Technical quality assurance during the TROG 03.04 RADAR prostate radiotherapy trial: Are the results reflected in observed toxicity rates?

Development of binational radiation therapy quality indicator reports for prostate cancer treatment using registry data

INTRODUCTION

Quality indicators (QIs) are metrics which seek to allow comparison of clinicians' and institutes' practice to best evidence-based practice. The Australia and New Zealand Prostate Cancer Outcomes Registry (PCOR-ANZ) is a bi-national clinical quality registry with coverage estimated to be over 60% of the men newly diagnosed with prostate cancer. We outline the production and ambition of institute-level QI reports to benchmark performance for radiation therapy in the treatment of prostate cancer.

METHODS

An expert clinician panel was assembled to create a list of candidate QIs based on a comprehensive literature review, and on modified Delphi-method and expert-consensus voting. A separate implementation group-including, clinicians, epidemiologists, data managers and data scientists-employed an evidence- and consensus- based approach to generate an effective QI report designed for automated production and regular distribution to participating institutes. Feedback from the recipient clinicians was sought to enable refinement of these reports.

RESULTS

Seven QIs, including three related to post-treatment symptoms, were deemed feasible to analyse with the currently available data. Utilising an existing report template employed for benchmarking of surgical indicators, a novel radiation therapy report was generated using registry data in a secure analytical environment. The first, beta version of these reports have been produced and confidentially distributed. It is planned to automatically generate these reports biannually and iteratively refine them based on the clinician input.

CONCLUSION

QI reports for the treatment of prostate cancer by radiation oncologists have been produced using data from Australia and New Zealand patients. These are being disseminated to institutes on a six-monthly basis allowing comparisons to de-identified peers. The reports aim to facilitate improving patient outcomes, deepen engagement with the radiation oncology community and increase the breadth of PCOR-ANZ coverage. Additional QIs will be included in future iterations of these reports as data matures.

Automatic radiotherapy delineation quality assurance on prostate MRI with deep learning in a multicentre clinical trial

Volume delineation quality assurance (QA) is particularly important in clinical trial settings where consistent protocol implementation is required, as outcomes will affect future as well current patients. Currently, where feasible, this is conducted manually, which is time consuming and resource intensive. Although previous studies mostly focused on automating delineation QA on CT, magnetic resonance imaging (MRI) is being increasingly used in radiotherapy treatment. In this work, we propose to perform automatic delineation QA on prostate MRI for both the clinical target volume (CTV) and organs-at-risk (OARs) by using delineations generated by 3D Unet variants as benchmarks for QA. These networks were trained on a small gold standard atlas set and applied on a multicentre radiotherapy clinical trial dataset to generate benchmark delineations. Then, a QA stage was designed to recommend 'pass', 'minor correction' and 'major correction' for each manual delineation in the trial set by thresholding its Dice similarity coefficient to the network generated delineation. Among all 3D Unet variants explored, the Unet with anatomical gates in an AtlasNet architecture performed the best in delineation QA, achieving an area under the receiver operating characteristics curve of 0.97, 0.92, 0.89 and 0.97 for identifying unacceptable (major correction) delineations with a sensitivity of 0.93, 0.73, 0.74 and 0.90 at a specificity of 0.93, 0.86, 0.86 and 0.95 for bladder, prostate CTV, rectum and gel spacer respectively. To the best of our knowledge, this is the first study to propose automated delineation QA for a multicentre radiotherapy clinical trial with treatment planning MRI. The methods proposed in this work can potentially improve the accuracy and consistency of CTV and OAR delineation in radiotherapy treatment planning.

Spatial descriptions of radiotherapy dose: normal tissue complication models and statistical associations

For decades, dose-volume information for segmented anatomy has provided the essential data for correlating radiotherapy dosimetry with treatment-induced complications. Dose-volume information has formed the basis for modelling those associations via normal tissue complication probability (NTCP) models and for driving treatment planning. Limitations to this approach have been identified. Many studies have emerged demonstrating that the incorporation of information describing the spatial nature of the dose distribution, and potentially its correlation with anatomy, can provide more robust associations with toxicity and seed more general NTCP models. Such approaches are culminating in the application of computationally intensive processes such as machine learning and the application of neural networks. The opportunities these approaches have for individualising treatment, predicting toxicity and expanding the solution space for radiation therapy are substantial and have clearly widespread and disruptive potential. Impediments to reaching that potential include issues associated with data collection, model generalisation and validation. This review examines the role of spatial models of complication and summarises relevant published studies. Sources of data for these studies, appropriate statistical methodology frameworks for processing spatial dose information and extracting relevant features are described. Spatial complication modelling is consolidated as a pathway to guiding future developments towards effective, complication-free radiotherapy treatment.

Association between measures of treatment quality and disease progression in prostate cancer radiotherapy: An exploratory analysis from the TROG 03.04 RADAR trial

INTRODUCTION

Quality assurance methods are incorporated into multicentre radiotherapy clinical trials for ensuring consistent application of trial protocol and quantifying treatment uncertainties. The study's purpose was to determine whether post-treatment disease progression is associated with measures of the quality of radiotherapy treatment.

METHODS

The TROG 03.04 RADAR trial tested the impact of androgen deprivation on prostate cancer patients receiving dose-escalated external beam radiation therapy. The trial incorporated a plan-review process and Level III dosimetric intercomparison at each centre, from which variables suggestive of treatment quality were collected. Kaplan-Meier statistics and Fine and Gray competing risk modelling were employed to test for associations between quality-related variables and the participant outcome local composite progression.

RESULTS

Increased 'dose-difference' at the prostatic apex and at the anterior rectal wall, between planned and measured dose, was associated with reduced progression. Participants whose treatment plans included clinical target volume (CTV) to planning target volume (PTV) margins exceeding protocol requirements also experienced reduced progression. Other quality-related variables, including total accrual from participating centres, measures of target coverage and other variations from protocol, were not significantly associated with progression.

CONCLUSIONS

This analysis has revealed the association of several treatment quality factors with disease progression. Increased dose and dose margin coverage in the prostate region can reduce disease progression. Extensive and rigorous monitoring has helped to maximise treatment quality, reducing the incidence of quality-indicator outliers, and thus reduce the chance of observing significant associations with progression rates.

Modeling Urinary Dysfunction After External Beam Radiation Therapy of the Prostate Using Bladder Dose-Surface Maps: Evidence of Spatially Variable Response of the Bladder Surface

PURPOSE

We assessed the association of the spatial distribution of dose to the bladder surface, described using dose-surface maps, with the risk of urinary dysfunction.

METHODS AND MATERIALS

The bladder dose-surface maps of 754 participants from the TROG 03.04-RADAR trial were generated from the volumetric data by virtually cutting the bladder at the sagittal slice, intersecting the bladder center-of-mass through to the bladder posterior and projecting the dose information on a 2-dimensional plane. Pixelwise dose comparisons were performed between patients with and without symptoms (dysuria, hematuria, incontinence, and an International Prostate Symptom Score increase of ≥10 [ΔIPSS10]). The results with and without permutation-based multiple-comparison adjustments are reported. The pixelwise multivariate analysis findings (peak-event model for dysuria, hematuria, and ΔIPSS10; event-count model for incontinence), with adjustments for clinical factors, are also reported.

RESULTS

The associations of the spatially specific dose measures to urinary dysfunction were dependent on the presence of specific symptoms. The doses received by the anteroinferior and, to lesser extent, posterosuperior surface of the bladder had the strongest relationship with the incidence of dysuria, hematuria, and ΔIPSS10, both with and without adjustment for clinical factors. For the doses to the posteroinferior region corresponding to the area of the trigone, the only symptom with significance was incontinence.

CONCLUSIONS

A spatially variable response of the bladder surface to the dose was found for symptoms of urinary dysfunction. Limiting the dose extending anteriorly might help reduce the risk of urinary dysfunction.