Dataset Information

Prediction model for regional or distant recurrence in endometrial cancer based on classical pathological and immunological parameters.

ABSTRACT:

Background

Adjuvant therapy increases disease-free survival in endometrial cancer (EC), but has no impact on overall survival and negatively influences the quality of life. We investigated the discriminatory power of classical and immunological predictors of recurrence in a cohort of EC patients and confirmed the findings in an independent validation cohort.

Methods

We reanalysed the data from 355 EC patients and tested our findings in an independent validation cohort of 72 patients with EC. Predictors were selected and Harrell's C-index for concordance was used to determine discriminatory power for disease-free survival in the total group and stratified for histological subtype.

Results

Predictors for recurrence were FIGO stage, lymphovascular space invasion and numbers of cytotoxic and memory T-cells. For high risk cancer, cytotoxic or memory T-cells predicted recurrence as well as a combination of FIGO stage and lymphovascular space invasion (C-index 0.67 and 0.71 vs 0.70). Recurrence was best predicted when FIGO stage, lymphovascular space invasion and numbers of cytotoxic cells were used in combination (C-index 0.82). Findings were confirmed in the validation cohort.

Conclusions

In high-risk EC, clinicopathological or immunological variables can predict regional or distant recurrence with equal accuracy, but the use of these variables in combination is more powerful.

SUBMITTER: Versluis MA

PROVIDER: S-EPMC4559831 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Publications

Prediction model for regional or distant recurrence in endometrial cancer based on classical pathological and immunological parameters.

Versluis M A MA de Jong R A RA Plat A A Bosse T T Smit V T VT Mackay H H Powell M M Leary A A Mileshkin L L Kitchener H C HC Crosbie E J EJ Edmondson R J RJ Creutzberg C L CL Hollema H H Daemen T T de Bock G H GH Nijman H W HW

British journal of cancer 20150728 5

<h4>Background</h4>Adjuvant therapy increases disease-free survival in endometrial cancer (EC), but has no impact on overall survival and negatively influences the quality of life. We investigated the discriminatory power of classical and immunological predictors of recurrence in a cohort of EC patients and confirmed the findings in an independent validation cohort.<h4>Methods</h4>We reanalysed the data from 355 EC patients and tested our findings in an independent validation cohort of 72 patien ...[more]

PMID: 26217922

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Project description:This study aimed to identify a distant-recurrence image biomarker in NSCLC by investigating correlations between heterogeneity functional gene expression and fluorine-18-2-fluoro-2-deoxy-D-glucose positron emission tomography (18F-FDG PET) image features of NSCLC patients. RNA-sequencing data and 18F-FDG PET images of 53 patients with NSCLC (19 with distant recurrence and 34 without recurrence) from The Cancer Imaging Archive and The Cancer Genome Atlas Program databases were used in a combined analysis. Weighted correlation network analysis was performed to identify gene groups related to distant recurrence. Genes were selected for functions related to distant recurrence. In total, 47 image features were extracted from PET images as radiomics. The relationship between gene expression and image features was estimated using a hypergeometric distribution test with the Pearson correlation method. The distant recurrence prediction model was validated by a random forest (RF) algorithm using image texture features and related gene expression. In total, 37 gene modules were identified by gene-expression pattern with weighted gene co-expression network analysis. The gene modules with the highest significance were selected (p-value < 0.05). Nine genes with high protein-protein interaction and area under the curve (AUC) were identified as hub genes involved in the proliferation function, which plays an important role in distant recurrence of cancer. Four image features (GLRLM_SRHGE, GLRLM_HGRE, SUVmean, and GLZLM_GLNU) and six genes were identified to be correlated (p-value < 0.1). AUCs (accuracy: 0.59, AUC: 0.729) from the 47 image texture features and AUCs (accuracy: 0.767, AUC: 0.808) from hub genes were calculated using the RF algorithm. AUCs (accuracy: 0.783, AUC: 0.912) from the four image texture features and six correlated genes and AUCs (accuracy: 0.738, AUC: 0.779) from only the four image texture features were calculated using the RF algorithm. The four image texture features validated by heterogeneity group gene expression were found to be related to cancer heterogeneity. The identification of these image texture features demonstrated that advanced prediction of NSCLC distant recurrence is possible using the image biomarker.

Dataset Information

Prediction model for regional or distant recurrence in endometrial cancer based on classical pathological and immunological parameters.

Background

Methods

Results

Conclusions

Publications

Prediction model for regional or distant recurrence in endometrial cancer based on classical pathological and immunological parameters.

Similar Datasets

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