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Purpose: The recently released Ethos therapy system (Varian Medical Systems) allows for online CBCT-guided adaptive radiation therapy (RT). The clinical introduction of multiple systems requires machine characterization and machine variation quantification to allow patient interchangeability between systems. Despite several clinical introductions, limited vendor-independent information on machine performance is available. Our aim was to determine the relevant dosimetric and mechanical characteristics of individual machines and to quantify machine variations., Methods: Six Ethos treatment machines, equipped with a 6-MV FFF beam including dual-layer MLC and kV-CBCT system, were recently introduced clinically after extensive machine characterization and pre-configured beam model verification. Point doses and profiles were measured and compared to vendor-provided reference data and dose calculations. Also, dose calculations were verified based on point measurements for non-standard fields and dose distributions for optimized treatment plans. Agreements between dose profiles (dose distributions) were quantified using 1D (3D) γ-analysis. Additionally, we quantified leaf transmission, dosimetric leaf gap (DLG) and couch attenuation, determined isocenter accuracy and kV-MV isocenter coincidence and verified the kV-CBCT system. Machine variations were quantified for all dosimetric and mechanical characteristics., Results: For all machines, distinct agreements were found between measurements and vendor-provided reference data as well as measurements and dose calculations. Mean γ 1%/1mm values for all profiles were below 0.30. All profiles, point measurements and dose distributions matched well among the six machines. Minimal machine variations were found in terms of DLG (0.05 mm), leaf transmission (0.001%), isocenter accuracy (0.08 mm), kV-MV isocenter coincidence (0.15 mm), couch attenuation (0.69%), and CBCT imaging dose (0.29 mGy)., Conclusions: This study demonstrates excellent agreement between individual Ethos therapy systems and vendor-provided reference data as well as a pre-configured beam model. Furthermore, our results show good consistency among all machines and provide valuable insights on relevant machine characteristics. The systematically obtained results provide benchmark data for future clinical introduction of Ethos therapy systems., (© 2023 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.)

Background Image-guided adaptive proton therapy (IGAPT) can potentially be applied to take into account interfraction motion while limiting organ at risk (OAR) dose in cervical cancer radiation therapy (RT). In this study, the potential dosimetric advantages of IGAPT compared with photon-based image-guided adaptive RT (IGART) were investigated. Material and methods For 13 cervical cancer patients, full and empty bladder planning computed tomography (CT) images and weekly CTs were acquired. Based on both primary clinical target volumes (pCTVs) [i.e. gross tumor volume (GTV), cervix, corpus-uterus and upper part of the vagina] on planning CTs, the pretreatment observed full range primary internal target volume (pITV) was interpolated to derive pITV subranges. Given corresponding ITVs (i.e. pITVs including lymph nodes), patient-specific photon and proton plan libraries were generated. Using all weekly CTs, IGART and IGAPT treatments were simulated by selecting library plans and recalculating the dose. For each recalculated IGART and IGAPT fraction, CTV (i.e. pCTV including lymph nodes) coverage was assessed and differences in fractionated substitutes of dose-volume histogram (DVH) parameters (V15Gy, V30Gy, V45Gy, Dmean, D2cc) for bladder, bowel and rectum were tested for significance (Wilcoxon signed-rank test). Also, differences in toxicity-related DVH parameters (rectum V30Gy, bowel V45Gy) were approximated based on accumulated dose distributions. Results In 92% (96%) of all recalculated IGAPT (IGART) fractions adequate CTV coverage (V95% >98%) was obtained. All dose parameters for bladder, bowel and rectum, except the fractionated substitute for rectum V45Gy, were improved using IGAPT. Also, IGAPT reduced the mean dose to bowel, bladder and rectum significantly (p < 0.01). In addition, an average decrease of rectum V30Gy and bowel V45Gy indicated reductions in toxicity probabilities when using IGAPT. Conclusion This study demonstrates the feasibility of IGAPT in cervical cancer using a plan-library based plan-of-the-day approach. Compared to photon-based IGART, IGAPT maintains target coverage while significant dose reductions for the bladder, bowel and rectum can be achieved.

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.

Purpose: To assess the reliability of magnetic resonance imaging (MRI) for evaluation of craniocaudal tumour extension by comparing the craniocaudal tumour extension on the pre-operative MRI and post-operative hysterectomy specimen in patients with early stage uterine cervical cancer., Materials and Methods: After approval of the institutional review board was acquired, pre-operative MRI and hysterectomy specimen of 21 women with early stage cervical cancer were re-evaluated. The craniocaudal extension on MRI was measured separately by two experienced radiologists and compared with corresponding measurements from the hysterectomy specimen, which were re-evaluated by an experienced pathologist., Results: Median craniocaudal extension of uterine cervical cancer on MRI was slightly smaller compared to histopathology (2.1 cm vs. 2.5 cm). The median underestimation was 0.4 cm (range -0.6 cm to 2.2 cm, mean 0.4 cm, standard deviation (SD) ±0.7 cm); Pearson's correlation was 0.83 (p < 0.001). In two patients (9%) MRI underestimated tumour craniocaudal extension by more than 1.8 cm., Conclusion: MRI represents the histopathological craniocaudal tumour extension in the majority of patients with early stage uterine cervical cancer, but with a systematic small underestimation of the real craniocaudal tumour extension.

Background: To safely optimize target volumes using magnetic resonance imaging (MRI) for uterine cervical cancer radiation therapy, MRI findings need to be validated. The aim of this study was to correlate pre-operatively acquired MRI and surgical specimen imaging for uterine cervical cancer patients using deformable image registration and quantify gross tumor volume (GTV) delineation discrepancies., Material and Methods: For 16 retrospectively selected early-stage uterine cervical cancer patients, the cervix-uterus structure, uterine cavity and the GTV were delineated on 2D pathology photos after macroscopic intersection and corresponding pre-operatively acquired T2-weighted 2D sagittal MR images. Segmentations of pathology photos and MR images were simultaneously registered using a three-step multi-image registration strategy. The registration outcome was evaluated by the Dice similarity coefficient (DSC) and the surface distance error (SDE). In addition, GTV expansions within the cervix-uterus structure needed to obtain 95% GTV coverage were determined., Results: After three-step multi-image registration, the median DSC and median SDE were 0.98 and 0.4 mm (cervix-uterus) and 0.90 and 0.4 mm (uterine cavity), respectively. The average SDE around the GTV was 0.7 mm (range, 0.1 mm - 2.6 mm). An underestimation of MRI-based GTV delineations was found when no margin was applied, indicated by a mean GTV coverage of 61%. To obtain 95% GTV coverage for 90% of the patients, a minimum 12.0 mm margin around MRI-based GTVs was needed., Conclusion: The presented three-step multi-image registration strategy was suitable and accurate to correlate MRI and pathology data for uterine cervical cancer patients. To cover the pathology-based GTV, a margin of at least 12.0 mm around GTV delineations on T2-weighted MRI is needed.

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.