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Staartjes VE, Regli L, Serra C. Letter to the editor regarding "Is Knosp enough? A novel classification for Acromegaly: a retrospective analysis of cure rates and outcome predictors in a large tertiary centre". Acta Neurochir (Wien) 2025; 167:128. [PMID: 40310556 PMCID: PMC12045803 DOI: 10.1007/s00701-025-06511-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2025] [Accepted: 03/31/2025] [Indexed: 05/02/2025]
Affiliation(s)
- Victor E Staartjes
- Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, CH-8091, Zurich, Switzerland.
| | - Luca Regli
- Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, CH-8091, Zurich, Switzerland
| | - Carlo Serra
- Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, CH-8091, Zurich, Switzerland
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Zhu Y, Liu X, Zhang K, El-Newehy M, Abdulhameed MM, Mo X, Cao L, Wang Y. Application of electrospinning and 3D-printing based bilayer composite scaffold in the skull base reconstruction during transnasal surgery. Colloids Surf B Biointerfaces 2024; 245:114337. [PMID: 39489988 DOI: 10.1016/j.colsurfb.2024.114337] [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: 08/05/2024] [Revised: 10/12/2024] [Accepted: 10/22/2024] [Indexed: 11/05/2024]
Abstract
Skull base defects are a common complication after transsphenoidal endoscopic surgery, and their commonly used autologous tissue repair has limited clinical outcomes. Tissue-engineered scaffolds prepared by advanced techniques of electrostatic spinning and three-dimensional (3D) printing was an effective way to solve this problem. In this study, soft tissue scaffolds consisting of centripetal nanofiber mats and 3D-printed hard tissue scaffolds consisting of porous structures were prepared, respectively. And the two layers were combined to obtain bilayer composite scaffolds. The physicochemical characterization proved that the nanofiber mat prepared by polylactide-polycaprolactone (PLCL) electrospinning had a uniform centripetal nanofiber structure, and the loaded bFGF growth factor could achieve a slow release for 14 days and exert its bioactivity to promote the proliferation of fibroblasts. The porous scaffolds prepared with polycaprolactone (PCL), and hydroxyapatite (HA) 3D printing have a 300 μm macroporous structure with good biocompatibility. In vivo experiments results demonstrated that the bilayer composite scaffold could promote soft tissue repair of the skull base membrane through the centripetal nanofiber structure and slow-release of bFGF factor. It also played the role of promoting the regeneration of the skull base bone tissue. In addition, the centripetal nanofiber structure also had a promotional effect on the regeneration of skull base bone tissue.
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Affiliation(s)
- Yiqian Zhu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China; National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China
| | - Xuezhe Liu
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, Shanghai Engineering Research Center of Nano-Biomaterials and Regenerative Medicine, College of Biological Science and Medical Engineering, Donghua University, Shanghai 201620, China
| | - Keyi Zhang
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China; National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China
| | - Mohamed El-Newehy
- Department of Chemistry, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
| | - Meera Moydeen Abdulhameed
- Department of Chemistry, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
| | - Xiumei Mo
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, Shanghai Engineering Research Center of Nano-Biomaterials and Regenerative Medicine, College of Biological Science and Medical Engineering, Donghua University, Shanghai 201620, China.
| | - Lei Cao
- Orthopaedic Traumatology, Trauma Center, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 201620, China.
| | - Yongfei Wang
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China; National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China.
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Chen Y, Zhong J, Li H, Lin K, Wei L, Wang S. Predictive modeling of arginine vasopressin deficiency after transsphenoidal pituitary adenoma resection by using multiple machine learning algorithms. Sci Rep 2024; 14:22210. [PMID: 39333611 PMCID: PMC11436865 DOI: 10.1038/s41598-024-72486-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Accepted: 09/09/2024] [Indexed: 09/29/2024] Open
Abstract
This study aimed to predict arginine vasopressin deficiency (AVP-D) following transsphenoidal pituitary adenoma surgery using machine learning algorithms. We reviewed 452 cases from December 2013 to December 2023, analyzing clinical and imaging data. Key predictors of AVP-D included sex, tumor height, preoperative and postoperative changes in sellar diaphragm height and pituitary stalk length, preoperative ACTH levels, changes in ACTH levels, and preoperative cortisol levels. Six machine learning algorithms were tested: logistic regression (LR), support vector classification (SVC), random forest (RF), decision tree (DT), k-nearest neighbors (KNN), and extreme gradient boosting (XGBoost). After cross-validation and parameter optimization, the random forest model demonstrated the highest performance, with an accuracy (ACC) of 0.882 and an AUC of 0.96. The decision tree model followed, achieving an accuracy of 0.843 and an AUC of 0.95. Other models showed lower performance: LR had an ACC of 0.522 and an AUC of 0.54; SVC had an ACC of 0.647 and an AUC of 0.67; KNN achieved an ACC of 0.64 and an AUC of 0.70; and XGBoost had an ACC of 0.794 and an AUC of 0.91. The study found that a shorter preoperative pituitary stalk length, significant intraoperative stretching, and lower preoperative ACTH and cortisol levels were associated with a higher likelihood of developing AVP-D post-surgery.
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Affiliation(s)
- Yuyang Chen
- Department of Neurosurgery, Fuzhou General Clinical Medical College, Fujian Medical University (900th Hospital), Fuzhou, 350025, China
| | - Jiansheng Zhong
- Department of Neurosurgery, Fuzhou General Clinical Medical College, Fujian Medical University (900th Hospital), Fuzhou, 350025, China
| | - Haixiang Li
- Department of Neurosurgery, Fuzhou General Clinical Medical College, Fujian Medical University (900th Hospital), Fuzhou, 350025, China
- Department of Neurosurgery, East Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China, FuZhou, China
| | - Kunzhe Lin
- Department of Neurosurgery, Fuzhou General Clinical Medical College, Fujian Medical University (900th Hospital), Fuzhou, 350025, China
| | - Liangfeng Wei
- Department of Neurosurgery, Fuzhou General Clinical Medical College, Fujian Medical University (900th Hospital), Fuzhou, 350025, China
| | - Shousen Wang
- Department of Neurosurgery, Fuzhou General Clinical Medical College, Fujian Medical University (900th Hospital), Fuzhou, 350025, China.
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Patel RV, Groff KJ, Bi WL. Applications and Integration of Radiomics for Skull Base Oncology. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1462:285-305. [PMID: 39523272 DOI: 10.1007/978-3-031-64892-2_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2024]
Abstract
Radiomics, a quantitative approach to extracting features from medical images, represents a new frontier in skull base oncology. Novel image analysis approaches have enabled us to capture patterns from images imperceptible by the human eye. This rich source of data can be combined with a range of clinical features, holding the potential to be a noninvasive source of biomarkers. Applications of radiomics in skull base pathologies have centered around three common tumor classes: meningioma, sellar/parasellar tumors, and vestibular schwannomas. Radiomic investigations can be categorized into five domains: tumor detection/segmentation, classification between tumor types, tumor grading, detection of tumor features, and prognostication. Various computational architectures have been employed across these domains, with deep-learning methods becoming more common versus machine learning. Across radiomic applications, contrast-enhanced T1-weighted MRI images remain the most utilized sequence for model development. Efforts to standardize and connect radiomic features to tumor biology have facilitated more clinically applicable radiomic models. Despite the advancement in model performance, several challenges continue to hinder translatability, including small sample sizes and model training on homogenous single institution data. To recognize the potential of radiomics for skull base oncology, prospective, multi-institutional collaboration will be the cornerstone for a validated radiomic technology.
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Affiliation(s)
- Ruchit V Patel
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Karenna J Groff
- New York University Grossman School of Medicine, New York, NY, USA
| | - Wenya Linda Bi
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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Suero Molina E, Di Ieva A. Artificial Intelligence, Radiomics, and Computational Modeling in Skull Base Surgery. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1462:265-283. [PMID: 39523271 DOI: 10.1007/978-3-031-64892-2_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2024]
Abstract
This chapter explores current artificial intelligence (AI), radiomics, and computational modeling applications in skull base surgery. AI advancements are providing opportunities to improve diagnostic accuracy, surgical planning, and postoperative care. Currently, computational models can assist in diagnosis, simulate surgical scenarios, and improve safety during surgical procedures by identifying critical structures. AI-powered technologies, such as liquid biopsy, machine learning, radiomic analysis, computer vision, and label-free optical imaging, aim to revolutionize skull base surgery. AI-driven advancements promise safer, more precise, and effective surgeries, improving patient outcomes and preoperative assessment.
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Affiliation(s)
- Eric Suero Molina
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, NSW, Australia.
- Department of Neurosurgery, University Hospital of Münster, Münster, Germany.
- Macquarie Neurosurgery & Spine, MQ Health, Macquarie University Hospital, Sydney, NSW, Australia.
| | - Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, NSW, Australia
- Macquarie Neurosurgery & Spine, MQ Health, Macquarie University Hospital, Sydney, NSW, Australia
- Department of Neurosurgery, Nepean Blue Mountains Local Health District, Kingswood, NSW, Australia
- Centre for Applied Artificial Intelligence, School of Computing, Macquarie University, Sydney, NSW, Australia
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Schonfeld E, Mordekai N, Berg A, Johnstone T, Shah A, Shah V, Haider G, Marianayagam NJ, Veeravagu A. Machine Learning in Neurosurgery: Toward Complex Inputs, Actionable Predictions, and Generalizable Translations. Cureus 2024; 16:e51963. [PMID: 38333513 PMCID: PMC10851045 DOI: 10.7759/cureus.51963] [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: 08/27/2023] [Accepted: 01/08/2024] [Indexed: 02/10/2024] Open
Abstract
Machine learning can predict neurosurgical diagnosis and outcomes, power imaging analysis, and perform robotic navigation and tumor labeling. State-of-the-art models can reconstruct and generate images, predict surgical events from video, and assist in intraoperative decision-making. In this review, we will detail the neurosurgical applications of machine learning, ranging from simple to advanced models, and their potential to transform patient care. As machine learning techniques, outputs, and methods become increasingly complex, their performance is often more impactful yet increasingly difficult to evaluate. We aim to introduce these advancements to the neurosurgical audience while suggesting major potential roadblocks to their safe and effective translation. Unlike the previous generation of machine learning in neurosurgery, the safe translation of recent advancements will be contingent on neurosurgeons' involvement in model development and validation.
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Affiliation(s)
- Ethan Schonfeld
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
| | | | - Alex Berg
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
| | - Thomas Johnstone
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
| | - Aaryan Shah
- School of Humanities and Sciences, Stanford University, Stanford, USA
| | - Vaibhavi Shah
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
| | - Ghani Haider
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
| | | | - Anand Veeravagu
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
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Lu B, Zhang Y, Liu C, Ma X, Liu G, Bie Z, Yang Z, Liu P. Intraoperative cerebrospinal fluid leakage and residual tumors in endoscopic transsphenoidal surgery for pituitary adenoma: risk analysis and nomogram development. Acta Neurochir (Wien) 2023; 165:4131-4142. [PMID: 37966528 DOI: 10.1007/s00701-023-05830-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 09/19/2023] [Indexed: 11/16/2023]
Abstract
BACKGROUND Endoscopic transsphenoidal surgery is the primary method used to treat pituitary adenomas (PAs) at present; however, this technique is associated with certain risks, including cerebrospinal fluid leakage (CFL) and residual tumors (RTs). In this study, we aimed to identify specific risk factors for intraoperative CFL (ioCFL) and postoperative RT in patients with pituitary adenoma and construct a corresponding nomogram for risk assessment. METHODS We collected a range of information from 782 patients who underwent endoscopic transsphenoidal PA resection in the Department of Neurosurgery at Beijing Tiantan Hospital between 2019 and 2021. Patients were then randomly assigned to training and validation groups (in a 8:2 ratio) with R software. Univariate and multivariable logistic regression models were then used to screen variables related to ioCFL and RT. These variables were then used to construct a predictive nomogram. Finally, the accuracy of the nomogram was validated by receiver operating characteristic curve (ROC) analysis, calibration plots, and decision curve analysis (DCA). RESULTS Univariate and multivariable logistic regression models identified four risk factors for ioCFL (Hardy grade, tumor size, position, and consistency) and five risk factors for RT (operation time, tumor size, consistency, Knosp grade, and primary/recurrence type). The area under the ROC curve (AUC) for the ioCFL risk model was 0.666 and 0.697 for the training and validation groups, respectively. For RT, the AUCs for the two groups were 0.788 and 0.754, respectively. The calibration plots for the ioCFL and RT models showed high calibration quality and DCA analysis yielded excellent efficiency with regards to clinical decision making. CONCLUSION Tumor size, growth characteristics, and invasion location were identified as the main factors affecting intraoperative CFL and RT. With our novel nomogram, surgeons can identify high-risk patients according to preoperative and intraoperative tumor performance and reduce the probability of complications.
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Affiliation(s)
- Bin Lu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Yu Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Chenan Liu
- Department of Gastrointestinal Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Xin Ma
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Gemingtian Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Zhixu Bie
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Zhijun Yang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Pinan Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
- Department of Neural Reconstruction, Beijing Key Laboratory of Central Nervous System Injury, Beijing Neurosurgical Institute, Capital Medical University, Beijing, People's Republic of China.
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Zanier O, Zoli M, Staartjes VE, Alalfi MO, Guaraldi F, Asioli S, Rustici A, Pasquini E, Faustini-Fustini M, Erlic Z, Hugelshofer M, Voglis S, Regli L, Mazzatenta D, Serra C. Development and external validation of clinical prediction models for pituitary surgery. BRAIN & SPINE 2023; 3:102668. [PMID: 38020983 PMCID: PMC10668061 DOI: 10.1016/j.bas.2023.102668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 08/14/2023] [Accepted: 08/25/2023] [Indexed: 12/01/2023]
Abstract
Introduction Gross total resection (GTR), Biochemical Remission (BR) and restitution of a priorly disrupted hypothalamus pituitary axis (new improvement, IMP) are important factors in pituitary adenoma (PA) resection surgery. Prediction of these metrics using simple and preoperatively available data might help improve patient care and contribute to a more personalized medicine. Research question This study aims to develop machine learning models predicting GTR, BR, and IMP in PA resection surgery, using preoperatively available data. Material and methods With data from patients undergoing endoscopic transsphenoidal surgery for PAs machine learning models for prediction of GTR, BR and IMP were developed and externally validated. Development was carried out on a registry from Bologna, Italy while external validation was conducted using patient data from Zurich, Switzerland. Results The model development cohort consisted of 1203 patients. GTR was achieved in 207 (17.2%, 945 (78.6%) missing), BR in 173 (14.4%, 992 (82.5%) missing) and IMP in 208 (17.3%, 167 (13.9%) missing) cases. In the external validation cohort 206 patients were included and GTR was achieved in 121 (58.7%, 32 (15.5%) missing), BR in 46 (22.3%, 145 (70.4%) missing) and IMP in 42 (20.4%, 7 (3.4%) missing) cases. The AUC at external validation amounted to 0.72 (95% CI: 0.63-0.80) for GTR, 0.69 (0.52-0.83) for BR, as well as 0.82 (0.76-0.89) for IMP. Discussion and conclusion All models showed adequate generalizability, performing similarly in training and external validation, confirming the possible potentials of machine learning in helping to adapt surgical therapy to the individual patient.
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Affiliation(s)
- Olivier Zanier
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Matteo Zoli
- IRCCS Istituto Delle Scienze Neurologiche di Bologna. Programma Neurochirurgia Ipofisi - Pituitary Unit, Bologna, Italy
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Italy
| | - Victor E. Staartjes
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | | | - Federica Guaraldi
- IRCCS Istituto Delle Scienze Neurologiche di Bologna. Programma Neurochirurgia Ipofisi - Pituitary Unit, Bologna, Italy
| | - Sofia Asioli
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Italy
- Azienda USL di Bologna, Anatomic Pathology Unit, Bologna, Italy
| | - Arianna Rustici
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Italy
| | - Ernesto Pasquini
- Azienda USL di Bologna, Bellaria Hospital, ENT Unit, Bologna, Italy
| | - Marco Faustini-Fustini
- IRCCS Istituto Delle Scienze Neurologiche di Bologna. Programma Neurochirurgia Ipofisi - Pituitary Unit, Bologna, Italy
| | - Zoran Erlic
- Department of Endocrinology, Diabetology and Clinical Nutrition, University Hospital Zurich (USZ) and University of Zurich (UZH), Zurich, Switzerland
| | - Michael Hugelshofer
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Stefanos Voglis
- 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
| | - Diego Mazzatenta
- IRCCS Istituto Delle Scienze Neurologiche di Bologna. Programma Neurochirurgia Ipofisi - Pituitary Unit, Bologna, Italy
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Italy
| | - 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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Chang H, Zhao K, Qiu J, Ji XJ, Chen WG, Li BY, Lv C, Xiong ZC, Chen SB, Shu XJ. Prediction of intraoperative cerebrospinal fluid leaks in endoscopic endonasal transsphenoidal pituitary surgery based on a deep neural network model trained with MRI images: a pilot study. Front Neurosci 2023; 17:1203698. [PMID: 37575298 PMCID: PMC10413098 DOI: 10.3389/fnins.2023.1203698] [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: 04/11/2023] [Accepted: 07/13/2023] [Indexed: 08/15/2023] Open
Abstract
Objective This study aimed to investigate the reliability of a deep neural network (DNN) model trained only on contrast-enhanced T1 (T1CE) images for predicting intraoperative cerebrospinal fluid (ioCSF) leaks in endoscopic transsphenoidal surgery (EETS). Methods 396 pituitary adenoma (PA) cases were reviewed, only primary PAs with Hardy suprasellar Stages A, B, and C were included in this study. The T1CE images of these patients were collected, and sagittal and coronal T1CE slices were selected for training the DNN model. The model performance was evaluated and tested, and its interpretability was explored. Results A total of 102 PA cases were enrolled in this study, 51 from the ioCSF leakage group, and 51 from the non-ioCSF leakage group. 306 sagittal and 306 coronal T1CE slices were collected as the original dataset, and data augmentation was applied before model training and testing. In the test dataset, the DNN model provided a single-slice prediction accuracy of 97.29%, a sensitivity of 98.25%, and a specificity of 96.35%. In clinical test, the accuracy of the DNN model in predicting ioCSF leaks in patients reached 84.6%. The feature maps of the model were visualized and the regions of interest for prediction were the tumor roof and suprasellar region. Conclusion In this study, the DNN model could predict ioCSF leaks based on preoperative T1CE images, especially in PAs in Hardy Stages A, B, and C. The region of interest in the model prediction-making process is similar to that of humans. DNN models trained with preoperative MRI images may provide a novel tool for predicting ioCSF leak risk for PA patients.
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Affiliation(s)
- Hui Chang
- School of Computer and Information Engineering and Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng, Henan Province, China
| | - Kai Zhao
- The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Jun Qiu
- Department of Critical Care Medicine, The Second People’s Hospital of Yibin, Yibin, Sichuan Province, China
| | - Xiang-Jun Ji
- Department of Neurosurgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu Province, China
| | - Wu-Gang Chen
- School of Computer and Information Engineering and Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng, Henan Province, China
| | - Bo-Yuan Li
- School of Computer and Information Engineering and Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng, Henan Province, China
| | - Cheng Lv
- School of Mathematics and Computer Sciences, Nanchang University, Nanchang, Jiangxi Province, China
| | - Zi-Cheng Xiong
- School of Computer and Information Engineering and Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng, Henan Province, China
| | - Sheng-Bo Chen
- School of Computer and Information Engineering and Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng, Henan Province, China
| | - Xu-Jun Shu
- Department of Neurosurgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu Province, China
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Fuse Y, Takeuchi K, Nishiwaki H, Imaizumi T, Nagata Y, Ohno K, Saito R. Machine learning models predict delayed hyponatremia post-transsphenoidal surgery using clinically available features. Pituitary 2023:10.1007/s11102-023-01311-w. [PMID: 36995457 DOI: 10.1007/s11102-023-01311-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/14/2023] [Indexed: 03/31/2023]
Abstract
PURPOSE Delayed hyponatremia (DHN), a unique complication, is the leading cause of unexpected readmission after pituitary surgery. Therefore, this study aimed to develop tools for predicting postoperative DHN in patients undergoing endoscopic transsphenoidal surgery (eTSS) for pituitary neuroendocrine tumors (PitNETs). METHODS This was a single-center, retrospective study involving 193 patients with PitNETs who underwent eTSS. The objective variable was DHN, defined as serum sodium levels < 135 mmol/L at ≥ 1 time between post operative days 3 and 9. We trained four machine learning models to predict this objective variable using the clinical variables available preoperatively and on the first postoperative day. The clinical variables included patient characteristics, pituitary-related hormone levels, blood test results, radiological findings, and postoperative complications. RESULTS The random forest (RF) model demonstrated the highest (0.759 ± 0.039) area under the curve of the receiver operating characteristic curve (ROC-AUC), followed by the support vector machine (0.747 ± 0.034), the light gradient boosting machine (LGBM: 0.738 ± 0.026), and the logistic regression (0.710 ± 0.028). The highest accuracy (0.746 ± 0.029) was observed in the LGBM model. The best-performing RF model was based on 24 features, nine of which were clinically available preoperatively. CONCLUSIONS The proposed machine learning models with pre- and post-resection features predicted DHN after the resection of PitNETs.
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Affiliation(s)
- Yutaro Fuse
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Kazuhito Takeuchi
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan.
| | - Hiroshi Nishiwaki
- Division of Neurogenetics, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Takahiro Imaizumi
- Department of Advanced Medicine, Nagoya University Hospital, Nagoya, Japan
| | - Yuichi Nagata
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Kinji Ohno
- Division of Neurogenetics, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Ryuta Saito
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
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CRANIAL Consortium. Machine learning driven prediction of cerebrospinal fluid rhinorrhoea following endonasal skull base surgery: A multicentre prospective observational study. Front Oncol 2023; 13:1046519. [PMID: 37035179 PMCID: PMC10076706 DOI: 10.3389/fonc.2023.1046519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 01/27/2023] [Indexed: 04/11/2023] Open
Abstract
Background Cerebrospinal fluid rhinorrhoea (CSFR) is a common complication following endonasal skull base surgery, a technique that is fundamental to the treatment of pituitary adenomas and many other skull base tumours. The CRANIAL study explored CSFR incidence and related risk factors, particularly skull base repair techniques, via a multicentre prospective observational study. We sought to use machine learning to leverage this complex multicentre dataset for CSFR prediction and risk factor analysis. Methods A dataset of 865 cases - 725 transsphenoidal approach (TSA) and 140 expanded endonasal approach (EEA) - with cerebrospinal fluid rhinorrhoea as the primary outcome, was used. Relevant variables were extracted from the data, and prediction variables were divided into two categories, preoperative risk factors; and repair techniques, with 6 and 11 variables respectively. Three types of machine learning models were developed in order to predict CSFR: logistic regression (LR); decision tree (DT); and neural network (NN). Models were validated using 5-fold cross-validation, compared via their area under the curve (AUC) evaluation metric, and key prediction variables were identified using their Shapley additive explanations (SHAP) score. Results CSFR rates were 3.9% (28/725) for the transsphenoidal approach and 7.1% (10/140) for the expanded endonasal approach. NNs outperformed LR and DT for CSFR prediction, with a mean AUC of 0.80 (0.70-0.90) for TSA and 0.78 (0.60-0.96) for EEA, when all risk factor and intraoperative repair data were integrated into the model. The presence of intraoperative CSF leak was the most prominent risk factor for CSFR. Elevated BMI and revision surgery were also associated with CSFR for the transsphenoidal approach. CSF diversion and gasket sealing appear to be strong predictors of the absence of CSFR for both approaches. Conclusion Neural networks are effective at predicting CSFR and uncovering key CSFR predictors in patients following endonasal skull base surgery, outperforming traditional statistical methods. These models will be improved further with larger and more granular datasets, improved NN architecture, and external validation. In the future, such predictive models could be used to assist surgical decision-making and support more individualised patient counselling.
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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: 6] [Impact Index Per Article: 3.0] [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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13
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Noh SH, Cho PG, Kim KN, Kim SH, Shin DA. Artificial Intelligence for Neurosurgery : Current State and Future Directions. J Korean Neurosurg Soc 2023; 66:113-120. [PMID: 36124365 PMCID: PMC10009243 DOI: 10.3340/jkns.2022.0130] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 09/12/2022] [Indexed: 11/27/2022] Open
Abstract
Artificial intelligence (AI) is a field of computer science that equips machines with human-like intelligence and enables them to learn, reason, and solve problems when presented with data in various formats. Neurosurgery is often at the forefront of innovative and disruptive technologies, which have similarly altered the course of acute and chronic diseases. In diagnostic imaging, such as X-rays, computed tomography, and magnetic resonance imaging, AI is used to analyze images. The use of robots in the field of neurosurgery is also increasing. In neurointensive care units, AI is used to analyze data and provide care to critically ill patients. Moreover, AI can be used to predict a patient's prognosis. Several AI applications have already been introduced in the field of neurosurgery, and many more are expected in the near future. Ultimately, it is our responsibility to keep pace with this evolution to provide meaningful outcomes and personalize each patient's care. Rather than blindly relying on AI in the future, neurosurgeons should gain a thorough understanding of it and use it to enhance their patient care.
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Affiliation(s)
- Sung Hyun Noh
- Department of Neurosurgery, Ajou University College of Medicine, Suwon, Korea.,Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Pyung Goo Cho
- Department of Neurosurgery, Ajou University College of Medicine, Suwon, Korea
| | - Keung Nyun Kim
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea.,Department of Neurosurgery, Spine and Spinal Cord Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Sang Hyun Kim
- Department of Neurosurgery, Ajou University College of Medicine, Suwon, Korea
| | - Dong Ah Shin
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea.,Department of Neurosurgery, Spine and Spinal Cord Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
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14
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Baussart B, Venier A, Jouinot A, Reuter G, Gaillard S. Closure strategy for endoscopic pituitary surgery: Experience from 3015 patients. Front Oncol 2023; 12:1067312. [PMID: 36686774 PMCID: PMC9846073 DOI: 10.3389/fonc.2022.1067312] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 12/05/2022] [Indexed: 01/05/2023] Open
Abstract
Introduction Effective strategies are required to ensure optimal management of the crucial closure step in endoscopic pituitary surgery. Many surgical techniques have been reported but no significant consensus has been defined. Methods Between January 2006 and March 2022, 3015 adult patients with pituitary adenomas were operated on by a single expert neurosurgical team, using a mononostril endoscopic endonasal approach. Based of preoperative risk factors of and operative findings, a detailed closure strategy was used. Body mass index >40, sellar floor lysis, number of surgeries>2, large skull base destruction, prior radiotherapy were considered as preoperative risk factors for closure failure. All patients treated with an expanded endonasal approach were excluded. Results Patients were mostly women (F/M ratio: 1.4) with a median age of 50 (range: 18 -89). Intraoperative CSF leak requiring specific surgical management was observed in 319/3015 (10.6%) of patients. If intraoperative leak occurred, patients with predictive risk factors were managed using a Foley balloon catheter in case of sellar floor lysis or BMI>40 and a multilayer repair strategy with a vascularized nasoseptal flap in other cases. Postoperative CSF leak occurred in 29/3015 (1%) of patients, while meningitis occurred in 24/3015 (0.8%) of patients. In patients with intraoperative leak, closure management failed in 11/319 (3.4%) of cases. Conclusion Based on our significant 16-year experience, our surgical management is reliable and easy to follow. With a planned and stepwise strategy, the closure step can be optimized and tailored to each patient with a very low failure rate.
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Affiliation(s)
- Bertrand Baussart
- Department of Neurosurgery, La Pitié-Salpêtrière University Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France,Université Paris Cité, Institut Cochin, CNRS, INSERM, Paris, France,*Correspondence: Bertrand Baussart, ;
| | - Alice Venier
- Department of Neurosurgery, Neurocenter of Southern Switzerland, Lugano, Switzerland
| | - Anne Jouinot
- Université Paris Cité, Institut Cochin, CNRS, INSERM, Paris, France
| | - Gilles Reuter
- Department of Neurosurgery, Centre Hospitalier Universitaire (CHU) de Liège, Bat B35, Domaine Universitaire du Sart-Tilman, Liège, Belgium
| | - Stephan Gaillard
- Department of Neurosurgery, La Pitié-Salpêtrière University Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
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15
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Mattogno PP, Caccavella VM, Giordano M, D'Alessandris QG, Chiloiro S, Tariciotti L, Olivi A, Lauretti L. Interpretable Machine Learning-Based Prediction of Intraoperative Cerebrospinal Fluid Leakage in Endoscopic Transsphenoidal Pituitary Surgery: A Pilot Study. J Neurol Surg B Skull Base 2022; 83:485-495. [PMID: 36091632 PMCID: PMC9462964 DOI: 10.1055/s-0041-1740621] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 11/12/2021] [Indexed: 01/18/2023] Open
Abstract
Purpose Transsphenoidal surgery (TSS) for pituitary adenomas can be complicated by the occurrence of intraoperative cerebrospinal fluid (CSF) leakage (IOL). IOL significantly affects the course of surgery predisposing to the development of postoperative CSF leakage, a major source of morbidity and mortality in the postoperative period. The authors trained and internally validated the Random Forest (RF) prediction model to preoperatively identify patients at high risk for IOL. A locally interpretable model-agnostic explanations (LIME) algorithm is employed to elucidate the main drivers behind each machine learning (ML) model prediction. Methods The data of 210 patients who underwent TSS were collected; first, risk factors for IOL were identified via conventional statistical methods (multivariable logistic regression). Then, the authors trained, optimized, and audited a RF prediction model. Results IOL reported in 45 patients (21.5%). The recursive feature selection algorithm identified the following variables as the most significant determinants of IOL: Knosp's grade, sellar Hardy's grade, suprasellar Hardy's grade, tumor diameter (on X, Y, and Z axes), intercarotid distance, and secreting status (nonfunctioning and growth hormone [GH] secreting). Leveraging the predictive values of these variables, the RF prediction model achieved an area under the curve (AUC) of 0.83 (95% confidence interval [CI]: 0.78; 0.86), significantly outperforming the multivariable logistic regression model (AUC = 0.63). Conclusion A RF model that reliably identifies patients at risk for IOL was successfully trained and internally validated. ML-based prediction models can predict events that were previously judged nearly unpredictable; their deployment in clinical practice may result in improved patient care and reduced postoperative morbidity and healthcare costs.
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Affiliation(s)
- Pier Paolo Mattogno
- Department of Neurosurgery, Fondazione Policlinico Universitario A. Gemell iIstituto di Ricovero e Cura a Carattere Scientifico Università Cattolica del Sacro Cuore, Rome, Italy
| | - Valerio M. Caccavella
- Department of Neurosurgery, Fondazione Policlinico Universitario A. Gemell iIstituto di Ricovero e Cura a Carattere Scientifico Università Cattolica del Sacro Cuore, Rome, Italy
| | - Martina Giordano
- Department of Neurosurgery, Fondazione Policlinico Universitario A. Gemell iIstituto di Ricovero e Cura a Carattere Scientifico Università Cattolica del Sacro Cuore, Rome, Italy
| | - Quintino G. D'Alessandris
- Department of Neurosurgery, Fondazione Policlinico Universitario A. Gemell iIstituto di Ricovero e Cura a Carattere Scientifico Università Cattolica del Sacro Cuore, Rome, Italy
| | - Sabrina Chiloiro
- Department of Endocrinology, Fondazione Policlinico Universitario A. Gemelli Istituto di Ricovero e Cura a Carattere Scientifico Università Cattolica del Sacro Cuore, Rome, Italy
| | - Leonardo Tariciotti
- Unit of Neurosurgery, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Cà Granda Ospedale Maggiore Policlinico, Milan, Italy
- University of Milan, Milan, Italy
| | - Alessandro Olivi
- Department of Neurosurgery, Fondazione Policlinico Universitario A. Gemell iIstituto di Ricovero e Cura a Carattere Scientifico Università Cattolica del Sacro Cuore, Rome, Italy
| | - Liverana Lauretti
- Department of Neurosurgery, Fondazione Policlinico Universitario A. Gemell iIstituto di Ricovero e Cura a Carattere Scientifico Università Cattolica del Sacro Cuore, Rome, Italy
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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: 1.7] [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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17
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Zanier O, Zoli M, Staartjes VE, Guaraldi F, Asioli S, Rustici A, Picciola VM, Pasquini E, Faustini-Fustini M, Erlic Z, Regli L, Mazzatenta D, Serra C. Machine learning-based clinical outcome prediction in surgery for acromegaly. Endocrine 2022; 75:508-515. [PMID: 34642894 PMCID: PMC8816764 DOI: 10.1007/s12020-021-02890-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 09/08/2021] [Indexed: 11/13/2022]
Abstract
PURPOSE Biochemical remission (BR), gross total resection (GTR), and intraoperative cerebrospinal fluid (CSF) leaks are important metrics in transsphenoidal surgery for acromegaly, and prediction of their likelihood using machine learning would be clinically advantageous. We aim to develop and externally validate clinical prediction models for outcomes after transsphenoidal surgery for acromegaly. METHODS Using data from two registries, we develop and externally validate machine learning models for GTR, BR, and CSF leaks after endoscopic transsphenoidal surgery in acromegalic patients. For the model development a registry from Bologna, Italy was used. External validation was then performed using data from Zurich, Switzerland. Gender, age, prior surgery, as well as Hardy and Knosp classification were used as input features. Discrimination and calibration metrics were assessed. RESULTS The derivation cohort consisted of 307 patients (43.3% male; mean [SD] age, 47.2 [12.7] years). GTR was achieved in 226 (73.6%) and BR in 245 (79.8%) patients. In the external validation cohort with 46 patients, 31 (75.6%) achieved GTR and 31 (77.5%) achieved BR. Area under the curve (AUC) at external validation was 0.75 (95% confidence interval: 0.59-0.88) for GTR, 0.63 (0.40-0.82) for BR, as well as 0.77 (0.62-0.91) for intraoperative CSF leaks. While prior surgery was the most important variable for prediction of GTR, age, and Hardy grading contributed most to the predictions of BR and CSF leaks, respectively. CONCLUSIONS Gross total resection, biochemical remission, and CSF leaks remain hard to predict, but machine learning offers potential in helping to tailor surgical therapy. We demonstrate the feasibility of developing and externally validating clinical prediction models for these outcomes after surgery for acromegaly and lay the groundwork for development of a multicenter model with more robust generalization.
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Affiliation(s)
- Olivier Zanier
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Matteo Zoli
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Programma Neurochirurgia Ipofisi-Pituitary Unit, Bologna, Italy
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy
| | - Victor E Staartjes
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Federica Guaraldi
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Programma Neurochirurgia Ipofisi-Pituitary Unit, Bologna, Italy
| | - Sofia Asioli
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy
- Azienda USL di Bologna, Anatomic Pathology Unit, Bologna, Italy
| | - Arianna Rustici
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Bologna, Italy
| | | | - Ernesto Pasquini
- Azienda USL di Bologna, Bellaria Hospital, ENT Unit, Bologna, Italy
| | - Marco Faustini-Fustini
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Programma Neurochirurgia Ipofisi-Pituitary Unit, Bologna, Italy
| | - Zoran Erlic
- Department of Endocrinology, Diabetology and Clinical Nutrition, University Hospital Zurich (USZ) and University of Zurich (UZH), 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
| | - Diego Mazzatenta
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Programma Neurochirurgia Ipofisi-Pituitary Unit, Bologna, Italy
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy
| | - 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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Abstract
Artificial intelligence (AI) has illuminated a clear path towards an evolving health-care system replete with enhanced precision and computing capabilities. Medical imaging analysis can be strengthened by machine learning as the multidimensional data generated by imaging naturally lends itself to hierarchical classification. In this Review, we describe the role of machine intelligence in image-based endocrine cancer diagnostics. We first provide a brief overview of AI and consider its intuitive incorporation into the clinical workflow. We then discuss how AI can be applied for the characterization of adrenal, pancreatic, pituitary and thyroid masses in order to support clinicians in their diagnostic interpretations. This Review also puts forth a number of key evaluation criteria for machine learning in medicine that physicians can use in their appraisals of these algorithms. We identify mitigation strategies to address ongoing challenges around data availability and model interpretability in the context of endocrine cancer diagnosis. Finally, we delve into frontiers in systems integration for AI, discussing automated pipelines and evolving computing platforms that leverage distributed, decentralized and quantum techniques.
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Affiliation(s)
| | - Ihab R Kamel
- Department of Imaging & Imaging Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Harrison X Bai
- Department of Imaging & Imaging Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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19
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Huang J, Shlobin NA, DeCuypere M, Lam SK. Deep Learning for Outcome Prediction in Neurosurgery: A Systematic Review of Design, Reporting, and Reproducibility. Neurosurgery 2022; 90:16-38. [PMID: 34982868 DOI: 10.1227/neu.0000000000001736] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 08/18/2021] [Indexed: 02/06/2023] Open
Abstract
Deep learning (DL) is a powerful machine learning technique that has increasingly been used to predict surgical outcomes. However, the large quantity of data required and lack of model interpretability represent substantial barriers to the validity and reproducibility of DL models. The objective of this study was to systematically review the characteristics of DL studies involving neurosurgical outcome prediction and to assess their bias and reporting quality. Literature search using the PubMed, Scopus, and Embase databases identified 1949 records of which 35 studies were included. Of these, 32 (91%) developed and validated a DL model while 3 (9%) validated a pre-existing model. The most commonly represented subspecialty areas were oncology (16 of 35, 46%), spine (8 of 35, 23%), and vascular (6 of 35, 17%). Risk of bias was low in 18 studies (51%), unclear in 5 (14%), and high in 12 (34%), most commonly because of data quality deficiencies. Adherence to transparent reporting of a multivariable prediction model for individual prognosis or diagnosis reporting standards was low, with a median of 12 transparent reporting of a multivariable prediction model for individual prognosis or diagnosis items (39%) per study not reported. Model transparency was severely limited because code was provided in only 3 studies (9%) and final models in 2 (6%). With the exception of public databases, no study data sets were readily available. No studies described DL models as ready for clinical use. The use of DL for neurosurgical outcome prediction remains nascent. Lack of appropriate data sets poses a major concern for bias. Although studies have demonstrated promising results, greater transparency in model development and reporting is needed to facilitate reproducibility and validation.
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Affiliation(s)
- Jonathan Huang
- Ann and Robert H. Lurie Children's Hospital, Division of Pediatric Neurosurgery, Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
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20
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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: 0.8] [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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21
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Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:319-331. [PMID: 34862556 DOI: 10.1007/978-3-030-85292-4_36] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Machine learning (ML) is a rapidly rising research tool in biomedical sciences whose applications include segmentation, classification, disease detection, and outcome prediction. With respect to traditional statistical methods, ML algorithms have the potential to learn and improve their predictive performance when fed with large data sets without the need of being specifically programmed. In recent years, this technology has been increasingly applied for tackling clinical issues in intracranial aneurysm (IA) research. Several studies attempted to provide reliable models for enhanced aneurysm detection. Convolutional neural networks trained with variable degrees of human interaction on data from diverse imaging modalities showed high sensitivity in aneurysm detection tasks, also outperforming expert image analysis. Algorithms were also shown to differentiate ruptured from unruptured IAs, with however limited clinical relevance. For prediction of rupture and stability assessment, ML was preliminarily shown to achieve better performance compared to conventional statistical methods and existing risk scores. ML-based complication and functional outcome prediction in the event of SAH have been more extensively reported, in contrast with periprocedural outcome investigation in unruptured IA patients. ML has the potential to be a game changer in IA patient management. Currently clinical translation of experimental results is limited.
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22
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Cai X, Zhu J, Yang J, Tang C, Yuan F, Cong Z, Ma C. Development and Validation of Nomogram to Preoperatively Predict Intraoperative Cerebrospinal Fluid Leakage in Endoscopic Pituitary Surgery: A Retrospective Cohort Study. Front Oncol 2021; 11:719494. [PMID: 34765541 PMCID: PMC8576331 DOI: 10.3389/fonc.2021.719494] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 10/08/2021] [Indexed: 12/23/2022] Open
Abstract
Background Pituitary adenomas (PAs) are the most common tumor of the sellar region. PA resection is the preferred treatment for patients with clear indications for surgery. Intraoperative cerebrospinal fluid (iCSF) leakage is a major complication of PA resection surgery. Risk factors for iCSF leakage have been studied previously, but a predictive nomogram has not yet been developed. We constructed a nomogram for preoperative prediction of iCSF leakage in endoscopic pituitary surgery. Methods A total of 232 patients who underwent endoscopic PA resection at the Department of Neurosurgery in Jinling Hospital between January of 2018 and October of 2020 were enrolled in this retrospective study. Patients treated by a board-certified neurosurgeon were randomly classified into a training cohort or a validation cohort 1. Patients treated by other qualified neurosurgeons were included in validation cohort 2. A range of demographic, clinical, radiological, and laboratory data were acquired from the medical records. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and uni- and multivariate logistic regression were utilized to analyze these features and develop a nomogram model. We used a receiver operating characteristic (ROC) curve and calibration curve to evaluate the predictive performance of the nomogram model. Results Variables were comparable between the training cohort and validation cohort 1. Tumor height and albumin were included in the final prediction model. The area under the curve (AUC) of the nomogram model was 0.733, 0.643, and 0.644 in training, validation 1, and validation 2 cohorts, respectively. The calibration curve showed satisfactory homogeneity between the predicted probability and actual observations. Nomogram performance was stable in the subgroup analysis. Conclusions Tumor height and albumin were the independent risk factors for iCSF leakage. The prediction model developed in this study is the first nomogram developed as a practical and effective tool to facilitate the preoperative prediction of iCSF leakage in endoscopic pituitary surgery, thus optimizing treatment decisions.
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Affiliation(s)
- Xiangming Cai
- School of Medicine, Southeast University, Nanjing, China
| | - Junhao Zhu
- School of Medicine, Nanjing University, Nanjing, China.,Department of Neurosurgery, Jinling Hospital, Nanjing, China
| | - Jin Yang
- Department of Neurosurgery, Jinling Hospital, Nanjing, China
| | - Chao Tang
- Department of Neurosurgery, Jinling Hospital, Nanjing, China
| | - Feng Yuan
- School of Medicine, Nanjing University, Nanjing, China.,Department of Neurosurgery, Jinling Hospital, Nanjing, China
| | - Zixiang Cong
- School of Medicine, Nanjing University, Nanjing, China.,Department of Neurosurgery, Jinling Hospital, Nanjing, China
| | - Chiyuan Ma
- School of Medicine, Southeast University, Nanjing, China.,School of Medicine, Nanjing University, Nanjing, China.,Department of Neurosurgery, Jinling Hospital, Nanjing, China.,School of Nanjing Medicine, Southern Medical University, Guangzhou, China
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Simultaneous brain structure segmentation in magnetic resonance images using deep convolutional neural networks. Radiol Phys Technol 2021; 14:358-365. [PMID: 34338999 DOI: 10.1007/s12194-021-00633-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 07/24/2021] [Accepted: 07/28/2021] [Indexed: 10/20/2022]
Abstract
In brain magnetic resonance imaging (MRI) examinations, rapidly acquired two-dimensional (2D) T1-weighted sagittal slices are typically used to confirm brainstem atrophy and the presence of signals in the posterior pituitary gland. Image segmentation is essential for the automatic evaluation of chronological changes in the brainstem and pituitary gland. Thus, the purpose of our study was to use deep learning to automatically segment internal organs (brainstem, corpus callosum, pituitary, cerebrum, and cerebellum) in midsagittal slices of 2D T1-weighted images. Deep learning for the automatic segmentation of seven regions in the images was accomplished using two different methods: patch-based segmentation and semantic segmentation. The networks used for patch-based segmentation were AlexNet, GoogLeNet, and ResNet50, whereas semantic segmentation was accomplished using SegNet, VGG16-weighted SegNet, and U-Net. The precision and Jaccard index were calculated, and the extraction accuracy of the six convolutional network (DCNN) systems was evaluated. The highest precision (0.974) was obtained with the VGG16-weighted SegNet, and the lowest precision (0.506) was obtained with ResNet50. Based on the data, calculation times, and Jaccard indices obtained in this study, segmentation on a 2D image may be considered a viable and effective approach. We found that the optimal automatic segmentation of organs (brainstem, corpus callosum, pituitary, cerebrum, and cerebellum) on brain sagittal T1-weighted images could be achieved using SegNet with VGG16.
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Raju B, Jumah F, Ashraf O, Narayan V, Gupta G, Sun H, Hilden P, Nanda A. Big data, machine learning, and artificial intelligence: a field guide for neurosurgeons. J Neurosurg 2021; 135:373-383. [PMID: 33007750 DOI: 10.3171/2020.5.jns201288] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 05/27/2020] [Indexed: 11/06/2022]
Abstract
Big data has transformed into a trend phrase in healthcare and neurosurgery, becoming a pervasive and inescapable phrase in everyday life. The upsurge in big data applications is a direct consequence of the drastic boom in information technology as well as the growing number of internet-connected devices called the Internet of Things in healthcare. Compared with business, marketing, and other sectors, healthcare applications are lagging due to a lack of technical knowledge among healthcare workers, technological limitations in acquiring and analyzing the data, and improper governance of healthcare big data. Despite these limitations, the medical literature is flooded with big data-related articles, and most of these are filled with abstruse terminologies such as machine learning, artificial intelligence, artificial neural network, and algorithm. Many of the recent articles are restricted to neurosurgical registries, creating a false impression that big data is synonymous with registries. Others advocate that the utilization of big data will be the panacea to all healthcare problems and research in the future. Without a proper understanding of these principles, it becomes easy to get lost without the ability to differentiate hype from reality. To that end, the authors give a brief narrative of big data analysis in neurosurgery and review its applications, limitations, and the challenges it presents for neurosurgeons and healthcare professionals naive to this field. Awareness of these basic concepts will allow neurosurgeons to understand the literature regarding big data, enabling them to make better decisions and deliver personalized care.
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Affiliation(s)
- Bharath Raju
- 1Department of Neurosurgery, Rutgers-Robert Wood Johnson Medical School and University Hospital; and
| | - Fareed Jumah
- 1Department of Neurosurgery, Rutgers-Robert Wood Johnson Medical School and University Hospital; and
| | - Omar Ashraf
- 1Department of Neurosurgery, Rutgers-Robert Wood Johnson Medical School and University Hospital; and
| | - Vinayak Narayan
- 1Department of Neurosurgery, Rutgers-Robert Wood Johnson Medical School and University Hospital; and
| | - Gaurav Gupta
- 1Department of Neurosurgery, Rutgers-Robert Wood Johnson Medical School and University Hospital; and
| | - Hai Sun
- 1Department of Neurosurgery, Rutgers-Robert Wood Johnson Medical School and University Hospital; and
| | - Patrick Hilden
- 2Rutgers Neurosurgery Health Outcomes, Policy, and Economics (HOPE) Center, New Brunswick, New Jersey
| | - Anil Nanda
- 1Department of Neurosurgery, Rutgers-Robert Wood Johnson Medical School and University Hospital; and
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25
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Staartjes VE, Stumpo V, Kernbach JM, Klukowska AM, Gadjradj PS, Schröder ML, Veeravagu A, Stienen MN, van Niftrik CHB, Serra C, Regli L. Machine learning in neurosurgery: a global survey. Acta Neurochir (Wien) 2020; 162:3081-3091. [PMID: 32812067 PMCID: PMC7593280 DOI: 10.1007/s00701-020-04532-1] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 08/10/2020] [Indexed: 12/11/2022]
Abstract
Background Recent technological advances have led to the development and implementation of machine learning (ML) in various disciplines, including neurosurgery. Our goal was to conduct a comprehensive survey of neurosurgeons to assess the acceptance of and attitudes toward ML in neurosurgical practice and to identify factors associated with its use. Methods The online survey consisted of nine or ten mandatory questions and was distributed in February and March 2019 through the European Association of Neurosurgical Societies (EANS) and the Congress of Neurosurgeons (CNS). Results Out of 7280 neurosurgeons who received the survey, we received 362 responses, with a response rate of 5%, mainly in Europe and North America. In total, 103 neurosurgeons (28.5%) reported using ML in their clinical practice, and 31.1% in research. Adoption rates of ML were relatively evenly distributed, with 25.6% for North America, 30.9% for Europe, 33.3% for Latin America and the Middle East, 44.4% for Asia and Pacific and 100% for Africa with only two responses. No predictors of clinical ML use were identified, although academic settings and subspecialties neuro-oncology, functional, trauma and epilepsy predicted use of ML in research. The most common applications were for predicting outcomes and complications, as well as interpretation of imaging. Conclusions This report provides a global overview of the neurosurgical applications of ML. A relevant proportion of the surveyed neurosurgeons reported clinical experience with ML algorithms. Future studies should aim to clarify the role and potential benefits of ML in neurosurgery and to reconcile these potential advantages with bioethical considerations.
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Affiliation(s)
- Victor E Staartjes
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.
- Amsterdam UMC, Vrije Universiteit Amsterdam, Neurosurgery, Amsterdam Movement Sciences, Amsterdam, The Netherlands.
- Department of Neurosurgery, Bergman Clinics, Amsterdam, The Netherlands.
| | - Vittorio Stumpo
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Julius M Kernbach
- Department of Neurosurgery, RWTH Aachen University Hospital, Aachen, Germany
| | - Anita M Klukowska
- Department of Neurosurgery, Bergman Clinics, Amsterdam, The Netherlands
- School of Medicine, University of Nottingham, Nottingham, UK
| | - Pravesh S Gadjradj
- Department of Neurosurgery, Leiden University Medical Centre, Leiden, The Netherlands
- Department of Neurosurgery, Erasmus MC, University Medical Centre, Rotterdam, The Netherlands
| | - Marc L Schröder
- Department of Neurosurgery, Bergman Clinics, Amsterdam, The Netherlands
| | - Anand Veeravagu
- Neurosurgery AI Lab, Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Martin N Stienen
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - Christiaan H B van Niftrik
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - Carlo Serra
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - Luca Regli
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
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26
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Muscas G, Matteuzzi T, Becattini E, Orlandini S, Battista F, Laiso A, Nappini S, Limbucci N, Renieri L, Carangelo BR, Mangiafico S, Della Puppa A. Development of machine learning models to prognosticate chronic shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage. Acta Neurochir (Wien) 2020; 162:3093-3105. [PMID: 32642833 PMCID: PMC7593274 DOI: 10.1007/s00701-020-04484-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 07/02/2020] [Indexed: 01/06/2023]
Abstract
BACKGROUND Shunt-dependent hydrocephalus significantly complicates subarachnoid hemorrhage (SAH), and reliable prognosis methods have been sought in recent years to reduce morbidity and costs associated with delayed treatment or neglected onset. Machine learning (ML) defines modern data analysis techniques allowing accurate subject-based risk stratifications. We aimed at developing and testing different ML models to predict shunt-dependent hydrocephalus after aneurysmal SAH. METHODS We consulted electronic records of patients with aneurysmal SAH treated at our institution between January 2013 and March 2019. We selected variables for the models according to the results of the previous works on this topic. We trained and tested four ML algorithms on three datasets: one containing binary variables, one considering variables associated with shunt-dependency after an explorative analysis, and one including all variables. For each model, we calculated AUROC, specificity, sensitivity, accuracy, PPV, and also, on the validation set, the NPV and the Matthews correlation coefficient (ϕ). RESULTS Three hundred eighty-six patients were included. Fifty patients (12.9%) developed shunt-dependency after a mean follow-up of 19.7 (± 12.6) months. Complete information was retrieved for 32 variables, used to train the models. The best models were selected based on the performances on the validation set and were achieved with a distributed random forest model considering 21 variables, with a ϕ = 0.59, AUC = 0.88; sensitivity and specificity of 0.73 (C.I.: 0.39-0.94) and 0.92 (C.I.: 0.84-0.97), respectively; PPV = 0.59 (0.38-0.77); and NPV = 0.96 (0.90-0.98). Accuracy was 0.90 (0.82-0.95). CONCLUSIONS Machine learning prognostic models allow accurate predictions with a large number of variables and a more subject-oriented prognosis. We identified a single best distributed random forest model, with an excellent prognostic capacity (ϕ = 0.58), which could be especially helpful in identifying low-risk patients for shunt-dependency.
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Affiliation(s)
- Giovanni Muscas
- Neurosurgery Clinic, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Largo Piero Palagi 1, 50137, Florence, Italy.
| | - Tommaso Matteuzzi
- Institute of Physics, Alma Mater Studiorum, University of Bologna, Bologna, Italy
| | - Eleonora Becattini
- Neurosurgery Clinic, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Largo Piero Palagi 1, 50137, Florence, Italy
| | - Simone Orlandini
- Neurosurgery Clinic, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Largo Piero Palagi 1, 50137, Florence, Italy
| | - Francesca Battista
- Neurosurgery Clinic, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Largo Piero Palagi 1, 50137, Florence, Italy
| | - Antonio Laiso
- Neurosurgery Clinic, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Largo Piero Palagi 1, 50137, Florence, Italy
- Interventional Neuroradiology Unit, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Florence, Italy
| | - Sergio Nappini
- Interventional Neuroradiology Unit, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Florence, Italy
| | - Nicola Limbucci
- Interventional Neuroradiology Unit, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Florence, Italy
| | - Leonardo Renieri
- Interventional Neuroradiology Unit, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Florence, Italy
| | | | - Salvatore Mangiafico
- Interventional Neuroradiology Unit, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Florence, Italy
| | - Alessandro Della Puppa
- Neurosurgery Clinic, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Largo Piero Palagi 1, 50137, Florence, Italy
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Voglis S, van Niftrik CHB, Staartjes VE, Brandi G, Tschopp O, Regli L, Serra C. Feasibility of machine learning based predictive modelling of postoperative hyponatremia after pituitary surgery. Pituitary 2020; 23:543-551. [PMID: 32488759 DOI: 10.1007/s11102-020-01056-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
PURPOSE Hyponatremia after pituitary surgery is a frequent finding with potential severe complications and the most common cause for readmission. Several studies have found parameters associated with postoperative hyponatremia, but no reliable specific predictor was described yet. This pilot study evaluates the feasibility of machine learning (ML) algorithms to predict postoperative hyponatremia after resection of pituitary lesions. METHODS Retrospective screening of a prospective registry of patients who underwent transsphenoidal surgery for pituitary lesions. Hyponatremia within 30 days after surgery was the primary outcome. Several pre- and intraoperative clinical, procedural and laboratory features were selected to train different ML algorithms. Trained models were compared using common performance metrics. Final model was internally validated on the testing dataset. RESULTS From 207 patients included in the study, 44 (22%) showed a hyponatremia within 30 days postoperatively. Hyponatremic measurements peaked directly postoperatively (day 0-1) and around day 7. Bootstrapped performance metrics of different trained ML-models showed largest area under the receiver operating characteristic curve (AUROC) for the boosted generalized linear model (67.1%), followed by the Naïve Bayes classifier (64.6%). The discriminative capability of the final model was assessed by predicting on unseen dataset. Large AUROC (84.3%; 67.0-96.4), sensitivity (81.8%) and specificity (77.5%) with an overall accuracy of 78.4% (66.7-88.2) was reached. CONCLUSION Our trained ML-model was able to learn the complex risk factor interactions and showed a high discriminative capability on unseen patient data. In conclusion, ML-methods can predict postoperative hyponatremia and thus potentially reduce morbidity and improve patient safety.
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Affiliation(s)
- Stefanos Voglis
- Department of Neurosurgery and Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinkstrasse 10, 8091, Zurich, Switzerland.
| | - Christiaan H B van Niftrik
- Department of Neurosurgery and Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinkstrasse 10, 8091, Zurich, Switzerland
| | - Victor E Staartjes
- Department of Neurosurgery and Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinkstrasse 10, 8091, Zurich, Switzerland
| | - Giovanna Brandi
- Neurosurgical Intensive Care Unit, Institute for Intensive Care Medicine, University Hospital and University of Zurich, Zurich, Switzerland
| | - Oliver Tschopp
- Department of Endocrinology, Diabetes, and Clinical Nutrition, University Hospital and University of Zurich, Zurich, Switzerland
| | - Luca Regli
- Department of Neurosurgery and Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinkstrasse 10, 8091, Zurich, Switzerland
| | - Carlo Serra
- Department of Neurosurgery and Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinkstrasse 10, 8091, Zurich, Switzerland
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Guerriero E, Ugga L, Cuocolo R. Artificial intelligence and pituitary adenomas: A review. Artif Intell Med Imaging 2020; 1:70-77. [DOI: 10.35711/aimi.v1.i2.70] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 07/15/2020] [Accepted: 08/21/2020] [Indexed: 02/06/2023] Open
Abstract
The aim of this review was to provide an overview of the main concepts in machine learning (ML) and to analyze the ML applications in the imaging of pituitary adenomas. After describing the clinical, pathological and imaging features of pituitary tumors, we defined the difference between ML and classical rule-based algorithms, we illustrated the fundamental ML techniques: supervised, unsupervised and reinforcement learning and explained the characteristic of deep learning, a ML approach employing networks inspired by brain’s structure. Pre-treatment assessment and neurosurgical outcome prediction were the potential ML applications using magnetic resonance imaging. Regarding pre-treatment assessment, ML methods were used to have information about tumor consistency, predict cavernous sinus invasion and high proliferative index, discriminate null cell adenomas, which respond to neo-adjuvant radiotherapy from other subtypes, predict somatostatin analogues response and visual pathway injury. Regarding neurosurgical outcome prediction, the following applications were discussed: Gross total resection prediction, evaluation of Cushing disease recurrence after transsphenoidal surgery and prediction of cerebrospinal fluid fistula’s formation after surgery. Although clinical applicability requires more replicability, generalizability and validation, results are promising, and ML software can be a potential power to facilitate better clinical decision making in pituitary tumor patients.
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Affiliation(s)
- Elvira Guerriero
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples 80131, Italy
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples 80131, Italy
| | - Renato Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples 80131, Italy
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Sorba EL, Staartjes VE, Voglis S, Tosic L, Brandi G, Tschopp O, Serra C, Regli L. Diabetes insipidus and syndrome of inappropriate antidiuresis (SIADH) after pituitary surgery: incidence and risk factors. Neurosurg Rev 2020; 44:1503-1511. [PMID: 32583307 DOI: 10.1007/s10143-020-01340-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 05/17/2020] [Accepted: 06/18/2020] [Indexed: 12/11/2022]
Abstract
Electrolyte disorders are relatively frequent and potentially serious complications after pituitary surgery. Both DI (diabetes insipidus) and SIADH (syndrome of inappropriate antidiuresis) can complicate and prolong hospital and intensive care unit stay, and the latter may even be preventable. We aim to assess the incidence of both electrolyte disorders and their risk factors. From a prospective registry of patients who underwent endoscopic transnasal transsphenoidal surgery (TSS) for pituitary adenoma, patients with postoperative DI and SIADH were identified. Univariable and multivariable statistics were carried out to identify factors independently associated with the occurrence of either DI or SIADH. A total of 174 patients were included, of which 73 (42%) were female. Mean age was 54 years (range 20-88). During postoperative hospital stay, 13 (7.5%) patients presenting with DI and 11 (6.3%) with SIADH were identified. Patients who developed DI after surgery had significantly longer hospital stays (p = 0.022), as did those who developed SIADH (p = 0.002). Four (2.3%) patients were discharged with a diagnosis of persistent DI, and 2 (1.1%) with the diagnosis of SIADH. At the last follow-up, 5 (2.9%) patients presented with persistent DI, while none of the patients suffered from SIADH. Younger age (odds ratio (OR) 0.97, 95% confidence interval (CI) 0.94-1.01, p = 0.166) and pituitary apoplexy (OR 2.69, 95% CI 0.53-10.65, p = 0.184) were weakly associated with the occurrence of DI. We identified younger age (OR 0.96, 95% CI 0.92-0.99, p = 0.045) and lower preoperative serum sodium (OR 0.83, 95% CI 0.71-0.95, p = 0.008) as independent risk factors for SIADH. Although we found a weak association among age, pituitary apoplexy, and the occurrence of DI, no independent predictor was identified for DI. For postoperative SIADH however, lower age and preoperative serum sodium were identified as significant predictors. None of these findings were sufficiently supported by preexisting literature. Both electrolyte disorders are exquisitely hard to predict preoperatively, and further research into their early detection and prevention is warranted.
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Affiliation(s)
- Elena L Sorba
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Victor E Staartjes
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
| | - Stefanos Voglis
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Lazar Tosic
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Giovanna Brandi
- Neurosurgical Intensive Care Unit, Institute for Intensive Care Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Oliver Tschopp
- Department of Endocrinology, Diabetes, and Clinical Nutrition, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Carlo Serra
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Luca Regli
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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30
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Staartjes VE, Serra C, Zoli M, Mazzatenta D, Pozzi F, Locatelli D, D'Avella E, Solari D, Cavallo LM, Regli L. Multicenter external validation of the Zurich Pituitary Score. Acta Neurochir (Wien) 2020; 162:1287-1295. [PMID: 32172439 DOI: 10.1007/s00701-020-04286-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 03/04/2020] [Indexed: 12/15/2022]
Abstract
PURPOSE Recently, the Zurich Pituitary Score (ZPS) has been proposed as a new quantitative preoperative classification scheme for predicting gross total resection (GTR), extent of resection (EOR), and residual tumor volume (RV) in endoscopic pituitary surgery. We evaluated the external validity of the ZPS. METHODS In three reference centers for pituitary surgery, the ZPS was applied and correlated to GTR, EOR, and RV. Furthermore, its inter-rater agreement was assessed. RESULTS A total of 485 patients (53% male; age, 53.8 ± 15.7) were included. ZPS grades I, II, III, and IV were observed in 110 (23%), 270 (56%), 64 (13%), and 41 (8%) patients, respectively. GTR was achieved in 358 (74%) cases, with mean EOR of 87.6% ± 20.3% and RV of 1.42 ± 2.80 cm3. With increasing ZPS grade, strongly significant decreasing trends for GTR (I, 92%; II, 77%; III, 67%; IV, 15%; p < 0.001) and EOR (I, 93.8%; II, 89.9%; III, 88.1%; IV, 75.4%; p < 0.001) were found. Similarly, RV increased steadily ([cm3] I, 0.16; II, 0.61; III, 2.01; IV, 3.84; p < 0.001). We observed intraclass correlation coefficients of 0.837 (95% CI, 0.804-0.865) for intercarotid distance and 0.964 (95% CI, 0.956-0.970) for adenoma diameter, and Cohen's kappa of 0.972 (95% CI, 0.952-0.992) for the ZPS grades. CONCLUSIONS Application of the ZPS in three external cohorts was successful. The ZPS generalized well in terms of GTR, EOR, and RV; demonstrated excellent inter-rater agreement; and can safely and effectively be applied as a quantitative classification of adenomas with relevance to surgical outcome.
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Affiliation(s)
- Victor E Staartjes
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.
| | - Carlo Serra
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - Matteo Zoli
- Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic and Pituitary Diseases, Division of Neurosurgery, IRCCS Institute of Neurological Sciences of Bologna, Bologna, Italy
| | - Diego Mazzatenta
- Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic and Pituitary Diseases, Division of Neurosurgery, IRCCS Institute of Neurological Sciences of Bologna, Bologna, Italy
| | - Fabio Pozzi
- Division of Neurosurgery, Ospedale di Circolo ASST Sette Laghi, University of Insubria, Varese, Italy
| | - Davide Locatelli
- Division of Neurosurgery, Ospedale di Circolo ASST Sette Laghi, University of Insubria, Varese, Italy
| | - Elena D'Avella
- Division of Neurosurgery, School of Medicine and Surgery, Università degli Studi di Napoli "Federico II", Naples, Italy
| | - Domenico Solari
- Division of Neurosurgery, School of Medicine and Surgery, Università degli Studi di Napoli "Federico II", Naples, Italy
| | - Luigi Maria Cavallo
- Division of Neurosurgery, School of Medicine and Surgery, Università degli Studi di Napoli "Federico II", Naples, Italy
| | - Luca Regli
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
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Saha A, Tso S, Rabski J, Sadeghian A, Cusimano MD. Machine learning applications in imaging analysis for patients with pituitary tumors: a review of the current literature and future directions. Pituitary 2020; 23:273-293. [PMID: 31907710 DOI: 10.1007/s11102-019-01026-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
PURPOSE To provide an overview of fundamental concepts in machine learning (ML), review the literature on ML applications in imaging analysis of pituitary tumors for the last 10 years, and highlight the future directions on potential applications of ML for pituitary tumor patients. METHOD We presented an overview of the fundamental concepts in ML, its various stages used in healthcare, and highlighted the key components typically present in an imaging-based tumor analysis pipeline. A search was conducted across four databases (PubMed, Ovid, Embase, and Google Scholar) to gather research articles from the past 10 years (2009-2019) involving imaging related to pituitary tumor and ML. We grouped the studies by imaging modalities and analyzed the ML tasks in terms of the data inputs, reference standards, methodologies, and limitations. RESULTS Of the 16 studies included in our analysis, 10 appeared in 2018-2019. Most of the studies utilized retrospective data and followed a semi-automatic ML pipeline. The studies included use of magnetic resonance imaging (MRI), facial photographs, surgical microscopic video, spectrometry, and spectroscopy imaging. The objectives of the studies covered 14 distinct applications and majority of the studies addressed a binary classification problem. Only five of the 11 MRI-based studies had an external validation or a holdout set to test the performance of a final trained model. CONCLUSION Through our concise evaluation and comparison of the studies using the concepts presented, we highlight future directions so that potential ML applications using different imaging modalities can be developed to benefit the clinical care of pituitary tumor patients.
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Affiliation(s)
- Ashirbani Saha
- Division of Neurosurgery, St. Michael's Hospital, Toronto, ON, Canada.
| | - Samantha Tso
- Division of Neurosurgery, St. Michael's Hospital, Toronto, ON, Canada
| | - Jessica Rabski
- Division of Neurosurgery, St. Michael's Hospital, Toronto, ON, Canada
| | | | - Michael D Cusimano
- Division of Neurosurgery, St. Michael's Hospital, Toronto, ON, Canada
- Department of Surgery, University of Toronto, Toronto, ON, Canada
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Fan Y, Li Y, Li Y, Feng S, Bao X, Feng M, Wang R. Development and assessment of machine learning algorithms for predicting remission after transsphenoidal surgery among patients with acromegaly. Endocrine 2020; 67:412-422. [PMID: 31673954 DOI: 10.1007/s12020-019-02121-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Accepted: 10/21/2019] [Indexed: 12/11/2022]
Abstract
PURPOSE Preoperative prediction of transsphenoidal surgical (TSS) response is important for determining individual treatment strategies for acromegaly. There is currently no accurate predictive model for TSS response for acromegaly. The current study sought to develop and validate machine learning (ML)-based models for preoperative prediction of TSS response for acromegaly. METHODS Six hundred sixty-eight patients with acromegaly were enrolled and divided into training (n = 534) and text datasets (n = 134) in this retrospective, data mining and ML study. The forward search algorithm was used to select features, and six ML algorithms were applied to construct TSS response prediction models. The performance of these ML models was validated using receiver operating characteristics analysis. Model calibration, discrimination ability, and clinical usefulness were also assessed. RESULTS Three hundred forty-nine (52.2%) patients achieved postoperative remission criteria and exhibited good TSS response. A univariate analysis was conducted and eight features, including age, hypertension, ophthalmic disorders, GH, IGF-1, nadir GH, maximal tumor diameter, and Knosp grade, were significantly associated with the TSS response in patients with acromegaly. After feature selection, the gradient boosting decision tree (GBDT), which was constructed with the eight significant features showed the best favorable discriminatory ability both the training (AUC = 0.8555) and validation (AUC = 0.8178) cohorts. The GBDT model showed good discrimination ability and calibration, with the highest levels of accuracy and specificity, and provided better estimates of TTS responses of patients with acromegaly compared with using only the Knosp grade. Decision curve analysis confirmed that the model was clinically useful. CONCLUSIONS ML-based models could aid neurosurgeons in the preoperative prediction of TTS response for patients with acromegaly, and could contribute to determining individual treatment strategies.
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Affiliation(s)
- Yanghua Fan
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuai Fu Yuan, Dongcheng District, 100730, Beijing, China
| | | | - Yichao Li
- DHC Software Co. Ltd, Beijing, China
| | - Shanshan Feng
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuai Fu Yuan, Dongcheng District, 100730, Beijing, China
| | - Xinjie Bao
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuai Fu Yuan, Dongcheng District, 100730, Beijing, China
| | - Ming Feng
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuai Fu Yuan, Dongcheng District, 100730, Beijing, China.
| | - Renzhi Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuai Fu Yuan, Dongcheng District, 100730, Beijing, China.
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Surgical and radiosurgical treatment strategies for Cushing’s disease. J Neurooncol 2019; 145:403-413. [DOI: 10.1007/s11060-019-03325-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 10/26/2019] [Indexed: 12/11/2022]
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