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Automated population-based planning for whole brain radiation therapy.

ABSTRACT: Treatment planning for whole-brain radiation treatment is technically a simple process, but in practice it takes valuable clinical time of repetitive and tedious tasks. This report presents a method that automatically segments the relevant target and normal tissues, and creates a treatment plan in only a few minutes after patient simulation. Segmentation of target and critical structures is performed automatically through morphological operations on the soft tissue and was validated by comparing with manual clinical segmentation using the Dice coefficient and Hausdorff distance. The treatment plan is generated by searching a database of previous cases for patients with similar anatomy. In this search, each database case is ranked in terms of similarity using a customized metric designed for sensitivity by including only geometrical changes that affect the dose distribution. The database case with the best match is automatically modified to replace relevant patient info and isocenter position while maintaining original beam and MLC settings. Fifteen patients with marginally acceptable treatment plans were used to validate the method. In each of these cases the anatomy was accurately segmented, but the beams and MLC settings led to a suboptimal treatment plan by either underdosing the brain or excessively irradiating critical normal tissues. For each case, the anatomy was automatically segmented with the proposed method, and the automated and anual segmentations were then compared. The mean Dice coefficient was 0.97, with a standard deviation of 0.008 for the brain, 0.85 ± 0.009 for the eyes, and 0.67 ± 0.11 for the lens. The mean Euclidian distance was 0.13 ± 0.13 mm for the brain, 0.27± 0.31 for the eye, and 2.34 ± 7.23 for the lens. Each case was then subsequently matched against a database of 70 validated treatment plans and the best matching plan (termed autoplanned), was compared retrospectively with the clinical plans in terms of brain coverage and maximum doses to critical structures. Maximum doses were reduced by a maximum of 8.37 Gy for the left eye (mean 2.08), 11.67 for the right eye (1.90) and, respectively, 25.44 (5.59) for the left lens and 24.40 (4.85) for the right lens. Time to generate the autoplan, including the segmentation, was 3-4min. Automated database- based matching is an alternative to classical treatment planning that improves quality while providing a cost-effective solution to planning through modifying previous validated plans to match a current patient's anatomy.

SUBMITTER: Schreibmann E

PROVIDER: S-EPMC5690177 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Automated population-based planning for whole brain radiation therapy.

Schreibmann Eduard E Fox Tim T Curran Walter W Shu Hui-Kuo HK Crocker Ian I

Journal of applied clinical medical physics 20150908 5

Treatment planning for whole-brain radiation treatment is technically a simple process, but in practice it takes valuable clinical time of repetitive and tedious tasks. This report presents a method that automatically segments the relevant target and normal tissues, and creates a treatment plan in only a few minutes after patient simulation. Segmentation of target and critical structures is performed automatically through morphological operations on the soft tissue and was validated by comparing ...[more]

PMID: 26699292

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Project description:Radiation therapy (RT) plays a vital role in the treatment of brain cancers, but it frequently results in cognitive decline in the patients who receive it. Because the underlying mechanisms for this decline remain poorly understood, the brain is typically treated as a single, uniform volume when evaluating the toxic effects of RT plans. This ignorance represents a significant deficit in the field of radiation oncology, as the technology exists to manipulate dose distributions to spare regions of the brain, but there exists no body of knowledge regarding what is critical to spare. This deficit exists due to the numerous confounding factors that are frequently associated with radiotherapy, including the tumors themselves, other treatments such as surgery and chemotherapy, and dose gradients across the brain. Here, we present a case in which a 57-year-old male patient received a uniform dose of radiation across the whole brain, did not receive concurrent chemotherapy, had minimal surgical intervention and a small tumor burden, and received resting-state functional magnetic resonance imaging (fMRI) scans both before and after RT. To our knowledge, this is the first study on the effects of whole-brain radiotherapy on functional network organization, and this patient's treatment regimen represents a rare and non-replicable opportunity to isolate the effects of radiation on functional connectivity. We observed substantial changes in the subject's behavior and functional network organization over a 12-month timeframe. Interestingly, the homogenous radiation dose to the brain had a heterogeneous effect on cortical networks, and the functional networks most affected correspond with observed cognitive behavioral deficits. This novel study suggests that the cognitive decline that occurs after whole-brain radiation therapy may be network specific and related to the disruption of large-scale distributed functional systems, and it indicates that fMRI is a promising avenue of study for optimizing cognitive outcomes after RT.

Project description:PURPOSE:As computed tomography (CT) imaging is the most commonly used modality for treatment planning in radiation therapy, metal artifacts in the planning CT images may complicate the target delineation and reduce the dose calculation accuracy. Although current CT scanners do provide certain correction steps, it is a common understanding that there is not a universal solution yet to the metal artifact reduction (MAR) in general. Particularly noting the importance of MAR for radiation treatment planning, we propose a novel MAR method in this work that recruits an additional tilted CT scan and synthesizes nearly metal-artifact-free CT images. METHODS:The proposed method is based on the facts that the most pronounced metal artifacts in CT images show up along the x-ray beam direction traversing multiple metallic objects and that a tilted CT scan can provide complementary information free of such metal artifacts in the earlier scan. Although the tilted CT scan would contain its own metal artifacts in the images, the artifacts may manifest in a different fashion leaving a chance to concatenate the two CT images with the metal artifacts much suppressed. We developed an image processing technique that uses the structural similarity (SSIM) for suppressing the metal artifacts. On top of the additional scan, we proposed to use an existing MAR method for each scan if necessary to further suppress the metal artifacts. RESULTS:The proposed method was validated by a simulation study using the pelvic region of an XCAT numerical phantom and also by an experimental study using the head part of the Rando phantom. The proposed method was found to effectively reduce the metal artifacts. Quantitative analyses revealed that the proposed method reduced the mean absolute percentages of the error by up to 86% and 89% in the simulation and experimental studies, respectively. CONCLUSIONS:It was confirmed that the proposed method, using complementary information acquired from an additional tilted CT scan, can provide nearly metal-artifact-free images for the treatment planning.

Dataset Information

Automated population-based planning for whole brain radiation therapy.

Publications

Automated population-based planning for whole brain radiation therapy.

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