An image-guided radiotherapy decision support framework incorporating a Bayesian network and visualization tool.

PURPOSE: To describe a Bayesian network (BN) and complementary visualization tool that aim to support decision-making during online cone-beam computed tomography (CBCT)-based image-guided radiotherapy (IGRT) for prostate cancer patients. METHODS: The BN was created to represent relationships between observed prostate, proximal seminal vesicle (PSV), bladder and rectum volume variations, an image feature alignment score (FASTV_OAR ), delivered dose, and treatment plan compliance (TPC). Variables influencing tumor volume (TV) targeting accuracy such as intrafraction motion, and contouring and couch shift errors were also represented. A score of overall TPC (FASglobal ) and factors such as image quality were used to inform the BN output node providing advice about proceeding with treatment. The BN was quantified using conditional probabilities generated from published studies, FASTV_OAR/global modeling, and a survey of IGRT decision-making practices. A new IGRT visualization tool (IGRTREV ), in the form of Mollweide projection plots, was developed to provide a global summary of residual errors after online CBCT-planning CT registration. Sensitivity and scenario analyses were undertaken to evaluate the performance of the BN and the relative influence of the network variables on TPC and the decision to proceed with treatment. The IGRTREV plots were evaluated in conjunction with the BN scenario testing, using additional test data generated from retrospective CBCT-planning CT soft-tissue registrations for 13/36 patients whose data were used in the FASTV_OAR/global modeling. RESULTS: Modeling of the TV targeting errors resulted in a very low probability of corrected distances between the CBCT and planning CT prostate or PSV volumes being within their thresholds. Strength of influence evaluation with and without the BN TV targeting error nodes indicated that rectum- and bladder-related network variables had the highest relative importance. When the TV targeting error nodes were excluded from the BN, TPC was sensitive to observed PSV and rectum variations while the decision to treat was sensitive to observed prostate and PSV variations. When root nodes were set so the PSV and rectum variations exceeded thresholds, the probability of low TPC increased to 40%. Prostate and PSV variations exceeding thresholds increased the likelihood of repositioning or repeating patient preparation to 43%. Scenario testing using the test data from 13 patients, demonstrated two cases where the BN provided increased high TPC probabilities, despite some of the prostate and PSV volume variation metrics not being within tolerance. The IGRTREV tool was effective in highlighting and quantifying where TV and OAR variations occurred, supporting the BN recommendation to reposition the patient or repeat their bladder and bowel preparation. In another case, the IGRTREV tool was also effective in highlighting where PSV volume variation significantly exceeded tolerance when the BN had indicated to proceed with treatment. CONCLUSIONS: This study has demonstrated that both the BN and IGRTREV plots are effective tools for inclusion in a decision support system for online CBCT-based IGRT for prostate cancer patients. Alternate approaches to modeling TV targeting errors need to be explored as well as extension of the BN to support offline IGRT decisions related to adaptive radiotherapy.

A feature alignment score for online cone-beam CT-based image-guided radiotherapy for prostate cancer.

PURPOSE: To develop a method for scoring online cone-beam CT (CBCT)-to-planning CT image feature alignment to inform prostate image-guided radiotherapy (IGRT) decision-making. The feasibility of incorporating volume variation metric thresholds predictive of delivering planned dose into weighted functions, was investigated. METHODS: Radiation therapists and radiation oncologists participated in workshops where they reviewed prostate CBCT-IGRT case examples and completed a paper-based survey of image feature matching practices. For 36 prostate cancer patients, one daily CBCT was retrospectively contoured then registered with their plan to simulate delivered dose if (a) no online setup corrections and (b) online image alignment and setup corrections, were performed. Survey results were used to select variables for inclusion in classification and regression tree (CART) and boosted regression trees (BRT) modeling of volume variation metric thresholds predictive of delivering planned dose to the prostate, proximal seminal vesicles (PSV), bladder, and rectum. Weighted functions incorporating the CART and BRT results were used to calculate a score of individual tumor and organ at risk image feature alignment (FASTV_OAR ). Scaled and weighted FASTV_OAR were then used to calculate a score of overall treatment compliance (FASglobal ) for a given CBCT-planning CT registration. The FASTV_OAR were assessed for sensitivity, specificity, and predictive power. FASglobal thresholds indicative of high, medium, or low overall treatment plan compliance were determined using coefficients from multiple linear regression analysis. RESULTS: Thirty-two participants completed the prostate CBCT-IGRT survey. While responses demonstrated consensus of practice for preferential ranking of planning CT and CBCT match features in the presence of deformation and rotation, variation existed in the specified thresholds for observed volume differences requiring patient repositioning or repeat bladder and bowel preparation. The CART and BRT modeling indicated that for a given registration, a Dice similarity coefficient >0.80 and >0.60 for the prostate and PSV, respectively, and a maximum Hausdorff distance <8.0 mm for both structures were predictive of delivered dose ± 5% of planned dose. A normalized volume difference <1.0 and a CBCT anterior rectum wall >1.0 mm anterior to the planning CT anterior rectum wall were predictive of delivered dose >5% of planned rectum dose. A normalized volume difference <0.88, and a CBCT bladder wall >13.5 mm inferior and >5.0 mm posterior to the planning CT bladder were predictive of delivered dose >5% of planned bladder dose. A FASTV_OAR >0 is indicative of delivery of planned dose. For calculated FASTV_OAR for the prostate, PSV, bladder, and rectum using test data, sensitivity was 0.56, 0.75, 0.89, and 1.00, respectively; specificity 0.90, 0.94, 0.59, and 1.00, respectively; positive predictive power 0.90, 0.86, 0.53, and 1.00, respectively; and negative predictive power 0.56, 0.89, 0.91, and 1.00, respectively. Thresholds for the calculated FASglobal of were low <60, medium 60-80, and high >80, with a 27% misclassification rate for the test data. CONCLUSIONS: A FASglobal incorporating nested FASTV_OAR and volume variation metric thresholds predictive of treatment plan compliance was developed, offering an alternative to pretreatment dose calculations to assess treatment delivery accuracy.

Cloud based toolbox for image analysis, processing and reconstruction tasks.

This chapter describes a novel way of carrying out image analysis, reconstruction and processing tasks using cloud based service provided on the Australian National eResearch Collaboration Tools and Resources (NeCTAR) infrastructure. The toolbox allows users free access to a wide range of useful blocks of functionalities (imaging functions) that can be connected together in workflows allowing creation of even more complex algorithms that can be re-run on different data sets, shared with others or additionally adjusted. The functions given are in the area of cellular imaging, advanced X-ray image analysis, computed tomography and 3D medical imaging and visualisation. The service is currently available on the website www.cloudimaging.net.au .