A.J.A.J. Van de Schoot

Beam configuration selection for robust intensity-modulated proton therapy in cervical cancer using Pareto front comparison

The Pareto front reflects the optimal trade-offs between conflicting objectives and can be used to quantify the effect of different beam configurations on plan robustness and dose-volume histogram parameters. Therefore, our aim was to develop and implement a method to automatically approach the Pareto front in robust intensity-modulated proton therapy (IMPT) planning. Additionally, clinically relevant Pareto fronts based on different beam configurations will be derived and compared to enable beam configuration selection in cervical cancer proton therapy. A method to iteratively approach the Pareto front by automatically generating robustly optimized IMPT plans was developed. To verify plan quality, IMPT plans were evaluated on robustness by simulating range and position errors and recalculating the dose. For five retrospectively selected cervical cancer patients, this method was applied for IMPT plans with three different beam configurations using two, three and four beams. 3D Pareto fronts were optimized on target coverage (CTV D(99%)) and OAR doses (rectum V30Gy; bladder V40Gy). Per patient, proportions of non-approved IMPT plans were determined and differences between patient-specific Pareto fronts were quantified in terms of CTV D(99%), rectum V(30Gy) and bladder V(40Gy) to perform beam configuration selection. Per patient and beam configuration, Pareto fronts were successfully sampled based on 200 IMPT plans of which on average 29% were non-approved plans. In all patients, IMPT plans based on the 2-beam set-up were completely dominated by plans with the 3-beam and 4-beam configuration. Compared to the 3-beam set-up, the 4-beam set-up increased the median CTV D(99%) on average by 0.2 Gy and decreased the median rectum V(30Gy) and median bladder V(40Gy) on average by 3.6% and 1.3%, respectively. This study demonstrates a method to automatically derive Pareto fronts in robust IMPT planning. For all patients, the defined four-beam configuration was found optimal in terms of plan robustness, target coverage and OAR sparing.

Generic method for automatic bladder segmentation on cone beam CT using a patient-specific bladder shape model

PURPOSE The aim of this study is to develop and validate a generic method for automatic bladder segmentation on cone beam computed tomography (CBCT), independent of gender and treatment position (prone or supine), using only pretreatment imaging data. METHODS Data of 20 patients, treated for tumors in the pelvic region with the entire bladder visible on CT and CBCT, were divided into four equally sized groups based on gender and treatment position. The full and empty bladder contour, that can be acquired with pretreatment CT imaging, were used to generate a patient-specific bladder shape model. This model was used to guide the segmentation process on CBCT. To obtain the bladder segmentation, the reference bladder contour was deformed iteratively by maximizing the cross-correlation between directional grey value gradients over the reference and CBCT bladder edge. To overcome incorrect segmentations caused by CBCT image artifacts, automatic adaptations were implemented. Moreover, locally incorrect segmentations could be adapted manually. After each adapted segmentation, the bladder shape model was expanded and new shape patterns were calculated for following segmentations. All available CBCTs were used to validate the segmentation algorithm. The bladder segmentations were validated by comparison with the manual delineations and the segmentation performance was quantified using the Dice similarity coefficient (DSC), surface distance error (SDE) and SD of contour-to-contour distances. Also, bladder volumes obtained by manual delineations and segmentations were compared using a Bland-Altman error analysis. RESULTS The mean DSC, mean SDE, and mean SD of contour-to-contour distances between segmentations and manual delineations were 0.87, 0.27 cm and 0.22 cm (female, prone), 0.85, 0.28 cm and 0.22 cm (female, supine), 0.89, 0.21 cm and 0.17 cm (male, supine) and 0.88, 0.23 cm and 0.17 cm (male, prone), respectively. Manual local adaptations improved the segmentation results significantly (p < 0.01) based on DSC (6.72%) and SD of contour-to-contour distances (0.08 cm) and decreased the 95% confidence intervals of the bladder volume differences. Moreover, expanding the shape model improved the segmentation results significantly (p < 0.01) based on DSC and SD of contour-to-contour distances. CONCLUSIONS This patient-specific shape model based automatic bladder segmentation method on CBCT is accurate and generic. Our segmentation method only needs two pretreatment imaging data sets as prior knowledge, is independent of patient gender and patient treatment position and has the possibility to manually adapt the segmentation locally.

OC-0081: Robust photon versus robust proton therapy planning with a library of plans for cervical cancer

Conclusion: A comparable PTV dose coverage between the 3 plans was found for rectal cancer, with a HI advantage for the PTV1 for the MRIdian plan. Differences were described for OaRs, especially for low dose areas (V5 Body). MRIdian allowed to reach dosimetrical goals comparable to RapidArc and IMRT gold standards. The evaluation of a possible reduction in PTV margin and a proper target coverage by MRI based gating will be analyzed when the system will become operative at Gemelli ART.

SU-C-WAB-05: Automatic Bladder Segmentation On CBCT for Plan Selection During Cervical ART

PURPOSE Adaptive radiotherapy (ART) with a plan-of-the-day (PotD) approach is suitable for cervical cancer patients, due to the highly mobile cervix-uterus. PotD selection is not obvious given the poor soft-tissue contrast of CBCT and bladder volume can be used as indirect measure of cervix-uterus position. The aim of this study is to automatically segment the bladder on CBCT, in order to automate plan selection. METHODS Four cervical cancer patients, treated in prone position on a bellyboard, with planning-CT and in total 25 CBCT (Elekta) images were included. A patient-specific statistical training set was developed to guide automatic bladder segmentation. This training set was built from full and empty bladder contour interpolations and principal component analysis was applied to model deformation patterns. Bladder segmentation on CBCT was obtained by consistently deforming the planning contour using the training set while maximizing the cross-correlation between directional gradient fields on both images. The segmentations could be improved by manually adding correction points. The training set could be expanded with the segmentation. The segmentations, acquired with expanded (ETs) or non-expanded training set (NETs), were validated with manual delineations by volume comparison and contour-to-contour distance. The volume range between empty and full bladder was divided into three equally-sized subranges, representing PotDs. The segmented and manually obtained volumes were classified into subranges, representing selected PotD. RESULTS Average volume correlations between segmentation and manual delineation were 0.84 (NETs) and 0.89 (ETs) and the mean absolute contour-to-contour distance was 0.59cm (NETs) and 1.21cm (ETs) using on average 2.48[0-4] (NETs) and 2.80[0-6] (ETs) correction points per segmentation. PotD selection using manual and segmented volumes (NETs and ETs) was equal in 92%. CONCLUSION Bladder volumes of cervical cancer patients can be detected automatically on CBCT using a patient-specific training set. The accuracy is sufficient for plan selection during cervical ART.