Margin and PTV volume reduction using a population based library of plans strategy for rectal cancer radiotherapy

Incorporating patient-specific prior clinical knowledge to improve clinical target volume auto-segmentation generalisability for online adaptive radiotherapy of rectal cancer: A multicenter validation

BACKGROUND & PURPOSE

Deep learning (DL) based auto-segmentation has shown to be beneficial for online adaptive radiotherapy (OART). However, auto-segmentation of clinical target volumes (CTV) is complex, as clinical interpretations are crucial in their definition. The resulting variation between clinicians and institutes hampers the generalizability of DL networks. In OART the CTV is delineated during treatment preparation which makes the clinician intent explicitly available during treatment. We studied whether multicenter generalisability improves when using this prior clinical knowledge, the pre-treatment delineation, as a patient-specific prior for DL models for online auto-segmentation of the mesorectal CTV.

MATERIAL & METHODS

We included intermediate risk or locally advanced rectal cancer patients from three centers. Patient-specific weight maps were created by combining the patient-specific CTV delineation on the pre-treatment scan with population-based variation of likely inter-fraction mesorectal CTV deformations. We trained two models to auto-segment the mesorectal CTV on in-house data, one with (MRI + prior) and one without (MRI-only) priors. Both models were applied to two external datasets. An external baseline model was trained without priors from scratch for one external center. Performance was evaluated on the DSC, surface Dice, 95HD and MSD.

RESULTS

For both external centers, the MRI + prior model outperformed the MRI-only model significantly on the segmentation metrics (p-values < 0.01). There was no significant difference between the external baseline model and the MRI + prior model.

CONCLUSION

Adding patient-specific weight maps makes the CTV segmentation model more robust to institutional preferences. Performance was comparable to a model trained locally from scratch. This makes this approach suitable for generalization to multiple centers.

Efficient strategy for magnetic resonance image-guided adaptive radiotherapy of rectal cancer using a library of reference plans

Background and purpose

Adaptive radiotherapy for patients with rectal cancer using a magnetic resonance-guided linear accelerator has limitations in managing bladder shape variations. Conventional couch shifts may result in missing the target while requiring a large margin. Conversely, fully adaptive strategy is time-consuming. Therefore, a more efficient strategy for online adaptive radiotherapy is required.

Materials and methods

This retrospective study included 50 fractions from 10 patients with rectal cancer undergoing preoperative radiotherapy. The proposed method involved preparing a library of reference plans (LoRP) based on diverse bladder shapes. For each fraction, a plan from the LoRP was selected based on daily bladder filling. This plan was compared with those generated by conventional couch shift and fully adaptive strategies. The clinical acceptability of the plans (i.e., per protocol, variation-acceptable, or unacceptable) was assessed.

Results

In per protocol criterion, 44 %, 6 %, and 100 % of the plans for LoRP, conventional couch shift, and fully adaptive strategies were achieved, respectively. In variation-acceptable criterion, 92 % of LoRP plans and 74 % of conventional couch shift plans were achieved. LoRP demonstrated 94 % target coverage (100 % prescription dose) in the fully adaptive strategy compared with 91 % in conventional couch shift strategy. The fully adaptive strategy had the best performance in sparing the intestine and colon. LoRP reduced the treatment session duration by more than a third (>20 min) compared with the fully adaptive strategy.

Conclusion

LoRP achieved adequate target coverage with a short treatment session duration, potentially increasing treatment efficiency and improving patient comfort.

Evaluation of Deep Learning Clinical Target Volumes Auto-Contouring for Magnetic Resonance Imaging-Guided Online Adaptive Treatment of Rectal Cancer

Purpose

Segmentation of clinical target volumes (CTV) on medical images can be time-consuming and is prone to interobserver variation (IOV). This is a problem for online adaptive radiation therapy, where CTV segmentation must be performed every treatment fraction, leading to longer treatment times and logistic challenges. Deep learning (DL)-based auto-contouring has the potential to speed up CTV contouring, but its current clinical use is limited. One reason for this is that it can be time-consuming to verify the accuracy of CTV contours produced using auto-contouring, and there is a risk of bias being introduced. To be accepted by clinicians, auto-contouring must be trustworthy. Therefore, there is a need for a comprehensive commissioning framework when introducing DL-based auto-contouring in clinical practice. We present such a framework and apply it to an in-house developed DL model for auto-contouring of the CTV in rectal cancer patients treated with MRI-guided online adaptive radiation therapy.

Methods and Materials

The framework for evaluating DL-based auto-contouring consisted of 3 steps: (1) Quantitative evaluation of the model's performance and comparison with IOV; (2) Expert observations and corrections; and (3) Evaluation of the impact on expected volumetric target coverage. These steps were performed on independent data sets. The framework was applied to an in-house trained nnU-Net model, using the data of 44 rectal cancer patients treated at our institution.

Results

The framework established that the model's performance after expert corrections was comparable to IOV, and although the model introduced a bias, this had no relevant impact on clinical practice. Additionally, we found a substantial time gain without reducing quality as determined by volumetric target coverage.

Conclusions

Our framework provides a comprehensive evaluation of the performance and clinical usability of target auto-contouring models. Based on the results, we conclude that the model is eligible for clinical use.

Dosimetric benefit of a library of plans versus single-plan strategy for pre-operative gastric cancer radiotherapy

BACKGROUND AND PURPOSE

The stomach experiences large volume and shape changes during pre-operative gastric radiotherapy. This study evaluates the dosimetric benefit for organs-at-risk (OARs) of a library of plans (LoP) compared to the traditional single-plan (SP) strategy.

MATERIALS AND METHODS

Twelve patients who received SP CBCT-guided pre-operative gastric radiotherapy (45 Gy; 25 fractions) were included. Clinical target volume (CTV) consisted of CTV_stomach (i.e., stomach + 10 mm uniform margin minus OARs) and CTV_LN (i.e., regional lymph node stations). For LoP, five stomach volumes (approximately equidistant with fixed volumes) were created using a previously developed stomach deformation model (volume = 150-750 mL). Appropriate planning target volume (PTV) margins were calculated for CTV_stomach (SP and LoP, separately) and CTV_LN. Treatment plans were automatically generated/optimized and the best-fitting library plan was manually selected for each daily CBCT. OARs (i.e., liver, kidneys, heart, spleen, spinal canal) doses were accumulated and dose-volume histogram (DVH) parameters were evaluated.

RESULTS

The non-isotropic PTV_stomach margins were significantly (p < 0.05) smaller for LoP than SP (median = 13.1 vs 19.8 mm). For each patient, the average PTV was smaller using a LoP (difference range 134-1151 mL). For all OARs except the kidneys, DVH parameters were significantly reduced using a LoP. Differences in mean dose (Dmean) for liver, heart and spleen ranged between -1.8 to 5.7 Gy. For LoP, a benefit of heart Dmean > 4 Gy and spleen Dmean > 2 Gy was found in 4 and 5 patients, respectively.

CONCLUSION

A LoP strategy for pre-operative gastric cancer reduced average PTV and reduced OAR dose compared to a SP strategy, thereby potentially reducing risks for radiation-induced toxicities.

Correcting rotational error in rectal cancer radiation therapy: Can planning target volume margins be safely reduced?

INTRODUCTION

The magnitude and impact of rotational error is unclear in rectal cancer radiation therapy. This study evaluates rotational errors in rectal cancer patients, and investigates the feasibility of planning target volume (PTV) margin reduction to decrease organs at risk (OAR) irradiation.

METHODS

In this study, 10 patients with rectal cancer were retrospectively selected. Rotational errors were assessed through image registration of daily cone beam computed tomography (CBCT) and planning CT scans. Two reference treatment plans (TPR ) with PTV margins of 5 mm and 10 mm were generated for each patient. Pre-determined rotational errors (±1°, ±3°, ±5°) were simulated to produce six manipulated treatment plans (TPM ) from each TPR . Differences in evaluated dose-volume metrics between TPR and TPM of each rotation were compared using Wilcoxon Signed-Rank Test. Clinical compliance was investigated for statistically significant dose-volume metrics.

RESULTS

Mean rotational errors in pitch, roll and yaw were -0.72 ± 1.81°, -0.04 ± 1.36° and 0.38 ± 0.96° respectively. Pitch resulted in the largest potential circumferential displacement of clinical target volume (CTV) at 1.42 ± 1.06 mm. Pre-determined rotational errors resulted in statistically significant differences in CTV, small bowel, femoral heads and iliac crests (P < 0.05). Only small bowel and iliac crests failed clinical compliance, with majority in the PTV 10 mm margin group.

CONCLUSION

Rotational errors affected clinical compliance for OAR dose but exerted minimal impact on CTV coverage even with reduced PTV margins. Both PTV margin reduction and rotational correction decreased irradiated volume of OAR. PTV margin reduction to 5 mm is feasible, and rotational corrections are recommended in rectal patients to further minimise OAR irradiation.

An online adaptive plan library approach for intensity modulated proton therapy for head and neck cancer

BACKGROUND AND PURPOSE

In intensity modulated proton therapy (IMPT), the impact of setup errors and anatomical changes is commonly mitigated by robust optimization with population-based setup robustness (SR) settings and offline replanning. In this study we propose and evaluate an alternative approach based on daily plan selection from patient-specific pre-treatment established plan libraries (PLs). Clinical implementation of the PL strategy would be rather straightforward compared to daily online re-planning.

MATERIALS AND METHODS

For 15 head-and-neck cancer patients, the planning CT was used to generate a PL with 5 plans, robustly optimized for increasing SR: 0, 1, 2, 3, 5 mm, and 3% range robustness. Repeat CTs (rCTs) and realistic setup and range uncertainty distributions were used for simulation of treatment courses for the PL approach, treatments with fixed SR (fSR₃) and a trigger-based offline adaptive schedule for 3 mm SR (fSR₃OfA). Daily plan selection in the PL approach was based only on recomputed dose to the CTV on the rCT.

RESULTS

Compared to using fSR₃ and fSR₃OfA, the risk of xerostomia grade ≥ II & III and dysphagia ≥ grade III were significantly reduced with the PL. For 6/15 patients the risk of xerostomia and/or dysphagia ≥ grade II could be reduced by > 2% by using PL. For the other patients, adherence to target coverage constraints was often improved. fSR₃OfA resulted in significantly improved coverage compared to PL for selected patients.

CONCLUSION

The proposed PL approach resulted in overall reduced NTCPs compared to fSR₃ and fSR₃OfA at limited cost in target coverage.

Effect of intrafraction adaptation on PTV margins for MRI guided online adaptive radiotherapy for rectal cancer

Purpose

To determine PTV margins for intrafraction motion in MRI-guided online adaptive radiotherapy for rectal cancer and the potential benefit of performing a 2nd adaptation prior to irradiation.

Methods

Thirty patients with rectal cancer received radiotherapy on a 1.5 T MR-Linac. On T2-weighted images for adaptation (MRI_adapt), verification prior to (MRI_ver) and after irradiation (MRI_post) of 5 treatment fractions per patient, the primary tumor GTV (GTV_prim) and mesorectum CTV (CTV_meso) were delineated. The structures on MRI_adapt were expanded to corresponding PTVs. We determined the required expansion margins such that on average over 5 fractions, 98% of CTV_meso and 95% of GTV_prim on MRI_post was covered in 90% of the patients. Furthermore, we studied the benefit of an additional adaptation, just prior to irradiation, by evaluating the coverage between the structures on MRI_ver and MRI_post. A threshold to assess the need for a secondary adaptation was determined by considering the overlap between MRI_adapt and MRI_ver.

Results

PTV margins for intrafraction motion without 2nd adaptation were 6.4 mm in the anterior direction and 4.0 mm in all other directions for CTV_meso and 5.0 mm isotropically for GTV_prim. A 2nd adaptation, applied for all fractions where the motion between MRI_adapt and MRI_ver exceeded 1 mm (36% of the fractions) would result in a reduction of the PTV_meso margin to 3.2 mm/2.0 mm. For PTV_prim a margin reduction to 3.5 mm is feasible when a 2nd adaptation is performed in fractions where the motion exceeded 4 mm (17% of the fractions).

Conclusion

We studied the potential benefit of intrafraction motion monitoring and a 2nd adaptation to reduce PTV margins in online adaptive MRIgRT in rectal cancer. Performing 2nd adaptations immediately after online replanning when motion exceeded 1 mm and 4 mm for CTV_meso and GTV_prim respectively, could result in a 30–50% margin reduction with limited reduction of dose to the bowel.

Gastric deformation models for adaptive radiotherapy: Personalized vs population-based strategy

BACKGROUND AND PURPOSE

To create a library of plans (LoP) for gastric cancer adaptive radiotherapy, accurate predictions of shape changes due to filling variations are essential. The ability of two strategies (personalized and population-based) to predict stomach shape based on filling was evaluated for volunteer and patient data to explore the potential for use in a LoP.

MATERIALS AND METHODS

For 19 healthy volunteers, stomachs were delineated on MRIs with empty (ES), half-full (HFS) and full stomach (FS). For the personalized strategy, a deformation vector field from HFS to corresponding ES was acquired and extrapolated to predict FS. For the population-based strategy, the average deformation vectors from HFS to FS of 18 volunteers were applied to the HFS of the remaining volunteer to predict FS (leave-one-out principle); thus, predictions were made for each volunteer. Reversed processes were performed to predict ES. To validate, for seven gastric cancer patients, the volunteer population-based model was applied to their pre-treatment CT to predict stomach shape on 2-3 repeat CTs. For all predictions, volume was made equal to true stomach volume.

RESULTS

FS predictions were satisfactory, with median Dice similarity coefficient (mDSC) of 0.91 (population-based) and 0.89 (personalized). ES predictions were poorer: mDSC = 0.82 for population-based; personalized strategy yielded unachievable volumes. Population-based shape predictions (both ES and FS) were comparable between patients (mDSC = 0.87) and volunteers (0.88).

CONCLUSION

The population-based model outperformed the personalized model and demonstrated its ability in predicting filling-dependent stomach shape changes and, therefore, its potential for use in a gastric cancer LoP.

Deformation trajectory prediction using a neural network trained on finite element data-application to library of CTVs creation for cervical cancer

Purpose. We propose a neural network for fast prediction of realistic, time-parametrized deformations between pairs of input segmentations. The proposed method was used to generate a library of planning CTVs for cervical cancer radiotherapy.Methods.A 3D convolutional neural network (CNN) was introduced to predict a stationary velocity field given the distance maps of the cervix CTV in empty and full bladder anatomy. Diffeomorphic deformation trajectories between the two states were obtained by time integration. Intermediate deformation states were used to populate a library of cervix CTVs. The network was trained on cervix CTV deformations of 20 patients generated by finite element modeling (FEM). Validation was performed on FEM data of 9 healthy volunteers. Additionally, for these subjects, CTV deformations were observed in a series of repeat MR scans as the bladder filled from empty to full. Predicted and FEM libraries were compared, and benchmarked against the observed deformations. Finally, for an independent test set of 20 patients the predicted libraries were evaluated clinically, and compared to the current method.Results.The median Dice score over the validation subjects between the predicted and FEM libraries was >0.95 throughout the deformation, with a median 90 percentile surface distance of <3 mm. The ability to cover observed CTVs was similar for both the FEM-based and the proposed method, with residual offsets being about twice as large as the difference between the two methods. Clinical evaluation showed improved library properties over the method currently used in clinic.Conclusions.We proposed a CNN trained on FEM deformations, which predicts the deformation trajectory between two input states of the cervix CTV in one forward pass. We applied this to CTV library prediction for cervical cancer. The network is able to mimic FEM deformations well, while being much faster and simpler in use.

Indigenous Groin Board Immobilization Reduces Planning Target Volume Margins in Groin Radiotherapy

Purpose:

To quantify the relative motion of pelvic and groin lymph nodes (PLN and GLN) and define indicative margins for image-guided radiotherapy based on bony anatomy for the frog-leg position (FLP) and groin immobilization board (GIB).

Materials and Methods:

Twenty patients with planning computed tomography (CT) scan and on treatment cone beam CTs (median = 8) for groin and pelvic radiotherapy were included in the study. Of these nine were treated with FLP and eleven with GIB. The PLN and GLN regions on the left and right were outlined in each scan. Systematic and random uncertainties were determined along with correlations between the motions of these regions. The clinical target volume to planning target volume (PTV) margins required to take motion into account was calculated for each immobilization.

Results:

The mean shifts for PLN and GLN were lesser but not statistically lower using GIB over FLP. There was significant concordance in the vertical, longitudinal and lateral motion of the pelvis and right groin (P = 0.015, 0.09 and 0.049, respectively), pelvis and left groin (P = 0.001, 0.048, and 0.006, respectively) and between left and right groin (P = 0.013, 0.01 and 0.07, respectively) for FLP and not GIB. The PTV margins required by Van Herk and Stroom's formula were reduced from 11 mm and 9 mm to 6 mm and 5 mm for pelvis; 12 mm and 11 mm to 7 mm and 6 mm for groin, respectively, using FLP over GIB.

Conclusions:

GIB brings concordance in shifts between pelvis and groin and between bilateral groins, thereby reducing the required PTV margins.

Feasibility of Conebeam CT-based online adaptive radiotherapy for neoadjuvant treatment of rectal cancer

Background

Online adaptive radiotherapy has the potential to reduce toxicity for patients treated for rectal cancer because smaller planning target volumes (PTV) margins around the entire clinical target volume (CTV) are required. The aim of this study is to describe the first clinical experience of a Conebeam CT (CBCT)-based online adaptive workflow for rectal cancer, evaluating timing of different steps in the workflow, plan quality, target coverage and patient compliance.

Methods

Twelve consecutive patients eligible for 5 × 5 Gy pre-operative radiotherapy were treated on a ring-based linear accelerator with a multidisciplinary team present at the treatment machine for each fraction. The accelerator is operated using an integrated software platform for both treatment planning and delivery. In all directions for all CTVs a PTV margin of 5 mm was used, except for the cranial/caudal borders of the total CTV where a margin of 8 mm was applied. A reference plan was generated based on a single planning CT. After aligning the patient the online adaptive procedure started with acquisition of a CBCT. The planning CT scan was registered to the CBCT using deformable registration and a synthetic CT scan was generated. With the support of artificial intelligence, structure guided deformation and the synthetic CT scan contours were adapted by the system to match the anatomy on the CBCT. If necessary, these contours were adjusted before a new plan was generated. A second and third CBCT were acquired to validate the new plan with respect to CTV coverage just before and after treatment delivery, respectively. Treatment was delivered using volumetric modulated arc treatment (VMAT). All steps in this process were defined and timed.

Results

On average the timeslot needed at the treatment machine was 34 min. The process of acquiring a CBCT, evaluating and adjusting the contours, creating the new plan and verifying the CTV on the CBCT scan took on average 20 min. Including delivery and post treatment verification this was 26 min. Manual adjustments of the target volumes were necessary in 50% of fractions. Plan quality, target coverage and patient compliance were excellent.

Conclusions

First clinical experience with CBCT-based online adaptive radiotherapy shows it is feasible for rectal cancer.

Trial registration Medical Research Involving Human Subjects Act (WMO) does not apply to this study and was retrospectively approved by the Medical Ethics review Committee of the Academic Medical Center (W21_087 # 21.097; Amsterdam University Medical Centers, Location Academic Medical Center, Amsterdam, The Netherlands).

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-021-01866-7.

MR-Guided Radiotherapy for Rectal Cancer: Current Perspective on Organ Preservation

Online MRI-guided radiotherapy (MRgRT) is one of the most recent technological advances in radiotherapy. MRgRT permits the visualization of tumorous and healthy tissue while the patient is on the treatment table and online daily plan adaptations following the observed anatomical changes. In the context of rectal cancer, online MRgRT is a very promising modality due to the pronounced geographical variability of tumor tissues and the surrounding healthy tissues. This current paper will discuss the possible applications of online MRgRT, in particular, in terms of radiotherapy dose escalation and response prediction in organ preservation approaches for rectal cancer.

Patterns of practice for adaptive and real-time radiation therapy (POP-ART RT) part II: Offline and online plan adaption for interfractional changes

PURPOSE

The POP-ART RT study aims to determine to what extent and how intrafractional real-time respiratory motion management (RRMM), and plan adaptation for interfractional anatomical changes (ART) are used in clinical practice and to understand barriers to implementation. Here we report on part II: ART using more than one plan per target per treatment course.

MATERIALS AND METHODS

A questionnaire on the current practice of ART, wishes for expansion or implementation, and barriers to implementation was distributed worldwide. Four types of ART were discriminated: daily online replanning, online plan library, protocolled offline replanning (all three based on a protocol), and ad-hoc offline replanning.

RESULTS

The questionnaire was completed by 177 centres from 40 countries. ART was used by 61% of respondents (31% with protocol) for a median (range) of 3 (1-8) tumour sites. CBCT/MVCT was the main imaging modality except for online daily replanning (11 users) where 10 users used MR. Two thirds of respondents wished to implement ART for a new tumour site; 40% of these had plans to do it in the next 2 years. Human/material resources and technical limitations were the main barriers to further use and implementation.

CONCLUSIONS

ART was used for a broad range of tumour sites, mainly with ad-hoc offline replanning and for a median of 3 tumour sites. There was a large interest in implementing ART for more tumour sites, mainly limited by human/material resources and technical limitations. Daily online replanning was primarily performed on MR-linacs.

First system for fully-automated multi-criterial treatment planning for a high-magnetic field MR-Linac applied to rectal cancer

Background and purpose: In this study we developed a workflow for fully-automated generation of deliverable IMRT plans for a 1.5 T MR-Linac (MRL) based on contoured CT scans, and we evaluated automated MRL planning for rectal cancer.Methods: The Monte Carlo dose calculation engine used in the clinical MRL TPS (Monaco, Elekta AB, Stockholm, Sweden), suited for high accuracy dose calculations in a 1.5 T magnetic field, was coupled to our in-house developed Erasmus-iCycle optimizer. Clinically deliverable plans for 23 rectal cancer patients were automatically generated in a two-step process, i.e., multi-criterial fluence map optimization with Erasmus-iCycle followed by a conversion into a deliverable IMRT plan in the clinical TPS. Automatically generated plans (AUTOplans) were compared to plans that were manually generated with the clinical TPS (MANplans).Results: With AUTOplanning large reductions in planning time and workload were obtained; 4-6 h mainly hands-on planning for MANplans vs ∼1 h of mainly computer computation time for AUTOplans. For equal target coverage, the bladder and bowel bag D_mean was reduced in the AUTOplans by 1.3 Gy (6.9%) on average with a maximum reduction of 4.5 Gy (23.8%). Dosimetric measurements at the MRL demonstrated clinically acceptable delivery accuracy for the AUTOplans.Conclusions: A system for fully automated multi-criterial planning for a 1.5 T MR-Linac was developed and tested for rectal cancer patients. Automated planning resulted in major reductions in planning workload and time, while plan quality improved. Negative impact of the high magnetic field on the dose distributions could be avoided.

Modeling Complex Deformations of the Sigmoid Colon Between External Beam Radiation Therapy and Brachytherapy Images of Cervical Cancer

PURPOSE

In this study, we investigated registration methods for estimating the large interfractional sigmoid deformations that occur between external beam radiation therapy (EBRT) and brachytherapy (BT) for cervical cancer.

METHODS AND MATERIALS

Sixty-three patients were retrospectively analyzed. The sigmoid colon was delineated on 2 computed tomography images acquired during EBRT (without applicator) and BT (with applicator) for each patient. Five registration approaches were compared to propagate the contour of the sigmoid from BT to EBRT anatomies: rigid registration, commercial hybrid (ANAtomically CONstrained Deformation Algorithm), controlling ROI surface projection of RayStation, and the classical and constrained symmetrical thin-plate spline robust point matching (sTPS-RPM) methods. Deformation of the sigmoid due to insertion of the BT applicator was reported. Registration performance was compared by using the Dice similarity coefficient (DSC), distance to agreement, and Hausdorff distance. The 2 sTPS-RPM methods were compared by using surface triangle quality criteria between deformed surfaces. Using the deformable approaches, the BT dose of the sigmoid was deformed toward the EBRT anatomy. The displacement and discrepancy between the deformable methods to propagate the planned D1cm³ and D2cm³ of the sigmoid from BT to EBRT anatomies were reported for 55 patients.

RESULTS

Large and complex deformations of the sigmoid were observed for each patient. Rigid registration resulted in poor sigmoid alignment with a mean DSC of 0.26. Using the contour to drive the deformation, ANAtomically CONstrained Deformation Algorithm was able to slightly improve the alignment of the sigmoid with a mean DSC of 0.57. Using only the sigmoid surface as controlling ROI, the mean DSC was improved to 0.79. The classical and constrained sTPS-RPM methods provided mean DSCs of 0.95 and 0.96, respectively, with an average inverse consistency error <1 mm. The constrained sTPS-RPM provided more realistic deformations and better surface topology of the deformed sigmoids. The planned mean (range) D1cm³ and D2cm³ of the sigmoid were 13.4 Gy (1-24.1) and 12.2 Gy (1-21.5) on the BT anatomy, respectively. Using the constrained sTPS-RPM to deform the sigmoid from BT to EBRT anatomies, these hotspots had a mean (range) displacement of 27.1 mm (6.8-81).

CONCLUSIONS

Large deformations of the sigmoid were observed between the EBRT and BT anatomies, suggesting that the D1cm³ and D2cm³ of the sigmoid would unlikely to be at the same position throughout treatment. The proposed constrained sTPS-RPM seems to be the preferred approach to manage the large deformation due to BT applicator insertion. Such an approach could be used to map the EBRT dose to the BT anatomy for personalized BT planning optimization.