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)

Chemical References

• EGFR protein, human
• ErbB Receptors

Topics

• Adenocarcinoma of Lung (diagnosis, genetics)
• Aged
• Aged, 80 and over
• ErbB Receptors (genetics)
• Female
• Humans
• Kaplan-Meier Estimate
• Lung Neoplasms (diagnosis, genetics)
• Male
• Middle Aged
• Mutation
• Nomograms
• Probability
• Proportional Hazards Models