Prognostic factors in stage III non-small-cell lung cancer.

PURPOSE: The aim of this study is to present an analysis investigating the association of patient characteristics with overall survival (OS) in individuals with stage III non-small-cell lung cancer (NSCLC). PATIENTS AND METHODS: Ten prognostic factors were analyzed in 203 patients with NSCLC who were enrolled in a phase III trial conducted by the Hoosier Oncology Group and US Oncology between 2002 and 2006. Eligible patients had untreated stage III NSCLC, forced expiratory volume in one second (FEV(1)) > or = 1 liter, baseline performance status of 0/1, and weight loss < 5% in 3 months preceding the trial. Univariate analysis, Cox proportional hazards regression, and parametric accelerated failure time models were performed to identify the factors that affected survival duration. Variables analyzed included age (< 70 years vs. > or = 70 years), sex, ethnicity, body mass index, performance status (0 vs. 1), FEV(1) (> 2 L vs. 1-2 L), smoking status (current vs. never/former), hemoglobin (Hb) level, use of positron emission tomography scan in staging, and stage (IIIA vs. IIIB). RESULTS: Median follow-up was 25.6 months and the median OS was 21.2 months. The univariate analysis showed that Hb levels > or = 12 were associated with an improved survival (P = .033). The multivariable parametric accelerated failure time model demonstrated the association of FEV(1) > 2 L (P = .014), and higher pretreatment Hb values (P = .007) as independent prognostic factors for OS. Similarly in the Cox regression, survival was influenced by Hb and FEV(1) > 2 L. CONCLUSION: This analysis suggests that FEV(1) > 2 L and higher pretreatment Hb values are associated with improved OS in patients with stage III NSCLC. These factors can be useful in predicting for more favorable outcomes in patients with stage III NSCLC and provide additional information when designing future studies.

Comparison of computational algorithms for the classification of liver cancer using SELDI mass spectrometry: a case study.

INTRODUCTION: As an alternative to DNA microarrays, mass spectrometry based analysis of proteomic patterns has shown great potential in cancer diagnosis. The ultimate application of this technique in clinical settings relies on the advancement of the technology itself and the maturity of the computational tools used to analyze the data. A number of computational algorithms constructed on different principles are available for the classification of disease status based on proteomic patterns. Nevertheless, few studies have addressed the difference in the performance of these approaches. In this report, we describe a comparative case study on the classification accuracy of hepatocellular carcinoma based on the serum proteomic pattern generated from a Surface Enhanced Laser Desorption/Ionization (SELDI) mass spectrometer. METHODS: Nine supervised classification algorithms are implemented in R software and compared for the classification accuracy. RESULTS: We found that the support vector machine with radial function is preferable as a tool for classification of hepatocellular carcinoma using features in SELDI mass spectra. Among the rest of the methods, random forest and prediction analysis of microarrays have better performance. A permutation-based technique reveals that the support vector machine with a radial function seems intrinsically superior in learning from the training data since it has a lower prediction error than others when there is essentially no differential signal. On the other hand, the performance of the random forest and prediction analysis of microarrays rely on their capability of capturing the signals with substantial differentiation between groups. CONCLUSIONS: Our finding is similar to a previous study, where classification methods based on the Matrix Assisted Laser Desorption/Ionization (MALDI) mass spectrometry are compared for the prediction accuracy of ovarian cancer. The support vector machine, random forest and prediction analysis of microarrays provide better prediction accuracy for hepatocellular carcinoma using SELDI proteomic data than six other approaches.

A longitudinal study of genital human papillomavirus infection in a cohort of closely followed adolescent women.

BACKGROUND: We performed a study to better characterize the natural history of genital human papillomavirus (HPV) infection in a cohort of closely followed adolescent women. METHODS: A cohort of 60 adolescent women was followed over a 2.2-year period, on average. A median of 41.5 self-collected vaginal and clinician-obtained cervical swabs were obtained from each subject. RESULTS: HPV was detected in 45.3% of all adequate specimens, by use of a polymerase chain reaction/reverse blot strip assay. Oncogenic--or high-risk (HR)--HPV types were detected in 38.6% of specimens, and nononcogenic--or low-risk (LR)--types were detected in 19.6% of specimens. During the entire study period, 49 of 60 subjects tested positive for HPV (cumulative prevalence, 81.7%). The most frequently detected HR types were HPV types 52, 16, and 59. Infections with multiple HPV types were common. The median duration of persistence of a specific HPV type was 168 days, and HR types were more persistent than LR types. Abnormal cervical cytological results occurred in 37% of the adolescent women and were significantly associated with HR HPV infection. CONCLUSIONS: The cumulative prevalence of HPV infection in sexually active adolescent women is extremely high, involves numerous HPV types, and frequently results in cervical dysplasia.