Dataset Information

Dynamic prognostication using conditional survival analysis for patients with operable lung adenocarcinoma.

ABSTRACT:

Purpose

To evaluate conditional survival among patients with surgically resected stage I-IIIa lung adenocarcinoma and identify changes in prognostic contributions for various prognostic factors over time.

Patients and methods

We performed conditional survival analysis at each t0 (=0, 1, 2, 3, 4, 5 years) for 723 consecutive patients who underwent surgical resection for lung adenocarcinoma, stratified by various clinico-demographic features, as well as pathologic and imaging (tumor-shadow disappearance ratio [TDR] on CT and maximum standardized uptake value [SUVmax] on PET) characteristics. Uni- and multivariableCox regression analyses were performed to evaluate relationships between those variables and conditional survival.

Results

Three-year conditional overall survival (OS) and disease-free survival (DFS) were 92.12% and 75.51% at baseline, but improved steadily up to 98.33% and 95.95% at 5 years after surgery. In contrast to demographic factors, pathologic (stage, subtype, pathologic grade and differentiation) and radiologic factors (TDR and SUVmax) maintained a statistically significant association with subseqeunt 3-year OS until 3 years after surgery. According to the multivariableanalysis, high SUVmax and low TDR value were independent predictors of subsequent 3-year OS and DFS at baseline, 1 and 2 years after surgery, respectively.

Conclusion

Our findings based on CS provide theoretical background for clinicians to plan longer period of surveillance following lung adenocarcinoma resection in survivors with preoperatively high SUVmax and low TDR on PET-CT and chest CT, respectively.

SUBMITTER: Kim W

PROVIDER: S-EPMC5458278 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Dynamic prognostication using conditional survival analysis for patients with operable lung adenocarcinoma.

Kim Wooil W Lee Ho Yun HY Jung Sin-Ho SH Woo Min-Ah MA Kim Hong Kwan HK Choi Yong Soo YS Kim Jhingook J Zo Jae Ill JI Shim Young Mog YM Han Joungho J Jeong Ji Yun JY Choi Joon Young JY Lee Kyung Soo KS

Oncotarget 20170501 19

<h4>Purpose</h4>To evaluate conditional survival among patients with surgically resected stage I-IIIa lung adenocarcinoma and identify changes in prognostic contributions for various prognostic factors over time.<h4>Patients and methods</h4>We performed conditional survival analysis at each t0 (=0, 1, 2, 3, 4, 5 years) for 723 consecutive patients who underwent surgical resection for lung adenocarcinoma, stratified by various clinico-demographic features, as well as pathologic and imaging (tumor ...[more]

PMID: 27793026

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Project description:ObjectivesTo investigate the prognostic utility of DTI and DSC-PWI perfusion-derived parameters in brain metastases patients.MethodsRetrospective analyses of DTI-derived parameters (MD, FA, CL, CP, and CS) and DSC-perfusion PWI-derived rCBVmax from 101 patients diagnosed with brain metastases prior to treatment were performed. Using semi-automated segmentation, DTI metrics and rCBVmax were quantified from enhancing areas of the dominant metastatic lesion. For each metric, patients were classified as short- and long-term survivors based on analysis of the best coefficient for each parameter and percentile to separate the groups. Kaplan-Meier analysis was used to compare mOS between these groups. Multivariate survival analysis was subsequently conducted. A correlative histopathologic analysis was performed in a subcohort (n = 10) with DTI metrics and rCBVmax on opposite ends of the spectrum.ResultsSignificant differences in mOS were observed for MDmin (p < 0.05), FA (p < 0.01), CL (p < 0.05), and CP (p < 0.01) and trend toward significance for rCBVmax (p = 0.07) between the two risk groups, in the univariate analysis. On multivariate analysis, the best predictive survival model was comprised of MDmin (p = 0.05), rCBVmax (p < 0.05), RPA (p < 0.0001), and number of lesions (p = 0.07). On histopathology, metastatic tumors showed significant differences in the amount of stroma depending on the combination of DTI metrics and rCBVmax values. Patients with high stromal content demonstrated poorer mOS.ConclusionPretreatment DTI-derived parameters, notably MDmin and rCBVmax, are promising imaging markers for prognostication of OS in patients with brain metastases. Stromal cellularity may be a contributing factor to these differences.Advances in knowledgeThe correlation of DTI-derived metrics and perfusion MRI with patient outcomes has not been investigated in patients with treatment naïve brain metastasis. DTI and DSC-PWI can aid in therapeutic decision-making by providing additional clinical guidance.

Project description:Lung adenocarcinoma (LAD) is difficult to diagnose as it tends to be small in size and metastasize early. The aim of the present study was to investigate prognostic factors for patients with LAD and establish a prognosis risk model. A training set consisting of clinical and RNA sequencing data from 503 patients with LAD, as well as expression data from a further 59 LAD and adjacent tissues, was obtained from The Cancer Genome Atlas. Additionally, a validation dataset was acquired from the Gene Expression Omnibus database (GSE26939), which included clinical and gene expression data from 115 patients. Using the DESeq2 package to compare expression between LAD and adjacent tissues, differentially expressed genes (DEGs) were identified. On the basis of survival and the random forests for survival, regression and classification package, genes for constructing the prognosis risk model were selected. The prognosis risk model was constructed and validated using the survival package. Subsequently, high- and low-risk groups were compared using the Limma package to identify DEGs, and enrichment analysis was performed using the web-based gene set analysis toolkit. A protein-protein interaction network was visualized using Cytoscape software. There were 18,567 DEGs between the LAD samples and the adjacent tissues, and 363 DEGs between the high- and low-risk groups. Of these, four genes were selected for constructing the prognosis risk model, myosin IE (MYO1E), endoplasmic reticulum oxidoreductase 1α (ERO1L), C1q and tumor necrosis factor-related protein 6 (C1QTNF6) and family with sequence similarity 83, member A (FAM83A). The survival time of high- and low-risk groups in the validation set were significantly different. Functional enrichment revealed that the genes that interacted with MYO1E, ERO1L, C1QTNF6 and FAM83A separately were enriched in 'cell cycle regulation', 'synthesis and assembly of nucleic acids', 'histone modification and cell cycle progression' and 'cell secretion process'. The four-gene prognosis risk model could potentially be used for predicting the survival of patients with LAD.

Dataset Information

Dynamic prognostication using conditional survival analysis for patients with operable lung adenocarcinoma.

Purpose

Patients and methods

Results

Conclusion

Publications

Dynamic prognostication using conditional survival analysis for patients with operable lung adenocarcinoma.

Similar Datasets

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