Refining Critical Structure Contouring in STereotactic Arrhythmia Radioablation (STAR): Benchmark Results and Consensus Guidelines from the STOPSTORM.eu Consortium.

BACKGROUND AND PURPOSE In patients with recurrent ventricular tachycardia (VT), STereotactic Arrhythmia Radioablation (STAR) shows promising results. The STOPSTORM consortium was established to investigate and harmonise STAR treatment in Europe. The primary goals of this benchmark study were to standardise contouring of organs at risk (OAR) for STAR, including detailed substructures of the heart, and accredit each participating centre. MATERIALS AND METHODS Centres within the STOPSTORM consortium were asked to delineate 31 OAR in three STAR cases. Delineation was reviewed by the consortium expert panel and after a dedicated workshop feedback and accreditation was provided to all participants. Further quantitative analysis was performed by calculating DICE similarity coefficients (DSC), median distance to agreement (MDA), and 95th percentile distance to agreement (HD95). RESULTS Twenty centres participated in this study. Based on DSC, MDA and HD95, the delineations of well-known OAR in radiotherapy were similar, such as lungs (median DSC=0.96, median MDA=0.1mm and median HD95=1.1mm) and aorta (median DSC=0.90, median MDA=0.1mm and median HD95=1.5mm). Some centres did not include the gastro-oesophageal junction, leading to differences in stomach and oesophagus delineations. For cardiac substructures, such as chambers (median DSC=0.83, median MDA=0.2mm and median HD95=0.5mm), valves (median DSC=0.16, median MDA=4.6mm and median HD95=16.0mm), coronary arteries (median DSC=0.4, median MDA=0.7mm and median HD95=8.3mm) and the sinoatrial and atrioventricular nodes (median DSC=0.29, median MDA=4.4mm and median HD95=11.4mm), deviations between centres occurred more frequently. After the dedicated workshop all centres were accredited and contouring consensus guidelines for STAR were established. CONCLUSION This STOPSTORM multi-centre critical structure contouring benchmark study showed high agreement for standard radiotherapy OAR. However, for cardiac substructures larger disagreement in contouring occurred, which may have significant impact on STAR treatment planning and dosimetry evaluation. To standardize OAR contouring, consensus guidelines for critical structure contouring in STAR were established

Segmentation of Planning Target Volume in CT Series for Total Marrow Irradiation Using U-Net

Radiotherapy (RT) is a key component in the treatment of various cancers, including Acute Lymphocytic Leukemia (ALL) and Acute Myelogenous Leukemia (AML). Precise delineation of organs at risk (OARs) and target areas is essential for effective treatment planning. Intensity Modulated Radiotherapy (IMRT) techniques, such as Total Marrow Irradiation (TMI) and Total Marrow and Lymph node Irradiation (TMLI), provide more precise radiation delivery compared to Total Body Irradiation (TBI). However, these techniques require time-consuming manual segmentation of structures in Computerized Tomography (CT) scans by the Radiation Oncologist (RO). In this paper, we present a deep learning-based auto-contouring method for segmenting Planning Target Volume (PTV) for TMLI treatment using the U-Net architecture. We trained and compared two segmentation models with two different loss functions on a dataset of 100 patients treated with TMLI at the Humanitas Research Hospital between 2011 and 2021. Despite challenges in lymph node areas, the best model achieved an average Dice score of 0.816 for PTV segmentation. Our findings are a preliminary but significant step towards developing a segmentation model that has the potential to save radiation oncologists a considerable amount of time. This could allow for the treatment of more patients, resulting in improved clinical practice efficiency and more reproducible contours

Reducing Tumour Volume Uncertainty for the Benefit of Radiation Therapy Cancer Patients

The efficacy of radiotherapy is dependent on its precision and accuracy. Increasingly conformal, modulated radiation fields can be reproducibly delivered to small, complex volumes within the human body. However, treatment is not without uncertainty. This thesis focuses on limitations in radiotherapy accuracy due to uncertainty in delineation of the volume requiring treatment

Auto-Segmentation of Target Volume and Organs-at-risks for Radiotherapy in Breast Cancer patients

목적: 유방암 방사선 치료에서 치료 체적에 대한 정확한 타겟 그리기는 중요하다. 하지만 방사선 치료 계획 과정에 타겟 그리기는 의료진의 부담을 주고 있으며, 의료진 간의 편차는 존재하고 있다. 본 연구에서는 Deep learning-based auto-segmentation (DLBAS)의 성능을 atlas-based segmentation solutions (ABAS)와 비교하고, 임상 의사의 관점에서 유용성을 평가하고, 최종적으로 외부 타당도 조사를 통하여 유방암 방사선 치료에서 자동 구획화의 가능성을 규명하고자 한다. 대상 및 방법: 유방암 방사선 치료 체적과 정상장기들에 대하여 한 명의 연구진에 의하여 구획화 정보를 생성하였다. Convolutional neural network 알고리즘을 이용하여 auto-contours를 생성하였고, Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD)를 이용하여 ABAS와 비교하였다. DLBAS에 의해 생성된 auto-contours의 질적인 평가를 조사하였고, manual contours와 방사선 치료 선량-체적 히스토그램을 비교하여 주요 선량평가분석을 시행하였다. 마지막으로 2개 기관의 11명의 전문가에게 manual contour를 그릴 것을 요청하여 데이터를 수집하였다. 외부 위원회를 통해 가장 최적의 치료 체적을 선정하였고, 나머지 10명의 contour와 DLBAS에 의해 생성된 auto-contour의 성능을 비교하여 순위 평가를 시행하였다. 결과: 제안된 DLBAS 모델은 대부분의 체적 (특히, 치료 체적과 심장 세부구조)에서 ABAS보다 더 일관된 결과와 높은 DSC와 낮은 HD 결과 값을 보였다. ABAS는 연조직의 정상장기와 조영제를 쓰지 않은 새로운 데이터 셋에서 DLBAS에 비해, 제한적인 성능을 보였다. 질적 평가를 위한 설문조사가 시행되었고, 중위수 8점으로 manual contour와 auto-contour 사이의 차이가 크지 않다고 대답하였으며, 임상에서 도움이 될 것으로 답변하였다. 또한 선량평가 분석 결과에서 차이는 미미하였다. 외부 검증 결과, 9개의 정상장기를 그리는데 평균 37분이 걸렸고, DLBAS는 6분이 걸렸다. Auto-contour는 전체 12개 중 1위 manual contour와 비교하였을 때 가장 DSC상 차이가 적었으며, HSD상 2번째로 차이가 적었다. 정상장기에서 가장 편차가 높았던 부위는 유방이었다. 결론: 유방 방사선 치료 계획에서 DLBAS의 실현가능성은 이번 연구에서 다각도로 검증되었다. 의료진의 최종 수정 과정은 필수적이지만, 앞으로 DLBAS는 방사선 치료를 도울 수 있는 훌륭한 가능성을 보여주었다.open박

Incorporating Cardiac Substructures Into Radiation Therapy For Improved Cardiac Sparing

Growing evidence suggests that radiation therapy (RT) doses to the heart and cardiac substructures (CS) are strongly linked to cardiac toxicities, though only the heart is considered clinically. This work aimed to utilize the superior soft-tissue contrast of magnetic resonance (MR) to segment CS, quantify uncertainties in their position, assess their effect on treatment planning and an MR-guided environment. Automatic substructure segmentation of 12 CS was completed using a novel hybrid MR/computed tomography (CT) atlas method and was improved upon using a 3-dimensional neural network (U-Net) from deep learning. Intra-fraction motion due to respiration was then quantified. The inter-fraction setup uncertainties utilizing a novel MR-linear accelerator were also quantified. Treatment planning comparisons were performed with and without substructure inclusions and methods to reduce radiation dose to sensitive CS were evaluated. Lastly, these described technologies (deep learning U-Net) were translated to an MR-linear accelerator and a segmentation pipeline was created. Automatic segmentations from the hybrid MR/CT atlas was able to generate accurate segmentations for the chambers and great vessels (Dice similarity coefficient (DSC) \u3e 0.75) but coronary artery segmentations were unsuccessful (DSC\u3c0.3). After implementing deep learning, DSC for the chambers and great vessels was ≥0.85 along with an improvement in the coronary arteries (DSC\u3e0.5). Similar accuracy was achieved when implementing deep learning for MR-guided RT. On average, automatic segmentations required ~10 minutes to generate per patient and deep learning only required 14 seconds. The inclusion of CS in the treatment planning process did not yield statistically significant changes in plan complexity, PTV, or OAR dose. Automatic segmentation results from deep learning pose major efficiency and accuracy gains for CS segmentation offering high potential for rapid implementation into radiation therapy planning for improved cardiac sparing. Introducing CS into RT planning for MR-guided RT presented an opportunity for more effective sparing with limited increase in plan complexity

Incorporating Cardiac Substructures Into Radiation Therapy For Improved Cardiac Sparing

Growing evidence suggests that radiation therapy (RT) doses to the heart and cardiac substructures (CS) are strongly linked to cardiac toxicities, though only the heart is considered clinically. This work aimed to utilize the superior soft-tissue contrast of magnetic resonance (MR) to segment CS, quantify uncertainties in their position, assess their effect on treatment planning and an MR-guided environment. Automatic substructure segmentation of 12 CS was completed using a novel hybrid MR/computed tomography (CT) atlas method and was improved upon using a 3-dimensional neural network (U-Net) from deep learning. Intra-fraction motion due to respiration was then quantified. The inter-fraction setup uncertainties utilizing a novel MR-linear accelerator were also quantified. Treatment planning comparisons were performed with and without substructure inclusions and methods to reduce radiation dose to sensitive CS were evaluated. Lastly, these described technologies (deep learning U-Net) were translated to an MR-linear accelerator and a segmentation pipeline was created. Automatic segmentations from the hybrid MR/CT atlas was able to generate accurate segmentations for the chambers and great vessels (Dice similarity coefficient (DSC) \u3e 0.75) but coronary artery segmentations were unsuccessful (DSC\u3c0.3). After implementing deep learning, DSC for the chambers and great vessels was ≥0.85 along with an improvement in the coronary arteries (DSC\u3e0.5). Similar accuracy was achieved when implementing deep learning for MR-guided RT. On average, automatic segmentations required ~10 minutes to generate per patient and deep learning only required 14 seconds. The inclusion of CS in the treatment planning process did not yield statistically significant changes in plan complexity, PTV, or OAR dose. Automatic segmentation results from deep learning pose major efficiency and accuracy gains for CS segmentation offering high potential for rapid implementation into radiation therapy planning for improved cardiac sparing. Introducing CS into RT planning for MR-guided RT presented an opportunity for more effective sparing with limited increase in plan complexity