1
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Sherwani MK, Marzullo A, De Momi E, Calimeri F. Lesion segmentation in lung CT scans using unsupervised adversarial learning. Med Biol Eng Comput 2022; 60:3203-3215. [PMID: 36125656 PMCID: PMC9486778 DOI: 10.1007/s11517-022-02651-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 07/28/2022] [Indexed: 12/01/2022]
Abstract
Lesion segmentation in medical images is difficult yet crucial for proper diagnosis and treatment. Identifying lesions in medical images is costly and time-consuming and requires highly specialized knowledge. For this reason, supervised and semi-supervised learning techniques have been developed. Nevertheless, the lack of annotated data, which is common in medical imaging, is an issue; in this context, interesting approaches can use unsupervised learning to accurately distinguish between healthy tissues and lesions, training the network without using the annotations. In this work, an unsupervised learning technique is proposed to automatically segment coronavirus disease 2019 (COVID-19) lesions on 2D axial CT lung slices. The proposed approach uses the technique of image translation to generate healthy lung images based on the infected lung image without the need for lesion annotations. Attention masks are used to improve the quality of the segmentation further. Experiments showed the capability of the proposed approaches to segment the lesions, and it outperforms a range of unsupervised lesion detection approaches. The average reported results for the test dataset based on the metrics: Dice Score, Sensitivity, Specificity, Structure Measure, Enhanced-Alignment Measure, and Mean Absolute Error are 0.695, 0.694, 0.961, 0.791, 0.875, and 0.082 respectively. The achieved results are promising compared with the state-of-the-art and could constitute a valuable tool for future developments.
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Affiliation(s)
- Moiz Khan Sherwani
- Department of Mathematics and Computer Science, University of Calabria, Rende, Italy.
| | - Aldo Marzullo
- Department of Mathematics and Computer Science, University of Calabria, Rende, Italy
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Francesco Calimeri
- Department of Mathematics and Computer Science, University of Calabria, Rende, Italy
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2
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Obara T, Blonski M, Brzenczek C, Mézières S, Gaudeau Y, Pouget C, Gauchotte G, Verger A, Vogin G, Moureaux JM, Duffau H, Rech F, Taillandier L. Adult Diffuse Low-Grade Gliomas: 35-Year Experience at the Nancy France Neurooncology Unit. Front Oncol 2020; 10:574679. [PMID: 33194684 PMCID: PMC7656991 DOI: 10.3389/fonc.2020.574679] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Accepted: 08/20/2020] [Indexed: 01/14/2023] Open
Abstract
Background To report survival, spontaneous prognostic factors, and treatment efficacy in a French monocentric cohort of diffuse low-grade glioma (DLGG) patients over 35 years of follow-up. Methods A monocentric retrospective study of 339 patients diagnosed with a new DLGG between 01/01/1982 and 01/01/2017 was created. Inclusion criteria were patient age ≥18 years at diagnosis and histological diagnosis of WHO grade II glioma (according to 1993, 2007, and 2016 WHO classifications). The survival parameters were estimated using the Kaplan-Meier method with a 95% confidence interval. Differences in survival were tested for statistical significance by the log-rank test. Factors were considered significant when p ≤ 0.1 and p ≤ 0.05 in the univariate and multivariate analyses, respectively. Results A total of 339 patients were included with a median follow-up of 8.7 years. The Kaplan-Meier median overall survival was 15.7 years. At the time of radiological diagnosis, Karnofsky Performance Status score and initial tumor volume were significant independent prognostic factors. Oncological prognostic factors were the extent of resection for patients who underwent surgery and the timing of radiotherapy for those concerned. In this study, patients who had delayed radiotherapy (provided remaining low grade) did not have worse survival compared with patients who had early radiotherapy. The functional capabilities of the patients were preserved enough so that they could remain independent during at least three quarters of the follow-up. Conclusion This large monocentric series spread over a long time clarifies the effects of different therapeutic strategies and their combination in the management of DLGG.
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Affiliation(s)
- Tiphaine Obara
- Centre de Recherche en Automatique Nancy France - UMR 7039 - BioSiS Department, Faculty of Medicine, Université de Lorraine, Vandoeuvre-lès-Nancy, France.,Neurology Departement, Neurooncology Unit, CHRU, Nancy, France
| | - Marie Blonski
- Centre de Recherche en Automatique Nancy France - UMR 7039 - BioSiS Department, Faculty of Medicine, Université de Lorraine, Vandoeuvre-lès-Nancy, France.,Neurology Departement, Neurooncology Unit, CHRU, Nancy, France
| | - Cyril Brzenczek
- Centre de Recherche en Automatique Nancy France - UMR 7039 - BioSiS Department, Faculty of Medicine, Université de Lorraine, Vandoeuvre-lès-Nancy, France
| | - Sophie Mézières
- Department of Mathematics, Elie Cartan Institute, Nancy, France.,INRIA Biology, Genetics and Statistics, Nancy, France
| | - Yann Gaudeau
- Centre de Recherche en Automatique Nancy France - UMR 7039 - BioSiS Department, Faculty of Medicine, Université de Lorraine, Vandoeuvre-lès-Nancy, France
| | - Celso Pouget
- Department of Pathology, CHRU, Nancy, France.,Centre de Ressources Biologiques, BB-0033-00035, CHRU Nancy, France
| | - Guillaume Gauchotte
- Department of Pathology, CHRU, Nancy, France.,Centre de Ressources Biologiques, BB-0033-00035, CHRU Nancy, France
| | - Antoine Verger
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, CHRU Nancy, France.,IADI, INSERM U1254, Lorraine University, Vandoeuvre-lès-Nancy, France
| | - Guillaume Vogin
- UMR 7365 CNRS, IMoPA Biopole Lorraine University Faculty of Medicine, Université de Lorraine, Vandoeuvre-lès-Nancy, France.,Department of Radiation Therapy, Baclese Radiation Therapy Centre, Esch/Alzette, Luxembourg
| | - Jean-Marie Moureaux
- Centre de Recherche en Automatique Nancy France - UMR 7039 - BioSiS Department, Faculty of Medicine, Université de Lorraine, Vandoeuvre-lès-Nancy, France
| | - Hugues Duffau
- Department of Neurosurgery, Montpellier University Medical Center, Gui de Chauliac Hospital, Montpellier, France.,Team "Plasticity of Central Nervous System, Stem Cells and Glial Tumors", U1051 Laboratory, National Institute for Health and Medical Research (INSERM), Institute for Neurosciences of Montpellier, Montpellier University Medical Center, Montpellier, France
| | - Fabien Rech
- Department of Neurosurgery, CHRU, Nancy, France
| | - Luc Taillandier
- Centre de Recherche en Automatique Nancy France - UMR 7039 - BioSiS Department, Faculty of Medicine, Université de Lorraine, Vandoeuvre-lès-Nancy, France.,Neurology Departement, Neurooncology Unit, CHRU, Nancy, France
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3
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Verburg N, de Witt Hamer PC. State-of-the-art imaging for glioma surgery. Neurosurg Rev 2020; 44:1331-1343. [PMID: 32607869 PMCID: PMC8121714 DOI: 10.1007/s10143-020-01337-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 05/25/2020] [Accepted: 06/15/2020] [Indexed: 11/29/2022]
Abstract
Diffuse gliomas are infiltrative primary brain tumors with a poor prognosis despite multimodal treatment. Maximum safe resection is recommended whenever feasible. The extent of resection (EOR) is positively correlated with survival. Identification of glioma tissue during surgery is difficult due to its diffuse nature. Therefore, glioma resection is imaging-guided, making the choice for imaging technique an important aspect of glioma surgery. The current standard for resection guidance in non-enhancing gliomas is T2 weighted or T2w-fluid attenuation inversion recovery magnetic resonance imaging (MRI), and in enhancing gliomas T1-weighted MRI with a gadolinium-based contrast agent. Other MRI sequences, like magnetic resonance spectroscopy, imaging modalities, such as positron emission tomography, as well as intraoperative imaging techniques, including the use of fluorescence, are also available for the guidance of glioma resection. The neurosurgeon’s goal is to find the balance between maximizing the EOR and preserving brain functions since surgery-induced neurological deficits result in lower quality of life and shortened survival. This requires localization of important brain functions and white matter tracts to aid the pre-operative planning and surgical decision-making. Visualization of brain functions and white matter tracts is possible with functional MRI, diffusion tensor imaging, magnetoencephalography, and navigated transcranial magnetic stimulation. In this review, we discuss the current available imaging techniques for the guidance of glioma resection and the localization of brain functions and white matter tracts.
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Affiliation(s)
- Niels Verburg
- Department of Neurosurgery and Cancer Center Amsterdam, Amsterdam UMC location VU University Medical Center, Amsterdam, The Netherlands. .,Division of Neurosurgery, Department of Clinical Neurosciences, Cambridge Brain Tumor Imaging Laboratory, University of Cambridge, Addenbrooke's Hospital, Hill Rd, Cambridge, CB2 0QQ, UK.
| | - Philip C de Witt Hamer
- Department of Neurosurgery and Cancer Center Amsterdam, Amsterdam UMC location VU University Medical Center, Amsterdam, The Netherlands
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4
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Darlix A, Mandonnet E, Freyschlag CF, Pinggera D, Forster MT, Voss M, Steinbach J, Loughrey C, Goodden J, Banna G, Di Blasi C, Foroglou N, Hottinger AF, Baron MH, Pallud J, Duffau H, Rutten GJ, Almairac F, Fontaine D, Taillandier L, Pessanha Viegas C, Albuquerque L, von Campe G, Urbanic-Purkart T, Blonski M. Chemotherapy and diffuse low-grade gliomas: a survey within the European Low-Grade Glioma Network. Neurooncol Pract 2019; 6:264-273. [PMID: 31386080 PMCID: PMC6660823 DOI: 10.1093/nop/npy051] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Diffuse low-grade gliomas (DLGGs) are rare and incurable tumors. Whereas maximal safe, functional-based surgical resection is the first-line treatment, the timing and choice of further treatments (chemotherapy, radiation therapy, or combined treatments) remain controversial. METHODS An online survey on the management of DLGG patients was sent to 28 expert centers from the European Low-Grade Glioma Network (ELGGN) in May 2015. It contained 40 specific questions addressing the modalities of use of chemotherapy in these patients. RESULTS The survey demonstrated a significant heterogeneity in practice regarding the initial management of DLGG patients and the use of chemotherapy. Interestingly, radiation therapy combined with the procarbazine, CCNU (lomustine), and vincristine regimen has not imposed itself as the gold-standard treatment after surgery, despite the results of the Radiation Therapy Oncology Group 9802 study. Temozolomide is largely used as first-line treatment after surgical resection for high-risk DLGG patients, or at progression. CONCLUSIONS The heterogeneity in the management of patients with DLGG demonstrates that many questions regarding the postoperative strategy and the use of chemotherapy remain unanswered. Our survey reveals a high recruitment potential within the ELGGN for retrospective or prospective studies to generate new data regarding these issues.
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Affiliation(s)
- Amélie Darlix
- Department of Medical Oncology, Institut du Cancer de Montpellier, University of Montpellier, France
| | | | | | - Daniel Pinggera
- Department of Neurosurgery, Medical University of Innsbruck, Austria
| | | | - Martin Voss
- Dr. Senckenberg Institute of Neurooncology, Goethe University Hospital, Frankfurt, Germany
| | - Joachim Steinbach
- Dr. Senckenberg Institute of Neurooncology, Goethe University Hospital, Frankfurt, Germany
| | | | - John Goodden
- Leeds General Infirmary and North East Paediatric Neuroscience Network, Leeds, United Kingdom
| | - Giuseppe Banna
- Department of Neurosurgery and Gammaknife, Cannizzaro General Hospital, Catania, Italy
| | - Concetta Di Blasi
- Department of Neurosurgery and Gammaknife, Cannizzaro General Hospital, Catania, Italy
| | - Nicolas Foroglou
- Aristotle University of Thessaloniki, Department of Neurosurgery, AHEPA University Hospital, Greece
| | - Andreas F Hottinger
- Departments of Clinical Neurosciences and Oncology, Centre Hospitalier Universitaire Vaudois and Lausanne University, Switzerland
| | | | - Johan Pallud
- Department of Neurosurgery, Sainte-Anne Hospital, Paris, France, and Paris Descartes University, Sorbonne Paris Cité, France
| | - Hugues Duffau
- Inserm, U894, IMA-Brain, Centre de Psychiatrie et Neurosciences, Paris, France
- Department of Neurosurgery, Montpellier University Hospital, France
| | - Geert-Jan Rutten
- Department of Neurosurgery, Elisabeth-Tweesteden Hospital, Tilburg, The Netherlands
| | - Fabien Almairac
- Department of Neurosurgery, University Hospital of Nice, France
| | - Denys Fontaine
- Department of Neurosurgery, University Hospital of Nice, France
| | - Luc Taillandier
- Department of Neurooncology, Nancy Neurological Hospital, France
| | | | | | - Gord von Campe
- Department of Neurosurgery, Medical University of Graz, Austria
| | | | - Marie Blonski
- Department of Neurooncology, Nancy Neurological Hospital, France
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5
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Visser M, Müller DMJ, van Duijn RJM, Smits M, Verburg N, Hendriks EJ, Nabuurs RJA, Bot JCJ, Eijgelaar RS, Witte M, van Herk MB, Barkhof F, de Witt Hamer PC, de Munck JC. Inter-rater agreement in glioma segmentations on longitudinal MRI. NEUROIMAGE-CLINICAL 2019; 22:101727. [PMID: 30825711 PMCID: PMC6396436 DOI: 10.1016/j.nicl.2019.101727] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Revised: 02/06/2019] [Accepted: 02/19/2019] [Indexed: 11/25/2022]
Abstract
Background Tumor segmentation of glioma on MRI is a technique to monitor, quantify and report disease progression. Manual MRI segmentation is the gold standard but very labor intensive. At present the quality of this gold standard is not known for different stages of the disease, and prior work has mainly focused on treatment-naive glioblastoma. In this paper we studied the inter-rater agreement of manual MRI segmentation of glioblastoma and WHO grade II-III glioma for novices and experts at three stages of disease. We also studied the impact of inter-observer variation on extent of resection and growth rate. Methods In 20 patients with WHO grade IV glioblastoma and 20 patients with WHO grade II-III glioma (defined as non-glioblastoma) both the enhancing and non-enhancing tumor elements were segmented on MRI, using specialized software, by four novices and four experts before surgery, after surgery and at time of tumor progression. We used the generalized conformity index (GCI) and the intra-class correlation coefficient (ICC) of tumor volume as main outcome measures for inter-rater agreement. Results For glioblastoma, segmentations by experts and novices were comparable. The inter-rater agreement of enhancing tumor elements was excellent before surgery (GCI 0.79, ICC 0.99) poor after surgery (GCI 0.32, ICC 0.92), and good at progression (GCI 0.65, ICC 0.91). For non-glioblastoma, the inter-rater agreement was generally higher between experts than between novices. The inter-rater agreement was excellent between experts before surgery (GCI 0.77, ICC 0.92), was reasonable after surgery (GCI 0.48, ICC 0.84), and good at progression (GCI 0.60, ICC 0.80). The inter-rater agreement was good between novices before surgery (GCI 0.66, ICC 0.73), was poor after surgery (GCI 0.33, ICC 0.55), and poor at progression (GCI 0.36, ICC 0.73). Further analysis showed that the lower inter-rater agreement of segmentation on postoperative MRI could only partly be explained by the smaller volumes and fragmentation of residual tumor. The median interquartile range of extent of resection between raters was 8.3% and of growth rate was 0.22 mm/year. Conclusion Manual tumor segmentations on MRI have reasonable agreement for use in spatial and volumetric analysis. Agreement in spatial overlap is of concern with segmentation after surgery for glioblastoma and with segmentation of non-glioblastoma by non-experts. Inter-rater agreement for longitudinal glioma segmentation was determined. Agreement between 4 experts was higher than between 4 novices. Three time-points of glioblastoma (WHO IV) and diffuse glioma (WHO II-III) are studied. Impact on extent of resection and growth rate measurements was determined.
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Affiliation(s)
- M Visser
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HZ Amsterdam, the Netherlands.
| | - D M J Müller
- Department of Neurosurgery, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HZ Amsterdam, the Netherlands; Brain Tumor Center, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HZ Amsterdam, the Netherlands
| | - R J M van Duijn
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HZ Amsterdam, the Netherlands
| | - M Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC, PO Box 2040, 3000 CA Rotterdam, the Netherlands
| | - N Verburg
- Department of Neurosurgery, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HZ Amsterdam, the Netherlands; Brain Tumor Center, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HZ Amsterdam, the Netherlands
| | - E J Hendriks
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HZ Amsterdam, the Netherlands
| | - R J A Nabuurs
- Department of Neurosurgery, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HZ Amsterdam, the Netherlands; Brain Tumor Center, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HZ Amsterdam, the Netherlands
| | - J C J Bot
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HZ Amsterdam, the Netherlands
| | - R S Eijgelaar
- Department of Radiotherapy, The Netherlands Cancer Institute, Plesmanlaan 121, 1006 BE Amsterdam, the Netherlands
| | - M Witte
- Department of Radiotherapy, The Netherlands Cancer Institute, Plesmanlaan 121, 1006 BE Amsterdam, the Netherlands
| | - M B van Herk
- Institute of Cancer Sciences, Manchester Cancer Research Centre, Division of Cancer Science, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Sciences Centre, Manchester M13 9PL, United Kingdom
| | - F Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HZ Amsterdam, the Netherlands; Institutes of Neurology and Healthcare Engineering, University College London, Gower St, Bloomsbury, London WC1E 6BT, United Kingdom
| | - P C de Witt Hamer
- Department of Neurosurgery, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HZ Amsterdam, the Netherlands
| | - J C de Munck
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HZ Amsterdam, the Netherlands
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6
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Thust SC, Heiland S, Falini A, Jäger HR, Waldman AD, Sundgren PC, Godi C, Katsaros VK, Ramos A, Bargallo N, Vernooij MW, Yousry T, Bendszus M, Smits M. Glioma imaging in Europe: A survey of 220 centres and recommendations for best clinical practice. Eur Radiol 2018. [PMID: 29536240 PMCID: PMC6028837 DOI: 10.1007/s00330-018-5314-5] [Citation(s) in RCA: 135] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Objectives At a European Society of Neuroradiology (ESNR) Annual Meeting 2015 workshop, commonalities in practice, current controversies and technical hurdles in glioma MRI were discussed. We aimed to formulate guidance on MRI of glioma and determine its feasibility, by seeking information on glioma imaging practices from the European Neuroradiology community. Methods Invitations to a structured survey were emailed to ESNR members (n=1,662) and associates (n=6,400), European national radiologists’ societies and distributed via social media. Results Responses were received from 220 institutions (59% academic). Conventional imaging protocols generally include T2w, T2-FLAIR, DWI, and pre- and post-contrast T1w. Perfusion MRI is used widely (85.5%), while spectroscopy seems reserved for specific indications. Reasons for omitting advanced imaging modalities include lack of facility/software, time constraints and no requests. Early postoperative MRI is routinely carried out by 74% within 24–72 h, but only 17% report a percent measure of resection. For follow-up, most sites (60%) issue qualitative reports, while 27% report an assessment according to the RANO criteria. A minority of sites use a reporting template (23%). Conclusion Clinical best practice recommendations for glioma imaging assessment are proposed and the current role of advanced MRI modalities in routine use is addressed. Key Points • We recommend the EORTC-NBTS protocol as the clinical standard glioma protocol. • Perfusion MRI is recommended for diagnosis and follow-up of glioma. • Use of advanced imaging could be promoted with increased education activities. • Most response assessment is currently performed qualitatively. • Reporting templates are not widely used, and could facilitate standardisation. Electronic supplementary material The online version of this article (10.1007/s00330-018-5314-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- S C Thust
- Lysholm Neuroradiology Department, National Hospital for Neurology and Neurosurgery, London, UK
- Department of Brain Rehabilitation and Repair, UCL Institute of Neurology, London, UK
- Imaging Department, University College London Hospital, London, UK
| | - S Heiland
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| | - A Falini
- Department of Neuroradiology, San Raffaele Scientific Institute, Milan, Italy
| | - H R Jäger
- Lysholm Neuroradiology Department, National Hospital for Neurology and Neurosurgery, London, UK
- Department of Brain Rehabilitation and Repair, UCL Institute of Neurology, London, UK
- Imaging Department, University College London Hospital, London, UK
| | - A D Waldman
- Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK
| | - P C Sundgren
- Institution for Clinical Sciences/Radiology, Lund University, Lund, Sweden
- Centre for Imaging and Physiology, Skåne University hospital, Lund, Sweden
| | - C Godi
- Department of Neuroradiology, San Raffaele Scientific Institute, Milan, Italy
| | - V K Katsaros
- General Anti-Cancer and Oncological Hospital "Agios Savvas", Athens, Greece
- Central Clinic of Athens, Athens, Greece
- University of Athens, Athens, Greece
| | - A Ramos
- Hospital 12 de Octubre, Madrid, Spain
| | - N Bargallo
- Image Diagnostic Centre, Hospital Clinic de Barcelona, Barcelona, Spain
- Magnetic Resonance Core Facility, Institut per la Recerca Biomedica August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - M W Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - T Yousry
- Lysholm Neuroradiology Department, National Hospital for Neurology and Neurosurgery, London, UK
| | - M Bendszus
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| | - M Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.
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7
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Ben Abdallah M, Blonski M, Wantz-Mézières S, Gaudeau Y, Taillandier L, Moureaux JM. Relevance of two manual tumour volume estimation methods for diffuse low-grade gliomas. Healthc Technol Lett 2018. [PMID: 29515811 PMCID: PMC5830888 DOI: 10.1049/htl.2017.0013] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Management of diffuse low-grade glioma (DLGG) relies extensively on tumour volume estimation from MRI datasets. Two methods are currently clinically used to define this volume: the commonly used three-diameters solution and the more rarely used software-based volume reconstruction from the manual segmentations approach. The authors conducted an initial study of inter-practitioners’ variability of software-based manual segmentations on DLGGs MRI datasets. A panel of 13 experts from various specialties and years of experience delineated 12 DLGGs’ MRI scans. A statistical analysis on the segmented tumour volumes and pixels indicated that the individual practitioner, the years of experience and the specialty seem to have no significant impact on the segmentation of DLGGs. This is an interesting result as it had not yet been demonstrated and as it encourages cross-disciplinary collaboration. Their second study was with the three-diameters method, investigating its impact and that of the software-based volume reconstruction from manual segmentations method on tumour volume. They relied on the same dataset and on a participant from the first study. They compared the average of tumour volumes acquired by software reconstruction from manual segmentations method with tumour volumes obtained with the three-diameters method. The authors found that there is no statistically significant difference between the volumes estimated with the two approaches. These results correspond to non-operated and easily delineable DLGGs and are particularly interesting for time-consuming CUBE MRIs. Nonetheless, the three-diameters method has limitations in estimating tumour volumes for resected DLGGs, for which case the software-based manual segmentation method becomes more appropriate.
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Affiliation(s)
| | - Marie Blonski
- Centre de Recherche en Automatique de Nancy (CRAN), Nancy, France.,Neuro-Oncology Unit, Nancy University Hospital, Nancy, France
| | | | - Yann Gaudeau
- Centre de Recherche en Automatique de Nancy (CRAN), Nancy, France.,Université de Strasbourg, Strasbourg, France
| | - Luc Taillandier
- Centre de Recherche en Automatique de Nancy (CRAN), Nancy, France.,Neuro-Oncology Unit, Nancy University Hospital, Nancy, France
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