Multi-observer contouring of male pelvic anatomy: Highly variable agreement across conventional and emerging structures of interest

Bladder trigone sparing radiotherapy in prostate cancer treatment

INTRODUCTION

Evidence suggests the bladder trigone to be a potential organ at risk (OAR) in predicting acute and late genitourinary (GU) side effects when treating prostate cancer with radiotherapy.

METHODS

A search of MEDLINE, Cinahl, EMBASE, PubMed, the Cochrane Database of Systematic Reviews and OpenGrey was conducted and no current or underway systematic reviews or scoping reviews on the topic were identified. A systematic literature review was carried out assessing the quality of this evidence. All evidence that prospectively or retrospectively reviewed radiotherapy or modelled radiotherapy dose to the bladder trigone were included. The search was conducted on the 8th July 2021 with 32 studies included in this review. This was repeated 10th June 2023 and two additional studies were identified. Any evidence published since this date have not been included and are a limitation of this review.

RESULTS

MRI imaging is recommended to assist in delineating the trigone which has been shown to have a high amount of inter-observer variability and the use of specific training may reduce this. Across all radiotherapy treatment modalities, trigone dose contributed to GU acute and late toxicity symptoms. Trigone motion is relative to prostate motion but further research is required to confirm if the prostate can be used as a reliable surrogate for trigone position. The dose tolerance given for specific trigone related toxicities is debated within the literature, and on analysis the authors of this review suggest bladder trigone dose limits: Dmean < 45.8 Gy, V61.0Gy < 40%, V59.8Gy < 25%, V42.5Gy-V41.0Gy < 91% and V47.4Gy-V43.2Gy < 91% with α/β of 3 Gy to reduce acute and late GU toxicities.

CONCLUSION

There is evidence to support further research into bladder trigone sparing radiotherapy to improve patient outcomes.

IMPLICATION FOR PRACTICE

Using the bladder trigone as an organ at risk is possible and the authors are currently seeking funding for a feasibility trial to further investigate this.

NRG Oncology Assessment of Artificial Intelligence Deep Learning-Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions

Deep learning neural networks (DLNN) in Artificial intelligence (AI) have been extensively explored for automatic segmentation in radiotherapy (RT). In contrast to traditional model-based methods, data-driven AI-based models for auto-segmentation have shown high accuracy in early studies in research settings and controlled environment (single institution). Vendor-provided commercial AI models are made available as part of the integrated treatment planning system (TPS) or as a stand-alone tool that provides streamlined workflow interacting with the main TPS. These commercial tools have drawn clinics' attention thanks to their significant benefit in reducing the workload from manual contouring and shortening the duration of treatment planning. However, challenges occur when applying these commercial AI-based segmentation models to diverse clinical scenarios, particularly in uncontrolled environments. Contouring nomenclature and guideline standardization has been the main task undertaken by the NRG Oncology. AI auto-segmentation holds the potential clinical trial participants to reduce interobserver variations, nomenclature non-compliance, and contouring guideline deviations. Meanwhile, trial reviewers could use AI tools to verify contour accuracy and compliance of those submitted datasets. In recognizing the growing clinical utilization and potential of these commercial AI auto-segmentation tools, NRG Oncology has formed a working group to evaluate the clinical utilization and potential of commercial AI auto-segmentation tools. The group will assess in-house and commercially available AI models, evaluation metrics, clinical challenges, and limitations, as well as future developments in addressing these challenges. General recommendations are made in terms of the implementation of these commercial AI models, as well as precautions in recognizing the challenges and limitations.

Deep-learning-based joint rigid and deformable contour propagation for magnetic resonance imaging-guided prostate radiotherapy

BACKGROUND

Deep learning-based unsupervised image registration has recently been proposed, promising fast registration. However, it has yet to be adopted in the online adaptive magnetic resonance imaging-guided radiotherapy (MRgRT) workflow.

PURPOSE

In this paper, we design an unsupervised, joint rigid, and deformable registration framework for contour propagation in MRgRT of prostate cancer.

METHODS

Three-dimensional pelvic T2-weighted MRIs of 143 prostate cancer patients undergoing radiotherapy were collected and divided into 110, 13, and 20 patients for training, validation, and testing. We designed a framework using convolutional neural networks (CNNs) for rigid and deformable registration. We selected the deformable registration network architecture among U-Net, MS-D Net, and LapIRN and optimized the training strategy (end-to-end vs. sequential). The framework was compared against an iterative baseline registration. We evaluated registration accuracy (the Dice and Hausdorff distance of the prostate and bladder contours), structural similarity index, and folding percentage to compare the methods. We also evaluated the framework's robustness to rigid and elastic deformations and bias field perturbations.

RESULTS

The end-to-end trained framework comprising LapIRN for the deformable component achieved the best median (interquartile range) prostate and bladder Dice of 0.89 (0.85-0.91) and 0.86 (0.80-0.91), respectively. This accuracy was comparable to the iterative baseline registration: prostate and bladder Dice of 0.91 (0.88-0.93) and 0.86 (0.80-0.92). The best models complete rigid and deformable registration in 0.002 (0.0005) and 0.74 (0.43) s (Nvidia Tesla V100-PCIe 32 GB GPU), respectively. We found that the models are robust to translations up to 52 mm, rotations up to 15∘ $^\circ$ , elastic deformations up to 40 mm, and bias fields.

CONCLUSIONS

Our proposed unsupervised, deep learning-based registration framework can perform rigid and deformable registration in less than a second with contour propagation accuracy comparable with iterative registration.

Impact of bias field correction on 0.35 T pelvic MR images: evaluation on generative adversarial network-based OARs' auto-segmentation and visual grading assessment

Purpose

Magnetic resonance imaging (MRI)-guided radiotherapy enables adaptive treatment plans based on daily anatomical changes and accurate organ visualization. However, the bias field artifact can compromise image quality, affecting diagnostic accuracy and quantitative analyses. This study aims to assess the impact of bias field correction on 0.35 T pelvis MRIs by evaluating clinical anatomy visualization and generative adversarial network (GAN) auto-segmentation performance.

Materials and methods

3D simulation MRIs from 60 prostate cancer patients treated on MR-Linac (0.35 T) were collected and preprocessed with the N4ITK algorithm for bias field correction. A 3D GAN architecture was trained, validated, and tested on 40, 10, and 10 patients, respectively, to auto-segment the organs at risk (OARs) rectum and bladder. The GAN was trained and evaluated either with the original or the bias-corrected MRIs. The Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95th) were computed for the segmented volumes of each patient. The Wilcoxon signed-rank test assessed the statistical difference of the metrics within OARs, both with and without bias field correction. Five radiation oncologists blindly scored 22 randomly chosen patients in terms of overall image quality and visibility of boundaries (prostate, rectum, bladder, seminal vesicles) of the original and bias-corrected MRIs. Bennett's S score and Fleiss' kappa were used to assess the pairwise interrater agreement and the interrater agreement among all the observers, respectively.

Results

In the test set, the GAN trained and evaluated on original and bias-corrected MRIs showed DSC/HD95th of 0.92/5.63 mm and 0.92/5.91 mm for the bladder and 0.84/10.61 mm and 0.83/9.71 mm for the rectum. No statistical differences in the distribution of the evaluation metrics were found neither for the bladder (DSC: p = 0.07; HD95th: p = 0.35) nor for the rectum (DSC: p = 0.32; HD95th: p = 0.63). From the clinical visual grading assessment, the bias-corrected MRI resulted mostly in either no change or an improvement of the image quality and visualization of the organs' boundaries compared with the original MRI.

Conclusion

The bias field correction did not improve the anatomy visualization from a clinical point of view and the OARs' auto-segmentation outputs generated by the GAN.

Improvement in male pelvis magnetic resonance image contouring following radiologist-delivered training

INTRODUCTION

The magnetic resonance linear accelerator (MRL) combines both magnetic resonance imaging and a linear accelerator, allowing for daily treatment adaptation. This study aimed to assess the impact of radiologist-delivered training in magnetic resonance (MR) contouring of relevant structures within the male pelvis.

METHODS

Two radiation oncologists, two radiation oncology registrars and seven radiation therapists completed contouring on 10 male pelvis MR datasets both pre- and post-training. A 2-hour MR anatomy training session was delivered by a radiologist, who also provided the 'gold standard' contours. The pre- and post-training contours were compared against the gold standard with Dice similarity coefficient (DSC) and Hausdorff distances calculated; and the pre- and post-confidence scores and timing were compared.

RESULTS

The improvement in DSC were significant in prostate, rectum and seminal vesicles, with a post-training median DSC of 0.87 ± 0.06, 0.92 ± 0.04 and 0.80 ± 0.14, respectively. The median Hausdorff improved with a median of 1.46 ± 0.78 mm, 0.52 ± 0.32 mm and 1.11 ± 0.86 mm for prostate, rectum and seminal vesicles, respectively. Bladder concordance was high both pre- and post-training. Urethra contours improved post-training, however, remained difficult to contour with a median post-DSC of 0.51 ± 0.24. Overall, confidence scoring improved (P < 0.001) and timing decreased by an average of 4.4 ± 16.4 min post-training.

CONCLUSION

Radiologist-delivered training improved concordance of male pelvis contouring on MR datasets. Further work is required in the identification of urethra on MRs. These findings are of importance in the MRL adaptive workflow.

Review and recommendations on deformable image registration uncertainties for radiotherapy applications

Deformable image registration (DIR) is a versatile tool used in many applications in radiotherapy (RT). DIR algorithms have been implemented in many commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of DIR can be large and difficult to quantify, resulting in barriers to clinical practice. Currently, there is no agreement in the RT community on how to quantify these uncertainties and determine thresholds that distinguish a good DIR result from a poor one. This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications. Recommendations are provided on how to handle these uncertainties for patient-specific use, commissioning, and research. Recommendations are also provided for developers and vendors to help users to understand DIR uncertainties and make the application of DIR in RT safer and more reliable.

Real-world validation of Artificial Intelligence-based Computed Tomography auto-contouring for prostate cancer radiotherapy planning

Background and purpose

Artificial Intelligence (AI)-based auto-contouring for treatment planning in radiotherapy needs extensive clinical validation, including the impact of editing after automatic segmentation. The aims of this study were to assess the performance of a commercial system for Clinical Target Volumes (CTVs) (prostate/seminal vesicles) and selected Organs at Risk (OARs) (rectum/bladder/femoral heads + femurs), evaluating also inter-observer variability (manual vs automatic + editing) and the reduction of contouring time.

Materials and methods

Two expert observers contoured CTVs/OARs of 20 patients in our Treatment Planning System (TPS). Computed Tomography (CT) images were sent to the automatic contouring workstation: automatic contours were generated and sent back to TPS, where observers could edit them if necessary. Inter- and intra-observer consistency was estimated using Dice Similarity Coefficients (DSC). Radiation oncologists were also asked to score the quality of automatic contours, ranging from 1 (complete re-contouring) to 5 (no editing). Contouring times (manual vs automatic + edit) were compared.

Results

DSCs (manual vs automatic only) were consistent with inter-observer variability (between 0.65 for seminal vesicles and 0.94 for bladder); editing further improved performances (range: 0.76-0.94). The median clinical score was 4 (little editing) and it was <4 in 3/2 patients for the two observers respectively. Inter-observer variability of automatic + editing contours improved significantly, being lower than manual contouring (e.g.: seminal vesicles: 0.83vs0.73; prostate: 0.86vs0.83; rectum: 0.96vs0.81). Oncologist contouring time reduced from 17 to 24 min of manual contouring time to 3-7 min of editing time for the two observers (p < 0.01).

Conclusion

Automatic contouring with a commercial AI-based system followed by editing can replace manual contouring, resulting in significantly reduced time for segmentation and better consistency between operators.

Deep learning for automated contouring of neurovascular structures on magnetic resonance imaging for prostate cancer patients

Background and purpose

Manual contouring of neurovascular structures on prostate magnetic resonance imaging (MRI) is labor-intensive and prone to considerable interrater disagreement. Our aim is to contour neurovascular structures automatically on prostate MRI by deep learning (DL) to improve workflow and interrater agreement.

Materials and methods

Segmentation of neurovascular structures was performed on pre-treatment 3.0 T MRI data of 131 prostate cancer patients (training [n = 105] and testing [n = 26]). The neurovascular structures include the penile bulb (PB), corpora cavernosa (CCs), internal pudendal arteries (IPAs), and neurovascular bundles (NVBs). Two DL networks, nnU-Net and DeepMedic, were trained for auto-contouring on prostate MRI and evaluated using volumetric Dice similarity coefficient (DSC), mean surface distances (MSD), Hausdorff distances, and surface DSC. Three radiation oncologists evaluated the DL-generated contours and performed corrections when necessary. Interrater agreement was assessed and the time required for manual correction was recorded.

Results

nnU-Net achieved a median DSC of 0.92 (IQR: 0.90-0.93) for the PB, 0.90 (IQR: 0.86-0.92) for the CCs, 0.79 (IQR: 0.77-0.83) for the IPAs, and 0.77 (IQR: 0.72-0.81) for the NVBs, which outperformed DeepMedic for each structure (p < 0.03). nnU-Net showed a median MSD of 0.24 mm for the IPAs and 0.71 mm for the NVBs. The median interrater DSC ranged from 0.93 to 1.00, with the majority of cases (68.9%) requiring manual correction times under two minutes.

Conclusions

DL enables reliable auto-contouring of neurovascular structures on pre-treatment MRI data, easing the clinical workflow in neurovascular-sparing MR-guided radiotherapy.

Assessing Interobserver Variability in the Delineation of Structures in Radiation Oncology: A Systematic Review

PURPOSE

The delineation of target volumes and organs at risk is the main source of uncertainty in radiation therapy. Numerous interobserver variability (IOV) studies have been conducted, often with unclear methodology and nonstandardized reporting. We aimed to identify the parameters chosen in conducting delineation IOV studies and assess their performances and limits.

METHODS AND MATERIALS

We conducted a systematic literature review to highlight major points of heterogeneity and missing data in IOV studies published between 2018 and 2021. For the main used metrics, we did in silico analyses to assess their limits in specific clinical situations.

RESULTS

All disease sites were represented in the 66 studies examined. Organs at risk were studied independently of tumor site in 29% of reviewed IOV studies. In 65% of studies, statistical analyses were performed. No gold standard (GS; ie, reference) was defined in 36% of studies. A single expert was considered as the GS in 21% of studies, without testing intraobserver variability. All studies reported both absolute and relative indices, including the Dice similarity coefficient (DSC) in 68% and the Hausdorff distance (HD) in 42%. Limitations were shown in silico for small structures when using the DSC and dependence on irregular shapes when using the HD. Variations in DSC values were large between studies, and their thresholds were inconsistent. Most studies (51%) included 1 to 10 cases. The median number of observers or experts was 7 (range, 2-35). The intraclass correlation coefficient was reported in only 9% of cases. Investigating the feasibility of studying IOV in delineation, a minimum of 8 observers with 3 cases, or 11 observers with 2 cases, was required to demonstrate moderate reproducibility.

CONCLUSIONS

Implementation of future IOV studies would benefit from a more standardized methodology: clear definitions of the gold standard and metrics and a justification of the tradeoffs made in the choice of the number of observers and number of delineated cases should be provided.

A Dosiomics Analysis Based on Linear Energy Transfer and Biological Dose Maps to Predict Local Recurrence in Sacral Chordomas after Carbon-Ion Radiotherapy

Carbon Ion Radiotherapy (CIRT) is one of the most promising therapeutic options to reduce Local Recurrence (LR) in Sacral Chordomas (SC). The aim of this work is to compare the performances of survival models fed with dosiomics features and conventional DVH metrics extracted from relative biological effectiveness (RBE)-weighted dose (D_RBE) and dose-averaged Linear Energy Transfer (LET_d) maps, towards the identification of possible prognostic factors for LR in SC patients treated with CIRT. This retrospective study included 50 patients affected by SC with a focus on patients that presented a relapse in a high-dose region. Survival models were built to predict both LR and High-Dose Local Recurrencies (HD-LR). The models were evaluated through Harrell Concordance Index (C-index) and patients were stratified into high/low-risk groups. Local Recurrence-free Kaplan-Meier curves were estimated and evaluated through log-rank tests. The model with highest performance (median(interquartile-range) C-index of 0.86 (0.22)) was built on features extracted from LET_d maps, with D_RBE models showing promising but weaker results (C-index of 0.83 (0.21), 0.80 (0.21)). Although the study should be extended to a wider patient population, LET_d maps show potential as a prognostic factor for SC HD-LR in CIRT, and dosiomics appears to be the most promising approach against more conventional methods (e.g., DVH-based).

Daily online contouring and re-planning versus translation-only correction in neurovascular-sparing magnetic resonance-guided radiotherapy for localized prostate cancer

Neurovascular bundle (NVB) and internal pudendal artery (IPA) sparing during magnetic resonance-guided radiotherapy (MRgRT) for prostate cancer aims for preservation of erectile function. Our present workflow involves daily online contouring and re-planning on a 1.5 T MR-linac, as alternative to conventional (rigid) translation-only corrections of the prostate. We compared planned dose for the NVB and IPA between strategies. Total planned dose was significantly lower with daily online contouring and re-planning for the NVB, but not for the IPA. For the NVB and IPA, the intrapatient difference between highest and lowest fraction dose was significantly smaller for the contouring and re-planning plans.

Simulation CT-based radiomics for prediction of response after neoadjuvant chemo-radiotherapy in patients with locally advanced rectal cancer

Background

To report on the discriminative ability of a simulation Computed Tomography (CT)-based radiomics signature for predicting response to treatment in patients undergoing neoadjuvant chemo-radiation for locally advanced adenocarcinoma of the rectum.

Methods

Consecutive patients treated at the Universities of Tübingen (from 1/1/07 to 31/12/10, explorative cohort) and Florence (from 1/1/11 to 31/12/17, external validation cohort) were considered in our dual-institution, retrospective analysis. Long-course neoadjuvant chemo-radiation was performed according to local policy. On simulation CT, the rectal Gross Tumor Volume was manually segmented. A feature selection process was performed yielding mineable data through an in-house developed software (written in Python 3.6). Model selection and hyper-parametrization of the model was performed using a fivefold cross validation approach. The main outcome measure of the study was the rate of pathologic good response, defined as the sum of Tumor regression grade (TRG) 3 and 4 according to Dworak’s classification.

Results

Two-hundred and one patients were included in our analysis, of whom 126 (62.7%) and 75 (37.3%) cases represented the explorative and external validation cohorts, respectively. Patient characteristics were well balanced between the two groups. A similar rate of good response to neoadjuvant treatment was obtained in in both cohorts (46% and 54.7%, respectively; p = 0.247). A total of 1150 features were extracted from the planning scans. A 5-metafeature complex consisting of Principal component analysis (PCA)-clusters (whose main components are LHL Grey-Level-Size-Zone: Large Zone Emphasis, Elongation, HHH Intensity Histogram Mean, HLL Run-Length: Run Level Variance and HHH Co-occurence: Cluster Tendency) in combination with 5-nearest neighbour model was the most robust signature. When applied to the explorative cohort, the prediction of good response corresponded to an average Area under the curve (AUC) value of 0.65 ± 0.02. When the model was tested on the external validation cohort, it ensured a similar accuracy, with a slightly lower predictive ability (AUC of 0.63).

Conclusions

Radiomics-based, data-mining from simulation CT scans was shown to be feasible and reproducible in two independent cohorts, yielding fair accuracy in the prediction of response to neoadjuvant chemo-radiation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-022-02053-y.

Interrater agreement of contouring of the neurovascular bundles and internal pudendal arteries in neurovascular-sparing magnetic resonance-guided radiotherapy for localized prostate cancer

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Interrater DSC of the NVB was 0.60 and 0.61 for the left and right side respectively.

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Interrater DSC of the IPA was 0.59 for the left and right side.

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Agreement was best for the inferior half (i.e. prostate apex to midgland) of the NVB.

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Agreement improved with MRI optimization and rater training.

Background and purpose

Radiation damage to neural and vascular tissue, such as the neurovascular bundles (NVBs) and internal pudendal arteries (IPAs), during radiotherapy for prostate cancer (PCa) may cause erectile dysfunction. Neurovascular-sparing magnetic resonance-guided adaptive radiotherapy (MRgRT) aims to preserve erectile function after treatment. However, the NVBs and IPAs are not routinely contoured in current radiotherapy practice. Before neurovascular-sparing MRgRT for PCa can be implemented, the interrater agreement of the contouring of the NVBs and IPAs on pre-treatment MRI needs to be assessed.

Materials and methods

Four radiation oncologists independently contoured the prostate, NVB, and IPA in an unselected consecutive series of 15 PCa patients, on pre-treatment MRI. Dice similarity coefficients (DSCs) for pairwise interrater agreement of contours were calculated. Additionally, the DCS of a subset of the inferior half of the NVB contours (i.e. approximately prostate midgland to apex level) was calculated.

Results

Median overall interrater DSC for the left and right NVB was 0.60 (IQR: 0.54 – 0.68) and 0.61 (IQR: 0.53 – 0.69) respectively and for the left and right IPA 0.59 (IQR: 0.53 – 0.64) and 0.59 (IQR: 0.52 – 0.64) respectively. Median overall interrater DSC for the inferior half of the left NVB was 0.67 (IQR: 0.58 – 0.74) and 0.67 (IQR: 0.61 – 0.71) for the right NVB.

Conclusion

We found that the interrater agreement for the contouring of the NVB and IPA improved with enhancement of the MRI sequence as well as further training of the raters. The agreement was best in the subset of the inferior half of the NVB, where a good agreement is clinically most relevant for neurovascular-sparing MRgRT for PCa.

Uncertainty in organ delineation using low-dose computed tomography images with high-strength iterative reconstruction technique in radiotherapy for prostate cancer

Introduction

This study aimed to investigate the uncertainty in organ delineation of low-dose computed tomography (CT) images using a high-strength iterative reconstruction (IR) during radiotherapy planning for the treatment of prostate cancer.

Methods

Two CT datasets were prepared with different dose levels by adjusting the reconstruction slice thickness. Two observers independently delineated the prostate, seminal vesicles, bladder and rectum on both images without referring to other modality images. The delineated organ volumes were compared between both images. Observer delineation variability was assessed using Dice similarity coefficient (DSC) and mean distance to agreement.

Results

No significant differences regarding the delineated organ volumes were observed between the low- and standard-dose images for all organs. Regarding inter-observer variability, the DSC was relatively high for both images, whereas mean distance to agreement was not significantly different between images (p > 0·05 for all). Intra-observer variability for each observer showed high DSC (>0·8 and >0·9 for seminal vesicles and other organs, respectively) but no significant differences in the mean distance to agreement (p > 0·05 for all).

Conclusions

Our results indicate that low-dose CT images with high-strength IR would be available for organ delineation in the radiotherapy treatment planning for prostate cancer.

Spatial descriptions of radiotherapy dose: normal tissue complication models and statistical associations

For decades, dose-volume information for segmented anatomy has provided the essential data for correlating radiotherapy dosimetry with treatment-induced complications. Dose-volume information has formed the basis for modelling those associations via normal tissue complication probability (NTCP) models and for driving treatment planning. Limitations to this approach have been identified. Many studies have emerged demonstrating that the incorporation of information describing the spatial nature of the dose distribution, and potentially its correlation with anatomy, can provide more robust associations with toxicity and seed more general NTCP models. Such approaches are culminating in the application of computationally intensive processes such as machine learning and the application of neural networks. The opportunities these approaches have for individualising treatment, predicting toxicity and expanding the solution space for radiation therapy are substantial and have clearly widespread and disruptive potential. Impediments to reaching that potential include issues associated with data collection, model generalisation and validation. This review examines the role of spatial models of complication and summarises relevant published studies. Sources of data for these studies, appropriate statistical methodology frameworks for processing spatial dose information and extracting relevant features are described. Spatial complication modelling is consolidated as a pathway to guiding future developments towards effective, complication-free radiotherapy treatment.

Reduction of inter-observer contouring variability in daily clinical practice through a retrospective, evidence-based intervention

BACKGROUND

Inter-observer variations (IOVs) arising during contouring can potentially impact plan quality and patient outcomes. Regular assessment of contouring IOV is not commonly performed in clinical practice due to the large time commitment required of clinicians from conventional methods. This work uses retrospective information from past treatment plans to facilitate a time-efficient, evidence-based intervention to reduce contouring IOV.

METHODS

The contours of 492 prostate cancer treatment plans created by four radiation oncologists were analyzed in this study. Structure volumes, lengths, and DVHs were extracted from the treatment planning system and stratified based on primary oncologist and inclusion of a pelvic lymph node (PLN) target. Inter-observer variations and their dosimetric consequences were assessed using Student's t-tests. Results of this analysis were presented at an intervention meeting, where new consensus contour definitions were agreed upon. The impact of the intervention was assessed one-year later by repeating the analysis on 152 new plans.

RESULTS

Significant IOV in prostate and PLN target delineation existed pre-intervention between oncologists, impacting dose to nearby OARs. IOV was also present for rectum and penile-bulb structures. Post-intervention, IOV decreased for all previously discordant structures. Dosimetric variations were also reduced. Although target contouring concordance increased significantly, some variations still persisted for PLN structures, highlighting remaining areas for improvement.

CONCLUSION

We detected significant contouring IOV in routine practice using easily accessible retrospective data and successfully decreased IOV in our clinic through a reflective intervention. Continued application of this approach may aid improvements in practice standardization and enhance quality of care.

Evaluation of daily online contour adaptation by radiation therapists for prostate cancer treatment on an MRI-guided linear accelerator

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Adapt-to-Shape (ATS) workflows enable high-precision MR-guided radiotherapy.

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Online, manual contour adaptation is a time-consuming step in the ATS workflow.

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Trained radiation therapists adapt contours for prostate cancer treatment.

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CTV contours by radiation therapists are well-suited for MR-Linac prostate cancer treatment.

Background and purpose

Magnetic resonance (MR)-guided linear accelerator (MR-Linac) systems have changed radiotherapy workflows. The addition of daily online contour adaptation allows for higher precision treatment, but also increases the workload of those involved. We train radiation therapists (RTTs) to perform daily online contour adaptation for MR-Linac treatment of prostate cancer (PCa) patients. The purpose of this study was to evaluate these prostate contours by performing an interfraction and interobserver analysis.

Materials and methods

Clinical target volume (CTV) contours generated online by RTTs from 30 low-intermediate risk PCa patients, treated with 5x7.25 Gy, were used. Two physicians (Observers) judged the RTTs contours and performed adaptations when necessary. Interfraction relative volume differences between the first and the subsequent fractions were calculated for the RTTs, Observer 1, and Observer 2. Additionally, interobserver dice’s similarity coefficient (DSC) for fraction 2–5 was calculated with the RTTs- and physician-adapted contours. Clinical acceptability of the RTTs contours was judged by a third observer.

Results

Mean (SD) online contour adaptation time was 12.6 (±3.8) minutes and overall median (interquartile range [IQR]) relative volume difference was 9.3% (4.4–13.0). Adaptations by the observers were mostly performed at the apex and base of the prostate. Median (IQR) interobserver DSC between RTTs and Observer 1, RTTs and Observer 2, and Observer 1 and 2 was 0.99 (0.98–1.00), 1.00 (0.98–1.00), and 1.00 (0.99–1.00), respectively. Contours were acceptable for clinical use in 113 (94.2%) fractions. Dose-volume histogram (DVH) analysis showed significant CTV underdosage for one of the seven identified outliers.

Conclusion

Daily online contour adaptation by RTTs is clinically feasible for MR-Linac treatment of PCa.

The urethral position may shift due to urethral catheter placement in the treatment planning for prostate radiation therapy

PURPOSE

To determine the best method to contour the planning organ at risk volume (PRV) for the urethra, this study aimed to investigate the displacement of a Foley catheter in the urethra with a soft and thin guide-wire.

METHODS

For each patient, the study used two sets of computed tomography (CT) images for radiation treatment planning (RT-CT): (1) set with a Foley urethral catheter (4.0 mm diameter) plus a guide-wire (0.46 mm diameter) in the first RT-CT and (2) set with a guide-wire alone in the second CT recorded 2 min after the first RT-CT. Using three fiducial markers in the prostate for image fusion, the displacement between the catheter and the guide-wire in the prostatic urethra was calculated. In 155 consecutive patients treated between 2011 and 2017, 5531 slices of RT-CT were evaluated.

RESULTS

Assuming that ≥3.0 mm of difference between the catheter and the guide-wire position was a significant displacement, the urethra with the catheter was displaced significantly from the urethra with the guide-wire alone in > 20% of the RT-CT slices in 23.2% (36/155) of the patients. The number of patients who showed ≥3.0 mm anterior displacement with the catheter in ≥20% RT-CT slices was significantly larger at the superior segment (38/155) than at the middle (14/155) and inferior segments (18/155) of the prostatic urethra (p < 0.0167).

CONCLUSIONS

The urethral position with a Foley catheter is different from the urethral position with a thin and soft guide-wire in a significant proportion of the patients. This should be taken into account for the PRV of the urethra to ensure precise radiotherapy such as in urethra-sparing radiotherapy.

Treatment planning for proton therapy: what is needed in the next 10 years?

Treatment planning is the process where the prescription of the radiation oncologist is translated into a deliverable treatment. With the complexity of contemporary radiotherapy, treatment planning cannot be performed without a computerized treatment planning system. Proton therapy (PT) enables highly conformal treatment plans with a minimum of dose to tissues outside the target volume, but to obtain the most optimal plan for the treatment, there are a multitude of parameters that need to be addressed. In this review areas of ongoing improvements and research in the field of PT treatment planning are identified and discussed. The main focus is on issues of immediate clinical and practical relevance to the PT community highlighting the needs for the near future but also in a longer perspective. We anticipate that the manual tasks performed by treatment planners in the future will involve a high degree of computational thinking, as many issues can be solved much better by e.g. scripting. More accurate and faster dose calculation algorithms are needed, automation for contouring and planning is required and practical tools to handle the variable biological efficiency in PT is urgently demanded just to mention a few of the expected improvements over the coming 10 years.