Intermittent Hormone Therapy Models Analysis and Bayesian Model Comparison for Prostate Cancer.

The prostate is an exocrine gland of the male reproductive system dependent on androgens (testosterone and dihydrotestosterone) for development and maintenance. First-line therapy for prostate cancer includes androgen deprivation therapy (ADT), depriving both the normal and malignant prostate cells of androgens required for proliferation and survival. A significant problem with continuous ADT at the maximum tolerable dose is the insurgence of cancer cell resistance. In recent years, intermittent ADT has been proposed as an alternative to continuous ADT, limiting toxicities and delaying time-to-progression. Several mathematical models with different biological resistance mechanisms have been considered to simulate intermittent ADT response dynamics. We present a comparison between 13 of these intermittent dynamical models and assess their ability to describe prostate-specific antigen (PSA) dynamics. The models are calibrated to longitudinal PSA data from the Canadian Prospective Phase II Trial of intermittent ADT for locally advanced prostate cancer. We perform Bayesian inference and model analysis over the models' space of parameters on- and off-treatment to determine each model's strength and weakness in describing the patient-specific PSA dynamics. Additionally, we carry out a classical Bayesian model comparison on the models' evidence to determine the models with the highest likelihood to simulate the clinically observed dynamics. Our analysis identifies several models with critical abilities to disentangle between relapsing and not relapsing patients, together with parameter intervals where the critical points' basin of attraction might be exploited for clinical purposes. Finally, within the Bayesian model comparison framework, we identify the most compelling models in the description of the clinical data.

An Adaptive Thresholding Method for BTV Estimation Incorporating PET Reconstruction Parameters: A Multicenter Study of the Robustness and the Reliability.

OBJECTIVE: The aim of this work was to assess robustness and reliability of an adaptive thresholding algorithm for the biological target volume estimation incorporating reconstruction parameters. METHOD: In a multicenter study, a phantom with spheres of different diameters (6.5-57.4 mm) was filled with (18)F-FDG at different target-to-background ratios (TBR: 2.5-70) and scanned for different acquisition periods (2-5 min). Image reconstruction algorithms were used varying number of iterations and postreconstruction transaxial smoothing. Optimal thresholds (TS) for volume estimation were determined as percentage of the maximum intensity in the cross section area of the spheres. Multiple regression techniques were used to identify relevant predictors of TS. RESULTS: The goodness of the model fit was high (R(2): 0.74-0.92). TBR was the most significant predictor of TS. For all scanners, except the Gemini scanners, FWHM was an independent predictor of TS. Significant differences were observed between scanners of different models, but not between different scanners of the same model. The shrinkage on cross validation was small and indicative of excellent reliability of model estimation. CONCLUSIONS: Incorporation of postreconstruction filtering FWHM in an adaptive thresholding algorithm for the BTV estimation allows obtaining a robust and reliable method to be applied to a variety of different scanners, without scanner-specific individual calibration.