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El Hachimy I, Kabelma D, Echcharef C, Hassani M, Benamar N, Hajji N. A comprehensive survey on the use of deep learning techniques in glioblastoma. Artif Intell Med 2024; 154:102902. [PMID: 38852314 DOI: 10.1016/j.artmed.2024.102902] [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: 10/12/2023] [Revised: 04/28/2024] [Accepted: 06/02/2024] [Indexed: 06/11/2024]
Abstract
Glioblastoma, characterized as a grade 4 astrocytoma, stands out as the most aggressive brain tumor, often leading to dire outcomes. The challenge of treating glioblastoma is exacerbated by the convergence of genetic mutations and disruptions in gene expression, driven by alterations in epigenetic mechanisms. The integration of artificial intelligence, inclusive of machine learning algorithms, has emerged as an indispensable asset in medical analyses. AI is becoming a necessary tool in medicine and beyond. Current research on Glioblastoma predominantly revolves around non-omics data modalities, prominently including magnetic resonance imaging, computed tomography, and positron emission tomography. Nonetheless, the assimilation of omic data-encompassing gene expression through transcriptomics and epigenomics-offers pivotal insights into patients' conditions. These insights, reciprocally, hold significant value in refining diagnoses, guiding decision- making processes, and devising efficacious treatment strategies. This survey's core objective encompasses a comprehensive exploration of noteworthy applications of machine learning methodologies in the domain of glioblastoma, alongside closely associated research pursuits. The study accentuates the deployment of artificial intelligence techniques for both non-omics and omics data, encompassing a range of tasks. Furthermore, the survey underscores the intricate challenges posed by the inherent heterogeneity of Glioblastoma, delving into strategies aimed at addressing its multifaceted nature.
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Affiliation(s)
| | | | | | - Mohamed Hassani
- Cancer Division, Faculty of medicine, Department of Biomolecular Medicine, Imperial College, London, United Kingdom
| | - Nabil Benamar
- Moulay Ismail University of Meknes, Meknes, Morocco; Al Akhawayn University in Ifrane, Ifrane, Morocco.
| | - Nabil Hajji
- Cancer Division, Faculty of medicine, Department of Biomolecular Medicine, Imperial College, London, United Kingdom; Department of Medical Biochemistry, Molecular Biology and Immunology, School of Medicine, Virgen Macarena University Hospital, University of Seville, Seville, Spain
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Jensen MP, Qiang Z, Khan DZ, Stoyanov D, Baldeweg SE, Jaunmuktane Z, Brandner S, Marcus HJ. Artificial intelligence in histopathological image analysis of central nervous system tumours: A systematic review. Neuropathol Appl Neurobiol 2024; 50:e12981. [PMID: 38738494 DOI: 10.1111/nan.12981] [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: 01/08/2024] [Revised: 04/05/2024] [Accepted: 04/10/2024] [Indexed: 05/14/2024]
Abstract
The convergence of digital pathology and artificial intelligence could assist histopathology image analysis by providing tools for rapid, automated morphological analysis. This systematic review explores the use of artificial intelligence for histopathological image analysis of digitised central nervous system (CNS) tumour slides. Comprehensive searches were conducted across EMBASE, Medline and the Cochrane Library up to June 2023 using relevant keywords. Sixty-eight suitable studies were identified and qualitatively analysed. The risk of bias was evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST) criteria. All the studies were retrospective and preclinical. Gliomas were the most frequently analysed tumour type. The majority of studies used convolutional neural networks or support vector machines, and the most common goal of the model was for tumour classification and/or grading from haematoxylin and eosin-stained slides. The majority of studies were conducted when legacy World Health Organisation (WHO) classifications were in place, which at the time relied predominantly on histological (morphological) features but have since been superseded by molecular advances. Overall, there was a high risk of bias in all studies analysed. Persistent issues included inadequate transparency in reporting the number of patients and/or images within the model development and testing cohorts, absence of external validation, and insufficient recognition of batch effects in multi-institutional datasets. Based on these findings, we outline practical recommendations for future work including a framework for clinical implementation, in particular, better informing the artificial intelligence community of the needs of the neuropathologist.
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Affiliation(s)
- Melanie P Jensen
- Pathology Department, Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, UK
- Briscoe Lab, The Francis Crick Institute, London, UK
| | - Zekai Qiang
- School of Medicine and Population Health, University of Sheffield Medical School, Sheffield, UK
| | - Danyal Z Khan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- Department of Computer Science, University College London, London, UK
| | - Danail Stoyanov
- Department of Computer Science, University College London, London, UK
| | - Stephanie E Baldeweg
- Department of Diabetes and Endocrinology, University College London Hospitals, London, UK
- Centre for Obesity and Metabolism, Department of Experimental and Translational Medicine, Division of Medicine, University College London, London, UK
| | - Zane Jaunmuktane
- Division of Neuropathology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, London, UK
- Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, UK
| | - Sebastian Brandner
- Division of Neuropathology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, London, UK
| | - Hani J Marcus
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- Department of Computer Science, University College London, London, UK
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Alzoubi I, Zhang L, Zheng Y, Loh C, Wang X, Graeber MB. PathoGraph: An Attention-Based Graph Neural Network Capable of Prognostication Based on CD276 Labelling of Malignant Glioma Cells. Cancers (Basel) 2024; 16:750. [PMID: 38398141 PMCID: PMC10886785 DOI: 10.3390/cancers16040750] [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: 12/18/2023] [Revised: 02/07/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
Computerized methods have been developed that allow quantitative morphological analyses of whole slide images (WSIs), e.g., of immunohistochemical stains. The latter are attractive because they can provide high-resolution data on the distribution of proteins in tissue. However, many immunohistochemical results are complex because the protein of interest occurs in multiple locations (in different cells and also extracellularly). We have recently established an artificial intelligence framework, PathoFusion which utilises a bifocal convolutional neural network (BCNN) model for detecting and counting arbitrarily definable morphological structures. We have now complemented this model by adding an attention-based graph neural network (abGCN) for the advanced analysis and automated interpretation of such data. Classical convolutional neural network (CNN) models suffer from limitations when handling global information. In contrast, our abGCN is capable of creating a graph representation of cellular detail from entire WSIs. This abGCN method combines attention learning with visualisation techniques that pinpoint the location of informative cells and highlight cell-cell interactions. We have analysed cellular labelling for CD276, a protein of great interest in cancer immunology and a potential marker of malignant glioma cells/putative glioma stem cells (GSCs). We are especially interested in the relationship between CD276 expression and prognosis. The graphs permit predicting individual patient survival on the basis of GSC community features. Our experiments lay a foundation for the use of the BCNN-abGCN tool chain in automated diagnostic prognostication using immunohistochemically labelled histological slides, but the method is essentially generic and potentially a widely usable tool in medical research and AI based healthcare applications.
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Affiliation(s)
- Islam Alzoubi
- School of Computer Science, The University of Sydney, J12/1 Cleveland St, Darlington, Sydney, NSW 2008, Australia; (I.A.); (L.Z.)
| | - Lin Zhang
- School of Computer Science, The University of Sydney, J12/1 Cleveland St, Darlington, Sydney, NSW 2008, Australia; (I.A.); (L.Z.)
| | - Yuqi Zheng
- Ken Parker Brain Tumour Research Laboratories, Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2050, Australia; (Y.Z.); (C.L.)
| | - Christina Loh
- Ken Parker Brain Tumour Research Laboratories, Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2050, Australia; (Y.Z.); (C.L.)
| | - Xiuying Wang
- School of Computer Science, The University of Sydney, J12/1 Cleveland St, Darlington, Sydney, NSW 2008, Australia; (I.A.); (L.Z.)
| | - Manuel B. Graeber
- Ken Parker Brain Tumour Research Laboratories, Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2050, Australia; (Y.Z.); (C.L.)
- University of Sydney Association of Professors (USAP), University of Sydney, Sydney, NSW 2006, Australia
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Boskind M, Nelapudi N, Williamson G, Mendez B, Juarez R, Zhang L, Blood AB, Wilson CG, Puglisi JL, Wilson SM. Improved Workflow for Analysis of Vascular Myocyte Time-Series and Line-Scan Ca 2+ Imaging Datasets. Int J Mol Sci 2023; 24:9729. [PMID: 37298681 PMCID: PMC10253939 DOI: 10.3390/ijms24119729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/22/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
Intracellular Ca2+ signals are key for the regulation of cellular processes ranging from myocyte contraction, hormonal secretion, neural transmission, cellular metabolism, transcriptional regulation, and cell proliferation. Measurement of cellular Ca2+ is routinely performed using fluorescence microscopy with biological indicators. Analysis of deterministic signals is reasonably straightforward as relevant data can be discriminated based on the timing of cellular responses. However, analysis of stochastic, slower oscillatory events, as well as rapid subcellular Ca2+ responses, takes considerable time and effort which often includes visual analysis by trained investigators, especially when studying signals arising from cells embedded in complex tissues. The purpose of the current study was to determine if full-frame time-series and line-scan image analysis workflow of Fluo-4 generated Ca2+ fluorescence data from vascular myocytes could be automated without introducing errors. This evaluation was addressed by re-analyzing a published "gold standard" full-frame time-series dataset through visual analysis of Ca2+ signals from recordings made in pulmonary arterial myocytes of en face arterial preparations. We applied a combination of data driven and statistical approaches with comparisons to our published data to assess the fidelity of the various approaches. Regions of interest with Ca2+ oscillations were detected automatically post hoc using the LCPro plug-in for ImageJ. Oscillatory signals were separated based on event durations between 4 and 40 s. These data were filtered based on cutoffs obtained from multiple methods and compared to the published manually curated "gold standard" dataset. Subcellular focal and rapid Ca2+ "spark" events from line-scan recordings were examined using SparkLab 5.8, which is a custom automated detection and analysis program. After filtering, the number of true positives, false positives, and false negatives were calculated through comparisons to visually derived "gold standard" datasets. Positive predictive value, sensitivity, and false discovery rates were calculated. There were very few significant differences between the automated and manually curated results with respect to quality of the oscillatory and Ca2+ spark events, and there were no systematic biases in the data curation or filtering techniques. The lack of statistical difference in event quality between manual data curation and statistically derived critical cutoff techniques leads us to believe that automated analysis techniques can be reliably used to analyze spatial and temporal aspects to Ca2+ imaging data, which will improve experiment workflow.
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Affiliation(s)
- Madison Boskind
- Lawrence D Longo MD Center for Perinatal Biology, School of Medicine, Loma Linda University, Loma Linda, CA 92373, USA; (M.B.); (N.N.); (G.W.); (B.M.); (L.Z.); (C.G.W.)
| | - Nikitha Nelapudi
- Lawrence D Longo MD Center for Perinatal Biology, School of Medicine, Loma Linda University, Loma Linda, CA 92373, USA; (M.B.); (N.N.); (G.W.); (B.M.); (L.Z.); (C.G.W.)
| | - Grace Williamson
- Lawrence D Longo MD Center for Perinatal Biology, School of Medicine, Loma Linda University, Loma Linda, CA 92373, USA; (M.B.); (N.N.); (G.W.); (B.M.); (L.Z.); (C.G.W.)
| | - Bobby Mendez
- Lawrence D Longo MD Center for Perinatal Biology, School of Medicine, Loma Linda University, Loma Linda, CA 92373, USA; (M.B.); (N.N.); (G.W.); (B.M.); (L.Z.); (C.G.W.)
| | - Rucha Juarez
- Advanced Imaging and Microscopy Core, Department of Basic Sciences, School of Medicine, Loma Linda University, Loma Linda, CA 92350, USA;
| | - Lubo Zhang
- Lawrence D Longo MD Center for Perinatal Biology, School of Medicine, Loma Linda University, Loma Linda, CA 92373, USA; (M.B.); (N.N.); (G.W.); (B.M.); (L.Z.); (C.G.W.)
| | - Arlin B. Blood
- Lawrence D Longo MD Center for Perinatal Biology, School of Medicine, Loma Linda University, Loma Linda, CA 92373, USA; (M.B.); (N.N.); (G.W.); (B.M.); (L.Z.); (C.G.W.)
| | - Christopher G. Wilson
- Lawrence D Longo MD Center for Perinatal Biology, School of Medicine, Loma Linda University, Loma Linda, CA 92373, USA; (M.B.); (N.N.); (G.W.); (B.M.); (L.Z.); (C.G.W.)
| | - Jose Luis Puglisi
- Department of Biostatistics, School of Medicine, California Northstate University, Elk Grove, CA 95757, USA;
| | - Sean M. Wilson
- Lawrence D Longo MD Center for Perinatal Biology, School of Medicine, Loma Linda University, Loma Linda, CA 92373, USA; (M.B.); (N.N.); (G.W.); (B.M.); (L.Z.); (C.G.W.)
- Advanced Imaging and Microscopy Core, Department of Basic Sciences, School of Medicine, Loma Linda University, Loma Linda, CA 92350, USA;
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Alzoubi I, Bao G, Zheng Y, Wang X, Graeber MB. Artificial intelligence techniques for neuropathological diagnostics and research. Neuropathology 2022. [PMID: 36443935 DOI: 10.1111/neup.12880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 10/17/2022] [Accepted: 10/23/2022] [Indexed: 12/03/2022]
Abstract
Artificial intelligence (AI) research began in theoretical neurophysiology, and the resulting classical paper on the McCulloch-Pitts mathematical neuron was written in a psychiatry department almost 80 years ago. However, the application of AI in digital neuropathology is still in its infancy. Rapid progress is now being made, which prompted this article. Human brain diseases represent distinct system states that fall outside the normal spectrum. Many differ not only in functional but also in structural terms, and the morphology of abnormal nervous tissue forms the traditional basis of neuropathological disease classifications. However, only a few countries have the medical specialty of neuropathology, and, given the sheer number of newly developed histological tools that can be applied to the study of brain diseases, a tremendous shortage of qualified hands and eyes at the microscope is obvious. Similarly, in neuroanatomy, human observers no longer have the capacity to process the vast amounts of connectomics data. Therefore, it is reasonable to assume that advances in AI technology and, especially, whole-slide image (WSI) analysis will greatly aid neuropathological practice. In this paper, we discuss machine learning (ML) techniques that are important for understanding WSI analysis, such as traditional ML and deep learning, introduce a recently developed neuropathological AI termed PathoFusion, and present thoughts on some of the challenges that must be overcome before the full potential of AI in digital neuropathology can be realized.
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Affiliation(s)
- Islam Alzoubi
- School of Computer Science The University of Sydney Sydney New South Wales Australia
| | - Guoqing Bao
- School of Computer Science The University of Sydney Sydney New South Wales Australia
| | - Yuqi Zheng
- Ken Parker Brain Tumour Research Laboratories Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney Camperdown New South Wales Australia
| | - Xiuying Wang
- School of Computer Science The University of Sydney Sydney New South Wales Australia
| | - Manuel B. Graeber
- Ken Parker Brain Tumour Research Laboratories Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney Camperdown New South Wales Australia
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