A new algorithm for finding survival coefficients employed in reliability equations

Product reliabilities are predicted from past failure rates and reasonable estimate of future failure rates. Algorithm is used to calculate probability that product will function correctly. Algorithm sums the probabilities of each survival pattern and number of permutations for that pattern, over all possible ways in which product can survive

Small median tumor diameter at cure threshold (<20 mm) among aggressive non-small cell lung cancers in male smokers predicts both chest X-ray and CT screening outcomes in a novel simulation framework

The effectiveness of population-wide lung cancer screening strategies depends on the underlying natural course of lung cancer. We evaluate the expected stage distribution in the Mayo CT screening study under an existing simulation model of non-small cell lung cancer (NSCLC) progression calibrated to the Mayo lung project (MLP). Within a likelihood framework, we evaluate whether the probability of 5-year NSCLC survival conditional on tumor diameter at detection depends significantly on screening detection modality, namely chest X-ray and computed tomography. We describe a novel simulation framework in which tumor progression depends on cellular proliferation and mutation within a stem cell compartment of the tumor. We fit this model to randomized trial data from the MLP and produce estimates of the median radiologic size at the cure threshold. We examine the goodness of model fit with respect to radiologic tumor size and 5-year NSCLC survival among incident cancers in both the MLP and Mayo CT studies. An existing model of NSCLC progression under-predicts the number of advanced-stage incident NSCLCs among males in the Mayo CT study (p-value = 0.004). The probability of 5-year NSCLC survival conditional on tumor diameter depends significantly on detection modality (p-value = 0.0312). In our new model, selected solution sets having a median tumor diameter of 16.2ﾖ22.1 mm at cure threshold among aggressive NSCLCs predict both MLP and Mayo CT outcomes. We conclude that the median lung tumor diameter at cure threshold among aggressive NSCLCs in male smokers may be small (<20 mm)

Exploring the uncertainties of early detection results: model-based interpretation of mayo lung project

Background: The Mayo Lung Project (MLP), a randomized controlled clinical trial of lung cancer screening conducted between 1971 and 1986 among male smokers aged 45 or above, demonstrated an increase in lung cancer survival since the time of diagnosis, but no reduction in lung cancer mortality. Whether this result necessarily indicates a lack of mortality benefit for screening remains controversial. A number of hypotheses have been proposed to explain the observed outcome, including over-diagnosis, screening sensitivity, and population heterogeneity (initial difference in lung cancer risks between the two trial arms). This study is intended to provide model-based testing for some of these important arguments.Method: Using a micro-simulation model, the MISCAN-lung model, we explore the possible influence of screening sensitivity, systematic error, over-diagnosis and population heterogeneity.Results: Calibrating screening sensitivity, systematic error, or over-diagnosis does not noticeably improve the fit of the model, whereas calibrating population heterogeneity helps the model predict lung cancer incidence better.Conclusions: Our conclusion is that the hypothesized imperfection in screening sensitivity, systematic error, and over-diagnosis do not in themselves explain the observed trial results. Model fit improvement achieved by accounting for population heterogeneity suggests a higher risk of cancer incidence in the intervention group as compared with the control group

Adult chest radiograph reporting by radiographers: Preliminary data from an in-house audit programme

Aim To examine the adult chest radiograph (CXR) reporting performance of a reporting radiographer in clinical practice using different audit systems; single radiologist and two radiologists, with clinical review of discordant cases. Materials and methods 100 chest radiographs (CXRs) were drawn randomly from a consecutive series of 4800 CXRs which had been reported during a nine month period at a district general hospital by a radiographer after two years of training. Diagnostic outcomes were normal or abnormal, and agreement with the reporting radiographer or not. There was 50% duplication of CXRs reported between three radiologists. Concordance rates were determined for the radiographer-radiologist and inter-radiologist interpretations. Independent clinical review of discordant cases was performed to establish the final diagnosis. Results Ninety-nine cases were reviewed, with 40 cases deemed abnormal by at least one radiologist. Consensus was found with the radiographers report in 59 normal and 33 abnormal CXRs reviewed by two radiologists (96.7% and 86.8% respectively). Seven CXR reports were discrepant with clinical review: mediastinal lymphadenopathy was missed by both radiologist and radiographer; linear atelectasis was reported by two radiologists but not the radiographer. Three cases were over-interpreted and on two occasions at least one radiologist agreed with the radiographer. There was very high concordance between the radiographer and each radiologist, 96%, 96% and 92% respectively. Conclusions This study suggested that regular audit, which incorporates case note review and discrepant reporting within a multidisciplinary setting, should contribute to safe practice

Gene-Expression Signature Predicts Postoperative Recurrence in Stage I Non-Small Cell Lung Cancer Patients

About 30% stage I non-small cell lung cancer (NSCLC) patients undergoing resection will recur. Robust prognostic markers are required to better manage therapy options. The purpose of this study is to develop and validate a novel gene-expression signature that can predict tumor recurrence of stage I NSCLC patients. Cox proportional hazards regression analysis was performed to identify recurrence-related genes and a partial Cox regression model was used to generate a gene signature of recurrence in the training dataset −142 stage I lung adenocarcinomas without adjunctive therapy from the Director's Challenge Consortium. Four independent validation datasets, including GSE5843, GSE8894, and two other datasets provided by Mayo Clinic and Washington University, were used to assess the prediction accuracy by calculating the correlation between risk score estimated from gene expression and real recurrence-free survival time and AUC of time-dependent ROC analysis. Pathway-based survival analyses were also performed. 104 probesets correlated with recurrence in the training dataset. They are enriched in cell adhesion, apoptosis and regulation of cell proliferation. A 51-gene expression signature was identified to distinguish patients likely to develop tumor recurrence (Dxy = −0.83, P<1e-16) and this signature was validated in four independent datasets with AUC >85%. Multiple pathways including leukocyte transendothelial migration and cell adhesion were highly correlated with recurrence-free survival. The gene signature is highly predictive of recurrence in stage I NSCLC patients, which has important prognostic and therapeutic implications for the future management of these patients

Magnetic resonance imaging for lung cancer detection: Experience in a population of more than 10,000 healthy individuals

<p>Abstract</p> <p>Background</p> <p>Recent refinements of lung MRI techniques have reduced the examination time and improved diagnostic sensitivity and specificity. We conducted a study to assess the feasibility of MRI for the detection of primary lung cancer in asymptomatic individuals.</p> <p>Methods</p> <p>A retrospective chart review was performed on images of lung parenchyma, which were extracted from whole-body MRI examinations between October 2000 and December 2007. 11,766 consecutive healthy individuals (mean age, 50.4 years; 56.8% male) were scanned using one of two 1.5-T scanners (Sonata and Sonata Maestro, Siemens Medical Solutions, Erlangen, Germany). The standard protocol included a quick whole-lung survey with T2-weighted 2-dimensional half Fourier acquisition single shot turbo spin echo (HASTE) and 3-dimensional volumetric interpolated breath-hold examination (VIBE). Total examination time was less than 10 minutes, and scanning time was only 5 minutes. Prompt referrals and follow-ups were arranged in cases of suspicious lung nodules.</p> <p>Results</p> <p>A total of 559 individuals (4.8%) had suspicious lung nodules. A total of 49 primary lung cancers were diagnosed in 46 individuals: 41 prevalence cancers and 8 incidence cancers. The overall detection rate of primary lung cancers was 0.4%. For smokers aged 51 to 70 years, the detection rate was 1.4%. TNM stage I disease accounted for 37 (75.5%). The mean size of detected lung cancers was 1.98 cm (median, 1.5 cm; range, 0.5-8.2 cm). The most histological types were adenocarcinoma in 38 (77.6%).</p> <p>Conclusion</p> <p>Rapid zero-dose MRI can be used for lung cancer detection in a healthy population.</p

Trends in the Statistical Assessment of Reliability

Changes in technology have had and will continue to have a strong effect on changes in the area of statistical assessment of reliability data. These changes include higher levels of integration in electronics, improvements in measurement technology and the deployment of sensors and smart chips into more products, dramatically improved computing power and storage technology, and the development of new, powerful statistical methods for graphics, inference, and experimental design and reliability test planning. This paper traces some of the history of the development of statistical methods for reliability assessment and makes some predictions about the future

Application of Biomarkers in Cancer Risk Management: Evaluation from Stochastic Clonal Evolutionary and Dynamic System Optimization Points of View

Aside from primary prevention, early detection remains the most effective way to decrease mortality associated with the majority of solid cancers. Previous cancer screening models are largely based on classification of at-risk populations into three conceptually defined groups (normal, cancer without symptoms, and cancer with symptoms). Unfortunately, this approach has achieved limited successes in reducing cancer mortality. With advances in molecular biology and genomic technologies, many candidate somatic genetic and epigenetic “biomarkers” have been identified as potential predictors of cancer risk. However, none have yet been validated as robust predictors of progression to cancer or shown to reduce cancer mortality. In this Perspective, we first define the necessary and sufficient conditions for precise prediction of future cancer development and early cancer detection within a simple physical model framework. We then evaluate cancer risk prediction and early detection from a dynamic clonal evolution point of view, examining the implications of dynamic clonal evolution of biomarkers and the application of clonal evolution for cancer risk management in clinical practice. Finally, we propose a framework to guide future collaborative research between mathematical modelers and biomarker researchers to design studies to investigate and model dynamic clonal evolution. This approach will allow optimization of available resources for cancer control and intervention timing based on molecular biomarkers in predicting cancer among various risk subsets that dynamically evolve over time

Diagnosis of lung cancer in individuals with solitary pulmonary nodules by plasma microRNA biomarkers

<p>Abstract</p> <p>Background</p> <p>Making a definitive preoperative diagnosis of solitary pulmonary nodules (SPNs) found by CT has been a clinical challenge. We previously demonstrated that microRNAs (miRNAs) could be used as biomarkers for lung cancer diagnosis. Here we investigate whether plasma microRNAs are useful in identifying lung cancer among individuals with CT-detected SPNs.</p> <p>Methods</p> <p>By using quantitative reverse transcriptase PCR analysis, we first determine plasma expressions of five miRNAs in a training set of 32 patients with malignant SPNs, 33 subjects with benign SPNs, and 29 healthy smokers to define a panel of miRNAs that has high diagnostic efficiency for lung cancer. We then validate the miRNA panel in a testing set of 76 patients with malignant SPNs and 80 patients with benign SPNs.</p> <p>Results</p> <p>In the training set, miR-21 and miR-210 display higher plasma expression levels, whereas miR-486-5p has lower expression level in patients with malignant SPNs, as compared to subjects with benign SPNs and healthy controls (all P ≤ 0.001). A logistic regression model with the best prediction was built on the basis of miR-21, miR-210, and miR-486-5p. The three miRNAs used in combination produced the area under receiver operating characteristic curve at 0.86 in distinguishing lung tumors from benign SPNs with 75.00% sensitivity and 84.95% specificity. Validation of the miRNA panel in the testing set confirms their diagnostic value that yields significant improvement over any single one.</p> <p>Conclusions</p> <p>The plasma miRNAs provide potential circulating biomarkers for noninvasively diagnosing lung cancer among individuals with SPNs, and could be further evaluated in clinical trials.</p