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Long Y, Ren J, Cheng F, Duan Y, Wang B, Sun Y, Sun Q, Bian L, Yi J, Qin Y, Huang R, Guo W, Jiang H, Liu C, Feng X, Qin L. Identifying gray matter alterations in Cushing's disease using machine learning: An interpretable approach. Med Phys 2024. [PMID: 38558279 DOI: 10.1002/mp.17032] [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: 06/05/2023] [Revised: 01/29/2024] [Accepted: 02/19/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Cushing's Disease (CD) is a rare clinical syndrome characterized by excessive secretion of adrenocorticotrophic hormone, leading to significant functional and structural brain alterations as observed in Magnetic Resonance Imaging (MRI). While traditional statistical analysis has been widely employed to investigate these MRI changes in CD, it has lacked the ability to predict individual-level outcomes. PURPOSE To address this problem, this paper has proposed an interpretable machine learning (ML) framework, including model-level assessment, feature-level assessment, and biology-level assessment to ensure a comprehensive analysis based on structural MRI of CD. METHODS The ML framework has effectively identified the changes in brain regions in the stage of model-level assessment, verified the effectiveness of these altered brain regions to predict CD from normal controls in the stage of feature-level assessment, and carried out a correlation analysis between altered brain regions and clinical symptoms in the stage of biology-level assessment. RESULTS The experimental results of this study have demonstrated that the Insula, Fusiform gyrus, Superior frontal gyrus, Precuneus, and the opercular portion of the Inferior frontal gyrus of CD showed significant alterations in brain regions. Furthermore, our study has revealed significant correlations between clinical symptoms and the frontotemporal lobes, insulin, and olfactory cortex, which also have been confirmed by previous studies. CONCLUSIONS The ML framework proposed in this study exhibits exceptional potential in uncovering the intricate pathophysiological mechanisms underlying CD, with potential applicability in diagnosing other diseases.
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Affiliation(s)
- Yue Long
- College of Computer, Chengdu University, Chengdu, China
| | - Jie Ren
- Department of Neurosurgery, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - FuChao Cheng
- College of Computer, Chengdu University, Chengdu, China
| | - YuMei Duan
- Department of Computer and Software, Chengdu Jincheng College, Chengdu, China
| | - BaoFeng Wang
- Department of Neurosurgery, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuhao Sun
- Department of Neurosurgery, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - QingFang Sun
- Department of Neurosurgery, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Neurosurgery, Rui Jin Lu Wan Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - LiuGuan Bian
- Department of Neurosurgery, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - JunChen Yi
- International Foundation ProgramInternational CollegeGuangxi University, Guangxi, China
| | - Ying Qin
- College of Computer, Chengdu University, Chengdu, China
| | | | - WeiTong Guo
- College of Computer, Chengdu University, Chengdu, China
| | - Hong Jiang
- Department of Neurosurgery, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Neurosurgery, Rui Jin Lu Wan Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chang Liu
- College of Computer, Chengdu University, Chengdu, China
| | - Xiao Feng
- College of Computer, Chengdu University, Chengdu, China
| | - Ling Qin
- College of Computer, Chengdu University, Chengdu, China
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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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3
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Koechli C, Zwahlen DR, Schucht P, Windisch P. Radiomics and machine learning for predicting the consistency of benign tumors of the central nervous system: A systematic review. Eur J Radiol 2023; 164:110866. [PMID: 37207398 DOI: 10.1016/j.ejrad.2023.110866] [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: 03/14/2023] [Revised: 04/28/2023] [Accepted: 05/03/2023] [Indexed: 05/21/2023]
Abstract
PURPOSE Predicting the consistency of benign central nervous system (CNS) tumors prior to surgery helps to improve surgical outcomes. This review summarizes and analyzes the literature on using radiomics and/or machine learning (ML) for consistency prediction. METHOD The Medical Literature Analysis and Retrieval System Online (MEDLINE) database was screened for studies published in English from January 1st 2000. Data was extracted according to the PRISMA guidelines and quality of the studies was assessed in compliance with the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). RESULTS Eight publications were included focusing on pituitary macroadenomas (n = 5), pituitary adenomas (n = 1), and meningiomas (n = 2) using a retrospective (n = 6), prospective (n = 1), and unknown (n = 1) study design with a total of 763 patients for the consistency prediction. The studies reported an area under the curve (AUC) of 0.71-0.99 for their respective best performing model regarding the consistency prediction. Of all studies, four articles validated their models internally whereas none validated their models externally. Two articles stated making data available on request with the remaining publications lacking information with regard to data availability. CONCLUSIONS The research on consistency prediction of CNS tumors is still at an early stage regarding the use of radiomics and different ML techniques. Best-practice procedures regarding radiomics and ML need to be followed more rigorously to facilitate the comparison between publications and, accordingly, the possible implementation into clinical practice in the future.
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Affiliation(s)
- Carole Koechli
- Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland; Universitätsklinik für Neurochirurgie, Bern University Hospital, 3010 Bern, Switzerland.
| | - Daniel R Zwahlen
- Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland
| | - Philippe Schucht
- Universitätsklinik für Neurochirurgie, Bern University Hospital, 3010 Bern, Switzerland
| | - Paul Windisch
- Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland
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Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current Evidence. Brain Sci 2023; 13:brainsci13030495. [PMID: 36979305 PMCID: PMC10046799 DOI: 10.3390/brainsci13030495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/08/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023] Open
Abstract
Background: The complex nature and heterogeneity involving pituitary surgery results have increased interest in machine learning (ML) applications for prediction of outcomes over the last decade. This study aims to systematically review the characteristics of ML models involving pituitary surgery outcome prediction and assess their reporting quality. Methods: We searched the PubMed, Scopus, and Web of Knowledge databases for publications on the use of ML to predict pituitary surgery outcomes. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) to assess report quality. Our search strategy was based on the terms “artificial intelligence”, “machine learning”, and “pituitary”. Results: 20 studies were included in this review. The principal models reported in each article were post-surgical endocrine outcomes (n = 10), tumor management (n = 3), and intra- and postoperative complications (n = 7). Overall, the included studies adhered to a median of 65% (IQR = 60–72%) of TRIPOD criteria, ranging from 43% to 83%. The median reported AUC was 0.84 (IQR = 0.80–0.91). The most popular algorithms were support vector machine (n = 5) and random forest (n = 5). Only two studies reported external validation and adherence to any reporting guideline. Calibration methods were not reported in 15 studies. No model achieved the phase of actual clinical applicability. Conclusion: Applications of ML in the prediction of pituitary outcomes are still nascent, as evidenced by the lack of any model validated for clinical practice. Although studies have demonstrated promising results, greater transparency in model development and reporting is needed to enable their use in clinical practice. Further adherence to reporting guidelines can help increase AI’s real-world utility and improve clinical practice.
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Alizadeh B, Alibabaei A, Ahmadi S, Maroufi SF, Ghafouri-Fard S, Nateghinia S. Designing predictive models for appraisal of outcome of neurosurgery patients using machine learning-based techniques. INTERDISCIPLINARY NEUROSURGERY 2023. [DOI: 10.1016/j.inat.2022.101658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022] Open
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Bove I, Franca RA, Ugga L, Solari D, Elefante A, De Caro MLDB, Cavallo LM. The "chameleon" sellar lesions: a case report of unexpected sellar lesions. Front Neurol 2023; 14:1149858. [PMID: 37168664 PMCID: PMC10164933 DOI: 10.3389/fneur.2023.1149858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 03/27/2023] [Indexed: 05/13/2023] Open
Abstract
Introduction The sellar region and its boundaries represent a challenging area, harboring a variety of tissues of different linings. Therefore, a variety of diseases can arise or involve in this area (i.e., neoplastic or not). A total of three challenging cases of "chameleon" sellar lesions treated via EEA were described, and the lesions mimicked radiological features of common sellar masses such as craniopharyngiomas and/or pituitary adenomas, and we also report a literature review of similar cases. Methods A retrospective analysis of three primary cases was conducted at the Università degli Studi di Napoli Federico II, Naples, Italy. Clinical information, radiological examinations, and pathology reports were illustrated. Results A total of three cases of so-called "chameleon" sellar lesions comprising two men and one woman were reported. Based on the intraoperative finding and pathological examination, we noticed that case 1 had suprasellar glioblastoma, case 2 had a primary neuroendocrine tumor, and case 3 had cavernous malformation. Conclusion Neurosurgeons should consider "unexpected" lesions of the sellar/suprasellar region in the preoperative differential diagnosis. A multidisciplinary approach with the collaboration of neurosurgeons, neuroradiologists, and pathologists plays a fundamental role. The recognition of unusual sellar lesions can help surgeons with better preoperative planning; so an endoscopic endonasal approach may represent a valid surgical technique to obtain decompression of the optic apparatus and vascular structures and finally a pathological diagnosis.
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Affiliation(s)
- Ilaria Bove
- Division of Neurosurgery, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Università degli Studi di Napoli “Federico II”, Naples, Italy
- *Correspondence: Ilaria Bove
| | - Raduan Ahmed Franca
- Department of Advanced Biomedical Sciences, Pathology Section, University of Naples “Federico II”, Naples, Italy
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples, Italy
| | - Domenico Solari
- Division of Neurosurgery, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Università degli Studi di Napoli “Federico II”, Naples, Italy
| | - Andrea Elefante
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples, Italy
| | | | - Luigi Maria Cavallo
- Division of Neurosurgery, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Università degli Studi di Napoli “Federico II”, Naples, Italy
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7
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DiRisio AC, Feng R, Shuman WH, Platt S, Price G, Dullea JT, Gilja S, D'Andrea MR, Delman BN, Bederson JB, Shrivastava RK. The Knosp Criteria Revisited: 3-Dimensional Volumetric Analysis as a Predictive Tool for Extent of Resection in Complex Endoscopic Pituitary Surgery. Neurosurgery 2023; 92:179-185. [PMID: 36170168 DOI: 10.1227/neu.0000000000002170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 07/29/2022] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND The Knosp criteria have been the historical standard for predicting cavernous sinus invasion, and therefore extent of surgical resection, of pituitary macroadenomas. Few studies have sought to reappraise the utility of this tool after recent advances in visualization and modeling of tumors in complex endoscopic surgery. OBJECTIVE To evaluate our proposed alternative method, using 3-dimensional (3D) volumetric imaging, and whether it can better predict extent of resection in nonfunctional pituitary adenomas. METHODS Patients who underwent endoscopic transsphenoidal resection of pituitary macroadenomas at our institution were reviewed. Information was collected on neurological, endocrine, and visual function. Volumetric segmentation was performed using 3D Slicer software. Relationship of tumor volume, clinical features, and Knosp grade on extent of resection was examined. RESULTS One hundred forty patients were identified who had transsphenoidal resection of nonfunctional pituitary adenomas. Macroadenomas had a median volume of 6 cm 3 (IQR 3.4-8.7), and 17% had a unilateral Knosp grade of at least 3B. On multiple logistic regression, only smaller log-transformed preoperative tumor volume was independently associated with increased odds of gross total resection (GTR; odds ratio: 0.27, 95% CI: 0.07-0.89, P < .05) when controlling for tumor proliferative status, age, and sex (area under the curve 0.67). The Knosp criteria did not independently predict GTR in this cohort ( P > .05, area under the curve 0.46). CONCLUSION Increasing use of volumetric 3D imaging may better anticipate extent of resection compared with the Knosp grade metric and may have a greater positive predictive value for GTR. More research is needed to validate these findings and implement them using automated methods.
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Affiliation(s)
- Aislyn C DiRisio
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Neurosurgery, University of California - Los Angeles, Los Angeles, California, USA
| | - Rui Feng
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - William H Shuman
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Neurosurgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Samantha Platt
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Radiology, New York University, New York, New York, USA
| | - Gabrielle Price
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jonathan T Dullea
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Shivee Gilja
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Megan R D'Andrea
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Bradley N Delman
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Joshua B Bederson
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Raj K Shrivastava
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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8
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Pandit AS, Khan DZ, Hanrahan JG, Dorward NL, Baldeweg SE, Nachev P, Marcus HJ. Historical and future trends in emergency pituitary referrals: a machine learning analysis. Pituitary 2022; 25:927-937. [PMID: 36085340 PMCID: PMC9462621 DOI: 10.1007/s11102-022-01269-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/02/2022] [Indexed: 11/03/2022]
Abstract
PURPOSE Acute pituitary referrals to neurosurgical services frequently necessitate emergency care. Yet, a detailed characterisation of pituitary emergency referral patterns, including how they may change prospectively is lacking. This study aims to evaluate historical and current pituitary referral patterns and utilise state-of-the-art machine learning tools to predict future service use. METHODS A data-driven analysis was performed using all available electronic neurosurgical referrals (2014-2021) to the busiest U.K. pituitary centre. Pituitary referrals were characterised and volumes were predicted using an auto-regressive moving average model with a preceding seasonal and trend decomposition using Loess step (STL-ARIMA), compared against a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) algorithm, Prophet and two standard baseline forecasting models. Median absolute, and median percentage error scoring metrics with cross-validation were employed to evaluate algorithm performance. RESULTS 462 of 36,224 emergency referrals were included (referring centres = 48; mean patient age = 56.7 years, female:male = 0.49:0.51). Emergency medicine and endocrinology accounted for the majority of referrals (67%). The most common presentations were headache (47%) and visual field deficits (32%). Lesions mainly comprised tumours or haemorrhage (85%) and involved the pituitary gland or fossa (70%). The STL-ARIMA pipeline outperformed CNN-LSTM, Prophet and baseline algorithms across scoring metrics, with standard accuracy being achieved for yearly predictions. Referral volumes significantly increased from the start of data collection with future projected increases (p < 0.001) and did not significantly reduce during the COVID-19 pandemic. CONCLUSION This work is the first to employ large-scale data and machine learning to describe and predict acute pituitary referral volumes, estimate future service demands, explore the impact of system stressors (e.g. COVID pandemic), and highlight areas for service improvement.
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Affiliation(s)
- A S Pandit
- High-Dimensional Neurology, Queen Square Institute of Neurology, University College London, London, UK
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - D Z Khan
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - J G Hanrahan
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - N L Dorward
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - S E Baldeweg
- Department of Diabetes and Endocrinology, University College London Hospital, London, UK
- Centre for Obesity & Metabolism, Department of Experimental & Translational Medicine, Division of Medicine, University College London, London, UK
| | - P Nachev
- High-Dimensional Neurology, Queen Square Institute of Neurology, University College London, London, UK
| | - H J Marcus
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK.
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK.
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9
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Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review. Cancers (Basel) 2022; 14:cancers14112676. [PMID: 35681655 PMCID: PMC9179850 DOI: 10.3390/cancers14112676] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/18/2022] [Accepted: 05/26/2022] [Indexed: 11/20/2022] Open
Abstract
Simple Summary Machine learning in radiology of the central nervous system has seen many interesting publications in the past few years. Since the focus has largely been on malignant tumors such as brain metastases and high-grade gliomas, we conducted a systematic review on benign tumors to summarize what has been published and where there might be gaps in the research. We found several studies that report good results, but the descriptions of methodologies could be improved to enable better comparisons and assessment of biases. Abstract Objectives: To summarize the available literature on using machine learning (ML) for the detection and segmentation of benign tumors of the central nervous system (CNS) and to assess the adherence of published ML/diagnostic accuracy studies to best practice. Methods: The MEDLINE database was searched for the use of ML in patients with any benign tumor of the CNS, and the records were screened according to PRISMA guidelines. Results: Eleven retrospective studies focusing on meningioma (n = 4), vestibular schwannoma (n = 4), pituitary adenoma (n = 2) and spinal schwannoma (n = 1) were included. The majority of studies attempted segmentation. Links to repositories containing code were provided in two manuscripts, and no manuscripts shared imaging data. Only one study used an external test set, which raises the question as to whether some of the good performances that have been reported were caused by overfitting and may not generalize to data from other institutions. Conclusions: Using ML for detecting and segmenting benign brain tumors is still in its infancy. Stronger adherence to ML best practices could facilitate easier comparisons between studies and contribute to the development of models that are more likely to one day be used in clinical practice.
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Chen YJ, Hsieh HP, Hung KC, Shih YJ, Lim SW, Kuo YT, Chen JH, Ko CC. Deep Learning for Prediction of Progression and Recurrence in Nonfunctioning Pituitary Macroadenomas: Combination of Clinical and MRI Features. Front Oncol 2022; 12:813806. [PMID: 35515108 PMCID: PMC9065347 DOI: 10.3389/fonc.2022.813806] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 03/22/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives A subset of non-functioning pituitary macroadenomas (NFMAs) may exhibit early progression/recurrence (P/R) after tumor resection. The purpose of this study was to apply deep learning (DL) algorithms for prediction of P/R in NFMAs. Methods From June 2009 to December 2019, 78 patients diagnosed with pathologically confirmed NFMAs, and who had undergone complete preoperative MRI and postoperative MRI follow-up for more than one year, were included. DL classifiers including multi-layer perceptron (MLP) and convolutional neural network (CNN) were used to build predictive models. Categorical and continuous clinical data were fed into the MLP model, and images of preoperative MRI (T2WI and contrast enhanced T1WI) were analyzed by the CNN model. MLP, CNN and multimodal CNN-MLP architectures were performed to predict P/R in NFMAs. Results Forty-two (42/78, 53.8%) patients exhibited P/R after surgery. The median follow-up time was 42 months, and the median time to P/R was 25 months. As compared with CNN using MRI (accuracy 83%, precision 87%, and AUC 0.84) or MLP using clinical data (accuracy 73%, precision 73%, and AUC 0.73) alone, the multimodal CNN-MLP model using both clinical and MRI features showed the best performance for prediction of P/R in NFMAs, with accuracy 83%, precision 90%, and AUC 0.85. Conclusions DL architecture incorporating clinical and MRI features performs well to predict P/R in NFMAs. Pending more studies to support the findings, the results of this study may provide valuable information for NFMAs treatment planning.
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Affiliation(s)
- Yan-Jen Chen
- Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan.,Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan
| | - Hsun-Ping Hsieh
- Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan
| | - Kuo-Chuan Hung
- Department of Anesthesiology, Chi Mei Medical Center, Tainan, Taiwan.,Department of Hospital and Health Care Administration, College of Recreation and Health Management, Chia Nan University of Pharmacy and Science, Tainan, Taiwan
| | - Yun-Ju Shih
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan
| | - Sher-Wei Lim
- Department of Neurosurgery, Chi Mei Medical Center, Tainan, Taiwan.,Department of Nursing, Min-Hwei College of Health Care Management, Tainan, Taiwan
| | - Yu-Ting Kuo
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan.,Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
| | - Jeon-Hor Chen
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States.,Department of Radiology, E-DA Hospital, I-Shou University, Kaohsiung, Taiwan
| | - Ching-Chung Ko
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan.,Department of Health and Nutrition, Chia Nan University of Pharmacy and Science, Tainan, Taiwan.,Institute of Biomedical Sciences, National Sun Yat-Sen University, Kaohsiung, Taiwan
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11
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Dai C, Sun B, Wang R, Kang J. The Application of Artificial Intelligence and Machine Learning in Pituitary Adenomas. Front Oncol 2022; 11:784819. [PMID: 35004306 PMCID: PMC8733587 DOI: 10.3389/fonc.2021.784819] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 12/02/2021] [Indexed: 12/28/2022] Open
Abstract
Pituitary adenomas (PAs) are a group of tumors with complex and heterogeneous clinical manifestations. Early accurate diagnosis, individualized management, and precise prediction of the treatment response and prognosis of patients with PA are urgently needed. Artificial intelligence (AI) and machine learning (ML) have garnered increasing attention to quantitatively analyze complex medical data to improve individualized care for patients with PAs. Therefore, we critically examined the current use of AI and ML in the management of patients with PAs, and we propose improvements for future uses of AI and ML in patients with PAs. AI and ML can automatically extract many quantitative features based on massive medical data; moreover, related diagnosis and prediction models can be developed through quantitative analysis. Previous studies have suggested that AI and ML have wide applications in early accurate diagnosis; individualized treatment; predicting the response to treatments, including surgery, medications, and radiotherapy; and predicting the outcomes of patients with PAs. In addition, facial imaging-based AI and ML, pathological picture-based AI and ML, and surgical microscopic video-based AI and ML have also been reported to be useful in assisting the management of patients with PAs. In conclusion, the current use of AI and ML models has the potential to assist doctors and patients in making crucial surgical decisions by providing an accurate diagnosis, response to treatment, and prognosis of PAs. These AI and ML models can improve the quality and safety of medical services for patients with PAs and reduce the complication rates of neurosurgery. Further work is needed to obtain more reliable algorithms with high accuracy, sensitivity, and specificity for the management of PA patients.
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Affiliation(s)
- Congxin Dai
- Department of Neurosurgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Bowen Sun
- Department of Neurosurgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Renzhi Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jun Kang
- Department of Neurosurgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China
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AIM in Endocrinology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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13
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Yan KX, Liu L, Li H. Application of machine learning in oral and maxillofacial surgery. Artif Intell Med Imaging 2021; 2:104-114. [DOI: 10.35711/aimi.v2.i6.104] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 12/20/2021] [Accepted: 12/28/2021] [Indexed: 02/06/2023] Open
Abstract
Oral and maxillofacial anatomy is extremely complex, and medical imaging is critical in the diagnosis and treatment of soft and bone tissue lesions. Hence, there exists accumulating imaging data without being properly utilized over the last decades. As a result, problems are emerging regarding how to integrate and interpret a large amount of medical data and alleviate clinicians’ workload. Recently, artificial intelligence has been developing rapidly to analyze complex medical data, and machine learning is one of the specific methods of achieving this goal, which is based on a set of algorithms and previous results. Machine learning has been considered useful in assisting early diagnosis, treatment planning, and prognostic estimation through extracting key features and building mathematical models by computers. Over the past decade, machine learning techniques have been applied to the field of oral and maxillofacial surgery and increasingly achieved expert-level performance. Thus, we hold a positive attitude towards developing machine learning for reducing the number of medical errors, improving the quality of patient care, and optimizing clinical decision-making in oral and maxillofacial surgery. In this review, we explore the clinical application of machine learning in maxillofacial cysts and tumors, maxillofacial defect reconstruction, orthognathic surgery, and dental implant and discuss its current problems and solutions.
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Affiliation(s)
- Kai-Xin Yan
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Lei Liu
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Hui Li
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
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Stumpo V, Staartjes VE, Regli L, Serra C. Machine Learning in Pituitary Surgery. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:291-301. [PMID: 34862553 DOI: 10.1007/978-3-030-85292-4_33] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Machine learning applications in neurosurgery are increasingly reported for diverse tasks such as faster and more accurate preoperative diagnosis, enhanced lesion characterization, as well as surgical outcome, complications and healthcare cost prediction. Even though the pertinent literature in pituitary surgery is less extensive with respect to other neurosurgical diseases, past research attempted to answer clinically relevant questions to better assist surgeons and clinicians. In the present chapter we review reported ML applications in pituitary surgery including differential diagnosis, preoperative lesion characterization (immunohistochemistry, cavernous sinus invasion, tumor consistency), surgical outcome and complication predictions (gross total resection, tumor recurrence, and endocrinological remission, cerebrospinal fluid leak, postoperative hyponatremia). Moreover, we briefly discuss from a practical standpoint the current barriers to clinical translation of machine learning research. On the topic of pituitary surgery, published reports can be considered mostly preliminary, requiring larger training populations and strong external validation. Thoughtful selection of clinically relevant outcomes of interest and transversal application of model development pipeline-together with accurate methodological planning and multicenter collaborations-have the potential to overcome current limitations and ultimately provide additional tools for more informed patient management.
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Affiliation(s)
- Vittorio Stumpo
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Victor E Staartjes
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
| | - Luca Regli
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Carlo Serra
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis. Neuroradiology 2021; 64:647-668. [PMID: 34839380 DOI: 10.1007/s00234-021-02845-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 10/21/2021] [Indexed: 01/03/2023]
Abstract
PURPOSE To systematically review the literature regarding the application of machine learning (ML) of magnetic resonance imaging (MRI) radiomics in common sellar tumors. To identify future directions for application of ML in sellar tumor MRI. METHODS PubMed, Medline, Embase, Google Scholar, Scopus, ArxIV, and bioRxiv were searched to identify relevant studies published between 2010 and September 2021. Studies were included if they specifically involved ML of MRI radiomics in the analysis of sellar masses. Risk of bias assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) Tool. RESULTS Fifty-eight articles were identified for review. All papers utilized retrospective data, and a quantitative systematic review was performed for thirty-one studies utilizing a public dataset which compared pituitary adenomas, meningiomas, and gliomas. One of the analyzed architectures yielded the highest classification accuracy of 0.996. The remaining twenty-seven articles were qualitatively reviewed and showed promising findings in predicting specific tumor characteristics such as tumor consistency, Ki-67 proliferative index, and post-surgical recurrence. CONCLUSION This review highlights the potential clinical application of ML using MRI radiomic data of the sellar region in diagnosis and predicting treatment outcomes. We describe future directions for practical application in the clinical care of patients with pituitary neuroendocrine and other sellar tumors.
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Liu Y, Wang X, Li J, Hao L, Zhao T, Zou H, Xu D. Deep Learning Technology in Pathological Image Analysis of Breast Tissue. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:9610830. [PMID: 34868535 PMCID: PMC8635881 DOI: 10.1155/2021/9610830] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/16/2021] [Accepted: 10/15/2021] [Indexed: 12/28/2022]
Abstract
To explore the application value of the multilevel pyramid convolutional neural network (MPCNN) model based on convolutional neural network (CNN) in breast histopathology image analysis, in this study, based on CNN algorithm and softmax classifier (SMC), a sparse autoencoder (SAE) is introduced to optimize it. The sliding window method is used to identify cells, and the CNN + SMC pathological image cell detection method is established. Furthermore, the local region active contour (LRAC) is introduced to optimize it and the LRAC fine segmentation model driven by local Gaussian distribution is established. On this basis, the sparse automatic encoder is further introduced to optimize it and the MPCNN model is established. The proposed algorithm is evaluated on the pathological image data set. The results showed that the Acc value, F value, and Re value of pathological cell detection of CNN + SMC algorithm were significantly higher than those of the other two algorithms (P < 0.05). The Dice, OL, Sen, and Spe values of pathological image regional segmentation of CNN algorithm were significantly higher than those of the other two algorithms, and the difference was statistically significant (P < 0.05). The accuracy, recall, and F-measure of the optimized CNN algorithm for detecting breast histopathological images were 85.25%, 89.27%, and 80.09%, respectively. In the two databases with segmentation standards, the segmentation accuracy of MPCNN is 55%, 73.1%, 78.8%, and 82.1%. In the deep convolution network model, the training time of the MPCNN algorithm is about 80 min. It shows that when the feature dimension is low, the feature map extracted by MPCNN is more effective than the traditional feature extraction method.
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Affiliation(s)
- Yanan Liu
- Medical Technology Department, Qiqihar Medical University, Qiqihar 161006, Heilongjiang, China
| | - Xiaoyan Wang
- Breast Department, Qiqihar First Hospital, Qiqihar 161006, Heilongjiang, China
| | - Jingyu Li
- Medical Technology Department, Qiqihar Medical University, Qiqihar 161006, Heilongjiang, China
| | - Liguo Hao
- Medical Technology Department, Qiqihar Medical University, Qiqihar 161006, Heilongjiang, China
| | - Tianyu Zhao
- Medical Technology Department, Qiqihar Medical University, Qiqihar 161006, Heilongjiang, China
| | - He Zou
- Medical Technology Department, Qiqihar Medical University, Qiqihar 161006, Heilongjiang, China
| | - Dongbin Xu
- Medical Technology Department, Qiqihar Medical University, Qiqihar 161006, Heilongjiang, China
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Hong N, Park Y, You SC, Rhee Y. AIM in Endocrinology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_328-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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18
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Zhang Y, Ko CC, Chen JH, Chang KT, Chen TY, Lim SW, Tsui YK, Su MY. Radiomics Approach for Prediction of Recurrence in Non-Functioning Pituitary Macroadenomas. Front Oncol 2020; 10:590083. [PMID: 33392084 PMCID: PMC7775655 DOI: 10.3389/fonc.2020.590083] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 11/19/2020] [Indexed: 02/06/2023] Open
Abstract
Objectives A subset of non-functioning pituitary macroadenomas (NFPAs) may exhibit early progression/recurrence (P/R) after surgical resection. The purpose of this study was to apply radiomics in predicting P/R in NFPAs. Methods Only patients who had undergone preoperative MRI and postoperative MRI follow-ups for more than 1 year were included in this study. From September 2010 to December 2017, 50 eligible patients diagnosed with pathologically confirmed NFPAs were identified. Preoperative coronal T2WI and contrast-enhanced (CE) T1WI imaging were analyzed by computer algorithms. For each imaging sequence, 32 first-order features and 75 texture features were extracted. Support vector machine (SVM) classifier was utilized to evaluate the importance of extracted parameters, and the most significant three parameters were used to build the prediction model. The SVM score was calculated based on the three selected features. Results Twenty-eight patients exhibited P/R (28/50, 56%) after surgery. The median follow-up time was 38 months, and the median time to P/R was 20 months. Visual disturbance, hypopituitarism, extrasellar extension, compression of the third ventricle, large tumor height and volume, failed optic chiasmatic decompression, and high SVM score were more frequently encountered in the P/R group (p < 0.05). In multivariate Cox hazards analysis, symptoms of sex hormones, hypopituitarism, and SVM score were high risk factors for P/R (p < 0.05) with hazard ratios of 10.71, 2.68, and 6.88. The three selected radiomics features were T1 surface-to-volume radio, T1 GLCM-informational measure of correlation, and T2 NGTDM-coarseness. The radiomics predictive model shows 25 true positive, 16 true negative, 6 false positive, and 3 false negative cases, with an accuracy of 82% and AUC of 0.78 in differentiating P/R from non-P/R NFPAs. For SVM score, optimal cut-off value of 0.537 and AUC of 0.87 were obtained for differentiation of P/R. Higher SVM scores were associated with shorter progression-free survival (p < 0.001). Conclusions Our preliminary results showed that objective and quantitative MR radiomic features can be extracted from NFPAs. Pending more studies and evidence to support the findings, radiomics analysis of preoperative MRI may have the potential to offer valuable information in treatment planning for NFPAs.
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Affiliation(s)
- Yang Zhang
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States
| | - Ching-Chung Ko
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan.,Department of Health and Nutrition, Chia Nan University of Pharmacy and Science, Tainan, Taiwan
| | - Jeon-Hor Chen
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States.,Department of Radiology, E-DA Hospital, I-Shou University, Kaohsiung, Taiwan
| | - Kai-Ting Chang
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States
| | - Tai-Yuan Chen
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan.,Graduate Institute of Medical Sciences, Chang Jung Christian University, Tainan, Taiwan
| | - Sher-Wei Lim
- Department of Neurosurgery, Chi-Mei Medical Center, Chiali, Tainan, Taiwan.,Department of Nursing, Min-Hwei College of Health Care Management, Tainan, Taiwan
| | - Yu-Kun Tsui
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States
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