Modeling intracranial second tumor risk and estimates of clinical toxicity with various radiation therapy techniques for patients with pituitary adenoma.

This study was designed to estimate the risk of radiation-associated tumors and clinical toxicity in the brain following fractionated radiation treatment of pituitary adenoma. A standard case of a patient with a pituitary adenoma was planned using 8 different dosimetric techniques. Total dose was 50.4 Gy (GyE) at daily fractionation of 1.8 Gy (GyE). All methods utilized the same CT simulation scan with designated target and normal tissue volumes. The excess risk of radiation-associated second tumors in the brain was calculated using the corresponding dose-volume histograms for the whole brain and based on the data published by the United Nation Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) and a risk model proposed by Schneider. The excess number of second tumor cases per 10,000 patients per year following radiation is 9.8 for 2-field photons, 18.4 with 3-field photons, 20.4 with photon intensity modulated radiation therapy (IMRT), and 25 with photon stereotactic radiotherapy (SRT). Proton radiation resulted in the following excess second tumor risks: 2-field 5 5.1, 3-field 5 12, 4-field 5 15, 5-field 5 16. Temporal lobe toxicity was highest for the 2-field photon plan. Proton radiation therapy achieves the best therapeutic ratio when evaluating plans for the treatment of pituitary adenoma. Temporal lobe toxicity can be reduced through the use of multiple fields but is achieved at the expense of exposing a larger volume of normal brain to radiation. Limiting the irradiated volume of normal brain by reducing the number of treatment fields is desirable to minimize excess risk of radiation-associated second tumors.

Rescoring the NIH chronic prostatitis symptom index: nothing new.

The National Institutes of Health-chronic prostatitis symptom index (NIH-CPSI) is a commonly used 13-item questionnaire for the assessment of symptom severity in men with chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS). For each item, score ranges are 0-1 (6 items), 0-3 (2 items), 0-5 (3 items), 0-6 (1 item) and 0-10 (1 item). This scoring system is straightforward, but items with wider score ranges are de facto weighted more, which could adversely affect the performance characteristics of the questionnaire. We rescored the NIH-CPSI so that equal weights were assigned to each item, and compared the performance of the standard and rescored questionnaires using the original validation dataset. Both the original and revised versions of the scoring algorithm discriminated similarly among groups of men with CP (n=151), benign prostatic hyperplasia (n=149) and controls (n=134). The internal consistency of the questionnaire was slightly better with the revised scoring, but values with the standard scoring were sufficiently high (Cronbach's >or=0.80). We conclude that although the rescored NIH-CPSI provides better face validity than the standard scoring algorithm, it requires additional calculation efforts and yields only marginal improvements in performance.

A Bayesian method for finding regulatory segments in DNA.

A goal of the human genome project is to determine the entire sequence of DNA (3 x 10(9) base pairs) found in chromosomes. The massive amounts of data produced by this project require interpretation. A Bayesian model is developed for locating regulatory regions in a DNA sequence. Regulatory regions are areas of DNA to which specific proteins bind and control whether or not a gene is transcribed to produce templates for protein synthesis. Each human cell contains the same DNA sequence. Thus the particular function of different cells is determined by the genes that are transcribed in that cell. A Hidden Markov chain is used to model whether a small interval of the DNA is in a regulatory region or not. This can be regarded as a changepoint problem where the changepoints are the start of a regulatory or nonregulatory region. The data consists of protein-binding elements, which are short subsequences, or "words," in the DNA sequence. Although these words can occur anywhere in the sequence, a larger number are expected in regulatory regions. Therefore, regulatory regions are detected by locating clusters of words. For a particular DNA sequence, the model automatically selects those words that best predict regions of interest. Markov chain Monte Carlo methods are used to explore the posterior distribution of the Hidden Markov chain. The model is tested by means of simulations, and applied to several DNA sequences.

A statistical model for locating regulatory regions in genomic DNA.

In addition to genes, chromosomal DNA contains sequences that serve as signals for turning on and off gene expression. These signals are thought to be distributed as clusters in the regulatory regions of genes. We develop a Bayesian model that views locating regulatory regions in genomic DNA as a change-point problem, with the beginning of regulatory and non-regulatory regions corresponding to the change points. The model is based on a hidden Markov chain. The data consist of nucleotide positions of protein-binding elements in a genomic DNA sequence. These positions are identified using a reference catalogue containing elements that interact with transcription factors implicated in controlling the expression of protein-encoding genes. Among the protein-binding elements in a genomic DNA sequence, the statistical model automatically selects those that tend to predict regulatory regions. We test the model using viral sequences that include known regulatory regions and provide the results obtained for human genomic DNA corresponding to the beta globin locus on chromosome 11.