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Updated: Jun 17, 2026
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Risk Stratification Using a Novel Nomogram for 2190 EGFR-Mutant NSCLC Patients Receiving the First or Second
John Wen-Cheng Chang1, Chen-Yang Huang1, Yueh-Fu Fang2
1Division of Hematology-Oncology, Department of Internal Medicine, Linkou Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan.
Abstract:
Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) are the standard treatment for EGFR mutation-positive (EGFRm+) non-small cell lung cancer (NSCLC). This study aimed to create a novel nomogram to help physicians suggest the optimal treatment for patients with EGFRm+ NSCLC. Records of 2190 patients with EGFRm+ NSCLC cancer who were treated with EGFR-TKIs (including gefitinib, erlotinib, and afatinib) at the branches of a hospital group between 2011 and 2018 were retrospectively reviewed. Their clinicopathological characteristics, clinical tumor response, progression-free survival (PFS), and overall survival (OS) data were collected. Univariate and multivariate analyses were performed to identify potential prognostic factors to create a nomogram for risk stratification. Univariate analysis identified 14 prognostic factors, and multivariate analysis confirmed the pretreatment independent factors, including Eastern Cooperative Oncology Group performance status, morphology, mutation, stage, EGFR-TKIs (gefitinib, erlotinib, or afatinib), and metastasis to liver, brain, bone, pleura, adrenal gland, and distant lymph nodes. Based on these factors, a novel nomogram was created and used to stratify the patients into five different risk groups for PFS and OS using recursive partitioning analysis. This risk stratification can provide additional information to clinicians and patients when determining the optimal therapeutic options for EGFRm+ NSCLC.
Insights
A new nomogram aids physicians in selecting optimal treatments for EGFR mutation-positive non-small cell lung cancer (NSCLC) patients receiving EGFR tyrosine kinase inhibitors (EGFR-TKIs). This tool stratifies patients by risk, improving therapeutic decisions for better progression-free survival (PFS) and overall survival (OS).
Area of Science:
- Oncology
- Medical Informatics
- Clinical Research
Background:
- Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) are standard therapy for EGFR mutation-positive (EGFRm+) non-small cell lung cancer (NSCLC).
- Optimal treatment selection for EGFRm+ NSCLC patients requires accurate prognostic assessment.
Purpose of the Study:
- To develop a novel nomogram for risk stratification in EGFRm+ NSCLC patients treated with EGFR-TKIs.
- To identify independent pretreatment prognostic factors influencing progression-free survival (PFS) and overall survival (OS).
Main Methods:
- Retrospective review of 2190 EGFRm+ NSCLC patients treated with gefitinib, erlotinib, or afatinib (2011-2018).
- Collection of clinicopathological data, tumor response, PFS, and OS.
- Univariate and multivariate analyses to identify prognostic factors; recursive partitioning analysis for risk stratification.
Main Results:
- Multivariate analysis identified Eastern Cooperative Oncology Group performance status, morphology, mutation, stage, specific EGFR-TKIs, and sites of metastasis as independent prognostic factors.
- A novel nomogram was created and validated for risk stratification into five groups based on PFS and OS.
- The nomogram effectively stratified patients, providing valuable information for treatment decisions.
Conclusions:
- The developed nomogram serves as a valuable tool for clinicians and patients in optimizing therapeutic strategies for EGFRm+ NSCLC.
- Risk stratification based on identified prognostic factors can enhance personalized treatment selection and improve patient outcomes.
