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Boelders SM, De Baene W, Postma E, Gehring K, Ong LL. Predicting Cognitive Functioning for Patients with a High-Grade Glioma: Evaluating Different Representations of Tumor Location in a Common Space. Neuroinformatics 2024:10.1007/s12021-024-09671-9. [PMID: 38900230 DOI: 10.1007/s12021-024-09671-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/31/2024] [Indexed: 06/21/2024]
Abstract
Cognitive functioning is increasingly considered when making treatment decisions for patients with a brain tumor in view of a personalized onco-functional balance. Ideally, one can predict cognitive functioning of individual patients to make treatment decisions considering this balance. To make accurate predictions, an informative representation of tumor location is pivotal, yet comparisons of representations are lacking. Therefore, this study compares brain atlases and principal component analysis (PCA) to represent voxel-wise tumor location. Pre-operative cognitive functioning was predicted for 246 patients with a high-grade glioma across eight cognitive tests while using different representations of voxel-wise tumor location as predictors. Voxel-wise tumor location was represented using 13 different frequently-used population average atlases, 13 randomly generated atlases, and 13 representations based on PCA. ElasticNet predictions were compared between representations and against a model solely using tumor volume. Preoperative cognitive functioning could only partly be predicted from tumor location. Performances of different representations were largely similar. Population average atlases did not result in better predictions compared to random atlases. PCA-based representation did not clearly outperform other representations, although summary metrics indicated that PCA-based representations performed somewhat better in our sample. Representations with more regions or components resulted in less accurate predictions. Population average atlases possibly cannot distinguish between functionally distinct areas when applied to patients with a glioma. This stresses the need to develop and validate methods for individual parcellations in the presence of lesions. Future studies may test if the observed small advantage of PCA-based representations generalizes to other data.
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Affiliation(s)
- S M Boelders
- Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands
- Department of Cognitive Sciences and AI, Tilburg University, Tilburg, The Netherlands
| | - W De Baene
- Department of Cognitive Neuropsychology, Tilburg University Tilburg, Warandelaan 2, P. O. Box 90153, Tilburg, 5000 LE, The Netherlands
| | - E Postma
- Department of Cognitive Sciences and AI, Tilburg University, Tilburg, The Netherlands
| | - K Gehring
- Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands.
- Department of Cognitive Neuropsychology, Tilburg University Tilburg, Warandelaan 2, P. O. Box 90153, Tilburg, 5000 LE, The Netherlands.
| | - L L Ong
- Department of Cognitive Sciences and AI, Tilburg University, Tilburg, The Netherlands
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Cepeda S, García-García S, Arrese I, Sarabia R. Non-navigated 2D intraoperative ultrasound: An unsophisticated surgical tool to achieve high standards of care in glioma surgery. J Neurooncol 2024; 167:387-396. [PMID: 38413458 DOI: 10.1007/s11060-024-04614-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 02/17/2024] [Indexed: 02/29/2024]
Abstract
PURPOSE In an era characterized by rapid progression in neurosurgical technologies, traditional tools such as the non-navigated two-dimensional intraoperative ultrasound (nn-2D-IOUS) risk being overshadowed. Against this backdrop, this study endeavors to provide a comprehensive assessment of the clinical efficacy and surgical relevance of nn-2D-IOUS, specifically in the context of glioma resections. METHODS This retrospective study undertaken at a single center evaluated 99 consecutive, non-selected patients diagnosed with both high-grade and low-grade gliomas. The primary objective was to assess the proficiency of nn-2D-IOUS in generating satisfactory image quality, identifying residual tumor tissue, and its influence on the extent of resection. To validate these results, early postoperative MRI data served as the reference standard. RESULTS The nn-2D-IOUS exhibited a high level of effectiveness, successfully generating good quality images in 79% of the patients evaluated. With a sensitivity rate of 68% and a perfect specificity of 100%, nn-2D-IOUS unequivocally demonstrated its utility in intraoperative residual tumor detection. Notably, when total tumor removal was the surgical objective, a resection exceeding 95% of the initial tumor volume was achieved in 86% of patients. Additionally, patients in whom residual tumor was not detected by nn-2D-IOUS, the mean volume of undetected tumor tissue was remarkably minimal, averaging at 0.29 cm3. CONCLUSION Our study supports nn-2D-IOUS's invaluable role in glioma surgery. The results highlight the utility of traditional technologies for enhanced surgical outcomes, even when compared to advanced alternatives. This is particularly relevant for resource-constrained settings and emphasizes optimizing existing tools for efficient patient care. NCT05873946 - 24/05/2023 - Retrospectively registered.
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Affiliation(s)
- Santiago Cepeda
- Department of Neurosurgery, University Hospital Río Hortega, Calle Dulzaina, 2, 47012, Valladolid, Spain.
| | - Sergio García-García
- Department of Neurosurgery, University Hospital Río Hortega, Calle Dulzaina, 2, 47012, Valladolid, Spain
| | - Ignacio Arrese
- Department of Neurosurgery, University Hospital Río Hortega, Calle Dulzaina, 2, 47012, Valladolid, Spain
| | - Rosario Sarabia
- Department of Neurosurgery, University Hospital Río Hortega, Calle Dulzaina, 2, 47012, Valladolid, Spain
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Johnstad C, Reinertsen I, Bouget D, Sagberg LM, Strand PS, Solheim O. Incidence, risk factors, and clinical implications of postoperative blood in or near the resection cavity after glioma surgery. BRAIN & SPINE 2024; 4:102818. [PMID: 38726240 PMCID: PMC11081780 DOI: 10.1016/j.bas.2024.102818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/07/2024] [Accepted: 04/20/2024] [Indexed: 05/12/2024]
Abstract
Introduction Postoperative hematomas that require reoperation are a serious, but uncommon complication to glioma surgery. However, smaller blood volumes are frequently observed, but their clinical significance is less known. Research question What are the incidence rates, risk factors, and patient-reported outcomes of all measurable blood in or near the resection cavity on postoperative MRI in diffuse glioma patients? Material and methods We manually segmented intradural and extradural blood from early postoperative MRI of 292 diffuse glioma resections. Potential associations between blood volume and tumor characteristics, demographics, and perioperative factors were explored using non-parametric methods. The assessed outcomes were generic and disease-specific patient-reported HRQoL. Results Out of the 292 MRI scans included, 184 (63%) had intradural blood, and 212 (73%) had extradural blood in or near the resection cavity. The median blood volumes were 0.4 mL and 3.0 mL, respectively. Intradural blood volume was associated with tumor volume, intraoperative blood loss, and EOR. Extradural blood volume was associated with age and tumor volume. Greater intradural blood volume was associated with less headache and cognitive improvement, but not after adjustments for tumor volume. Discussion and conclusions Postoperative blood on early postoperative MRI is common. Intradural blood volumes tend to be larger in patients with larger tumors, more intraoperative blood loss, or undergoing subtotal resections. Extradural blood volumes tend to be larger in younger patients with larger tumors. Postoperative blood in or near the resection cavity that does not require reoperation does not seem to affect HRQoL in diffuse glioma patients.
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Affiliation(s)
- Claes Johnstad
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ingerid Reinertsen
- Department of Health Research, SINTEF Digital, Trondheim, Norway
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - David Bouget
- Department of Health Research, SINTEF Digital, Trondheim, Norway
| | - Lisa M. Sagberg
- Department of Neurosurgery, St. Olav’s Hospital, Trondheim University Hospital, Trondheim, Norway
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Per S. Strand
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neurosurgery, St. Olav’s Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Ole Solheim
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neurosurgery, St. Olav’s Hospital, Trondheim University Hospital, Trondheim, Norway
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Zimmermann MLM, Breedt LC, Centeno EGZ, Reijneveld JC, Santos FAN, Stam CJ, van Lingen MR, Schoonheim MM, Hillebrand A, Douw L. The relationship between pathological brain activity and functional network connectivity in glioma patients. J Neurooncol 2024; 166:523-533. [PMID: 38308803 PMCID: PMC10876827 DOI: 10.1007/s11060-024-04577-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 01/17/2024] [Indexed: 02/05/2024]
Abstract
PURPOSE Glioma is associated with pathologically high (peri)tumoral brain activity, which relates to faster progression. Functional connectivity is disturbed locally and throughout the entire brain, associating with symptomatology. We, therefore, investigated how local activity and network measures relate to better understand how the intricate relationship between the tumor and the rest of the brain may impact disease and symptom progression. METHODS We obtained magnetoencephalography in 84 de novo glioma patients and 61 matched healthy controls. The offset of the power spectrum, a proxy of neuronal activity, was calculated for 210 cortical regions. We calculated patients' regional deviations in delta, theta and lower alpha network connectivity as compared to controls, using two network measures: clustering coefficient (local connectivity) and eigenvector centrality (integrative connectivity). We then tested group differences in activity and connectivity between (peri)tumoral, contralateral homologue regions, and the rest of the brain. We also correlated regional offset to connectivity. RESULTS As expected, patients' (peri)tumoral activity was pathologically high, and patients showed higher clustering and lower centrality than controls. At the group-level, regionally high activity related to high clustering in controls and patients alike. However, within-patient analyses revealed negative associations between regional deviations in brain activity and clustering, such that pathologically high activity coincided with low network clustering, while regions with 'normal' activity levels showed high network clustering. CONCLUSION Our results indicate that pathological activity and connectivity co-localize in a complex manner in glioma. This insight is relevant to our understanding of disease progression and cognitive symptomatology.
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Affiliation(s)
- Mona L M Zimmermann
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- Cancer Center Amsterdam, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Lucas C Breedt
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Eduarda G Z Centeno
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Univ. Bordeaux, CNRS, IMN, UMR 5293, Bordeaux, France
| | - Jaap C Reijneveld
- Department of Neurology, Stichting Epilepsie Instellingen Nederland, Heemstede, The Netherlands
| | - Fernando A N Santos
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Dutch Institute for Emergent Phenomena (DIEP), Institute for Advanced Studies, University of Amsterdam, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Clinical Neurophysiology and MEG Center, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Marike R van Lingen
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Menno M Schoonheim
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Amsterdam Neuroscience, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Linda Douw
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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Eidex Z, Ding Y, Wang J, Abouei E, Qiu RLJ, Liu T, Wang T, Yang X. Deep learning in MRI-guided radiation therapy: A systematic review. J Appl Clin Med Phys 2024; 25:e14155. [PMID: 37712893 PMCID: PMC10860468 DOI: 10.1002/acm2.14155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 05/10/2023] [Accepted: 08/21/2023] [Indexed: 09/16/2023] Open
Abstract
Recent advances in MRI-guided radiation therapy (MRgRT) and deep learning techniques encourage fully adaptive radiation therapy (ART), real-time MRI monitoring, and the MRI-only treatment planning workflow. Given the rapid growth and emergence of new state-of-the-art methods in these fields, we systematically review 197 studies written on or before December 31, 2022, and categorize the studies into the areas of image segmentation, image synthesis, radiomics, and real time MRI. Building from the underlying deep learning methods, we discuss their clinical importance and current challenges in facilitating small tumor segmentation, accurate x-ray attenuation information from MRI, tumor characterization and prognosis, and tumor motion tracking. In particular, we highlight the recent trends in deep learning such as the emergence of multi-modal, visual transformer, and diffusion models.
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Affiliation(s)
- Zach Eidex
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
- School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
| | - Yifu Ding
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Jing Wang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Elham Abouei
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Richard L. J. Qiu
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Tian Liu
- Department of Radiation OncologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Tonghe Wang
- Department of Medical PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
- School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
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Helland RH, Ferles A, Pedersen A, Kommers I, Ardon H, Barkhof F, Bello L, Berger MS, Dunås T, Nibali MC, Furtner J, Hervey-Jumper S, Idema AJS, Kiesel B, Tewari RN, Mandonnet E, Müller DMJ, Robe PA, Rossi M, Sagberg LM, Sciortino T, Aalders T, Wagemakers M, Widhalm G, Witte MG, Zwinderman AH, Majewska PL, Jakola AS, Solheim O, Hamer PCDW, Reinertsen I, Eijgelaar RS, Bouget D. Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks. Sci Rep 2023; 13:18897. [PMID: 37919325 PMCID: PMC10622432 DOI: 10.1038/s41598-023-45456-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 10/19/2023] [Indexed: 11/04/2023] Open
Abstract
Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.
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Affiliation(s)
- Ragnhild Holden Helland
- Department of Health Research, SINTEF Digital, 7465, Trondheim, Norway.
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, NO-7491, Trondheim, Norway.
| | - Alexandros Ferles
- Cancer Center Amsterdam, Brain Tumor Center, Amsterdam University Medical Centers, 1081 HV, Amsterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Vrije Universiteit, 1081 HV, Amsterdam, The Netherlands
| | - André Pedersen
- Department of Health Research, SINTEF Digital, 7465, Trondheim, Norway
| | - Ivar Kommers
- Cancer Center Amsterdam, Brain Tumor Center, Amsterdam University Medical Centers, 1081 HV, Amsterdam, The Netherlands
- Department of Neurosurgery, Amsterdam University Medical Centers, Vrije Universiteit, 1081 HV, Amsterdam, The Netherlands
| | - Hilko Ardon
- Department of Neurosurgery, Twee Steden Hospital, 5042 AD, Tilburg, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Vrije Universiteit, 1081 HV, Amsterdam, The Netherlands
- Institutes of Neurology and Healthcare Engineering, University College London, London, WC1E 6BT, UK
| | - Lorenzo Bello
- Neurosurgical Oncology Unit, Department of Oncology and Hemato-oncology, Humanitas Research Hospital, Università Degli Studi di Milano, 20122, Milan, Italy
| | - Mitchel S Berger
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Tora Dunås
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, 405 30, Gothenburg, Sweden
| | | | - Julia Furtner
- Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, 1090, Vienna, Austria
- Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Faculty of Medicine and Dentistry, Danube Private University, 3500, Krems, Austria
| | - Shawn Hervey-Jumper
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Albert J S Idema
- Department of Neurosurgery, Northwest Clinics, 1815 JD, Alkmaar, The Netherlands
| | - Barbara Kiesel
- Department of Neurosurgery, Medical University Vienna, 1090, Vienna, Austria
| | - Rishi Nandoe Tewari
- Department of Neurosurgery, Haaglanden Medical Center, 2512 VA, The Hague, The Netherlands
| | - Emmanuel Mandonnet
- Department of Neurological Surgery, Hôpital Lariboisière, 75010, Paris, France
| | - Domenique M J Müller
- Cancer Center Amsterdam, Brain Tumor Center, Amsterdam University Medical Centers, 1081 HV, Amsterdam, The Netherlands
- Department of Neurosurgery, Amsterdam University Medical Centers, Vrije Universiteit, 1081 HV, Amsterdam, The Netherlands
| | - Pierre A Robe
- Department of Neurology and Neurosurgery, University Medical Center Utrecht, 3584 CX, Utrecht, The Netherlands
| | - Marco Rossi
- Department of Medical Biotechnology and Translational Medicine, Università Degli Studi di Milano, 20122, Milan, Italy
| | - Lisa M Sagberg
- Department of Neurosurgery, St. Olavs hospital, Trondheim University Hospital, 7030, Trondheim, Norway
- Department of Public Health and Nursing, Norwegian University of Science and Technology, 7491, Trondheim, Norway
| | | | - Tom Aalders
- Department of Neurosurgery, Isala, 8025 AB, Zwolle, The Netherlands
| | - Michiel Wagemakers
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, 9713 GZ, Groningen, The Netherlands
| | - Georg Widhalm
- Department of Neurosurgery, Medical University Vienna, 1090, Vienna, Austria
| | - Marnix G Witte
- Department of Radiation Oncology, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands
| | - Aeilko H Zwinderman
- Department of Clinical Epidemiology and Biostatistics, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ, Amsterdam, The Netherlands
| | - Paulina L Majewska
- Department of Neurology and Neurosurgery, University Medical Center Utrecht, 3584 CX, Utrecht, The Netherlands
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, 7491, Trondheim, Norway
| | - Asgeir S Jakola
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, 405 30, Gothenburg, Sweden
- Department of Neurosurgery, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Ole Solheim
- Department of Neurology and Neurosurgery, University Medical Center Utrecht, 3584 CX, Utrecht, The Netherlands
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, 7491, Trondheim, Norway
| | - Philip C De Witt Hamer
- Cancer Center Amsterdam, Brain Tumor Center, Amsterdam University Medical Centers, 1081 HV, Amsterdam, The Netherlands
- Department of Neurosurgery, Amsterdam University Medical Centers, Vrije Universiteit, 1081 HV, Amsterdam, The Netherlands
| | - Ingerid Reinertsen
- Department of Health Research, SINTEF Digital, 7465, Trondheim, Norway
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, NO-7491, Trondheim, Norway
| | - Roelant S Eijgelaar
- Cancer Center Amsterdam, Brain Tumor Center, Amsterdam University Medical Centers, 1081 HV, Amsterdam, The Netherlands
- Department of Neurosurgery, Amsterdam University Medical Centers, Vrije Universiteit, 1081 HV, Amsterdam, The Netherlands
| | - David Bouget
- Department of Health Research, SINTEF Digital, 7465, Trondheim, Norway
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Bouget D, Alsinan D, Gaitan V, Helland RH, Pedersen A, Solheim O, Reinertsen I. Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting. Sci Rep 2023; 13:15570. [PMID: 37730820 PMCID: PMC10511510 DOI: 10.1038/s41598-023-42048-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 09/05/2023] [Indexed: 09/22/2023] Open
Abstract
For patients suffering from central nervous system tumors, prognosis estimation, treatment decisions, and postoperative assessments are made from the analysis of a set of magnetic resonance (MR) scans. Currently, the lack of open tools for standardized and automatic tumor segmentation and generation of clinical reports, incorporating relevant tumor characteristics, leads to potential risks from inherent decisions' subjectivity. To tackle this problem, the proposed Raidionics open-source software has been developed, offering both a user-friendly graphical user interface and stable processing backend. The software includes preoperative segmentation models for each of the most common tumor types (i.e., glioblastomas, lower grade gliomas, meningiomas, and metastases), together with one early postoperative glioblastoma segmentation model. Preoperative segmentation performances were quite homogeneous across the four different brain tumor types, with an average Dice around 85% and patient-wise recall and precision around 95%. Postoperatively, performances were lower with an average Dice of 41%. Overall, the generation of a standardized clinical report, including the tumor segmentation and features computation, requires about ten minutes on a regular laptop. The proposed Raidionics software is the first open solution enabling an easy use of state-of-the-art segmentation models for all major tumor types, including preoperative and postsurgical standardized reports.
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Affiliation(s)
- David Bouget
- Department of Health Research, SINTEF Digital, 7465, Trondheim, Norway
| | - Demah Alsinan
- Department of Health Research, SINTEF Digital, 7465, Trondheim, Norway
| | - Valeria Gaitan
- Department of Health Research, SINTEF Digital, 7465, Trondheim, Norway
| | - Ragnhild Holden Helland
- Department of Health Research, SINTEF Digital, 7465, Trondheim, Norway
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway
| | - André Pedersen
- Department of Health Research, SINTEF Digital, 7465, Trondheim, Norway
| | - Ole Solheim
- Department of Neurosurgery, St. Olavs Hospital, Trondheim University Hospital, 7491, Trondheim, Norway
- Norwegian University of Science and Technology (NTNU), Department of Neuromedicine and Movement Science, 7491, Trondheim, Norway
| | - Ingerid Reinertsen
- Department of Health Research, SINTEF Digital, 7465, Trondheim, Norway.
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway.
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8
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Eidex Z, Ding Y, Wang J, Abouei E, Qiu RL, Liu T, Wang T, Yang X. Deep Learning in MRI-guided Radiation Therapy: A Systematic Review. ARXIV 2023:arXiv:2303.11378v2. [PMID: 36994167 PMCID: PMC10055493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
MRI-guided radiation therapy (MRgRT) offers a precise and adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematically reviewed. MRI-guided radiation therapy offers a precise, adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematically reviewed with emphasis placed on underlying methods. Studies are further categorized into the areas of segmentation, synthesis, radiomics, and real time MRI. Finally, clinical implications, current challenges, and future directions are discussed.
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Affiliation(s)
- Zach Eidex
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
- School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA
| | - Yifu Ding
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Jing Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Elham Abouei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Richard L.J. Qiu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Tian Liu
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Tonghe Wang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
- School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA
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