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- Eekers, Danielle B. P., et al.
(author)
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The EPTN consensus-based atlas for CT- and MR-based contouring in neuro-oncology
- 2018
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In: Radiotherapy and Oncology. - : ELSEVIER IRELAND LTD. - 0167-8140 .- 1879-0887. ; 128:1, s. 37-43
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Journal article (peer-reviewed)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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- Zegers, Catharina M L, et al.
(author)
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In Vivo Quantification of Hypoxic and Metabolic Status of NSCLC Tumors Using [18F]HX4 and [18F]FDG-PET/CT Imaging.
- 2014
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In: Clinical cancer research. - 1078-0432. ; 20:24, s. 6389-6397
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Journal article (peer-reviewed)abstract
- PURPOSE: Increased tumor metabolism and hypoxia are related to poor prognosis in solid tumors, including non-small cell lung cancer (NSCLC). PET imaging is a noninvasive technique that is frequently used to visualize and quantify tumor metabolism and hypoxia. The aim of this study was to perform an extensive comparison of tumor metabolism using 2[(18)F]fluoro-2-deoxy-d-glucose (FDG)-PET and hypoxia using HX4-PET imaging.EXPERIMENTAL DESIGN: FDG- and HX4-PET/CT images of 25 patients with NSCLC were coregistered. At a global tumor level, HX4 and FDG parameters were extracted from the gross tumor volume (GTV). The HX4 high-fraction (HX4-HF) and HX4 high-volume (HX4-HV) were defined using a tumor-to-blood ratio > 1.4. For FDG high-fraction (FDG-HF) and FDG high-volume (FDG-HV), a standardized uptake value (SUV) > 50% of SUVmax was used. We evaluated the spatial correlation between HX4 and FDG uptake within the tumor, to quantify the (mis)match between volumes with a high FDG and high HX4 uptake.RESULTS: At a tumor level, significant correlations were observed between FDG and HX4 parameters. For the primary GTV, the HX4-HF was three times smaller compared with the FDG-HF. In 53% of the primary lesions, less than 1 cm(3) of the HX4-HV was outside the FDG-HV; for 37%, this volume was 1.9 to 12 cm(3). Remarkably, a distinct uptake pattern was observed in 11%, with large hypoxic volumes localized outside the FDG-HV.CONCLUSION: Hypoxic tumor volumes are smaller than metabolic active volumes. Approximately half of the lesions showed a good spatial correlation between the PET tracers. In the other cases, a (partial) mismatch was observed. The addition of HX4-PET imaging has the potential to individualize patient treatment. Clin Cancer Res; 1-9. ©2014 AACR.
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3. |
- Grau, Cai, et al.
(author)
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Towards a European prospective data registry for particle therapy
- 2024
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In: Radiotherapy and Oncology. - : Elsevier. - 0167-8140 .- 1879-0887. ; 196
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Journal article (peer-reviewed)abstract
- The evidence for the value of particle therapy (PT) is still sparse. While randomized trials remain a cornerstone for robust comparisons with photon-based radiotherapy, data registries collecting real-world data can play a crucial role in building evidence for new developments. This Perspective describes how the European Particle Therapy Network (EPTN) is actively working on establishing a prospective data registry encompassing all patients undergoing PT in European centers. Several obstacles and hurdles are discussed, for instance harmonization of nomenclature and structure of technical and dosimetric data and data protection issues. A preferred approach is the adoption of a federated data registry model with transparent and agile governance to meet European requirements for data protection, transfer, and processing. Funding of the registry, especially for operation after the initial setup process, remains a major challenge.
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4. |
- Zegers, Catharina M L, et al.
(author)
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Hypoxia imaging with [(18)F]HX4 PET in NSCLC patients : Defining optimal imaging parameters.
- 2013
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In: Radiotherapy and Oncology. - : Elsevier BV. - 0167-8140 .- 1879-0887. ; 109:1, s. 58-64
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Journal article (peer-reviewed)abstract
- BACKGROUND AND PURPOSE: [(18)F]HX4 is a promising hypoxia PET-tracer. Uptake, spatio-temporal stability and optimal acquisition parameters for [(18)F]HX4 PET imaging were evaluated in non-small cell lung cancer (NSCLC) patients.MATERIALS AND METHODS: [(18)F]HX4 PET/CT images of 15 NSCLC patients were acquired 2h and 4h after injection (p.i.). Maximum standardized-uptake-value (SUVmax), tumor-to-blood-ratio (TBRmax), hypoxic fraction (HF) and contrast-to-noise-ratio (CNR) were determined for all lesions. To evaluate spatio-temporal stability, DICE-similarity and Pearson correlation coefficients were calculated. Optimal acquisition-duration was assessed by comparing 30, 20, 10 and 5min acquisitions.RESULTS: Considerable uptake (TBR >1.4) was observed in 18/25 target lesions. TBRmax increased significantly from 2h (1.6±0.3) to 4h p.i. (2.0±0.6). Uptake patterns at 2h and 4h p.i. showed a strong correlation (R=0.77±0.10) with a DICE similarity coefficient of 0.69±0.08 for the 30% highest uptake volume. Reducing acquisition-time resulted in significant changes in SUVmax and CNR. TBRmax and HF were only affected for scan-times of 5min.CONCLUSIONS: The majority of NSCLC lesions showed considerable [(18)F]HX4 uptake. The heterogeneous uptake pattern was stable between 2h and 4h p.i. [(18)F]HX4 PET imaging at 4h p.i. is superior to 2h p.i. to reach highest contrast. Acquisition time may be reduced to 10min without significant effects on TBRmax and HF.
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5. |
- Unkelbach, Jan, et al.
(author)
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The role of computational methods for automating and improving clinical target volume definition
- 2020
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In: Radiotherapy and Oncology. - : Elsevier BV. - 0167-8140 .- 1879-0887. ; 153, s. 15-25
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Journal article (peer-reviewed)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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