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749 results on '"Vogelius, Ivan"'

1. Using Biometric Sensor Data to Monitor Cancer Patients During Radiotherapy: Protocol for the OncoWatch Feasibility Study

Author

Holländer-Mieritz, Cecilie, Vogelius, Ivan R, Kristensen, Claus A, Green, Allan, Rindum, Judith L, and Pappot, Helle

Subjects
Medicine, Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundPatients with head and neck cancer (HNC) experience severe side effects during radiotherapy (RT). Ongoing technological advances in wearable biometric sensors allow for the collection of objective data (eg, physical activity and heart rate), which might, in the future, help detect and counter side effects before they become severe. A smartwatch such as the Apple Watch allows for objective data monitoring outside the hospital with minimal effort from the patient. To determine whether such tools can be implemented in the oncological setting, feasibility studies are needed. ObjectiveThis protocol describes the design of the OncoWatch 1.0 feasibility study that assesses the adherence of patients with HNC to an Apple Watch during RT. MethodsA prospective, single-cohort trial will be conducted at the Department of Oncology, Rigshospitalet (Copenhagen, Denmark). Patients aged ≥18 years intended for primary or postoperative curatively intended RT for HNC will be recruited. Consenting patients will be asked to wear an Apple Watch on the wrist during and until 2 weeks after RT. The study will include 10 patients. Data on adherence, data acquisition, and biometric data will be collected. Demographic data, objective toxicity scores, and hospitalizations will be documented. ResultsThe primary outcome is to determine if it is feasible for the patients to wear a smartwatch continuously (minimum 12 hours/day) during RT. Furthermore, we will explore how the heart rate and physical activity change over the treatment course. ConclusionsThe study will assess the feasibility of using the Apple Watch for home monitoring of patients with HNC. Our findings may provide novel insights into the patient’s activity levels and variations in heart rate during the treatment course. The knowledge obtained from this study will be essential for further investigating how biometric data can be used as part of symptom monitoring for patients with HNC. Trial RegistrationClinicalTrials.gov NCT04613232; https://clinicaltrials.gov/ct2/show/NCT04613232 International Registered Report Identifier (IRRID)PRR1-10.2196/26096

Published
2021
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2. Prediction of post-radiotherapy recurrence volumes in head and neck squamous cell carcinoma using 3D U-Net segmentation

Author

Kutnár, Denis, Vogelius, Ivan R, Håkansson, Katrin Elisabet, Petersen, Jens, Friborg, Jeppe, Specht, Lena, Bernsdorf, Mogens, Gothelf, Anita, Kristensen, Claus, and Smith, Abraham George

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Locoregional recurrences (LRR) are still a frequent site of treatment failure for head and neck squamous cell carcinoma (HNSCC) patients. Identification of high risk subvolumes based on pretreatment imaging is key to biologically targeted radiation therapy. We investigated the extent to which a Convolutional neural network (CNN) is able to predict LRR volumes based on pre-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET)/computed tomography (CT) scans in HNSCC patients and thus the potential to identify biological high risk volumes using CNNs. For 37 patients who had undergone primary radiotherapy for oropharyngeal squamous cell carcinoma, five oncologists contoured the relapse volumes on recurrence CT scans. Datasets of pre-treatment FDG-PET/CT, gross tumour volume (GTV) and contoured relapse for each of the patients were randomly divided into training (n=23), validation (n=7) and test (n=7) datasets. We compared a CNN trained from scratch, a pre-trained CNN, a SUVmax threshold approach, and using the GTV directly. The SUVmax threshold method included 5 out of the 7 relapse origin points within a volume of median 4.6 cubic centimetres (cc). Both the GTV contour and best CNN segmentations included the relapse origin 6 out of 7 times with median volumes of 28 and 18 cc respectively. The CNN included the same or greater number of relapse volume POs, with significantly smaller relapse volumes. Our novel findings indicate that CNNs may predict LRR, yet further work on dataset development is required to attain clinically useful prediction accuracy.

Published
2023

3. Localise to segment: crop to improve organ at risk segmentation accuracy

Author

Smith, Abraham George, Kutnár, Denis, Vogelius, Ivan Richter, Darkner, Sune, and Petersen, Jens

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Increased organ at risk segmentation accuracy is required to reduce cost and complications for patients receiving radiotherapy treatment. Some deep learning methods for the segmentation of organs at risk use a two stage process where a localisation network first crops an image to the relevant region and then a locally specialised network segments the cropped organ of interest. We investigate the accuracy improvements brought about by such a localisation stage by comparing to a single-stage baseline network trained on full resolution images. We find that localisation approaches can improve both training time and stability and a two stage process involving both a localisation and organ segmentation network provides a significant increase in segmentation accuracy for the spleen, pancreas and heart from the Medical Segmentation Decathlon dataset. We also observe increased benefits of localisation for smaller organs. Source code that recreates the main results is available at \href{https://github.com/Abe404/localise_to_segment}{this https URL}.

Published
2023

4. Explicit Temporal Embedding in Deep Generative Latent Models for Longitudinal Medical Image Synthesis

Author

Schön, Julian, Selvan, Raghavendra, Nygård, Lotte, Vogelius, Ivan Richter, and Petersen, Jens

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Medical imaging plays a vital role in modern diagnostics and treatment. The temporal nature of disease or treatment progression often results in longitudinal data. Due to the cost and potential harm, acquiring large medical datasets necessary for deep learning can be difficult. Medical image synthesis could help mitigate this problem. However, until now, the availability of GANs capable of synthesizing longitudinal volumetric data has been limited. To address this, we use the recent advances in latent space-based image editing to propose a novel joint learning scheme to explicitly embed temporal dependencies in the latent space of GANs. This, in contrast to previous methods, allows us to synthesize continuous, smooth, and high-quality longitudinal volumetric data with limited supervision. We show the effectiveness of our approach on three datasets containing different longitudinal dependencies. Namely, modeling a simple image transformation, breathing motion, and tumor regression, all while showing minimal disentanglement. The implementation is made available online at https://github.com/julschoen/Temp-GAN.

Published
2023

11. RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy

Author

Smith, Abraham George, Petersen, Jens, Terrones-Campos, Cynthia, Berthelsen, Anne Kiil, Forbes, Nora Jarrett, Darkner, Sune, Specht, Lena, and Vogelius, Ivan Richter

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

Organ-at-risk contouring is still a bottleneck in radiotherapy, with many deep learning methods falling short of promised results when evaluated on clinical data. We investigate the accuracy and time-savings resulting from the use of an interactive-machine-learning method for an organ-at-risk contouring task. We compare the method to the Eclipse contouring software and find strong agreement with manual delineations, with a dice score of 0.95. The annotations created using corrective-annotation also take less time to create as more images are annotated, resulting in substantial time savings compared to manual methods, with hearts that take 2 minutes and 2 seconds to delineate on average, after 923 images have been delineated, compared to 7 minutes and 1 seconds when delineating manually. Our experiment demonstrates that interactive-machine-learning with corrective-annotation provides a fast and accessible way for non computer-scientists to train deep-learning models to segment their own structures of interest as part of routine clinical workflows. Source code is available at \href{https://github.com/Abe404/RootPainter3D}{this HTTPS URL}.

Published
2021
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19. Cine-MRI Simulation to Evaluate Tumor Tracking

Author

Tascón-Vidarte, José D., Wahlstedt, Isak, Jomier, Julien, Erleben, Kenny, Vogelius, Ivan R., Darkner, Sune, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Svoboda, David, editor, Burgos, Ninon, editor, Wolterink, Jelmer M., editor, and Zhao, Can, editor

Published
2021
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23. Primary Hypothyroidism in Childhood Cancer Survivors Treated With Radiation Therapy: A PENTEC Comprehensive Review

Author

Klinische Fysica RT, Endocrinologie patientenzorg, Brain, Child Health, Cancer, Milano, Michael T, Vargo, John A, Yorke, Ellen D, Ronckers, Cécile M, Kremer, Leontien C, Chafe, Susan M J, van Santen, Hanneke M, Marks, Lawrence B, Bentzen, Søren M, Constine, Louis S, Vogelius, Ivan R, Klinische Fysica RT, Endocrinologie patientenzorg, Brain, Child Health, Cancer, Milano, Michael T, Vargo, John A, Yorke, Ellen D, Ronckers, Cécile M, Kremer, Leontien C, Chafe, Susan M J, van Santen, Hanneke M, Marks, Lawrence B, Bentzen, Søren M, Constine, Louis S, and Vogelius, Ivan R

Published
2024

24. MR-guided stereotactic radiotherapy of infra-diaphragmatic oligometastases:Evaluation of toxicity and dosimetric parameters

Author

van Overeem Felter, Mette, Møller, Pia Krause, Josipovic, Mirjana, Bekke, Susanne Nørring, Bernchou, Uffe, Serup-Hansen, Eva, Madsen, Kasper, Parikh, Parag J., Kim, Joshua, Geertsen, Poul, Behrens, Claus P., Vogelius, Ivan R., Pøhl, Mette, Schytte, Tine, Persson, Gitte Fredberg, van Overeem Felter, Mette, Møller, Pia Krause, Josipovic, Mirjana, Bekke, Susanne Nørring, Bernchou, Uffe, Serup-Hansen, Eva, Madsen, Kasper, Parikh, Parag J., Kim, Joshua, Geertsen, Poul, Behrens, Claus P., Vogelius, Ivan R., Pøhl, Mette, Schytte, Tine, and Persson, Gitte Fredberg

Abstract

Background and purposeThe SOFT trial is a prospective, multicenter, phase 2 trial investigating magnetic resonance (MR)-guided stereotactic ablative radiotherapy (SABR) for abdominal, soft tissue metastases in patients with oligometastatic disease (OMD) (clinicaltrials.govID NCT04407897). We present the primary endpoint analysis of 1-year treatment-related toxicity (TRAE).Materials and methodsPatients with up to five oligometastases from non-hematological cancers were eligible for inclusion. A risk-adapted strategy prioritized fixed organs at risk (OAR) constraints over target coverage. Fractionation schemes were 45–67.5 Gy in 3–8 fractions. The primary endpoint was grade ≥ 4 TRAE within 12 months post-SABR. The association between the risk of gastrointestinal (GI) toxicity and clinical and dosimetric parameters was tested using a normal tissue complication probability model.ResultsWe included 121 patients with 147 oligometastatic targets, mainly located in the liver (41 %), lymph nodes (35 %), or adrenal glands (14 %). Nearly half of all targets (48 %, n = 71) were within 10 mm of a radiosensitive OAR. No grade 4 or 5 TRAEs, 3.5 % grade 3 TRAEs, and 43.7 % grade 2 TRAEs were reported within the first year of follow-up. We found a significant association between grade ≥ 2 GI toxicity and the parameters GI OAR D0.1cc, D1cc, and D20cc.ConclusionIn this phase II study of MR-guided SABR of oligometastases in the infra-diaphragmatic region, we found a low incidence of toxicity despite half of the lesions being within 10 mm of a radiosensitive OAR. GI OAR D0.1cc, D1cc, and D20cc were associated with grade ≥ 2 GI toxicity., Background and purpose: The SOFT trial is a prospective, multicenter, phase 2 trial investigating magnetic resonance (MR)-guided stereotactic ablative radiotherapy (SABR) for abdominal, soft tissue metastases in patients with oligometastatic disease (OMD) (clinicaltrials.gov ID NCT04407897). We present the primary endpoint analysis of 1-year treatment-related toxicity (TRAE). Materials and methods: Patients with up to five oligometastases from non-hematological cancers were eligible for inclusion. A risk-adapted strategy prioritized fixed organs at risk (OAR) constraints over target coverage. Fractionation schemes were 45–67.5 Gy in 3–8 fractions. The primary endpoint was grade ≥ 4 TRAE within 12 months post-SABR. The association between the risk of gastrointestinal (GI) toxicity and clinical and dosimetric parameters was tested using a normal tissue complication probability model. Results: We included 121 patients with 147 oligometastatic targets, mainly located in the liver (41 %), lymph nodes (35 %), or adrenal glands (14 %). Nearly half of all targets (48 %, n = 71) were within 10 mm of a radiosensitive OAR. No grade 4 or 5 TRAEs, 3.5 % grade 3 TRAEs, and 43.7 % grade 2 TRAEs were reported within the first year of follow-up. We found a significant association between grade ≥ 2 GI toxicity and the parameters GI OAR D0.1cc, D1cc, and D20cc. Conclusion: In this phase II study of MR-guided SABR of oligometastases in the infra-diaphragmatic region, we found a low incidence of toxicity despite half of the lesions being within 10 mm of a radiosensitive OAR. GI OAR D0.1cc, D1cc, and D20cc were associated with grade ≥ 2 GI toxicity.

Published
2024

27. Uncertainty of Scintillator-Based Field-Output Factor Measurements in MR-Linacs With the Two-Channel Chromatic Stem Removal Technique

Author

Klavsen, Mads Fjelbro, primary, Boye, Kristian, additional, Riis, Hans Lynggaard, additional, Ehrbar, Stefanie, additional, Baumgartl, Michael, additional, Rippke, Carolin, additional, Buchele, Carolin, additional, Renkamp, Katharina, additional, Santurio, Grichar Valdes, additional, Behrens, Claus P., additional, Vogelius, Ivan Richter, additional, Ankjærgaard, Christina, additional, and Andersen, Claus E., additional

Published
2024
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28. Dosimetric analysis of proximal bronchial tree subsegments to assess the risk of severe toxicity after stereotactic body radiation therapy of ultra-central lung tumors

Author

Ahmadsei, Maiwand, primary, Jegarajah, Vinojaa, additional, Dal Bello, Riccardo, additional, Christ, Sebastian M., additional, Mayinger, Michael M., additional, Sabrina Stark, Luisa, additional, Willmann, Jonas, additional, Vogelius, Ivan R., additional, Balermpas, Panagiotis, additional, Andratschke, Nicolaus, additional, Tanadini-Lang, Stephanie, additional, and Guckenberger, Matthias, additional

Published
2023
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37. A national repository of complete radiotherapy plans: design, Results, and experiences

Author

Krogh, Simon Long, primary, Brink, Carsten, additional, Lorenzen, Ebbe Laugaard, additional, Samsøe, Eva, additional, Vogelius, Ivan Richter, additional, Zukauskaite, Ruta, additional, Vrou Offersen, Birgitte, additional, Eriksen, Jesper Grau, additional, Hansen, Olfred, additional, Johansen, Jørgen, additional, Olloni, Agon, additional, Ruhlmann, Christina H., additional, Hoffmann, Lone, additional, Nissen, Henrik Dahl, additional, Skovmos Nielsen, Martin, additional, Andersen, Karen, additional, Grau, Cai, additional, and Hansen, Christian Rønn, additional

Published
2023
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47. 3017: SABR of bony oligometastases, 1-year, efficacy, and toxicity analyses from the Bony-M trial

Author

Spindler, Nicklas J., Felter, Mette v O, Hansen, Olfred, Nielsen, Tine B., Suppli, Morten H., Josipovic, Mirjana, Poulsen, Laurids Ø, Gaard-Petersen, Fatma, Andersen, Michael B., Sand, Hella M B, Johansen, Marie, Madsen, Kasper, Kornerup, Josefine S., Alsaker, Mirjam D., Serup-Hansen, Eva, Eefsen, Rikke L., Vogelius, Ivan R., Geertsen, Poul, Behrens, Claus P., and Persson, Gitte F.

Published
2024
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