SU-E-T-739: The Logistic Regression and Cox Regression Model for Predicting Local Recurrence, Distant Metastases, and Overall Survival of Rectal Cancer Patients

2015 ◽  
Vol 42 (6Part24) ◽  
pp. 3506-3507
Author(s):  
Z Zhou ◽  
Z Zhang ◽  
W Hu ◽  
H Zhong ◽  
J Wang
Keyword(s):  
Rectal Cancer ◽  
Overall Survival ◽  
Logistic Regression ◽  
Local Recurrence ◽  
Regression Model ◽  
Cancer Patients ◽  
Cox Regression ◽  
Distant Metastases ◽  
Cox Regression Model

scholarly journals Glioblastoma Distance From the Subventricular Neural Stem Cell Niche Does Not Correlate With Survival

2020 ◽  
Vol 10 ◽  
Author(s):  
Akshitkumar M. Mistry ◽  
Nishit Mummareddy ◽  
Sanjana Salwi ◽  
Larry T. Davis ◽  
Rebecca A. Ihrie
Keyword(s):  
Overall Survival ◽  
Stem Cell ◽  
Regression Model ◽  
Neural Stem Cell ◽  
Stem Cell Niche ◽  
Tumor Volume ◽  
Cox Regression ◽  
Cox Regression Model ◽  
Cell Niche ◽  
The Relationship

ObjectiveTo determine the relationship between survival and glioblastoma distance from the ventricular-subventricular neural stem cell niche (VSVZ).Methods502 pre-operative gadolinium-enhanced, T1-weighted MRIs with glioblastoma retrieved from an institutional dataset (n = 252) and The Cancer Imaging Atlas (n=250) were independently reviewed. The shortest distance from the tumor contrast enhancement to the nearest lateral ventricular wall, the location of the VSVZ, was measured (GBM-VSVZDist). The relationship of GBM-VSVZDist with the proportion of glioblastomas at each distance point and overall survival was explored with a Pearson’s correlation and Cox regression model, respectively, adjusting for the well-established glioblastoma prognosticators.Results244/502 glioblastomas had VSVZ contact. The proportion of non-VSVZ-contacting glioblastomas correlated inversely with GBM-VSVZDist (partial Pearson’s correlation adjusted for tumor volume R=-0.79, p=7.11x10-7). A fit of the Cox regression model adjusted for age at diagnosis, Karnofsky performance status score, post-operative treatment with temozolomide and/or radiotherapy, IDH1/2 mutation status, MGMT promoter methylation status, tumor volume, and extent of resection demonstrated a significantly decreased overall survival only when glioblastoma contacted the VSVZ. Overall survival did not correlate with GBM-VSVZDist.ConclusionsIn the two independent cohorts analyzed, glioblastomas at diagnosis were found in close proximity or in contact with the VSVZ with a proportion that decreased linearly with GBM-VSVZDist. Patient survival was only influenced by the presence or absence of a gadolinium-enhanced glioblastoma contact with the VSVZ. These results may guide analyses to test differential effectiveness of VSVZ radiation in VSVZ-contacting and non-contacting glioblastomas and/or inform patient selection criteria in clinical trials of glioblastoma radiation.

Evaluation of microRNA-10b expression as a novel predictive marker of metastases development and patients’ survival in breast cancer.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 576-576
Author(s):  
Paola Parrella ◽  
Raffaela Barbano ◽  
Barbara Pasculli ◽  
Andrea Fontana ◽  
Massimiliano Copetti ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Model ◽  
Cox Regression ◽  
Rna Extraction ◽  
Distant Metastases ◽  
Pathological Examination ◽  
Breast Cancer Cell Lines ◽  
Cox Regression Model ◽  
Increased Risk

576 Background: MicroRNA-10b was found highly expressed in metastatic breast cancer cell lines and able to generate metastases in mice models. The aim of this study is to evaluate the putative association between miR10b expression and disease progression. Methods: We selectedfrom our tumor bank 150 consecutive breast cancers with at least three years follow up. For each case frozen paired tumor and normal tissue and complete clinical data were available. Pathological examination was performed to ensure that each tumour sample contained more than 70% of cancer cells resulting in 114 samples suitable for RNA extraction. RNA quality was measured and only samples with RIN≥7.0 were analyzed (n=101) by a relative quantification method. Results: miR10b relative expression in tumor to normal samples (RERs) was significantly higher in the subgroup of patients with metastases (median 0.25 IQR 0.11-1.02) as compared with patients without metastases (median 0.09 IQR 0.04-0.29) (P=0.023 Mann Whitney Test). The association between miR-10b RERs and survival was evaluated in the group of patients without metastases at diagnosis (n=90). In univariate Cox regression model, patients with high miR-10b RERs had a higher risk of distant metastases development (HR 4.91, P=0.02) and disease related death (HR 6.02; P=0.01). In a multivariate Cox regression model adjusted for tumor size, lymph node metastases, grade, ER, PgR status, and Ki67 labeling index (n=79), higher miR-10b RERs were still associated with increased risk of distant metastases development (HR18.84; P<0.001) and disease related death (HR 13.39; P=0.003) (Table). Conclusions: We show that in breast cancer patients miR-10b expression is associated with worse prognosis on a short term follow up. These results suggest that miR-10b expression could be used for individual patient’s risk assessment and perhaps as potential therapeutical target. [Table: see text]

scholarly journals Expression of La Ribonucleoprotein Domain Family Member 4B (LARP4B) in Liver Cancer and Their Clinical and Prognostic Significance

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yanqing Li ◽  
Yan Jiao ◽  
Yang Li ◽  
Yanan Liu
Keyword(s):  
Survival Analysis ◽  
Liver Cancer ◽  
Regression Model ◽  
Cancer Patients ◽  
Clinical Relevance ◽  
Cox Regression ◽  
Diagnostic Ability ◽  
Cox Regression Model ◽  
Diagnosis And Prognosis ◽  
Chi Squared

Background and Objective. Liver cancer is a common malignant tumor with few poor diagnostic and prognostic markers, which greatly shortens the potential life span of patients. The RNA-binding protein la ribonucleoprotein 4B (LARP4B) has a la motif (lam) that is important in the process of cancer. We aimed to explore the role of LARP4B in the diagnosis and prognosis of liver cancer. Methods. The Cancer Genome Atlas (TCGA) database was searched to detect LARP4B gene expression in liver cancer. The clinical relevance and diagnostic ability of LARP4B were evaluated by a chi-squared test and a receiver operating characteristic (ROC) curve, respectively. Survival and risk factors of patients with liver cancer were assessed by survival analysis and univariate/multivariate Cox regression model. Additionally, we carried out gene set enrichment analysis (GSEA) to identify LARP4B-related signaling pathways in liver cancer. Results. LARP4B mRNA was highly expressed in liver cancer tissues and was correlated with survival status. The chi-squared test showed that LARP4B had clinical relevance, while ROC curves showed that LARP4B had good diagnostic ability. Survival analysis showed that liver cancer patients with high LARP4B expression had shorter overall/relapse-free survival. The univariate/multivariate Cox regression model indicated that high LARP4B expression may be an independent risk factor for the prognosis of liver cancer patients. Finally, we found that genes involved in the G2M checkpoint, E2F targets, and mitotic spindle were differentially enriched in the high LARP4B-expression phenotype. Conclusions. LARP4B is a potential independent biomarker for diagnosis and prognosis in liver cancer patients.

scholarly journals Modeling Recidivism through Bayesian Regression Models and Deep Neural Networks

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 639
Author(s):  
Rolando de la Cruz ◽  
Oslando Padilla ◽  
Mauricio A. Valle ◽  
Gonzalo A. Ruz
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Cox Regression ◽  
Bayesian Regression ◽  
Rate Model ◽  
Cure Rate ◽  
Cox Regression Model ◽  
Cure Rate Model ◽  
Random Survival Forest

This study aims to analyze and explore criminal recidivism with different modeling strategies: one based on an explanation of the phenomenon and another based on a prediction task. We compared three common statistical approaches for modeling recidivism: the logistic regression model, the Cox regression model, and the cure rate model. The parameters of these models were estimated from a Bayesian point of view. Additionally, for prediction purposes, we compared the Cox proportional model, a random survival forest, and a deep neural network. To conduct this study, we used a real dataset that corresponds to a cohort of individuals which consisted of men convicted of sexual crimes against women in 1973 in England and Wales. The results show that the logistic regression model tends to give more precise estimations of the probabilities of recidivism both globally and with the subgroups considered, but at the expense of running a model for each moment of the time that is of interest. The cure rate model with a relatively simple distribution, such as Weibull, provides acceptable estimations, and these tend to be better with longer follow-up periods. The Cox regression model can provide the most biased estimations with certain subgroups. The prediction results show the deep neural network’s superiority compared to the Cox proportional model and the random survival forest.

scholarly journals Large-scale gene expression analysis reveals robust gene signatures for prognosis prediction in lung adenocarcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6980 ◽  
Author(s):  
Yiyan Songyang ◽  
Wei Zhu ◽  
Cong Liu ◽  
Lin-lin Li ◽  
Wei Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
Regression Model ◽  
Expression Analysis ◽  
Large Scale ◽  
Gene Expression Analysis ◽  
Cox Regression ◽  
Cox Regression Model ◽  
Gene Signatures

Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death worldwide. High mortality in LUAD motivates us to stratify the patients into high- and low-risk groups, which is beneficial for the clinicians to design a personalized therapeutic regimen. To robustly predict the risk, we identified a set of robust prognostic gene signatures and critical pathways based on ten gene expression datasets by the meta-analysis-based Cox regression model, 25 of which were selected as predictors of multivariable Cox regression model by MMPC algorithm. Gene set enrichment analysis (GSEA) identified the Aurora-A pathway, the Aurora-B pathway, and the FOXM1 transcription factor network as prognostic pathways in LUAD. Moreover, the three prognostic pathways were also the biological processes of G2-M transition, suggesting that hyperactive G2-M transition in cell cycle was an indicator of poor prognosis in LUAD. The validation in the independent datasets suggested that overall survival differences were observed not only in all LUAD patients, but also in those with a specific TNM stage, gender, and age group. The comprehensive analysis demonstrated that prognostic signatures and the prognostic model by the large-scale gene expression analysis were more robust than models built by single data based gene signatures in LUAD overall survival prediction.

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