No abstract

We confirmed the safety of active surveillance as a treatment option for men with low-risk prostate cancer; however, some changes could be made to the follow-up protocol to safely increase the number of men who remain on active surveillance.

Asymptomatic men with low-risk, early-stage prostate cancer are eligible for active surveillance (AS), which offers a means to monitor the cancer while delaying treatment. However, AS operates within a unique set of circumstances that advocate monitoring, rather than immediate treatment, and men's health practices are central to coping with the inherent uncertainty of living with an untreated cancer. A qualitative study was completed to describe the range of men's self-management strategies used to overcome AS-related uncertainty. The study findings reveal two strategies. First, positioning prostate cancer as benign through stoicism and solitary discourses were common to men intent on "living a normal life." Second, men committed to "doing something extra" complemented AS protocols, and often collaborated with their wives to focus on diet as an adjunct therapy. Although most participants exhibited typical men's health practices, it is clear that tailored AS psychosocial interventions will benefit men and their families.

Summary Patients who were previously treated for prostate cancer with radiation therapy are monitored at regular intervals using a laboratory test called Prostate Specific Antigen (PSA). If the value of the PSA test starts to rise, this is an indication that the prostate cancer is more likely to recur, and the patient may wish to initiate new treatments. Such patients could be helped in making medical decisions by an accurate estimate of the probability of recurrence of the cancer in the next few years. In this paper, we describe the methodology for giving the probability of recurrence for a new patient, as implemented on a web-based calculator. The methods use a joint longitudinal survival model. The model is developed on a training dataset of 2,386 patients and tested on a dataset of 846 patients. Bayesian estimation methods are used with one Markov chain Monte Carlo (MCMC) algorithm developed for estimation of the parameters from the training dataset and a second quick MCMC developed for prediction of the risk of recurrence that uses the longitudinal PSA measures from a new patient.