Ten-year biochemical disease control for patients with prostate cancer treated with cryosurgery as primary therapy.

OBJECTIVES: Prostate cryosurgery has been increasingly used for the management of localized prostate cancer since its introduction in a minimally invasive form in the early 1990s. We performed a retrospective study of the largest and most mature patient group treated with cryosurgery reported thus far. METHODS: We retrospectively analyzed the data from 370 patients treated consecutively from 1991 to 1996 with a focus on the determination of biochemical disease-free survival for a group of patients with T1 to T3 prostate cancer who had undergone prostate cryosurgery as primary monotherapy. Only patients with no previous radiotherapy, hormonal therapy, or surgery were included. RESULTS: The median follow-up was 12.55 years. Using a nadir plus 2 ng/dL definition, Kaplan-Meier analysis demonstrated a biochemical disease-free survival rate at 10 years of 80.56%, 74.16%, and 45.54% for low, moderate, and high-risk groups, respectively. The 10-year negative biopsy rate was 76.96%. CONCLUSIONS: The results for this pilot group of patients who underwent percutaneous prostate cryosurgery monotherapy demonstrated biochemical disease-free survival rates that overlap with those of similar groups of patients treated under similar circumstances using other types of nonextirpative monotherapy.

Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes.

BACKGROUND: Gene expression data frequently contain missing values, however, most down-stream analyses for microarray experiments require complete data. In the literature many methods have been proposed to estimate missing values via information of the correlation patterns within the gene expression matrix. Each method has its own advantages, but the specific conditions for which each method is preferred remains largely unclear. In this report we describe an extensive evaluation of eight current imputation methods on multiple types of microarray experiments, including time series, multiple exposures, and multiple exposures x time series data. We then introduce two complementary selection schemes for determining the most appropriate imputation method for any given data set. RESULTS: We found that the optimal imputation algorithms (LSA, LLS, and BPCA) are all highly competitive with each other, and that no method is uniformly superior in all the data sets we examined. The success of each method can also depend on the underlying "complexity" of the expression data, where we take complexity to indicate the difficulty in mapping the gene expression matrix to a lower-dimensional subspace. We developed an entropy measure to quantify the complexity of expression matrixes and found that, by incorporating this information, the entropy-based selection (EBS) scheme is useful for selecting an appropriate imputation algorithm. We further propose a simulation-based self-training selection (STS) scheme. This technique has been used previously for microarray data imputation, but for different purposes. The scheme selects the optimal or near-optimal method with high accuracy but at an increased computational cost. CONCLUSION: Our findings provide insight into the problem of which imputation method is optimal for a given data set. Three top-performing methods (LSA, LLS and BPCA) are competitive with each other. Global-based imputation methods (PLS, SVD, BPCA) performed better on mcroarray data with lower complexity, while neighbour-based methods (KNN, OLS, LSA, LLS) performed better in data with higher complexity. We also found that the EBS and STS schemes serve as complementary and effective tools for selecting the optimal imputation algorithm.