| 1. |
- Ångström-Brännström, Charlotte, et al.
(författare)
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Children Undergoing Radiotherapy : Swedish Parents' Experiences and Suggestions for Improvement
- 2015
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Ingår i: PLOS ONE. - : Public library science. - 1932-6203. ; 10:10
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Tidskriftsartikel (refereegranskat)abstract
- Approximately 300 children, from 0 to 18 years old, are diagnosed with cancer in Sweden every year. Of these children, 80-90 of them undergo radiotherapy treatment for their cancer. Although radiotherapy is an encounter with advanced technology, few studies have investigated the child's and the parent's view of the procedure. As part of an ongoing multi-center study aimed to improve patient preparation and the care environment in pediatric radiotherapy, this article reports the findings from interviews with parents at baseline. The aim of the present study was twofold: to describe parents' experience when their child undergoes radiotherapy treatment, and to report parents' suggestions for improvements during radiotherapy for their children. Sixteen mothers and sixteen fathers of children between 2-16 years old with various cancer diagnoses were interviewed. Data were analyzed using content analysis. The findings showed that cancer and treatment turns people's lives upside down, affecting the entire family. Further, the parents experience the child's suffering and must cope with intense feelings. Radiotherapy treatment includes preparation by skilled and empathetic staff. The parents gradually find that they can deal with the process; and lastly, parents have suggestions for improvements during the radiotherapy treatment. An overarching theme emerged: that despair gradually turns to a sense of security, with a sustained focus on and close interaction with the child. In conclusion, an extreme burden was experienced around the start of radiotherapy, though parents gradually coped with the process.
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| 2. |
- Gustafsson, Christian, et al.
(författare)
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Registration free automatic identification of gold fiducial markers in MRI target delineation images for prostate radiotherapy
- 2017
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Ingår i: Medical physics (Lancaster). - : Wiley. - 0094-2405. ; 44:11, s. 5563-5574
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: The superior soft tissue contrast of magnetic resonance imaging (MRI) compared to computed tomography (CT) has urged the integration of MRI and elimination of CT in radiotherapy treatment (RT) for prostate. An intraprostatic gold fiducial marker (GFM) appears hyperintense on CT. On T2-weighted (T2w) MRI target delineation images, the GFM appear as a small signal void similar to calcifications and post biopsy fibrosis. It can therefore be difficult to identify the markers without CT. Detectability of GFMs can be improved using additional MR images, which are manually registered to target delineation images. This task requires manual labor, and is associated with interoperator differences and image registration errors. The aim of this work was to develop and evaluate an automatic method for identification of GFMs directly in the target delineation images without the need for image registration.Methods: T2w images, intended for target delineation, and multiecho gradient echo (MEGRE) images intended for GFM identification, were acquired for prostate cancer patients. Signal voids in the target delineation images were identified as GFM candidates. The GFM appeared as round, symmetric, signal void with increasing area for increasing echo time in the MEGRE images. These image features were exploited for automatic identification of GFMs in a MATLAB model using a patient training dataset (n = 20). The model was validated on an independent patient dataset (n = 40). The distances between the identified GFM in the target delineation images and the GFM in CT images were measured. A human observatory study was conducted to validate the use of MEGRE images.Results: The sensitivity, specificity, and accuracy of the automatic method and the observatory study was 84%, 74%, 81% and 98%, 94%, 97%, respectively. The mean absolute difference in the GFM distances for the automatic method and observatory study was 1.28 1.25 mm and 1.14 +/- 1.06 mm, respectively.Conclusions: Multiecho gradient echo images were shown to be a feasible and reliable way to perform GFM identification. For clinical practice, visual inspection of the results from the automatic method is needed at the current stage.
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| 3. |
- Jonsson, Joakim, 1984-, et al.
(författare)
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Treatment planning of intracranial targets on MRI derived substitute CT data
- 2013
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Ingår i: Radiotherapy and Oncology. - : Elsevier BV. - 0167-8140 .- 1879-0887. ; 108:1, s. 118-122
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: The use of magnetic resonance imaging (MRI) as a complement to computed tomography (CT) in the target definition procedure for radiotherapy is increasing. To eliminate systematic uncertainties due to image registration, a workflow based entirely on MRI may be preferable. In the present pilot study, we investigate dose calculation accuracy for automatically generated substitute CT (s-CT) images of the head based on MRI. We also produce digitally reconstructed radiographs (DRRs) from s-CT data to evaluate the feasibility of patient positioning based on MR images. METHODS AND MATERIALS: Five patients were included in the study. The dose calculation was performed on CT, s-CT, s-CT data without inhomogeneity correction and bulk density assigned MRI images. Evaluation of the results was performed using point dose and dose volume histogram (DVH) comparisons, and gamma index evaluation. RESULTS: The results demonstrate that the s-CT images improves the dose calculation accuracy compared to the method of non-inhomogeneity corrected dose calculations (mean improvement 2.0 percentage points) and that it performs almost identically to the method of bulk density assignment. The s-CT based DRRs appear to be adequate for patient positioning of intra-cranial targets, although further investigation is needed on this subject. CONCLUSIONS: The s-CT method is very fast and yields data that can be used for treatment planning without sacrificing accuracy.
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| 4. |
- Nyholm, Tufve, et al.
(författare)
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A national approach for automated collection of standardized and population-based radiation therapy data in Sweden
- 2016
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Ingår i: Radiotherapy and Oncology. - : Elsevier BV. - 0167-8140 .- 1879-0887. ; 119:2, s. 344-350
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To develop an infrastructure for structured and automated collection of interoperable radiation therapy (RT) data into a national clinical quality registry. Materials and methods: The present study was initiated in 2012 with the participation of seven of the 15 hospital departments delivering RT in Sweden. A national RT nomenclature and a database for structured unified storage of RT data at each site (Medical Information Quality Archive, MIQA) have been developed. Aggregated data from the MIQA databases are sent to a national RT registry located on the same IT platform (INCA) as the national clinical cancer registries. Results: The suggested naming convention has to date been integrated into the clinical workflow at 12 of 15 sites, and MIQA is installed at six of these. Involvement of the remaining 3/15 RT departments is ongoing, and they are expected to be part of the infrastructure by 2016. RT data collection from ARIA (R), Mosaiq (R), Eclipse (TM), and Oncentra (R) is supported. Manual curation of RT-structure information is needed for approximately 10% of target volumes, but rarely for normal tissue structures, demonstrating a good compliance to the RT nomenclature. Aggregated dose/volume descriptors are calculated based on the information in MIQA and sent to INCA using a dedicated service (MIQA2INCA). Correct linkage of data for each patient to the clinical cancer registries on the INCA platform is assured by the unique Swedish personal identity number. Conclusions: An infrastructure for structured and automated prospective collection of syntactically inter operable RT data into a national clinical quality registry for RT data is under implementation. Future developments include adapting MIQA to other treatment modalities (e.g. proton therapy and brachytherapy) and finding strategies to harmonize structure delineations. How the RT registry should comply with domain-specific ontologies such as the Radiation Oncology Ontology (ROO) is under discussion.
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| 5. |
- Vu, Minh Hoang, 1988-
(författare)
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Resource efficient automatic segmentation of medical images
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cancer is one of the leading causes of death worldwide. In 2020, there were around 10 million cancer deaths and nearly 20 million new cancer cases in the world. Radiation therapy is essential in cancer treatments because half of the cancer patients receive radiation therapy at some point. During a radiotherapy treatment planning (RTP), an oncologist must manually outline two types of areas of the patient’s body: target, which will be treated, and organs-at-risks (OARs), which are essential to avoid. This step is called delineation. The purpose of the delineation is to generate a sufficient dose plan that can provide adequate radiation dose to a tumor and limit the radiation exposure to healthy tissue. Therefore, accurate delineations are essential to achieve this goal.Delineation is tedious and demanding for oncologists because it requires hours of concentrating work doing a repeated job. This is a RTP bottleneck which is often time- and resource-intensive. Current software, such as atlasbased techniques, can assist with this procedure by registering the patient’s anatomy to a predetermined anatomical map. However, the atlas-based methods are often slowed down and erroneous for patients with abnormal anatomies.In recent years, deep learning (DL) methods, particularly convolutional neural networks (CNNs), have led to breakthroughs in numerous medical imaging applications. The core benefits of CNNs are weight sharing and that they can automatically detect important visual features. A typical application of CNNs for medical images is to automatically segment tumors, organs, and structures, which is assumed to save radiation oncologists much time when delineating. This thesis contributes to resource efficient automatic segmentation and covers different aspects of resource efficiency.In Paper I, we proposed a novel end-to-end cascaded network for semantic segmentation in brain tumors in the multi-modal magnetic resonance imaging challenge in 2019. The proposed method used the hierarchical structure of the tumor sub-regions and was one of the top-ranking teams in the task of quantification of uncertainty in segmentation. A follow-up work to this paper was ranked second in the same task in the same challenge a year later.We systematically assessed the segmentation performance and computational costs of the technique called pseudo-3D as a function of the number of input slices in Paper II. We compared the results to typical two-dimensional (2D) and three-dimensional (3D) CNNs and a method called triplanar orthogonal 2D. The typical pseudo-3D approach considers adjacent slices to be several image input channels. We discovered that a substantial benefit from employing multiple input slices was apparent for a specific input size.We introduced a novel loss function in Paper III to address diverse issues, including imbalanced datasets, partially labeled data, and incremental learning. The proposed loss function adjusts to the given data to use all accessible data, even if some lack annotations. We show that the suggested loss function also performs well in an incremental learning context, where an existing model can be modified to incorporate the delineations of newly appearing organs semi-automatically.In Paper IV, we proposed a novel method for compressing high-dimensional activation maps, which are the primary source of memory use in modern systems. We examined three distinct compression methods for the activation maps to accomplishing this. We demonstrated that the proposed method induces a regularization effect that acts on the layer weight gradients. By employing the proposed technique, we reduced activation map memory usage by up to 95%.We investigated the use of generative adversarial networks (GANs) to enlarge a small dataset by generating synthetic images in Paper V. We use the real and generated data during training CNNs for the downstream segmentation tasks. Inspired by an existing GAN, we proposed a conditional version to generate high-dimensional and high-quality medical images of different modalities and their corresponding label maps. We evaluated the quality of the generated medical images and the effect of this augmentation on the performance of the segmentation task on six datasets.
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| 6. |
- Andersson, Jonas, 1975-, et al.
(författare)
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Artificial intelligence and the medical physics profession-A Swedish perspective
- 2021
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Ingår i: Physica Medica-European Journal of Medical Physics. - : Elsevier BV. - 1120-1797 .- 1724-191X. ; 88, s. 218-225
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Tidskriftsartikel (refereegranskat)abstract
- Background: There is a continuous and dynamic discussion on artificial intelligence (AI) in present-day society. AI is expected to impact on healthcare processes and could contribute to a more sustainable use of resources allocated to healthcare in the future. The aim for this work was to establish a foundation for a Swedish perspective on the potential effect of AI on the medical physics profession. Materials and methods: We designed a survey to gauge viewpoints regarding AI in the Swedish medical physics community. Based on the survey results and present-day situation in Sweden, a SWOT analysis was performed on the implications of AI for the medical physics profession. Results: Out of 411 survey recipients, 163 responded (40%). The Swedish medical physicists with a professional license believed (90%) that AI would change the practice of medical physics but did not foresee (81%) that AI would pose a risk to their practice and career. The respondents were largely positive to the inclusion of AI in educational programmes. According to self-assessment, the respondents' knowledge of and workplace preparedness for AI was generally low. Conclusions: From the survey and SWOT analysis we conclude that AI will change the medical physics profession and that there are opportunities for the profession associated with the adoption of AI in healthcare. To overcome the weakness of limited AI knowledge, potentially threatening the role of medical physicists, and build upon the strong position in Swedish healthcare, medical physics education and training should include learning objectives on AI.
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| 7. |
- Björeland, Ulrika, 1974-
(författare)
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MRI in prostate cancer : implications for target volume
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Prostate cancer (PCa) is the most common cancer among men, with 10 000 new cases per year in Sweden [1]. To diagnose PCa, magnetic resonance imaging (MRI) is used to identify and classify the disease. The patient’s treatment strategy depends on PCa classification and clinical data, which are weighted together into a risk group classification from 1–5. For patients with higher risk classes (>3), radiotherapy together with hormone therapy is a common treatment option [2].In radiotherapy (RT), individual treatment plans are created based on the patient’s anatomy. These plans are based on computed tomography (CT), often supplemented with MRI images. MRI and CT complement each other, as MRI has better soft tissue contrast and CT has better bone contrast. Based on the images, the volumes to be treated (target) and the volumes to be avoided (risk organs) are defined. Prostate RT is complex, and there are uncertainties regarding the patient's internal movements and how the patient is positioned before each treatment. To account for these uncertainties, the radiation field is expanded (extended margins to target) to ensure that the treatment volume receives its radiotherapy. RT is most often given in fractions. Fractionation, dose, and treatment volume depend on the patient’s risk category. The treatment area can be, for example, only prostate, prostate with extra radiation dose (boost) to an intraprostatic tumour, or prostate with lymph node (LN) irradiation. LN irradiation is most often given for preventive purposes for PCa with a risk classification >4, which means no cancer has been identified, but any microscopic spread to the LNs is being treated profylactically.In RT, target identification is essential both in the treatment planning images (CT/MRI) and at treatment. Studies have shown that PCa often re-occurs in or near the volume of the dominant (often largest) intraprostatic tumour [3, 4], and this volume is relevant for boosting. For patients treated with hormone therapy before radiotherapy, tumour identification is complicated. Hormones change the tumour characteristics, affecting the image contrast and making the tumour difficult to identify. To study this, we investigated whether texture analysis could identify the tumour volume after hormone therapy (paper II). However, even with texture analysis, the tumour was difficult to identify. A follow-up study examined whether the image information in MRI images taken before hormone therapy could indicate how the treatment fell out (paper IV). However, no correlation was seen between image features and the progression of PCa.Identifying the target and correctly positioning the patient for each treatment fraction is the most important procedure in radiotherapy. The prostate is a mobile organ; therefore, intraprostatic fiducial markers are inserted before treatment planning to reduce positioning uncertainties. Each radiotherapy session begins with an X-ray image where the markers are visible, and the radiation can be delivered based on the markers' position. The markers are also used as guidance for large target volumes, such as for prostate with LN irradiation. With better knowledge of the prostate and LN movements, the margins can potentially be reduced, followed by reduced radiation dose to healthy tissue and therefore reduced side effects for patients. Movements in the radiotherapy volume were the focus of paper I. Using MRI images, the movements of the prostate and LNs were measured during the course of radiotherapy, and we found that LN movement is independent of the movement of the prostate and that the movement varies in the target volume.In addition to the recurrence of PCa in the tumour area, there is an increased risk of recurrence in the prostate periphery close to the rectum. Since the rectum and prostate are in contact for some patients, RT must be adapted to make rectum side effects tolerable. One way to increase the distance between the prostate and the rectum is to inject a gel between the two organs. The distance makes it easier to achieve a better dose distribution to the PCa. This idea resulted in paper III, where patients were given a gel between the prostate and rectum. MRI was used to check the stability of the gel during the course of RT and was evaluated together with long-term follow-up of the patient’s well-being and acceptance of the gel. We found that the radiation dose to the rectum was lower with a spacer, although the spacer was not completely stable during treatment.
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| 8. |
- Edmund, Jens M., et al.
(författare)
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A review of substitute CT generation for MRI-only radiation therapy
- 2017
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Ingår i: Radiation Oncology. - : Springer Science and Business Media LLC. - 1748-717X. ; 12
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Forskningsöversikt (refereegranskat)abstract
- Radiotherapy based on magnetic resonance imaging as the sole modality (MRI-only RT) is an area of growing scientific interest due to the increasing use of MRI for both target and normal tissue delineation and the development of MR based delivery systems. One major issue in MRI-only RT is the assignment of electron densities (ED) to MRI scans for dose calculation and a similar need for attenuation correction can be found for hybrid PET/MR systems. The ED assigned MRI scan is here named a substitute CT (sCT). In this review, we report on a collection of typical performance values for a number of main approaches encountered in the literature for sCT generation as compared to CT. A literature search in the Scopus database resulted in 254 papers which were included in this investigation. A final number of 50 contributions which fulfilled all inclusion criteria were categorized according to applied method, MRI sequence/contrast involved, number of subjects included and anatomical site investigated. The latter included brain, torso, prostate and phantoms. The contributions geometric and/or dosimetric performance metrics were also noted. The majority of studies are carried out on the brain for 5-10 patients with PET/MR applications in mind using a voxel based method. T1 weighted images are most commonly applied. The overall dosimetric agreement is in the order of 0.3-2.5%. A strict gamma criterion of 1% and 1mm has a range of passing rates from 68 to 94% while less strict criteria show pass rates > 98%. The mean absolute error (MAE) is between 80 and 200 HU for the brain and around 40 HU for the prostate. The Dice score for bone is between 0.5 and 0.95. The specificity and sensitivity is reported in the upper 80s% for both quantities and correctly classified voxels average around 84%. The review shows that a variety of promising approaches exist that seem clinical acceptable even with standard clinical MRI sequences. A consistent reference frame for method benchmarking is probably necessary to move the field further towards a widespread clinical implementation.
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| 9. |
- Grefve, Josefine, et al.
(författare)
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Histopathology-validated gross tumor volume delineations of intraprostatic lesions using PSMA-positron emission tomography/multiparametric magnetic resonance imaging
- 2024
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Ingår i: Physics and Imaging in Radiation Oncology. - : Elsevier. - 2405-6316. ; 31
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Tidskriftsartikel (refereegranskat)abstract
- Background and purpose: Dose escalation in external radiotherapy of prostate cancer shows promising results in terms of biochemical disease-free survival. Boost volume delineation guidelines are sparse which may cause high interobserver variability. The aim of this research was to characterize gross tumor volume (GTV) delineations based on multiparametric magnetic resonance imaging (mpMRI) and prostate specific membrane antigen-positron emission tomography (PSMA-PET) in relation to histopathology-validated Gleason grade 4 and 5 regions.Material and methods: The study participants were examined with [68Ga]PSMA-PET/mpMRI prior to radical prostatectomy. Four radiation oncologists delineated GTVs in 15 study participants, on four different image types; T2-weighted (T2w), diffusion weighted imaging (DWI), dynamic contrast enhanced (DCE) and PSMA-PET scans separately. The simultaneous truth and performance level estimation (STAPLE) algorithm was used to generate combined GTVs. GTVs were subsequently compared to histopathology. We analysed how Dice similarity coefficient (DSC) and lesion coverage are affected by using single versus multiple image types as well as by adding a clinical target volume (CTV) margin.Results: Median DSC (STAPLE) for different GTVs varied between 0.33 and 0.52. GTVPSMA-PET/mpMRI generated the highest median lesion coverage at 0.66. Combining different image types achieved similar lesion coverage as adding a CTV margin to contours from a single image type, while reducing non-malignant tissue inclusion within the target volume.Conclusion: The combined use of mpMRI or PSMA-PET/mpMRI shows promise, achieving higher DSC and lesion coverage while minimizing non-malignant tissue inclusion, in comparison to the use of a single image type with an added CTV margin.
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| 10. |
- Grönlund, Eric, et al.
(författare)
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Dose painting of prostate cancer based on Gleason score correlations with apparent diffusion coefficients
- 2018
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Ingår i: Acta Oncologica. - : Taylor & Francis. - 0284-186X .- 1651-226X. ; 57:5, s. 574-581
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Tidskriftsartikel (refereegranskat)abstract
- Background: Gleason scores for prostate cancer correlates with an increased recurrence risk after radiotherapy (RT). Furthermore, higher Gleason scores correlates with decreasing apparent diffusion coefficient (ADC) data from diffusion weighted MRI (DWI-MRI). Based on these observations, we present a formalism for dose painting prescriptions of prostate volumes based on ADC images mapped to Gleason score driven dose-responses.Methods: The Gleason score driven dose-responses were derived from a learning data set consisting of pre-RT biopsy data and post-RT outcomes for 122 patients treated with a homogeneous dose to the prostate. For a test data set of 18 prostate cancer patients with pre-RT ADC images, we mapped the ADC data to the Gleason driven dose-responses by using probability distributions constructed from published Gleason score correlations with ADC data. We used the Gleason driven dose-responses to optimize dose painting prescriptions that maximize the tumor control probability (TCP) with equal average dose as for the learning sets homogeneous treatment dose.Results: The dose painting prescriptions increased the estimated TCP compared to the homogeneous dose by 0–51% for the learning set and by 4–30% for the test set. The potential for individual TCP gains with dose painting correlated with increasing Gleason score spread and larger prostate volumes. The TCP gains were also found to be larger for patients with a low expected TCP for the homogeneous dose prescription.Conclusions: We have from retrospective treatment data demonstrated a formalism that yield ADC driven dose painting prescriptions for prostate volumes that potentially can yield significant TCP increases without increasing dose burdens as compared to a homogeneous treatment dose. This motivates further development of the approach to consider more accurate ADC to Gleason mappings, issues with delivery robustness of heterogeneous dose distributions, and patient selection criteria for design of clinical trials.
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