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Akinduro OO, Ghaith AK, Loizos M, Lopez AO, Goyal A, de Macêdo Filho L, Ghanem M, Jarrah R, Moniz Garcia DP, Abode-Iyamah K, Kalani MA, Chen SG, Krauss WE, Clarke MJ, Bydon M, Quiñones-Hinojosa A. What Factors Predict the Development of Neurologic Deficits Following Resection of Intramedullary Spinal Cord Tumors: A Multi-Center Study. World Neurosurg 2024; 182:e34-e44. [PMID: 37952880 DOI: 10.1016/j.wneu.2023.11.010] [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: 07/12/2023] [Revised: 11/02/2023] [Accepted: 11/03/2023] [Indexed: 11/14/2023]
Abstract
BACKGROUND Intramedullary spinal cord tumors are challenging to resect, and their postoperative neurological outcomes are often difficult to predict, with few studies assessing this outcome. METHODS We reviewed the medical records of all patients surgically treated for Intramedullary spinal cord tumors at our multisite tertiary care institution (Mayo Clinic Arizona, Mayo Clinic Florida, Mayo Clinic Rochester) between June 2002 and May 2020. Variables that were significant in the univariate analyses were included in a multivariate logistic regression. "MissForest" operating on the Random Forest algorithm, was used for data imputation, and K-prototype was used for data clustering. Heatmaps were added to show correlations between postoperative neurological deficit and all other included variables. Shapley Additive exPlanations were implemented to understand each feature's importance. RESULTS Our query resulted in 315 patients, with 160 meeting the inclusion criteria. There were 53 patients with astrocytoma, 66 with ependymoma, and 41 with hemangioblastoma. The mean age (standard deviation) was 42.3 (17.5), and 48.1% of patients were women (n = 77/160). Multivariate analysis revealed that pathologic grade >3 (OR = 1.55; CI = [0.67, 3.58], P = 0.046 predicted a new neurological deficit. Random Forest algorithm (supervised machine learning) found age, use of neuromonitoring, histology of the tumor, performing a midline myelotomy, and tumor location to be the most important predictors of new postoperative neurological deficits. CONCLUSIONS Tumor grade/histology, age, use of neuromonitoring, and myelotomy type appeared to be most predictive of postoperative neurological deficits. These results can be used to better inform patients of perioperative risk.
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Affiliation(s)
| | - Abdul Karim Ghaith
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota, USA; Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, USA
| | - Michaelides Loizos
- Department of Neurological Surgery, Mayo Clinic, Jacksonville, Florida, USA
| | | | - Anshit Goyal
- Department of Neurological Surgery, Mayo Clinic, Jacksonville, Florida, USA
| | | | - Marc Ghanem
- Gilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, Byblos, Lebanon
| | - Ryan Jarrah
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota, USA; Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, USA
| | | | | | - Maziyar A Kalani
- Department of Neurological Surgery, Mayo Clinic, Phoenix, Florida, USA
| | - Selby G Chen
- Department of Neurological Surgery, Mayo Clinic, Jacksonville, Florida, USA
| | - William E Krauss
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota, USA; Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, USA
| | - Michelle J Clarke
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota, USA; Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, USA
| | - Mohamad Bydon
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota, USA; Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, USA
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Jujjavarapu C, Suri P, Pejaver V, Friedly J, Gold LS, Meier E, Cohen T, Mooney SD, Heagerty PJ, Jarvik JG. Predicting decompression surgery by applying multimodal deep learning to patients' structured and unstructured health data. BMC Med Inform Decis Mak 2023; 23:2. [PMID: 36609379 PMCID: PMC9824905 DOI: 10.1186/s12911-022-02096-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 12/29/2022] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Low back pain (LBP) is a common condition made up of a variety of anatomic and clinical subtypes. Lumbar disc herniation (LDH) and lumbar spinal stenosis (LSS) are two subtypes highly associated with LBP. Patients with LDH/LSS are often started with non-surgical treatments and if those are not effective then go on to have decompression surgery. However, recommendation of surgery is complicated as the outcome may depend on the patient's health characteristics. We developed a deep learning (DL) model to predict decompression surgery for patients with LDH/LSS. MATERIALS AND METHOD We used datasets of 8387 and 8620 patients from a prospective study that collected data from four healthcare systems to predict early (within 2 months) and late surgery (within 12 months after a 2 month gap), respectively. We developed a DL model to use patients' demographics, diagnosis and procedure codes, drug names, and diagnostic imaging reports to predict surgery. For each prediction task, we evaluated the model's performance using classical and generalizability evaluation. For classical evaluation, we split the data into training (80%) and testing (20%). For generalizability evaluation, we split the data based on the healthcare system. We used the area under the curve (AUC) to assess performance for each evaluation. We compared results to a benchmark model (i.e. LASSO logistic regression). RESULTS For classical performance, the DL model outperformed the benchmark model for early surgery with an AUC of 0.725 compared to 0.597. For late surgery, the DL model outperformed the benchmark model with an AUC of 0.655 compared to 0.635. For generalizability performance, the DL model outperformed the benchmark model for early surgery. For late surgery, the benchmark model outperformed the DL model. CONCLUSIONS For early surgery, the DL model was preferred for classical and generalizability evaluation. However, for late surgery, the benchmark and DL model had comparable performance. Depending on the prediction task, the balance of performance may shift between DL and a conventional ML method. As a result, thorough assessment is needed to quantify the value of DL, a relatively computationally expensive, time-consuming and less interpretable method.
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Affiliation(s)
- Chethan Jujjavarapu
- Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Box 358047, Seattle, WA, 98195, USA
| | - Pradeep Suri
- Clinical Learning, Evidence and Research Center, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA, 98105, USA
- Department of Rehabilitation Medicine, University of Washington, 1959 NE Pacific St, Seattle, WA, 98195, USA
| | - Vikas Pejaver
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Janna Friedly
- Clinical Learning, Evidence and Research Center, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA, 98105, USA
- Department of Rehabilitation Medicine, University of Washington, 1959 NE Pacific St, Seattle, WA, 98195, USA
| | - Laura S Gold
- Clinical Learning, Evidence and Research Center, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA, 98105, USA
- Department of Radiology, University of Washington, 1959 NE Pacific Street, Seattle, WA, 98195, USA
| | - Eric Meier
- Clinical Learning, Evidence and Research Center, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA, 98105, USA
- Department of Biostatistics, University of Washington, Box 357232, Seattle, WA, 98195-7232, USA
- Center for Biomedical Statistics, University of Washington, Seattle, WA, USA
| | - Trevor Cohen
- Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Box 358047, Seattle, WA, 98195, USA
| | - Sean D Mooney
- Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Box 358047, Seattle, WA, 98195, USA
| | - Patrick J Heagerty
- Department of Biostatistics, University of Washington, Box 357232, Seattle, WA, 98195-7232, USA
- Center for Biomedical Statistics, University of Washington, Seattle, WA, USA
| | - Jeffrey G Jarvik
- Clinical Learning, Evidence and Research Center, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA, 98105, USA.
- Department of Radiology, University of Washington, 1959 NE Pacific Street, Seattle, WA, 98195, USA.
- Department of Neurological Surgery, University of Washington, 1959 NE Pacific Street, Seattle, WA, 98195, USA.
- Department of Health Services, University of Washington, Box 357660, Seattle, WA, 98195-7660, USA.
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Ren G, Yu K, Xie Z, Wang P, Zhang W, Huang Y, Wang Y, Wu X. Current Applications of Machine Learning in Spine: From Clinical View. Global Spine J 2022; 12:1827-1840. [PMID: 34628966 PMCID: PMC9609532 DOI: 10.1177/21925682211035363] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
STUDY DESIGN Narrative review. OBJECTIVES This review aims to present current applications of machine learning (ML) in spine domain to clinicians. METHODS We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text. RESULTS Total 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost. CONCLUSIONS ML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models' assistance in real work.
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Affiliation(s)
- GuanRui Ren
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Kun Yu
- Nanjing Jiangbei Hospital, Nanjing,
Jiangsu, China
| | - ZhiYang Xie
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - PeiYang Wang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Wei Zhang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Yong Huang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - YunTao Wang
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China,YunTao Wang, Department of Spine Surgery,
Zhongda Hospital, School of Medicine, Southeast University, No. 87, Dingjiaqiao
Road, Nanjing, Jiangsu 210009, China.
| | - XiaoTao Wu
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China,XiaoTao Wu, Department of Spine Surgery,
Zhongda Hospital, School of Medicine, Southeast University, No. 87, Dingjiaqiao
Road, Nanjing, Jiangsu 210009, China.
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Saravi B, Hassel F, Ülkümen S, Zink A, Shavlokhova V, Couillard-Despres S, Boeker M, Obid P, Lang GM. Artificial Intelligence-Driven Prediction Modeling and Decision Making in Spine Surgery Using Hybrid Machine Learning Models. J Pers Med 2022; 12:jpm12040509. [PMID: 35455625 PMCID: PMC9029065 DOI: 10.3390/jpm12040509] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 03/18/2022] [Accepted: 03/19/2022] [Indexed: 12/22/2022] Open
Abstract
Healthcare systems worldwide generate vast amounts of data from many different sources. Although of high complexity for a human being, it is essential to determine the patterns and minor variations in the genomic, radiological, laboratory, or clinical data that reliably differentiate phenotypes or allow high predictive accuracy in health-related tasks. Convolutional neural networks (CNN) are increasingly applied to image data for various tasks. Its use for non-imaging data becomes feasible through different modern machine learning techniques, converting non-imaging data into images before inputting them into the CNN model. Considering also that healthcare providers do not solely use one data modality for their decisions, this approach opens the door for multi-input/mixed data models which use a combination of patient information, such as genomic, radiological, and clinical data, to train a hybrid deep learning model. Thus, this reflects the main characteristic of artificial intelligence: simulating natural human behavior. The present review focuses on key advances in machine and deep learning, allowing for multi-perspective pattern recognition across the entire information set of patients in spine surgery. This is the first review of artificial intelligence focusing on hybrid models for deep learning applications in spine surgery, to the best of our knowledge. This is especially interesting as future tools are unlikely to use solely one data modality. The techniques discussed could become important in establishing a new approach to decision-making in spine surgery based on three fundamental pillars: (1) patient-specific, (2) artificial intelligence-driven, (3) integrating multimodal data. The findings reveal promising research that already took place to develop multi-input mixed-data hybrid decision-supporting models. Their implementation in spine surgery may hence be only a matter of time.
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Affiliation(s)
- Babak Saravi
- Department of Orthopedics and Trauma Surgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79108 Freiburg, Germany; (S.Ü.); (P.O.); (G.M.L.)
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (F.H.); (A.Z.)
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, 5020 Salzburg, Austria;
- Correspondence:
| | - Frank Hassel
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (F.H.); (A.Z.)
| | - Sara Ülkümen
- Department of Orthopedics and Trauma Surgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79108 Freiburg, Germany; (S.Ü.); (P.O.); (G.M.L.)
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (F.H.); (A.Z.)
| | - Alisia Zink
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (F.H.); (A.Z.)
| | - Veronika Shavlokhova
- Department of Oral and Maxillofacial Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany;
| | - Sebastien Couillard-Despres
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, 5020 Salzburg, Austria;
- Austrian Cluster for Tissue Regeneration, 1200 Vienna, Austria
| | - Martin Boeker
- Intelligence and Informatics in Medicine, Medical Center Rechts der Isar, School of Medicine, Technical University of Munich, 81675 Munich, Germany;
| | - Peter Obid
- Department of Orthopedics and Trauma Surgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79108 Freiburg, Germany; (S.Ü.); (P.O.); (G.M.L.)
| | - Gernot Michael Lang
- Department of Orthopedics and Trauma Surgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79108 Freiburg, Germany; (S.Ü.); (P.O.); (G.M.L.)
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