1
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N. Kachouie N, Deebani W, Shutaywi M, Christiani DC. Lung cancer clustering by identification of similarities and discrepancies of DNA copy numbers using maximal information coefficient. PLoS One 2024; 19:e0301131. [PMID: 38739669 PMCID: PMC11090345 DOI: 10.1371/journal.pone.0301131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 03/11/2024] [Indexed: 05/16/2024] Open
Abstract
Lung cancer is the second most diagnosed cancer and the first cause of cancer related death for men and women in the United States. Early detection is essential as patient survival is not optimal and recurrence rate is high. Copy number (CN) changes in cancer populations have been broadly investigated to identify CN gains and deletions associated with the cancer. In this research, the similarities between cancer and paired peripheral blood samples are identified using maximal information coefficient (MIC) and the spatial locations with substantially high MIC scores in each chromosome are used for clustering analysis. The results showed that a sizable reduction of feature set can be obtained using only a subset of locations with high MIC values. The clustering performance was evaluated using both true rate and normalized mutual information (NMI). Clustering results using the reduced feature set outperformed the performance of clustering using entire feature set in several chromosomes that are highly associated with lung cancer with several identified oncogenes.
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Affiliation(s)
- Nezamoddin N. Kachouie
- Department of Mathematics and Systems Engineering, Florida Institute of Technology, Melbourne, FL, United States of America
| | - Wejdan Deebani
- Mathematics Department, College of Science and Arts, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Meshal Shutaywi
- Mathematics Department, College of Science and Arts, King Abdulaziz University, Jeddah, Saudi Arabia
| | - David C. Christiani
- Department of Environmental Health, Harvard School of Public Health, Boston, MA, United States of America
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, United States of America
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2
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Ding Z, Zhang W, Wang H, Ke H, Su D, Wang Q, Bian K, Su F, Xu K. An automatic diagnostic system for the urodynamic study applying in lower urinary tract dysfunction. Int Urol Nephrol 2024; 56:441-449. [PMID: 37755608 DOI: 10.1007/s11255-023-03795-8] [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: 06/12/2023] [Accepted: 08/18/2023] [Indexed: 09/28/2023]
Abstract
OBJECTIVE To establish an automatic diagnostic system based on machine learning for preliminarily analysis of urodynamic study applying in lower urinary tract dysfunction (LUTD). METHODS The eight most common conditions of LUTDs were included in the present study. A total of 527 eligible patients with complete data, from the year of 2015 to 2020, were enrolled in this study. In total, two global parameters (patients' age and sex) and 13 urodynamic parameters were considered to be the input for machine learning algorithms. Three machine learning approaches were applied and evaluated in this study, including Decision Tree (DT), Logistic Regression (LR), and Support Vector Machine (SVM). RESULTS By applying machine learning algorithms into the 8 common LUTDs, the DT models achieved the AUC of 0.63-0.98, the LR models achieved the AUC of 0.73-0.99, and the SVM models achieved the AUC of 0.64-1.00. For mutually exclusive diagnoses of underactive detrusor and acontractile detrusor, we developed a classification model that classifies the patients into either of these two diseases or double-negative class. For this classification method, the DT models achieved the AUC of 0.82-0.85 and the SVM models achieved the AUC of 0.86-0.90. Among all these models, the LR and the SVM models showed better performance. The best model of these diagnostic tasks achieved an average AUC of 0.90 (0.90 ± 0.08). CONCLUSIONS An automatic diagnostic system was developed using three machine learning models in urodynamic studies. This automated machine learning process could lead to promising assistance and enhancements of diagnosis and provide more useful reference for LUTD treatment.
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Affiliation(s)
- Zehua Ding
- Department of Urology, Peking University People's Hospital, Beijing, China
| | - Weiyu Zhang
- Department of Urology, Peking University People's Hospital, Beijing, China
| | - Huanrui Wang
- Department of Urology, Peking University People's Hospital, Beijing, China
| | - Hanwei Ke
- Department of Urology, Peking University People's Hospital, Beijing, China
| | - Dongyu Su
- Department of Urology, Peking University People's Hospital, Beijing, China
| | - Qi Wang
- Department of Urology, Peking University People's Hospital, Beijing, China
| | - Kaigui Bian
- School of Computer Science, Peking University, Beijing, China
| | - Feng Su
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Kexin Xu
- Department of Urology, Peking University People's Hospital, Beijing, China.
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3
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Walton NA, Nagarajan R, Wang C, Sincan M, Freimuth RR, Everman DB, Walton DC, McGrath SP, Lemas DJ, Benos PV, Alekseyenko AV, Song Q, Gamsiz Uzun E, Taylor CO, Uzun A, Person TN, Rappoport N, Zhao Z, Williams MS. Enabling the clinical application of artificial intelligence in genomics: a perspective of the AMIA Genomics and Translational Bioinformatics Workgroup. J Am Med Inform Assoc 2024; 31:536-541. [PMID: 38037121 PMCID: PMC10797281 DOI: 10.1093/jamia/ocad211] [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: 02/11/2023] [Revised: 10/09/2023] [Accepted: 10/26/2023] [Indexed: 12/02/2023] Open
Abstract
OBJECTIVE Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space. PROCESS A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members. CONCLUSIONS Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of larger datasets is crucial to emulating the success AI is achieving in other domains. It is important that AI methods do not exacerbate existing socio-economic, racial, and ethnic disparities. Genomic data standards are critical to effectively scale such technologies across institutions. With so much uncertainty, complexity and novelty in genomics and medicine, and with an evolving regulatory environment, the current focus should be on using these technologies in an interface with clinicians that emphasizes the value each brings to clinical decision-making.
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Affiliation(s)
- Nephi A Walton
- Division of Medical Genetics, University of Utah School of Medicine, Salt Lake City, UT 84112 ,United States
| | - Radha Nagarajan
- Enterprise Information Services, Cedars-Sinai Medical Center, Los Angeles, CA 90025, United States
- Information Services Department, Children’s Hospital of Orange County, Orange, CA 92868, United States
| | - Chen Wang
- Division of Computational Biology, Department of Quantitative Health Sciences, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, United States
| | - Murat Sincan
- Flatiron Health, New York, NY 10013, United States
- Department of Internal Medicine, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD 57107, United States
| | - Robert R Freimuth
- Department of Artificial Intelligence and Informatics, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, United States
| | - David B Everman
- EverMed Genetics and Genomics Consulting LLC, Greenville, SC 29607, United States
| | | | - Scott P McGrath
- CITRIS Health, CITRIS and Banatao Institute, University of California Berkeley, Berkeley, CA 94720, United States
| | - Dominick J Lemas
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32610, United States
| | - Panayiotis V Benos
- Department of Epidemiology, University of Florida, Gainesville, FL 32610, United States
| | - Alexander V Alekseyenko
- Department of Public Health Sciences, Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC 29403, United States
| | - Qianqian Song
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32610, United States
| | - Ece Gamsiz Uzun
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Providence, RI 02915, United States
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI 02915, United States
| | - Casey Overby Taylor
- Departments of Medicine and Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21205, United States
| | - Alper Uzun
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI 02915, United States
- Legorreta Cancer Center, Brown University, Providence, RI 02915, United States
| | - Thomas Nate Person
- Department of Bioinformatics and Genomics, Huck Institutes of the Life Sciences, Penn State University, Bloomsburg, PA 16802, United States
| | - Nadav Rappoport
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States
| | - Marc S Williams
- Department of Genomic Health, Geisinger, Danville, PA 17822, United States
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S Alshuhri M, Al-Musawi SG, Al-Alwany AA, Uinarni H, Rasulova I, Rodrigues P, Alkhafaji AT, Alshanberi AM, Alawadi AH, Abbas AH. Artificial intelligence in cancer diagnosis: Opportunities and challenges. Pathol Res Pract 2024; 253:154996. [PMID: 38118214 DOI: 10.1016/j.prp.2023.154996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/20/2023] [Accepted: 11/27/2023] [Indexed: 12/22/2023]
Abstract
Since cancer is one of the world's top causes of death, early diagnosis is critical to improving patient outcomes. Artificial intelligence (AI) has become a viable technique for cancer diagnosis by using machine learning algorithms to examine large volumes of data for accurate and efficient diagnosis. AI has the potential to alter the way cancer is detected fundamentally. Still, it has several disadvantages, such as requiring a large amount of data, technological limitations, and ethical concerns. This overview looks at the possibilities and restrictions of AI in cancer detection, as well as current applications and possible future developments. We can better understand how to use AI to improve patient outcomes and reduce cancer mortality rates by looking at its potential for cancer detection.
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Affiliation(s)
- Mohammed S Alshuhri
- Radiology and Medical Imaging Department, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Kharj, Saudi Arabia
| | | | | | - Herlina Uinarni
- Department of Anatomy, School of Medicine and Health Sciences Atma Jaya Catholic University of Indonesia, Indonesia; Radiology department of Pantai Indah Kapuk Hospital Jakarta, Jakarta, Indonesia.
| | - Irodakhon Rasulova
- School of Humanities, Natural & Social Sciences, New Uzbekistan University, 54 Mustaqillik Ave., Tashkent 100007, Uzbekistan; Department of Public Health, Samarkand State Medical University, Amir Temur Street 18, Samarkand, Uzbekistan
| | - Paul Rodrigues
- Department of Computer Engineering, College of Computer Science, King Khalid University, Al-Faraa, Abha, Asir, Kingdom of Saudi Arabia
| | | | - Asim Muhammed Alshanberi
- Department of Community Medicine & Pilgrim Healthcare, Umm Alqura University, Makkah 24382, Saudi Arabia; General Medicine Practice Program, Batterjee Medical College, Jeddah 21442, Saudi Arabia
| | - Ahmed Hussien Alawadi
- College of Technical Engineering, the Islamic University, Najaf, Iraq; College of Technical Engineering, the Islamic University of Al Diwaniyah, Iraq; College of Technical Engineering, the Islamic University of Babylon, Iraq
| | - Ali Hashim Abbas
- College of Technical Engineering, Imam Ja'afar Al-Sadiq University, Al-Muthanna 66002, Iraq
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5
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Hussain W, Mabrok M, Gao H, Rabhi FA, Rashed EA. Revolutionising healthcare with artificial intelligence: A bibliometric analysis of 40 years of progress in health systems. Digit Health 2024; 10:20552076241258757. [PMID: 38817839 PMCID: PMC11138196 DOI: 10.1177/20552076241258757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 05/14/2024] [Indexed: 06/01/2024] Open
Abstract
The development of artificial intelligence (AI) has revolutionised the medical system, empowering healthcare professionals to analyse complex nonlinear big data and identify hidden patterns, facilitating well-informed decisions. Over the last decade, there has been a notable trend of research in AI, machine learning (ML), and their associated algorithms in health and medical systems. These approaches have transformed the healthcare system, enhancing efficiency, accuracy, personalised treatment, and decision-making. Recognising the importance and growing trend of research in the topic area, this paper presents a bibliometric analysis of AI in health and medical systems. The paper utilises the Web of Science (WoS) Core Collection database, considering documents published in the topic area for the last four decades. A total of 64,063 papers were identified from 1983 to 2022. The paper evaluates the bibliometric data from various perspectives, such as annual papers published, annual citations, highly cited papers, and most productive institutions, and countries. The paper visualises the relationship among various scientific actors by presenting bibliographic coupling and co-occurrences of the author's keywords. The analysis indicates that the field began its significant growth in the late 1970s and early 1980s, with significant growth since 2019. The most influential institutions are in the USA and China. The study also reveals that the scientific community's top keywords include 'ML', 'Deep Learning', and 'Artificial Intelligence'.
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Affiliation(s)
- Walayat Hussain
- Peter Faber Business School, Australian Catholic University, North Sydney, Australia
| | - Mohamed Mabrok
- Department of Mathematics and Statistics, Qatar University, Doha, Qatar
| | - Honghao Gao
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Fethi A. Rabhi
- School of Computer Science and Engineering, University of New South Wales (UNSW), Sydney, Australia
| | - Essam A. Rashed
- Graduate School of Information Science, University of Hyogo, Kobe, Japan
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6
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Pan I, Huang RY. Artificial intelligence in neuroimaging of brain tumors: reality or still promise? Curr Opin Neurol 2023; 36:549-556. [PMID: 37973024 DOI: 10.1097/wco.0000000000001213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
PURPOSE OF REVIEW To provide an updated overview of artificial intelligence (AI) applications in neuro-oncologic imaging and discuss current barriers to wider clinical adoption. RECENT FINDINGS A wide variety of AI applications in neuro-oncologic imaging have been developed and researched, spanning tasks from pretreatment brain tumor classification and segmentation, preoperative planning, radiogenomics, prognostication and survival prediction, posttreatment surveillance, and differentiating between pseudoprogression and true disease progression. While earlier studies were largely based on data from a single institution, more recent studies have demonstrated that the performance of these algorithms are also effective on external data from other institutions. Nevertheless, most of these algorithms have yet to see widespread clinical adoption, given the lack of prospective studies demonstrating their efficacy and the logistical difficulties involved in clinical implementation. SUMMARY While there has been significant progress in AI and neuro-oncologic imaging, clinical utility remains to be demonstrated. The next wave of progress in this area will be driven by prospective studies measuring outcomes relevant to clinical practice and go beyond retrospective studies which primarily aim to demonstrate high performance.
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Affiliation(s)
- Ian Pan
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School
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7
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Higgins H, Nakhla A, Lotfalla A, Khalil D, Doshi P, Thakkar V, Shirini D, Bebawy M, Ammari S, Lopci E, Schwartz LH, Postow M, Dercle L. Recent Advances in the Field of Artificial Intelligence for Precision Medicine in Patients with a Diagnosis of Metastatic Cutaneous Melanoma. Diagnostics (Basel) 2023; 13:3483. [PMID: 37998619 PMCID: PMC10670510 DOI: 10.3390/diagnostics13223483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 10/27/2023] [Accepted: 10/31/2023] [Indexed: 11/25/2023] Open
Abstract
Standard-of-care medical imaging techniques such as CT, MRI, and PET play a critical role in managing patients diagnosed with metastatic cutaneous melanoma. Advancements in artificial intelligence (AI) techniques, such as radiomics, machine learning, and deep learning, could revolutionize the use of medical imaging by enhancing individualized image-guided precision medicine approaches. In the present article, we will decipher how AI/radiomics could mine information from medical images, such as tumor volume, heterogeneity, and shape, to provide insights into cancer biology that can be leveraged by clinicians to improve patient care both in the clinic and in clinical trials. More specifically, we will detail the potential role of AI in enhancing detection/diagnosis, staging, treatment planning, treatment delivery, response assessment, treatment toxicity assessment, and monitoring of patients diagnosed with metastatic cutaneous melanoma. Finally, we will explore how these proof-of-concept results can be translated from bench to bedside by describing how the implementation of AI techniques can be standardized for routine adoption in clinical settings worldwide to predict outcomes with great accuracy, reproducibility, and generalizability in patients diagnosed with metastatic cutaneous melanoma.
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Affiliation(s)
- Hayley Higgins
- Department of Clinical Medicine, Touro College of Osteopathic Medicine, Middletown, NY 10940, USA; (A.L.); (M.B.)
| | - Abanoub Nakhla
- Department of Clinical Medicine, American University of the Caribbean School of Medicine, 33027 Cupecoy, Sint Maarten, The Netherlands;
| | - Andrew Lotfalla
- Department of Clinical Medicine, Touro College of Osteopathic Medicine, Middletown, NY 10940, USA; (A.L.); (M.B.)
| | - David Khalil
- Department of Clinical Medicine, Campbell University School of Osteopathic Medicine, Lillington, NC 27546, USA; (D.K.); (P.D.); (V.T.)
| | - Parth Doshi
- Department of Clinical Medicine, Campbell University School of Osteopathic Medicine, Lillington, NC 27546, USA; (D.K.); (P.D.); (V.T.)
| | - Vandan Thakkar
- Department of Clinical Medicine, Campbell University School of Osteopathic Medicine, Lillington, NC 27546, USA; (D.K.); (P.D.); (V.T.)
| | - Dorsa Shirini
- Department of Radiology, Shahid Beheshti University of Medical Sciences, Tehran 1981619573, Iran;
| | - Maria Bebawy
- Department of Clinical Medicine, Touro College of Osteopathic Medicine, Middletown, NY 10940, USA; (A.L.); (M.B.)
| | - Samy Ammari
- Département d’Imagerie Médicale Biomaps, UMR1281 INSERM, CEA, CNRS, Gustave Roussy, Université Paris-Saclay, 94800 Villejuif, France;
- ELSAN Département de Radiologie, Institut de Cancérologie Paris Nord, 95200 Sarcelles, France
| | - Egesta Lopci
- Nuclear Medicine Unit, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy;
| | - Lawrence H. Schwartz
- Department of Radiology, New York-Presbyterian, Columbia University Irving Medical Center, New York, NY 10032, USA;
| | - Michael Postow
- Melanoma Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Weill Cornell Medical College, New York, NY 10065, USA
| | - Laurent Dercle
- Department of Radiology, Shahid Beheshti University of Medical Sciences, Tehran 1981619573, Iran;
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Vijayakumar S, Lee VV, Leong QY, Hong SJ, Blasiak A, Ho D. Physicians' Perspectives on AI in Clinical Decision Support Systems: Interview Study of the CURATE.AI Personalized Dose Optimization Platform. JMIR Hum Factors 2023; 10:e48476. [PMID: 37902825 PMCID: PMC10644191 DOI: 10.2196/48476] [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: 04/25/2023] [Revised: 08/24/2023] [Accepted: 09/10/2023] [Indexed: 10/31/2023] Open
Abstract
BACKGROUND Physicians play a key role in integrating new clinical technology into care practices through user feedback and growth propositions to developers of the technology. As physicians are stakeholders involved through the technology iteration process, understanding their roles as users can provide nuanced insights into the workings of these technologies that are being explored. Therefore, understanding physicians' perceptions can be critical toward clinical validation, implementation, and downstream adoption. Given the increasing prevalence of clinical decision support systems (CDSSs), there remains a need to gain an in-depth understanding of physicians' perceptions and expectations toward their downstream implementation. This paper explores physicians' perceptions of integrating CURATE.AI, a novel artificial intelligence (AI)-based and clinical stage personalized dosing CDSSs, into clinical practice. OBJECTIVE This study aims to understand physicians' perspectives of integrating CURATE.AI for clinical work and to gather insights on considerations of the implementation of AI-based CDSS tools. METHODS A total of 12 participants completed semistructured interviews examining their knowledge, experience, attitudes, risks, and future course of the personalized combination therapy dosing platform, CURATE.AI. Interviews were audio recorded, transcribed verbatim, and coded manually. The data were thematically analyzed. RESULTS Overall, 3 broad themes and 9 subthemes were identified through thematic analysis. The themes covered considerations that physicians perceived as significant across various stages of new technology development, including trial, clinical implementation, and mass adoption. CONCLUSIONS The study laid out the various ways physicians interpreted an AI-based personalized dosing CDSS, CURATE.AI, for their clinical practice. The research pointed out that physicians' expectations during the different stages of technology exploration can be nuanced and layered with expectations of implementation that are relevant for technology developers and researchers.
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Affiliation(s)
- Smrithi Vijayakumar
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
| | - V Vien Lee
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
| | - Qiao Ying Leong
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
| | - Soo Jung Hong
- Department of Communications and New Media, National University of Singapore, Singapore, Singapore
| | - Agata Blasiak
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Dean Ho
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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9
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Ibrahim M, Muhammad Q, Zamarud A, Eiman H, Fazal F. Navigating Glioblastoma Diagnosis and Care: Transformative Pathway of Artificial Intelligence in Integrative Oncology. Cureus 2023; 15:e44214. [PMID: 37645667 PMCID: PMC10461885 DOI: 10.7759/cureus.44214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/27/2023] [Indexed: 08/31/2023] Open
Abstract
Glioblastoma multiforme (GBM), an aggressive brain tumor with high recurrence rates and limited survival, presents a pressing need for accurate and timely diagnosis. The interpretation of MRI can be complex and subjective. Artificial Intelligence (AI) has emerged as a promising solution, leveraging its potential to revolutionize diagnostic imaging. Radiomics treats images as numerical data and extracts intricate features from images, including subtle patterns that elude human observation. By integrating radiomics with genetics through radiogenomics, AI aids in tumor classification, identifying specific mutations and genetic traits. Furthermore, AI's impact extends to treatment planning. GBM's heterogeneity and infiltrative growth complicate delineation for treatment purposes. AI-driven segmentation techniques provide accurate 2D and 3D delineations, optimizing surgical and radiotherapeutic planning. Predictive features like angiogenesis and tumor volumes enable AI models to anticipate postop complications and survival rates. It can also aid in distinguishing posttreatment radiation effects from tumor recurrence. Despite these merits, concerns linger. The quality of medical data, transparency of AI techniques, and ethical considerations require thorough addressing. Collaborative efforts between neurosurgeons, data scientists, ethicists, and regulatory bodies are imperative for AI's ethical development and implementation. Transparent communication and patient consent are vital, fostering trust and understanding in AI-augmented medical care. In conclusion, AI holds immense promise in diagnosing and managing aggressive brain tumors like GBM. Its ability to analyze complex radiological data, integrate genetics, and aid in treatment planning underscores its potential to transform patient care. However, carefully considering ethical, technical, and regulatory aspects is crucial for realizing AI's full potential in oncology.
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Affiliation(s)
- Muhammad Ibrahim
- Department of Medicine, Rawalpindi Medical University, Rawalpindi, PAK
| | | | - Aroosa Zamarud
- Department of Neurosurgery, Stanford Health Care, Palo Alto, USA
| | - Hadia Eiman
- Department of Medicine, Rawalpindi Medical University, Rawalpindi, PAK
| | - Faizan Fazal
- Department of Medicine, Rawalpindi Medical University, Rawalpindi, PAK
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10
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Kumari S, Gupta R, Ambasta RK, Kumar P. Multiple therapeutic approaches of glioblastoma multiforme: From terminal to therapy. Biochim Biophys Acta Rev Cancer 2023; 1878:188913. [PMID: 37182666 DOI: 10.1016/j.bbcan.2023.188913] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 04/24/2023] [Accepted: 05/10/2023] [Indexed: 05/16/2023]
Abstract
Glioblastoma multiforme (GBM) is an aggressive brain cancer showing poor prognosis. Currently, treatment methods of GBM are limited with adverse outcomes and low survival rate. Thus, advancements in the treatment of GBM are of utmost importance, which can be achieved in recent decades. However, despite aggressive initial treatment, most patients develop recurrent diseases, and the overall survival rate of patients is impossible to achieve. Currently, researchers across the globe target signaling events along with tumor microenvironment (TME) through different drug molecules to inhibit the progression of GBM, but clinically they failed to demonstrate much success. Herein, we discuss the therapeutic targets and signaling cascades along with the role of the organoids model in GBM research. Moreover, we systematically review the traditional and emerging therapeutic strategies in GBM. In addition, we discuss the implications of nanotechnologies, AI, and combinatorial approach to enhance GBM therapeutics.
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Affiliation(s)
- Smita Kumari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India
| | - Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India.
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11
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Pitarch C, Ribas V, Vellido A. AI-Based Glioma Grading for a Trustworthy Diagnosis: An Analytical Pipeline for Improved Reliability. Cancers (Basel) 2023; 15:3369. [PMID: 37444479 DOI: 10.3390/cancers15133369] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 06/22/2023] [Accepted: 06/25/2023] [Indexed: 07/15/2023] Open
Abstract
Glioma is the most common type of tumor in humans originating in the brain. According to the World Health Organization, gliomas can be graded on a four-stage scale, ranging from the most benign to the most malignant. The grading of these tumors from image information is a far from trivial task for radiologists and one in which they could be assisted by machine-learning-based decision support. However, the machine learning analytical pipeline is also fraught with perils stemming from different sources, such as inadvertent data leakage, adequacy of 2D image sampling, or classifier assessment biases. In this paper, we analyze a glioma database sourced from multiple datasets using a simple classifier, aiming to obtain a reliable tumor grading and, on the way, we provide a few guidelines to ensure such reliability. Our results reveal that by focusing on the tumor region of interest and using data augmentation techniques we significantly enhanced the accuracy and confidence in tumor classifications. Evaluation on an independent test set resulted in an AUC-ROC of 0.932 in the discrimination of low-grade gliomas from high-grade gliomas, and an AUC-ROC of 0.893 in the classification of grades 2, 3, and 4. The study also highlights the importance of providing, beyond generic classification performance, measures of how reliable and trustworthy the model's output is, thus assessing the model's certainty and robustness.
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Affiliation(s)
- Carla Pitarch
- Computer Science Department, Universitat Politècnica de Catalunya (UPC), 08034 Barcelona, Spain
- Eurecat, Technology Centre of Catalonia, Digital Health Unit, 08005 Barcelona, Spain
| | - Vicent Ribas
- Computer Science Department, Universitat Politècnica de Catalunya (UPC), 08034 Barcelona, Spain
| | - Alfredo Vellido
- Computer Science Department, Universitat Politècnica de Catalunya (UPC), 08034 Barcelona, Spain
- Centro de Investigación Biomédica en Red (CIBER), 28029 Madrid, Spain
- Intelligent Data Science and Artificial Intelligence Research Center (IDEAI-UPC), 08034 Barcelona, Spain
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12
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Nath G, Coursey A, Ekong J, Rastegari E, Sengupta S, Dag AZ, Delen D. Determining the temporal factors of survival associated with brain and nervous system cancer patients: A hybrid machine learning methodology. INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2023. [DOI: 10.1080/20479700.2023.2196101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Affiliation(s)
- Gopal Nath
- Department of Mathematics and Statistics, Murray State University, Murray, KY, USA
| | - Austin Coursey
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Joseph Ekong
- Department of Industrial Engineering, Western New England University, Springfield, MA, USA
| | - Elham Rastegari
- Department of Business, Intelligence and Analytics, Creighton University, Omaha, NE, USA
| | - Saptarshi Sengupta
- Department of Computer Science, San José State University, San José, CA, USA
| | - Asli Z. Dag
- Heider College of Business, Creighton University, Omaha, NE, USA
| | - Dursun Delen
- Spears School of Business, Oklahoma State University, Stillwater, OK, USA
- Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkey
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13
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Papp L, Rasul S, Spielvogel CP, Krajnc D, Poetsch N, Woehrer A, Patronas EM, Ecsedi B, Furtner J, Mitterhauser M, Rausch I, Widhalm G, Beyer T, Hacker M, Traub-Weidinger T. Sex-specific radiomic features of L-[S-methyl- 11C] methionine PET in patients with newly-diagnosed gliomas in relation to IDH1 predictability. Front Oncol 2023; 13:986788. [PMID: 36816966 PMCID: PMC9936222 DOI: 10.3389/fonc.2023.986788] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 01/23/2023] [Indexed: 02/05/2023] Open
Abstract
Introduction Amino-acid positron emission tomography (PET) is a validated metabolic imaging approach for the diagnostic work-up of gliomas. This study aimed to evaluate sex-specific radiomic characteristics of L-[S-methyl-11Cmethionine (MET)-PET images of glioma patients in consideration of the prognostically relevant biomarker isocitrate dehydrogenase (IDH) mutation status. Methods MET-PET of 35 astrocytic gliomas (13 females, mean age 41 ± 13 yrs. and 22 males, mean age 46 ± 17 yrs.) and known IDH mutation status were included. All patients underwent radiomic analysis following imaging biomarker standardization initiative (IBSI)-conform guidelines both from standardized uptake value (SUV) and tumor-to-background ratio (TBR) PET values. Aligned Monte Carlo (MC) 100-fold split was utilized for SUV and TBR dataset pairs for both sex and IDH-specific analysis. Borderline and outlier scores were calculated for both sex and IDH-specific MC folds. Feature ranking was performed by R-squared ranking and Mann-Whitney U-test together with Bonferroni correction. Correlation of SUV and TBR radiomics in relation to IDH mutational status in male and female patients were also investigated. Results There were no significant features in either SUV or TBR radiomics to distinguish female and male patients. In contrast, intensity histogram coefficient of variation (ih.cov) and intensity skewness (stat.skew) were identified as significant to predict IDH +/-. In addition, IDH+ females had significant ih.cov deviation (0.031) and mean stat.skew (-0.327) differences compared to IDH+ male patients (0.068 and -0.123, respectively) with two-times higher standard deviations of the normal brain background MET uptake as well. Discussion We demonstrated that female and male glioma patients have significantly different radiomic profiles in MET PET imaging data. Future IDH prediction models shall not be built on mixed female-male cohorts, but shall rely on sex-specific cohorts and radiomic imaging biomarkers.
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Affiliation(s)
- Laszlo Papp
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Sazan Rasul
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Clemens P. Spielvogel
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria,Christian Doppler Laboratory for Applied Metabolomics , Medical University of Vienna, Vienna, Austria
| | - Denis Krajnc
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Nina Poetsch
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Adelheid Woehrer
- Clinical Institute of Neurology, Medical University of Vienna, Vienna, Austria
| | - Eva-Maria Patronas
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria,Division of Pharmaceutical Technology and Biopharmaceutics, Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
| | - Boglarka Ecsedi
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Julia Furtner
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Markus Mitterhauser
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria,Ludwig Boltzmann Institute Applied Diagnostics, Medical University of Vienna, Vienna, Austria
| | - Ivo Rausch
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Georg Widhalm
- Clinical University of Neuro-Surgery, Medical University of Vienna, Vienna, Austria
| | - Thomas Beyer
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Tatjana Traub-Weidinger
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria,*Correspondence: Tatjana Traub-Weidinger,
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14
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Automation: A revolutionary vision of artificial intelligence in theranostics. Bull Cancer 2023; 110:233-241. [PMID: 36509576 DOI: 10.1016/j.bulcan.2022.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 10/12/2022] [Accepted: 10/26/2022] [Indexed: 12/13/2022]
Abstract
The last two decades have witnessed an extraordinary evolution of automation and artificial intelligence (AI), which has become an integral part of our daily lives. Lately, AI has also been assimilated in the field of medicine to upgrade overall healthcare system and encourage personalized treatment. Theranostics literally meaning combination of diagnosis and therapeutics, is a targeted pharmacotherapy, based on specific targeted diagnostic tests. Numerous theranostic agents/biomarkers are available which can identify the most beneficial treatment, correct dose or predict response to a medicine, thus, maximizing drug efficacy, minimizing toxicity and providing informed treatment choice. For instance, a statistics based Cluster-FLIM technology provides precise data on drug-receptor binding behavior in biological tissues using fluorescence real experimental imaging. Automated Idylla™ qPCR System is another approach in oncology to determine the EGFR mutations at initial stage as well as during the treatment and also assists the oncologist in designing the treatment protocol. Recent incorporation of automation and AI in theranostics has brought a drastic change in early detection and treatment protocols for various diseases such as cancer and diabetes. Also, it leads to quick analysis of number of diverse experimental datum with accuracy. The approach mainly uses computer algorithms to unveil relevant and significant information from clinical data, thereby assisting in making accurate, logical and pertinent decisions. This review highlights the emerging uses/role of automation and AI in theranostics, technical difficulties and focuses on its future prospects to facilitate a patient specific, reliable and efficient pharmacotherapy.
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15
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Assessing Metabolic Markers in Glioblastoma Using Machine Learning: A Systematic Review. Metabolites 2023; 13:metabo13020161. [PMID: 36837779 PMCID: PMC9958885 DOI: 10.3390/metabo13020161] [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: 12/26/2022] [Revised: 01/14/2023] [Accepted: 01/18/2023] [Indexed: 01/24/2023] Open
Abstract
Glioblastoma (GBM) is a common and deadly brain tumor with late diagnoses and poor prognoses. Machine learning (ML) is an emerging tool that can create highly accurate diagnostic and prognostic prediction models. This paper aimed to systematically search the literature on ML for GBM metabolism and assess recent advancements. A literature search was performed using predetermined search terms. Articles describing the use of an ML algorithm for GBM metabolism were included. Ten studies met the inclusion criteria for analysis: diagnostic (n = 3, 30%), prognostic (n = 6, 60%), or both (n = 1, 10%). Most studies analyzed data from multiple databases, while 50% (n = 5) included additional original samples. At least 2536 data samples were run through an ML algorithm. Twenty-seven ML algorithms were recorded with a mean of 2.8 algorithms per study. Algorithms were supervised (n = 24, 89%), unsupervised (n = 3, 11%), continuous (n = 19, 70%), or categorical (n = 8, 30%). The mean reported accuracy and AUC of ROC were 95.63% and 0.779, respectively. One hundred six metabolic markers were identified, but only EMP3 was reported in multiple studies. Many studies have identified potential biomarkers for GBM diagnosis and prognostication. These algorithms show promise; however, a consensus on even a handful of biomarkers has not yet been made.
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16
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Nittas V, Daniore P, Landers C, Gille F, Amann J, Hubbs S, Puhan MA, Vayena E, Blasimme A. Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging. PLOS DIGITAL HEALTH 2023; 2:e0000189. [PMID: 36812620 PMCID: PMC9931290 DOI: 10.1371/journal.pdig.0000189] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 01/02/2023] [Indexed: 02/04/2023]
Abstract
Machine learning has become a key driver of the digital health revolution. That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field's potential, limitations, and future directions. Most reported strengths and promises included: improved (a) analytic power, (b) efficiency (c) decision making, and (d) equity. Most reported challenges included: (a) structural barriers and imaging heterogeneity, (b) scarcity of well-annotated, representative and interconnected imaging datasets (c) validity and performance limitations, including bias and equity issues, and (d) the still missing clinical integration. The boundaries between strengths and challenges, with cross-cutting ethical and regulatory implications, remain blurred. The literature emphasizes explainability and trustworthiness, with a largely missing discussion about the specific technical and regulatory challenges surrounding these concepts. Future trends are expected to shift towards multi-source models, combining imaging with an array of other data, in a more open access, and explainable manner.
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Affiliation(s)
- Vasileios Nittas
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, Faculty of Medicine, Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Paola Daniore
- Institute for Implementation Science in Health Care, Faculty of Medicine, University of Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Switzerland
| | - Constantin Landers
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Felix Gille
- Institute for Implementation Science in Health Care, Faculty of Medicine, University of Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Switzerland
| | - Julia Amann
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Shannon Hubbs
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Milo Alan Puhan
- Epidemiology, Biostatistics and Prevention Institute, Faculty of Medicine, Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Effy Vayena
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Alessandro Blasimme
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
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17
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Artificial Intelligence and Precision Medicine: A New Frontier for the Treatment of Brain Tumors. LIFE (BASEL, SWITZERLAND) 2022; 13:life13010024. [PMID: 36675973 PMCID: PMC9866715 DOI: 10.3390/life13010024] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/08/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
Brain tumors are a widespread and serious neurological phenomenon that can be life- threatening. The computing field has allowed for the development of artificial intelligence (AI), which can mimic the neural network of the human brain. One use of this technology has been to help researchers capture hidden, high-dimensional images of brain tumors. These images can provide new insights into the nature of brain tumors and help to improve treatment options. AI and precision medicine (PM) are converging to revolutionize healthcare. AI has the potential to improve cancer imaging interpretation in several ways, including more accurate tumor genotyping, more precise delineation of tumor volume, and better prediction of clinical outcomes. AI-assisted brain surgery can be an effective and safe option for treating brain tumors. This review discusses various AI and PM techniques that can be used in brain tumor treatment. These new techniques for the treatment of brain tumors, i.e., genomic profiling, microRNA panels, quantitative imaging, and radiomics, hold great promise for the future. However, there are challenges that must be overcome for these technologies to reach their full potential and improve healthcare.
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18
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Martin P, Holloway L, Metcalfe P, Koh ES, Brighi C. Challenges in Glioblastoma Radiomics and the Path to Clinical Implementation. Cancers (Basel) 2022; 14:3897. [PMID: 36010891 PMCID: PMC9406186 DOI: 10.3390/cancers14163897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 08/04/2022] [Accepted: 08/09/2022] [Indexed: 11/17/2022] Open
Abstract
Radiomics is a field of medical imaging analysis that focuses on the extraction of many quantitative imaging features related to shape, intensity and texture. These features are incorporated into models designed to predict important clinical or biological endpoints for patients. Attention for radiomics research has recently grown dramatically due to the increased use of imaging and the availability of large, publicly available imaging datasets. Glioblastoma multiforme (GBM) patients stand to benefit from this emerging research field as radiomics has the potential to assess the biological heterogeneity of the tumour, which contributes significantly to the inefficacy of current standard of care therapy. Radiomics models still require further development before they are implemented clinically in GBM patient management. Challenges relating to the standardisation of the radiomics process and the validation of radiomic models impede the progress of research towards clinical implementation. In this manuscript, we review the current state of radiomics in GBM, and we highlight the barriers to clinical implementation and discuss future validation studies needed to advance radiomics models towards clinical application.
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Affiliation(s)
- Philip Martin
- Centre for Medical and Radiation Physics, School of Physics, University of Wollongong, Wollongong, NSW 2522, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
| | - Lois Holloway
- Centre for Medical and Radiation Physics, School of Physics, University of Wollongong, Wollongong, NSW 2522, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical Campus, School of Medicine, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Peter Metcalfe
- Centre for Medical and Radiation Physics, School of Physics, University of Wollongong, Wollongong, NSW 2522, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
| | - Eng-Siew Koh
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical Campus, School of Medicine, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Caterina Brighi
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- ACRF Image X Institute, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
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19
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Sajid MI, Ahmed S, Waqar U, Tariq J, Chundrigarh M, Balouch SS, Abaidullah S. SARS-CoV-2: Has artificial intelligence stood the test of time. Chin Med J (Engl) 2022; 135:1792-1802. [PMID: 36195992 PMCID: PMC9521771 DOI: 10.1097/cm9.0000000000002058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Indexed: 02/04/2023] Open
Abstract
ABSTRACT Artificial intelligence (AI) has proven time and time again to be a game-changer innovation in every walk of life, including medicine. Introduced by Dr. Gunn in 1976 to accurately diagnose acute abdominal pain and list potential differentials, AI has since come a long way. In particular, AI has been aiding in radiological diagnoses with good sensitivity and specificity by using machine learning algorithms. With the coronavirus disease 2019 pandemic, AI has proven to be more than just a tool to facilitate healthcare workers in decision making and limiting physician-patient contact during the pandemic. It has guided governments and key policymakers in formulating and implementing laws, such as lockdowns and travel restrictions, to curb the spread of this viral disease. This has been made possible by the use of social media to map severe acute respiratory syndrome coronavirus 2 hotspots, laying the basis of the "smart lockdown" strategy that has been adopted globally. However, these benefits might be accompanied with concerns regarding privacy and unconsented surveillance, necessitating authorities to develop sincere and ethical government-public relations.
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Affiliation(s)
- Mir Ibrahim Sajid
- Medical College, Aga Khan University, Stadium Road, Karachi, Pakistan
| | - Shaheer Ahmed
- Medlcal College, Islamabad Medical and Dental College, Main Murree Road, Islamabad, Pakistan
| | - Usama Waqar
- Medical College, Aga Khan University, Stadium Road, Karachi, Pakistan
| | - Javeria Tariq
- Medical College, Aga Khan University, Stadium Road, Karachi, Pakistan
| | | | - Samira Shabbir Balouch
- Oral and Maxillofacial Surgery, King Edward Medical University, Neela Gumbad, Lahore, Pakistan
| | - Sajid Abaidullah
- King Edward Medical University, Neela Gumbad, Lahore, Pakistan
- North Medical Ward, Mayo Hospital, Neela Gumbad, Lahore, Pakistan
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20
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Qin D, Yang G, Jing H, Tan Y, Zhao B, Zhang H. Tumor Progression and Treatment-Related Changes: Radiological Diagnosis Challenges for the Evaluation of Post Treated Glioma. Cancers (Basel) 2022; 14:cancers14153771. [PMID: 35954435 PMCID: PMC9367286 DOI: 10.3390/cancers14153771] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 07/25/2022] [Accepted: 07/27/2022] [Indexed: 12/30/2022] Open
Abstract
Simple Summary Glioma is the most common primary malignant tumor of the adult central nervous system. Despite aggressive multimodal treatment, its prognosis remains poor. During follow-up, it remains challenging to distinguish treatment-related changes from tumor progression in treated patients with gliomas due to both share clinical symptoms and morphological imaging characteristics (with new and/or increasing enhancing mass lesions). The early effective identification of tumor progression and treatment-related changes is of great significance for the prognosis and treatment of gliomas. We believe that advanced neuroimaging techniques can provide additional information for distinguishing both at an early stage. In this article, we focus on the research of magnetic resonance imaging technology and artificial intelligence in tumor progression and treatment-related changes. Finally, it provides new ideas and insights for clinical diagnosis. Abstract As the most common neuro-epithelial tumors of the central nervous system in adults, gliomas are highly malignant and easy to recurrence, with a dismal prognosis. Imaging studies are indispensable for tracking tumor progression (TP) or treatment-related changes (TRCs). During follow-up, distinguishing TRCs from TP in treated patients with gliomas remains challenging as both share similar clinical symptoms and morphological imaging characteristics (with new and/or increasing enhancing mass lesions) and fulfill criteria for progression. Thus, the early identification of TP and TRCs is of great significance for determining the prognosis and treatment. Histopathological biopsy is currently the gold standard for TP and TRC diagnosis. However, the invasive nature of this technique limits its clinical application. Advanced imaging methods (e.g., diffusion magnetic resonance imaging (MRI), perfusion MRI, magnetic resonance spectroscopy (MRS), positron emission tomography (PET), amide proton transfer (APT) and artificial intelligence (AI)) provide a non-invasive and feasible technical means for identifying of TP and TRCs at an early stage, which have recently become research hotspots. This paper reviews the current research on using the abovementioned advanced imaging methods to identify TP and TRCs of gliomas. First, the review focuses on the pathological changes of the two entities to establish a theoretical basis for imaging identification. Then, it elaborates on the application of different imaging techniques and AI in identifying the two entities. Finally, the current challenges and future prospects of these techniques and methods are discussed.
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Affiliation(s)
- Danlei Qin
- College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China;
- Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Shanxi Medical University School, Hospital of Stomatology, Taiyuan 030001, China
| | - Guoqiang Yang
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (Y.T.)
| | - Hui Jing
- Department of MRI, The Six Hospital, Shanxi Medical University, Taiyuan 030008, China;
| | - Yan Tan
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (Y.T.)
| | - Bin Zhao
- College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China;
- Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Shanxi Medical University School, Hospital of Stomatology, Taiyuan 030001, China
- Correspondence: (B.Z.); (H.Z.)
| | - Hui Zhang
- College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China;
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (Y.T.)
- Intelligent Imaging Big Data and Functional Nano-imaging Engineering Research Center of Shanxi Province, Taiyuan 030001, China
- Correspondence: (B.Z.); (H.Z.)
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21
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Sajid MI, Ahmed S, Waqar U, Tariq J, Chundrigarh M, Balouch SS, Abaidullah S. Application in medicine: Has artificial intelligence stood the test of time. Chin Med J (Engl) 2022; Publish Ahead of Print:00029330-990000000-00090. [PMID: 35899989 DOI: 10.1097/cm9.00000000000020s8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Indexed: 11/26/2022] Open
Abstract
ABSTRACT Artificial intelligence (AI) has proven time and time again to be a game-changer innovation in every walk of life, including medicine. Introduced by Dr. Gunn in 1976 to accurately diagnose acute abdominal pain and list potential differentials, AI has since come a long way. In particular, AI has been aiding in radiological diagnoses with good sensitivity and specificity by using machine learning algorithms. With the coronavirus disease 2019 pandemic, AI has proven to be more than just a tool to facilitate healthcare workers in decision making and limiting physician-patient contact during the pandemic. It has guided governments and key policymakers in formulating and implementing laws, such as lockdowns and travel restrictions, to curb the spread of this viral disease. This has been made possible by the use of social media to map severe acute respiratory syndrome coronavirus 2 hotspots, laying the basis of the "smart lockdown" strategy that has been adopted globally. However, these benefits might be accompanied with concerns regarding privacy and unconsented surveillance, necessitating authorities to develop sincere and ethical government-public relations.
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Affiliation(s)
- Mir Ibrahim Sajid
- Medical College, Aga Khan University, Stadium Road, Karachi, Pakistan
| | - Shaheer Ahmed
- Medlcal College, Islamabad Medical and Dental College, Main Murree Road, Islamabad, Pakistan
| | - Usama Waqar
- Medical College, Aga Khan University, Stadium Road, Karachi, Pakistan
| | - Javeria Tariq
- Medical College, Aga Khan University, Stadium Road, Karachi, Pakistan
| | | | - Samira Shabbir Balouch
- Oral and Maxillofacial Surgery, King Edward Medical University, Neela Gumbad, Lahore, Pakistan
| | - Sajid Abaidullah
- King Edward Medical University, Neela Gumbad, Lahore, Pakistan
- North Medical Ward, Mayo Hospital, Neela Gumbad, Lahore, Pakistan
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22
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Bumes E, Fellner C, Fellner FA, Fleischanderl K, Häckl M, Lenz S, Linker R, Mirus T, Oefner PJ, Paar C, Proescholdt MA, Riemenschneider MJ, Rosengarth K, Weis S, Wendl C, Wimmer S, Hau P, Gronwald W, Hutterer M. Validation Study for Non-Invasive Prediction of IDH Mutation Status in Patients with Glioma Using In Vivo 1H-Magnetic Resonance Spectroscopy and Machine Learning. Cancers (Basel) 2022; 14:cancers14112762. [PMID: 35681741 PMCID: PMC9179368 DOI: 10.3390/cancers14112762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 05/24/2022] [Accepted: 05/31/2022] [Indexed: 11/23/2022] Open
Abstract
Simple Summary The enzyme isocitrate dehydrogenase (IDH) affects glioma cell metabolism in multiple ways. Mutation of IDH is not only indicative of the presence of astrocytoma or oligodendroglioma but it also comes with a better prognosis and constitutes a promising therapeutic target. Therefore, determination of IDH mutation status is essential in clinical practice. In most patients, tissue can be obtained by resection or biopsy to determine IDH status histologically. However, in some cases, this is not possible for technical reasons. We recently showed in a small cohort of patients that non-invasive determination of IDH mutation status using proton magnetic resonance spectroscopy (1H-MRS) at 3.0 Tesla (T) together with machine learning techniques is feasible in a standard clinical setting and with acceptable effort. Here, we demonstrate that our approach showed comparably good results in sensitivity (82.6%) and specificity (72.7%) in a larger validation cohort employing 1H-MRS at 1.5 T in a retrospective, distinct setting. We concluded that our method works well regardless of the magnetic field strength and scanner used, and thus, may improve patient care. Abstract The isocitrate dehydrogenase (IDH) mutation status is an indispensable prerequisite for diagnosis of glioma (astrocytoma and oligodendroglioma) according to the WHO classification of brain tumors 2021 and is a potential therapeutic target. Usually, immunohistochemistry followed by sequencing of tumor tissue is performed for this purpose. In clinical routine, however, non-invasive determination of IDH mutation status is desirable in cases where tumor biopsy is not possible and for monitoring neuro-oncological therapies. In a previous publication, we presented reliable prediction of IDH mutation status employing proton magnetic resonance spectroscopy (1H-MRS) on a 3.0 Tesla (T) scanner and machine learning in a prospective cohort of 34 glioma patients. Here, we validated this approach in an independent cohort of 67 patients, for which 1H-MR spectra were acquired at 1.5 T between 2002 and 2007, using the same data analysis approach. Despite different technical conditions, a sensitivity of 82.6% (95% CI, 61.2–95.1%) and a specificity of 72.7% (95% CI, 57.2–85.0%) could be achieved. We concluded that our 1H-MRS based approach can be established in a routine clinical setting with affordable effort and time, independent of technical conditions employed. Therefore, the method provides a non-invasive tool for determining IDH status that is well-applicable in an everyday clinical setting.
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Affiliation(s)
- Elisabeth Bumes
- Department of Neurology and Wilhelm Sander-NeuroOncology Unit, Regensburg University Hospital, 93055 Regensburg, Germany; (R.L.); (P.H.); (M.H.)
- Correspondence: ; Tel.: +49-941-944-18751
| | - Claudia Fellner
- Department of Radiology and Division of Neuroradiology, Regensburg University Hospital, 93055 Regensburg, Germany; (C.F.); (C.W.)
| | - Franz A. Fellner
- Central Institute of Radiology, Kepler University Hospital, 4021 Linz, Austria;
| | - Karin Fleischanderl
- Division of Molecular Pathology, Neuromed Campus, Kepler University Hospital, 4020 Linz, Austria; (K.F.); (S.L.)
| | - Martina Häckl
- Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany; (M.H.); (T.M.); (P.J.O.); (W.G.)
| | - Stefan Lenz
- Division of Molecular Pathology, Neuromed Campus, Kepler University Hospital, 4020 Linz, Austria; (K.F.); (S.L.)
| | - Ralf Linker
- Department of Neurology and Wilhelm Sander-NeuroOncology Unit, Regensburg University Hospital, 93055 Regensburg, Germany; (R.L.); (P.H.); (M.H.)
| | - Tim Mirus
- Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany; (M.H.); (T.M.); (P.J.O.); (W.G.)
| | - Peter J. Oefner
- Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany; (M.H.); (T.M.); (P.J.O.); (W.G.)
| | - Christian Paar
- Institute of Laboratory Medicine, Kepler University Hospital, 4021 Linz, Austria;
| | | | | | - Katharina Rosengarth
- Department of Neurosurgery, Regensburg University Hospital, 93053 Regensburg, Germany; (M.A.P.); (K.R.)
| | - Serge Weis
- Division of Neuropathology, Neuromed Campus, Kepler University Hospital, 4020 Linz, Austria;
| | - Christina Wendl
- Department of Radiology and Division of Neuroradiology, Regensburg University Hospital, 93055 Regensburg, Germany; (C.F.); (C.W.)
| | - Sibylle Wimmer
- Institute of Neuroradiology, Neuromed Campus, Kepler University Hospital, 4020 Linz, Austria;
| | - Peter Hau
- Department of Neurology and Wilhelm Sander-NeuroOncology Unit, Regensburg University Hospital, 93055 Regensburg, Germany; (R.L.); (P.H.); (M.H.)
| | - Wolfram Gronwald
- Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany; (M.H.); (T.M.); (P.J.O.); (W.G.)
| | - Markus Hutterer
- Department of Neurology and Wilhelm Sander-NeuroOncology Unit, Regensburg University Hospital, 93055 Regensburg, Germany; (R.L.); (P.H.); (M.H.)
- Department of Neurology with Acute Geriatrics, Saint John of God Hospital Linz, 4021 Linz, Austria
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Bernstock JD, Gary SE, Klinger N, Valdes PA, Ibn Essayed W, Olsen HE, Chagoya G, Elsayed G, Yamashita D, Schuss P, Gessler FA, Peruzzi PP, Bag A, Friedman GK. Standard clinical approaches and emerging modalities for glioblastoma imaging. Neurooncol Adv 2022; 4:vdac080. [PMID: 35821676 PMCID: PMC9268747 DOI: 10.1093/noajnl/vdac080] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Glioblastoma (GBM) is the most common primary adult intracranial malignancy and carries a dismal prognosis despite an aggressive multimodal treatment regimen that consists of surgical resection, radiation, and adjuvant chemotherapy. Radiographic evaluation, largely informed by magnetic resonance imaging (MRI), is a critical component of initial diagnosis, surgical planning, and post-treatment monitoring. However, conventional MRI does not provide information regarding tumor microvasculature, necrosis, or neoangiogenesis. In addition, traditional MRI imaging can be further confounded by treatment-related effects such as pseudoprogression, radiation necrosis, and/or pseudoresponse(s) that preclude clinicians from making fully informed decisions when structuring a therapeutic approach. A myriad of novel imaging modalities have been developed to address these deficits. Herein, we provide a clinically oriented review of standard techniques for imaging GBM and highlight emerging technologies utilized in disease characterization and therapeutic development.
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Affiliation(s)
- Joshua D Bernstock
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts, USA
| | - Sam E Gary
- Medical Scientist Training Program, University of Alabama at Birmingham, Birmingham , AL, USA
| | - Neil Klinger
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts, USA
| | - Pablo A Valdes
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts, USA
| | - Walid Ibn Essayed
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts, USA
| | - Hannah E Olsen
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts, USA
| | - Gustavo Chagoya
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham , AL, USA
| | - Galal Elsayed
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham , AL, USA
| | - Daisuke Yamashita
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham , AL, USA
| | - Patrick Schuss
- Department of Neurosurgery, Unfallkrankenhaus Berlin , Berlin, Germany
| | | | - Pier Paolo Peruzzi
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts, USA
| | - Asim Bag
- Department of Diagnostic Imaging, St. Jude Children’s Research Hospital , Memphis, TN USA
| | - Gregory K Friedman
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham , AL, USA
- Division of Pediatric Hematology and Oncology, Department of Pediatrics, University of Alabama at Birmingham , Birmingham, AL, USA
- Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham , AL, USA
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24
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Li X, Wang C, Chen G, Zou W, Deng Y, Zhou F. EIF4A3-induced circCCNB1 (hsa_circ_0001495) promotes glioma progression by elevating CCND1 through interacting miR-516b-5p and HuR. Metab Brain Dis 2022; 37:819-833. [PMID: 35038081 DOI: 10.1007/s11011-021-00899-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 12/26/2021] [Indexed: 01/29/2023]
Abstract
To explore the functions of circRNA cyclin B1 (circCCNB1) in glioma and its possible mechanisms. The expression of circCCNB1, eukaryotic translation initiation factor 4A3 (EIF4A3), cyclin D1 (CCND1) and miR-516b-5p was determined by qRT-PCR, western blot or immunohistochemistry (IHC) assay. The feature of circCCNB1 was analyzed by Actinomycin D (ActD), RNase R and subcellular fraction assays. The molecule relationships were analyzed by RIP, dual-luciferase reporter and RNA pull-down assays. CCK-8, EdU and colony formation assays were performed to analyze cell proliferation. Flow cytometry analysis was executed to estimate the cell cycle. Murine xenograft model assay was used for the role of circCCNB1 in vivo. CircCCNB1 was overexpressed in glioma tissues and cells. EIF4A3 positively regulated circCCNB1 expression. CircCCNB1 knockdown repressed glioma cell proliferation and cell cycle process in vitro and blocked tumor growth in vivo. CircCCNB1 knockdown reduced CCND1 expression in glioma cells and CCND1 overexpression bated the effect of circCCNB1 knockdown on glioma cell growth. CircCCNB1 interacted with HuR to elevate CCND1 expression. miR-516b-5p could interact with circCCNB1 and CCND1. CircCCNB1 regulated glioma cell progression and CCND1 expression by miR-516b-5p and HuR. CircCCNB1 aggravated glioma cell growth by elevating CCND1 through targeting miR-516b-5p and HuR.
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Affiliation(s)
- Xiaoli Li
- Department of Neurology, Renmin Hospital, Hubei University of Medicine, No. 39, Chaoyang Middle Road, Maojian District, Shiyan, 442000, China
| | - Chengmou Wang
- Department of Neurology, Renmin Hospital, Hubei University of Medicine, No. 39, Chaoyang Middle Road, Maojian District, Shiyan, 442000, China
| | - Guanghui Chen
- Department of Neurology, Renmin Hospital, Hubei University of Medicine, No. 39, Chaoyang Middle Road, Maojian District, Shiyan, 442000, China
| | - Wenqin Zou
- Department of Neurology, Renmin Hospital, Hubei University of Medicine, No. 39, Chaoyang Middle Road, Maojian District, Shiyan, 442000, China
| | - Yanqing Deng
- Department of Neurology, Renmin Hospital, Hubei University of Medicine, No. 39, Chaoyang Middle Road, Maojian District, Shiyan, 442000, China
| | - Faming Zhou
- Department of Neurology, Renmin Hospital, Hubei University of Medicine, No. 39, Chaoyang Middle Road, Maojian District, Shiyan, 442000, China.
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25
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Booth TC, Wiegers EC, Warnert EAH, Schmainda KM, Riemer F, Nechifor RE, Keil VC, Hangel G, Figueiredo P, Álvarez-Torres MDM, Henriksen OM. High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 2: Spectroscopy, Chemical Exchange Saturation, Multiparametric Imaging, and Radiomics. Front Oncol 2022; 11:811425. [PMID: 35340697 PMCID: PMC8948428 DOI: 10.3389/fonc.2021.811425] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 12/28/2021] [Indexed: 01/16/2023] Open
Abstract
Objective To summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and to highlight the latest bench-to-bedside developments. Methods The current evidence regarding the potential for monitoring biomarkers was reviewed and individual modalities of metabolism and/or chemical composition imaging discussed. Perfusion, permeability, and microstructure imaging were similarly analyzed in Part 1 of this two-part review article and are valuable reading as background to this article. We appraise the clinic readiness of all the individual modalities and consider methodologies involving machine learning (radiomics) and the combination of MRI approaches (multiparametric imaging). Results The biochemical composition of high-grade gliomas is markedly different from healthy brain tissue. Magnetic resonance spectroscopy allows the simultaneous acquisition of an array of metabolic alterations, with choline-based ratios appearing to be consistently discriminatory in treatment response assessment, although challenges remain despite this being a mature technique. Promising directions relate to ultra-high field strengths, 2-hydroxyglutarate analysis, and the use of non-proton nuclei. Labile protons on endogenous proteins can be selectively targeted with chemical exchange saturation transfer to give high resolution images. The body of evidence for clinical application of amide proton transfer imaging has been building for a decade, but more evidence is required to confirm chemical exchange saturation transfer use as a monitoring biomarker. Multiparametric methodologies, including the incorporation of nuclear medicine techniques, combine probes measuring different tumor properties. Although potentially synergistic, the limitations of each individual modality also can be compounded, particularly in the absence of standardization. Machine learning requires large datasets with high-quality annotation; there is currently low-level evidence for monitoring biomarker clinical application. Conclusion Advanced MRI techniques show huge promise in treatment response assessment. The clinical readiness analysis highlights that most monitoring biomarkers require standardized international consensus guidelines, with more facilitation regarding technique implementation and reporting in the clinic.
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Affiliation(s)
- Thomas C. Booth
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
- Department of Neuroradiology, King’s College Hospital NHS Foundation Trust, London, United Kingdom
| | - Evita C. Wiegers
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | | | - Kathleen M. Schmainda
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Frank Riemer
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Ruben E. Nechifor
- Department of Clinical Psychology and Psychotherapy International Institute for the Advanced Studies of Psychotherapy and Applied Mental Health, Babes-Bolyai University, Cluj-Napoca, Romania
| | - Vera C. Keil
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, Netherlands
| | - Gilbert Hangel
- Department of Neurosurgery & High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria
| | - Patrícia Figueiredo
- Department of Bioengineering and Institute for Systems and Robotics - Lisboa, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | | | - Otto M. Henriksen
- Department of Clinical Physiology, Nuclear medicine and PET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
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26
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Aftab K, Aamir FB, Mallick S, Mubarak F, Pope WB, Mikkelsen T, Rock JP, Enam SA. Radiomics for precision medicine in glioblastoma. J Neurooncol 2022; 156:217-231. [PMID: 35020109 DOI: 10.1007/s11060-021-03933-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 12/20/2021] [Indexed: 11/26/2022]
Abstract
INTRODUCTION Being the most common primary brain tumor, glioblastoma presents as an extremely challenging malignancy to treat with dismal outcomes despite treatment. Varying molecular epidemiology of glioblastoma between patients and intra-tumoral heterogeneity explains the failure of current one-size-fits-all treatment modalities. Radiomics uses machine learning to identify salient features of the tumor on brain imaging and promises patient-specific management in glioblastoma patients. METHODS We performed a comprehensive review of the available literature on studies investigating the role of radiomics and radiogenomics models for the diagnosis, stratification, prognostication as well as treatment planning and monitoring of glioblastoma. RESULTS Classifiers based on a combination of various MRI sequences, genetic information and clinical data can predict non-invasive tumor diagnosis, overall survival and treatment response with reasonable accuracy. However, the use of radiomics for glioblastoma treatment remains in infancy as larger sample sizes, standardized image acquisition and data extraction techniques are needed to develop machine learning models that can be translated effectively into clinical practice. CONCLUSION Radiomics has the potential to transform the scope of glioblastoma management through personalized medicine.
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Affiliation(s)
- Kiran Aftab
- Section of Neurosurgery, Department of Surgery, Aga Khan University, Karachi, Pakistan
| | | | - Saad Mallick
- Medical College, Aga Khan University, Karachi, Pakistan
| | - Fatima Mubarak
- Department of Radiology, Aga Khan University, Karachi, Pakistan
| | - Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Tom Mikkelsen
- Departments of Neurology and Neurosurgery, Henry Ford Hospital, Detroit, MI, USA
| | - Jack P Rock
- Department of Neurosurgery, Henry Ford Health System, Detroit, MI, USA
| | - Syed Ather Enam
- Section of Neurosurgery, Department of Surgery, Aga Khan University, Karachi, Pakistan.
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27
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Lotan E, Zhang B, Dogra S, Wang W, Carbone D, Fatterpekar G, Oermann E, Lui Y. Development and Practical Implementation of a Deep Learning-Based Pipeline for Automated Pre- and Postoperative Glioma Segmentation. AJNR Am J Neuroradiol 2022; 43:24-32. [PMID: 34857514 PMCID: PMC8757542 DOI: 10.3174/ajnr.a7363] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 09/22/2021] [Indexed: 01/03/2023]
Abstract
BACKGROUND AND PURPOSE Quantitative volumetric segmentation of gliomas has important implications for diagnosis, treatment, and prognosis. We present a deep-learning model that accommodates automated preoperative and postoperative glioma segmentation with a pipeline for clinical implementation. Developed and engineered in concert, the work seeks to accelerate clinical realization of such tools. MATERIALS AND METHODS A deep learning model, autoencoder regularization-cascaded anisotropic, was developed, trained, and tested fusing key elements of autoencoder regularization with a cascaded anisotropic convolutional neural network. We constructed a dataset consisting of 437 cases with 40 cases reserved as a held-out test and the remainder split 80:20 for training and validation. We performed data augmentation and hyperparameter optimization and used a mean Dice score to evaluate against baseline models. To facilitate clinical adoption, we developed the model with an end-to-end pipeline including routing, preprocessing, and end-user interaction. RESULTS The autoencoder regularization-cascaded anisotropic model achieved median and mean Dice scores of 0.88/0.83 (SD, 0.09), 0.89/0.84 (SD, 0.08), and 0.81/0.72 (SD, 0.1) for whole-tumor, tumor core/resection cavity, and enhancing tumor subregions, respectively, including both preoperative and postoperative follow-up cases. The overall total processing time per case was ∼10 minutes, including data routing (∼1 minute), preprocessing (∼6 minute), segmentation (∼1-2 minute), and postprocessing (∼1 minute). Implementation challenges were discussed. CONCLUSIONS We show the feasibility and advantages of building a coordinated model with a clinical pipeline for the rapid and accurate deep learning segmentation of both preoperative and postoperative gliomas. The ability of the model to accommodate cases of postoperative glioma is clinically important for follow-up. An end-to-end approach, such as used here, may lead us toward successful clinical translation of tools for quantitative volume measures for glioma.
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Affiliation(s)
- E. Lotan
- From the Department of Radiology (E.L., B.Z., S.D., D.C., G.F., E.K.O., Y.W.L.)
| | - B. Zhang
- From the Department of Radiology (E.L., B.Z., S.D., D.C., G.F., E.K.O., Y.W.L.)
| | - S. Dogra
- From the Department of Radiology (E.L., B.Z., S.D., D.C., G.F., E.K.O., Y.W.L.)
| | | | - D. Carbone
- From the Department of Radiology (E.L., B.Z., S.D., D.C., G.F., E.K.O., Y.W.L.)
| | - G. Fatterpekar
- From the Department of Radiology (E.L., B.Z., S.D., D.C., G.F., E.K.O., Y.W.L.)
| | - E.K. Oermann
- From the Department of Radiology (E.L., B.Z., S.D., D.C., G.F., E.K.O., Y.W.L.),Neurosurgery, School of Medicine (E.K.O.), NYU Langone Health, New York, New York
| | - Y.W. Lui
- From the Department of Radiology (E.L., B.Z., S.D., D.C., G.F., E.K.O., Y.W.L.)
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28
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Ebrahimi Zade A, Shahabi Haghighi S, Soltani M. Deep Neural Networks for Neuro-oncology: Towards Patient Individualized Design of Chemo-Radiation Therapy for Glioblastoma Patients. J Biomed Inform 2022; 127:104006. [DOI: 10.1016/j.jbi.2022.104006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/04/2022] [Accepted: 01/25/2022] [Indexed: 11/28/2022]
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Srinivasan M, Thangaraj SR, Ramasubramanian K, Thangaraj PP, Ramasubramanian KV. Exploring the Current Trends of Artificial Intelligence in Stem Cell Therapy: A Systematic Review. Cureus 2021; 13:e20083. [PMID: 34873560 PMCID: PMC8635466 DOI: 10.7759/cureus.20083] [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] [Accepted: 12/01/2021] [Indexed: 12/16/2022] Open
Abstract
The concept of healing in medicine has been taking a new form where scientists and researchers are in pursuance of regenerative medicine. Until now, doctors have "reacted" to disease by treating the symptoms; however, modern medicine is transforming toward regeneration rather than reactive treatment, which is where stem cell therapy comes into the play-the concept of replacing damaged cells with brand new cells that perform the same function better. Stem cell treatment is currently being used to treat autoimmune, inflammatory, neurological, orthopedic, and traumatic disorders, with various research being undertaken for a wide range of diseases. It could also be the answer to anti-aging and a disease-free state. Despite the benefits, numerous errors could prevail in treating patients with stem cells. With the advancement of technology and research in the modern period, medicine is beginning to turn to artificial intelligence (AI) to address the complicated errors that could occur in regenerative medicine. For successful treatment, one must achieve precision and accuracy when analyzing healthy and productive stem cells that possess all the properties of a native cell. This review intends to discuss and study the application of AI in stem cell therapy and how it influences how medicine is practiced, thus creating a path to a regenerative future with negligible adverse effects. The following databases were used for a literature search: PubMed, Google Scholar, PubMed Central, and Institute of Electrical and Electronics Engineers (IEEE) Xplore. After a thorough analysis, studies were chosen, keeping in mind the inclusion and exclusion criteria set by the authors of this review, which comprised reports published within the last six years in the English language. The authors also made sure to include studies that sufficed the quality of each report assessed using appropriate quality appraisal tools, after which eight reports were found to be eligible and were included in this review. This research mainly revolves around machine learning, deep neural networks (DNN), and other subclasses of AI encompassed in these categories. While there are concerns and limitations in implementing various mediums of AI in stem cell therapy, the analysis of the eligible studies concluded that artificial intelligence provides significant benefits to the global healthcare ecosystem in numerous ways, such as determining the viability, functionality, biosafety, and bioefficacy of stem cells, as well as appropriate patient selection. Applying AI to this novelty brings out the precision, accuracy, and a revolution in regenerative medicine. In addition, stem cell therapy is not currently FDA approved (except for the blood-forming stem cells) and, to date, is considered experimental with no clear outline of risks and benefits. Given this limitation, studies are conducted regularly around the world in hopes for a concrete conclusion where technological advances such as AI could help in shaping the future of regenerative medicine.
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Affiliation(s)
- Mirra Srinivasan
- Internal Medicine, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA
| | | | - Krishnamurthy Ramasubramanian
- Computer Science and Engineering, Koneru Lakshmaiah University, Koneru Lakshmaiah Education Foundation (KLEF), Hyderabad, IND
| | - Padma Pradha Thangaraj
- Instrumentation Engineering, Madras Institute of Technology, Anna University, Chennai, IND
| | - Krishna Vyas Ramasubramanian
- Computer Science and Engineering, Artificial Intelligence and Machine Learning, Vellore Institute of Technology, Chennai, IND
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Pasquini L, Napolitano A, Lucignani M, Tagliente E, Dellepiane F, Rossi-Espagnet MC, Ritrovato M, Vidiri A, Villani V, Ranazzi G, Stoppacciaro A, Romano A, Di Napoli A, Bozzao A. AI and High-Grade Glioma for Diagnosis and Outcome Prediction: Do All Machine Learning Models Perform Equally Well? Front Oncol 2021; 11:601425. [PMID: 34888226 PMCID: PMC8649764 DOI: 10.3389/fonc.2021.601425] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 11/02/2021] [Indexed: 12/30/2022] Open
Abstract
Radiomic models outperform clinical data for outcome prediction in high-grade gliomas (HGG). However, lack of parameter standardization limits clinical applications. Many machine learning (ML) radiomic models employ single classifiers rather than ensemble learning, which is known to boost performance, and comparative analyses are lacking in the literature. We aimed to compare ML classifiers to predict clinically relevant tasks for HGG: overall survival (OS), isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor vIII (EGFR) amplification, and Ki-67 expression, based on radiomic features from conventional and advanced magnetic resonance imaging (MRI). Our objective was to identify the best algorithm for each task. One hundred fifty-six adult patients with pathologic diagnosis of HGG were included. Three tumoral regions were manually segmented: contrast-enhancing tumor, necrosis, and non-enhancing tumor. Radiomic features were extracted with a custom version of Pyradiomics and selected through Boruta algorithm. A Grid Search algorithm was applied when computing ten times K-fold cross-validation (K=10) to get the highest mean and lowest spread of accuracy. Model performance was assessed as AUC-ROC curve mean values with 95% confidence intervals (CI). Extreme Gradient Boosting (xGB) obtained highest accuracy for OS (74,5%), Adaboost (AB) for IDH mutation (87.5%), MGMT methylation (70,8%), Ki-67 expression (86%), and EGFR amplification (81%). Ensemble classifiers showed the best performance across tasks. High-scoring radiomic features shed light on possible correlations between MRI and tumor histology.
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Affiliation(s)
- Luca Pasquini
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
- Neuroradiology Unit, Neuroscience, Mental Health and Sensory Organs (NESMOS) Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Martina Lucignani
- Medical Physics Department, Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Emanuela Tagliente
- Medical Physics Department, Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Francesco Dellepiane
- Neuroradiology Unit, Neuroscience, Mental Health and Sensory Organs (NESMOS) Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
| | - Maria Camilla Rossi-Espagnet
- Neuroradiology Unit, Neuroscience, Mental Health and Sensory Organs (NESMOS) Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Matteo Ritrovato
- Unit of Health Technology Assessment (HTA), Biomedical Technology Risk Manager, Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Antonello Vidiri
- Radiology and Diagnostic Imaging Department, Regina Elena National Cancer Institute, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Veronica Villani
- Neuro-Oncology Unit, Regina Elena National Cancer Institute, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Giulio Ranazzi
- Department of Clinical and Molecular Medicine, Surgical Pathology Units, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
| | - Antonella Stoppacciaro
- Department of Clinical and Molecular Medicine, Surgical Pathology Units, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
| | - Andrea Romano
- Neuroradiology Unit, Neuroscience, Mental Health and Sensory Organs (NESMOS) Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
| | - Alberto Di Napoli
- Neuroradiology Unit, Neuroscience, Mental Health and Sensory Organs (NESMOS) Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
- Radiology Department, Castelli Romani Hospital, Rome, Italy
| | - Alessandro Bozzao
- Neuroradiology Unit, Neuroscience, Mental Health and Sensory Organs (NESMOS) Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
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31
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Ruini C, Schlingmann S, Jonke Ž, Avci P, Padrón-Laso V, Neumeier F, Koveshazi I, Ikeliani IU, Patzer K, Kunrad E, Kendziora B, Sattler E, French LE, Hartmann D. Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy. Cancers (Basel) 2021; 13:cancers13215522. [PMID: 34771684 PMCID: PMC8583634 DOI: 10.3390/cancers13215522] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/22/2021] [Accepted: 10/29/2021] [Indexed: 01/02/2023] Open
Abstract
Image classification with convolutional neural networks (CNN) offers an unprecedented opportunity to medical imaging. Regulatory agencies in the USA and Europe have already cleared numerous deep learning/machine learning based medical devices and algorithms. While the field of radiology is on the forefront of artificial intelligence (AI) revolution, conventional pathology, which commonly relies on examination of tissue samples on a glass slide, is falling behind in leveraging this technology. On the other hand, ex vivo confocal laser scanning microscopy (ex vivo CLSM), owing to its digital workflow features, has a high potential to benefit from integrating AI tools into the assessment and decision-making process. Aim of this work was to explore a preliminary application of CNN in digitally stained ex vivo CLSM images of cutaneous squamous cell carcinoma (cSCC) for automated detection of tumor tissue. Thirty-four freshly excised tissue samples were prospectively collected and examined immediately after resection. After the histologically confirmed ex vivo CLSM diagnosis, the tumor tissue was annotated for segmentation by experts, in order to train the MobileNet CNN. The model was then trained and evaluated using cross validation. The overall sensitivity and specificity of the deep neural network for detecting cSCC and tumor free areas on ex vivo CLSM slides compared to expert evaluation were 0.76 and 0.91, respectively. The area under the ROC curve was equal to 0.90 and the area under the precision-recall curve was 0.85. The results demonstrate a high potential of deep learning models to detect cSCC regions on digitally stained ex vivo CLSM slides and to distinguish them from tumor-free skin.
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Affiliation(s)
- Cristel Ruini
- Department of Dermatology and Allergy, University Hospital, LMU Munich, 80337 Munich, Germany; (S.S.); (P.A.); (K.P.); (E.K.); (B.K.); (E.S.); (L.E.F.); (D.H.)
- PhD School in Clinical and Experimental Medicine, University of Modena and Reggio Emilia, 41125 Modena, Italy
- Correspondence:
| | - Sophia Schlingmann
- Department of Dermatology and Allergy, University Hospital, LMU Munich, 80337 Munich, Germany; (S.S.); (P.A.); (K.P.); (E.K.); (B.K.); (E.S.); (L.E.F.); (D.H.)
| | - Žan Jonke
- Munich Innovation Labs GmbH, 80336 Munich, Germany; (Ž.J.); (V.P.-L.)
| | - Pinar Avci
- Department of Dermatology and Allergy, University Hospital, LMU Munich, 80337 Munich, Germany; (S.S.); (P.A.); (K.P.); (E.K.); (B.K.); (E.S.); (L.E.F.); (D.H.)
| | | | - Florian Neumeier
- M3i Industry-in-Clinic-Platform GmbH, 80336 Munich, Germany; (F.N.); (I.K.); (I.U.I.)
| | - Istvan Koveshazi
- M3i Industry-in-Clinic-Platform GmbH, 80336 Munich, Germany; (F.N.); (I.K.); (I.U.I.)
| | - Ikenna U. Ikeliani
- M3i Industry-in-Clinic-Platform GmbH, 80336 Munich, Germany; (F.N.); (I.K.); (I.U.I.)
| | - Kathrin Patzer
- Department of Dermatology and Allergy, University Hospital, LMU Munich, 80337 Munich, Germany; (S.S.); (P.A.); (K.P.); (E.K.); (B.K.); (E.S.); (L.E.F.); (D.H.)
| | - Elena Kunrad
- Department of Dermatology and Allergy, University Hospital, LMU Munich, 80337 Munich, Germany; (S.S.); (P.A.); (K.P.); (E.K.); (B.K.); (E.S.); (L.E.F.); (D.H.)
| | - Benjamin Kendziora
- Department of Dermatology and Allergy, University Hospital, LMU Munich, 80337 Munich, Germany; (S.S.); (P.A.); (K.P.); (E.K.); (B.K.); (E.S.); (L.E.F.); (D.H.)
| | - Elke Sattler
- Department of Dermatology and Allergy, University Hospital, LMU Munich, 80337 Munich, Germany; (S.S.); (P.A.); (K.P.); (E.K.); (B.K.); (E.S.); (L.E.F.); (D.H.)
| | - Lars E. French
- Department of Dermatology and Allergy, University Hospital, LMU Munich, 80337 Munich, Germany; (S.S.); (P.A.); (K.P.); (E.K.); (B.K.); (E.S.); (L.E.F.); (D.H.)
- Dr. Phillip Frost Department of Dermatology & Cutaneous Surgery, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Daniela Hartmann
- Department of Dermatology and Allergy, University Hospital, LMU Munich, 80337 Munich, Germany; (S.S.); (P.A.); (K.P.); (E.K.); (B.K.); (E.S.); (L.E.F.); (D.H.)
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Álvarez-Torres MDM, Fuster-García E, Balaña C, Puig J, García-Gómez JM. Lack of Benefit of Extending Temozolomide Treatment in Patients with High Vascular Glioblastoma with Methylated MGMT. Cancers (Basel) 2021; 13:5420. [PMID: 34771583 PMCID: PMC8582449 DOI: 10.3390/cancers13215420] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 10/25/2021] [Accepted: 10/25/2021] [Indexed: 11/25/2022] Open
Abstract
In this study, we evaluated the benefit on survival of the combination of methylation of O6-methylguanine-DNA methyltransferase (MGMT) promotor gene and moderate vascularity in glioblastoma using a retrospective dataset of 123 patients from a multicenter cohort. MRI processing and calculation of relative cerebral blood volume (rCBV), used to define moderate- and high-vascular groups, were performed with the automatic ONCOhabitats method. We assessed the previously proposed rCBV threshold (10.7) and the new calculated ones (9.1 and 9.8) to analyze the association with survival for different populations according to vascularity and MGMT methylation status. We found that patients included in the moderate-vascular group had longer survival when MGMT is methylated (significant median survival difference of 174 days, p = 0.0129*). However, we did not find significant differences depending on the MGMT methylation status for the high-vascular group (p = 0.9119). In addition, we investigated the combined correlation of MGMT methylation status and rCBV with the prognostic effect of the number of temozolomide cycles, and only significant results were found for the moderate-vascular group. In conclusion, there is a lack of benefit of extending temozolomide treatment for patients with high vascular glioblastomas, even presenting MGMT methylation. Preliminary results suggest that patients with moderate vascularity and methylated MGMT glioblastomas would benefit more from prolonged adjuvant chemotherapy.
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Affiliation(s)
- María del Mar Álvarez-Torres
- Biomedical Data Science Laboratory, Instituto Universitario de Tecnologías de la Información y Comunicaciones, Universitat Politècnica de València, 46022 Valencia, Spain; (E.F.-G.); (J.M.G.-G.)
| | - Elies Fuster-García
- Biomedical Data Science Laboratory, Instituto Universitario de Tecnologías de la Información y Comunicaciones, Universitat Politècnica de València, 46022 Valencia, Spain; (E.F.-G.); (J.M.G.-G.)
- Department of Diagnostic Physics, Oslo University Hospital, 0450 Oslo, Norway
| | - Carmen Balaña
- Institut Catala d’Oncologia (ICO), Applied Research Group in Oncology (B-ARGO Group), Institut Investigació Germans Trias i Pujol (IGTP), 08916 Badalona, Spain;
| | - Josep Puig
- Institut de Diagnostic per la Image (IDI), Hospital Dr. Josep Trueta, 17007 Girona, Spain;
| | - Juan M. García-Gómez
- Biomedical Data Science Laboratory, Instituto Universitario de Tecnologías de la Información y Comunicaciones, Universitat Politècnica de València, 46022 Valencia, Spain; (E.F.-G.); (J.M.G.-G.)
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Khan AA, Ibad H, Ahmed KS, Hoodbhoy Z, Shamim SM. Deep learning applications in neuro-oncology. Surg Neurol Int 2021; 12:435. [PMID: 34513198 PMCID: PMC8422419 DOI: 10.25259/sni_433_2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 07/30/2021] [Indexed: 11/04/2022] Open
Abstract
Deep learning (DL) is a relatively newer subdomain of machine learning (ML) with incredible potential for certain applications in the medical field. Given recent advances in its use in neuro-oncology, its role in diagnosing, prognosticating, and managing the care of cancer patients has been the subject of many research studies. The gamut of studies has shown that the landscape of algorithmic methods is constantly improving with each iteration from its inception. With the increase in the availability of high-quality data, more training sets will allow for higher fidelity models. However, logistical and ethical concerns over a prospective trial comparing prognostic abilities of DL and physicians severely limit the ability of this technology to be widely adopted. One of the medical tenets is judgment, a facet of medical decision making in DL that is often missing because of its inherent nature as a "black box." A natural distrust for newer technology, combined with a lack of autonomy that is normally expected in our current medical practices, is just one of several important limitations in implementation. In our review, we will first define and outline the different types of artificial intelligence (AI) as well as the role of AI in the current advances of clinical medicine. We briefly highlight several of the salient studies using different methods of DL in the realm of neuroradiology and summarize the key findings and challenges faced when using this nascent technology, particularly ethical challenges that could be faced by users of DL.
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Affiliation(s)
- Adnan A Khan
- Medical College, Aga Khan University, Karachi, Sindh, Pakistan
| | - Hamza Ibad
- Medical College, Aga Khan University, Karachi, Sindh, Pakistan
| | | | - Zahra Hoodbhoy
- Department of Pediatrics, Aga Khan University, Karachi, Sindh, Pakistan
| | - Shahzad M Shamim
- Department of Neurosurgery, Aga Khan University, Karachi, Sindh, Pakistan
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Ensemble based machine learning approach for prediction of glioma and multi-grade classification. Comput Biol Med 2021; 137:104829. [PMID: 34508971 DOI: 10.1016/j.compbiomed.2021.104829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 08/17/2021] [Accepted: 08/31/2021] [Indexed: 11/22/2022]
Abstract
Glioma is the most pernicious cancer of the nervous system, with histological grade influencing the survival of patients. Despite many studies on the multimodal treatment approach, survival time remains brief. In this study, a novel two-stage ensemble of an ensemble-type machine learning-based predictive framework for glioma detection and its histograde classification is proposed. In the proposed framework, five characteristics belonging to 135 subjects were considered: human telomerase reverse transcriptase (hTERT), chitinase-like protein (YKL-40), interleukin 6 (IL-6), tissue inhibitor of metalloproteinase-1 (TIMP-1) and neutrophil/lymphocyte ratio (NLR). These characteristics were examined using distinctive ensemble-based machine learning classifiers and combination strategies to develop a computer-aided diagnostic system for the non-invasive prediction of glioma cases and their grade. In the first stage, the analysis was conducted to classify glioma cases and control subjects. Machine learning approaches were applied in the second stage to classify the recognised glioma cases into three grades, from grade II, which has a good prognosis, to grade IV, which is also known as glioblastoma. All experiments were evaluated with a five-fold cross-validation method, and the classification results were analysed using different statistical parameters. The proposed approach obtained a high value of accuracy and other statistical parameters compared with other state-of-the-art machine learning classifiers. Therefore, the proposed framework can be utilised for designing other intervention strategies for the prediction of glioma cases and their grades.
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Richardson ML, Garwood ER, Lee Y, Li MD, Lo HS, Nagaraju A, Nguyen XV, Probyn L, Rajiah P, Sin J, Wasnik AP, Xu K. Noninterpretive Uses of Artificial Intelligence in Radiology. Acad Radiol 2021; 28:1225-1235. [PMID: 32059956 DOI: 10.1016/j.acra.2020.01.012] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 01/08/2020] [Accepted: 01/09/2020] [Indexed: 12/12/2022]
Abstract
We deem a computer to exhibit artificial intelligence (AI) when it performs a task that would normally require intelligent action by a human. Much of the recent excitement about AI in the medical literature has revolved around the ability of AI models to recognize anatomy and detect pathology on medical images, sometimes at the level of expert physicians. However, AI can also be used to solve a wide range of noninterpretive problems that are relevant to radiologists and their patients. This review summarizes some of the newer noninterpretive uses of AI in radiology.
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Affiliation(s)
| | - Elisabeth R Garwood
- Department of Radiology, University of Massachusetts, Worcester, Massachusetts
| | - Yueh Lee
- Department of Radiology, University of North Carolina, Chapel Hill, North Carolina
| | - Matthew D Li
- Department of Radiology, Harvard Medical School/Massachusetts General Hospital, Boston, Massachusets
| | - Hao S Lo
- Department of Radiology, University of Washington, Seattle, Washington
| | - Arun Nagaraju
- Department of Radiology, University of Chicago, Chicago, Illinois
| | - Xuan V Nguyen
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Linda Probyn
- Department of Radiology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario
| | - Prabhakar Rajiah
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Jessica Sin
- Department of Radiology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire
| | - Ashish P Wasnik
- Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Kali Xu
- Department of Medicine, Santa Clara Valley Medical Center, Santa Clara, California
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Zhao J, Jiang Y, Chen L, Ma Y, Zhang H, Zhou J, Li H, Jing Z. The EIF4A3/CASC2/RORA Feedback Loop Regulates the Aggressive Phenotype in Glioblastomas. Front Oncol 2021; 11:699933. [PMID: 34408982 PMCID: PMC8366401 DOI: 10.3389/fonc.2021.699933] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 07/16/2021] [Indexed: 12/25/2022] Open
Abstract
Glioblastoma (GBM) is a common and refractory subtype of high-grade glioma with a poor prognosis. The epithelial-mesenchymal transition (EMT) is an important cause of enhanced glioblastoma invasiveness and tumor recurrence. Our previous study found that retinoic acid receptor-related orphan receptor A (RORA) is a nuclear receptor and plays an important role in inhibiting proliferation and tumorigenesis of glioma. We further confirmed RORA was downregulated in GBM. Thus, we determined whether RORA was involved in the migration, invasion, and EMT of GBM. Human GBM cell lines, U87 and T98G, and patient-derived glioma stem cells (GSCs), GSC2C and GSC4D, were used for in vitro and in vivo experiments. The expressions of RORA, CASC2, and EIF4A3 in GBM cells and GSCs were detected by RT-qPCR and western blotting. The biological effects of RORA, CASC2, and EIF4A3 on GBM migration, invasion, and EMT were evaluated using the migration assay, transwell assay, immunofluorescence staining, and xenograft experiments. We found that RORA inhibited the migration, invasion, and EMT of GBM. CASC2 could bind to, maintain the stability, and promote the nuclear translocation of RORA protein. EIF4A3 could downregulate CASC2 expression via inducing its cleavage, while RORA transcriptionally inhibited EIF4A3 expression, which formed a feedback loop among EIF4A3/CASC2/RORA. Moreover, gene set enrichment analysis (GSEA) and in vitro and in vivo experiments showed RORA inhibited the aggressiveness of GBM by negatively regulating the TGF-β1/Smad signaling pathway. Therefore, The EIF4A3/CASC2/RORA feedback loop regulated TGF-β1/Smad signaling pathway might become a promising therapeutic strategy for GBM treatment.
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Affiliation(s)
- Junshuang Zhao
- Department of Neurosurgery, The First Hospital of China Medical University, Shenyang, China
| | - Yang Jiang
- Department of Neurosurgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Lian Chen
- Department of Neurosurgery, The First Hospital of China Medical University, Shenyang, China
| | - Yue Ma
- Department of Pulmonary and Critical Care Medicine, Shengjing Hospital of China Medical University, Shenyang, China
| | - Haiying Zhang
- International Education College, Liaoning University of Traditional Chinese Medicine, Shenyang, China
| | - Jinpeng Zhou
- Department of Neurosurgery, The First Hospital of China Medical University, Shenyang, China
| | - Hao Li
- Department of Neurosurgery, The First Hospital of China Medical University, Shenyang, China
| | - Zhitao Jing
- Department of Neurosurgery, The First Hospital of China Medical University, Shenyang, China
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Majewska P, Sagberg LM, Reinertsen I, Gulati S, Jakola AS, Solheim O. What is the current clinico-radiological diagnostic accuracy for intracranial tumours? Acta Neurol Scand 2021; 144:142-148. [PMID: 33960409 DOI: 10.1111/ane.13430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 03/05/2021] [Accepted: 03/09/2021] [Indexed: 11/30/2022]
Abstract
OBJECTIVE To determine the diagnostic accuracy of routine clinico-radiological workup for a population-based selection of intracranial tumours. METHODS In this prospective cohort study, we included consecutive adult patients who underwent a primary surgical intervention for a suspected intracranial tumour between 2015 and 2019 at a single-neurosurgical centre. The treating team estimated the expected diagnosis prior to surgery using predefined groups. The expected diagnosis was compared to final histopathology and the accuracy of preoperative clinico-radiological diagnosis (sensitivity, specificity, positive and negative predictive values) was calculated. RESULTS 392 patients were included in the data analysis, of whom 319 underwent a primary surgical resection and 73 were operated with a diagnostic biopsy only. The diagnostic accuracy varied between different tumour types. The overall sensitivity, specificity and diagnostic mismatch rate of clinico-radiological diagnosis was 85.8%, 97.7% and 4.0%, respectively. For gliomas (including differentiation between low-grade and high-grade gliomas), the same diagnostic accuracy measures were found to be 82.2%, 97.2% and 5.6%, respectively. The most common diagnostic mismatch was between low-grade gliomas, high-grade gliomas and metastases. Accuracy of 90.2% was achieved for differentiation between diffuse low-grade gliomas and high-grade gliomas. CONCLUSIONS The current accuracy of a preoperative clinico-radiological diagnosis of brain tumours is high. Future non-invasive diagnostic methods need to outperform our results in order to add much value in a routine clinical setting in unselected patients.
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Affiliation(s)
- Paulina Majewska
- Department of Neurosurgery St. Olav’s University Hospital Trondheim Norway
| | - Lisa Millgård Sagberg
- Department of Neurosurgery St. Olav’s University Hospital Trondheim Norway
- Department of Public Health and Nursing NTNU Trondheim Norway
| | | | - Sasha Gulati
- Department of Neurosurgery St. Olav’s University Hospital Trondheim Norway
- Department of Neuromedicine and Movement Science NTNU Trondheim Norway
| | - Asgeir Store Jakola
- Department of Neurosurgery St. Olav’s University Hospital Trondheim Norway
- Department of Neurosurgery Sahlgrenska University Hospital Gothenburg Sweden
- Institute of Neuroscience and Physiology Department of Clinical Neurosciences Sahlgrenska Academy Gothenburg
| | - Ole Solheim
- Department of Neurosurgery St. Olav’s University Hospital Trondheim Norway
- SINTEF Trondheim Norway
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Chromatin insulation dynamics in glioblastoma: challenges and future perspectives of precision oncology. Clin Epigenetics 2021; 13:150. [PMID: 34332627 PMCID: PMC8325855 DOI: 10.1186/s13148-021-01139-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Accepted: 07/23/2021] [Indexed: 12/13/2022] Open
Abstract
Glioblastoma (GBM) is the most aggressive primary brain tumor, having a poor prognosis and a median overall survival of less than two years. Over the last decade, numerous findings regarding the distinct molecular and genetic profiles of GBM have led to the emergence of several therapeutic approaches. Unfortunately, none of them has proven to be effective against GBM progression and recurrence. Epigenetic mechanisms underlying GBM tumor biology, including histone modifications, DNA methylation, and chromatin architecture, have become an attractive target for novel drug discovery strategies. Alterations on chromatin insulator elements (IEs) might lead to aberrant chromatin remodeling via DNA loop formation, causing oncogene reactivation in several types of cancer, including GBM. Importantly, it is shown that mutations affecting the isocitrate dehydrogenase (IDH) 1 and 2 genes, one of the most frequent genetic alterations in gliomas, lead to genome-wide DNA hypermethylation and the consequent IE dysfunction. The relevance of IEs has also been observed in a small population of cancer stem cells known as glioma stem cells (GSCs), which are thought to participate in GBM tumor initiation and drug resistance. Recent studies revealed that epigenomic alterations, specifically chromatin insulation and DNA loop formation, play a crucial role in establishing and maintaining the GSC transcriptional program. This review focuses on the relevance of IEs in GBM biology and their implementation as a potential theranostic target to stratify GBM patients and develop novel therapeutic approaches. We will also discuss the state-of-the-art emerging technologies using big data analysis and how they will settle the bases on future diagnosis and treatment strategies in GBM patients.
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Fully automated analysis combining [ 18F]-FET-PET and multiparametric MRI including DSC perfusion and APTw imaging: a promising tool for objective evaluation of glioma progression. Eur J Nucl Med Mol Imaging 2021; 48:4445-4455. [PMID: 34173008 PMCID: PMC8566389 DOI: 10.1007/s00259-021-05427-8] [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: 03/05/2021] [Accepted: 05/24/2021] [Indexed: 12/15/2022]
Abstract
Purpose To evaluate diagnostic accuracy of fully automated analysis of multimodal imaging data using [18F]-FET-PET and MRI (including amide proton transfer-weighted (APTw) imaging and dynamic-susceptibility-contrast (DSC) perfusion) in differentiation of tumor progression from treatment-related changes in patients with glioma. Material and methods At suspected tumor progression, MRI and [18F]-FET-PET data as part of a retrospective analysis of an observational cohort of 66 patients/74 scans (51 glioblastoma and 23 lower-grade-glioma, 8 patients included at two different time points) were automatically segmented into necrosis, FLAIR-hyperintense, and contrast-enhancing areas using an ensemble of deep learning algorithms. In parallel, previous MR exam was processed in a similar way to subtract preexisting tumor areas and focus on progressive tumor only. Within these progressive areas, intensity statistics were automatically extracted from [18F]-FET-PET, APTw, and DSC-derived cerebral-blood-volume (CBV) maps and used to train a Random Forest classifier with threefold cross-validation. To evaluate contribution of the imaging modalities to the classifier’s performance, impurity-based importance measures were collected. Classifier performance was compared with radiology reports and interdisciplinary tumor board assessments. Results In 57/74 cases (77%), tumor progression was confirmed histopathologically (39 cases) or via follow-up imaging (18 cases), while remaining 17 cases were diagnosed as treatment-related changes. The classification accuracy of the Random Forest classifier was 0.86, 95% CI 0.77–0.93 (sensitivity 0.91, 95% CI 0.81–0.97; specificity 0.71, 95% CI 0.44–0.9), significantly above the no-information rate of 0.77 (p = 0.03), and higher compared to an accuracy of 0.82 for MRI (95% CI 0.72–0.9), 0.81 for [18F]-FET-PET (95% CI 0.7–0.89), and 0.81 for expert consensus (95% CI 0.7–0.89), although these differences were not statistically significant (p > 0.1 for all comparisons, McNemar test). [18F]-FET-PET hot-spot volume was single-most important variable, with relevant contribution from all imaging modalities. Conclusion Automated, joint image analysis of [18F]-FET-PET and advanced MR imaging techniques APTw and DSC perfusion is a promising tool for objective response assessment in gliomas. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05427-8.
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Chen L, Zhou J, Li L, Zhao J, Li H, Zheng W, Xu J, Jing Z. SLC39A7 promotes malignant behaviors in glioma via the TNF-α-mediated NF-κB signaling pathway. J Cancer 2021; 12:4530-4541. [PMID: 34149917 PMCID: PMC8210565 DOI: 10.7150/jca.54158] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 05/03/2021] [Indexed: 11/05/2022] Open
Abstract
Purpose: Several studies have indicated that SLC39A7 plays an important role in tumor progression; however, little is known about the function and mechanism of SLC39A7 in glioma. In this study, we aimed to explore the role of SLC39A7 in glioma development. Patients and methods: Bioinformatic analysis was used to predict the role of SLC39A7 in glioma. Cell viability and Edu assays were used to detect the proliferation of glioma cells. A transwell assay was used to measure the invasion and migration of glioma cells. Western blotting, qPCR and ELISA were used to detect the expression of all molecules. Results: SLC39A7 was found to be highly expressed in high-grade glioma patients with a poor prognosis. Our results indicated that SLC39A7 significantly promoted the proliferation, invasion and migration of glioma cells. Furthermore, SLC39A7 promoted tumorigenesis in orthotopic models. We determined that SLC39A7 promotes the malignant behaviors of glioma by activating the TNF-α-mediated NF-κB signaling pathway. Conclusion: Our study revealed that SLC39A7 promotes the proliferation, invasion and migration of glioma cells via the TNF-α-mediated NF-κB signaling pathway, which provides potential targets for glioma therapy.
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Affiliation(s)
- Lian Chen
- Department of Neurosurgery, the First Hospital of China Medical University, NO. 155 North Nanjing Street, Shenyang, 110001 China
| | - Jinpeng Zhou
- Department of Neurosurgery, the First Hospital of China Medical University, NO. 155 North Nanjing Street, Shenyang, 110001 China
| | - Long Li
- Department of Neurosurgery, the First Hospital of China Medical University, NO. 155 North Nanjing Street, Shenyang, 110001 China
| | - Junshuang Zhao
- Department of Neurosurgery, the First Hospital of China Medical University, NO. 155 North Nanjing Street, Shenyang, 110001 China
| | - Hao Li
- Department of Neurosurgery, the First Hospital of China Medical University, NO. 155 North Nanjing Street, Shenyang, 110001 China
| | - Wei Zheng
- Department of Histology and Embryology, College of Basic Medical Science, China Medical University, NO. 77 Puhe Road, Shenyang, 110122 China
| | - Jinkun Xu
- Department of Neurosurgery, the First Hospital of China Medical University, NO. 155 North Nanjing Street, Shenyang, 110001 China
| | - Zhitao Jing
- Department of Neurosurgery, the First Hospital of China Medical University, NO. 155 North Nanjing Street, Shenyang, 110001 China
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van Kempen EJ, Post M, Mannil M, Kusters B, ter Laan M, Meijer FJA, Henssen DJHA. Accuracy of Machine Learning Algorithms for the Classification of Molecular Features of Gliomas on MRI: A Systematic Literature Review and Meta-Analysis. Cancers (Basel) 2021; 13:cancers13112606. [PMID: 34073309 PMCID: PMC8198025 DOI: 10.3390/cancers13112606] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 05/17/2021] [Accepted: 05/18/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Glioma prognosis and treatment are based on histopathological characteristics and molecular profile. Following the World Health Organization (WHO) guidelines (2016), the most important molecular diagnostic markers include IDH1/2-genotype and 1p/19q codeletion status, although more recent publications also include ARTX genotype and TERT- and MGMT promoter methylation. Machine learning algorithms (MLAs), however, were described to successfully determine these molecular characteristics non-invasively by using magnetic resonance imaging (MRI) data. The aim of this review and meta-analysis was to define the diagnostic accuracy of MLAs with regard to these different molecular markers. We found high accuracies of MLAs to predict each individual molecular marker, with IDH1/2-genotype being the most investigated and the most accurate. Radiogenomics could therefore be a promising tool for discriminating genetically determined gliomas in a non-invasive fashion. Although encouraging results are presented here, large-scale, prospective trials with external validation groups are warranted. Abstract Treatment planning and prognosis in glioma treatment are based on the classification into low- and high-grade oligodendroglioma or astrocytoma, which is mainly based on molecular characteristics (IDH1/2- and 1p/19q codeletion status). It would be of great value if this classification could be made reliably before surgery, without biopsy. Machine learning algorithms (MLAs) could play a role in achieving this by enabling glioma characterization on magnetic resonance imaging (MRI) data without invasive tissue sampling. The aim of this study is to provide a performance evaluation and meta-analysis of various MLAs for glioma characterization. Systematic literature search and meta-analysis were performed on the aggregated data, after which subgroup analyses for several target conditions were conducted. This study is registered with PROSPERO, CRD42020191033. We identified 724 studies; 60 and 17 studies were eligible to be included in the systematic review and meta-analysis, respectively. Meta-analysis showed excellent accuracy for all subgroups, with the classification of 1p/19q codeletion status scoring significantly poorer than other subgroups (AUC: 0.748, p = 0.132). There was considerable heterogeneity among some of the included studies. Although promising results were found with regard to the ability of MLA-tools to be used for the non-invasive classification of gliomas, large-scale, prospective trials with external validation are warranted in the future.
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Affiliation(s)
- Evi J. van Kempen
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands; (E.J.v.K.); (M.P.); (F.J.A.M.)
| | - Max Post
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands; (E.J.v.K.); (M.P.); (F.J.A.M.)
| | - Manoj Mannil
- Clinic of Radiology, University Hospital Münster, WWU University of Münster, 48149 Münster, Germany;
| | - Benno Kusters
- Department of Pathology, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands;
| | - Mark ter Laan
- Department of Neurosurgery, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands;
| | - Frederick J. A. Meijer
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands; (E.J.v.K.); (M.P.); (F.J.A.M.)
| | - Dylan J. H. A. Henssen
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands; (E.J.v.K.); (M.P.); (F.J.A.M.)
- Correspondence:
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Kobayashi K, Miyake M, Takahashi M, Hamamoto R. Observing deep radiomics for the classification of glioma grades. Sci Rep 2021; 11:10942. [PMID: 34035410 PMCID: PMC8149679 DOI: 10.1038/s41598-021-90555-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 05/13/2021] [Indexed: 12/22/2022] Open
Abstract
Deep learning is a promising method for medical image analysis because it can automatically acquire meaningful representations from raw data. However, a technical challenge lies in the difficulty of determining which types of internal representation are associated with a specific task, because feature vectors can vary dynamically according to individual inputs. Here, based on the magnetic resonance imaging (MRI) of gliomas, we propose a novel method to extract a shareable set of feature vectors that encode various parts in tumor imaging phenotypes. By applying vector quantization to latent representations, features extracted by an encoder are replaced with a fixed set of feature vectors. Hence, the set of feature vectors can be used in downstream tasks as imaging markers, which we call deep radiomics. Using deep radiomics, a classifier is established using logistic regression to predict the glioma grade with 90% accuracy. We also devise an algorithm to visualize the image region encoded by each feature vector, and demonstrate that the classification model preferentially relies on feature vectors associated with the presence or absence of contrast enhancement in tumor regions. Our proposal provides a data-driven approach to enhance the understanding of the imaging appearance of gliomas.
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Affiliation(s)
- Kazuma Kobayashi
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, Japan.
| | - Mototaka Miyake
- Department of Diagnostic Radiology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Masamichi Takahashi
- Department of Neurosurgery and Neuro-Oncology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Ryuji Hamamoto
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, Japan
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Alves CAPF, Löbel U, Martin-Saavedra JS, Toescu S, Tsunemi MH, Teixeira SR, Mankad K, Hargrave D, Jacques TS, da Costa Leite C, Gonçalves FG, Vossough A, D'Arco F. A Diagnostic Algorithm for Posterior Fossa Tumors in Children: A Validation Study. AJNR Am J Neuroradiol 2021; 42:961-968. [PMID: 33664107 DOI: 10.3174/ajnr.a7057] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 11/23/2020] [Indexed: 12/14/2022]
Abstract
BACKGROUND AND PURPOSE Primary posterior fossa tumors comprise a large group of neoplasias with variable aggressiveness and short and long-term outcomes. This study aimed to validate the clinical usefulness of a radiologic decision flow chart based on previously published neuroradiologic knowledge for the diagnosis of posterior fossa tumors in children. MATERIALS AND METHODS A retrospective study was conducted (from January 2013 to October 2019) at 2 pediatric referral centers, Children's Hospital of Philadelphia, United States, and Great Ormond Street Hospital, United Kingdom. Inclusion criteria were younger than 18 years of age and histologically and molecularly confirmed posterior fossa tumors. Subjects with no available preoperative MR imaging and tumors located primarily in the brain stem were excluded. Imaging characteristics of the tumors were evaluated following a predesigned, step-by-step flow chart. Agreement between readers was tested with the Cohen κ, and each diagnosis was analyzed for accuracy. RESULTS A total of 148 cases were included, with a median age of 3.4 years (interquartile range, 2.1-6.1 years), and a male/female ratio of 1.24. The predesigned flow chart facilitated identification of pilocytic astrocytoma, ependymoma, and medulloblastoma sonic hedgehog tumors with high sensitivity and specificity. On the basis of the results, the flow chart was adjusted so that it would also be able to better discriminate atypical teratoid/rhabdoid tumors and medulloblastoma groups 3 or 4 (sensitivity = 75%-79%; specificity = 92%-99%). Moreover, our adjusted flow chart was useful in ruling out ependymoma, pilocytic astrocytomas, and medulloblastoma sonic hedgehog tumors. CONCLUSIONS The modified flow chart offers a structured tool to aid in the adjunct diagnosis of pediatric posterior fossa tumors. Our results also establish a useful starting point for prospective clinical studies and for the development of automated algorithms, which may provide precise and adequate diagnostic tools for these tumors in clinical practice.
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Affiliation(s)
- C A P F Alves
- From the Division of Neuroradiology (C.A.P.F.A., J.S.M.-S. S.R.T., F.G.G., A.V.), Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Radiology (C.d.C.L.), Hospital das Clinicas, Faculdade de Medicina de Sao Paulo, Sao Paulo
| | - U Löbel
- Radiology Department (U.L., K.M., F.D.), UCL Great Ormond Street Hospital for Children, London, UK
| | - J S Martin-Saavedra
- From the Division of Neuroradiology (C.A.P.F.A., J.S.M.-S. S.R.T., F.G.G., A.V.), Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - S Toescu
- Department of Neurosurgery (S.T.), UCL Great Ormond Street Hospital for Children, London, UK
| | - M H Tsunemi
- Department of Biostatistics (M.H.T.), Instituto de Biociências, São Paulo State University, São Paul, Brazil
| | - S R Teixeira
- From the Division of Neuroradiology (C.A.P.F.A., J.S.M.-S. S.R.T., F.G.G., A.V.), Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - K Mankad
- Radiology Department (U.L., K.M., F.D.), UCL Great Ormond Street Hospital for Children, London, UK
| | - D Hargrave
- Pediatric Oncology Unit (D.H.), UCL Great Ormond Street Institute of Child Health, London, UK
| | - T S Jacques
- Developmental Biology and Cancer Programme (T.S.J.), UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - C da Costa Leite
- Department of Radiology (C.d.C.L.), Hospital das Clinicas, Faculdade de Medicina de Sao Paulo, Sao Paulo
| | - F G Gonçalves
- From the Division of Neuroradiology (C.A.P.F.A., J.S.M.-S. S.R.T., F.G.G., A.V.), Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - A Vossough
- From the Division of Neuroradiology (C.A.P.F.A., J.S.M.-S. S.R.T., F.G.G., A.V.), Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - F D'Arco
- Radiology Department (U.L., K.M., F.D.), UCL Great Ormond Street Hospital for Children, London, UK
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44
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Booth TC, Thompson G, Bulbeck H, Boele F, Buckley C, Cardoso J, Dos Santos Canas L, Jenkinson D, Ashkan K, Kreindler J, Huskens N, Luis A, McBain C, Mills SJ, Modat M, Morley N, Murphy C, Ourselin S, Pennington M, Powell J, Summers D, Waldman AD, Watts C, Williams M, Grant R, Jenkinson MD. A Position Statement on the Utility of Interval Imaging in Standard of Care Brain Tumour Management: Defining the Evidence Gap and Opportunities for Future Research. Front Oncol 2021; 11:620070. [PMID: 33634034 PMCID: PMC7900557 DOI: 10.3389/fonc.2021.620070] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 01/06/2021] [Indexed: 12/19/2022] Open
Abstract
OBJECTIV E To summarise current evidence for the utility of interval imaging in monitoring disease in adult brain tumours, and to develop a position for future evidence gathering while incorporating the application of data science and health economics. METHODS Experts in 'interval imaging' (imaging at pre-planned time-points to assess tumour status); data science; health economics, trial management of adult brain tumours, and patient representatives convened in London, UK. The current evidence on the use of interval imaging for monitoring brain tumours was reviewed. To improve the evidence that interval imaging has a role in disease management, we discussed specific themes of data science, health economics, statistical considerations, patient and carer perspectives, and multi-centre study design. Suggestions for future studies aimed at filling knowledge gaps were discussed. RESULTS Meningioma and glioma were identified as priorities for interval imaging utility analysis. The "monitoring biomarkers" most commonly used in adult brain tumour patients were standard structural MRI features. Interval imaging was commonly scheduled to provide reported imaging prior to planned, regular clinic visits. There is limited evidence relating interval imaging in the absence of clinical deterioration to management change that alters morbidity, mortality, quality of life, or resource use. Progression-free survival is confounded as an outcome measure when using structural MRI in glioma. Uncertainty from imaging causes distress for some patients and their caregivers, while for others it provides an important indicator of disease activity. Any study design that changes imaging regimens should consider the potential for influencing current or planned therapeutic trials, ensure that opportunity costs are measured, and capture indirect benefits and added value. CONCLUSION Evidence for the value, and therefore utility, of regular interval imaging is currently lacking. Ongoing collaborative efforts will improve trial design and generate the evidence to optimise monitoring imaging biomarkers in standard of care brain tumour management.
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Affiliation(s)
- Thomas C. Booth
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
- Department of Neuroradiology, King’s College Hospital NHS Foundation Trust, London, United Kingdom
| | - Gerard Thompson
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | | | - Florien Boele
- Leeds Institute of Medical Research at St James’s, St James’s University Hospital, Leeds, United Kingdom
- Faculty of Medicine and Health, Leeds Institute of Health Sciences, University of Leeds, Leeds, United Kingdom
| | | | - Jorge Cardoso
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Liane Dos Santos Canas
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | | | - Keyoumars Ashkan
- Department of Neurosurgery, King’s College Hospital NHS Foundation Trust, London, United Kingdom
| | | | - Nicky Huskens
- The Tessa Jowell Brain Cancer Mission, London, United Kingdom
| | - Aysha Luis
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Catherine McBain
- Department of Oncology, Christie Hospital NHS Foundation Trust, Manchester, United Kingdom
| | - Samantha J. Mills
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Nick Morley
- Department of Radiology, Wales Research and Diagnostic PET Imaging Centre, Cardiff University School of Medicine, Cardiff, United Kingdom
| | - Caroline Murphy
- King’s College Trials Unit, King’s College London, London, United Kingdom
| | - Sebastian Ourselin
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Mark Pennington
- King’s Health Economics, King’s College London, London, United Kingdom
| | - James Powell
- Department of Oncology, Velindre Cancer Centre, Cardiff, United Kingdom
| | - David Summers
- Department of Neuroradiology, Western General Hospital, Edinburgh, United Kingdom
| | - Adam D. Waldman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Colin Watts
- Birmingham Brain Cancer Program, University of Birmingham, Birmingham, United Kingdom
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
| | - Matthew Williams
- Department of Neuro-oncology, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Robin Grant
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Michael D. Jenkinson
- Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
- Department of Neurosurgery, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
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45
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Ebrahimi Zade A, Shahabi Haghighi S, Soltani M. A neuro evolutionary algorithm for patient calibrated prediction of survival in Glioblastoma patients. J Biomed Inform 2021; 115:103694. [PMID: 33545332 DOI: 10.1016/j.jbi.2021.103694] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 01/28/2021] [Accepted: 01/29/2021] [Indexed: 01/30/2023]
Abstract
BACKGROUND AND OBJECTIVES Glioblastoma multiforme (GBM) is the most common and malignant type of primary brain tumors. Radiation therapy (RT) plus concomitant and adjuvant Temozolomide (TMZ) constitute standard treatment of GBM. Existing models for GBM growth do not consider the effect of different schedules on tumor growth and patient survival. However, clinical trials show that treatment schedule and drug dosage significantly affect patient survival. The goal is to provide a patient calibrated model for predicting survival according to the treatment schedule. METHODS We propose a top-down method based on artificial neural networks (ANN) and genetic algorithm (GA) to predict survival of GBM patients. A feed forward undercomplete Autoencoder network is integrated with the neuro-evolutionary (NE) algorithm in order to extract a compressed representation of input clinical data. The proposed NE algorithm uses GA to obtain optimal architecture of a multi-layer perceptron (MLP). Taguchi L16 orthogonal design of experiments is used to tune parameters of the proposed NE algorithm. Finally, the optimal MLP is used to predict survival of GBM patients. RESULTS Data from 8 related clinical trials have been collected and integrated to train the model. From 847 evaluable cases, 719 were used for train and validation and the remaining 128 cases were used to test the model. Mean absolute error of the predictions on the test data is 0.087 months which shows excellent performance of the proposed model in predicting survival of the patients. Also, the results show that the proposed NE algorithm is superior to other existing models in both the mean and variability of the prediction error.
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Affiliation(s)
- Amir Ebrahimi Zade
- Faculty of Industrial Engineering and Systems Management, Amirkabir University of Technology, Tehran, Iran
| | | | - M Soltani
- Faculty of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran; Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran; Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, Canada; Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada
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46
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Reuter G, Moïse M, Roll W, Martin D, Lombard A, Scholtes F, Stummer W, Suero Molina E. Conventional and advanced imaging throughout the cycle of care of gliomas. Neurosurg Rev 2021; 44:2493-2509. [PMID: 33411093 DOI: 10.1007/s10143-020-01448-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 11/18/2020] [Accepted: 11/23/2020] [Indexed: 10/22/2022]
Abstract
Although imaging of gliomas has evolved tremendously over the last decades, published techniques and protocols are not always implemented into clinical practice. Furthermore, most of the published literature focuses on specific timepoints in glioma management. This article reviews the current literature on conventional and advanced imaging techniques and chronologically outlines their practical relevance for the clinical management of gliomas throughout the cycle of care. Relevant articles were located through the Pubmed/Medline database and included in this review. Interpretation of conventional and advanced imaging techniques is crucial along the entire process of glioma care, from diagnosis to follow-up. In addition to the described currently existing techniques, we expect deep learning or machine learning approaches to assist each step of glioma management through tumor segmentation, radiogenomics, prognostication, and characterization of pseudoprogression. Thorough knowledge of the specific performance, possibilities, and limitations of each imaging modality is key for their adequate use in glioma management.
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Affiliation(s)
- Gilles Reuter
- Department of Neurosurgery, University Hospital of Liège, Liège, Belgium. .,GIGA-CRC In-vivo Imaging Center, ULiege, Liège, Belgium.
| | - Martin Moïse
- Department of Radiology, University Hospital of Liège, Liège, Belgium
| | - Wolfgang Roll
- Department of Nuclear Medicine, University Hospital of Münster, Münster, Germany
| | - Didier Martin
- Department of Neurosurgery, University Hospital of Liège, Liège, Belgium
| | - Arnaud Lombard
- Department of Neurosurgery, University Hospital of Liège, Liège, Belgium
| | - Félix Scholtes
- Department of Neurosurgery, University Hospital of Liège, Liège, Belgium.,Department of Neuroanatomy, University of Liège, Liège, Belgium
| | - Walter Stummer
- Department of Neurosurgery, University Hospital of Münster, Münster, Germany
| | - Eric Suero Molina
- Department of Neurosurgery, University Hospital of Münster, Münster, Germany
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47
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Riva M, Lopci E, Gay LG, Nibali MC, Rossi M, Sciortino T, Castellano A, Bello L. Advancing Imaging to Enhance Surgery: From Image to Information Guidance. Neurosurg Clin N Am 2021; 32:31-46. [PMID: 33223024 DOI: 10.1016/j.nec.2020.08.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Conventional magnetic resonance imaging (cMRI) has an established role as a crucial disease parameter in the multidisciplinary management of glioblastoma, guiding diagnosis, treatment planning, assessment, and follow-up. Yet, cMRI cannot provide adequate information regarding tissue heterogeneity and the infiltrative extent beyond the contrast enhancement. Advanced magnetic resonance imaging and PET and newer analytical methods are transforming images into data (radiomics) and providing noninvasive biomarkers of molecular features (radiogenomics), conveying enhanced information for improving decision making in surgery. This review analyzes the shift from image guidance to information guidance that is relevant for the surgical treatment of glioblastoma.
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Affiliation(s)
- Marco Riva
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Via Festa del Perdono 7, Milan 20122, Italy; IRCCS Istituto Ortopedico Galeazzi, U.O. Neurochirurgia Oncologica, Milan, Italy.
| | - Egesta Lopci
- Unit of Nuclear Medicine, Humanitas Clinical and Research Center - IRCCS, Via Manzoni 56, Rozzano, Milan 20089, Italy. https://twitter.com/LopciEgesta
| | - Lorenzo G Gay
- IRCCS Istituto Ortopedico Galeazzi, U.O. Neurochirurgia Oncologica, Milan, Italy; Department of Oncology and Hemato-Oncology, Via Festa del Perdono 7, Milan 20122, Italy
| | - Marco Conti Nibali
- IRCCS Istituto Ortopedico Galeazzi, U.O. Neurochirurgia Oncologica, Milan, Italy; Department of Oncology and Hemato-Oncology, Via Festa del Perdono 7, Milan 20122, Italy. https://twitter.com/dr_mcn
| | - Marco Rossi
- IRCCS Istituto Ortopedico Galeazzi, U.O. Neurochirurgia Oncologica, Milan, Italy; Department of Oncology and Hemato-Oncology, Via Festa del Perdono 7, Milan 20122, Italy
| | - Tommaso Sciortino
- IRCCS Istituto Ortopedico Galeazzi, U.O. Neurochirurgia Oncologica, Milan, Italy; Department of Oncology and Hemato-Oncology, Via Festa del Perdono 7, Milan 20122, Italy
| | - Antonella Castellano
- Neuroradiology Unit and CERMAC, Vita-Salute San Raffaele University, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan 20123, Italy. https://twitter.com/antocastella
| | - Lorenzo Bello
- IRCCS Istituto Ortopedico Galeazzi, U.O. Neurochirurgia Oncologica, Milan, Italy; Department of Oncology and Hemato-Oncology, Via Festa del Perdono 7, Milan 20122, Italy
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48
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Towards effective machine learning in medical imaging analysis: A novel approach and expert evaluation of high-grade glioma 'ground truth' simulation on MRI. Int J Med Inform 2020; 146:104348. [PMID: 33285357 DOI: 10.1016/j.ijmedinf.2020.104348] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 11/16/2020] [Accepted: 11/18/2020] [Indexed: 12/21/2022]
Abstract
PURPOSE/OBJECTIVE(S) Gliomas are uniformly fatal brain tumours with significant neurological and quality of life detriment to patients. Improvement in outcomes has remained largely unchanged in nearly 20 years. MRI (magnetic resonance imaging) is often used in diagnosis and management. Machine learning analyses of large-scale MRI data are pivotal in advancing the diagnosis, management and improve outcomes in neuro-oncology. A common challenge to robust machine learning approaches is the lack of large 'ground truth' datasets in supervised learning for building classification and prediction models. The creation of these datasets relies on human-expert input and is time-consuming and subjective error-prone, limiting effective machine learning applications. Simulation of mechanistic aspects such as geometry, location and physical properties of brain tumours can generate large-scale ground-truth datasets allowing for comparison of analysis techniques in clinical applications. We aimed to develop a transparent and convenient method for building 'ground truth' presentations of simulated glioma lesions on anatomical MRI. MATERIALS/METHODS The simulation workflow was created using the Feature Manipulation Engine (FME®), a data integration platform specializing in the spatial data processing. By compiling and integrating FME's functions to read, integrate, transform, validate, save, and display MRI data, and experimenting with ways to manipulate the parameters concerning location, size, shape, and signal intensity with the presentations of glioma, we were able to generate simulated appearances of high-grade gliomas on gadolinium-based high-resolution 3D T1-weighted MRI (1 mm3). Data of patients with canonical high-grade tumours were used as real-world tumours for validating the accuracy of the simulation. Twenty raters who are experienced with brain tumour interpretation on MRI independently completed a survey, designed to distinguish simulated and real-world brain tumours. Sensitivity and specificity were calculated for assessing the performance of the approach with the binary classification of simulated vs real-world tumours. Correlation and regression were used in run time analysis, assessing the software toolset's efficiency in producing different numbers of simulated lesions. Differences in the group means were examined using the non-parametric Kruskal-Wallis test. RESULTS The simulation method was developed as an interpretable and useful workflow for the easy creation of tumour simulations and incorporation into 3D MRI. A linear increase in the running time and memory usage was observed with an increasing number of generated lesions. The respondents' accuracy rate ranged between 33.3 and 83.3 %. The sensitivity and specificity were low for a human expert to differentiate simulated lesions from real gliomas (0.43 and 0.58) or vice versa (0.65 and 0.62). The mean scores ranking the real-world gliomas did not differ between the simulated and real tumours. CONCLUSION The reliable and user-friendly software method can allow for robust simulation of high-grade glioma on MRI. Ongoing research efforts include optimizing the workflow for generating glioma datasets as well as adapting it to simulating additional MRI brain changes.
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Brar K, Hachem LD, Badhiwala JH, Mau C, Zacharia BE, de Moraes FY, Pirouzmand F, Mansouri A. Management of Diffuse Low-Grade Glioma: The Renaissance of Robust Evidence. Front Oncol 2020; 10:575658. [PMID: 33117714 PMCID: PMC7560299 DOI: 10.3389/fonc.2020.575658] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 08/31/2020] [Indexed: 12/20/2022] Open
Abstract
The surgical management of diffuse low-grade gliomas (DLGGs) has undergone a paradigm shift toward striving for maximal safe resection when feasible. While extensive observational data supports this transition, unbiased evidence in the form of high quality randomized-controlled trials (RCTs) is lacking. Furthermore, despite a high volume of molecular, genetic, and imaging data, the field of neuro-oncology lacks personalized care algorithms for individuals with DLGGs based on a robust foundation of evidence. In this manuscript, we (1) discuss the logistical and philosophical challenges hindering the development of surgical RCTs for DLGGs, (2) highlight the potential impact of well-designed international prospective observational registries, (3) discuss ways in which cutting-edge computational techniques can be harnessed to generate maximal insight from high volumes of multi-faceted data, and (4) outline a comprehensive plan of action that will enable a multi-disciplinary approach to future DLGG management, integrating advances in clinical medicine, basic molecular research and large-scale data mining.
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Affiliation(s)
- Karanbir Brar
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Laureen D Hachem
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Jetan H Badhiwala
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Christine Mau
- Department of Neurosurgery, Penn State Health, Hershey, PA, United States
| | - Brad E Zacharia
- Department of Neurosurgery, Penn State Health, Hershey, PA, United States.,Penn State Cancer Institute, Hershey, PA, United States
| | - Fabio Ynoe de Moraes
- Division of Radiation Oncology, Department of Oncology, Kingston General Hospital, Queen's University, Kingston, ON, Canada
| | - Farhad Pirouzmand
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Alireza Mansouri
- Department of Neurosurgery, Penn State Health, Hershey, PA, United States.,Penn State Cancer Institute, Hershey, PA, United States
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50
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MRI Radiomic Features to Predict IDH1 Mutation Status in Gliomas: A Machine Learning Approach using Gradient Tree Boosting. Int J Mol Sci 2020; 21:ijms21218004. [PMID: 33121211 PMCID: PMC7662499 DOI: 10.3390/ijms21218004] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/23/2020] [Accepted: 10/25/2020] [Indexed: 12/25/2022] Open
Abstract
Patients with gliomas, isocitrate dehydrogenase 1 (IDH1) mutation status have been studied as a prognostic indicator. Recent advances in machine learning (ML) have demonstrated promise in utilizing radiomic features to study disease processes in the brain. We investigate whether ML analysis of multiparametric radiomic features from preoperative Magnetic Resonance Imaging (MRI) can predict IDH1 mutation status in patients with glioma. This retrospective study included patients with glioma with known IDH1 status and preoperative MRI. Radiomic features were extracted from Fluid-Attenuated Inversion Recovery (FLAIR) and Diffused Weighted Imaging (DWI). The dataset was split into training, validation, and testing sets by stratified sampling. Synthetic Minority Oversampling Technique (SMOTE) was applied to the training sets. eXtreme Gradient Boosting (XGBoost) classifiers were trained, and the hyperparameters were tuned. Receiver operating characteristic curve (ROC), accuracy, and f1-scores were collected. A total of 100 patients (age: 55 ± 15, M/F 60/40); with IDH1 mutant (n = 22) and IDH1 wildtype (n = 78) were included. The best performance was seen with a DWI-trained XGBoost model, which achieved ROC with Area Under the Curve (AUC) of 0.97, accuracy of 0.90, and f1-score of 0.75 on the test set. The FLAIR-trained XGBoost model achieved ROC with AUC of 0.95, accuracy of 0.90, f1-score of 0.75 on the test set. A model that was trained on combined FLAIR-DWI radiomic features did not provide incremental accuracy. The results show that a XGBoost classifier using multiparametric radiomic features derived from preoperative MRI can predict IDH1 mutation status with > 90% accuracy.
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