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Survival Analysis - Top 30 Publications

High ECT2 expression is an independent prognostic factor for poor overall survival and recurrence-free survival in non-small cell lung adenocarcinoma.

Different subtypes of non-small cell lung cancer (NSCLC) have distinct sites of origin, histologies, genetic and epigenetic changes. In this study, we explored the mechanisms of ECT2 dysregulation and compared its prognostic value in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). In addition, we also investigated the enrichment of ECT2 co-expressed genes in KEGG pathways in LUAD and LUSC. Bioinformatic analysis was performed based on data from the Cancer Genome Atlas (TCGA)-LUAD and TCGA-LUSC. Results showed that ECT2 expression was significantly upregulated in both LUAD and LUSC compared with normal lung tissues. ECT2 expression was considerably higher in LUSC than in LUAD. The level of ECT2 DNA methylation was significantly lower in LUSC than in LUAD. ECT2 mutation was observed in 5% of LUAD and in 51% of LUSC cases. Amplification was the predominant alteration. LUAD patients with ECT2 amplification had significantly worse disease-free survival (p = 0.022). High ECT2 expression was associated with unfavorable overall survival (OS) (p<0.0001) and recurrence-free survival (RFS) (p = 0.001) in LUAD patients. Nevertheless, these associations were not observed in patients with LUSC. The following univariate and multivariate analysis showed that the high ECT2 expression was an independent prognostic factor for poor OS (HR: 2.039, 95%CI: 1.457-2.852, p<0.001) and RFS (HR: 1.715, 95%CI: 1.210-2.432, p = 0.002) in LUAD patients, but not in LUSC patients. Among 518 genes co-expressed with ECT2 in LUAD and 386 genes co-expressed with ECT2 in LUSC, there were only 98 genes in the overlapping cluster. Some of the genes related KEGG pathways in LUAD were not observed in LUSC. These differences might help to explain the different prognostic value of ECT2 in LUAD and LUSC, which are also worthy of further studies.

Estimation of the Most Influential Factors for Survival Probability Prediction of Prostate Cancer Patients.

The objective of the study was to address some important questions related to prostate cancer treatments and survivorship. One of possibility to improve the survival probability of prostate cancer patients is to improve predictive strategies. Therefore in this article was created short-term multistep ahead predictive model for survival probability prediction of prostate cancer patients. Neuro-fuzzy model was used to select the most important inputs for the predictive model. As the inputs, current and time lagged variables were used. The results could be useful for simplification of predictive models to avoid multiple inputs.

Effects of socioeconomic status on esophageal adenocarcinoma stage at diagnosis, receipt of treatment, and survival: A population-based cohort study.

The incidence of esophageal adenocarcinoma (EAC) is increasing worldwide and has overtaken squamous histology in occurrence. We studied the impact of socioeconomic status (SES) on EAC stage at diagnosis, receipt of treatment, and survival. A population-based retrospective cohort study was conducted using Ontario Cancer Registry-linked administrative health data. Multinomial logistic regression was used to examine the association between SES (income quintile) and stage at EAC diagnosis and EAC treatment. Survival times following EAC diagnosis were estimated using Kaplan-Meier method. Cox proportional-hazards regression analysis was used to examine the association between SES and EAC survival. Between 2003-2012, 2,125 EAC cases were diagnosed. Median survival for the lowest-SES group was 10.9 months compared to 11.6 months for the highest-SES group; the 5-year survival was 9.8% vs. 15.0%. Compared to individuals in the highest-SES group, individuals in the lowest-SES category experienced no significant difference in EAC treatment (91.6% vs. 93.3%, P = 0.314) and deaths (78.9% vs. 75.6%, P = 0.727). After controlling for covariates, no significant associations were found between SES and cancer stage at diagnosis and EAC treatment. Additionally, after controlling for age, gender, urban/rural residence, birth country, health region, aggregated diagnosis groups, cancer stage, treatment, and year of diagnosis, no significant association was found between SES and EAC survival. Moreover, increased mortality risk was observed among those with older age (P = 0.001), advanced-stage of EAC at diagnosis (P < 0.001), and those receiving chemotherapy alone, radiotherapy alone, or surgery plus chemotherapy (P < 0.001). Adjusted proportional-hazards model findings suggest that there is no association between SES and EAC survival. While the unadjusted model suggests reduced survival among individuals in lower income quintiles, this is no longer significant after adjusting for any covariate. Additionally, there is an apparent association between SES and survival when considering only those individuals diagnosed with stage 0-III EAC. These analyses suggest that the observed direct relationship between SES and survival is explained by patient-level factors including receipt of treatment, something that is potentially modifiable.

A Proposal for Progression-Free Survival Assessment in Patients with Early Progressive Cancer.

Progression-free survival (PFS), which is evaluated in oncology clinical trials, is determined based on tumor progression evaluated according to an assessment schedule. There is possibly a bias in median PFS and hazard ratio (HR) for PFS depending on the assessment schedule referring to randomized controlled trials (RCTs) in patients with metastatic colorectal cancer.

Outcomes of patients with severe influenza infection admitted to intensive care units: a retrospective study in a medical centre.

This study assessed clinical manifestations and prognostic factors of critically ill patients with severe influenza admitted to the intensive care unit (ICU) in Taiwan's recent outbreak.

Survival inequalities in patients with lung cancer in France: A nationwide cohort study (the TERRITOIRE Study).

The French healthcare system is a universal healthcare system with no financial barrier to access to health services and cancer drugs. The objective of the study is to investigate associations between, on the one hand, incidence and survival of patients diagnosed with lung cancer in France and, on the other, the socioeconomic deprivation and population density of their municipality of residence. A national, longitudinal analysis using data from the French National Hospital database crossed with the population density of the municipality and a social deprivation index based on census data aggregated at the municipality level. For lung cancer diagnosed at the metastatic stage, one-year and two-year survival was not associated with the population density of the municipality of residence. In contrast, mortality was higher for people living in very deprived, deprived and privileged areas compared to very privileged areas (hazard ratios at two years: 1.19 [1.13-1.25], 1.14 [1.08-1.20] and 1.10 [1.04-1.16] respectively). Similar associations are also observed in patients diagnosed with non-metastatic disease (hazard ratios at two years: 1.21 [1.13-1.30], 1.15 [1.08-1.23] and 1.10 [1.03-1.18] for people living in very deprived, deprived and privileged areas compared to very privileged areas). Despite a universal healthcare coverage, survival inequalities in patients with lung cancer can be observed in France with respect to certain socioeconomic indicators.

Racial disparities in cancer-related survival in patients with squamous cell carcinoma of the esophagus in the US between 1973 and 2013.

Esophageal cancer makes up approximately 1% of all diagnosed cancers in the US. There is a persistent disparity in incidence and cancer-related mortality rates among different races for esophageal squamous cell carcinoma (SCC). Most previous studies investigated racial disparities between black and white patients, occasionally examining disparities for Hispanic patients. Studies including Asians/Pacific Islanders (API) as a subgroup are rare. Our objective was to determine whether there is an association between race and cancer-related survival in patients with esophageal SCC.

Replication and validation of genetic polymorphisms associated with survival after allogeneic blood or marrow transplant.

Multiple candidate gene-association studies of non-HLA single-nucleotide polymorphisms (SNPs) and outcomes after blood or marrow transplant (BMT) have been conducted. We identified 70 publications reporting 45 SNPs in 36 genes significantly associated with disease-related mortality, progression-free survival, transplant-related mortality, and/or overall survival after BMT. Replication and validation of these SNP associations were performed using DISCOVeRY-BMT (Determining the Influence of Susceptibility COnveying Variants Related to one-Year mortality after BMT), a well-powered genome-wide association study consisting of 2 cohorts, totaling 2888 BMT recipients with acute myeloid leukemia, acute lymphoblastic leukemia, or myelodysplastic syndrome, and their HLA-matched unrelated donors, reported to the Center for International Blood and Marrow Transplant Research. Gene-based tests were used to assess the aggregate effect of SNPs on outcome. None of the previously reported significant SNPs replicated at P < .05 in DISCOVeRY-BMT. Validation analyses showed association with one previously reported donor SNP at P < .05 and survival; more associations would be anticipated by chance alone. No gene-based tests were significant at P < .05. Functional annotation with publicly available data shows these candidate SNPs most likely do not have biochemical function; only 13% of candidate SNPs correlate with gene expression or are predicted to impact transcription factor binding. Of these, half do not impact the candidate gene of interest; the other half correlate with expression of multiple genes. These findings emphasize the peril of pursing candidate approaches and the importance of adequately powered tests of unbiased genome-wide associations with BMT clinical outcomes given the ultimate goal of improving patient outcomes.

Assessment of imatinib as first-line treatment of chronic myeloid leukemia: 10-year survival results of the randomized CML study IV and impact of non-CML determinants.

Chronic myeloid leukemia (CML)-study IV was designed to explore whether treatment with imatinib (IM) at 400 mg/day (n=400) could be optimized by doubling the dose (n=420), adding interferon (IFN) (n=430) or cytarabine (n=158) or using IM after IFN-failure (n=128). From July 2002 to March 2012, 1551 newly diagnosed patients in chronic phase were randomized into a 5-arm study. The study was powered to detect a survival difference of 5% at 5 years. After a median observation time of 9.5 years, 10-year overall survival was 82%, 10-year progression-free survival was 80% and 10-year relative survival was 92%. Survival between IM400 mg and any experimental arm was not different. In a multivariate analysis, risk group, major-route chromosomal aberrations, comorbidities, smoking and treatment center (academic vs other) influenced survival significantly, but not any form of treatment optimization. Patients reaching the molecular response milestones at 3, 6 and 12 months had a significant survival advantage. For responders, monotherapy with IM400 mg provides a close to normal life expectancy independent of the time to response. Survival is more determined by patients' and disease factors than by initial treatment selection. Although improvements are also needed for refractory disease, more life-time can currently be gained by carefully addressing non-CML determinants of survival.

High RBM3 expression is associated with an improved survival and oxaliplatin response in patients with metastatic colorectal cancer.

High expression of the RNA-binding motif protein 3 (RBM3) has been shown to correlate, with prolonged survival in several malignant diseases and with the benefit of platinum-based chemotherapy in ovarian cancer. The aim of this study was to evaluate RBM3 in metastatic colorectal cancer (mCRC) as a prognostic factor for overall survival and in relation to benefit of first-line chemotherapy.

A Novel Derivation Predicting Survival After Primary Tumor Resection in Stage IV Colorectal Cancer: Validation of a Prognostic Scoring Model and an Online Calculator to Provide Individualized Survival Estimation.

A prognostic scoring model has been devised previously to predict survival following primary tumor resection in patients with metastatic colorectal cancer and unresectable metastases. This has yet to be validated.

Metastasis-Free Survival Is a Strong Surrogate of Overall Survival in Localized Prostate Cancer.

Purpose Adjuvant therapy for intermediate-risk and high-risk localized prostate cancer decreases the number of deaths from this disease. Surrogates for overall survival (OS) could expedite the evaluation of new adjuvant therapies. Methods By June 2013, 102 completed or ongoing randomized trials were identified and individual patient data were collected from 28 trials with 28,905 patients. Disease-free survival (DFS) and metastasis-free survival (MFS) were determined for 21,140 patients from 24 trials and 12,712 patients from 19 trials, respectively. We evaluated the surrogacy of DFS and MFS for OS by using a two-stage meta-analytic validation model by determining the correlation of an intermediate clinical end point with OS and the correlation of treatment effects on both the intermediate clinical end point and OS. Results Trials enrolled patients from 1987 to 2011. After a median follow-up of 10 years, 45% of 21,140 men and 45% of 12,712 men experienced a DFS and MFS event, respectively. For DFS and MFS, 61% and 90% of the patients, respectively, were from radiation trials, and 63% and 66%, respectively, had high-risk disease. At the patient level, Kendall's τ correlation with OS was 0.85 and 0.91 for DFS and MFS, respectively. At the trial level, R2 was 0.86 (95% CI, 0.78 to 0.90) and 0.83 (95% CI, 0.71 to 0.88) from weighted linear regression of 8-year OS rates versus 5-year DFS and MFS rates, respectively. Treatment effects-measured by log hazard ratios-for the surrogates and OS were well correlated ( R2, 0.73 [95% CI, 0.53 to 0.82] for DFS and 0.92 [95% CI, 0.81 to 0.95] for MFS). Conclusion MFS is a strong surrogate for OS for localized prostate cancer that is associated with a significant risk of death from prostate cancer.

Epistasis in genomic and survival data of cancer patients.

Cancer aggressiveness and its effect on patient survival depends on mutations in the tumor genome. Epistatic interactions between the mutated genes may guide the choice of anticancer therapy and set predictive factors of its success. Inhibitors targeting synthetic lethal partners of genes mutated in tumors are already utilized for efficient and specific treatment in the clinic. The space of possible epistatic interactions, however, is overwhelming, and computational methods are needed to limit the experimental effort of validating the interactions for therapy and characterizing their biomarkers. Here, we introduce SurvLRT, a statistical likelihood ratio test for identifying epistatic gene pairs and triplets from cancer patient genomic and survival data. Compared to established approaches, SurvLRT performed favorable in predicting known, experimentally verified synthetic lethal partners of PARP1 from TCGA data. Our approach is the first to test for epistasis between triplets of genes to identify biomarkers of synthetic lethality-based therapy. SurvLRT proved successful in identifying the known gene TP53BP1 as the biomarker of success of the therapy targeting PARP in BRCA1 deficient tumors. Search for other biomarkers for the same interaction revealed a region whose deletion was a more significant biomarker than deletion of TP53BP1. With the ability to detect not only pairwise but twelve different types of triple epistasis, applicability of SurvLRT goes beyond cancer therapy, to the level of characterization of shapes of fitness landscapes.

A score test for comparing cross-sectional survival data with a fraction of non-susceptible patients and its application in clinical immunology.

In cross-sectional studies of time-to-event data collected by patient examinations at a single random point in time, a fraction of them will not experience the event regardless of the length of the follow-up time. This is the case in clinical immunology studies that include a mixed population, with both immune-reactive and immune-tolerant (or non-susceptible) patients. In these cases, classical tests of current status data may perform poorly. New methods for testing these data are needed.

Appropriate Use of Effective Dose in Radiation Protection and Risk Assessment.

Effective dose was introduced by the ICRP for the single, over-arching purpose of setting limits for radiation protection. Effective dose is a derived quantity or mathematical construct and not a physical, measurable quantity. The formula for calculating effective dose to a reference model incorporates terms to account for all radiation types, organ and tissue radiosensitivities, population groups, and multiple biological endpoints. The properties and appropriate applications of effective dose are not well understood by many within and outside the health physics profession; no other quantity in radiation protection has been more confusing or misunderstood. According to ICRP Publication 103, effective dose is to be used for "prospective dose assessment for planning and optimization in radiological protection, and retrospective demonstration of compliance for regulatory purposes." In practice, effective dose has been applied incorrectly to predict cancer risk among exposed persons. The concept of effective dose applies generally to reference models only and not to individual subjects. While conceived to represent a measure of cancer risk or heritable detrimental effects, effective dose is not predictive of future cancer risk. The formula for calculating effective dose incorporates committee-selected weighting factors for radiation quality and organ sensitivity; however, the organ weighting factors are averaged across all ages and both genders and thus do not apply to any specific individual or radiosensitive subpopulations such as children and young women. Further, it is not appropriate to apply effective dose to individual medical patients because patient-specific parameters may vary substantially from the assumptions used in generalized models. Also, effective dose is not applicable to therapeutic uses of radiation, as its mathematical underpinnings pertain only to observed late (stochastic) effects of radiation exposure and do not account for short-term adverse tissue reactions. The weighting factors incorporate substantial uncertainties, and linearity of the dose-response function at low dose is uncertain and highly disputed. Since effective dose is not predictive of future cancer incidence, it follows that effective dose should never be used to estimate future cancer risk from specific sources of radiation exposure. Instead, individual assessments of potential detriment should only be based on organ or tissue radiation absorbed dose, together with best scientific understanding of the corresponding dose-response relationships.

Comorbidities in the diseasome are more apparent than real: What Bayesian filtering reveals about the comorbidities of depression.

Comorbidity patterns have become a major source of information to explore shared mechanisms of pathogenesis between disorders. In hypothesis-free exploration of comorbid conditions, disease-disease networks are usually identified by pairwise methods. However, interpretation of the results is hindered by several confounders. In particular a very large number of pairwise associations can arise indirectly through other comorbidity associations and they increase exponentially with the increasing breadth of the investigated diseases. To investigate and filter this effect, we computed and compared pairwise approaches with a systems-based method, which constructs a sparse Bayesian direct multimorbidity map (BDMM) by systematically eliminating disease-mediated comorbidity relations. Additionally, focusing on depression-related parts of the BDMM, we evaluated correspondence with results from logistic regression, text-mining and molecular-level measures for comorbidities such as genetic overlap and the interactome-based association score. We used a subset of the UK Biobank Resource, a cross-sectional dataset including 247 diseases and 117,392 participants who filled out a detailed questionnaire about mental health. The sparse comorbidity map confirmed that depressed patients frequently suffer from both psychiatric and somatic comorbid disorders. Notably, anxiety and obesity show strong and direct relationships with depression. The BDMM identified further directly co-morbid somatic disorders, e.g. irritable bowel syndrome, fibromyalgia, or migraine. Using the subnetwork of depression and metabolic disorders for functional analysis, the interactome-based system-level score showed the best agreement with the sparse disease network. This indicates that these epidemiologically strong disease-disease relations have improved correspondence with expected molecular-level mechanisms. The substantially fewer number of comorbidity relations in the BDMM compared to pairwise methods implies that biologically meaningful comorbid relations may be less frequent than earlier pairwise methods suggested. The computed interactive comprehensive multimorbidity views over the diseasome are available on the web at Co=MorNet:

Random survival forest with space extensions for censored data.

Prediction capability of a classifier usually improves when it is built from an extended variable space by adding new variables from randomly combination of two or more original variables. However, its usefulness in survival analysis of censored time-to-event data is yet to be verified. In this research, we investigate the plausibility of space extension technique, originally proposed for classification purpose, to survival analysis. By combing random subspace, bagging and extended space techniques, we develop a random survival forest with space extensions algorithm. According to statistical analysis results, we show that the proposed model outperforms or at least comparable to popular survival models such as random survival forest, rotation survival forest, Cox proportional hazard and boosting survival models on well-known benchmark datasets.

Development and validation of a prognostic scoring model for Mycobacterium avium complex lung disease: an observational cohort study.

Patients with Mycobacterium avium complex (MAC) lung disease (LD) have a heterogeneous prognosis. This study aimed to develop and validate a prognostic scoring model for these patients using independent risk factors for survival.

Risk Prediction Models for Incident Heart Failure: Beyond Statistical Validity.

Survival comparison between radical surgery and definitive chemoradiation in 267 esophageal squamous cell carcinomas in a single institution: A propensity-matched study.

To compare radical surgery with definitive chemoradiation (CRT) for esophageal squamous cell carcinoma using propensity score (PS) matching at our single institution.

Prognostic significance of marital status in breast cancer survival: A population-based study.

Research shows that married cancer patients have lower mortality than unmarried patients but few data exist for breast cancer. We assessed total mortality associated with marital status, with attention to differences by race/ethnicity, tumor subtype, and neighborhood socioeconomic status (nSES). We included, from the population-based California Cancer Registry, women ages 18 and older with invasive breast cancer diagnosed between 2005 and 2012 with follow-up through December 2013. We estimated mortality rate ratios (MRR) and 95% confidence intervals (CI) for total mortality by nSES, race/ethnicity, and tumor subtype. Among 145,564 breast cancer cases, 42.7% were unmarried at the time of diagnosis. In multivariable-adjusted models, the MRR (95% CI) for unmarried compared to married women was 1.28 (1.24-1.32) for total mortality. Significant interactions were observed by race/ethnicity (P<0.001), tumor subtype (P<0.001), and nSES (P = 0.009). Higher MRRs were observed for non-Hispanic whites and Asians/Pacific Islanders than for blacks or Hispanics, and for HR+/HER2+ tumors than other subtypes. Assessment of interactive effect between marital status and nSES showed that unmarried women living in low SES neighborhoods had a higher risk of dying compared with married women in high SES neighborhoods (MRR = 1.60; 95% CI: 1.53-1.67). Unmarried breast cancer patients have higher total mortality than married patients; the association varies by race/ethnicity, tumor subtype, and nSES. Unmarried status should be further evaluated as a breast cancer prognostic factor. Identification of underlying causes of the marital status associations is needed to design interventions that could improve survival for unmarried breast cancer patients.

Overall survival in elderly patients with colorectal cancer: A population-based study in the Caribbean.

Population-based Cancer registries (PBCR) play an important role in cancer surveillance and research. The aim of this study was to examine overall survival in elderly patients with colorectal cancer (CRC) by analysing data from the Martinique PBCR between 1993 and 2012.

Population effect model identifies gene expression predictors of survival outcomes in lung adenocarcinoma for both Caucasian and Asian patients.

We analyzed and integrated transcriptome data from two large studies of lung adenocarcinomas on distinct populations. Our goal was to investigate the variable gene expression alterations between paired tumor-normal tissues and prospectively identify those alterations that can reliably predict lung disease related outcomes across populations.

Advanced Stage at Presentation Remains a Major Factor Contributing to Breast Cancer Survival Disparity between Public and Private Hospitals in a Middle-Income Country.

Background: Survival disparities in cancer are known to occur between public and private hospitals. We compared breast cancer presentation, treatment and survival between a public academic hospital and a private hospital in a middle-income country. Methods: The demographics, clinical characteristics, treatment and overall survival (OS) of 2767 patients with invasive breast carcinoma diagnosed between 2001 and 2011 in the public hospital were compared with 1199 patients from the private hospital. Results: Compared to patients in the private hospital, patients from the public hospital were older at presentation, and had more advanced cancer stages. They were also more likely to receive mastectomy and chemotherapy but less radiotherapy. The five-year OS in public patients was significantly lower than in private patients (71.6% vs. 86.8%). This difference was largely attributed to discrepancies in stage at diagnosis and, although to a much smaller extent, to demographic differences and treatment disparities. Even following adjustment for these factors, patients in the public hospital remained at increased risk of mortality compared to their counterparts in the private hospital (Hazard Ratio: 1.59; 95% Confidence Interval: 1.36-1.85). Conclusion: Late stage at diagnosis appears to be a major contributing factor explaining the breast cancer survival disparity between public and private patients in this middle-income setting.

Correction of Selection Bias in Survey Data: Is the Statistical Cure Worse Than the Bias?

In previous articles in the American Journal of Epidemiology (Am J Epidemiol. 2013;177(5):431-442) and American Journal of Public Health (Am J Public Health. 2013;103(10):1895-1901), Masters et al. reported age-specific hazard ratios for the contrasts in mortality rates between obesity categories. They corrected the observed hazard ratios for selection bias caused by what they postulated was the nonrepresentativeness of the participants in the National Health Interview Study that increased with age, obesity, and ill health. However, it is possible that their regression approach to remove the alleged bias has not produced, and in general cannot produce, sensible hazard ratio estimates. First, one must consider how many nonparticipants there might have been in each category of obesity and of age at entry and how much higher the mortality rates would have to be in nonparticipants than in participants in these same categories. What plausible set of numerical values would convert the ("biased") decreasing-with-age hazard ratios seen in the data into the ("unbiased") increasing-with-age ratios that they computed? Can these values be encapsulated in (and can sensible values be recovered from) 1 additional internal variable in a regression model? Second, one must examine the age pattern of the hazard ratios that have been adjusted for selection. Without the correction, the hazard ratios are attenuated with increasing age. With it, the hazard ratios at older ages are considerably higher, but those at younger ages are well below 1. Third, one must test whether the regression approach suggested by Masters et al. would correct the nonrepresentativeness that increased with age and ill health that I introduced into real and hypothetical data sets. I found that the approach did not recover the hazard ratio patterns present in the unselected data sets: The corrections overshot the target at older ages and undershot it at lower ages.

Influence of time interval from diagnosis to treatment on survival for oral cavity cancer: A nationwide cohort study.

We aimed to explore the relationship between the time interval from diagnosis to treatment and survival of oral cavity squamous cell carcinoma patients.

Piecewise-linear criterion functions in oblique survival tree induction.

Recursive partitioning is a common, assumption-free method of survival data analysis. It focuses mainly on univariate trees, which use splits based on a single variable in each internal node. In this paper, I provide an extension of an oblique survival tree induction technique, in which axis-parallel splits are replaced by hyperplanes, dividing the feature space into areas with a homogeneous survival experience.

The prognostic efficacy and improvements of the 7th edition Union for International Cancer Control tumor-node-metastasis classifications for Chinese patients with gastric cancer: Results based on a retrospective three-decade population study.

This study aimed to evaluate survival trends for patients with gastric cancer in northeast China in the most recent three decades and analyze the applicability of the UICC tumor-node-metastasis (TNM) classification 7th edition for Chinese patients with gastric cancer. A review of all inpatient and outpatient records of patients with gastric cancer was conducted in the first hospital of China Medical University and the Liaoning Cancer Hospital and Institute. All patients who met the inclusion criteria and were seen from January 1980 through December 2009 were included in the study. The primary outcome was 5-year survival, which was analyzed according to decade of diagnosis and TNM classifications. From 1980 through 2009, the 5-year survival rates for patients with gastric cancer (n=2414) increased from 39.1% to 57.3%. Decade of diagnosis was significantly associated with patient survival (p = 0.013), and the 5-year survival rate in the 2000s was remarkably higher than that in the 1980s and 1990s (p = 0.004 and 0.049, respectively). When classified according to the UICC TNM classification of gastric cancer 7th edition, the prognoses of stage IIIA and stage IIIB patients were not significantly different (p = 0.077). However, if stage T4b and stage N0 patients were classified as stage IIIA, the prognoses of stage IIIA and stage IIIB patients were significantly different (p < 0.001). Hence, there was a significant difference in survival during the three time periods in Northeast China. Classifying stage T4b and stage N0 patients as stage IIIA according to the 7th edition of UICC gastric cancer TNM classifications better stratified Chinese patients and predicted prognoses.

CT Findings, Radiologic-Pathologic Correlation, and Imaging Predictors of Survival for Patients With Interstitial Pneumonia With Autoimmune Features.

The objective of this study is to determine the CT findings and patterns of interstitial pneumonia with autoimmune features (IPAF) and to assess whether imaging can predict survival for patients with IPAF.

The predictive value of baseline LDL-TG level on major adverse cardiovascular events in a followed up cohort population.

We aimed at identifying the predictive roles of Low-Density Lipoprotein Triglycerides (LDL-TG) for major adverse cardiovascular events (MACEs).