Resumen de Statistical modeling of bladder motion and deformation in prostate cancer radiotherapy
Prostate cancer is the most common cancer amongst the male population in most developed countries. It is the most common cancer amongst the male population in France (73.609 cases in 2014) and in Colombia (9564 cases in 2014). It is also the third most common cause of cancer deaths in males in both countries (9.3% and 7.1% in France and in Colombia in 2014, respectively). One of the standard treatment methods is external radiotherapy, which involves delivering ionizing radiation to a clinical target, namely the prostate and seminal vesicles. Due to the uncertain location of organs during treatment, which involves around forty (40) radiation fractions delivering a total dose ranging from 70 to 80Gy, safety margins are defined around the tumor target upon treatment planning. The radiation units are expressed in Grays, abbreviated as Gy, which represents 1 Jule/Kg. This leads to portions of healthy organs neighboring the prostate or organs at risk – the bladder and rectum – to be included in the target volume, potentially resulting in adverse events affecting patients' urinary (hematuria and cystitis, among others) or rectal (rectal bleeding, fecal incontinence, etc.) functions. Several studies have shown that increasing dose delivery to the prostate leads to improved local cancer control, up to approximately 80Gy. However, such dose increases are limited by their associated risks of treatment-related toxicity involving the organs at risk. The bladder is notorious for presenting the largest inter-fraction shape variations during treatment, caused by continuous changes in volume. These variations in shape introduce geometric uncertainties that render assessment of the actual dose delivered to the bladder during treatment difficult, thereby leading to dose uncertainties that limit the possibility of modeling dose-volume response for late genitourinary (GU) toxicity. The Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) project has stated that a similar dose-response to that of late gastrointestinal (GI) toxicity is far from being established. The dosimetric variables obtained from the planning CT prove to be very poor surrogates for the real delivered dose. As a result, it appears crucial to quantify uncertainties produced by inter-fraction bladder variations in order to determine dosimetric factors that affect late GU complications. The aim of this thesis was thus to characterize and predict uncertainties produced by geometric variations of the bladder between fractions, using solely the planning CT as input information. In clinical practice, a single CT scan is only available for a typical patient during the treatment planning while on-treatment CTs/CBCTs are seldom available. In this thesis, we thereby used a population approach to obtain enough data to learn the most important directions of bladder motion and deformation using principal components analysis (PCA). As in groundwork, these directions were then used to develop population-based models in order to predict and quantify geometrical uncertainties of the bladder. However, we use a longitudinal analysis in order to properly characterize both patient-specific variance and modes from the population. We proposed to use mixed-effects (ME) models and hierarchical PCA to separate intra and inter-patient variability to control confounding cohort effects. Other than using PCA, bladder shapes were represented by using spherical harmonics (SPHARM) which additionally enables data compression without losing information. Subsequently, we presented PCA models as a tool to quantify dose uncertainties produced by bladder motion and deformation between fractions. We then estimated mean and variance of the dose delivered to the bladder using PCA-based models via Monte Carlo simulation and dose integration; and subsequently, we compared the estimated accumulated doses with the accumulated dose derived from non-rigid registration and patient's available images. We also calculated average voxel doses, local dose variability and dose-volume histogram uncertainties.
