Practical management of patients with non-small-cell lung cancer treated with gefitinib.

PURPOSE: The use of gefitinib, the first drug approved to inhibit the epidermal growth factor receptor tyrosine kinase, is indicated in patients with non-small-cell lung cancer with tumors progressive after chemotherapy. The unique mechanism of action of this agent leads to distinctive patterns of response and toxicity in persons with lung cancer. Many of the principles of management relevant to gefitinib are distinct from those with conventional cytotoxic drugs. To meet this need, we present practical guidelines on the use of gefitinib in patients with non-small-cell lung cancer. METHODS: This article reviews gefitinib's indications, dosing, response phenomena, and patterns of relapse in individuals with radiographic response. RESULTS: We present our recommendations for the management of rash and diarrhea caused by this agent. CONCLUSION: This information can guide practitioners and help them inform their patients about what to expect when they receive gefitinib.     

CT appearances of hydatid disease at various locations

Hydatid disease has characteristic imaging features on CT, which allow accurate preoperative diagnosis in most cases. However, when it occurs at unusual locations the diagnosis is often difficult, especially as the imaging appearance varies at different sites. In this article we have presented a pictorial review of the CT features of disease due to Echinococcus granulosus at various sites in the human body.     

Bayesian estimation of a random effects heteroscedastic probit model

Summary Bayesian analysis is given of a random effects binary probit model that allows for heteroscedasticity. Real and simulated examples illustrate the approach and show that ignoring heteroscedasticity when it exists may lead to biased estimates and poor prediction. The computation is carried out by an efficient Markov chain Monte Carlo sampling scheme that generates the parameters in blocks. We use the Bayes factor, cross‐validation of the predictive density, the deviance information criterion and Receiver Operating Characteristic (ROC) curves for model comparison.