Dataset Information

Spatial optimization for radiation therapy of brain tumours.

ABSTRACT: Glioblastomas are the most common primary brain tumours. They are known for their highly aggressive growth and invasion, leading to short survival times. Treatments for glioblastomas commonly involve a combination of surgical intervention, chemotherapy, and external beam radiation therapy (XRT). Previous works have not only successfully modelled the natural growth of glioblastomas in vivo, but also show potential for the prediction of response to radiation prior to treatment. This suggests that the efficacy of XRT can be optimized before treatment in order to yield longer survival times. However, while current efforts focus on optimal scheduling of radiotherapy treatment, they do not include a similarly sophisticated spatial optimization. In an effort to improve XRT, we present a method for the spatial optimization of radiation profiles. We expand upon previous results in the general problem and examine the more physically reasonable cases of 1-step and 2-step radiation profiles during the first and second XRT fractions. The results show that by including spatial optimization in XRT, while retaining a constant prescribed total dose amount, we are able to increase the total cell kill from the clinically-applied uniform case.

SUBMITTER: Meaney C

PROVIDER: S-EPMC6599149 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Spatial optimization for radiation therapy of brain tumours.

Meaney Cameron C Stastna Marek M Kardar Mehran M Kohandel Mohammad M

PloS one 20190628 6

Glioblastomas are the most common primary brain tumours. They are known for their highly aggressive growth and invasion, leading to short survival times. Treatments for glioblastomas commonly involve a combination of surgical intervention, chemotherapy, and external beam radiation therapy (XRT). Previous works have not only successfully modelled the natural growth of glioblastomas in vivo, but also show potential for the prediction of response to radiation prior to treatment. This suggests that ...[more]

PMID: 31251755

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Project description:AimThis study aimed to explore associations between radiation dose and patient-reported outcomes in patients with a primary non-glioblastoma brain tumour treated with radiation therapy (RT), with a focus on health-related quality-of-life (HRQoL) and self-reported cognitive function.MethodsIn this cross-sectional study, 78 patients who had received RT for a non-glioblastoma primary brain tumour, underwent neuropsychological testing and completed questionnaires on HRQoL, cognitive function, fatigue, depression, anxiety and perceived stress. The study explores the association between HRQoL scores, self-reported cognitive function and radiation doses to total brain, brainstem, hippocampus, thalamus, temporal lobes and frontal lobes. In addition, we examined correlations between neuropsychological test scores and self-reported cognitive function.ResultsThe median time between RT and testing was 4.6 years (range 1-9 years). Patients who had received high mean radiation doses to the total brain had low HRQoL scores (Cohen's d = 0.50, p = 0.04), brainstem (d = 0.65, p = 0.01) and hippocampus (d = 0.66, p = 0.01). High mean doses to the total brain were also associated with low scores on self-reported cognitive functioning (Cohen's d = 0.64, p = 0.02), brainstem (d = 0.55, p = 0.03), hippocampus (d = 0.76, p < 0.01), temporal lobes (d = 0.70, p < 0.01) and thalamus (d = 0.64, p = 0.01). Self-reported cognitive function correlated well with neuropsychological test scores (correlation range 0.27-0.54.).ConclusionsHigh radiation doses to specific brain structures may be associated with impaired HRQoL and self-reported cognitive function with potentially negative implications to patients' daily lives. Patient-reported outcomes of treatment-related side-effects and their associations with radiation doses to the brain and its sub-structures may provide important information on radiation tolerance to the brain and sub-structures.

Project description:PurposeOnline adaptive radiotherapy (ART) requires accurate and efficient auto-segmentation of target volumes and organs-at-risk (OARs) in mostly cone-beam computed tomography (CBCT) images, which often have severe artifacts and lack soft-tissue contrast, making direct segmentation very challenging. Propagating expert-drawn contours from the pretreatment planning CT through traditional or deep learning (DL)-based deformable image registration (DIR) can achieve improved results in many situations. Typical DL-based DIR models are population based, that is, trained with a dataset for a population of patients, and so they may be affected by the generalizability problem.MethodsIn this paper, we propose a method called test-time optimization (TTO) to refine a pretrained DL-based DIR population model, first for each individual test patient, and then progressively for each fraction of online ART treatment. Our proposed method is less susceptible to the generalizability problem and thus can improve overall performance of different DL-based DIR models by improving model accuracy, especially for outliers. Our experiments used data from 239 patients with head-and-neck squamous cell carcinoma to test the proposed method. First, we trained a population model with 200 patients and then applied TTO to the remaining 39 test patients by refining the trained population model to obtain 39 individualized models. We compared each of the individualized models with the population model in terms of segmentation accuracy.ResultsThe average improvement of the Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95) of segmentation can be up to 0.04 (5%) and 0.98 mm (25%), respectively, with the individualized models compared to the population model over 17 selected OARs and a target of 39 patients. Although the average improvement may seem mild, we found that the improvement for outlier patients with structures of large anatomical changes is significant. The number of patients with at least 0.05 DSC improvement or 2 mm HD95 improvement by TTO averaged over the 17 selected structures for the state-of-the-art architecture VoxelMorph is 10 out of 39 test patients. By deriving the individualized model using TTO from the pretrained population model, TTO models can be ready in about 1 min. We also generated the adapted fractional models for each of the 39 test patients by progressively refining the individualized models using TTO to CBCT images acquired at later fractions of online ART treatment. When adapting the individualized model to a later fraction of the same patient, the model can be ready in less than a minute with slightly improved accuracy.ConclusionsThe proposed TTO method is well suited for online ART and can boost segmentation accuracy for DL-based DIR models, especially for outlier patients where the pretrained models fail.

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Spatial optimization for radiation therapy of brain tumours.

Publications

Spatial optimization for radiation therapy of brain tumours.

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