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Bhatia A, Khalvati F, Ertl-Wagner BB. Artificial Intelligence in the Future Landscape of Pediatric Neuroradiology: Opportunities and Challenges. AJNR Am J Neuroradiol 2024; 45:549-553. [PMID: 38176730 PMCID: PMC11288527 DOI: 10.3174/ajnr.a8086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 10/17/2023] [Indexed: 01/06/2024]
Abstract
This paper will review how artificial intelligence (AI) will play an increasingly important role in pediatric neuroradiology in the future. A safe, transparent, and human-centric AI is needed to tackle the quadruple aim of improved health outcomes, enhanced patient and family experience, reduced costs, and improved well-being of the healthcare team in pediatric neuroradiology. Equity, diversity and inclusion, data safety, and access to care will need to always be considered. In the next decade, AI algorithms are expected to play an increasingly important role in access to care, workflow management, abnormality detection, classification, response prediction, prognostication, report generation, as well as in the patient and family experience in pediatric neuroradiology. Also, AI algorithms will likely play a role in recognizing and flagging rare diseases and in pattern recognition to identify previously unknown disorders. While AI algorithms will play an important role, humans will not only need to be in the loop, but in the center of pediatric neuroimaging. AI development and deployment will need to be closely watched and monitored by experts in the field. Patient and data safety need to be at the forefront, and the risks of a dependency on technology will need to be contained. The applications and implications of AI in pediatric neuroradiology will differ from adult neuroradiology.
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Affiliation(s)
- Aashim Bhatia
- From the Children's Hospital of Philadelphia (A.B.), Philadelphia, Pennsylvania
| | - Farzad Khalvati
- Hospital for Sick Children (F.K., B.B.E.-W.), Toronto, Ontario, Canada
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Cui J, Miao X, Yanghao X, Qin X. Bibliometric research on the developments of artificial intelligence in radiomics toward nervous system diseases. Front Neurol 2023; 14:1171167. [PMID: 37360350 PMCID: PMC10288367 DOI: 10.3389/fneur.2023.1171167] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 05/16/2023] [Indexed: 06/28/2023] Open
Abstract
Background The growing interest suggests that the widespread application of radiomics has facilitated the development of neurological disease diagnosis, prognosis, and classification. The application of artificial intelligence methods in radiomics has increasingly achieved outstanding prediction results in recent years. However, there are few studies that have systematically analyzed this field through bibliometrics. Our destination is to study the visual relationships of publications to identify the trends and hotspots in radiomics research and encourage more researchers to participate in radiomics studies. Methods Publications in radiomics in the field of neurological disease research can be retrieved from the Web of Science Core Collection. Analysis of relevant countries, institutions, journals, authors, keywords, and references is conducted using Microsoft Excel 2019, VOSviewer, and CiteSpace V. We analyze the research status and hot trends through burst detection. Results On October 23, 2022, 746 records of studies on the application of radiomics in the diagnosis of neurological disorders were retrieved and published from 2011 to 2023. Approximately half of them were written by scholars in the United States, and most were published in Frontiers in Oncology, European Radiology, Cancer, and SCIENTIFIC REPORTS. Although China ranks first in the number of publications, the United States is the driving force in the field and enjoys a good academic reputation. NORBERT GALLDIKS and JIE TIAN published the most relevant articles, while GILLIES RJ was cited the most. RADIOLOGY is a representative and influential journal in the field. "Glioma" is a current attractive research hotspot. Keywords such as "machine learning," "brain metastasis," and "gene mutations" have recently appeared at the research frontier. Conclusion Most of the studies focus on clinical trial outcomes, such as the diagnosis, prediction, and prognosis of neurological disorders. The radiomics biomarkers and multi-omics studies of neurological disorders may soon become a hot topic and should be closely monitored, particularly the relationship between tumor-related non-invasive imaging biomarkers and the intrinsic micro-environment of tumors.
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Ak M, Toll SA, Hein KZ, Colen RR, Khatua S. Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology. AJNR Am J Neuroradiol 2021; 43:792-801. [PMID: 34649914 DOI: 10.3174/ajnr.a7297] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 07/19/2021] [Indexed: 12/24/2022]
Abstract
Exponential technologic advancements in imaging, high-performance computing, and artificial intelligence, in addition to increasing access to vast amounts of diverse data, have revolutionized the role of imaging in medicine. Radiomics is defined as a high-throughput feature-extraction method that unlocks microscale quantitative data hidden within standard-of-care medical imaging. Radiogenomics is defined as the linkage between imaging and genomics information. Multiple radiomics and radiogenomics studies performed on conventional and advanced neuro-oncology image modalities show that they have the potential to differentiate pseudoprogression from true progression, classify tumor subgroups, and predict recurrence, survival, and mutation status with high accuracy. In this article, we outline the technical steps involved in radiomics and radiogenomics analyses with the use of artificial intelligence methods and review current applications in adult and pediatric neuro-oncology.
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Affiliation(s)
- M Ak
- From the Department of Radiology (M.A., R.R.C.), University of Pittsburgh, Pittsburgh, Pennsylvania.,Hillman Cancer Center (M.A., R.R.C.), University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - S A Toll
- Department of Hematology-Oncology (S.A.T.), Children's Hospital of Michigan, Detroit, Michigan
| | - K Z Hein
- Department of Leukemia (K.Z.H.), The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - R R Colen
- From the Department of Radiology (M.A., R.R.C.), University of Pittsburgh, Pittsburgh, Pennsylvania.,Hillman Cancer Center (M.A., R.R.C.), University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - S Khatua
- Department of Pediatric Hematology-Oncology (S.K.), Mayo Clinic, Rochester, Minnesota.
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Hou M, Sun JH. Emerging applications of radiomics in rectal cancer: State of the art and future perspectives. World J Gastroenterol 2021; 27:3802-3814. [PMID: 34321845 PMCID: PMC8291019 DOI: 10.3748/wjg.v27.i25.3802] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 05/06/2021] [Accepted: 05/21/2021] [Indexed: 02/06/2023] Open
Abstract
Rectal cancer (RC) is the third most commonly diagnosed cancer and has a high risk of mortality, although overall survival rates have improved. Preoperative assessments and predictions, including risk stratification, responses to therapy, long-term clinical outcomes, and gene mutation status, are crucial to guide the optimization of personalized treatment strategies. Radiomics is a novel approach that enables the evaluation of the heterogeneity and biological behavior of tumors by quantitative extraction of features from medical imaging. As these extracted features cannot be captured by visual inspection, the field holds significant promise. Recent studies have proved the rapid development of radiomics and validated its diagnostic and predictive efficacy. Nonetheless, existing radiomics research on RC is highly heterogeneous due to challenges in workflow standardization and limitations of objective cohort conditions. Here, we present a summary of existing research based on computed tomography and magnetic resonance imaging. We highlight the most salient issues in the field of radiomics and analyze the most urgent problems that require resolution. Our review provides a cutting-edge view of the use of radiomics to detect and evaluate RC, and will benefit researchers dedicated to using this state-of-the-art technology in the era of precision medicine.
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Affiliation(s)
- Min Hou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, Zhejiang Province, China
| | - Ji-Hong Sun
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, Zhejiang Province, China
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Shaikh F, Dupont-Roettger D, Dehmeshki J, Awan O, Kubassova O, Bisdas S. The Role of Imaging Biomarkers Derived From Advanced Imaging and Radiomics in the Management of Brain Tumors. Front Oncol 2020; 10:559946. [PMID: 33072586 PMCID: PMC7539039 DOI: 10.3389/fonc.2020.559946] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 08/13/2020] [Indexed: 01/22/2023] Open
Affiliation(s)
- Faiq Shaikh
- Image Analysis Group, Philadelphia, PA, United States
| | | | - Jamshid Dehmeshki
- Image Analysis Group, Philadelphia, PA, United States.,Department of Computer Science, Kingston University, Kingston-upon-Thames, United Kingdom
| | - Omer Awan
- Department of Radiology, University of Maryland Medical Center, Baltimore, MD, United States
| | | | - Sotirios Bisdas
- Department of Neuroradiology, University College London, London, United Kingdom
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Korn RL, Rahmanuddin S, Borazanci E. Use of Precision Imaging in the Evaluation of Pancreas Cancer. Cancer Treat Res 2019; 178:209-236. [PMID: 31209847 DOI: 10.1007/978-3-030-16391-4_8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Pancreas cancer is an aggressive and fatal disease that will become one of the leading causes of cancer mortality by 2030. An all-out effort is underway to better understand the basic biologic mechanisms of this disease ranging from early development to metastatic disease. In order to change the course of this disease, diagnostic radiology imaging may play a vital role in providing a precise, noninvasive method for early diagnosis and assessment of treatment response. Recent progress in combining medical imaging, advanced image analysis and artificial intelligence, termed radiomics, can offer an innovate approach in detecting the earliest changes of tumor development as well as a rapid method for the detection of response. In this chapter, we introduce the principles of radiomics and demonstrate how it can provide additional information into tumor biology, early detection, and response assessments advancing the goals of precision imaging to deliver the right treatment to the right person at the right time.
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Affiliation(s)
- Ronald L Korn
- Virginia G Piper Cancer Center at HonorHealth, Scottsdale, AZ, USA. .,Translational Genomics Research Institute, An Affiliate of City of Hope, Phoenix, AZ, USA. .,Imaging Endpoints Core Lab, Scottsdale, AZ, USA.
| | | | - Erkut Borazanci
- Virginia G Piper Cancer Center at HonorHealth, Scottsdale, AZ, USA.,Translational Genomics Research Institute, An Affiliate of City of Hope, Phoenix, AZ, USA
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Katsila T, Matsoukas MT, Patrinos GP, Kardamakis D. Pharmacometabolomics Informs Quantitative Radiomics for Glioblastoma Diagnostic Innovation. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2018; 21:429-439. [PMID: 28816643 DOI: 10.1089/omi.2017.0087] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Applications of omics systems biology technologies have enormous promise for radiology and diagnostics in surgical fields. In this context, the emerging fields of radiomics (a systems scale approach to radiology using a host of technologies, including omics) and pharmacometabolomics (use of metabolomics for patient and disease stratification and guiding precision medicine) offer much synergy for diagnostic innovation in surgery, particularly in neurosurgery. This synthesis of omics fields and applications is timely because diagnostic accuracy in central nervous system tumors still challenges decision-making. Considering the vast heterogeneity in brain tumors, disease phenotypes, and interindividual variability in surgical and chemotherapy outcomes, we believe that diagnostic accuracy can be markedly improved by quantitative radiomics coupled to pharmacometabolomics and related health information technologies while optimizing economic costs of traditional diagnostics. In this expert review, we present an innovation analysis on a systems-level multi-omics approach toward diagnostic accuracy in central nervous system tumors. For this, we suggest that glioblastomas serve as a useful application paradigm. We performed a literature search on PubMed for articles published in English between 2006 and 2016. We used the search terms "radiomics," "glioblastoma," "biomarkers," "pharmacogenomics," "pharmacometabolomics," "pharmacometabonomics/pharmacometabolomics," "collaborative informatics," and "precision medicine." A list of the top 4 insights we derived from this literature analysis is presented in this study. For example, we found that (i) tumor grading needs to be better refined, (ii) diagnostic precision should be improved, (iii) standardization in radiomics is lacking, and (iv) quantitative radiomics needs to prove clinical implementation. We conclude with an interdisciplinary call to the metabolomics, pharmacy/pharmacology, radiology, and surgery communities that pharmacometabolomics coupled to information technologies (chemoinformatics tools, databases, collaborative systems) can inform quantitative radiomics, thus translating Big Data and information growth to knowledge growth, rational drug development and diagnostics innovation for glioblastomas, and possibly in other brain tumors.
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Affiliation(s)
- Theodora Katsila
- 1 Department of Pharmacy, School of Health Sciences, University of Patras , Patras, Greece
| | | | - George P Patrinos
- 1 Department of Pharmacy, School of Health Sciences, University of Patras , Patras, Greece .,2 Department of Pathology, College of Medicine and Health Sciences, United Arab Emirates University , Al Ain, United Arab Emirates
| | - Dimitrios Kardamakis
- 3 Department of Radiation Oncology, University of Patras Medical School , Patras, Greece
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Wu J, Tha KK, Xing L, Li R. Radiomics and radiogenomics for precision radiotherapy. JOURNAL OF RADIATION RESEARCH 2018; 59:i25-i31. [PMID: 29385618 PMCID: PMC5868194 DOI: 10.1093/jrr/rrx102] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 12/14/2017] [Indexed: 06/07/2023]
Abstract
Imaging plays an important role in the diagnosis and staging of cancer, as well as in radiation treatment planning and evaluation of therapeutic response. Recently, there has been significant interest in extracting quantitative information from clinical standard-of-care images, i.e. radiomics, in order to provide a more comprehensive characterization of image phenotypes of the tumor. A number of studies have demonstrated that a deeper radiomic analysis can reveal novel image features that could provide useful diagnostic, prognostic or predictive information, improving upon currently used imaging metrics such as tumor size and volume. Furthermore, these imaging-derived phenotypes can be linked with genomic data, i.e. radiogenomics, in order to understand their biological underpinnings or further improve the prediction accuracy of clinical outcomes. In this article, we will provide an overview of radiomics and radiogenomics, including their rationale, technical and clinical aspects. We will also present some examples of the current results and some emerging paradigms in radiomics and radiogenomics for clinical oncology, with a focus on potential applications in radiotherapy. Finally, we will highlight the challenges in the field and suggest possible future directions in radiomics to maximize its potential impact on precision radiotherapy.
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Affiliation(s)
- Jia Wu
- Department of Radiation Oncology, Stanford University School of Medicine, 875 Blake Wilbur Drive, Stanford, CA 94305-5847, USA
| | - Khin Khin Tha
- Global Station for Quantum Biomedical Science and Engineering, Global Institute for Cooperative Research and Education, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo 060-8638, Japan
| | - Lei Xing
- Department of Radiation Oncology, Stanford University School of Medicine, 875 Blake Wilbur Drive, Stanford, CA 94305-5847, USA
- Global Station for Quantum Biomedical Science and Engineering, Global Institute for Cooperative Research and Education, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo 060-8638, Japan
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University School of Medicine, 875 Blake Wilbur Drive, Stanford, CA 94305-5847, USA
- Global Station for Quantum Biomedical Science and Engineering, Global Institute for Cooperative Research and Education, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo 060-8638, Japan
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Keil VC, Warmuth-Metz M, Reh C, Enkirch SJ, Reinert C, Beier D, Jones DTW, Pietsch T, Schild HH, Hattingen E, Hau P. Imaging Biomarkers for Adult Medulloblastomas: Genetic Entities May Be Identified by Their MR Imaging Radiophenotype. AJNR Am J Neuroradiol 2017; 38:1892-1898. [PMID: 28798218 DOI: 10.3174/ajnr.a5313] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Accepted: 05/24/2017] [Indexed: 01/11/2023]
Abstract
BACKGROUND AND PURPOSE The occurrence of medulloblastomas in adults is rare; nevertheless, these tumors can be subdivided into genetic and histologic entities each having distinct prognoses. This study aimed to identify MR imaging biomarkers to classify these entities and to uncover differences in MR imaging biomarkers identified in pediatric medulloblastomas. MATERIALS AND METHODS Eligible preoperative MRIs from 28 patients (11 women; 22-53 years of age) of the Multicenter Pilot-study for the Therapy of Medulloblastoma of Adults (NOA-7) cohort were assessed by 3 experienced neuroradiologists. Lesions and perifocal edema were volumetrized and multiparametrically evaluated for classic morphologic characteristics, location, hydrocephalus, and Chang criteria. To identify MR imaging biomarkers, we correlated genetic entities sonic hedgehog (SHH) TP53 wild type, wingless (WNT), and non-WNT/non-SHH medulloblastomas (in adults, Group 4), and histologic entities were correlated with the imaging criteria. These MR imaging biomarkers were compared with corresponding data from a pediatric study. RESULTS There were 19 SHH TP53 wild type (69%), 4 WNT-activated (14%), and 5 Group 4 (17%) medulloblastomas. Six potential MR imaging biomarkers were identified, 3 of which, hydrocephalus (P = .03), intraventricular macrometastases (P = .02), and hemorrhage (P = .04), when combined, could identify WNT medulloblastoma with 100% sensitivity and 88.3% specificity (95% CI, 39.8%-100.0% and 62.6%-95.3%). WNT-activated nuclear β-catenin accumulating medulloblastomas were smaller than the other entities (95% CI, 5.2-22.3 cm3 versus 35.1-47.6 cm3; P = .03). Hemorrhage was exclusively present in non-WNT/non-SHH medulloblastomas (P = .04; n = 2/5). MR imaging biomarkers were all discordant from those identified in the pediatric cohort. Desmoplastic/nodular medulloblastomas were more rarely in contact with the fourth ventricle (4/15 versus 7/13; P = .04). CONCLUSIONS MR imaging biomarkers can help distinguish histologic and genetic medulloblastoma entities in adults and appear to be different from those identified in children.
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Affiliation(s)
- V C Keil
- From the Department of Radiology and Neuroradiology (V.C.K., C.R., S.J.E., H.H.S., E.H.), University Hospital Bonn, Bonn, Germany
| | - M Warmuth-Metz
- Institute for Diagnostic and Interventional Neuroradiology (M.W.-M.), University Hospital Würzburg, Würzburg, Germany
| | - C Reh
- From the Department of Radiology and Neuroradiology (V.C.K., C.R., S.J.E., H.H.S., E.H.), University Hospital Bonn, Bonn, Germany
- Wilhelm Sander-Therapieeinheit NeuroOnkologie (C.R., P.H.)
- Department of Neurology (C.R., P.H.), University Hospital Regensburg, Regensburg, Germany
| | - S J Enkirch
- From the Department of Radiology and Neuroradiology (V.C.K., C.R., S.J.E., H.H.S., E.H.), University Hospital Bonn, Bonn, Germany
| | - C Reinert
- From the Department of Radiology and Neuroradiology (V.C.K., C.R., S.J.E., H.H.S., E.H.), University Hospital Bonn, Bonn, Germany
| | - D Beier
- Department of Neurology (D.B.), University Hospital Odense and Clinical Institute, University of Southern Denmark, Odense, Denmark
- Department of Neurology (D.B.), University of Regensburg, Regensburg, Germany
| | - D T W Jones
- Deutsches Krebsforschungszentrum (D.T.W.J.), Division of Pediatric Neurooncology, Heidelberg, Germany
| | - T Pietsch
- Department of Neuropathology (T.P.), Brain Tumor Reference Center of the German Society for Neuropathology and Neuroanatomy, Bonn, Germany
| | - H H Schild
- From the Department of Radiology and Neuroradiology (V.C.K., C.R., S.J.E., H.H.S., E.H.), University Hospital Bonn, Bonn, Germany
| | - E Hattingen
- From the Department of Radiology and Neuroradiology (V.C.K., C.R., S.J.E., H.H.S., E.H.), University Hospital Bonn, Bonn, Germany
| | - P Hau
- Wilhelm Sander-Therapieeinheit NeuroOnkologie (C.R., P.H.)
- Department of Neurology (C.R., P.H.), University Hospital Regensburg, Regensburg, Germany
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