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Sánchez-Marqués R, García V, Sánchez JS. A data-centric machine learning approach to improve prediction of glioma grades using low-imbalance TCGA data. Sci Rep 2024; 14:17195. [PMID: 39060383 PMCID: PMC11282236 DOI: 10.1038/s41598-024-68291-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Accepted: 07/22/2024] [Indexed: 07/28/2024] Open
Abstract
Accurate prediction and grading of gliomas play a crucial role in evaluating brain tumor progression, assessing overall prognosis, and treatment planning. In addition to neuroimaging techniques, identifying molecular biomarkers that can guide the diagnosis, prognosis and prediction of the response to therapy has aroused the interest of researchers in their use together with machine learning and deep learning models. Most of the research in this field has been model-centric, meaning it has been based on finding better performing algorithms. However, in practice, improving data quality can result in a better model. This study investigates a data-centric machine learning approach to determine their potential benefits in predicting glioma grades. We report six performance metrics to provide a complete picture of model performance. Experimental results indicate that standardization and oversizing the minority class increase the prediction performance of four popular machine learning models and two classifier ensembles applied on a low-imbalanced data set consisting of clinical factors and molecular biomarkers. The experiments also show that the two classifier ensembles significantly outperform three of the four standard prediction models. Furthermore, we conduct a comprehensive descriptive analysis of the glioma data set to identify relevant statistical characteristics and discover the most informative attributes using four feature ranking algorithms.
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Affiliation(s)
- Raquel Sánchez-Marqués
- Fundación Estatal, Salud, Infancia y Bienestar Social, 28029, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, 28029, Madrid, Spain
| | - Vicente García
- Dept. Electrical and Computer Engineering, Instituto de Ingeniería y Tecnología, Universidad Autónoma de Ciudad Juárez, 32310, Ciudad Juárez, Mexico.
| | - J Salvador Sánchez
- Dept. Computer Languages and Systems, Institute of New Imaging Technologies, Universitat Jaume I, 12071, Castelló, Spain
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Lindner T, Bolar DS, Achten E, Barkhof F, Bastos-Leite AJ, Detre JA, Golay X, Günther M, Wang DJJ, Haller S, Ingala S, Jäger HR, Jahng GH, Juttukonda MR, Keil VC, Kimura H, Ho ML, Lequin M, Lou X, Petr J, Pinter N, Pizzini FB, Smits M, Sokolska M, Zaharchuk G, Mutsaerts HJMM. Current state and guidance on arterial spin labeling perfusion MRI in clinical neuroimaging. Magn Reson Med 2023; 89:2024-2047. [PMID: 36695294 PMCID: PMC10914350 DOI: 10.1002/mrm.29572] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/16/2022] [Accepted: 12/19/2022] [Indexed: 01/26/2023]
Abstract
This article focuses on clinical applications of arterial spin labeling (ASL) and is part of a wider effort from the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group to update and expand on the recommendations provided in the 2015 ASL consensus paper. Although the 2015 consensus paper provided general guidelines for clinical applications of ASL MRI, there was a lack of guidance on disease-specific parameters. Since that time, the clinical availability and clinical demand for ASL MRI has increased. This position paper provides guidance on using ASL in specific clinical scenarios, including acute ischemic stroke and steno-occlusive disease, arteriovenous malformations and fistulas, brain tumors, neurodegenerative disease, seizures/epilepsy, and pediatric neuroradiology applications, focusing on disease-specific considerations for sequence optimization and interpretation. We present several neuroradiological applications in which ASL provides unique information essential for making the diagnosis. This guidance is intended for anyone interested in using ASL in a routine clinical setting (i.e., on a single-subject basis rather than in cohort studies) building on the previous ASL consensus review.
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Affiliation(s)
- Thomas Lindner
- Department of Diagnostic and Interventional Neuroradiology, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Divya S. Bolar
- Center for Functional Magnetic Resonance Imaging, Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Eric Achten
- Department of Radiology and Nuclear Medicine, Ghent University, Ghent, Belgium
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Amsterdam, The Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, UK
| | | | - John A. Detre
- Department of Neurology, University of Pennsylvania, Philadelphia PA USA
| | - Xavier Golay
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Matthias Günther
- (1) University Bremen, Germany; (2) Fraunhofer MEVIS, Bremen, Germany; (3) mediri GmbH, Heidelberg, Germany
| | - Danny JJ Wang
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles CA USA
| | - Sven Haller
- (1) CIMC - Centre d’Imagerie Médicale de Cornavin, Place de Cornavin 18, 1201 Genève 1201 Genève (2) Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden (3) Faculty of Medicine of the University of Geneva, Switzerland. Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, P. R. China
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Hans R Jäger
- UCL Queen Square Institute of Neuroradiology, University College London, London, UK
| | - Geon-Ho Jahng
- Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Meher R. Juttukonda
- (1) Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown MA USA (2) Department of Radiology, Harvard Medical School, Boston MA USA
| | - Vera C. Keil
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Hirohiko Kimura
- Department of Radiology, Faculty of Medical sciences, University of Fukui, Fukui, JAPAN
| | - Mai-Lan Ho
- Nationwide Children’s Hospital and The Ohio State University, Columbus, OH, USA
| | - Maarten Lequin
- Division Imaging & Oncology, Department of Radiology & Nuclear Medicine | University Medical Center Utrecht & Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Xin Lou
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Jan Petr
- (1) Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany (2) Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Nandor Pinter
- Dent Neurologic Institute, Buffalo, NY, USA. University at Buffalo Neurosurgery, Buffalo, NY, USA
| | - Francesca B. Pizzini
- Radiology Institute, Dept. of Diagnostic and Public Health, University of Verona, Verona, Italy
| | - Marion Smits
- (1) Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands (2) The Brain Tumour Centre, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Magdalena Sokolska
- Department of Medical Physics and Biomedical Engineering University College London Hospitals NHS Foundation Trust, UK
| | | | - Henk JMM Mutsaerts
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Amsterdam, The Netherlands
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Pinter NK. The Right Imaging Protocol for the Right Patient. Continuum (Minneap Minn) 2023; 29:16-26. [PMID: 36795871 DOI: 10.1212/con.0000000000001209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
OBJECTIVE This article provides a high-level overview of the challenge of choosing the right imaging approach for an individual patient. It also presents a generalizable approach that can be applied to practice regardless of specific imaging technologies. ESSENTIAL POINTS This article constitutes an introduction to the in-depth, topic-focused analyses in the rest of this issue. It examines the broad principles that guide placing a patient on the right diagnostic trajectory, illustrated with real-life examples of current protocol recommendations and cases of advanced imaging techniques, as well as some thought experiments. Thinking about diagnostic imaging strictly in terms of imaging protocols is often inefficient because these protocols can be vague and have numerous variations. Broadly defined protocols may be sufficient, but their successful use often depends largely on the particular circumstances, with special emphasis on the relationship between neurologists and radiologists.
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