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Awuah WA, Adebusoye FT, Wellington J, David L, Salam A, Weng Yee AL, Lansiaux E, Yarlagadda R, Garg T, Abdul-Rahman T, Kalmanovich J, Miteu GD, Kundu M, Mykolaivna NI. Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery. World Neurosurg X 2024; 23:100301. [PMID: 38577317 PMCID: PMC10992893 DOI: 10.1016/j.wnsx.2024.100301] [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: 04/21/2023] [Revised: 07/23/2023] [Accepted: 02/21/2024] [Indexed: 04/06/2024] Open
Abstract
Neurosurgeons receive extensive technical training, which equips them with the knowledge and skills to specialise in various fields and manage the massive amounts of information and decision-making required throughout the various stages of neurosurgery, including preoperative, intraoperative, and postoperative care and recovery. Over the past few years, artificial intelligence (AI) has become more useful in neurosurgery. AI has the potential to improve patient outcomes by augmenting the capabilities of neurosurgeons and ultimately improving diagnostic and prognostic outcomes as well as decision-making during surgical procedures. By incorporating AI into both interventional and non-interventional therapies, neurosurgeons may provide the best care for their patients. AI, machine learning (ML), and deep learning (DL) have made significant progress in the field of neurosurgery. These cutting-edge methods have enhanced patient outcomes, reduced complications, and improved surgical planning.
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Affiliation(s)
| | | | - Jack Wellington
- Cardiff University School of Medicine, Cardiff University, Wales, United Kingdom
| | - Lian David
- Norwich Medical School, University of East Anglia, United Kingdom
| | - Abdus Salam
- Department of Surgery, Khyber Teaching Hospital, Peshawar, Pakistan
| | | | | | - Rohan Yarlagadda
- Rowan University School of Osteopathic Medicine, Stratford, NJ, USA
| | - Tulika Garg
- Government Medical College and Hospital Chandigarh, India
| | | | | | | | - Mrinmoy Kundu
- Institute of Medical Sciences and SUM Hospital, Bhubaneswar, India
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Li Z, Holzgreve A, Unterrainer LM, Ruf VC, Quach S, Bartos LM, Suchorska B, Niyazi M, Wenter V, Herms J, Bartenstein P, Tonn JC, Unterrainer M, Albert NL, Kaiser L. Combination of pre-treatment dynamic [ 18F]FET PET radiomics and conventional clinical parameters for the survival stratification in patients with IDH-wildtype glioblastoma. Eur J Nucl Med Mol Imaging 2023; 50:535-545. [PMID: 36227357 PMCID: PMC9816231 DOI: 10.1007/s00259-022-05988-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 10/03/2022] [Indexed: 01/11/2023]
Abstract
PURPOSE The aim of this study was to build and evaluate a prediction model which incorporates clinical parameters and radiomic features extracted from static as well as dynamic [18F]FET PET for the survival stratification in patients with newly diagnosed IDH-wildtype glioblastoma. METHODS A total of 141 patients with newly diagnosed IDH-wildtype glioblastoma and dynamic [18F]FET PET prior to surgical intervention were included. Patients with a survival time ≤ 12 months were classified as short-term survivors. First order, shape, and texture radiomic features were extracted from pre-treatment static (tumor-to-background ratio; TBR) and dynamic (time-to-peak; TTP) images, respectively, and randomly divided into a training (n = 99) and a testing cohort (n = 42). After feature normalization, recursive feature elimination was applied for feature selection using 5-fold cross-validation on the training cohort, and a machine learning model was constructed to compare radiomic models and combined clinical-radiomic models with selected radiomic features and clinical parameters. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values were calculated to assess the predictive performance for identifying short-term survivors in both the training and testing cohort. RESULTS A combined clinical-radiomic model comprising six clinical parameters and six selected dynamic radiomic features achieved highest predictability of short-term survival with an AUC of 0.74 (95% confidence interval, 0.60-0.88) in the independent testing cohort. CONCLUSIONS This study successfully built and evaluated prediction models using [18F]FET PET-based radiomic features and clinical parameters for the individualized assessment of short-term survival in patients with a newly diagnosed IDH-wildtype glioblastoma. The combination of both clinical parameters and dynamic [18F]FET PET-based radiomic features reached highest accuracy in identifying patients at risk. Although the achieved accuracy level remained moderate, our data shows that the integration of dynamic [18F]FET PET radiomic data into clinical prediction models may improve patient stratification beyond established prognostic markers.
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Affiliation(s)
- Zhicong Li
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
| | - Adrien Holzgreve
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Lena M Unterrainer
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Viktoria C Ruf
- Center for Neuropathology and Prion Research, LMU Munich, Munich, Germany
| | - Stefanie Quach
- Department of Neurosurgery, University Hospital, LMU Munich, Munich, Germany
| | - Laura M Bartos
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Bogdana Suchorska
- Department of Neurosurgery, University Hospital, LMU Munich, Munich, Germany
- Department of Neurosurgery, Sana Hospital, Duisburg, Germany
| | - Maximilian Niyazi
- Department of Radiotherapy, University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Vera Wenter
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Jochen Herms
- Center for Neuropathology and Prion Research, LMU Munich, Munich, Germany
| | - Peter Bartenstein
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Joerg-Christian Tonn
- Department of Neurosurgery, University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Marcus Unterrainer
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Nathalie L Albert
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Lena Kaiser
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
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Krauze AV, Zhuge Y, Zhao R, Tasci E, Camphausen K. AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models. JOURNAL OF BIOTECHNOLOGY AND BIOMEDICINE 2022; 5:1-19. [PMID: 35106480 PMCID: PMC8802234 DOI: 10.26502/jbb.2642-91280046] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The interpretation of imaging in medicine in general and in oncology specifically remains problematic due to several limitations which include the need to incorporate detailed clinical history, patient and disease-specific history, clinical exam features, previous and ongoing treatment, and account for the dependency on reproducible human interpretation of multiple factors with incomplete data linkage. To standardize reporting, minimize bias, expedite management, and improve outcomes, the use of Artificial Intelligence (AI) has gained significant prominence in imaging analysis. In oncology, AI methods have as a result been explored in most cancer types with ongoing progress in employing AI towards imaging for oncology treatment, assessing treatment response, and understanding and communicating prognosis. Challenges remain with limited available data sets, variability in imaging changes over time augmented by a growing heterogeneity in analysis approaches. We review the imaging analysis workflow and examine how hand-crafted features also referred to as traditional Machine Learning (ML), Deep Learning (DL) approaches, and hybrid analyses, are being employed in AI-driven imaging analysis in central nervous system tumors. ML, DL, and hybrid approaches coexist, and their combination may produce superior results although data in this space is as yet novel, and conclusions and pitfalls have yet to be fully explored. We note the growing technical complexities that may become increasingly separated from the clinic and enforce the acute need for clinician engagement to guide progress and ensure that conclusions derived from AI-driven imaging analysis reflect that same level of scrutiny lent to other avenues of clinical research.
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Affiliation(s)
- A V Krauze
- Center for Cancer Research, National Cancer Institute, NIH, Building 10, Room B2-3637, Bethesda, USA
| | - Y Zhuge
- Center for Cancer Research, National Cancer Institute, NIH, Building 10, Room B2-3637, Bethesda, USA
| | - R Zhao
- University of British Columbia, Faculty of Medicine, 317 - 2194 Health Sciences Mall, Vancouver, Canada
| | - E Tasci
- Center for Cancer Research, National Cancer Institute, NIH, Building 10, Room B2-3637, Bethesda, USA
| | - K Camphausen
- Center for Cancer Research, National Cancer Institute, NIH, Building 10, Room B2-3637, Bethesda, USA
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Krauze AV, Camphausen K. Molecular Biology in Treatment Decision Processes-Neuro-Oncology Edition. Int J Mol Sci 2021; 22:13278. [PMID: 34948075 PMCID: PMC8703419 DOI: 10.3390/ijms222413278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/02/2021] [Accepted: 12/03/2021] [Indexed: 11/30/2022] Open
Abstract
Computational approaches including machine learning, deep learning, and artificial intelligence are growing in importance in all medical specialties as large data repositories are increasingly being optimised. Radiation oncology as a discipline is at the forefront of large-scale data acquisition and well positioned towards both the production and analysis of large-scale oncologic data with the potential for clinically driven endpoints and advancement of patient outcomes. Neuro-oncology is comprised of malignancies that often carry poor prognosis and significant neurological sequelae. The analysis of radiation therapy mediated treatment and the potential for computationally mediated analyses may lead to more precise therapy by employing large scale data. We analysed the state of the literature pertaining to large scale data, computational analysis, and the advancement of molecular biomarkers in neuro-oncology with emphasis on radiation oncology. We aimed to connect existing and evolving approaches to realistic avenues for clinical implementation focusing on low grade gliomas (LGG), high grade gliomas (HGG), management of the elderly patient with HGG, rare central nervous system tumors, craniospinal irradiation, and re-irradiation to examine how computational analysis and molecular science may synergistically drive advances in personalised radiation therapy (RT) and optimise patient outcomes.
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Affiliation(s)
- Andra V. Krauze
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, Bethesda, MD 20892, USA;
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Habib A, Jovanovich N, Hoppe M, Ak M, Mamindla P, R. Colen R, Zinn PO. MRI-Based Radiomics and Radiogenomics in the Management of Low-Grade Gliomas: Evaluating the Evidence for a Paradigm Shift. J Clin Med 2021; 10:1411. [PMID: 33915813 PMCID: PMC8036428 DOI: 10.3390/jcm10071411] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 03/23/2021] [Accepted: 03/25/2021] [Indexed: 12/29/2022] Open
Abstract
Low-grade gliomas (LGGs) are tumors that affect mostly adults. These neoplasms are comprised mainly of oligodendrogliomas and diffuse astrocytomas. LGGs remain vexing to current management and therapeutic modalities although they exhibit more favorable survival rates compared with high-grade gliomas (HGGs). The specific genetic subtypes that these tumors exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists, epileptologists, neurooncologists and neurosurgeons. Currently, the diagnosis of an LGG pivots mainly around the preliminary radiological findings and the subsequent definitive surgical diagnosis (via surgical sampling). The introduction of radiomics as a high throughput quantitative imaging technique that allows for improved diagnostic, prognostic and predictive indices has created more interest for such techniques in cancer research and especially in neurooncology (MRI-based classification of LGGs, predicting Isocitrate dehydrogenase (IDH) and Telomerase reverse transcriptase (TERT) promoter mutations and predicting LGG associated seizures). Radiogenomics refers to the linkage of imaging findings with the tumor/tissue genomics. Numerous applications of radiomics and radiogenomics have been described in the clinical context and management of LGGs. In this review, we describe the recently published studies discussing the potential application of radiomics and radiogenomics in LGGs. We also highlight the potential pitfalls of the above-mentioned high throughput computerized techniques and, most excitingly, explore the use of machine learning artificial intelligence technologies as standalone and adjunct imaging tools en route to enhance a personalized MRI-based tumor diagnosis and management plan design.
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Affiliation(s)
- Ahmed Habib
- Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA;
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA; (N.J.); (M.H.); (M.A.); (P.M.); (R.R.C.)
| | - Nicolina Jovanovich
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA; (N.J.); (M.H.); (M.A.); (P.M.); (R.R.C.)
| | - Meagan Hoppe
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA; (N.J.); (M.H.); (M.A.); (P.M.); (R.R.C.)
| | - Murat Ak
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA; (N.J.); (M.H.); (M.A.); (P.M.); (R.R.C.)
- Department of Diagnostic Radiology, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA
| | - Priyadarshini Mamindla
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA; (N.J.); (M.H.); (M.A.); (P.M.); (R.R.C.)
| | - Rivka R. Colen
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA; (N.J.); (M.H.); (M.A.); (P.M.); (R.R.C.)
- Department of Diagnostic Radiology, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA
| | - Pascal O. Zinn
- Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA;
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA; (N.J.); (M.H.); (M.A.); (P.M.); (R.R.C.)
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