Project description:Background: One of the main fields of lung cancer research is identifying patients who are at high risk of post-resection recurrence. Individual recurrence risk evaluation by accurate but simple and reproducible method is needed for the clinical practice. Results: The log-rank test and further selection by our criteria of assayability generated 87 genes from microarray data with significant level 5%. Of these, by PTQ-PCR, the expression of most significant 18 genes was obtained. Using these gene expression information and clinical parameters, by stepwise variable selection method, the recurrence prediction model, which composed of 6 genes (CALB1, MMP7, SLC1A7, GSTA1, CCL19, IFI44) and pStage and cell differentiation, were developed. Validation into the two independent cohorts showed good results of the proposed model (p=0.0314, 0.0305, respectively). The predicted median recurrence-free survival times for each patient were reflected real ones well. Conclusions: Our method of individualized recurrence risk prediction is accurate, technically simple and reproducible to be used in clinical practice. Therefore, it would be useful in customizing the lung cancer management strategies. Keywords: Recurrence Free Survival Analysis

2007-11-20 | GSE8894 | GEO

SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data.

Project description: Not available

| S-EPMC9338425 | biostudies-literature

Using Microsoft Excel® to Build a Customized Partial-Interval Data Collection System.

Project description: Not available

| S-EPMC6269379 | biostudies-literature

Survival analysis: part II - applied clinical data analysis.

Project description: Not available

| S-EPMC6781220 | biostudies-literature

Accelerated Failure Time Survival Model to Analyze Morris Water Maze Latency Data.

Project description: Not available