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