Patient Factors Influencing Respiratory-Related Clinician Actions in Chronic Obstructive Pulmonary Disease Screening.

Introduction: The purpose of this study was to identify patient-related factors that may explain the increased likelihood of receiving a respiratory-related clinician action in patients identified to be at risk for chronic obstructive pulmonary disease in a U.S.-based pragmatic study of chronic obstructive pulmonary disease screening.

Effect of tiotropium on night-time awakening and daily rescue medication use in patients with COPD.

Background: Several small studies found night-time awakenings due to COPD symptoms were associated with decreased health status. In this study, night-time awakenings in patients with COPD were examined and effects of tiotropium therapy evaluated.

Methods: This study was a post hoc, exploratory, pooled analysis of twin, multicenter, double-blind, randomized, placebo-controlled, parallel-group trials.

Postoperative systems models more accurately predict risk of significant disease progression than standard risk groups and a 10-year postoperative nomogram: potential impact on the receipt of adjuvant therapy after surgery.

Objective: To compare the performance of a systems-based risk assessment tool with standard defined risk groups and the 10-year postoperative nomogram for predicting disease progression, including biochemical relapse and clinical (systemic) failure.

Patients And Methods: Clinical variables, biometric profiles and outcome results from a training cohort comprising 373 patients in a published postoperative systems-based prognostic model were obtained. Patients were stratified according to D'Amico standard risk groups, Kattan 10-year postoperative nomogram and prognostic scores from the postoperative tissue model.

A systems-based modelling approach using transurethral resection of the prostate (TURP) specimens yielded incremental prognostic significance to Gleason when predicting long-term outcome in men with localized prostate cancer.

Objective: To develop a systems-based model for predicting prostate cancer-specific survival (PCSS) using a conservatively managed cohort with clinically localized prostate cancer and long-term follow-up.

Patients And Methods: Transurethral prostate (TURP) specimens in tissue microarray format and medical records from a 758 patient cohort were obtained. Slides were stained with haematoxylin and eosin (H&E), imaged and digitally outlined for invasive tumour.

Personalized prediction of tumor response and cancer progression on prostate needle biopsy.

Purpose: To our knowledge in patients with prostate cancer there are no available tests except clinical variables to determine the likelihood of disease progression. We developed a patient specific, biology driven tool to predict outcome at diagnosis. We also investigated whether biopsy androgen receptor levels predict a durable response to therapy after secondary treatment.

Cost effectiveness of risk-prediction tools in selecting patients for immediate post-prostatectomy treatment.

Background And Objective: Ideally, tests that predict the risk of cancer recurrence should be capable of guiding treatment decisions that are both therapeutically effective and cost effective. This paper evaluates the cost effectiveness of two tools that identify patients at high risk for biochemical (prostate-specific antigen) recurrence of prostate cancer after prostatectomy, the hypothesis being that accurate classification of high-risk patients will allow more appropriate use of secondary (adjuvant/salvage) treatment and may improve on current clinical practice. These risk-prediction tools are the Kattan postoperative nomogram, which uses clinicopathologic features, and the Prostate Px test, which employs additional morphometric and immunofluorescence features of the prostate specimen to predict risk of biochemical recurrence.

A systems pathology model for predicting overall survival in patients with refractory, advanced non-small-cell lung cancer treated with gefitinib.

Purpose: To identify clinical and biometric features associated with overall survival of patients with advanced refractory non-small-cell lung cancer (NSCLC) treated with gefitinib.

Experimental Design: One hundred and nine diagnostic NSCLC samples were analysed for EGFR mutation status, EGFR immunohistochemistry, histologic morphometry and quantitative immunofluorescence of 15 markers. Support vector regression modelling using the concordance index was employed to predict overall survival.

Comparison of models to predict clinical failure after radical prostatectomy.

Background: Models are available to accurately predict biochemical disease recurrence (BCR) after radical prostatectomy (RP). Because not all patients experiencing BCR will progress to metastatic disease, it is appealing to determine postoperatively which patients are likely to manifest systemic disease.

Methods: The study cohort consisted of 881 patients undergoing RP between 1985 and 2003.

Systems pathology approach for the prediction of prostate cancer progression after radical prostatectomy.

Purpose: For patients with prostate cancer treated by radical prostatectomy, no current personalized tools predict clinical failure (CF; metastasis and/or androgen-independent disease). We developed such a tool through integration of clinicopathologic data with image analysis and quantitative immunofluorescence of prostate cancer tissue.

Patients And Methods: A prospectively designed algorithm was applied retrospectively to a cohort of 758 patients with clinically localized or locally advanced prostate cancer.

Improved prediction of prostate cancer recurrence through systems pathology.

We have developed an integrated, multidisciplinary methodology, termed systems pathology, to generate highly accurate predictive tools for complex diseases, using prostate cancer for the prototype. To predict the recurrence of prostate cancer following radical prostatectomy, defined by rising serum prostate-specific antigen (PSA), we used machine learning to develop a model based on clinicopathologic variables, histologic tumor characteristics, and cell type-specific quantification of biomarkers. The initial study was based on a cohort of 323 patients and identified that high levels of the androgen receptor, as detected by immunohistochemistry, were associated with a reduced time to PSA recurrence.