Trial reportJournal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer2011
Development and validation of a quantitative real-time polymerase chain reaction classifier for lung cancer prognosis.
Trial report in Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 32 citations in OpenAlex.
- Analysis and development of risk prediction models for chronic opioid use after surgery: a cohort study using the nationwide database.Korean journal of anesthesiology · 2025Article
- Gene Expression Profiling as a Potential Tool for Precision Oncology in Non-Small Cell Lung Cancer.Cancers · 2021Review
- Predictors of colorectal cancer survival using cox regression and random survival forests models based on gene expression data.PloS one · 2021Article
- Machine-learning algorithms for predicting hospital re-admissions in sickle cell disease.British journal of haematology · 2021Article
- Machine learning approach to predict postoperative opioid requirements in ambulatory surgery patients.PloS one · 2020Article
- Article
- Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions.JAMA network open · 2019Article
- A Predictive 7-Gene Assay and Prognostic Protein Biomarkers for Non-small Cell Lung Cancer.EBioMedicine · 2018Article
- Application of random survival forests in understanding the determinants of under-five child mortality in Uganda in the presence of covariates that satisfy the proportional and non-proportional hazards assumption.BMC research notes · 2017Article
- A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-to-event data.BMC medical research methodology · 2017Article
- Data-adaptive Shrinkage via the Hyperpenalized EM Algorithm.Statistics in biosciences · 2015Article
- Integrating Tumor and Stromal Gene Expression Signatures With Clinical Indices for Survival Stratification of Early-Stage Non-Small Cell Lung Cancer.Journal of the National Cancer Institute · 2015Article
- Using machine learning to examine medication adherence thresholds and risk of hospitalization.Medical care · 2015Article
- Incorporating auxiliary information for improved prediction using combination of kernel machines.Statistical methodology · 2015Article
- C4.4A as a biomarker in pulmonary adenocarcinoma and squamous cell carcinoma.World journal of clinical oncology · 2014Review
- Metformin and Not Diabetes Influences the Survival of Resected Early Stage NSCLC Patients.Journal of cancer science & therapy · 2014Article
- Epithelial-mesenchymal transition-associated secretory phenotype predicts survival in lung cancer patients.Carcinogenesis · 2014Article
- BAYESIAN SHRINKAGE METHODS FOR PARTIALLY OBSERVED DATA WITH MANY PREDICTORS.The annals of applied statistics · 2013Article
- Incorporating auxiliary information for improved prediction in high-dimensional datasets: an ensemble of shrinkage approaches.Biostatistics (Oxford, England) · 2013Article
- Characterization of vitamin D receptor (VDR) in lung adenocarcinoma.Lung cancer (Amsterdam, Netherlands) · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 1 institution in 1 country.
Funding
Abstract
introductionThis prospective study aimed to develop a robust and clinically applicable method to identify patients with high-risk early-stage lung cancer and then to validate this method for use in future translational studies.
methodsThree published Affymetrix microarray data sets representing 680 primary tumors were used in the survival-related gene selection procedure using clustering, Cox model, and random survival forest analysis. A final set of 91 genes was selected and tested as a predictor of survival using a quantitative real-time polymerase chain reaction-based assay using an independent cohort of 101 lung adenocarcinomas.
resultsThe random survival forest model built from 91 genes in the training set predicted patient survival in an independent cohort of 101 lung adenocarcinomas, with a prediction error rate of 26.6%. The mortality risk index was significantly related to survival (Cox model p < 0.00001) and separated all patients into low-, medium-, and high-risk groups (hazard ratio = 1.00, 2.82, 4.42). The mortality risk index was also related to survival in stage 1 patients (Cox model p = 0.001), separating patients into low-, medium-, and high-risk groups (hazard ratio = 1.00, 3.29, 3.77).
conclusionsThe development and validation of this robust quantitative real-time polymerase chain reaction platform allows prediction of patient survival with early-stage lung cancer. Utilization will now allow investigators to evaluate it prospectively by incorporation into new clinical trials with the goal of personalized treatment of patients with lung cancer and improving patient survival.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.