| 1. |
- Eekers, Danielle B. P., et al.
(författare)
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The EPTN consensus-based atlas for CT- and MR-based contouring in neuro-oncology
- 2018
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Ingår i: Radiotherapy and Oncology. - : ELSEVIER IRELAND LTD. - 0167-8140 .- 1879-0887. ; 128:1, s. 37-43
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To create a digital, online atlas for organs at risk (OAR) delineation in neuro-oncology based on high-quality computed tomography (Cr) and magnetic resonance (MR) imaging. Methods: CT and 3 Tesla (3T) MR images (slice thickness 1 mm with intravenous contrast agent) were obtained from the same patient and subsequently fused. In addition, a 7T MR without intravenous contrast agent was obtained from a healthy volunteer. Based on discussion between experienced radiation oncologists, the clinically relevant organs at risk (OARs) to be included in the atlas for neuro-oncology were determined, excluding typical head and neck OARs previously published. The draft atlas was delineated by a senior radiation oncologist, 2 residents in radiation oncology, and a senior neuro-radiologist incorporating relevant available literature. The proposed atlas was then critically reviewed and discussed by European radiation oncologists until consensus was reached. Results: The online atlas includes one CT-scan at two different window settings and one MR scan (3T) showing the OARs in axial, coronal and sagittal view. This manuscript presents the three-dimensional descriptions of the fifteen consensus OARs for neuro-oncology. Among these is a new OAR relevant for neuro-cognition, the posterior cerebellum (illustrated on 7T MR images). Conclusion: In order to decrease inter- and intra-observer variability in delineating OARs relevant for neuro-oncology and thus derive consistent dosimetric data, we propose this atlas to be used in photon and particle therapy. The atlas is available online at w.cancerdata.c and will be updated whenever required.
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| 2. |
- Gupta, Vikas, et al.
(författare)
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Fast and robust adaptation of organs-at-risk delineations from planning scans to match daily anatomy in pre-treatment scans for online-adaptive radiotherapy of abdominal tumors
- 2018
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Ingår i: Radiotherapy and Oncology. - : ELSEVIER IRELAND LTD. - 0167-8140 .- 1879-0887. ; 127:2, s. 332-338
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To validate a novel deformable image registration (DIR) method for online adaptation of planning organ-at-risk (OAR) delineations to match daily anatomy during hypo-fractionated RT of abdominal tumors. Materials and methods: For 20 liver cancer patients, planning OAR delineations were adapted to daily anatomy using the DIR on corresponding repeat CTs. The DIRs accuracy was evaluated for the entire cohort by comparing adapted and expert-drawn OAR delineations using geometric (Dice Similarity Coefficient (DSC), Modified Hausdorff Distance (MHD) and Mean Surface Error (MSE)) and dosimetric (D-max and D-mean) measures. Results: For all OARs, DIR achieved average DSC, MHD and MSE of 86%, 2.1 mm, and 1.7 mm, respectively, within 20 s for each repeat CT. Compared to the baseline (translations), the average improvements ranged from 2% (in heart) to 24% (in spinal cord) in DSC, and 25% (in heart) to 44% (in right kidney) in MHD and MSE. Furthermore, differences in dose statistics (D-max, D-mean and D-2%) using delineations from an expert and the proposed DIR were found to be statistically insignificant (p amp;gt; 0.01). Conclusion: The validated DIR showed potential for online-adaptive radiotherapy of abdominal tumors as it achieved considerably high geometric and dosimetric correspondences with the expert-drawn OAR delineations, albeit in a fraction of time required by experts. (C) 2018 The Authors. Published by Elsevier B.V.
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| 3. |
- Wang, Yibing, et al.
(författare)
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An individualized strategy to estimate the effect of deformable registration uncertainty on accumulated dose in the upper abdomen
- 2018
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Ingår i: Physics in Medicine and Biology. - : IOP PUBLISHING LTD. - 0031-9155 .- 1361-6560. ; 63:12
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Tidskriftsartikel (refereegranskat)abstract
- In the abdomen, it is challenging to assess the accuracy of deformable image registration (DIR) for individual patients, due to the lack of clear anatomical landmarks, which can hamper clinical applications that require high accuracy DIR, such as adaptive radiotherapy. In this study, we propose and evaluate a methodology for estimating the impact of uncertainties in DIR on calculated accumulated dose in the upper abdomen, in order to aid decision making in adaptive treatment approaches. Sixteen liver metastasis patients treated with SBRT were evaluated. Each patient had one planning and three daily treatment CT-scans. Each daily CT scan was deformably registered 132 times to the planning CT-scan, using a wide range of parameter settings for the registration algorithm. A subset of realistic registrations was then objectively selected based on distances between mapped and target contours. The underlying 3D transformations of these registrations were used to assess the corresponding uncertainties in voxel positions, and delivered dose, with a focus on accumulated maximum doses in the hollow OARs, i.e. esophagus, stomach, and duodenum. The number of realistic registrations varied from 5 to 109, depending on the patient, emphasizing the need for individualized registration parameters. Considering for all patients the realistic registrations, the 99th percentile of the voxel position uncertainties was 5.6 +/- 3.3 mm. This translated into a variation (difference between 1st and 99th percentile) in accumulated Dmax in hollow OARs of up to 3.3 Gy. For one patient a violation of the accumulated stomach dose outside the uncertainty band was detected. The observed variation in accumulated doses in the OARs related to registration uncertainty, emphasizes the need to investigate the impact of this uncertainty for any DIR algorithm prior to clinical use for dose accumulation. The proposed method for assessing on an individual patient basis the impact of uncertainties in DIR on accumulated dose is in principle applicable for all DIR algorithms allowing variation in registration parameters.
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