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Mukherjee T, Pournik O, Lim Choi Keung SN, Arvanitis TN. Clinical Decision Support Systems for Brain Tumour Diagnosis and Prognosis: A Systematic Review. Cancers (Basel) 2023; 15:3523. [PMID: 37444633 DOI: 10.3390/cancers15133523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/02/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023] Open
Abstract
CDSSs are being continuously developed and integrated into routine clinical practice as they assist clinicians and radiologists in dealing with an enormous amount of medical data, reduce clinical errors, and improve diagnostic capabilities. They assist detection, classification, and grading of brain tumours as well as alert physicians of treatment change plans. The aim of this systematic review is to identify various CDSSs that are used in brain tumour diagnosis and prognosis and rely on data captured by any imaging modality. Based on the 2020 preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol, the literature search was conducted in PubMed and Engineering Village Compendex databases. Different types of CDSSs identified through this review include Curiam BT, FASMA, MIROR, HealthAgents, and INTERPRET, among others. This review also examines various CDSS tool types, system features, techniques, accuracy, and outcomes, to provide the latest evidence available in the field of neuro-oncology. An overview of such CDSSs used to support clinical decision-making in the management and treatment of brain tumours, along with their benefits, challenges, and future perspectives has been provided. Although a CDSS improves diagnostic capabilities and healthcare delivery, there is lack of specific evidence to support these claims. The absence of empirical data slows down both user acceptance and evaluation of the actual impact of CDSS on brain tumour management. Instead of emphasizing the advantages of implementing CDSS, it is important to address its potential drawbacks and ethical implications. By doing so, it can promote the responsible use of CDSS and facilitate its faster adoption in clinical settings.
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Affiliation(s)
- Teesta Mukherjee
- Department of Electronic, Electrical and Systems Engineering, School of Engineering, College of Engineering and Physical Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Omid Pournik
- Department of Electronic, Electrical and Systems Engineering, School of Engineering, College of Engineering and Physical Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Sarah N Lim Choi Keung
- Department of Electronic, Electrical and Systems Engineering, School of Engineering, College of Engineering and Physical Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Theodoros N Arvanitis
- Department of Electronic, Electrical and Systems Engineering, School of Engineering, College of Engineering and Physical Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
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Julià-Sapé M, Griffiths JR, Tate AR, Howe FA, Acosta D, Postma G, Underwood J, Majós C, Arús C. Classification of brain tumours from MR spectra: the INTERPRET collaboration and its outcomes. NMR IN BIOMEDICINE 2015; 28:1772-1787. [PMID: 26768492 DOI: 10.1002/nbm.3439] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2014] [Revised: 07/15/2015] [Accepted: 10/01/2015] [Indexed: 06/05/2023]
Abstract
The INTERPRET project was a multicentre European collaboration, carried out from 2000 to 2002, which developed a decision-support system (DSS) for helping neuroradiologists with no experience of MRS to utilize spectroscopic data for the diagnosis and grading of human brain tumours. INTERPRET gathered a large collection of MR spectra of brain tumours and pseudo-tumoural lesions from seven centres. Consensus acquisition protocols, a standard processing pipeline and strict methods for quality control of the aquired data were put in place. Particular emphasis was placed on ensuring the diagnostic certainty of each case, for which all cases were evaluated by a clinical data validation committee. One outcome of the project is a database of 304 fully validated spectra from brain tumours, pseudotumoural lesions and normal brains, along with their associated images and clinical data, which remains available to the scientific and medical community. The second is the INTERPRET DSS, which has continued to be developed and clinically evaluated since the project ended. We also review here the results of the post-INTERPRET period. We evaluate the results of the studies with the INTERPRET database by other consortia or research groups. A summary of the clinical evaluations that have been performed on the post-INTERPRET DSS versions is also presented. Several have shown that diagnostic certainty can be improved for certain tumour types when the INTERPRET DSS is used in conjunction with conventional radiological image interpretation. About 30 papers concerned with the INTERPRET single-voxel dataset have so far been published. We discuss stengths and weaknesses of the DSS and the lessons learned. Finally we speculate on how the INTERPRET concept might be carried into the future.
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Affiliation(s)
- Margarida Julià-Sapé
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain
- Departament de Bioquímica i Biologia Molecular, Unitat de Bioquímica de Biociències, Universitat Autònoma de Barcelona (UAB), Cerdanyola del Vallès, Spain
- Institut de Biotecnologia i de Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), Cerdanyola del Vallès, Spain
| | | | - A Rosemary Tate
- School of Informatics, University of Sussex, Falmer, Brighton, UK
| | - Franklyn A Howe
- Cardiovascular and Cell Sciences Research Institute, St George's, University of London, London, UK
| | - Dionisio Acosta
- CHIME, University College London, The Farr Institute of Health Informatics Research, London, UK
| | - Geert Postma
- Radboud University Nijmegen, Institute for Molecules and Materials, Analytical Chemistry, Nijmegen, The Netherlands
| | | | - Carles Majós
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain
- Institut de Diagnòstic per la Imatge (IDI), CSU de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Carles Arús
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain
- Departament de Bioquímica i Biologia Molecular, Unitat de Bioquímica de Biociències, Universitat Autònoma de Barcelona (UAB), Cerdanyola del Vallès, Spain
- Institut de Biotecnologia i de Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), Cerdanyola del Vallès, Spain
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Mocioiu V, Ortega-Martorell S, Olier I, Jablonski M, Starcukova J, Lisboa P, Arús C, Julià-Sapé M. From raw data to data-analysis for magnetic resonance spectroscopy--the missing link: jMRUI2XML. BMC Bioinformatics 2015; 16:378. [PMID: 26552737 PMCID: PMC4640235 DOI: 10.1186/s12859-015-0796-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2015] [Accepted: 10/27/2015] [Indexed: 11/23/2022] Open
Abstract
Background Magnetic resonance spectroscopy provides metabolic information about living tissues in a non-invasive way. However, there are only few multi-centre clinical studies, mostly performed on a single scanner model or data format, as there is no flexible way of documenting and exchanging processed magnetic resonance spectroscopy data in digital format. This is because the DICOM standard for spectroscopy deals with unprocessed data. This paper proposes a plugin tool developed for jMRUI, namely jMRUI2XML, to tackle the latter limitation. jMRUI is a software tool for magnetic resonance spectroscopy data processing that is widely used in the magnetic resonance spectroscopy community and has evolved into a plugin platform allowing for implementation of novel features. Results jMRUI2XML is a Java solution that facilitates common preprocessing of magnetic resonance spectroscopy data across multiple scanners. Its main characteristics are: 1) it automates magnetic resonance spectroscopy preprocessing, and 2) it can be a platform for outputting exchangeable magnetic resonance spectroscopy data. The plugin works with any kind of data that can be opened by jMRUI and outputs in extensible markup language format. Data processing templates can be generated and saved for later use. The output format opens the way for easy data sharing- due to the documentation of the preprocessing parameters and the intrinsic anonymization - for example for performing pattern recognition analysis on multicentre/multi-manufacturer magnetic resonance spectroscopy data. Conclusions jMRUI2XML provides a self-contained and self-descriptive format accounting for the most relevant information needed for exchanging magnetic resonance spectroscopy data in digital form, as well as for automating its processing. This allows for tracking the procedures the data has undergone, which makes the proposed tool especially useful when performing pattern recognition analysis. Moreover, this work constitutes a first proposal for a minimum amount of information that should accompany any magnetic resonance processed spectrum, towards the goal of achieving better transferability of magnetic resonance spectroscopy studies. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0796-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Victor Mocioiu
- Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, UAB, Cerdanyola del Vallès, Barcelona, 08193, Spain. .,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina CIBER-BBN, Cerdanyola del Vallès, Barcelona, Spain. .,Institut de Biotecnologia i Biomedicina (IBB), Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Barcelona, Spain.
| | - Sandra Ortega-Martorell
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina CIBER-BBN, Cerdanyola del Vallès, Barcelona, Spain. .,School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK.
| | - Iván Olier
- Institute of Biotechnology, The University of Manchester, Manchester, UK.
| | - Michal Jablonski
- Institute of Scientific Instruments of the CAS, v. v. i, Brno, Czech Republic.
| | - Jana Starcukova
- Institute of Scientific Instruments of the CAS, v. v. i, Brno, Czech Republic.
| | - Paulo Lisboa
- School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK.
| | - Carles Arús
- Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, UAB, Cerdanyola del Vallès, Barcelona, 08193, Spain. .,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina CIBER-BBN, Cerdanyola del Vallès, Barcelona, Spain. .,Institut de Biotecnologia i Biomedicina (IBB), Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Barcelona, Spain.
| | - Margarida Julià-Sapé
- Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, UAB, Cerdanyola del Vallès, Barcelona, 08193, Spain. .,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina CIBER-BBN, Cerdanyola del Vallès, Barcelona, Spain. .,Institut de Biotecnologia i Biomedicina (IBB), Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Barcelona, Spain.
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Julià-Sapé M, Majós C, Camins À, Samitier A, Baquero M, Serrallonga M, Doménech S, Grivé E, Howe FA, Opstad K, Calvar J, Aguilera C, Arús C. Multicentre evaluation of the INTERPRET decision support system 2.0 for brain tumour classification. NMR IN BIOMEDICINE 2014; 27:1009-1018. [PMID: 25042391 DOI: 10.1002/nbm.3144] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2013] [Revised: 04/14/2014] [Accepted: 05/03/2014] [Indexed: 06/03/2023]
Abstract
In a previous study, we have shown the added value of (1) H MRS for the neuroradiological characterisation of adult human brain tumours. In that study, several methods of MRS analysis were used, and a software program, the International Network for Pattern Recognition of Tumours Using Magnetic Resonance Decision Support System 1.0 (INTERPRET DSS 1.0), with a short-TE classifier, provided the best results. Since then, the DSS evolved into a version 2.0 that contains an additional long-TE classifier. This study has two objectives. First, to determine whether clinicians with no experience of spectroscopy are comparable with spectroscopists in the use of the system, when only minimum training in the use of the system was given. Second, to assess whether or not a version with another TE is better than the initial version. We undertook a second study with the same cases and nine evaluators to assess whether the diagnostic accuracy of DSS 2.0 was comparable with the values obtained with DSS 1.0. In the second study, the analysis protocol was flexible in comparison with the first one to mimic a clinical environment. In the present study, on average, each case required 5.4 min by neuroradiologists and 9 min by spectroscopists for evaluation. Most classes and superclasses of tumours gave the same results as with DSS 1.0, except for astrocytomas of World Health Organization (WHO) grade III, in which performance measured as the area under the curve (AUC) decreased: AUC = 0.87 (0.72-1.02) with DSS 1.0 and AUC = 0.62 (0.55-0.70) with DSS 2.0. When analysing the performance of radiologists and spectroscopists with respect to DSS 1.0, the results were the same for most classes. Having data with two TEs instead of one did not affect the results of the evaluation.
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Affiliation(s)
- Margarida Julià-Sapé
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain; Departament de Bioquímica i Biologia Molecular, Unitat de Bioquímica de Biociències, Edifici Cs, Universitat Autònoma de Barcelona, UAB, Cerdanyola del Vallès, Spain; Institut de Biotecnologia i de Biomedicina (IBB), Universitat Autònoma de Barcelona, UAB, Cerdanyola del Vallès, Spain
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Tsolaki E, Kousi E, Svolos P, Kapsalaki E, Theodorou K, Kappas C, Tsougos I. Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques. World J Radiol 2014; 6:72-81. [PMID: 24778769 PMCID: PMC4000611 DOI: 10.4329/wjr.v6.i4.72] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2013] [Revised: 01/23/2014] [Accepted: 03/18/2014] [Indexed: 02/06/2023] Open
Abstract
In recent years, advanced magnetic resonance imaging (MRI) techniques, such as magnetic resonance spectroscopy, diffusion weighted imaging, diffusion tensor imaging and perfusion weighted imaging have been used in order to resolve demanding diagnostic problems such as brain tumor characterization and grading, as these techniques offer a more detailed and non-invasive evaluation of the area under study. In the last decade a great effort has been made to import and utilize intelligent systems in the so-called clinical decision support systems (CDSS) for automatic processing, classification, evaluation and representation of MRI data in order for advanced MRI techniques to become a part of the clinical routine, since the amount of data from the aforementioned techniques has gradually increased. Hence, the purpose of the current review article is two-fold. The first is to review and evaluate the progress that has been made towards the utilization of CDSS based on data from advanced MRI techniques. The second is to analyze and propose the future work that has to be done, based on the existing problems and challenges, especially taking into account the new imaging techniques and parameters that can be introduced into intelligent systems to significantly improve their diagnostic specificity and clinical application.
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Sáez C, Martí-Bonmatí L, Alberich-Bayarri A, Robles M, García-Gómez JM. Randomized pilot study and qualitative evaluation of a clinical decision support system for brain tumour diagnosis based on SV ¹H MRS: evaluation as an additional information procedure for novice radiologists. Comput Biol Med 2013; 45:26-33. [PMID: 24480160 DOI: 10.1016/j.compbiomed.2013.11.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2013] [Revised: 11/13/2013] [Accepted: 11/18/2013] [Indexed: 11/28/2022]
Abstract
The results of a randomized pilot study and qualitative evaluation of the clinical decision support system Curiam BT are reported. We evaluated the system's feasibility and potential value as a radiological information procedure complementary to magnetic resonance (MR) imaging to assist novice radiologists in diagnosing brain tumours using MR spectroscopy (1.5 and 3.0T). Fifty-five cases were analysed at three hospitals according to four non-exclusive diagnostic questions. Our results show that Curiam BT improved the diagnostic accuracy in all the four questions. Additionally, we discuss the findings of the users' feedback about the system, and the further work to optimize it for real environments and to conduct a large clinical trial.
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Affiliation(s)
- Carlos Sáez
- Grupo de Informática Biomédica (IBIME), Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València Camino de Vera s/n, 46022 Valéncia, Spain.
| | - Luis Martí-Bonmatí
- Department of Radiology, Hospital Quirón Valencia, Valencia, Spain; Radiology, Department of Medicine, Universidad de Valencia, Spain
| | | | - Montserrat Robles
- Grupo de Informática Biomédica (IBIME), Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València Camino de Vera s/n, 46022 Valéncia, Spain
| | - Juan M García-Gómez
- Grupo de Informática Biomédica (IBIME), Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València Camino de Vera s/n, 46022 Valéncia, Spain
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Accurate classification of childhood brain tumours by in vivo ¹H MRS - a multi-centre study. Eur J Cancer 2012; 49:658-67. [PMID: 23036849 DOI: 10.1016/j.ejca.2012.09.003] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2012] [Revised: 07/14/2012] [Accepted: 09/07/2012] [Indexed: 11/22/2022]
Abstract
AIMS To evaluate the accuracy of single-voxel Magnetic Resonance Spectroscopy ((1)H MRS) as a non-invasive diagnostic aid for paediatric brain tumours in a multi-national study. Our hypotheses are (1) that automated classification based on (1)H MRS provides an accurate non-invasive diagnosis in multi-centre datasets and (2) using a protocol which increases the metabolite information improves the diagnostic accuracy. METHODS Seventy-eight patients under 16 years old with histologically proven brain tumours from 10 international centres were investigated. Discrimination of 29 medulloblastomas, 11 ependymomas and 38 pilocytic astrocytomas (PILOAs) was evaluated. Single-voxel MRS was undertaken prior to diagnosis (1.5 T Point-Resolved Spectroscopy (PRESS), Proton Brain Exam (PROBE) or Stimulated Echo Acquisition Mode (STEAM), echo time (TE) 20-32 ms and 135-136 ms). MRS data were processed using two strategies, determination of metabolite concentrations using TARQUIN software and automatic feature extraction with Peak Integration (PI). Linear Discriminant Analysis (LDA) was applied to this data to produce diagnostic classifiers. An evaluation of the diagnostic accuracy was performed based on resampling to measure the Balanced Accuracy Rate (BAR). RESULTS The accuracy of the diagnostic classifiers for discriminating the three tumour types was found to be high (BAR 0.98) when a combination of TE was used. The combination of both TEs significantly improved the classification performance (p<0.01, Tukey's test) compared with the use of one TE alone. Other tumour types were classified accurately as glial or primitive neuroectodermal (BAR 1.00). CONCLUSION (1)H MRS has excellent accuracy for the non-invasive diagnosis of common childhood brain tumours particularly if the metabolite information is maximised and should become part of routine clinical assessment for these children.
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