Delineation Of Organs At Risk Research Articles

Multiple organ segmentation framework for brain metastasis radiotherapy

Radiotherapy is a preferred treatment for brain metastases, which kills cancer cells via high doses of radiation meanwhile hardly avoiding damage to surrounding healthy cells. Therefore, the delineation of organs-at-risk (OARs) is vital in treatment planning to minimize radiation-induced toxicity. However, the following aspects make OAR delineation a challenging task: extremely imbalanced organ sizes, ambiguous boundaries, and complex anatomical structures. To alleviate these challenges, we imitate how specialized clinicians delineate OARs and present a novel cascaded multi-OAR segmentation framework, called OAR-SegNet. OAR-SegNet comprises two distinct levels of segmentation networks: an Anatomical-Prior-Guided network (APG-Net) and a Point-Cloud-Guided network (PCG-Net). Specifically, APG-Net handles segmentation for all organs, where multi-view segmentation modules and a deep prior loss are designed under the guidance of prior knowledge. After APG-Net, PCG-Net refines small organs through the mini-segmentation and the point-cloud alignment heads. The mini-segmentation head is further equipped with the deep prior feature. Extensive experiments were conducted to demonstrate the superior performance of the proposed method compared to other state-of-the-art medical segmentation methods.

Beyond boundaries: exploring radiographers’ experiences and solutions in organ-at-risk delineation for radiotherapy planning

Abstract Introduction: This study aimed to assess the perception of radiographers on the difficulties encountered during the delineation of organs at risk (OARs) for radiotherapy planning and the methods that could be used to facilitate this process. Methodology: A self-designed questionnaire was distributed to all radiographers (n = 29) employed at the radiotherapy department in Malta. The survey assessed the challenges faced by the radiographers during the delineation of various OARs and inquired about the perceived effectiveness of specific measures that could be used to facilitate the delineation process using a Likert scale ranging from 1 (not difficult/effective) to 5 (very difficult/effective). Results: The response rate was 79%. Overall, the delineation of OARs was rated as slightly difficult (mean score 1.95 ± 0.33). Nevertheless, some OARs, such as the parotid, stomach and brain stem, were deemed more difficult to contour with a mean score of 3 or higher. The implementation of peer review was perceived as the most influential factor in reducing delineation difficulty (mean score 4.59 ± 0.58), followed by contouring training (mean score 4.48 ± 0.58) and training on artificial intelligence (AI) (mean score 4.35 ± 0.48). Conclusion: The introduction of peer review, training and AI could facilitate the delineation of OARs.

Geometric and dosimetric analysis of CT- and MR-based automatic contouring for the EPTN contouring atlas in neuro-oncology

PurposeAtlas-based and deep-learning contouring (DLC) are methods for automatic segmentation of organs-at-risk (OARs). The European Particle Therapy Network (EPTN) published a consensus-based atlas for delineation of OARs in neuro-oncology. In this study, geometric and dosimetric evaluation of automatically-segmented neuro-oncological OARs was performed using CT- and MR-models following the EPTN-contouring atlas. MethodsImage and contouring data from 76 neuro-oncological patients were included. Two atlas-based models (CT-atlas and MR-atlas) and one DLC-model (MR-DLC) were created. Manual contours on registered CT-MR-images were used as ground-truth. Results were analyzed in terms of geometrical (volumetric Dice similarity coefficient (vDSC), surface DSC (sDSC), added path length (APL), and mean slice-wise Hausdorff distance (MSHD)) and dosimetrical accuracy. Distance-to-tumor analysis was performed to analyze to which extent the location of the OAR relative to planning target volume (PTV) has dosimetric impact, using Wilcoxon rank-sum tests. ResultsCT-atlas outperformed MR-atlas for 22/26 OARs. MR-DLC outperformed MR-atlas for all OARs. Highest median (95 %CI) vDSC and sDSC were found for the brainstem in MR-DLC: 0.92 (0.88–0.95) and 0.84 (0.77–0.89) respectively, as well as lowest MSHD: 0.27 (0.22–0.39)cm. Median dose differences (ΔD) were within ± 1 Gy for 24/26(92 %) OARs for all three models. Distance-to-tumor showed a significant correlation for ΔDmax,0.03cc-parameters when splitting the data in ≤ 4 cm and > 4 cm OAR-distance (p < 0.001). ConclusionMR-based DLC and CT-based atlas-contouring enable high-quality segmentation. It was shown that a combination of both CT- and MR-autocontouring models results in the best quality.

Integrating Workshop Training and Artificial Intelligence (AI) to Contour Organs at Risk (OARs) in Thoracic Radiation Therapy

This work aimed to quantify and explore the effects of expert training and AI on improving the consistency in Organs at Risk (OAR) delineations on the and the concordance to RTOG Atlas for future work of multi-center clinical trials. This is a result of an on-site clinical training workshop/Global Collaborative Oncology Group (GCOG) and an online cross-over study of AI application on key thoracic OARs. The participating experts were provided with the same de-identified cases of stage III Non-Small Cell Lung Cancer (NSCLC) and instructions on structure contouring and plan assessment criteria according to RTOG1106. Required OARs included lungs minus gross tumor volume, heart, spinal cord, esophagus and brachial plexus. The reference OAR contours were generated by one expert investigator of a RTOG trial on locally advanced NSCLC. The consistency between contours from the physicians and the concordance of contours to the Standard OAR contours were evaluated by the dice similarity coefficient (DSC). Post-workshop contours and AI-assistant contours were further assessed for consistency and concordance. A total of 35 centers participated in the workshop, 32 of them with complete DICOM files were eligible for the DSC analysis. Following the given instructions and atlas before training, for RTOG 1106, this experiment showed the mean DSC of the lung, heart, spinal cord, esophagus and brachial plexus were 0.91±0.15, 0.84±0.17, 0.72±0.22, 0.73±0.16 and 0.34±0.11, respectively. The DSC of the lung and heart of this case with earlier stage of disease were 0.96±0.06 and 0.88±0.07, respectively, which were better than that of the first case with mode locally advanced disease. The mean DSC of Contours after GCOG training of lung, heart, spinal cord, esophagus and brachial plexus were 0.92±0.15, 0.81±0.24, 0.70±0.26, 0.70±0.24 and 0.26±0.14, respectively. In the crossover experiment, the mean DSC of contours without AI assistance of lung, heart, spinal cord, esophagus and brachial plexus were 0.94±0.02, 0.87±0.07, 0.85±0.04, 0.81±0.04, and 0.38±0.12, respectively. The mean DSC of contours with AI assistance of lung, heart, spinal cord, esophagus and brachial plexus were 0.95±0.01, 0.91±0.04, 0.84±0.06, 0.81±0.02 and 0.40±0.05, respectively. The mean decreased time of delineating the lung, heart, spinal cord, esophagus and brachial plexus were 29 mins, 3 mins, 6 min, 4 min and 3 min, respectively, with a total of 45 mins for one case. This study demonstrated 1) remarkable variations in OAR delineation among 32 investigators, 2) notable improvement in concordance after the training workshop, and 3) significant improvement in consistency with AI assistance, along with time-saving of treating physicians. More training with better AI tools may further improve the contouring consistency, which is important for clinical practice and clinical trial.

P03.14.A DUMMY RUN QUALITY ASSURANCE STUDY IN THE ONGOING PHASE 3 RANDOMIZED PRO-GLIO TRIAL

Abstract BACKGROUND PRO-GLIO is an ongoing multicenter phase 3 randomized controlled trial in Norway and Sweden. In this trial, 224 adult patients with isocitrate dehydrogenase (IDH) -mutated diffuse gliomas grade 2-3, astrocytomas and oligodendrogliomas are randomized 1:1 between proton and photon radiotherapy. Study inclusion commenced in Oslo University Hospital (OUH) in January 2022, with other study-centers following. A number of outcomes will be assessed, including several survival, quality of life, and cognition parameters. For patients with diffuse gliomas delineation of target volumes when planning radiotherapy may be inherently difficult, not least due to the diffuse infiltrative growth pattern. There are also interobserver variability in delineation of organs at risk (OAR) and treatment planning. These factors may influence the planned and delivered treatment and thereby potentially affect clinical outcome. As part of the PRO-GLIO trial, all participating study-centers must undergo a qualifying radiotherapy dummy run procedure prior to initiation of patient inclusion to minimize the variations of these factors and to ensure protocol adherence. MATERIAL AND METHODS In the dummy run procedure, all centers delineate target volumes and OAR on a given case, and thereafter perform dose planning based on a consensus structure set for the same case. Swedish centers prepare photon and proton therapy plans, whereas Norwegian centers prepare photon plans only. Delineations are evaluated based on pre-defined quantitative and qualitative parameters. For all volumes - gross target volume (GTV), clinical target volume (CTV), and OARs - a Dice similarity coefficient (DSC) is calculated. RESULTS References values were made from consensus delineations among five radiation oncologists from OUH and Sahlgrenska University Hospital. For each of the target volumes and OARs, the DSC was cross calculated for the five experts, and the lowest value was used as a reference during the evaluation of submitted contours. Initial analysis has been performed based on data from the 6 centers which have completed the procedure. Mean DSC for GTV is 0.78, for CTV 0.87, for optic chiasm 0.42, for brain stem surface 0.92, and for right and left hippocampus 0.61 and 0.59, respectively. An inherent weakness of the DSC is that it is sensitive to the volume of the organ. This is seen in our data with the highest value for the largest volume (CTV) and a lower value for the optic chiasm. Thus, a qualitative evaluation was also performed. All submitted contours were approved. CONCLUSION The dummy run procedure revealed differences in OAR and target volume definition between centers and experts. Nonetheless, a DSC of 0.87 for CTV is re-assuring and prove a high coherence between participating centers. A dummy run procedure seems to be a valuable tool to ensure protocol adherence and draw attention to OAR and target volume delineation.

MultiTrans: Multi-scale feature fusion transformer with transfer learning strategy for multiple organs segmentation of head and neck CT images

Radiotherapy with precise segmentation of head and neck organs at risk (OARs) is one of the important treatment methods for head and neck cancer. In routine clinical practice, OARs are manually segmented by doctors to avoid irreversible adverse reactions caused by radiotherapy, which is time-consuming and laborious. To assist doctors in OARs segmentation, a MultiTrans framework with a multi-scale feature fusion module was proposed in this paper. In the multi-scale feature fusion module, the original image and the feature map of CNN were fused together to form a compound feature map for more complete high-resolution global information. In addition, the global information was also fully utilized in MultiTrans by using the feature map restored from the compound feature map in the skip connection. The multi-scale interactive high-resolution information can make full use of medical image information and obtain features more comprehensively, thus improve the segmentation accuracy. Experiments showed that MultiTrans had an average Dice score coefficient (DSC) of 74.01% in all organs, effectively improved segmentation accuracy. In addition, we proposed a transfer learning strategy for small organs by transferring the weight parameters of organs with a large amount of data to organs with a small amount of data to speed up the convergence of MultiTrans and reduce the demand for data volume in the MultiTrans. With this strategy, the average DSC of small organs was obviously increased, making the segmentation of small organs more accurate. The proposed framework and transfer learning strategy have the potential of assisting doctors in OARs delineation.

Quality Assurance based on Deep Learning for Pelvic OARs Delineation in Radiotherapy.

Correct delineation of organs at risk (OARs) is an important step for radiotherapy and it is also a time-consuming process that depends on many factors. An automatic quality assurance (QA) method based on deep learning (DL) was proposed to improve efficiency for detecting contouring errors of OARs. A total of 180 planning CT scan sets at the pelvic site and the corresponding OARs contours from clinics were enrolled in this study. Among them, 140 cases were randomly chosen as the training datasets, 20 cases were used as the validation datasets, and the remaining 20 cases were used as the test datasets. DL-based models were trained through data curation for data cleaning based on the Dice similarity coefficient and the 95th percentile Hausdorff distance between the original contours and the predicted contours. All contouring errors could be classified into two types; minor modification required and major modification required. The pass criteria were established using Bias- Corrected and Accelerated bootstrap on 20 manually reviewed validation datasets. The performance of the QA method was evaluated with the metrics of sensitivity, specificity, the area under the receiving operator characteristic curve (AUC), and detection rate sensitivity on the 20 test datasets. For all OARs, segmentation results after data curation were superior to those without. The sensitivity of the QA method was greater than 0.890 and the specificity was higher than 0.975. The AUCs were 0.948, 0.966, 0.965, and 0.932 for the bladder, right femoral head, left femoral head, and rectum, respectively. Almost all major errors could be detected by the automatic QA method, and the lowest detection rate sensitivity of minor errors was 0.863 for the rectum. QA of OARs is an important step for the correct implementation of radiotherapy. The DL-based QA method proposed in this study showed a high potential to automatically detect contouring errors with high precision. The method can be integrated into the existing radiotherapy procedures to improve the efficiency of delineating the OARs.

Magnetic resonance imaging organ at risk delineation for nasopharyngeal radiotherapy: Measuring the effectiveness of an educational intervention.

Magnetic resonance imaging (MRI) demonstrates superior soft tissue contrast and is increasingly being used in radiotherapy planning. This study evaluated the impact of an education workshop in minimising inter-observer variation (IOV) for nasopharyngeal organs at risk (OAR) delineation on MRI. Ten observers delineated 14 OARs on 4 retrospective nasopharyngeal MRI data sets. Standard contouring guidelines were provided pre-workshop. Following an education workshop on MRI OAR delineation, observers blinded to their original contours repeated the 14 OAR delineations. For comparison, reference volumes were delineated by two head and neck radiation oncologists. IOV was evaluated using dice similarity coefficient (DSC), Hausdorff distance (HD) and relative volume. Location of largest deviations was evaluated with centroid values. Observer confidence pre- and post-workshop was also recorded using a 6-point Likert scale. The workshop was deemed beneficial for an OAR if ≥50% of observers mean scores improved in any metric and ≥50% of observers' confidence improved. All OARs had ≥50% of observers improve in at least one metric. Base of tongue, larynx, spinal cord and right temporal lobe were the only OARs achieving a mean DSC score of ≥0.7. Base of tongue, left and right lacrimal glands, larynx, left optic nerve and right parotid gland all exhibited statistically significant HD improvements post-workshop (P < 0.05). Brainstem and left and right temporal lobes all had statistically significant relative volume improvements post-workshop (P < 0.05). Post-workshop observer confidence improvement was observed for all OARs (P < 0.001). The educational workshop reduced IOV and improved observers' confidence when delineating nasopharyngeal OARs on MRI.

Comparisons of Clinical and Reference Standard Contours to AI Auto-Segmentation: An Evaluation of 5 Commercial Models in Head and Neck Organ at Risk Delineation

<h3>Purpose/Objective(s)</h3> Auto-segmentation of organs at risk (OARs) using artificial intelligence (AI) has the potential to improve the quality of radiation oncology contouring while also improving efficiency. OAR segmentation models can be used to standardize across centers and within cooperative group trials and as quality benchmarks. However, variation exists in the delineation and quality of OARs in commercially available products. We evaluated 5 commercially available products to assess whether models could outperform the clinical OAR contours relative to the reference standard. <h3>Materials/Methods</h3> We archived clinically used contours of 35 H&N patients; additionally, disease site experts contoured 42 anatomically correct structures on each scan using international cooperative group and departmental OAR consensus guidelines. The disease site expert contours were considered the reference standard, while the clinically treated contours were used as the benchmark values. We applied the 5 commercially available AI based auto-segmentation tools to the same 35 CT datasets, generating a total of 4395 structures. To compare these structures volumetric and overlap Dice Similarity Coefficient (DSC) were evaluated. Overlap DSC only compares CT slices where contours from both structure sets are present, reducing the impact on DSC values for differing contouring practices for tubular OARs. <h3>Results</h3> Models 1-5 generated a range of 16 to 27 structures. The least commonly delineated structures were the external auditory canals, mastoids, and nasal cavity. For structures contoured by all models, the most variation as measured by volumetric DSC was found in the spinal cord and oral cavity, whereas the least variation was found in the brain. Detailed evaluation of each structure across these platforms has been analyzed and is available for presentation as well as evaluation of modes of failure. Briefly, there were 33 complete contour misses out of 4395 inferences (0.75%). Selecting the 9 OARs delineated by all 5 models, the overall median volumetric DSC values were 0.84, 0.86, 0.80, 0.79, and 0.83, respectively. Median volumetric DSC for all structures in each model were 0.75, 0.74, 0.71, 0.69, and 0.61, with 2 of 5 models performing at or better than clinical contours when compared to the reference standard. <h3>Conclusion</h3> Across 5 commercially available AI segmentation models, 2 of 5 models performed at or above the clinical standard, with significant heterogeneity in the number and definition of OARs. No models performed to expert qualitative review, which was based on international cooperative group consensus guidelines. Available segmentation tools have significant room for improvement and more formal consensus standards of OARs are needed. Saliency models by voxel and second check quality systems can be developed by employing more than 1 model.

Evaluation of AI vs. Clinical Experts SBRT-Thorax Computed Tomography OARs Delineation

<h3>Purpose/Objective(s)</h3> Organ-at-risk (OAR) delineation is an essential step for radiotherapy treatment (RT) planning. Standard practice during the last decade referred to manual delineation of OARs from medical experts. Manual delineation is the current standard of practice and can be a tedious, time-consuming process, prone to errors due to intra and inter-observer variations. This is the case for lung stereotactic body radiation therapy (SBRT) due to the variety of structures to be contoured. The latest advances of artificial intelligence methods offer new perspectives towards a fully automatic delineation. This study aims at evaluating an AI-based auto-contouring solution (AC) and compare its clinical acceptability against contours delineated by experts (EC). <h3>Materials/Methods</h3> In this study, a CE/FDA cleared anatomically preserving ensemble deep-learning architecture contouring solution was used. The AI-solution was trained using more than 300+ fully annotated multi-centric SBRT cases according to the ESTRO guidelines (male and female) cases. A fully external cohort of 30 additional patients was considered to assess performance quantitatively and qualitatively. In terms of quantitative metrics, the Dice coefficient was used while qualitative assessment was done through a scoring mechanism: A/acceptable, B/ acceptable after minor corrections, and C/ not acceptable for clinical use. In terms of reference/comparisons with clinical experts, two independent annotations were used while treatment experts' contours were blended blindly with the AI-solution at 50/50 ratio. Random blending at the patient level was performed guaranteeing that, among contours being evaluated per patient and OAR, the 50/50 split was satisfied. <h3>Results</h3> The mean Dice coefficient between expert and AI was 88 % which dropped to 87% among experts. Overall clinical acceptability after aggregating blinded evaluations for the combined categories (A+B) was 95% for the AC which dropped to 81% for EC. When looking at the overall acceptability of contours that do not require any modifications (A), the AC (67%) outperformed the EC (48%) by significant margin. AC outperformed the EC on 14 structures. <h3>Conclusion</h3> This work reports clinical evaluation of AC solution on CT scans for Thorax-SBRT. Our results suggest that the deep learning model could be a viable clinical alternative to the human expert offering treatment standardization and potentially better outcomes.

Clinical Validation of a Deep-Learning Segmentation Software in Head and Neck: An Early Analysis in a Developing Radiation Oncology Center.

Background: Organs at risk (OARs) delineation is a crucial step of radiotherapy (RT) treatment planning workflow. Time-consuming and inter-observer variability are main issues in manual OAR delineation, mainly in the head and neck (H & N) district. Deep-learning based auto-segmentation is a promising strategy to improve OARs contouring in radiotherapy departments. A comparison of deep-learning-generated auto-contours (AC) with manual contours (MC) was performed by three expert radiation oncologists from a single center. Methods: Planning computed tomography (CT) scans of patients undergoing RT treatments for H&N cancers were considered. CT scans were processed by Limbus Contour auto-segmentation software, a commercial deep-learning auto-segmentation based software to generate AC. H&N protocol was used to perform AC, with the structure set consisting of bilateral brachial plexus, brain, brainstem, bilateral cochlea, pharyngeal constrictors, eye globes, bilateral lens, mandible, optic chiasm, bilateral optic nerves, oral cavity, bilateral parotids, spinal cord, bilateral submandibular glands, lips and thyroid. Manual revision of OARs was performed according to international consensus guidelines. The AC and MC were compared using the Dice similarity coefficient (DSC) and 95% Hausdorff distance transform (DT). Results: A total of 274 contours obtained by processing CT scans were included in the analysis. The highest values of DSC were obtained for the brain (DSC 1.00), left and right eye globes and the mandible (DSC 0.98). The structures with greater MC editing were optic chiasm, optic nerves and cochleae. Conclusions: In this preliminary analysis, deep-learning auto-segmentation seems to provide acceptable H&N OAR delineations. For less accurate organs, AC could be considered a starting point for review and manual adjustment. Our results suggest that AC could become a useful time-saving tool to optimize workload and resources in RT departments.

FcTC-UNet: Fine-grained Combination of Transformer and CNN for Thoracic Organs Segmentation.

Precise segmentation of organs at risk (OARs) in computed tomography (CT) images is an essential step for lung cancer radiotherapy. However, the manual delineation of OARs is time-consuming and subject to inter-observer variation. Although U-like architecture has achieved great success in medical image segmentation recently, it exhibits the limitations in modeling long-range dependencies. As an alternative structure, Transformers have emerged due to the outstanding capability of capturing the global contextual information provided by Self-Attention(SA) mechanism. However, Transformers need more computational cost than CNNs for introducing the SA module. In this paper, we propose a novel module named fine-grained combination of Transformer and CNN(FcTC). FcTC module is composed of dual-path extractor and fusing unit to effectively extract local information and model long-distance dependency. Then we build FcTC-UNet to automatically segment the OARs in thoracic CT images. The experiments results demonstrate that the proposed method achieves better performance over other state-of-the-art methods.

A Unified 3D Framework for Organs-at-Risk Localization and Segmentation for Radiation Therapy Planning.

Automatic localization and segmentation of organs-at-risk (OAR) in CT are essential pre-processing steps in medical image analysis tasks, such as radiation therapy planning. For instance, the segmentation of OAR surrounding tumors enables the maximization of radiation to the tumor area without compromising the healthy tissues. However, the current medical workflow requires manual delineation of OAR, which is prone to errors and is annotator-dependent. In this work, we aim to introduce a unified 3D pipeline for OAR localization-segmentation rather than novel localization or segmentation architectures. To the best of our knowledge, our proposed framework fully enables the exploitation of 3D context information inherent in medical imaging. In the first step, a 3D multi-variate regression network predicts organs' centroids and bounding boxes. Secondly, 3D organ-specific segmentation networks are leveraged to generate a multi-organ segmentation map. Our method achieved an overall Dice score of 0.9260 ± 0.18% on the VISCERAL dataset containing CT scans with varying fields of view and multiple organs.

A dosemetric and radiobiological impact of VMAT and 3DCRT on lumbosacral plexuses, an underestimated organ at risk in cervical cancer patients.

BackgroundThe purpose of this study was to evaluate dosimetric and radiobiological difference between volumetric modulated arc therapy (VMAT) and 3-dimensional conformal radiotherapy (3DCRT) in organ at risk (OAR) lumbosacral plexus (LSP) in cervical cancer patients.Materials and methods30 patients of cervical cancer who were treated using 3DCRT or VMAT along with chemotherapy followed by brachytherapy were enrolled. LSP was delineated retrospectively. Dosimetric and radiobiological difference was evaluated. Patients were followed for radiation induced lumbosacral plexopathy (RILSP).ResultsMedian follow-up was 12 months (3–16 months). 53.3% of patients were treated by 3DCRT and 46.7% by VMAT. The mean (±SD) LSP volume: 119.03 ± 15 cm3. The mean volume percentages (%) of the LSP: V5, V10, V20, V30, V40, V50, V55, and V60 were 100%, 99.8%, 99.2%, 94.3%, 84.03%, 59.7%, 0%, 0%, respectively. All patients received doses to the LSP in excess of 50 Gy, one patient received 55 Gy. A statistically significant difference was observed in the median value of V20, V30, V40, V50, D50, P2, P4, P7, P8, P9, and P10 across two different techniques of radiotherapy — VMAT and 3DCRT. None of the patients presented with RILSP. NTCP value was less in VMAT plans compared to 3DCRT, which is also statistically significant.ConclusionRILSP is a rare and often refractory complication of pelvic radiotherapy. Advance radiotherapy technique with proper OAR delineation and constraint can prevent the occurrence of RILSP. VMAT has potential benefits for the probability of dose reduction in LSP. Further studies are required focusing on dose distribution in LSP–OAR and radiotherapy modality.

Development and evaluation of an automated EPTN-consensus based organ at risk atlas in the brain on MRI

Background and purposeDuring radiotherapy treatment planning, avoidance of organs at risk (OARs) is important. An international consensus-based delineation guideline was recently published with 34 OARs in the brain. We developed an MR-based OAR autosegmentation atlas and evaluated its performance compared to manual delineation. Materials and methodsAnonymized cerebral T1-weighted MR scans (voxel size 0.9 × 0.9 × 0.9 mm3) were available. OARs were manually delineated according to international consensus. Fifty MR scans were used to develop the autosegmentation atlas in a commercially available treatment planning system (Raystation®). The performance of this atlas was tested on another 40 MR scans by automatically delineating 34 OARs, as defined by the 2018 EPTN consensus. Spatial overlap between manual and automated delineations was determined by calculating the Dice similarity coefficient (DSC). Two radiation oncologists determined the quality of each automatically delineated OAR. The time needed to delineate all OARs manually or to adjust automatically delineated OARs was determined. ResultsDSC was ≥ 0.75 in 31 (91 %) out of 34 automated OAR delineations. Delineations were rated by radiation oncologists as excellent or good in 29 (85 %) out 34 OAR delineations, while 4 were rated fair (12 %) and 1 was rated poor (3 %). Interobserver agreement between the radiation oncologists ranged from 77-100 % per OAR. The time to manually delineate all OARs was 88.5 minutes, while the time needed to adjust automatically delineated OARs was 15.8 minutes. ConclusionAutosegmentation of OARs enables high-quality contouring within a limited time. Accurate OAR delineation helps to define OAR constraints to mitigate serious complications and helps with the development of NTCP models.

Thorough design and pre-trial quality assurance (QA) decrease dosimetric impact of delineation and dose planning variability in the STRICTLUNG and STARLUNG trials for stereotactic body radiotherapy (SBRT) of central and ultra-central lung tumours

IntroductionSBRT of central lung tumours implies significant risk of toxicity. We are initiating two phase II trials prescribing 56 Gy/eight fractions to PTV, allowing for dose escalation of GTV. We prioritize organs at risk (OAR) constraints over target coverage, making the treatment plans very sensitive to OAR delineation variations. The aim of this study is to quantify the dosimetric impact of contouring variations and to provide a thorough description of pre-trial quality assurance to be used in upcoming trials to provide consistent clinical care. Materials and methodsDelineation: Seven physicians delineated OAR in three rounds, with evaluations in-between. For each patient case, seven treatment plans, repeatedly using each of the OAR structure sets from the seven physicians, were made and compared to evaluate the dosimetric effect of delineation variability.Treatment planning: Treatment plans for seven cases were made at six departments in two rounds, with discussion in-between. ResultsOAR delineation variation between centres resulted in high variabilities in OAR dose for simulated plans and led to potential overdosage of the lobar bronchus (constraint: D0.03cc < 45 Gy), with maximum doses ranging between 58 Gy (first round), and 50 Gy (third round). For mediastinal tissue, the constraint (D0.03cc < 45 Gy) was violated for the majority of the delineations in all three rounds, with maximum doses of 84 Gy (first round), and 72 Gy (third round).For the treatment planning study, the range of the standard deviation for GTV mean dose was 12.8–18.5 Gy (first round) and 2.8–3.5 Gy (second round). ConclusionsEven small variations in OAR delineation led to high OAR overdosage. The study demonstrates the importance of having extensive QA procedures in place before initiating clinical trials on dose escalation in SBRT.

3D Lightweight Network for Simultaneous Registration and Segmentation of Organs-at-Risk in CT Images of Head and Neck Cancer.

Image-guided radiation therapy (IGRT) is the most effective treatment for head and neck cancer. The successful implementation of IGRT requires accurate delineation of organ-at-risk (OAR) in the computed tomography (CT) images. In routine clinical practice, OARs are manually segmented by oncologists, which is time-consuming, laborious, and subjective. To assist oncologists in OAR contouring, we proposed a three-dimensional (3D) lightweight framework for simultaneous OAR registration and segmentation. The registration network was designed to align a selected OAR template to a new image volume for OAR localization. A region of interest (ROI) selection layer then generated ROIs of OARs from the registration results, which were fed into a multiview segmentation network for accurate OAR segmentation. To improve the performance of registration and segmentation networks, a centre distance loss was designed for the registration network, an ROI classification branch was employed for the segmentation network, and further, context information was incorporated to iteratively promote both networks' performance. The segmentation results were further refined with shape information for final delineation. We evaluated registration and segmentation performances of the proposed framework using three datasets. On the internal dataset, the Dice similarity coefficient (DSC) of registration and segmentation was 69.7% and 79.6%, respectively. In addition, our framework was evaluated on two external datasets and gained satisfactory performance. These results showed that the 3D lightweight framework achieved fast, accurate and robust registration and segmentation of OARs in head and neck cancer. The proposed framework has the potential of assisting oncologists in OAR delineation.

Three-dimensional deep neural network for automatic delineation of cervical cancer in planning computed tomography images.

PurposeRadiation therapy is an essential treatment modality for cervical cancer, while accurate and efficient segmentation methods are needed to improve the workflow. In this study, a three‐dimensional V‐net model is proposed to automatically segment clinical target volume (CTV) and organs at risk (OARs), and to provide prospective guidance for low lose area.Material and methodsA total of 130 CT datasets were included. Ninety cases were randomly selected as the training data, with 10 cases used as the validation data, and the remaining 30 cases as testing data. The V‐net model was implemented with Tensorflow package to segment the CTV and OARs, as well as regions of 5 Gy, 10 Gy, 15 Gy, and 20 Gy isodose lines covered. The auto‐segmentation by V‐net was compared to auto‐segmentation by U‐net. Four representative parameters were calculated to evaluate the accuracy of the delineation, including Dice similarity coefficients (DSC), Jaccard index (JI), average surface distance (ASD), and Hausdorff distance (HD).ResultsThe V‐net and U‐net achieved the average DSC value for CTV of 0.85 and 0.83, average JI values of 0.77 and 0.75, average ASD values of 2.58 and 2.26, average HD of 11.2 and 10.08, respectively. As for the OARs, the performance of the V‐net model in the colon was significantly better than the U‐net model (p = 0.046), and the performance in the kidney, bladder, femoral head, and pelvic bones were comparable to the U‐net model. For prediction of low‐dose areas, the average DSC of the patients’ 5 Gy dose area in the test set were 0.88 and 0.83, for V‐net and U‐net, respectively.ConclusionsIt is feasible to use the V‐Net model to automatically segment cervical cancer CTV and OARs to achieve a more efficient radiotherapy workflow. In the delineation of most target areas and OARs, the performance of V‐net is better than U‐net. It also offers advantages with its feature of predicting the low‐dose area prospectively before radiation therapy (RT).

Prospective Evaluation of Prostate and Organs at Risk Segmentation Software for MRI-based Prostate Radiation Therapy.

The segmentation of the prostate and surrounding organs at risk (OARs) is a necessary workflow step for performing dose-volume histogram analyses of prostate radiation therapy procedures. Low-dose-rate prostate brachytherapy (LDRPBT) is a curative prostate radiation therapy treatment that delivers a single fraction of radiation over a period of days. Prior studies have demonstrated the feasibility of fully convolutional networks to segment the prostate and surrounding OARs for LDRPBT dose-volume histogram analyses. However, performance evaluations have been limited to measures of global similarity between algorithm predictions and a reference. To date, the clinical use of automatic segmentation algorithms for LDRPBT has not been evaluated, to the authors' knowledge. The purpose of this work was to assess the performance of fully convolutional networks for prostate and OAR delineation on a prospectively identified cohort of patients who underwent LDRPBT by using clinically relevant metrics. Thirty patients underwent LDRPBT and were imaged with fully balanced steady-state free precession MRI after implantation. Custom automatic segmentation software was used to segment the prostate and four OARs. Dose-volume histogram analyses were performed by using both the original automatically generated contours and the physician-refined contours. Dosimetry parameters of the prostate, external urinary sphincter, and rectum were compared without and with the physician refinements. This study observed that physician refinements to the automatic contours did not significantly affect dosimetry parameters. Keywords: MRI, Neural Networks, Radiation Therapy, Radiation Therapy/Oncology, Genital/Reproductive, Prostate, Segmentation, Dosimetry Supplemental material is available for this article. © RSNA, 2022.

3D-T2W-TSE radiotherapy treatment planning MRI using compressed sensing acceleration for prostate cancer: Image quality and delineation value.

To assess the image quality and delineation value of compressed sensing (CS)-accelerated 3D T2W turbo-spin-echo (TSE) sequence for radiotherapy treatment planning (RTP) of prostate cancer. An optimized CS-accelerated 3D-T2W-TSE was determined by volunteer imaging and applied for clinical RTP-MRI. This optimized CS-accelerated planning MRI and the standardized adaptive MRI acquired at 1.5T were retrospectively analyzed in 26 prostate cancer patients who were to receive MR-guided radiotherapy. Signal-to-noise ratio (SNR) and relative contrast ratio (CR) were quantitatively assessed. Image quality and artifacts were qualitatively assessed using a five-point scale rating. Delineation value in the prostate and organs-at-risk (OARs) was also rated and compared. Wilcoxon signed-rank test was used for SNR, relative CR, and rating comparisons. The interobserver rating agreement was evaluated by percent agreement. Significantly better SNR and relative CR in the prostate, rectum, bowel, penis, and penile bulb, while significantly worse in the cauda equina, were observed on the planning MRI. Significantly better ratings of image quality and artifacts were given to the planning MRI, with much less Gibbs ringing and reconstruction artifacts. Significantly better delineation value rating was achieved on the planning MRI in the prostate, seminal vesicle, rectum, penis, penile bulb, and testes, while significantly worse in the cauda equina. A strong to almost perfect interobserver rating agreement was obtained. This study suggested that CS acceleration is applicable and valuable in prostate RTP-MRI. CS-accelerated 3D-T2W-TSE images should benefit the delineation of prostate and many OARs.