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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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Mut M, Zhang M, Gupta I, Fletcher PT, Farzad F, Nwafor D. Augmented surgical decision-making for glioblastoma: integrating AI tools into education and practice. Front Neurol 2024; 15:1387958. [PMID: 38911587 PMCID: PMC11191873 DOI: 10.3389/fneur.2024.1387958] [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: 02/19/2024] [Accepted: 05/28/2024] [Indexed: 06/25/2024] Open
Abstract
Surgical decision-making for glioblastoma poses significant challenges due to its complexity and variability. This study investigates the potential of artificial intelligence (AI) tools in improving "decision-making processes" for glioblastoma surgery. A systematic review of literature identified 10 relevant studies, primarily focused on predicting resectability and surgery-related neurological outcomes. AI tools, especially rooted in radiomics and connectomics, exhibited promise in predicting resection extent through precise tumor segmentation and tumor-network relationships. However, they demonstrated limited effectiveness in predicting postoperative neurological due to dynamic and less quantifiable nature of patient-related factors. Recognizing these challenges, including limited datasets and the interpretability requirement in medical applications, underscores the need for standardization, algorithm optimization, and addressing variability in model performance and then further validation in clinical settings. While AI holds potential, it currently does not possess the capacity to emulate the nuanced decision-making process utilized by experienced neurosurgeons in the comprehensive approach to glioblastoma surgery.
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Affiliation(s)
- Melike Mut
- Department of Neurosurgery, University of Virginia, Charlottesville, VA, United States
| | - Miaomiao Zhang
- Department of Electrical and Computer Engineering, Department of Computer Science, University of Virginia, Charlottesville, VA, United States
| | - Ishita Gupta
- Department of Electrical and Computer Engineering, Department of Computer Science, University of Virginia, Charlottesville, VA, United States
| | - P. Thomas Fletcher
- Department of Electrical and Computer Engineering, Department of Computer Science, University of Virginia, Charlottesville, VA, United States
| | - Faraz Farzad
- Department of Neurosurgery, University of Virginia, Charlottesville, VA, United States
| | - Divine Nwafor
- Department of Neurosurgery, University of Virginia, Charlottesville, VA, United States
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Wang Y, Wang Z, Guo X, Cao Y, Xing H, Wang Y, Xing B, Wang Y, Yao Y, Ma W. Artificial neural network identified a 20-gene panel in predicting immunotherapy response and survival benefits after anti-PD1/PD-L1 treatment in glioblastoma patients. Cancer Med 2024; 13:e7218. [PMID: 38733169 PMCID: PMC11087814 DOI: 10.1002/cam4.7218] [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: 07/16/2023] [Revised: 04/03/2024] [Accepted: 04/15/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND Immune checkpoint inhibitors (ICIs) are a promising immunotherapy approach, but glioblastoma clinical trials have not yielded satisfactory results. OBJECTIVE To screen glioblastoma patients who may benefit from immunotherapy. METHODS Eighty-one patients receiving anti-PD1/PD-L1 treatment from a large-scale clinical trial and 364 patients without immunotherapy from The Cancer Genome Atlas (TCGA) were included. Patients in the ICI-treated cohort were divided into responders and nonresponders according to overall survival (OS), and the most critical responder-relevant features were screened using random forest (RF). We constructed an artificial neural network (ANN) model and verified its predictive value with immunotherapy response and OS. RESULTS We defined two groups of ICI-treated glioblastoma patients with large differences in survival benefits as nonresponders (OS ≤6 months, n = 18) and responders (OS ≥17 months, n = 8). No differentially mutated genes were observed between responders and nonresponders. We performed RF analysis to select the most critical responder-relevant features and developed an ANN with 20 input variables, five hidden neurons and one output neuron. Receiver operating characteristic analysis and the DeLong test demonstrated that the ANN had the best performance in predicting responders, with an AUC of 0.97. Survival analysis indicated that ANN-predicted responders had significantly better OS rates than nonresponders. CONCLUSION The 20-gene panel developed by the ANN could be a promising biomarker for predicting immunotherapy response and prognostic benefits in ICI-treated GBM patients and may guide oncologists to accurately select potential responders for the preferential use of ICIs.
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Affiliation(s)
- Yaning Wang
- Department of Neurosurgery, Center for Malignant Brain Tumors, National Glioma MDT Alliance, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking UnionMedical CollegeBeijingChina
| | - Zihao Wang
- Department of Neurosurgery, Center for Malignant Brain Tumors, National Glioma MDT Alliance, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking UnionMedical CollegeBeijingChina
| | - Xiaopeng Guo
- Department of Neurosurgery, Center for Malignant Brain Tumors, National Glioma MDT Alliance, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking UnionMedical CollegeBeijingChina
| | - Yaning Cao
- Department of Neurosurgery, Center for Malignant Brain Tumors, National Glioma MDT Alliance, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking UnionMedical CollegeBeijingChina
| | - Hao Xing
- Department of Neurosurgery, Center for Malignant Brain Tumors, National Glioma MDT Alliance, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking UnionMedical CollegeBeijingChina
| | - Yuekun Wang
- Department of Neurosurgery, Center for Malignant Brain Tumors, National Glioma MDT Alliance, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking UnionMedical CollegeBeijingChina
| | - Bing Xing
- Department of Neurosurgery, Center for Malignant Brain Tumors, National Glioma MDT Alliance, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking UnionMedical CollegeBeijingChina
| | - Yu Wang
- Department of Neurosurgery, Center for Malignant Brain Tumors, National Glioma MDT Alliance, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking UnionMedical CollegeBeijingChina
| | - Yong Yao
- Department of Neurosurgery, Center for Malignant Brain Tumors, National Glioma MDT Alliance, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking UnionMedical CollegeBeijingChina
| | - Wenbin Ma
- Department of Neurosurgery, Center for Malignant Brain Tumors, National Glioma MDT Alliance, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking UnionMedical CollegeBeijingChina
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4
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Agadi K, Dominari A, Tebha SS, Mohammadi A, Zahid S. Neurosurgical Management of Cerebrospinal Tumors in the Era of Artificial Intelligence : A Scoping Review. J Korean Neurosurg Soc 2023; 66:632-641. [PMID: 35831137 PMCID: PMC10641423 DOI: 10.3340/jkns.2021.0213] [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: 08/23/2021] [Revised: 10/06/2021] [Accepted: 03/14/2022] [Indexed: 11/27/2022] Open
Abstract
Central nervous system tumors are identified as tumors of the brain and spinal cord. The associated morbidity and mortality of cerebrospinal tumors are disproportionately high compared to other malignancies. While minimally invasive techniques have initiated a revolution in neurosurgery, artificial intelligence (AI) is expediting it. Our study aims to analyze AI's role in the neurosurgical management of cerebrospinal tumors. We conducted a scoping review using the Arksey and O'Malley framework. Upon screening, data extraction and analysis were focused on exploring all potential implications of AI, classification of these implications in the management of cerebrospinal tumors. AI has enhanced the precision of diagnosis of these tumors, enables surgeons to excise the tumor margins completely, thereby reducing the risk of recurrence, and helps to make a more accurate prediction of the patient's prognosis than the conventional methods. AI also offers real-time training to neurosurgeons using virtual and 3D simulation, thereby increasing their confidence and skills during procedures. In addition, robotics is integrated into neurosurgery and identified to increase patient outcomes by making surgery less invasive. AI, including machine learning, is rigorously considered for its applications in the neurosurgical management of cerebrospinal tumors. This field requires further research focused on areas clinically essential in improving the outcome that is also economically feasible for clinical use. The authors suggest that data analysts and neurosurgeons collaborate to explore the full potential of AI.
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Affiliation(s)
- Kuchalambal Agadi
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
| | - Asimina Dominari
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
- Aristotle University of Thessaloniki School of Medicine, Thessaloniki, Greece
| | - Sameer Saleem Tebha
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
- Department of Neurosurgery and Neurology, Jinnah Medical and Dental College, Karachi, Pakistan
| | - Asma Mohammadi
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
| | - Samina Zahid
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
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Khan DZ, Hanrahan JG, Baldeweg SE, Dorward NL, Stoyanov D, Marcus HJ. Current and Future Advances in Surgical Therapy for Pituitary Adenoma. Endocr Rev 2023; 44:947-959. [PMID: 37207359 PMCID: PMC10502574 DOI: 10.1210/endrev/bnad014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 03/14/2023] [Accepted: 05/17/2023] [Indexed: 05/21/2023]
Abstract
The vital physiological role of the pituitary gland, alongside its proximity to critical neurovascular structures, means that pituitary adenomas can cause significant morbidity or mortality. While enormous advancements have been made in the surgical care of pituitary adenomas, numerous challenges remain, such as treatment failure and recurrence. To meet these clinical challenges, there has been an enormous expansion of novel medical technologies (eg, endoscopy, advanced imaging, artificial intelligence). These innovations have the potential to benefit each step of the patient's journey, and ultimately, drive improved outcomes. Earlier and more accurate diagnosis addresses this in part. Analysis of novel patient data sets, such as automated facial analysis or natural language processing of medical records holds potential in achieving an earlier diagnosis. After diagnosis, treatment decision-making and planning will benefit from radiomics and multimodal machine learning models. Surgical safety and effectiveness will be transformed by smart simulation methods for trainees. Next-generation imaging techniques and augmented reality will enhance surgical planning and intraoperative navigation. Similarly, surgical abilities will be augmented by the future operative armamentarium, including advanced optical devices, smart instruments, and surgical robotics. Intraoperative support to surgical team members will benefit from a data science approach, utilizing machine learning analysis of operative videos to improve patient safety and orientate team members to a common workflow. Postoperatively, neural networks leveraging multimodal datasets will allow early detection of individuals at risk of complications and assist in the prediction of treatment failure, thus supporting patient-specific discharge and monitoring protocols. While these advancements in pituitary surgery hold promise to enhance the quality of care, clinicians must be the gatekeepers of the translation of such technologies, ensuring systematic assessment of risk and benefit prior to clinical implementation. In doing so, the synergy between these innovations can be leveraged to drive improved outcomes for patients of the future.
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Affiliation(s)
- Danyal Z Khan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
| | - John G Hanrahan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
| | - Stephanie E Baldeweg
- Department of Diabetes & Endocrinology, University College London Hospitals NHS Foundation Trust, London NW1 2BU, UK
- Centre for Obesity and Metabolism, Department of Experimental and Translational Medicine, Division of Medicine, University College London, London WC1E 6BT, UK
| | - Neil L Dorward
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
- Digital Surgery Ltd, Medtronic, London WD18 8WW, UK
| | - Hani J Marcus
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
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6
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Chirica C, Haba D, Cojocaru E, Mazga AI, Eva L, Dobrovat BI, Chirica SI, Stirban I, Rotundu A, Leon MM. One Step Forward-The Current Role of Artificial Intelligence in Glioblastoma Imaging. Life (Basel) 2023; 13:1561. [PMID: 37511936 PMCID: PMC10381280 DOI: 10.3390/life13071561] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/07/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023] Open
Abstract
Artificial intelligence (AI) is rapidly integrating into diagnostic methods across many branches of medicine. Significant progress has been made in tumor assessment using AI algorithms, and research is underway on how image manipulation can provide information with diagnostic, prognostic and treatment impacts. Glioblastoma (GB) remains the most common primary malignant brain tumor, with a median survival of 15 months. This paper presents literature data on GB imaging and the contribution of AI to the characterization and tracking of GB, as well as recurrence. Furthermore, from an imaging point of view, the differential diagnosis of these tumors can be problematic. How can an AI algorithm help with differential diagnosis? The integration of clinical, radiomics and molecular markers via AI holds great potential as a tool for enhancing patient outcomes by distinguishing brain tumors from mimicking lesions, classifying and grading tumors, and evaluating them before and after treatment. Additionally, AI can aid in differentiating between tumor recurrence and post-treatment alterations, which can be challenging with conventional imaging methods. Overall, the integration of AI into GB imaging has the potential to significantly improve patient outcomes by enabling more accurate diagnosis, precise treatment planning and better monitoring of treatment response.
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Affiliation(s)
- Costin Chirica
- Doctoral School, Grigore T. Popa University of Medicine and Pharmacy, 16 Universitatii Str., 700115 Iasi, Romania
| | - Danisia Haba
- Department of Oral and Maxillofacial Surgery, Faculty of Dental Medicine, Grigore T. Popa University of Medicine and Pharmacy, 16 Universitatii Str., 700115 Iasi, Romania
| | - Elena Cojocaru
- Department of Morphofunctional Sciences I, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Andreea Isabela Mazga
- Faculty of General Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
| | - Lucian Eva
- Department of Anatomy, Apollonia University, 11 Pacurari Str., 700535 Iasi, Romania
| | - Bogdan Ionut Dobrovat
- Department of Radiology, Emergency Hospital Professor Doctor Nicolae Oblu, 700309 Iasi, Romania
| | - Sabina Ioana Chirica
- Faculty of General Medicine, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Ioana Stirban
- Department of Neurosurgery, Emergency Hospital Professor Doctor Nicolae Oblu, 700309 Iasi, Romania
| | - Andreea Rotundu
- Doctoral School, Grigore T. Popa University of Medicine and Pharmacy, 16 Universitatii Str., 700115 Iasi, Romania
| | - Maria Magdalena Leon
- Doctoral School, Grigore T. Popa University of Medicine and Pharmacy, 16 Universitatii Str., 700115 Iasi, Romania
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D'Amico RS, White TG, Shah HA, Langer DJ. I Asked a ChatGPT to Write an Editorial About How We Can Incorporate Chatbots Into Neurosurgical Research and Patient Care…. Neurosurgery 2023; 92:663-664. [PMID: 36757199 DOI: 10.1227/neu.0000000000002414] [Citation(s) in RCA: 38] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 01/11/2023] [Indexed: 02/10/2023] Open
Affiliation(s)
- Randy S D'Amico
- Department of Neurological Surgery, Lenox Hill Hospital/Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New York, New York, USA
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8
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Luo J, Pan M, Mo K, Mao Y, Zou D. Emerging role of artificial intelligence in diagnosis, classification and clinical management of glioma. Semin Cancer Biol 2023; 91:110-123. [PMID: 36907387 DOI: 10.1016/j.semcancer.2023.03.006] [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: 10/15/2022] [Revised: 03/05/2023] [Accepted: 03/08/2023] [Indexed: 03/12/2023]
Abstract
Glioma represents a dominant primary intracranial malignancy in the central nervous system. Artificial intelligence that mainly includes machine learning, and deep learning computational approaches, presents a unique opportunity to enhance clinical management of glioma through improving tumor segmentation, diagnosis, differentiation, grading, treatment, prediction of clinical outcomes (prognosis, and recurrence), molecular features, clinical classification, characterization of the tumor microenvironment, and drug discovery. A growing body of recent studies apply artificial intelligence-based models to disparate data sources of glioma, covering imaging modalities, digital pathology, high-throughput multi-omics data (especially emerging single-cell RNA sequencing and spatial transcriptome), etc. While these early findings are promising, future studies are required to normalize artificial intelligence-based models to improve the generalizability and interpretability of the results. Despite prominent issues, targeted clinical application of artificial intelligence approaches in glioma will facilitate the development of precision medicine of this field. If these challenges can be overcome, artificial intelligence has the potential to profoundly change the way patients with or at risk of glioma are provided with more rational care.
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Affiliation(s)
- Jiefeng Luo
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China
| | - Mika Pan
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China
| | - Ke Mo
- Clinical Research Center, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China
| | - Yingwei Mao
- Department of Biology, Pennsylvania State University, University Park, PA 16802, USA.
| | - Donghua Zou
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China; Clinical Research Center, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China.
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Katsos K, Johnson SE, Ibrahim S, Bydon M. Current Applications of Machine Learning for Spinal Cord Tumors. Life (Basel) 2023; 13:life13020520. [PMID: 36836877 PMCID: PMC9962966 DOI: 10.3390/life13020520] [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: 01/10/2023] [Revised: 02/01/2023] [Accepted: 02/06/2023] [Indexed: 02/16/2023] Open
Abstract
Spinal cord tumors constitute a diverse group of rare neoplasms associated with significant mortality and morbidity that pose unique clinical and surgical challenges. Diagnostic accuracy and outcome prediction are critical for informed decision making and can promote personalized medicine and facilitate optimal patient management. Machine learning has the ability to analyze and combine vast amounts of data, allowing the identification of patterns and the establishment of clinical associations, which can ultimately enhance patient care. Although artificial intelligence techniques have been explored in other areas of spine surgery, such as spinal deformity surgery, precise machine learning models for spinal tumors are lagging behind. Current applications of machine learning in spinal cord tumors include algorithms that improve diagnostic precision by predicting genetic, molecular, and histopathological profiles. Furthermore, artificial intelligence-based systems can assist surgeons with preoperative planning and surgical resection, potentially reducing the risk of recurrence and consequently improving clinical outcomes. Machine learning algorithms promote personalized medicine by enabling prognostication and risk stratification based on accurate predictions of treatment response, survival, and postoperative complications. Despite their promising potential, machine learning models require extensive validation processes and quality assessments to ensure safe and effective translation to clinical practice.
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Affiliation(s)
- Konstantinos Katsos
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
- Mayo Clinic Neuro-Informatics Laboratory, Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
| | - Sarah E. Johnson
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
- Mayo Clinic Neuro-Informatics Laboratory, Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
| | - Sufyan Ibrahim
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
- Mayo Clinic Neuro-Informatics Laboratory, Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
| | - Mohamad Bydon
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
- Mayo Clinic Neuro-Informatics Laboratory, Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USA
- Correspondence:
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10
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Jimenez AE, Porras JL, Azad TD, Shah PP, Jackson CM, Gallia G, Bettegowda C, Weingart J, Mukherjee D. Machine Learning Models for Predicting Postoperative Outcomes following Skull Base Meningioma Surgery. J Neurol Surg B Skull Base 2022; 83:635-645. [PMID: 36393884 PMCID: PMC9653296 DOI: 10.1055/a-1885-1447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 06/20/2022] [Indexed: 10/17/2022] Open
Abstract
Objective While predictive analytic techniques have been used to analyze meningioma postoperative outcomes, to our knowledge, there have been no studies that have investigated the utility of machine learning (ML) models in prognosticating outcomes among skull base meningioma patients. The present study aimed to develop models for predicting postoperative outcomes among skull base meningioma patients, specifically prolonged hospital length of stay (LOS), nonroutine discharge disposition, and high hospital charges. We also validated the predictive performance of our models on out-of-sample testing data. Methods Patients who underwent skull base meningioma surgery between 2016 and 2019 at an academic institution were included in our study. Prolonged hospital LOS and high hospital charges were defined as >4 days and >$47,887, respectively. Elastic net logistic regression algorithms were trained to predict postoperative outcomes using 70% of available data, and their predictive performance was evaluated on the remaining 30%. Results A total of 265 patients were included in our final analysis. Our cohort was majority female (77.7%) and Caucasian (63.4%). Elastic net logistic regression algorithms predicting prolonged LOS, nonroutine discharge, and high hospital charges achieved areas under the receiver operating characteristic curve of 0.798, 0.752, and 0.592, respectively. Further, all models were adequately calibrated as determined by the Spiegelhalter Z -test ( p >0.05). Conclusion Our study developed models predicting prolonged hospital LOS, nonroutine discharge disposition, and high hospital charges among skull base meningioma patients. Our models highlight the utility of ML as a tool to aid skull base surgeons in providing high-value health care and optimizing clinical workflows.
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Affiliation(s)
- Adrian E. Jimenez
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States
| | - Jose L. Porras
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States
| | - Tej D. Azad
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States
| | - Pavan P. Shah
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States
| | - Christopher M. Jackson
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States
| | - Gary Gallia
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States
| | - Chetan Bettegowda
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States
| | - Jon Weingart
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States
| | - Debraj Mukherjee
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States
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11
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Predicting access to postoperative treatment after glioblastoma resection: an analysis of neighborhood-level disadvantage using the Area Deprivation Index (ADI). J Neurooncol 2022; 158:349-357. [PMID: 35503190 DOI: 10.1007/s11060-022-04020-9] [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: 02/03/2022] [Accepted: 04/16/2022] [Indexed: 10/18/2022]
Abstract
PURPOSE Social determinants of health (SDoH)-socioeconomic and environmental factors-impact outcomes. The Area Deprivation Index (ADI), a composite of seventeen SDoH factors, has been correlated with poorer outcomes. We aimed to compare outcomes and treatment access for glioblastoma, a universally fatal malignant brain tumor, in patients more (ADI 34-100%) versus less disadvantaged (ADI 0-33%). METHODS A 5-year retrospective study of Rhode Island Hospital and Mayo Clinic databases was conducted from 2012 to 2017 for patients ≥ 18 years with glioblastoma. Patient addresses were matched to ADI percentiles and grouped into more (top 66% ADI) and less disadvantaged. Adjusted multivariable regressions were used to compare outcomes between groups. RESULTS A total of 434 patients met inclusion; 92.9% were insured, 56.2% were more disadvantaged (n = 244), and the more disadvantaged cohort was younger on average (62 years). After adjustment, the more disadvantaged group had decreased odds of receiving gross total resection (adjusted odds ratio (aOR) 0.43, 95% CI [0.27-0.68]; p < 0.001). This cohort also had decreased odds of undergoing chemotherapy (aOR 0.51[0.26-0.98]), radiation (aOR 0.39[0.20-0.77]), chemoradiation (aOR 0.42[0.23-0.77]), tumor-treating fields (aOR 0.39[0.16-0.93]), and clinical trial participation (aOR 0.47[0.25-0.91]). No differences in length of survival or postoperative Karnofsky Performance Status Scale were observed. CONCLUSION More disadvantaged glioblastoma patients had decreased odds of receiving gross total resection. They also exhibited decreased odds of receiving standard of care like chemoradiation as well as participating in a clinical trial, compared to the less disadvantaged group. More research is needed to identify modifiable SDoH barriers to post-operative treatment in disadvantaged patients with glioblastoma.
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Maier-Hein L, Eisenmann M, Sarikaya D, März K, Collins T, Malpani A, Fallert J, Feussner H, Giannarou S, Mascagni P, Nakawala H, Park A, Pugh C, Stoyanov D, Vedula SS, Cleary K, Fichtinger G, Forestier G, Gibaud B, Grantcharov T, Hashizume M, Heckmann-Nötzel D, Kenngott HG, Kikinis R, Mündermann L, Navab N, Onogur S, Roß T, Sznitman R, Taylor RH, Tizabi MD, Wagner M, Hager GD, Neumuth T, Padoy N, Collins J, Gockel I, Goedeke J, Hashimoto DA, Joyeux L, Lam K, Leff DR, Madani A, Marcus HJ, Meireles O, Seitel A, Teber D, Ückert F, Müller-Stich BP, Jannin P, Speidel S. Surgical data science - from concepts toward clinical translation. Med Image Anal 2022; 76:102306. [PMID: 34879287 PMCID: PMC9135051 DOI: 10.1016/j.media.2021.102306] [Citation(s) in RCA: 85] [Impact Index Per Article: 42.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 11/03/2021] [Accepted: 11/08/2021] [Indexed: 02/06/2023]
Abstract
Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.
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Affiliation(s)
- Lena Maier-Hein
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany; Medical Faculty, Heidelberg University, Heidelberg, Germany.
| | - Matthias Eisenmann
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Duygu Sarikaya
- Department of Computer Engineering, Faculty of Engineering, Gazi University, Ankara, Turkey; LTSI, Inserm UMR 1099, University of Rennes 1, Rennes, France
| | - Keno März
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Anand Malpani
- The Malone Center for Engineering in Healthcare, The Johns Hopkins University, Baltimore, Maryland, USA
| | | | - Hubertus Feussner
- Department of Surgery, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Stamatia Giannarou
- The Hamlyn Centre for Robotic Surgery, Imperial College London, London, United Kingdom
| | - Pietro Mascagni
- ICube, University of Strasbourg, CNRS, France; IHU Strasbourg, Strasbourg, France
| | | | - Adrian Park
- Department of Surgery, Anne Arundel Health System, Annapolis, Maryland, USA; Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Carla Pugh
- Department of Surgery, Stanford University School of Medicine, Stanford, California, USA
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - Swaroop S Vedula
- The Malone Center for Engineering in Healthcare, The Johns Hopkins University, Baltimore, Maryland, USA
| | - Kevin Cleary
- The Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, D.C., USA
| | | | - Germain Forestier
- L'Institut de Recherche en Informatique, Mathématiques, Automatique et Signal (IRIMAS), University of Haute-Alsace, Mulhouse, France; Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
| | - Bernard Gibaud
- LTSI, Inserm UMR 1099, University of Rennes 1, Rennes, France
| | - Teodor Grantcharov
- University of Toronto, Toronto, Ontario, Canada; The Li Ka Shing Knowledge Institute of St. Michael's Hospital, Toronto, Ontario, Canada
| | - Makoto Hashizume
- Kyushu University, Fukuoka, Japan; Kitakyushu Koga Hospital, Fukuoka, Japan
| | - Doreen Heckmann-Nötzel
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hannes G Kenngott
- Department for General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Nassir Navab
- Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany; Department of Computer Science, The Johns Hopkins University, Baltimore, Maryland, USA
| | - Sinan Onogur
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tobias Roß
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany; Medical Faculty, Heidelberg University, Heidelberg, Germany
| | - Raphael Sznitman
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Russell H Taylor
- Department of Computer Science, The Johns Hopkins University, Baltimore, Maryland, USA
| | - Minu D Tizabi
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Martin Wagner
- Department for General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Gregory D Hager
- The Malone Center for Engineering in Healthcare, The Johns Hopkins University, Baltimore, Maryland, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, Maryland, USA
| | - Thomas Neumuth
- Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Leipzig, Germany
| | - Nicolas Padoy
- ICube, University of Strasbourg, CNRS, France; IHU Strasbourg, Strasbourg, France
| | - Justin Collins
- Division of Surgery and Interventional Science, University College London, London, United Kingdom
| | - Ines Gockel
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, Leipzig University Hospital, Leipzig, Germany
| | - Jan Goedeke
- Pediatric Surgery, Dr. von Hauner Children's Hospital, Ludwig-Maximilians-University, Munich, Germany
| | - Daniel A Hashimoto
- University Hospitals Cleveland Medical Center, Case Western Reserve University, Cleveland, Ohio, USA; Surgical AI and Innovation Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Luc Joyeux
- My FetUZ Fetal Research Center, Department of Development and Regeneration, Biomedical Sciences, KU Leuven, Leuven, Belgium; Center for Surgical Technologies, Faculty of Medicine, KU Leuven, Leuven, Belgium; Department of Obstetrics and Gynecology, Division Woman and Child, Fetal Medicine Unit, University Hospitals Leuven, Leuven, Belgium; Michael E. DeBakey Department of Surgery, Texas Children's Hospital and Baylor College of Medicine, Houston, Texas, USA
| | - Kyle Lam
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Daniel R Leff
- Department of BioSurgery and Surgical Technology, Imperial College London, London, United Kingdom; Hamlyn Centre for Robotic Surgery, Imperial College London, London, United Kingdom; Breast Unit, Imperial Healthcare NHS Trust, London, United Kingdom
| | - Amin Madani
- Department of Surgery, University Health Network, Toronto, Ontario, Canada
| | - Hani J Marcus
- National Hospital for Neurology and Neurosurgery, and UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Ozanan Meireles
- Massachusetts General Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Alexander Seitel
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Dogu Teber
- Department of Urology, City Hospital Karlsruhe, Karlsruhe, Germany
| | - Frank Ückert
- Institute for Applied Medical Informatics, Hamburg University Hospital, Hamburg, Germany
| | - Beat P Müller-Stich
- Department for General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Pierre Jannin
- LTSI, Inserm UMR 1099, University of Rennes 1, Rennes, France
| | - Stefanie Speidel
- Division of Translational Surgical Oncology, National Center for Tumor Diseases (NCT/UCC) Dresden, Dresden, Germany; Centre for Tactile Internet with Human-in-the-Loop (CeTI), TU Dresden, Dresden, Germany
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13
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Sidpra J, Marcus AP, Löbel U, Toescu SM, Yecies D, Grant G, Yeom K, Mirsky DM, Marcus HJ, Aquilina K, Mankad K. Improved prediction of postoperative paediatric cerebellar mutism syndrome using an artificial neural network. Neurooncol Adv 2022; 4:vdac003. [PMID: 35233531 PMCID: PMC8882257 DOI: 10.1093/noajnl/vdac003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
Postoperative paediatric cerebellar mutism syndrome (pCMS) is a common but severe complication which may arise following the resection of posterior fossa tumours in children. Two previous studies have aimed to preoperatively predict pCMS, with varying results. In this work, we examine the generalisation of these models and determine if pCMS can be predicted more accurately using an artificial neural network (ANN).
Methods
An overview of reviews was performed to identify risk factors for pCMS, and a retrospective dataset collected as per these defined risk factors from children undergoing resection of primary posterior fossa tumours. The ANN was trained on this dataset and its performance evaluated in comparison to logistic regression and other predictive indices via analysis of receiver operator characteristic curves. Area under the curve (AUC) and accuracy were calculated and compared using a Wilcoxon signed rank test, with p<0.05 considered statistically significant.
Results
204 children were included, of whom 80 developed pCMS. The performance of the ANN (AUC 0.949; accuracy 90.9%) exceeded that of logistic regression (p<0.05) and both external models (p<0.001).
Conclusion
Using an ANN, we show improved prediction of pCMS in comparison to previous models and conventional methods.
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Affiliation(s)
- Jai Sidpra
- University College London Medical School, London, WC1E 6DE, UK
- Developmental Biology and Cancer Section, University College London Great Ormond Street Institute of Child Health, London, WC1N 1EH, UK
- Department of Neuroradiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, WC1N 3JH, UK
| | - Adam P Marcus
- Department of Brain Sciences and Computing, Imperial College London, London, SW7 2BU, UK
| | - Ulrike Löbel
- Department of Neuroradiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, WC1N 3JH, UK
| | - Sebastian M Toescu
- Department of Neurosurgery, Great Ormond Street Hospital for Children NHS Foundation Trust, London, WC1N 3JH, UK
- Developmental Imaging and Biophysics Section, University College London Great Ormond Street Institute of Child Health, London WC1N 1EH, UK
| | - Derek Yecies
- Department of Neurosurgery, Lucile Packard Children’s Hospital, Stanford, CA 94304, USA
| | - Gerald Grant
- Department of Neurosurgery, Lucile Packard Children’s Hospital, Stanford, CA 94304, USA
| | - Kristen Yeom
- Department of Neuroradiology, Lucile Packard Children’s Hospital, Stanford, CA 94304, USA
| | - David M Mirsky
- Department of Radiology, Children’s Hospital Colorado, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Hani J Marcus
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, WC1N 3BG, UK
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, WC1E 6BT, UK
| | - Kristian Aquilina
- Developmental Biology and Cancer Section, University College London Great Ormond Street Institute of Child Health, London, WC1N 1EH, UK
- Department of Neurosurgery, Great Ormond Street Hospital for Children NHS Foundation Trust, London, WC1N 3JH, UK
| | - Kshitij Mankad
- Developmental Biology and Cancer Section, University College London Great Ormond Street Institute of Child Health, London, WC1N 1EH, UK
- Department of Neuroradiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, WC1N 3JH, UK
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Williams S, Layard Horsfall H, Funnell JP, Hanrahan JG, Khan DZ, Muirhead W, Stoyanov D, Marcus HJ. Artificial Intelligence in Brain Tumour Surgery-An Emerging Paradigm. Cancers (Basel) 2021; 13:cancers13195010. [PMID: 34638495 PMCID: PMC8508169 DOI: 10.3390/cancers13195010] [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: 09/07/2021] [Revised: 10/02/2021] [Accepted: 10/03/2021] [Indexed: 01/01/2023] Open
Abstract
Artificial intelligence (AI) platforms have the potential to cause a paradigm shift in brain tumour surgery. Brain tumour surgery augmented with AI can result in safer and more effective treatment. In this review article, we explore the current and future role of AI in patients undergoing brain tumour surgery, including aiding diagnosis, optimising the surgical plan, providing support during the operation, and better predicting the prognosis. Finally, we discuss barriers to the successful clinical implementation, the ethical concerns, and we provide our perspective on how the field could be advanced.
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Affiliation(s)
- Simon Williams
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
- Correspondence:
| | - Hugo Layard Horsfall
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - Jonathan P. Funnell
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - John G. Hanrahan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - Danyal Z. Khan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - William Muirhead
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - Danail Stoyanov
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - Hani J. Marcus
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
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Abstract
Neurosurgeons receive extensive and lengthy training to equip themselves with various technical skills, and neurosurgery require a great deal of pre-, intra- and postoperative clinical data collection, decision making, care and recovery. The last decade has seen a significant increase in the importance of artificial intelligence (AI) in neurosurgery. AI can provide a great promise in neurosurgery by complementing neurosurgeons' skills to provide the best possible interventional and noninterventional care for patients by enhancing diagnostic and prognostic outcomes in clinical treatment and help neurosurgeons with decision making during surgical interventions to improve patient outcomes. Furthermore, AI is playing a pivotal role in the production, processing and storage of clinical and experimental data. AI usage in neurosurgery can also reduce the costs associated with surgical care and provide high-quality healthcare to a broader population. Additionally, AI and neurosurgery can build a symbiotic relationship where AI helps to push the boundaries of neurosurgery, and neurosurgery can help AI to develop better and more robust algorithms. This review explores the role of AI in interventional and noninterventional aspects of neurosurgery during pre-, intra- and postoperative care, such as diagnosis, clinical decision making, surgical operation, prognosis, data acquisition, and research within the neurosurgical arena.
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Affiliation(s)
- Mohammad Mofatteh
- Sir William Dunn School of Pathology, Medical Sciences Division, University of Oxford, South Parks Road, Oxford OX1 3RE, United Kingdom
- Lincoln College, University of Oxford, Turl Street, Oxford OX1 3DR, United Kingdom
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16
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Brennan PM, Borchert R, Coulter C, Critchley GR, Hall B, Holliman D, Phang I, Jefferies SJ, Keni S, Lee L, Liaquat I, Marcus HJ, Thomson S, Thorne L, Vintu M, Wiggins AN, Jenkinson MD, Erridge S. Second surgery for progressive glioblastoma: a multi-centre questionnaire and cohort-based review of clinical decision-making and patient outcomes in current practice. J Neurooncol 2021; 153:99-107. [PMID: 33791952 PMCID: PMC8131335 DOI: 10.1007/s11060-021-03748-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 03/25/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE Glioblastoma prognosis is poor. Treatment options are limited at progression. Surgery may benefit, but no quality guidelines exist to inform patient selection. We sought to describe variations in surgical management at progression, highlight where further evidence is needed, and build towards a consensus strategy. METHODS Current practice in selection of patients with progressive GBM for second surgery was surveyed online amongst specialists in the UK and Europe. We complemented this with an assessment of practice in a retrospective cohort study from six United Kingdom neurosurgical units. We used descriptive statistics to analyse the data. RESULTS 234 questionnaire responses were received. Maintaining or improving patient quality of life was key to decision making, with variation as to whether patient age, performance status or intended extent of resection was relevant. MGMT methylation status was not important. Half considered no minimum time after first surgery. 288 patients were reported in the cohort analysis. Median time to second surgery from first surgery 390 days. Median overall survival 815 days, with no association between time to second surgery and time to death (p = 0.874). CONCLUSIONS This is the most wide-ranging examination of contemporaneous practice in management of GBM progression. Without evidence-based guidelines, the variation is unsurprising. We propose consensus guidelines for consideration, to reduce heterogeneity in decision making, support data collection and analysis of factors influencing outcomes, and to inform clinical trials to establish whether second surgery improves patient outcomes, or simply selects to patients already performing well.
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Affiliation(s)
- P M Brennan
- Translational Neurosurgery, Centre for Clinical Brain Sciences, University of Edinburgh, Chancellor's Building, Edinburgh BioQuarter, 49 Little France Crescent, Edinburgh, EH16 4SB, UK.
| | - R Borchert
- Addenbrookes University Hospital, Cambridge, UK
| | - C Coulter
- Royal Victoria Hospital, Newcastle, UK
| | - G R Critchley
- Brighton and Sussex University Hospitals NHS Trust, Brighton, UK
| | - B Hall
- Institute of Translational Medicine, University of Liverpool, Liverpool, UK
- Department of Neurosurgery, The Walton Centre NHS Foundation Trust, Liverpool, UK
| | | | - I Phang
- Lancashire teaching Hospitals, Preston, UK
| | | | - S Keni
- University of Edinburgh medical School, Edinburgh, UK
| | - L Lee
- University of Edinburgh medical School, Edinburgh, UK
| | - I Liaquat
- Department of Clinical Neuroscience, NHS Lothian, Edinburgh, UK
| | - H J Marcus
- National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | | | - L Thorne
- University College London Hospitals, London, UK
| | - M Vintu
- Brighton and Sussex University Hospitals NHS Trust, Brighton, UK
| | - A N Wiggins
- Department of Clinical Neuroscience, NHS Lothian, Edinburgh, UK
| | - M D Jenkinson
- Institute of Translational Medicine, University of Liverpool, Liverpool, UK
- Department of Neurosurgery, The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - S Erridge
- Department of Clinical Neuroscience, NHS Lothian, Edinburgh, UK
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