Related Articles

Introduction

Identification of individuals at high risk for lung cancer should be of value to individuals, patients, clinicians, and researchers. Existing prediction models have only modest capabilities to classify persons at risk accurately.

Methods

Prospective data from 70 962 control subjects in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) were used in models for the general population (model 1) and for a subcohort of ever-smokers (N = 38 254) (model 2). Both models included age, socioeconomic status (education), body mass index, family history of lung cancer, chronic obstructive pulmonary disease, recent chest x-ray, smoking status (never, former, or current), pack-years smoked, and smoking duration. Model 2 also included smoking quit-time (time in years since ever-smokers permanently quit smoking). External validation was performed with 44 223 PLCO intervention arm participants who completed a supplemental questionnaire and were subsequently followed. Known available risk factors were included in logistic regression models. Bootstrap optimism-corrected estimates of predictive performance were calculated (internal validation). Nonlinear relationships for age, pack-years smoked, smoking duration, and quit-time were modeled using restricted cubic splines. All reported P values are two-sided.

Results

During follow-up (median 9.2 years) of the control arm subjects, 1040 lung cancers occurred. During follow-up of the external validation sample (median 3.0 years), 213 lung cancers occurred. For models 1 and 2, bootstrap optimism-corrected receiver operator characteristic area under the curves were 0.857 and 0.805, and calibration slopes (model-predicted probabilities vs observed probabilities) were 0.987 and 0.979, respectively. In the external validation sample, models 1 and 2 had area under the curves of 0.841 and 0.784, respectively. These models had high discrimination in women, men, whites, and nonwhites.

Conclusion

The PLCO lung cancer risk models demonstrate high discrimination and calibration.

Prostate specific antigen (PSA) is widely used as a diagnostic biomarker for prostate cancer (PC). However, due to its low predictive performance, many patients without PC suffer from the harms of unnecessary prostate needle biopsies. The present study aims to evaluate the reproducibility and performance of a genetic risk prediction model in Japanese and estimate its utility as a diagnostic biomarker in a clinical scenario. We created a logistic regression model incorporating 16 SNPs that were significantly associated with PC in a genome-wide association study of Japanese population using 689 cases and 749 male controls. The model was validated by two independent sets of Japanese samples comprising 3,294 cases and 6,281 male controls. The areas under curve (AUC) of the model were 0.679, 0.655, and 0.661 for the samples used to create the model and those used for validation. The AUCs were not significantly altered in samples with PSA 1–10 ng/ml. 24.2% and 9.7% of the patients had odds ratio <0.5 (low risk) or >2 (high risk) in the model. Assuming the overall positive rate of prostate needle biopsies to be 20%, the positive biopsy rates were 10.7% and 42.4% for the low and high genetic risk groups respectively. Our genetic risk prediction model for PC was highly reproducible, and its predictive performance was not influenced by PSA. The model could have a potential to affect clinical decision when it is applied to patients with gray-zone PSA, which should be confirmed in future clinical studies.

Because existing risk prediction models for lung cancer were developed in white populations, they may not be appropriate for predicting risk among African-Americans. Therefore, a need exists to construct and validate a risk prediction model for lung cancer that is specific to African-Americans. We analyzed data from 491 African-Americans with lung cancer and 497 matched African-American controls to identify specific risks and incorporate them into a multivariable risk model for lung cancer and estimate the 5-year absolute risk of lung cancer. We performed internal and external validations of the risk model using data on additional cases and controls from the same ongoing multiracial/ethnic lung cancer case-control study from which the model-building data were obtained as well as data from two different lung cancer studies in metropolitan Detroit, respectively. We also compared our African-American model with our previously developed risk prediction model for whites. The final risk model included smoking-related variables [smoking status, pack-years smoked, age at smoking cessation (former smokers), and number of years since smoking cessation (former smokers)], self- reported physician diagnoses of chronic obstructive pulmonary disease or hay fever, and exposures to asbestos or wood dusts. Our risk prediction model for African-Americans exhibited good discrimination [75% (95% confidence interval, 0.67−0.82)] for our internal data and moderate discrimination [63% (95% confidence interval, 0.57−0.69)] for the external data group, which is an improvement over the Spitz model for white subjects. Existing lung cancer prediction models may not be appropriate for predicting risk for African-Americans because (a) they were developed using white populations, (b) level of risk is different for risk factors that African-American share with whites, and (c) unique group-specific risk factors exist for African-Americans. This study developed and validated a risk prediction model for lung cancer that is specific to African-Americans and thus more precise in predicting their risks. These findings highlight the importance of conducting further ethnic-specific analyses of disease risk.

Objective

An independent cohort of acute lung injury (ALI) patients was used to evaluate the external validity of a simple prediction model for short-term mortality previously developed using data from ARDS Network (ARDSNet) trials.

Design, Setting, and Patients

Data for external validation were obtained from a prospective cohort study of ALI patients from 13 ICUs at four teaching hospitals in Baltimore, Maryland.

Measurements and Main Results

Of the 508 non-trauma, ALI patients eligible for this analysis, 234 (46%) died in-hospital. Discrimination of the ARDSNet prediction model for inhospital mortality, evaluated by the area under the receiver operator characteristics curves (AUC), was 0.67 for our external validation dataset versus 0.70 and 0.68 using APACHE II and the ARDSNet validation dataset, respectively. In evaluating calibration of the model, predicted versus observed in-hospital mortality for the external validation dataset was similar for both low risk (ARDSNet model score = 0) and high risk (score = 3 or 4+) patient strata. However, for intermediate risk (score = 1 or 2) patients, observed in-hospital mortality was substantially higher than predicted mortality (25.3% vs. 16.5% and 40.6% vs. 31.0% for score = 1 and 2, respectively). Sensitivity analyses limiting our external validation data set to only those patients meeting the ARDSNet trial eligibility criteria and to those who received mechanical ventilation in compliance with the ARDSNet ventilation protocol, did not substantially change the model’s discrimination or improve its calibration.

Conclusions

Evaluation of the ARDSNet prediction model using an external ALI cohort demonstrated similar discrimination of the model as was observed with the ARDSNet validation dataset. However, there were substantial differences in observed versus predicted mortality among intermediate risk ALI patients. The ARDSNet model provided reasonable, but imprecise, estimates of predicted mortality when applied to our external validation cohort of ALI patients.

Background

A combination of biomarkers in a multivariate model may predict disease with greater accuracy than a single biomarker employed alone. We developed a non-linear method of multivariate analysis, weighted digital analysis (WDA), and evaluated its ability to predict lung cancer employing volatile biomarkers in the breath.

Methods

WDA generates a discriminant function to predict membership in disease vs no disease groups by determining weight, a cutoff value, and a sign for each predictor variable employed in the model. The weight of each predictor variable was the area under the curve (AUC) of the receiver operating characteristic (ROC) curve minus a fixed offset of 0.55, where the AUC was obtained by employing that predictor variable alone, as the sole marker of disease. The sign (±) was used to invert the predictor variable if a lower value indicated a higher probability of disease. When employed to predict the presence of a disease in a particular patient, the discriminant function was determined as the sum of the weights of all predictor variables that exceeded their cutoff values. The algorithm that generates the discriminant function is deterministic because parameters are calculated from each individual predictor variable without any optimization or adjustment. We employed WDA to re-evaluate data from a recent study of breath biomarkers of lung cancer, comprising the volatile organic compounds (VOCs) in the alveolar breath of 193 subjects with primary lung cancer and 211 controls with a negative chest CT.

Results

The WDA discriminant function accurately identified patients with lung cancer in a model employing 30 breath VOCs (ROC curve AUC = 0.90; sensitivity = 84.5%, specificity = 81.0%). These results were superior to multi-linear regression analysis of the same data set (AUC= 0.74, sensitivity = 68.4, specificity = 73.5%). WDA test accuracy did not vary appreciably with TNM (tumor, node, metastasis) stage of disease, and results were not affected by tobacco smoking (ROC curve AUC =0.92 in current smokers, 0.90 in former smokers). WDA was a robust predictor of lung cancer: random removal of 1/3 of the VOCs did not reduce the AUC of the ROC curve by >10% (99.7% CI).

Conclusions

A test employing WDA of breath VOCs predicted lung cancer with accuracy similar to chest computed tomography. The algorithm identified dependencies that were not apparent with traditional linear methods. WDA appears to provide a useful new technique for non-linear multivariate analysis of data.

Introduction

International Collaborative Effort on Chronic Obstructive Lung Disease: Exacerbation Risk Index Cohorts (ICE COLD ERIC) is a prospective cohort study with chronic obstructive pulmonary disease (COPD) patients from Switzerland and The Netherlands designed to develop and validate practical COPD risk indices that predict the clinical course of COPD patients in primary care. This paper describes the characteristics of the cohorts at baseline.

Material and methods

Standardized assessments included lung function, patient history, self-administered questionnaires, exercise capacity, and a venous blood sample for analysis of biomarkers and genetics.

Results

A total of 260 Dutch and 151 Swiss patients were included. Median age was 66 years, 57% were male, 38% were current smokers, 55% were former smokers, and 76% had at least one and 40% had two or more comorbidities with cardiovascular disease being the most prevalent one. The use of any pulmonary and cardiovascular drugs was 84% and 66%, respectively. Although lung function results (median forced expiratory volume in 1 second [FEV1] was 59% of predicted) were similar across the two cohorts, Swiss patients reported better COPD-specific health-related quality of life (Chronic Respiratory Questionnaire) and had higher exercise capacity.

Discussion

COPD patients in the ICE COLD ERIC study represent a wide range of disease severities and the prevalence of multimorbidity is high. The rich variation in these primary care cohorts offers good opportunities to learn more about the clinical course of COPD.

Affordable early screening in subjects with high risk of lung cancer has great potential to improve survival from this deadly disease. We measured gene expression from lung tissue and peripheral whole blood (PWB) from adenocarcinoma cases and controls to identify dysregulated lung cancer genes that could be tested in blood to improve identification of at-risk patients in the future. Genome-wide mRNA expression analysis was conducted in 153 subjects (73 adenocarcinoma cases, 80 controls) from the Environment And Genetics in Lung cancer Etiology (EAGLE) study using PWB and paired snap-frozen tumor and non-involved lung tissue samples. Analyses were conducted using unpaired t-tests, linear mixed effects and ANOVA models. The area under the receiver operating characteristic curve (AUC) was computed to assess the predictive accuracy of the identified biomarkers. We identified 50 dysregulated genes in stage I adenocarcinoma versus control PWB samples (False Discovery Rate ≤0.1, fold change ≥1.5 or ≤0.66). Among them, eight (TGFBR3, RUNX3, TRGC2, TRGV9, TARP, ACP1, VCAN, and TSTA3) differentiated paired tumor versus non-involved lung tissue samples in stage I cases, suggesting a similar pattern of lung cancer-related changes in PWB and lung tissue. These results were confirmed in two independent gene expression analyses in a blood-based case-control study (n=212) and a tumor-non tumor paired tissue study (n=54). The eight genes discriminated patients with lung cancer from healthy controls with high accuracy (AUC=0.81, 95% CI=0.74–0.87). Our finding suggests the use of gene expression from PWB for the identification of early detection markers of lung cancer in the future.

Lung cancer is the leading cause of cancer death in the United States, and the majority of diagnoses are made in former smokers. While avoidance of tobacco abuse and smoking cessation clearly will have the greatest impact on lung cancer development, effective chemoprevention could prove to be more effective than treatment of established disease. Chemoprevention is the use of dietary or pharmaceutical agents to reverse or inhibit the carcinogenic process and has been successfully applied to common malignancies other than lung. Despite previous studies in lung cancer chemoprevention failing to identify effective agents, our ability to determine higher risk populations and the understanding of lung tumor and pre-malignant biology continues to advance. Additional biomarkers of risk continue to be investigated and validated. The World Health Organization/International Association for the Study of Lung Cancer classification for lung cancer now recognizes distinct histologic lesions that can be reproducibly graded as precursors of non–small cell lung cancer. For example, carcinogenesis in the bronchial epithelium starts with normal epithelium and progresses through hyperplasia, metaplasia, dysplasia, and carcinoma in situ to invasive squamous cell cancer. Similar precursor lesions exist for adenocarcinoma, and these pre-malignant lesions are targeted by chemopreventive agents in current and future trials. At this time, chemopreventive agents can only be recommended as part of well-designed clinical trials, and multiple trials are currently in progress and additional trials are in the planning stages. This review will discuss the principles of chemoprevention, summarize the completed trials, and discuss ongoing and potential future trials with a focus on targeted pathways.

Gene promoter hypermethylation in sputum is a promising biomarker for predicting lung cancer. Identifying factors that predispose smokers to methylation of multiple gene promoters in the lung could impact strategies for early detection and chemoprevention. This study evaluated the hypothesis that double-strand break repair capacity and sequence variation in genes in this pathway are associated with a high methylation index in a cohort of current and former cancer-free smokers. A 50% reduction in the mean level of double-strand break repair capacity was seen in lymphocytes from smokers with a high methylation index, defined as ≥ 3 of 8 genes methylated in sputum, compared to smokers with no genes methylated. The classification accuracy for predicting risk for methylation was 88%. Single nucleotide polymorphisms within the MRE11A, CHEK2, XRCC3, DNA-Pkc, and NBN DNA repair genes were highly associated with the methylation index. A 14.5-fold increased odds for high methylation was seen for persons with ≥ 7 risk alleles of these genes. Promoter activity of the MRE11A gene that plays a critical role in recognition of DNA damage and activation of ATM was reduced in persons with the risk allele. Collectively, ours is the first population-based study to identify double-strand break DNA repair capacity and specific genes within this pathway as critical determinants for gene methylation in sputum, that is, in turn, associated with elevated risk for lung cancer.

Background

Lung cancer is a complex polygenic disease. Although recent genome-wide association (GWA) studies have identified multiple susceptibility loci for lung cancer, most of these variants have not been validated in a Chinese population. In this study, we investigated whether a genetic risk score combining multiple.

Methods

Five single-nucleotide polymorphisms (SNPs) identified in previous GWA or large cohort studies were genotyped in 5068 Chinese case–control subjects. The genetic risk score (GRS) based on these SNPs was estimated by two approaches: a simple risk alleles count (cGRS) and a weighted (wGRS) method. The area under the receiver operating characteristic (ROC) curve (AUC) in combination with the bootstrap resampling method was used to assess the predictive performance of the genetic risk score for lung cancer.

Results

Four independent SNPs (rs2736100, rs402710, rs4488809 and rs4083914), were found to be associated with a risk of lung cancer. The wGRS based on these four SNPs was a better predictor than cGRS. Using a liability threshold model, we estimated that these four SNPs accounted for only 4.02% of genetic variance in lung cancer. Smoking history contributed significantly to lung cancer (P < 0.001) risk [AUC = 0.619 (0.603-0.634)], and incorporated with wGRS gave an AUC value of 0.639 (0.621-0.652) after adjustment for over-fitting. This model shows promise for assessing lung cancer risk in a Chinese population.

Conclusion

Our results indicate that although genetic variants related to lung cancer only added moderate discriminatory accuracy, it still improved the predictive ability of the assessment model in Chinese population.

OBJECTIVES:

It has been recognized that patients with non-small cell lung cancer who are lifelong never-smokers constitute a distinct clinical entity. The aim of this study was to assess clinical risk factors for survival among never-smokers with non-small cell lung cancer.

METHODS:

All consecutive non-small cell lung cancer patients diagnosed (n = 285) between May 2005 and May 2009 were included. The clinical characteristics of never-smokers and ever-smokers (former and current) were compared using chi-squared or Student's t tests. Survival curves were calculated using the Kaplan-Meier method, and log-rank tests were used for survival comparisons. A Cox proportional hazards regression analysis was evaluated by adjusting for age (continuous variable), gender (female vs. male), smoking status (never- vs. ever-smoker), the Karnofsky Performance Status Scale (continuous variable), histological type (adenocarcinoma vs. non-adenocarcinoma), AJCC staging (early vs. advanced staging), and treatment (chemotherapy and/or radiotherapy vs. the best treatment support).

RESULTS:

Of the 285 non-small cell lung cancer patients, 56 patients were never-smokers. Univariate analyses indicated that the never-smoker patients were more likely to be female (68% vs. 32%) and have adenocarcinoma (70% vs. 51%). Overall median survival was 15.7 months (95% CI: 13.2 to 18.2). The never-smoker patients had a better survival rate than their counterpart, the ever-smokers. Never-smoker status, higher Karnofsky Performance Status, early staging, and treatment were independent and favorable prognostic factors for survival after adjusting for age, gender, and adenocarcinoma in multivariate analysis.

CONCLUSIONS:

Epidemiological differences exist between never- and ever-smokers with lung cancer. Overall survival among never-smokers was found to be higher and independent of gender and histological type.

Mammographic percent density (PD) is a strong risk factor for breast cancer, but there has been relatively little systematic evaluation of other features in mammographic images that might additionally predict breast cancer risk. We evaluated the association of a large number of image texture features with risk of breast cancer using a clinic-based case-control study of digitized film mammograms, all with screening mammograms prior to breast cancer diagnosis. The sample was split into training (123 cases, 258 controls) and validation (123 cases, 264 controls) datasets. Age and body mass index (BMI)-adjusted Odds Ratios (ORs) per standard deviation change in the feature, 95% confidence intervals, and the area under the receiver operator characteristic curve (AUC) were obtained using logistic regression. A bootstrap approach was used to identify the strongest features in the training dataset, and results for features that validated in the second half of the sample were reported using the full dataset. The mean age at mammography was 64.0 ± 10.2 years, and the mean time from mammography to breast cancer was 3.7 ± 1.0 (range 2.0-5.9 years). PD was associated with breast cancer risk (OR=1.49; 1.25-1.78). The strongest features that validated from each of several classes (Markovian, run-length, Laws, wavelet and Fourier) showed similar ORs as PD and predicted breast cancer at a similar magnitude (AUC=0.58-0.60) as PD (AUC=0.58). All of these features were automatically calculated (unlike PD), and measure texture at a coarse scale. These features were moderately correlated with PD (r = 0.39-0.64), and after adjustment for PD, each of the features attenuated only slightly and retained statistical significance. However, simultaneous inclusion of these features in a model with PD did not significantly improve the ability to predict breast cancer.

A physiologically based pharmacokinetic (PBPK) model was developed that provides a comprehensive description of the kinetics of trichloroethylene (TCE) and its metabolites, trichloroethanol (TCOH), trichloroacetic acid (TCA), and dichloroacetic acid (DCA), in the mouse, rat, and human for both oral and inhalation exposure. The model includes descriptions of the three principal target tissues for cancer identified in animal bioassays: liver, lung, and kidney. Cancer dose metrics provided in the model include the area under the concentration curve (AUC) for TCA and DCA in the plasma, the peak concentration and AUC for chloral in the tracheobronchial region of the lung, and the production of a thioacetylating intermediate from dichlorovinylcysteine in the kidney. Additional dose metrics provided for noncancer risk assessment include the peak concentrations and AUCs for TCE and TCOH in the blood, as well as the total metabolism of TCE divided by the body weight. Sensitivity and uncertainty analyses were performed on the model to evaluate its suitability for use in a pharmacokinetic risk assessment for TCE. Model predictions of TCE, TCA, DCA, and TCOH concentrations in rodents and humans are in good agreement with a variety of experimental data, suggesting that the model should provide a useful basis for evaluating cross-species differences in pharmacokinetics for these chemicals. In the case of the lung and kidney target tissues, however, only limited data are available for establishing cross-species pharmacokinetics. As a result, PBPK model calculations of target tissue dose for lung and kidney should be used with caution.

Rationale: Accurate, early identification of patients at risk for developing acute lung injury (ALI) provides the opportunity to test and implement secondary prevention strategies.

Objectives: To determine the frequency and outcome of ALI development in patients at risk and validate a lung injury prediction score (LIPS).

Methods: In this prospective multicenter observational cohort study, predisposing conditions and risk modifiers predictive of ALI development were identified from routine clinical data available during initial evaluation. The discrimination of the model was assessed with area under receiver operating curve (AUC). The risk of death from ALI was determined after adjustment for severity of illness and predisposing conditions.

Measurements and Main Results: Twenty-two hospitals enrolled 5,584 patients at risk. ALI developed a median of 2 (interquartile range 1–4) days after initial evaluation in 377 (6.8%; 148 ALI-only, 229 adult respiratory distress syndrome) patients. The frequency of ALI varied according to predisposing conditions (from 3% in pancreatitis to 26% after smoke inhalation). LIPS discriminated patients who developed ALI from those who did not with an AUC of 0.80 (95% confidence interval, 0.78–0.82). When adjusted for severity of illness and predisposing conditions, development of ALI increased the risk of in-hospital death (odds ratio, 4.1; 95% confidence interval, 2.9–5.7).

Conclusions: ALI occurrence varies according to predisposing conditions and carries an independently poor prognosis. Using routinely available clinical data, LIPS identifies patients at high risk for ALI early in the course of their illness. This model will alert clinicians about the risk of ALI and facilitate testing and implementation of ALI prevention strategies.

Clinical trial registered with www.clinicaltrials.gov (NCT00889772).

Background:

We developed a web-based, prognostic tool for extremity and trunk wall soft tissue sarcoma to predict 10-year sarcoma-specific survival. External validation was performed.

Methods:

Patients referred during 1987–2002 to Helsinki University Central Hospital are included. External validation was obtained from the Lund University Hospital register. Cox proportional hazards models were fitted with the Helsinki data. The previously described model (SIN) includes size, necrosis, and vascular invasion. The extended model (SAM) includes the SIN factors and in addition depth, location, grade, and size on a continuous scale. Models were statistically compared according to accuracy (area under the ROC curve=AUC) of 10-year sarcoma-specific survival prediction.

Results:

The AUC of the SAM model in10-year survival prediction in the Helsinki patient series was 0.81 as compared with 0.74 for the SIN model (P=0.0007). The corresponding AUCs in the external validation series were 0.77 for the SAM model and 0.73 for the SIN model (P=0.03). A web-based calculator for the SAM model is available at http://www.prognomics.org/sam.

Conclusion:

Addition of grade, depth, and location as well as tumour size on a continuous scale significantly improved the accuracy of the prognostic model when compared with a model that includes only size, necrosis, and vascular invasion.

Purpose

The primary hypothesis to be tested in this study was that the diagnostic performance (as assessed by the area under the receiver operator characteristic curve, AUC) of a multianalyte panel to correctly identify women with ovarian cancer was significantly greater than that for CA-125 alone.

Methods

A retrospective, case–control study (phase II biomarker trial) was conducted that involved 362 plasma samples obtained from women with ovarian cancer (n = 150) and healthy controls (n = 212). A multivariate classification model was developed that incorporated five biomarkers of ovarian cancer, CA-125; C-reactive protein (CRP); serum amyloid A (SAA); interleukin 6 (IL-6); and interleukin 8 (IL-8) from a modelling cohort (n = 179). The performance of the model was evaluated using an independent validation cohort (n = 183) and compared with of CA-125 alone.

Results

The AUC for the biomarker panel was significantly greater than the AUC for CA-125 alone for a validation cohort (p < 0.01) and an early stage disease cohort (i.e. Stages I and II; p < 0.01). At a threshold of 0.3, the sensitivity and specificity of the multianalyte panel were 94.1 and 91.3%, respectively, for the validation cohort and 92.3 and 91.3%, respectively for an early stage disease cohort.

Conclusions

The use of a panel of plasma biomarkers for the identification of women with ovarian cancer delivers a significant increase in diagnostic performance when compared to the performance of CA-125 alone.

Aberrant promoter hypermethylation is one of the major mechanisms in carcinogenesis and some critical growth regulatory genes have shown commonality in methylation across solid tumors. Twenty-six genes, 14 identified through methylation in colon and breast cancers, were evaluated using primary lung adenocarcinomas (n = 175) from current, former and never smokers. Tumor specificity of methylation was validated through comparison of 14 lung cancer cell lines to normal human bronchial epithelial cells derived from bronchoscopy of 20 cancer-free smokers. Twenty-five genes were methylated in 11–81% of primary tumors. Prevalence for methylation of TNFRSF10C, BHLHB5 and BOLL was significantly higher in adenocarcinomas from never smokers than smokers. The relation between methylation of individual genes was examined using pairwise comparisons. A significant association was seen between 138 (42%) of the possible 325 pairwise comparisons. Most notably, methylation of MMP2, BHLHB4 or p16 was significantly associated with methylation of 16–19 other genes, thus predicting for a widespread methylation phenotype. Kaplan–Meier log-rank test and proportional hazard models identified a significant association between methylation of SULF2 (a pro-growth, -angiogenesis and -migration gene) and better patient survival (hazard ratio = 0.23). These results demonstrate a high degree of commonality for targeted silencing of genes between lung and other solid tumors and suggest that promoter hypermethylation in cancer is a highly co-ordinated event.

Background

Prostate-specific antigen (PSA) is widely used to detect prostate cancer. The low positive predictive value of elevated PSA results in large numbers of unnecessary prostate biopsies. We set out to determine whether a multivariable model including four kallikrein forms (total, free, and intact PSA, and human kallikrein 2 (hK2)) could predict prostate biopsy outcome in previously unscreened men with elevated total PSA.

Methods

The study cohort comprised 740 men in Göteborg, Sweden, undergoing biopsy during the first round of the European Randomized study of Screening for Prostate Cancer. We calculated the area-under-the-curve (AUC) for predicting prostate cancer at biopsy. AUCs for a model including age and PSA (the 'laboratory' model) and age, PSA and digital rectal exam (the 'clinical' model) were compared with those for models that also included additional kallikreins.

Results

Addition of free and intact PSA and hK2 improved AUC from 0.68 to 0.83 and from 0.72 to 0.84, for the laboratory and clinical models respectively. Using a 20% risk of prostate cancer as the threshold for biopsy would have reduced the number of biopsies by 424 (57%) and missed only 31 out of 152 low-grade and 3 out of 40 high-grade cancers.

Conclusion

Multiple kallikrein forms measured in blood can predict the result of biopsy in previously unscreened men with elevated PSA. A multivariable model can determine which men should be advised to undergo biopsy and which might be advised to continue screening, but defer biopsy until there was stronger evidence of malignancy.

Background

Early identification of patients at risk of developing acute lung injury (ALI) is critical for potential preventive strategies. We aimed to derive and validate an acute lung injury prediction score (EDLIPS) in a multicenter sample of emergency department (ED) patients.

Methods

We performed a subgroup analysis of 4,361 ED patients enrolled in the previously reported multicenter observational study. ED risk factors and conditions associated with subsequent ALI development were identified and included in the EDLIPS model. Scores were derived and validated using logistic regression analyses. The model was assessed with the area under the receiver-operating curve (AUC) and compared to the original LIPS model (derived from a population of elective high-risk surgical and ED patients) and the Acute Physiology and Chronic Health Evaluation (APACHE II) score.

Results

The incidence of ALI was 7.0% (303/4361). EDLIPS discriminated patients who developed ALI from those who did not with an AUC of 0.78 (95% CI 0.75, 0.82), better than the APACHE II AUC 0.70 (p ≤ 0.001) and similar to the original LIPS score AUC 0.80 (p = 0.07). At an EDLIPS cutoff of 5 (range −0.5, 15) positive and negative likelihood ratios (95% CI) for ALI development were 2.74 (2.43, 3.07) and 0.39 (0.30, 0.49), respectively, with a sensitivity 0.72(0.64, 0.78), specificity 0.74 (0.72, 0.76), and positive and negative predictive value of 0.18 (0.15, 0.21) and 0.97 (0.96, 0.98).

Conclusion

EDLIPS may help identify patients at risk for ALI development early in the course of their ED presentation. This novel model may detect at-risk patients for treatment optimization and identify potential patients for ALI prevention trials.

Evidence from human and animal research indicates that choline metabolic pathways may be activated during a variety of diseases, including cancer. We report results of a case-control study of 2821 lung cancer cases and 2923 controls that assessed associations of choline and betaine dietary intakes with lung cancer. Using multivariable logistic regression analyses, we report a significant association between higher betaine intake and lower lung cancer risk that varied by smoking status. Specifically, no significant association was observed between betaine intake and lung cancer among never-smokers. However, higher betaine intake was significantly associated with reduced lung cancer risk among smokers, and the protective effect was more evident among current than former smokers: for former and current smokers, the ORs (95% CI) of lung cancer for individuals with highest as compared to lowest quartiles of intake were 0.70(0.55–0.88) and 0.51(0.39–0.66) respectively. Significant linear trend of higher betaine intake and lower lung cancer risk was observed among both former (ptrend = 0.002) and current (ptrend<0.0001) smokers. A similar protective effect was also observed with choline intake both in overall analysis as well as among current smokers, with p-values for chi-square tests being 0.001 and 0.004 respectively, but the effect was less evident, as no linear trend was observed. Our results suggest that choline and betaine intake, especially higher betaine intake, may be protective against lung cancer through mitigating the adverse effect of smoking.

Background Non-uniform reporting of relevant relationships and metrics hampers critical appraisal of the clinical utility of C-reactive protein (CRP) measurement for prediction of later coronary events.

Methods We evaluated the predictive performance of CRP in the Northwick Park Heart Study (NPHS-II) and the Edinburgh Artery Study (EAS) comparing discrimination by area under the ROC curve (AUC), calibration and reclassification. We set the findings in the context of a systematic review of published studies comparing different available and imputed measures of prediction. Risk estimates per-quantile of CRP were pooled using a random effects model to infer the shape of the CRP-coronary event relationship.

Results NPHS-II and EAS (3441 individuals, 309 coronary events): CRP alone provided modest discrimination for coronary heart disease (AUC 0.61 and 0.62 in NPHS-II and EAS, respectively) and only modest improvement in the discrimination of a Framingham-based risk score (FRS) (increment in AUC 0.04 and –0.01, respectively). Risk models based on FRS alone and FRS + CRP were both well calibrated and the net reclassification improvement (NRI) was 8.5% in NPHS-II and 8.8% in EAS with four risk categories, falling to 4.9% and 3.0% for 10-year coronary disease risk threshold of 15%. Systematic review (31 prospective studies 84 063 individuals, 11 252 coronary events): pooled inferred values for the AUC for CRP alone were 0.59 (0.57, 0.61), 0.59 (0.57, 0.61) and 0.57 (0.54, 0.61) for studies of <5, 5–10 and >10 years follow up, respectively. Evidence from 13 studies (7201 cases) indicated that CRP did not consistently improve performance of the Framingham risk score when assessed by discrimination, with AUC increments in the range 0–0.15. Evidence from six studies (2430 cases) showed that CRP provided statistically significant but quantitatively small improvement in calibration of models based on established risk factors in some but not all studies. The wide overlap of CRP values among people who later suffered events and those who did not appeared to be explained by the consistently log-normal distribution of CRP and a graded continuous increment in coronary risk across the whole range of values without a threshold, such that a large proportion of events occurred among the many individuals with near average levels of CRP.

Conclusions CRP does not perform better than the Framingham risk equation for discrimination. The improvement in risk stratification or reclassification from addition of CRP to models based on established risk factors is small and inconsistent. Guidance on the clinical use of CRP measurement in the prediction of coronary events may require updating in light of this large comparative analysis.

Background

Identifying people at higher risk of having squamous dysplasia, the precursor lesion for esophageal squamous cell carcinoma (ESCC), would allow targeted endoscopic screening.

Methods

We used multivariate logistic regression models to predict ESCC and dysplasia as outcomes. The ESCC model was based on data from the Golestan Case-Control Study (total n=871; cases=300), and the dysplasia model was based on data from a cohort of subjects from a GI clinic in Northeast Iran (total n=724; cases=26). In each of these analyses, we fit a model including all risk factors known in this region to be associated with ESCC. Individual risks were calculated using the linear combination of estimated regression coefficients and individual-specific values for covariates. We used cross-validation to determine the area under the curve (AUC) and to find the optimal cut points for each of the models.

Results

The model had an area under the curve of 0.77 (95% CI: 0.74–0.80) to predict ESCC with 74% sensitivity and 70.4% specificity for the optimum cut point. The area under the curve was 0.71 (95% CI: 0.64–0.79) for dysplasia diagnosis, and the classification table optimized at 61.5% sensitivity and 69.5% specificity. In this population, the positive and negative predictive values for diagnosis of dysplasia were 6.8% and 97.8%, respectively.

Conclusion

Our models were able to discriminate between ESCC cases and controls in about 77%, and between individuals with and without squamous dysplasia in about 70% of the cases. Using risk factors to predict individual risk of ESCC or squamous dysplasia still has limited application in clinical practice, but such models may be suitable for selecting high risk individuals in research studies, or increasing the pretest probability for other screening strategies.

Background

Sera from lung cancer patients contain autoantibodies that react with tumor associated antigens (TAAs) that reflect genetic over-expression, mutation, or other anomalies of cell cycle, growth, signaling, and metabolism pathways.

Methods

We performed immunoassays to detect autoantibodies to ten tumor associated antigens (TAAs) selected on the basis of previous studies showing that they had preferential specificity for certain cancers. Sera examined were from lung cancer patients (22); smokers with ground-glass opacities (GGOs) (46), benign solid nodules (55), or normal CTs (35); and normal non-smokers (36). Logistic regression models based on the antibody biomarker levels among the high risk and lung cancer groups were developed to identify the combinations of biomarkers that predict lung cancer in these cohorts.

Results

Statistically significant differences in the distributions of each of the biomarkers were identified among all five groups. Using Receiver Operating Characteristic (ROC) curves based on age, c-myc, Cyclin A, Cyclin B1, Cyclin D1, CDK2, and survivin, we obtained a sensitivity = 81% and specificity = 97% for the classification of cancer vs smokers(no nodules, solid nodules, or GGO) and correctly predicted 31/36 healthy controls as noncancer.

Conclusion

A pattern of autoantibody reactivity to TAAs may distinguish patients with lung cancer versus smokers with normal CTs, stable solid nodules, ground glass opacities, or normal healthy never smokers.

Lung cancer is the leading cause of cancer death worldwide and nearly 90% of cases are attributable to smoking. Quitting smoking and early diagnosis of lung cancer, through computed tomographic screening, are the only ways to reduce mortality from lung cancer. Recent epidemiological studies show that risk prediction for lung cancer is optimized by using multivariate risk models that include age, smoking exposure, history of chronic obstructive pulmonary disease (COPD), family history of lung cancer, and body mass index. It has also been shown that COPD predates lung cancer in 65–70% of cases, conferring a four- to sixfold greater risk of lung cancer compared to smokers with normal lung function. Genome-wide association studies of smokers have identified a number of genetic variants associated with COPD or lung cancer. In a case–control study, where smokers with normal lungs were compared to smokers who had spirometry-defined COPD or histology confirmed lung cancer, several of these variants were shown to overlap, conferring the same susceptibility or protective effects on both COPD and lung cancer (independent of COPD status). In this perspective article, we show how combining clinical data with genetic variants can help identify heavy smokers at the greatest risk of lung cancer. Using this approach, we found that gene-based risk testing helped engage smokers in risk mitigating activities like quitting smoking and undertaking lung cancer screening. We suggest that such an approach could facilitate the targeted selection of smokers for cost-effective life-saving interventions.

Occupational medicine physicians are frequently asked to establish cancer causation in patients with both workplace and non-workplace exposures. This is especially difficult in cases involving beryllium for which the data on human carcinogenicity are limited and controversial. In this report we present the case of a 73-year-old former technician at a government research facility who was recently diagnosed with lung cancer. The patient is a former smoker who has worked with both beryllium and asbestos. He was referred to the University of California, San Francisco, Occupational and Environmental Medicine Clinic at San Francisco General Hospital for an evaluation of whether past workplace exposures may have contributed to his current disease. The goal of this paper is to provide an example of the use of data-based risk estimates to determine causation in patients with multiple exposures. To do this, we review the current knowledge of lung cancer risks in former smokers and asbestos workers, and evaluate the controversies surrounding the epidemiologic data linking beryllium and cancer. Based on this information, we estimated that the patient's risk of lung cancer from asbestos was less than his risk from tobacco smoke, whereas his risk from beryllium was approximately equal to his risk from smoking. Based on these estimates, the patient's workplace was considered a probable contributing factor to his development of lung cancer.