1
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Gill SK, Rose HEL, Wilson M, Rodriguez Gutierrez D, Worthington L, Davies NP, MacPherson L, Hargrave DR, Saunders DE, Clark CA, Payne GS, Leach MO, Howe FA, Auer DP, Jaspan T, Morgan PS, Grundy RG, Avula S, Pizer B, Arvanitis TN, Peet AC. Characterisation of paediatric brain tumours by their MRS metabolite profiles. NMR IN BIOMEDICINE 2024; 37:e5101. [PMID: 38303627 DOI: 10.1002/nbm.5101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 11/20/2023] [Accepted: 12/04/2023] [Indexed: 02/03/2024]
Abstract
1H-magnetic resonance spectroscopy (MRS) has the potential to improve the noninvasive diagnostic accuracy for paediatric brain tumours. However, studies analysing large, comprehensive, multicentre datasets are lacking, hindering translation to widespread clinical practice. Single-voxel MRS (point-resolved single-voxel spectroscopy sequence, 1.5 T: echo time [TE] 23-37 ms/135-144 ms, repetition time [TR] 1500 ms; 3 T: TE 37-41 ms/135-144 ms, TR 2000 ms) was performed from 2003 to 2012 during routine magnetic resonance imaging for a suspected brain tumour on 340 children from five hospitals with 464 spectra being available for analysis and 281 meeting quality control. Mean spectra were generated for 13 tumour types. Mann-Whitney U-tests and Kruskal-Wallis tests were used to compare mean metabolite concentrations. Receiver operator characteristic curves were used to determine the potential for individual metabolites to discriminate between specific tumour types. Principal component analysis followed by linear discriminant analysis was used to construct a classifier to discriminate the three main central nervous system tumour types in paediatrics. Mean concentrations of metabolites were shown to differ significantly between tumour types. Large variability existed across each tumour type, but individual metabolites were able to aid discrimination between some tumour types of importance. Complete metabolite profiles were found to be strongly characteristic of tumour type and, when combined with the machine learning methods, demonstrated a diagnostic accuracy of 93% for distinguishing between the three main tumour groups (medulloblastoma, pilocytic astrocytoma and ependymoma). The accuracy of this approach was similar even when data of marginal quality were included, greatly reducing the proportion of MRS excluded for poor quality. Children's brain tumours are strongly characterised by MRS metabolite profiles readily acquired during routine clinical practice, and this information can be used to support noninvasive diagnosis. This study provides both key evidence and an important resource for the future use of MRS in the diagnosis of children's brain tumours.
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Affiliation(s)
- Simrandip K Gill
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
| | - Heather E L Rose
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
| | - Martin Wilson
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
| | | | - Lara Worthington
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
- Department of Imaging and Medical Physics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Nigel P Davies
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
- Department of Imaging and Medical Physics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | | | - Darren R Hargrave
- Paediatric Oncology Unit, Great Ormond Street Hospital For Sick Children, London, UK
| | - Dawn E Saunders
- Paediatric Oncology Unit, Great Ormond Street Hospital For Sick Children, London, UK
| | - Christopher A Clark
- Developmental Imaging and Biophysics Section, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Geoffrey S Payne
- CRUK Cancer Imaging Centre, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | - Martin O Leach
- CRUK Cancer Imaging Centre, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | - Franklyn A Howe
- Neurosciences Research Section, Molecular and Clinical Sciences Research Institute, St George's, University of London, London, UK
| | - Dorothee P Auer
- The Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
- Radiological Sciences, Department of Clinical Neuroscience, University of Nottingham, Nottingham, UK
- Neuroradiology, Nottingham University Hospital, Queen's Medical Centre, Nottingham, UK
| | - Tim Jaspan
- The Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
- Neuroradiology, Nottingham University Hospital, Queen's Medical Centre, Nottingham, UK
| | - Paul S Morgan
- Medical Physics, Nottingham University Hospital, Queen's Medical Centre, Nottingham, UK
- The Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
| | - Richard G Grundy
- The Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
| | - Shivaram Avula
- Department of Radiology, Alder Hey Children's NHS Foundation Trust, Liverpool, UK
| | - Barry Pizer
- Department of Paediatric Oncology, Alder Hey Children's NHS Foundation Trust, Liverpool, UK
| | - Theodoros N Arvanitis
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham, UK
| | - Andrew C Peet
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
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2
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Tsang B, Gupta A, Takahashi MS, Baffi H, Ola T, Doria AS. Applications of artificial intelligence in magnetic resonance imaging of primary pediatric cancers: a scoping review and CLAIM score assessment. Jpn J Radiol 2023; 41:1127-1147. [PMID: 37395982 DOI: 10.1007/s11604-023-01437-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 04/18/2023] [Indexed: 07/04/2023]
Abstract
PURPOSES To review the uses of AI for magnetic resonance (MR) imaging assessment of primary pediatric cancer and identify common literature topics and knowledge gaps. To assess the adherence of the existing literature to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines. MATERIALS AND METHODS A scoping literature search using MEDLINE, EMBASE and Cochrane databases was performed, including studies of > 10 subjects with a mean age of < 21 years. Relevant data were summarized into three categories based on AI application: detection, characterization, treatment and monitoring. Readers independently scored each study using CLAIM guidelines, and inter-rater reproducibility was assessed using intraclass correlation coefficients. RESULTS Twenty-one studies were included. The most common AI application for pediatric cancer MR imaging was pediatric tumor diagnosis and detection (13/21 [62%] studies). The most commonly studied tumor was posterior fossa tumors (14 [67%] studies). Knowledge gaps included a lack of research in AI-driven tumor staging (0/21 [0%] studies), imaging genomics (1/21 [5%] studies), and tumor segmentation (2/21 [10%] studies). Adherence to CLAIM guidelines was moderate in primary studies, with an average (range) of 55% (34%-73%) CLAIM items reported. Adherence has improved over time based on publication year. CONCLUSION The literature surrounding AI applications of MR imaging in pediatric cancers is limited. The existing literature shows moderate adherence to CLAIM guidelines, suggesting that better adherence is required for future studies.
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Affiliation(s)
- Brian Tsang
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Department of Diagnostic Imaging, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada
| | - Aaryan Gupta
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Department of Diagnostic Imaging, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada
| | - Marcelo Straus Takahashi
- Instituto de Radiologia do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (InRad/HC-FMUSP), São Paulo, SP, Brazil
- Instituto da Criança do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (ICr/HC-FMUSP), São Paulo, SP, Brazil
- DasaInova, Diagnósticos da América SA (Dasa), São Paulo, SP, Brazil
| | | | - Tolulope Ola
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Department of Diagnostic Imaging, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada
| | - Andrea S Doria
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.
- Department of Diagnostic Imaging, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada.
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Ungan G, Pons-Escoda A, Ulinic D, Arús C, Vellido A, Julià-Sapé M. Using Single-Voxel Magnetic Resonance Spectroscopy Data Acquired at 1.5T to Classify Multivoxel Data at 3T: A Proof-of-Concept Study. Cancers (Basel) 2023; 15:3709. [PMID: 37509372 PMCID: PMC10377805 DOI: 10.3390/cancers15143709] [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: 04/22/2023] [Revised: 06/26/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
In vivo magnetic resonance spectroscopy (MRS) has two modalities, single-voxel (SV) and multivoxel (MV), in which one or more contiguous grids of SVs are acquired. PURPOSE To test whether MV grids can be classified with models trained with SV. METHODS Retrospective study. Training dataset: Multicenter multiformat SV INTERPRET, 1.5T. Testing dataset: MV eTumour, 3T. Two classification tasks were completed: 3-class (meningioma vs. aggressive vs. normal) and 4-class (meningioma vs. low-grade glioma vs. aggressive vs. normal). Five different methods were tested for feature selection. The classification was implemented using linear discriminant analysis (LDA), random forest, and support vector machines. The evaluation was completed with balanced error rate (BER) and area under the curve (AUC) on both sets. The accuracy in class prediction was calculated by developing a solid tumor index (STI) and segmentation accuracy with the Dice score. RESULTS The best method was sequential forward feature selection combined with LDA, with AUCs = 0.95 (meningioma), 0.89 (aggressive), 0.82 (low-grade glioma), and 0.82 (normal). STI was 66% (4-class task) and 71% (3-class task) because two cases failed completely and two more had suboptimal STI as defined by us. DISCUSSION The reasons for failure in the classification of the MV test set were related to the presence of artifacts.
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Affiliation(s)
- Gülnur Ungan
- Centro de Investigación Biomédica en Red (CIBER), 28029 Madrid, Spain
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), 08193 Barcelona, Spain
| | - Albert Pons-Escoda
- Group de Neuro-Oncologia, Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Hospital Universitari de Bellvitge, 08908 Barcelona, Spain
| | - Daniel Ulinic
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), 08193 Barcelona, Spain
| | - Carles Arús
- Centro de Investigación Biomédica en Red (CIBER), 28029 Madrid, Spain
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), 08193 Barcelona, Spain
| | - Alfredo Vellido
- Centro de Investigación Biomédica en Red (CIBER), 28029 Madrid, Spain
- IDEAI-UPC Research Center, UPC BarcelonaTech, 08034 Barcelona, Spain
| | - Margarida Julià-Sapé
- Centro de Investigación Biomédica en Red (CIBER), 28029 Madrid, Spain
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), 08193 Barcelona, Spain
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van de Sande DMJ, Merkofer JP, Amirrajab S, Veta M, van Sloun RJG, Versluis MJ, Jansen JFA, van den Brink JS, Breeuwer M. A review of machine learning applications for the proton MR spectroscopy workflow. Magn Reson Med 2023. [PMID: 37402235 DOI: 10.1002/mrm.29793] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 06/20/2023] [Accepted: 06/20/2023] [Indexed: 07/06/2023]
Abstract
This literature review presents a comprehensive overview of machine learning (ML) applications in proton MR spectroscopy (MRS). As the use of ML techniques in MRS continues to grow, this review aims to provide the MRS community with a structured overview of the state-of-the-art methods. Specifically, we examine and summarize studies published between 2017 and 2023 from major journals in the MR field. We categorize these studies based on a typical MRS workflow, including data acquisition, processing, analysis, and artificial data generation. Our review reveals that ML in MRS is still in its early stages, with a primary focus on processing and analysis techniques, and less attention given to data acquisition. We also found that many studies use similar model architectures, with little comparison to alternative architectures. Additionally, the generation of artificial data is a crucial topic, with no consistent method for its generation. Furthermore, many studies demonstrate that artificial data suffers from generalization issues when tested on in vivo data. We also conclude that risks related to ML models should be addressed, particularly for clinical applications. Therefore, output uncertainty measures and model biases are critical to investigate. Nonetheless, the rapid development of ML in MRS and the promising results from the reviewed studies justify further research in this field.
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Affiliation(s)
- Dennis M J van de Sande
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Julian P Merkofer
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Sina Amirrajab
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Mitko Veta
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Ruud J G van Sloun
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Philips Research, Philips Research, Eindhoven, The Netherlands
| | | | - Jacobus F A Jansen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | | | - Marcel Breeuwer
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- MR R&D - Clinical Science, Philips Healthcare, Best, The Netherlands
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5
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Machine Learning in the Classification of Pediatric Posterior Fossa Tumors: A Systematic Review. Cancers (Basel) 2022; 14:cancers14225608. [PMID: 36428701 PMCID: PMC9688156 DOI: 10.3390/cancers14225608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/02/2022] [Accepted: 11/11/2022] [Indexed: 11/17/2022] Open
Abstract
Background: Posterior fossa tumors (PFTs) are a morbid group of central nervous system tumors that most often present in childhood. While early diagnosis is critical to drive appropriate treatment, definitive diagnosis is currently only achievable through invasive tissue collection and histopathological analyses. Machine learning has been investigated as an alternative means of diagnosis. In this systematic review and meta-analysis, we evaluated the primary literature to identify all machine learning algorithms developed to classify and diagnose pediatric PFTs using imaging or molecular data. Methods: Of the 433 primary papers identified in PubMed, EMBASE, and Web of Science, 25 ultimately met the inclusion criteria. The included papers were extracted for algorithm architecture, study parameters, performance, strengths, and limitations. Results: The algorithms exhibited variable performance based on sample size, classifier(s) used, and individual tumor types being investigated. Ependymoma, medulloblastoma, and pilocytic astrocytoma were the most studied tumors with algorithm accuracies ranging from 37.5% to 94.5%. A minority of studies compared the developed algorithm to a trained neuroradiologist, with three imaging-based algorithms yielding superior performance. Common algorithm and study limitations included small sample sizes, uneven representation of individual tumor types, inconsistent performance reporting, and a lack of application in the clinical environment. Conclusions: Artificial intelligence has the potential to improve the speed and accuracy of diagnosis in this field if the right algorithm is applied to the right scenario. Work is needed to standardize outcome reporting and facilitate additional trials to allow for clinical uptake.
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6
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Zhao D, Grist JT, Rose HEL, Davies NP, Wilson M, MacPherson L, Abernethy LJ, Avula S, Pizer B, Gutierrez DR, Jaspan T, Morgan PS, Mitra D, Bailey S, Sawlani V, Arvanitis TN, Sun Y, Peet AC. Metabolite selection for machine learning in childhood brain tumour classification. NMR IN BIOMEDICINE 2022; 35:e4673. [PMID: 35088473 DOI: 10.1002/nbm.4673] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 12/01/2021] [Accepted: 12/02/2021] [Indexed: 06/14/2023]
Abstract
MRS can provide high accuracy in the diagnosis of childhood brain tumours when combined with machine learning. A feature selection method such as principal component analysis is commonly used to reduce the dimensionality of metabolite profiles prior to classification. However, an alternative approach of identifying the optimal set of metabolites has not been fully evaluated, possibly due to the challenges of defining this for a multi-class problem. This study aims to investigate metabolite selection from in vivo MRS for childhood brain tumour classification. Multi-site 1.5 T and 3 T cohorts of patients with a brain tumour and histological diagnosis of ependymoma, medulloblastoma and pilocytic astrocytoma were retrospectively evaluated. Dimensionality reduction was undertaken by selecting metabolite concentrations through multi-class receiver operating characteristics and compared with principal component analysis. Classification accuracy was determined through leave-one-out and k-fold cross-validation. Metabolites identified as crucial in tumour classification include myo-inositol (P < 0.05, AUC = 0 . 81 ± 0 . 01 ), total lipids and macromolecules at 0.9 ppm (P < 0.05, AUC = 0 . 78 ± 0 . 01 ) and total creatine (P < 0.05, AUC = 0 . 77 ± 0 . 01 ) for the 1.5 T cohort, and glycine (P < 0.05, AUC = 0 . 79 ± 0 . 01 ), total N-acetylaspartate (P < 0.05, AUC = 0 . 79 ± 0 . 01 ) and total choline (P < 0.05, AUC = 0 . 75 ± 0 . 01 ) for the 3 T cohort. Compared with the principal components, the selected metabolites were able to provide significantly improved discrimination between the tumours through most classifiers (P < 0.05). The highest balanced classification accuracy determined through leave-one-out cross-validation was 85% for 1.5 T 1 H-MRS through support vector machine and 75% for 3 T 1 H-MRS through linear discriminant analysis after oversampling the minority. The study suggests that a group of crucial metabolites helps to achieve better discrimination between childhood brain tumours.
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Affiliation(s)
- Dadi Zhao
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
| | - James T Grist
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
| | - Heather E L Rose
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
| | - Nigel P Davies
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
- Imaging and Medical Physics, University Hospitals Birmingham, Birmingham, UK
| | - Martin Wilson
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK
| | | | | | | | - Barry Pizer
- Paediatric Oncology, Alder Hey Children's Hospital, Liverpool, UK
| | - Daniel R Gutierrez
- Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
- Medical Physics, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Tim Jaspan
- Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
- Neuroradiology, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Paul S Morgan
- Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
- Medical Physics, Nottingham University Hospitals NHS Trust, Nottingham, UK
- Division of Clinical Neuroscience, University of Nottingham, Nottingham, UK
| | - Dipayan Mitra
- Neuroradiology, The Newcastle upon Tyne Hospitals, Newcastle upon Tyne, UK
| | - Simon Bailey
- Paediatric Oncology, Great North Children's Hospital, Newcastle upon Tyne, UK
| | - Vijay Sawlani
- Radiology, Queen Elizabeth Hospital Birmingham, Birmingham, UK
- School of Psychology, University of Birmingham, Birmingham, UK
| | - Theodoros N Arvanitis
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
- Institute of Digital Healthcare, WMG, University of Warwick, Coventry, UK
| | - Yu Sun
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
- University of Birmingham and Southeast University Joint Research Centre for Biomedical Engineering, Suzhou, China
| | - Andrew C Peet
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
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7
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Davies NP, Rose HEL, Manias KA, Natarajan K, Abernethy LJ, Oates A, Janjua U, Davies P, MacPherson L, Arvanitis TN, Peet AC. Added value of magnetic resonance spectroscopy for diagnosing childhood cerebellar tumours. NMR IN BIOMEDICINE 2022; 35:e4630. [PMID: 34647377 DOI: 10.1002/nbm.4630] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 08/20/2021] [Accepted: 09/15/2021] [Indexed: 06/13/2023]
Abstract
1 H-magnetic resonance spectroscopy (MRS) provides noninvasive metabolite profiles with the potential to aid the diagnosis of brain tumours. Prospective studies of diagnostic accuracy and comparisons with conventional MRI are lacking. The aim of the current study was to evaluate, prospectively, the diagnostic accuracy of a previously established classifier for diagnosing the three major childhood cerebellar tumours, and to determine added value compared with standard reporting of conventional imaging. Single-voxel MRS (1.5 T, PRESS, TE 30 ms, TR 1500 ms, spectral resolution 1 Hz/point) was acquired prospectively on 39 consecutive cerebellar tumours with histopathological diagnoses of pilocytic astrocytoma, ependymoma or medulloblastoma. Spectra were analysed with LCModel and predefined quality control criteria were applied, leaving 33 cases in the analysis. The MRS diagnostic classifier was applied to this dataset. A retrospective analysis was subsequently undertaken by three radiologists, blind to histopathological diagnosis, to determine the change in diagnostic certainty when sequentially viewing conventional imaging, MRS and a decision support tool, based on the classifier. The overall classifier accuracy, evaluated prospectively, was 91%. Incorrectly classified cases, two anaplastic ependymomas, and a rare histological variant of medulloblastoma, were not well represented in the original training set. On retrospective review of conventional MRI, MRS and the classifier result, all radiologists showed a significant increase (Wilcoxon signed rank test, p < 0.001) in their certainty of the correct diagnosis, between viewing the conventional imaging and MRS with the decision support system. It was concluded that MRS can aid the noninvasive diagnosis of posterior fossa tumours in children, and that a decision support classifier helps in MRS interpretation.
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Affiliation(s)
- Nigel P Davies
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Department of Medical Physics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Birmingham Women's and Children's Hospital NHS Foundation Trust, Birmingham, UK
| | - Heather E L Rose
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Women's and Children's Hospital NHS Foundation Trust, Birmingham, UK
| | - Karen A Manias
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Women's and Children's Hospital NHS Foundation Trust, Birmingham, UK
| | - Kal Natarajan
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Department of Medical Physics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | | | - Adam Oates
- Birmingham Women's and Children's Hospital NHS Foundation Trust, Birmingham, UK
| | - Umair Janjua
- Birmingham Women's and Children's Hospital NHS Foundation Trust, Birmingham, UK
| | - Paul Davies
- Birmingham Women's and Children's Hospital NHS Foundation Trust, Birmingham, UK
| | - Lesley MacPherson
- Birmingham Women's and Children's Hospital NHS Foundation Trust, Birmingham, UK
| | - Theodoros N Arvanitis
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Women's and Children's Hospital NHS Foundation Trust, Birmingham, UK
- Institute of Digital Healthcare, WMG, University of Warwick, Coventry, UK
| | - Andrew C Peet
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Women's and Children's Hospital NHS Foundation Trust, Birmingham, UK
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8
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Ramesh S, Chokkara S, Shen T, Major A, Volchenboum SL, Mayampurath A, Applebaum MA. Applications of Artificial Intelligence in Pediatric Oncology: A Systematic Review. JCO Clin Cancer Inform 2021; 5:1208-1219. [PMID: 34910588 PMCID: PMC8812636 DOI: 10.1200/cci.21.00102] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 10/05/2021] [Accepted: 11/05/2021] [Indexed: 12/12/2022] Open
Abstract
PURPOSE There is a need for an improved understanding of clinical and biologic risk factors in pediatric cancer to improve patient outcomes. Machine learning (ML) represents the application of computational inference from advanced statistical methods that can be applied to increasing amount of data available for study in pediatric oncology. The goal of this systematic review was to systematically characterize the state of ML in pediatric oncology and highlight advances and opportunities in the field. METHODS We conducted a systematic review of the Embase, Scopus, and MEDLINE databases for applications of ML in pediatric oncology. Query results from all three databases were aggregated and duplicate studies were removed. RESULTS A total of 42 unique articles that examined the applications of ML in pediatric oncology met inclusion criteria for review. We identified 20 studies of CNS tumors, 13 of solid tumors, and nine of leukemia. ML tasks included classification, prediction of treatment response, and dose optimization with a variety of methods being used including neural network, k-nearest neighbor, random forest, naive Bayes, and support vector machines. Strengths of the identified studies included matching or outperforming physician comparators via automated analysis and predicting therapeutic response. Common limitations included significant heterogeneity in reporting standards, clinical applicability, small sample sizes, and missing external validation cohorts. CONCLUSION We identified areas where ML can enhance clinical care in ways that may not otherwise be achievable. Although ML promises enormous potential in improving diagnostics, decision making, and monitoring for children with cancer, the field remains in early stages and future work will be aided by standards and guidelines to ensure rigorous methodologic design and maximizing clinical utility.
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Affiliation(s)
- Siddhi Ramesh
- Pritzker School of Medicine, University of Chicago, Chicago, IL
| | - Sukarn Chokkara
- Pritzker School of Medicine, University of Chicago, Chicago, IL
| | - Timothy Shen
- Pritzker School of Medicine, University of Chicago, Chicago, IL
| | - Ajay Major
- Department of Medicine, Section of Hematology/Oncology, University of Chicago, Chicago, IL
| | - Samuel L. Volchenboum
- Department of Pediatrics, Section of Hematology/Oncology, University of Chicago, Chicago, IL
| | - Anoop Mayampurath
- Department of Pediatrics, Section of Hematology/Oncology, University of Chicago, Chicago, IL
| | - Mark A. Applebaum
- Department of Pediatrics, Section of Hematology/Oncology, University of Chicago, Chicago, IL
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9
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Huang J, Shlobin NA, Lam SK, DeCuypere M. Artificial Intelligence Applications in Pediatric Brain Tumor Imaging: A Systematic Review. World Neurosurg 2021; 157:99-105. [PMID: 34648981 DOI: 10.1016/j.wneu.2021.10.068] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 10/04/2021] [Indexed: 01/23/2023]
Abstract
OBJECTIVE Artificial intelligence (AI) has facilitated the analysis of medical imaging given increased computational capacity and medical data availability in recent years. Although many applications for AI in the imaging of brain tumors have been proposed, their potential clinical impact remains to be explored. A systematic review was performed to examine the role of AI in the analysis of pediatric brain tumor imaging. METHODS PubMed, Embase, and Scopus were searched for relevant articles up to January 27, 2021. RESULTS Literature search identified 298 records, of which 22 studies were included. The most commonly studied tumors were posterior fossa tumors including brainstem glioma, ependymoma, medulloblastoma, and pilocytic astrocytoma (15, 68%). Tumor diagnosis was the most frequently performed task (14, 64%), followed by tumor segmentation (3, 14%) and tumor detection (3, 14%). Of the 6 studies comparing AI to clinical experts, 5 demonstrated superiority of AI for tumor diagnosis. Other tasks including tumor segmentation, attenuation correction of positron emission tomography scans, image registration for patient positioning, and dose calculation for radiotherapy were performed with high accuracy comparable with clinical experts. No studies described use of the AI tool in routine clinical practice. CONCLUSIONS AI methods for analysis of pediatric brain tumor imaging have increased exponentially in recent years. However, adoption of these methods in clinical practice requires further characterization of validity and utility. Implementation of these methods may streamline clinical workflows by improving diagnostic accuracy and automating basic imaging analysis tasks.
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Affiliation(s)
- Jonathan Huang
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Division of Pediatric Neurosurgery, Ann and Robert H. Lurie Children's Hospital, Chicago, Illinois, USA
| | - Nathan A Shlobin
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Division of Pediatric Neurosurgery, Ann and Robert H. Lurie Children's Hospital, Chicago, Illinois, USA
| | - Sandi K Lam
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Division of Pediatric Neurosurgery, Ann and Robert H. Lurie Children's Hospital, Chicago, Illinois, USA
| | - Michael DeCuypere
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Division of Pediatric Neurosurgery, Ann and Robert H. Lurie Children's Hospital, Chicago, Illinois, USA.
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Attallah O. CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes. Front Neuroinform 2021; 15:663592. [PMID: 34122031 PMCID: PMC8193683 DOI: 10.3389/fninf.2021.663592] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 04/26/2021] [Indexed: 12/28/2022] Open
Abstract
Childhood medulloblastoma (MB) is a threatening malignant tumor affecting children all over the globe. It is believed to be the foremost common pediatric brain tumor causing death. Early and accurate classification of childhood MB and its classes are of great importance to help doctors choose the suitable treatment and observation plan, avoid tumor progression, and lower death rates. The current gold standard for diagnosing MB is the histopathology of biopsy samples. However, manual analysis of such images is complicated, costly, time-consuming, and highly dependent on the expertise and skills of pathologists, which might cause inaccurate results. This study aims to introduce a reliable computer-assisted pipeline called CoMB-Deep to automatically classify MB and its classes with high accuracy from histopathological images. This key challenge of the study is the lack of childhood MB datasets, especially its four categories (defined by the WHO) and the inadequate related studies. All relevant works were based on either deep learning (DL) or textural analysis feature extractions. Also, such studies employed distinct features to accomplish the classification procedure. Besides, most of them only extracted spatial features. Nevertheless, CoMB-Deep blends the advantages of textural analysis feature extraction techniques and DL approaches. The CoMB-Deep consists of a composite of DL techniques. Initially, it extracts deep spatial features from 10 convolutional neural networks (CNNs). It then performs a feature fusion step using discrete wavelet transform (DWT), a texture analysis method capable of reducing the dimension of fused features. Next, the CoMB-Deep explores the best combination of fused features, enhancing the performance of the classification process using two search strategies. Afterward, it employs two feature selection techniques on the fused feature sets selected in the previous step. A bi-directional long-short term memory (Bi-LSTM) network; a DL-based approach that is utilized for the classification phase. CoMB-Deep maintains two classification categories: binary category for distinguishing between the abnormal and normal cases and multi-class category to identify the subclasses of MB. The results of the CoMB-Deep for both classification categories prove that it is reliable. The results also indicate that the feature sets selected using both search strategies have enhanced the performance of Bi-LSTM compared to individual spatial deep features. CoMB-Deep is compared to related studies to verify its competitiveness, and this comparison confirmed its robustness and outperformance. Hence, CoMB-Deep can help pathologists perform accurate diagnoses, reduce misdiagnosis risks that could occur with manual diagnosis, accelerate the classification procedure, and decrease diagnosis costs.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt
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11
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Franco P, Huebschle I, Simon-Gabriel CP, Dacca K, Schnell O, Beck J, Mast H, Urbach H, Wuertemberger U, Prinz M, Hosp JA, Delev D, Mader I, Heiland DH. Mapping of Metabolic Heterogeneity of Glioma Using MR-Spectroscopy. Cancers (Basel) 2021; 13:cancers13102417. [PMID: 34067701 PMCID: PMC8155922 DOI: 10.3390/cancers13102417] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 05/13/2021] [Accepted: 05/15/2021] [Indexed: 01/04/2023] Open
Abstract
Simple Summary Radiomics is a research field that integrates radiological and genetic information, but the application of the techniques that have been developed to this purpose have not been widely established in daily clinical practice. The purpose of our study is the development of a straightforward tool that can easily be used to preoperatively predict and correlate the metabolic signature of different CNS-lesions. Particularly in gliomas, we hope to integrate the molecular profile of these tumors into our prediction model. Our goal is to deliver an open-software tool with the intention of advancing the diagnostic work-up of gliomas to the latest standards. Abstract Proton magnetic resonance spectroscopy (1H-MRS) delivers information about the non-invasive metabolic landscape of brain pathologies. 1H-MRS is used in clinical setting in addition to MRI for diagnostic, prognostic and treatment response assessments, but the use of this radiological tool is not entirely widespread. The importance of developing automated analysis tools for 1H-MRS lies in the possibility of a straightforward application and simplified interpretation of metabolic and genetic data that allow for incorporation into the daily practice of a broad audience. Here, we report a prospective clinical imaging trial (DRKS00019855) which aimed to develop a novel MR-spectroscopy-based algorithm for in-depth characterization of brain lesions and prediction of molecular traits. Dimensional reduction of metabolic profiles demonstrated distinct patterns throughout pathologies. We combined a deep autoencoder and multi-layer linear discriminant models for voxel-wise prediction of the molecular profile based on MRS imaging. Molecular subtypes were predicted by an overall accuracy of 91.2% using a classifier score. Our study indicates a first step into combining the metabolic and molecular traits of lesions for advancing the pre-operative diagnostic workup of brain tumors and improve personalized tumor treatment.
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Affiliation(s)
- Pamela Franco
- Department of Neurosurgery, Medical Center-University of Freiburg, 79106 Freiburg, Germany; (I.H.); (K.D.); (O.S.); (J.B.); (D.H.H.)
- Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (C.P.S.-G.); (H.U.); (U.W.); (M.P.); (J.A.H.); (I.M.)
- Correspondence: ; Tel.: +49-(0)-761-270-50010; Fax: +49-(0)-761-270-51020
| | - Irene Huebschle
- Department of Neurosurgery, Medical Center-University of Freiburg, 79106 Freiburg, Germany; (I.H.); (K.D.); (O.S.); (J.B.); (D.H.H.)
- Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (C.P.S.-G.); (H.U.); (U.W.); (M.P.); (J.A.H.); (I.M.)
| | - Carl Philipp Simon-Gabriel
- Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (C.P.S.-G.); (H.U.); (U.W.); (M.P.); (J.A.H.); (I.M.)
- Department of Radiology, Medical Center-University of Freiburg, 79106 Freiburg, Germany
| | - Karam Dacca
- Department of Neurosurgery, Medical Center-University of Freiburg, 79106 Freiburg, Germany; (I.H.); (K.D.); (O.S.); (J.B.); (D.H.H.)
- Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (C.P.S.-G.); (H.U.); (U.W.); (M.P.); (J.A.H.); (I.M.)
| | - Oliver Schnell
- Department of Neurosurgery, Medical Center-University of Freiburg, 79106 Freiburg, Germany; (I.H.); (K.D.); (O.S.); (J.B.); (D.H.H.)
- Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (C.P.S.-G.); (H.U.); (U.W.); (M.P.); (J.A.H.); (I.M.)
| | - Juergen Beck
- Department of Neurosurgery, Medical Center-University of Freiburg, 79106 Freiburg, Germany; (I.H.); (K.D.); (O.S.); (J.B.); (D.H.H.)
- Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (C.P.S.-G.); (H.U.); (U.W.); (M.P.); (J.A.H.); (I.M.)
| | - Hansjoerg Mast
- Department of Neuroradiology, Medical Center-University of Freiburg, 79106 Freiburg, Germany;
| | - Horst Urbach
- Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (C.P.S.-G.); (H.U.); (U.W.); (M.P.); (J.A.H.); (I.M.)
- Department of Neuroradiology, Medical Center-University of Freiburg, 79106 Freiburg, Germany;
| | - Urs Wuertemberger
- Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (C.P.S.-G.); (H.U.); (U.W.); (M.P.); (J.A.H.); (I.M.)
- Department of Neuroradiology, Medical Center-University of Freiburg, 79106 Freiburg, Germany;
| | - Marco Prinz
- Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (C.P.S.-G.); (H.U.); (U.W.); (M.P.); (J.A.H.); (I.M.)
- Institute of Neuropathology, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
- Signaling Research Centers BIOSS and CIBSS, University of Freiburg, 79106 Freiburg, Germany
- Center for Basics in NeuroModulation (NeuroModulBasics), Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Jonas A. Hosp
- Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (C.P.S.-G.); (H.U.); (U.W.); (M.P.); (J.A.H.); (I.M.)
- Department of Neurology and Neuroscience, Medical Center-University of Freiburg, 79106 Freiburg, Germany
| | - Daniel Delev
- Department of Neurosurgery, RWTH University of Aachen, 52074 Aachen, Germany;
| | - Irina Mader
- Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (C.P.S.-G.); (H.U.); (U.W.); (M.P.); (J.A.H.); (I.M.)
- Department of Neuroradiology, Medical Center-University of Freiburg, 79106 Freiburg, Germany;
- Specialist Centre for Radiology, Schoen Clinic, 83569 Vogtareuth, Germany
| | - Dieter Henrik Heiland
- Department of Neurosurgery, Medical Center-University of Freiburg, 79106 Freiburg, Germany; (I.H.); (K.D.); (O.S.); (J.B.); (D.H.H.)
- Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (C.P.S.-G.); (H.U.); (U.W.); (M.P.); (J.A.H.); (I.M.)
- Microenvironment and Immunology Research Laboratory, Medical Center-University of Freiburg, 79106 Freiburg, Germany
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Attallah O. MB-AI-His: Histopathological Diagnosis of Pediatric Medulloblastoma and its Subtypes via AI. Diagnostics (Basel) 2021; 11:359. [PMID: 33672752 PMCID: PMC7924641 DOI: 10.3390/diagnostics11020359] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 02/11/2021] [Accepted: 02/11/2021] [Indexed: 12/17/2022] Open
Abstract
Medulloblastoma (MB) is a dangerous malignant pediatric brain tumor that could lead to death. It is considered the most common pediatric cancerous brain tumor. Precise and timely diagnosis of pediatric MB and its four subtypes (defined by the World Health Organization (WHO)) is essential to decide the appropriate follow-up plan and suitable treatments to prevent its progression and reduce mortality rates. Histopathology is the gold standard modality for the diagnosis of MB and its subtypes, but manual diagnosis via a pathologist is very complicated, needs excessive time, and is subjective to the pathologists' expertise and skills, which may lead to variability in the diagnosis or misdiagnosis. The main purpose of the paper is to propose a time-efficient and reliable computer-aided diagnosis (CADx), namely MB-AI-His, for the automatic diagnosis of pediatric MB and its subtypes from histopathological images. The main challenge in this work is the lack of datasets available for the diagnosis of pediatric MB and its four subtypes and the limited related work. Related studies are based on either textural analysis or deep learning (DL) feature extraction methods. These studies used individual features to perform the classification task. However, MB-AI-His combines the benefits of DL techniques and textural analysis feature extraction methods through a cascaded manner. First, it uses three DL convolutional neural networks (CNNs), including DenseNet-201, MobileNet, and ResNet-50 CNNs to extract spatial DL features. Next, it extracts time-frequency features from the spatial DL features based on the discrete wavelet transform (DWT), which is a textural analysis method. Finally, MB-AI-His fuses the three spatial-time-frequency features generated from the three CNNs and DWT using the discrete cosine transform (DCT) and principal component analysis (PCA) to produce a time-efficient CADx system. MB-AI-His merges the privileges of different CNN architectures. MB-AI-His has a binary classification level for classifying among normal and abnormal MB images, and a multi-classification level to classify among the four subtypes of MB. The results of MB-AI-His show that it is accurate and reliable for both the binary and multi-class classification levels. It is also a time-efficient system as both the PCA and DCT methods have efficiently reduced the training execution time. The performance of MB-AI-His is compared with related CADx systems, and the comparison verified the powerfulness of MB-AI-His and its outperforming results. Therefore, it can support pathologists in the accurate and reliable diagnosis of MB and its subtypes from histopathological images. It can also reduce the time and cost of the diagnosis procedure which will correspondingly lead to lower death rates.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria 1029, Egypt
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13
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Quon JL, Jin MC, Seekins J, Yeom KW. Harnessing the potential of artificial neural networks for pediatric patient management. Artif Intell Med 2021. [DOI: 10.1016/b978-0-12-821259-2.00021-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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14
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Davendralingam N, Sebire NJ, Arthurs OJ, Shelmerdine SC. Artificial intelligence in paediatric radiology: Future opportunities. Br J Radiol 2021; 94:20200975. [PMID: 32941736 PMCID: PMC7774693 DOI: 10.1259/bjr.20200975] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 09/04/2020] [Indexed: 12/13/2022] Open
Abstract
Artificial intelligence (AI) has received widespread and growing interest in healthcare, as a method to save time, cost and improve efficiencies. The high-performance statistics and diagnostic accuracies reported by using AI algorithms (with respect to predefined reference standards), particularly from image pattern recognition studies, have resulted in extensive applications proposed for clinical radiology, especially for enhanced image interpretation. Whilst certain sub-speciality areas in radiology, such as those relating to cancer screening, have received wide-spread attention in the media and scientific community, children's imaging has been hitherto neglected.In this article, we discuss a variety of possible 'use cases' in paediatric radiology from a patient pathway perspective where AI has either been implemented or shown early-stage feasibility, while also taking inspiration from the adult literature to propose potential areas for future development. We aim to demonstrate how a 'future, enhanced paediatric radiology service' could operate and to stimulate further discussion with avenues for research.
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Affiliation(s)
- Natasha Davendralingam
- Department of Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
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15
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EkŞİ Z, ÇakiroĞlu M, Öz C, AralaŞmak A, Karadelİ HH, Özcan ME. Differentiation of relapsing-remitting and secondary progressive multiple sclerosis: a magnetic resonance spectroscopy study based on machine learning. ARQUIVOS DE NEURO-PSIQUIATRIA 2020; 78:789-796. [PMID: 33331515 DOI: 10.1590/0004-282x20200094] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 06/04/2020] [Indexed: 11/22/2022]
Abstract
INTRODUCTION Magnetic resonance imaging (MRI) is the most important tool for diagnosis and follow-up in multiple sclerosis (MS). The discrimination of relapsing-remitting MS (RRMS) from secondary progressive MS (SPMS) is clinically difficult, and developing the proposal presented in this study would contribute to the process. OBJECTIVE This study aimed to ensure the automatic classification of healthy controls, RRMS, and SPMS by using MR spectroscopy and machine learning methods. METHODS MR spectroscopy (MRS) was performed on a total of 91 participants, distributed into healthy controls (n=30), RRMS (n=36), and SPMS (n=25). Firstly, MRS metabolites were identified using signal processing techniques. Secondly, feature extraction was performed based on MRS Spectra. N-acetylaspartate (NAA) was the most significant metabolite in differentiating MS types. Lastly, binary classifications (healthy controls-RRMS and RRMS-SPMS) were carried out according to features obtained by the Support Vector Machine algorithm. RESULTS RRMS cases were differentiated from healthy controls with 85% accuracy, 90.91% sensitivity, and 77.78% specificity. RRMS and SPMS were classified with 83.33% accuracy, 81.81% sensitivity, and 85.71% specificity. CONCLUSIONS A combined analysis of MRS and computer-aided diagnosis may be useful as a complementary imaging technique to determine MS types.
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Affiliation(s)
- Ziya EkŞİ
- Sakarya University, Department of Computer Engineering, Sakarya, Turkey
| | - Murat ÇakiroĞlu
- Sakarya University, Department of Mechatronic Engineering, Sakarya, Turkey
| | - Cemil Öz
- Sakarya University, Department of Computer Engineering, Sakarya, Turkey
| | - Ayse AralaŞmak
- Memorial Bahçelievler Hospital, Department of Radiology, Istanbul, Turkey
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16
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Payabvash S, Aboian M, Tihan T, Cha S. Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings. Front Oncol 2020; 10:71. [PMID: 32117728 PMCID: PMC7018938 DOI: 10.3389/fonc.2020.00071] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Accepted: 01/15/2020] [Indexed: 12/16/2022] Open
Abstract
We applied machine learning algorithms for differentiation of posterior fossa tumors using apparent diffusion coefficient (ADC) histogram analysis and structural MRI findings. A total of 256 patients with intra-axial posterior fossa tumors were identified, of whom 248 were included in machine learning analysis, with at least 6 representative subjects per each tumor pathology. The ADC histograms of solid components of tumors, structural MRI findings, and patients' age were applied to construct decision models using Classification and Regression Tree analysis. We also compared different machine learning classification algorithms (i.e., naïve Bayes, random forest, neural networks, support vector machine with linear and polynomial kernel) for dichotomized differentiation of the 5 most common tumors in our cohort: metastasis (n = 65), hemangioblastoma (n = 44), pilocytic astrocytoma (n = 43), ependymoma (n = 27), and medulloblastoma (n = 26). The decision tree model could differentiate seven tumor histopathologies with terminal nodes yielding up to 90% accurate classification rates. In receiver operating characteristics (ROC) analysis, the decision tree model achieved greater area under the curve (AUC) for differentiation of pilocytic astrocytoma (p = 0.020); and atypical teratoid/rhabdoid tumor ATRT (p = 0.001) from other types of neoplasms compared to the official clinical report. However, neuroradiologists' interpretations had greater accuracy in differentiating metastases (p = 0.001). Among different machine learning algorithms, random forest models yielded the highest accuracy in dichotomized classification of the 5 most common tumor types; and in multiclass differentiation of all tumor types random forest yielded an averaged AUC of 0.961 in training datasets, and 0.873 in validation samples. Our study demonstrates the potential application of machine learning algorithms and decision trees for accurate differentiation of brain tumors based on pretreatment MRI. Using easy to apply and understandable imaging metrics, the proposed decision tree model can help radiologists with differentiation of posterior fossa tumors, especially in tumors with similar qualitative imaging characteristics. In particular, our decision tree model provided more accurate differentiation of pilocytic astrocytomas from ATRT than by neuroradiologists in clinical reads.
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Affiliation(s)
- Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Mariam Aboian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Tarik Tihan
- Department of Pathology, University of California, San Francisco, San Francisco, CA, United States
| | - Soonmee Cha
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
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17
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Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning: A multi-site study. NEUROIMAGE-CLINICAL 2020; 25:102172. [PMID: 32032817 PMCID: PMC7005468 DOI: 10.1016/j.nicl.2020.102172] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 12/04/2019] [Accepted: 01/10/2020] [Indexed: 12/12/2022]
Abstract
The imaging and subsequent accurate diagnosis of paediatric brain tumours presents a radiological challenge, with magnetic resonance imaging playing a key role in providing tumour specific imaging information. Diffusion weighted and perfusion imaging are commonly used to aid the non-invasive diagnosis of children's brain tumours, but are usually evaluated by expert qualitative review. Quantitative studies are mainly single centre and single modality. The aim of this work was to combine multi-centre diffusion and perfusion imaging, with machine learning, to develop machine learning based classifiers to discriminate between three common paediatric tumour types. The results show that diffusion and perfusion weighted imaging of both the tumour and whole brain provide significant features which differ between tumour types, and that combining these features gives the optimal machine learning classifier with >80% predictive precision. This work represents a step forward to aid in the non-invasive diagnosis of paediatric brain tumours, using advanced clinical imaging.
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18
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Bi Q, Goodman KE, Kaminsky J, Lessler J. What is Machine Learning? A Primer for the Epidemiologist. Am J Epidemiol 2019; 188:2222-2239. [PMID: 31509183 DOI: 10.1093/aje/kwz189] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 07/29/2019] [Accepted: 08/14/2019] [Indexed: 12/22/2022] Open
Abstract
Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on "Big Data," it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods.
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Affiliation(s)
- Qifang Bi
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
| | - Katherine E Goodman
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
| | - Joshua Kaminsky
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
| | - Justin Lessler
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
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Julià-Sapé M, Candiota AP, Arús C. Cancer metabolism in a snapshot: MRS(I). NMR IN BIOMEDICINE 2019; 32:e4054. [PMID: 30633389 DOI: 10.1002/nbm.4054] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 11/02/2018] [Accepted: 11/05/2018] [Indexed: 06/09/2023]
Abstract
The contribution of MRS(I) to the in vivo evaluation of cancer-metabolism-derived metrics, mostly since 2016, is reviewed here. Increased carbon consumption by tumour cells, which are highly glycolytic, is now being sampled by 13 C magnetic resonance spectroscopic imaging (MRSI) following the injection of hyperpolarized [1-13 C] pyruvate (Pyr). Hot-spots of, mostly, increased lactate dehydrogenase activity or flow between Pyr and lactate (Lac) have been seen with cancer progression in prostate (preclinical and in humans), brain and pancreas (both preclinical) tumours. Therapy response is usually signalled by decreased Lac/Pyr 13 C-labelled ratio with respect to untreated or non-responding tumour. For therapeutic agents inducing tumour hypoxia, the 13 C-labelled Lac/bicarbonate ratio may be a better metric than the Lac/Pyr ratio. 31 P MRSI may sample intracellular pH changes from brain tumours (acidification upon antiangiogenic treatment, basification at fast proliferation and relapse). The steady state tumour metabolome pattern is still in use for cancer evaluation. Metrics used for this range from quantification of single oncometabolites (such as 2-hydroxyglutarate in mutant IDH1 glial brain tumours) to selected metabolite ratios (such as total choline to N-acetylaspartate (plain ratio or CNI index)) or the whole 1 H MRSI(I) pattern through pattern recognition analysis. These approaches have been applied to address different questions such as tumour subtype definition, following/predicting the response to therapy or defining better resection or radiosurgery limits.
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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
| | - Ana Paula Candiota
- 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
| | - 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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Sakai K, Yamada K. Machine learning studies on major brain diseases: 5-year trends of 2014–2018. Jpn J Radiol 2018; 37:34-72. [DOI: 10.1007/s11604-018-0794-4] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Accepted: 11/14/2018] [Indexed: 12/17/2022]
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