Dataset Information

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.

SUBMITTER: Chen HC

PROVIDER: S-EPMC6697331 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

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

Chen Hsi-Chieh HC Tan Elise Chia-Hui EC Liao Chih-Hsien CH Lin Zhong-Zhe ZZ Yang Ming-Chin MC

PloS one 20190816 8

<h4>Introduction</h4>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 tr ...[more]

PMID: 31419239

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Project description:Background: There is limited data on the relative survival rate of first-line therapy of gefitinib, erlotinib (first-generation epidermal growth factor receptor-tyrosine kinase inhibitor [EGFR-TKI]), and afatinib (second-generation EGFR-TKI) in patients with EGFR-mutated advanced lung adenocarcinoma in real-world data, especially in the Asian population. This study aimed to compare the relative survival rate of gefitinib, erlotinib, and afatinib in patients with EGFR-mutated advanced lung adenocarcinoma by real-world data in Taiwan. Methods: This retrospective cohort population-based study included untreated adult patients diagnosed with advanced lung adenocarcinoma who were identified in the Taiwan National Health Insurance Research Database between 2014 and 2017. The date of EGFR-mutated advanced lung adenocarcinoma diagnosis was referred as index date. This outcome evaluated overall survival (OS) and time to treatment failure (TTF) between gefitinib, erlotinib, and afatinib. Switching EGFR-TKIs or chemotherapy and new development of brain metastases were proxies of TTF. Estimated relative treatment effects on OS and TTF among EGFR-TKIs were adjusted by inverse probability of treatment weighting (IPTW) in Cox proportional hazards model. Propensity score (PS) matched pair analyses were performed as sensitivity analyses. Results: The study cohort included 3,695 patients initiated with gefitinib, 3,310 with erlotinib, and 3,041 with afatinib. The mean age among the three treatment groups was 70.4 (±11.6), 66.8 (±11.6), and 64.3 (±11.4) years, and the female percentage was 70.4, 58.6, and 57.7%, respectively. Afatinib showed longer median OS than gefitinib (23.9 vs. 21.3 months; adjusted hazard ratio (aHR), 0.87; p < 0.001) and erlotinib (23.9 vs. 21.8 months; aHR, 0.87; p = 0.001). Consistent results were observed with TTF outcomes. For patients with brain metastases at diagnosis, afatinib showed similar OS with erlotinib (p = 0.917) but superior to gefitinib (p = 0.028). PS matching had similar results with IPTW adjustment in the study population. Conclusion: Afatinib as first-line therapy had better survival outcomes for EGFR-mutated advanced lung adenocarcinoma than gefitinib and erlotinib in the Taiwan population. Both erlotinib and afatinib demonstrated superior treatment effect in patients with initial brain metastases than gefitinib.

Project description:BackgroundTriple negative breast cancer (TNBC) is an aggressive and heterogeneous disease. Nomograms predicting outcomes of TNBC are needed for risk management.MethodsNomograms were based on an analysis of 296 non-metastatic TNBC patients treated at Sun Yat-sen Memorial Hospital from 2002 to 2014. The end points were disease-free survival (DFS) and overall survival (OS). Predictive accuracy and discriminative ability were evaluated by concordance index (C-index), area under the curve (AUC) and calibration curve, and compared with the American Joint Committee on Cancer (AJCC) staging system, PREDICT and CancerMath. Models were subjected to bootstrap internal validation and external validation using independent cohorts of 191 patients from the second Xiangya Hospital and Peking University Shenzhen Hospital between 2007 and 2012.ResultsOn multivariable analysis of training cohort, independent prognostic factors were stromal tumor-infiltrating lymphocytes (TILs), tumor size, node status, and Ki67 index, which were then selected into the nomograms. The calibration curves for probability of DFS and OS showed optimal agreement between nomogram prediction and actual observation. The C-index of nomograms was significantly higher than that of the seventh and eighth AJCC staging system for predicting DFS (training: 0.743 vs 0.666 (P = 0.003) and 0.664 (P = 0.024); validation: 0.784 vs 0.632 (P = 0.02) and 0.607 (P = 0.002)) and OS (training: 0.791 vs 0.683 (P = 0.004) and 0.677 (P < 0.001); validation: 0.783 vs 0.656 (P = 0.006) and 0.606 (P = 0.001)). Our nomograms had larger AUCs compared with PREDICT and CancerMath. In addition, the nomograms showed good performance in stratifying different risk groups of patients both in the training and validation cohorts.ConclusionWe have developed novel and practical nomograms that can provide individual prediction of DFS and OS for TNBC based on stromal TILs, tumor size, node status, and Ki67 index. Our nomograms may help clinicians in risk consulting and selection of long term survivors.

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Project description:BackgroundMany patients with gastric cancer are at a locally advanced stage during initial diagnosis. TNM staging is inaccurate in predicting survival. This study aims to develop two more accurate survival prediction models for patients with locally advanced gastric cancer (LAGC) and guide clinical decision-making.MethodsWe recruited 2794 patients diagnosed with LAGC (2010-2015) from the Surveillance, Epidemiology, and End Results (SEER) database and performed external validation using data from 115 patients with LAGC at Yantai Affiliated Hospital of Binzhou Medical University. Univariate and multifactorial survival analyses were screened for meaningful independent prognostic factors and were used to build survival prediction models. Concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were evaluated for nomograms. Finally, the differences and relationships of survival and prognosis between the three different risk groups were described using the Kaplan-Meier method.ResultsCox proportional risk regression model analysis identified independent prognostic factors for patients with LAGC, and variables associated with overall survival (OS) included age, race, marital status, T-stage, N-stage, grade, histologic type, surgery, and chemotherapy. Variables associated with cancer-specific survival (CSS) included age, race, T-stage, N-stage, grade, histological type, surgery, and chemotherapy. In the training cohort, C-index of nomogram for predicting OS was 0.722 (95% confidence interval [95% CI]: 0.708-0.736] and CSS was 0.728 (95% CI: 0.713-0.743). In the external validation cohort, C-index of nomogram for predicted OS was 0.728 (95% CI:0.672-0.784) and CSS was 0.727 (95% CI:0.668-0.786). The calibration curves showed good concordance between the predicted and actual results. C-index, ROC, and DCA results indicated that our nomograms could more accurately predict OS and CSS than TNM staging and had a higher clinical benefit. Finally, to facilitate clinical use, we set up two web servers based on nomograms.ConclusionThe nomograms established in this study have better risk assessment ability than the clinical staging system, which can help clinicians predict the individual survival of LAGC patients more accurately and thus develop appropriate treatment strategies.

Dataset Information

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

Introduction

Material and methods

Results

Conclusions

Publications

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

Similar Datasets

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