Voxel-based population analysis for correlating local dose and rectal toxicity in prostate cancer radiotherapy

Abstract

The majority of current models utilized for predicting toxicity in prostate cancer radiotherapy are based on dose-volume histograms. One of their main drawbacks is the lack of spatial accuracy, since they consider the organs as a whole volume and thus ignore the heterogeneous intra-organ radio-sensitivity. In this paper, we propose a dose-image-based framework to reveal the relationships between local dose and toxicity. In this approach, the three-dimensional (3D) planned dose distributions across a population are non-rigidly registered into a common coordinate system and compared at a voxel level, therefore enabling the identification of 3D anatomical patterns, which may be responsible for toxicity, at least to some extent. Additionally, different metrics were employed in order to assess the quality of the dose mapping. The value of this approach was demonstrated by prospectively analyzing rectal bleeding (≥Grade 1 at 2 years) according to the CTCAE v3.0 classification in a series of 105 patients receiving 80 Gy to the prostate by intensity modulated radiation therapy (IMRT). Within the patients presenting bleeding, a significant dose excess (6 Gy on average, p < 0.01) was found in a region of the anterior rectal wall. This region, close to the prostate (1 cm), represented less than 10% of the rectum. This promising voxel-wise approach allowed subregions to be defined within the organ that may be involved in toxicity and, as such, must be considered during the inverse IMRT planning step.

Prostate external beam radiotherapy combined with high-dose-rate brachytherapy: dose-volume parameters from deformably-registered plans correlate with late gastrointestinal complications

“…2, the median overlap is 80.4 % for alignment of EBRT/registered HDR rectum structure volumes. A general structure overlap of 70 % is considered to be the starting point for satisfactory structure-correspondence in the radiotherapy context [22,23]. The registrations have also previously been extensively evaluated using structure-correspondence metrics, image similarity metrics and qualitative visual inspection by authors (initials CRM, VL and CIT) [21].…”

“…When considering the distribution-adding findings in isolation it should be noted that errors in contouring and registration accuracy will confound the distributionadding parameters that have been correlated with toxicity. A median overlap of 80.4 % for the rectum volume correspondence across all patients would indicate the registrations are satisfactory as a general structure overlap of 70 % is considered to be the starting point for satisfactory structure-correspondence in the radiotherapy context [22,23]. The proximity of the HDR rectum to the HDR catheters makes parameters obtained by registration and distribution-adding sensitive to small localised variations in contouring and/or registration accuracy across the prostate/rectum interface.…”

A Voxel-Based Approach to Explore Local Dose Differences Associated With Radiation-Induced Lung Damage

“…Recently, 2-dimensional or 3-dimensional (3D) dose distribution based methods, collectively referred to as voxel-based (VB) methods, which evaluate dose–response relationships and overcome the organ-based philosophy of NTCP modeling, have been proposed as alternative approaches to predict urinary (2), gastrointestinal (3), or rectal toxicity (4) after radiation therapy for prostate cancer.…”

Early detection of cardiovascular changes after radiotherapy for breast cancer: Protocol for a European multicenter prospective cohort (MEDIRAD EARLY HEART Study) (Preprint)

“…Mathematical models have been developed in recent years with the aim of using dosimetric data to estimate a complication probability: the NTCP models (Normal Tissue Complication Probability). Research on parameters to optimize the NTCP models (physical imaging, dosimetry, clinical) are a major challenge to improve knowledge on the relationship between a received dose and toxicity to healthy tissue [37,38]. In addition, the enrichment with biological data of these models is fundamental to improve risk prediction.…”