| 1. |
- Bailey, D. L., et al.
(författare)
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Combined PET/MRI : Global Warming-Summary Report of the 6th International Workshop on PET/MRI, March 27-29, 2017, Tubingen, Germany
- 2018
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Ingår i: Molecular Imaging and Biology. - : SPRINGER. - 1536-1632 .- 1860-2002. ; 20:1, s. 4-20
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Forskningsöversikt (refereegranskat)abstract
- The 6th annual meeting to address key issues in positron emission tomography (PET)/magnetic resonance imaging (MRI) was held again in Tubingen, Germany, from March 27 to 29, 2017. Over three days of invited plenary lectures, round table discussions and dialogue board deliberations, participants critically assessed the current state of PET/MRI, both clinically and as a research tool, and attempted to chart future directions. The meeting addressed the use of PET/MRI and workflows in oncology, neurosciences, infection, inflammation and chronic pain syndromes, as well as deeper discussions about how best to characterise the tumour microenvironment, optimise the complementary information available from PET and MRI, and how advanced data mining and bioinformatics, as well as information from liquid biomarkers (circulating tumour cells and nucleic acids) and pathology, can be integrated to give a more complete characterisation of disease phenotype. Some issues that have dominated previous meetings, such as the accuracy of MR-based attenuation correction (AC) of the PET scan, were finally put to rest as having been adequately addressed for the majority of clinical situations. Likewise, the ability to standardise PET systems for use in multicentre trials was confirmed, thus removing a perceived barrier to larger clinical imaging trials. The meeting openly questioned whether PET/MRI should, in all cases, be used as a whole-body imaging modality or whether in many circumstances it would best be employed to give an in-depth study of previously identified disease in a single organ or region. The meeting concluded that there is still much work to be done in the integration of data from different fields and in developing a common language for all stakeholders involved. In addition, the participants advocated joint training and education for individuals who engage in routine PET/MRI. It was agreed that PET/MRI can enhance our understanding of normal and disrupted biology, and we are in a position to describe the in vivo nature of disease processes, metabolism, evolution of cancer and the monitoring of response to pharmacological interventions and therapies. As such, PET/MRI is a key to advancing medicine and patient care.
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| 2. |
- 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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| 3. |
- Unkelbach, Jan, et al.
(författare)
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The role of computational methods for automating and improving clinical target volume definition
- 2020
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Ingår i: Radiotherapy and Oncology. - : Elsevier BV. - 0167-8140 .- 1879-0887. ; 153, s. 15-25
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Tidskriftsartikel (refereegranskat)abstract
- Treatment planning in radiotherapy distinguishes three target volume concepts: the gross tumor volume(GTV), the clinical target volume (CTV), and the planning target volume (PTV). Over time, GTV definitionand PTV margins have improved through the development of novel imaging techniques and better imageguidance, respectively. CTV definition is sometimes considered the weakest element in the planning pro-cess. CTV definition is particularly complex since the extension of microscopic disease cannot be seenusing currently available in-vivo imaging techniques. Instead, CTV definition has to incorporate knowl-edge of the patterns of tumor progression. While CTV delineation has largely been considered the domainof radiation oncologists, this paper, arising from a 2019 ESTRO Physics research workshop, discusses thecontributions that medical physics and computer science can make by developing computational meth-ods to support CTV definition. First, we overview the role of image segmentation algorithms, which mayin part automate CTV delineation through segmentation of lymph node stations or normal tissues repre-senting anatomical boundaries of microscopic tumor progression. The recent success of deep convolu-tional neural networks has also enabled learning entire CTV delineations from examples. Second, wediscuss the use of mathematical models of tumor progression for CTV definition, using as example theapplication of glioma growth models to facilitate GTV-to-CTV expansion for glioblastoma that is consis-tent with neuroanatomy. We further consider statistical machine learning models to quantify lymphaticmetastatic progression of tumors, which may eventually improve elective CTV definition. Lastly, we dis-cuss approaches to incorporate uncertainty in CTV definition into treatment plan optimization as well asgeneral limitations of the CTV concept in the case of infiltrating tumors without natural boundaries.
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