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Thakur U, Ramachandran S, Mazal AT, Cheng J, Le L, Chhabra A. Multiparametric whole-body MRI of patients with neurofibromatosis type I: spectrum of imaging findings. Skeletal Radiol 2024:10.1007/s00256-024-04765-6. [PMID: 39105762 DOI: 10.1007/s00256-024-04765-6] [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] [Received: 03/27/2024] [Revised: 07/20/2024] [Accepted: 07/22/2024] [Indexed: 08/07/2024]
Abstract
Neurofibromatosis (NF) type I is a neuroectodermal and mesodermal dysplasia caused by a mutation of the neurofibromin tumor suppressor gene. Phenotypic features of NF1 vary, and patients develop benign peripheral nerve sheath tumors and malignant neoplasms, such as malignant peripheral nerve sheath tumor, malignant melanoma, and astrocytoma. Multiparametric whole-body MR imaging (WBMRI) plays a critical role in disease surveillance. Multiparametric MRI, typically used in prostate imaging, is a general term for a technique that includes multiple sequences, i.e. anatomic, diffusion, and Dixon-based pre- and post-contrast imaging. This article discusses the value of multiparametric WBMRI and illustrates the spectrum of whole-body lesions of NF1 in a single imaging setting. Examples of lesions include those in the skin (tumors and axillary freckling), soft tissues (benign and malignant peripheral nerve sheath tumors, visceral plexiform, and diffuse lesions), bone and joints (nutrient nerve lesions, non-ossifying fibromas, intra-articular neurofibroma, etc.), spine (acute-angled scoliosis, dural ectasia, intraspinal tumors, etc.), and brain/skull (optic nerve glioma, choroid plexus xanthogranuloma, sphenoid wing dysplasia, cerebral hamartomas, etc.). After reading this article, the reader will gain knowledge of the variety of lesions encountered with NF1 and their WBMRI appearances. Timely identification of such lesions can aid in accurate diagnosis and appropriate patient management.
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Affiliation(s)
- Uma Thakur
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75235, USA
| | - Shyam Ramachandran
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75235, USA
| | - Alexander T Mazal
- Department of Radiology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Jonathan Cheng
- Department of Plastic Surgery, UT Southwestern Medical Center, Dallas, TX, USA
| | - Lu Le
- Department of Dermatology and Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA
| | - Avneesh Chhabra
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75235, USA.
- Department of Orthopedic Surgery, UT Southwestern Medical Center, Dallas, TX, USA.
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Jansma CYMN, Wan X, Acem I, Spaanderman DJ, Visser JJ, Hanff D, Taal W, Verhoef C, Klein S, Martin E, Starmans MPA. Preoperative Classification of Peripheral Nerve Sheath Tumors on MRI Using Radiomics. Cancers (Basel) 2024; 16:2039. [PMID: 38893158 PMCID: PMC11170987 DOI: 10.3390/cancers16112039] [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: 04/25/2024] [Revised: 05/21/2024] [Accepted: 05/24/2024] [Indexed: 06/21/2024] Open
Abstract
Malignant peripheral nerve sheath tumors (MPNSTs) are aggressive soft-tissue tumors prevalent in neurofibromatosis type 1 (NF1) patients, posing a significant risk of metastasis and recurrence. Current magnetic resonance imaging (MRI) imaging lacks decisiveness in distinguishing benign peripheral nerve sheath tumors (BPNSTs) and MPNSTs, necessitating invasive biopsies. This study aims to develop a radiomics model using quantitative imaging features and machine learning to distinguish MPNSTs from BPNSTs. Clinical data and MRIs from MPNST and BPNST patients (2000-2019) were collected at a tertiary sarcoma referral center. Lesions were manually and semi-automatically segmented on MRI scans, and radiomics features were extracted using the Workflow for Optimal Radiomics Classification (WORC) algorithm, employing automated machine learning. The evaluation was conducted using a 100× random-split cross-validation. A total of 35 MPNSTs and 74 BPNSTs were included. The T1-weighted (T1w) MRI radiomics model outperformed others with an area under the curve (AUC) of 0.71. The incorporation of additional MRI scans did not enhance performance. Combining T1w MRI with clinical features achieved an AUC of 0.74. Experienced radiologists achieved AUCs of 0.75 and 0.66, respectively. Radiomics based on T1w MRI scans and clinical features show some ability to distinguish MPNSTs from BPNSTs, potentially aiding in the management of these tumors.
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Affiliation(s)
- Christianne Y. M. N. Jansma
- Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus MC Cancer Institute University Hospital Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands; (I.A.); (C.V.)
- Department of Plastic and Reconstructive Surgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands;
| | - Xinyi Wan
- Department of Radiology & Nuclear Medicine, Erasmus MC Cancer Institute University Hospital Rotterdam, 3015 GD Rotterdam, The Netherlands; (X.W.); (D.J.S.); (J.J.V.); (D.H.); (S.K.); (M.P.A.S.)
| | - Ibtissam Acem
- Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus MC Cancer Institute University Hospital Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands; (I.A.); (C.V.)
| | - Douwe J. Spaanderman
- Department of Radiology & Nuclear Medicine, Erasmus MC Cancer Institute University Hospital Rotterdam, 3015 GD Rotterdam, The Netherlands; (X.W.); (D.J.S.); (J.J.V.); (D.H.); (S.K.); (M.P.A.S.)
| | - Jacob J. Visser
- Department of Radiology & Nuclear Medicine, Erasmus MC Cancer Institute University Hospital Rotterdam, 3015 GD Rotterdam, The Netherlands; (X.W.); (D.J.S.); (J.J.V.); (D.H.); (S.K.); (M.P.A.S.)
| | - David Hanff
- Department of Radiology & Nuclear Medicine, Erasmus MC Cancer Institute University Hospital Rotterdam, 3015 GD Rotterdam, The Netherlands; (X.W.); (D.J.S.); (J.J.V.); (D.H.); (S.K.); (M.P.A.S.)
| | - Walter Taal
- Department of Neurology, Erasmus MC Cancer Institute University Hospital Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands;
| | - Cornelis Verhoef
- Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus MC Cancer Institute University Hospital Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands; (I.A.); (C.V.)
| | - Stefan Klein
- Department of Radiology & Nuclear Medicine, Erasmus MC Cancer Institute University Hospital Rotterdam, 3015 GD Rotterdam, The Netherlands; (X.W.); (D.J.S.); (J.J.V.); (D.H.); (S.K.); (M.P.A.S.)
| | - Enrico Martin
- Department of Plastic and Reconstructive Surgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands;
| | - Martijn P. A. Starmans
- Department of Radiology & Nuclear Medicine, Erasmus MC Cancer Institute University Hospital Rotterdam, 3015 GD Rotterdam, The Netherlands; (X.W.); (D.J.S.); (J.J.V.); (D.H.); (S.K.); (M.P.A.S.)
- Department of Pathology, Erasmus MC Cancer Institute University Hospital Rotterdam, 3015 GD Rotterdam, The Netherlands
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Gitto S, Cuocolo R, Huisman M, Messina C, Albano D, Omoumi P, Kotter E, Maas M, Van Ooijen P, Sconfienza LM. CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies. Insights Imaging 2024; 15:54. [PMID: 38411750 PMCID: PMC10899555 DOI: 10.1186/s13244-024-01614-x] [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: 12/22/2023] [Accepted: 01/09/2024] [Indexed: 02/28/2024] Open
Abstract
OBJECTIVE To systematically review radiomic feature reproducibility and model validation strategies in recent studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas, thus updating a previous version of this review which included studies published up to 2020. METHODS A literature search was conducted on EMBASE and PubMed databases for papers published between January 2021 and March 2023. Data regarding radiomic feature reproducibility and model validation strategies were extracted and analyzed. RESULTS Out of 201 identified papers, 55 were included. They dealt with radiomics of bone (n = 23) or soft-tissue (n = 32) tumors. Thirty-two (out of 54 employing manual or semiautomatic segmentation, 59%) studies included a feature reproducibility analysis. Reproducibility was assessed based on intra/interobserver segmentation variability in 30 (55%) and geometrical transformations of the region of interest in 2 (4%) studies. At least one machine learning validation technique was used for model development in 34 (62%) papers, and K-fold cross-validation was employed most frequently. A clinical validation of the model was reported in 38 (69%) papers. It was performed using a separate dataset from the primary institution (internal test) in 22 (40%), an independent dataset from another institution (external test) in 14 (25%) and both in 2 (4%) studies. CONCLUSIONS Compared to papers published up to 2020, a clear improvement was noted with almost double publications reporting methodological aspects related to reproducibility and validation. Larger multicenter investigations including external clinical validation and the publication of databases in open-access repositories could further improve methodology and bring radiomics from a research area to the clinical stage. CRITICAL RELEVANCE STATEMENT An improvement in feature reproducibility and model validation strategies has been shown in this updated systematic review on radiomics of bone and soft-tissue sarcomas, highlighting efforts to enhance methodology and bring radiomics from a research area to the clinical stage. KEY POINTS • 2021-2023 radiomic studies on CT and MRI of musculoskeletal sarcomas were reviewed. • Feature reproducibility was assessed in more than half (59%) of the studies. • Model clinical validation was performed in 69% of the studies. • Internal (44%) and/or external (29%) test datasets were employed for clinical validation.
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Affiliation(s)
- Salvatore Gitto
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Merel Huisman
- Radboud University Medical Center, Department of Radiology and Nuclear Medicine, Nijmegen, The Netherlands
| | - Carmelo Messina
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Dipartimento di Scienze Biomediche, Chirurgiche ed Odontoiatriche, Università degli Studi di Milano, Milan, Italy
| | - Patrick Omoumi
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Elmar Kotter
- Department of Radiology, Freiburg University Medical Center, Freiburg, Germany
| | - Mario Maas
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands
| | - Peter Van Ooijen
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Luca Maria Sconfienza
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy.
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
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Jin Z, Wang C, Wang D, Li X, Guo W, Chen T. Malignant and Benign Peripheral Nerve Sheath Tumors in a Single Center: Value of Clinical and Ultrasound Features for the Diagnosis of Malignant Peripheral Nerve Sheath Tumor Compared With Magnetic Resonance Imaging. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2024; 43:21-31. [PMID: 37772628 DOI: 10.1002/jum.16330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 08/16/2023] [Accepted: 08/27/2023] [Indexed: 09/30/2023]
Abstract
OBJECTIVES This study aimed to investigate the combined use of ultrasonography and clinical features for the differentiation of malignant peripheral nerve sheath tumors (MPNST) from benign peripheral nerve sheath tumors (BPNST) and to compare the efficacy of ultrasonography with that of magnetic resonance imaging (MRI). METHODS This retrospective study included 28 MPNSTs and a control group of 57 BPNSTs. All patients underwent an ultrasound scan using the Logiq E9 (GE Health Care, Milwaukee, WI) or EPIQ7 equipment (Philips Medical System, Bothell, WA). A 3.0-T MRI machine (Ingenia; Philips Healthcare, Best, the Netherlands) was used for scanning, and conventional MRI was performed on different regions based on the patient's clinical situation. The following variables were evaluated: palpable mass, pain, nerve symptoms, maximum diameter, location, shape, boundary, encapsulation, echogenicity, echo homogeneity, presence of a cystic component, calcification, target sign, posterior echo, and intertumoral vascularity of the tumors. The diagnostic efficacy of ultrasonography and clinical factors was compared with that of MRI. Independent factors for predicting MPNST versus BPNST were also assessed. RESULTS The parameters of location, shape, boundary, encapsulation, and vascularity were significantly different between MPNSTs and BPNSTs. Multiple logistic regression analysis showed that shape, boundary, and vascularity were independent predictors of MPNSTs. The sensitivity, specificity, and Youden index of the three clinical and ultrasound factors (shape, boundary, and vascularity) were 0.89, 0.81, and 0.69, respectively, whereas those of MRI were 0.71, 0.89, and 0.61, respectively. No significant differences in the area under the curve (AUC) of the three combined clinical and ultrasound factors and those of MRI were found (P > .05). CONCLUSIONS MRI was useful in the differential diagnosis between MPNSTs and BPNSTs. However, the combination of clinical and ultrasound diagnoses can achieve the same effect as MRI, including shape, boundary, and vasculature.
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Affiliation(s)
- Zhenzhen Jin
- Department of Ultrasound, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China
| | - Chao Wang
- National Center for Orthopaedics, Beijing Research Institute of Traumatology and Orthopaedics, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China
| | - Dandan Wang
- Department of Ultrasound, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China
| | - Xintong Li
- Department of Radiology, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China
| | - Wen Guo
- Department of Ultrasound, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China
| | - Tao Chen
- Department of Ultrasound, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China
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Bhandarkar AR, Spinner RJ. Commentary: Natural History of Brachial Plexus, Peripheral Nerve, and Spinal Schwannomas. Neurosurgery 2022; 91:e151-e152. [PMID: 36083176 DOI: 10.1227/neu.0000000000002131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 07/07/2022] [Indexed: 12/15/2022] Open
Affiliation(s)
- Archis R Bhandarkar
- Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, USA.,Department of Neurological Surgery, Mayo Clinic Alix School of Medicine, Rochester, Minnesota, USA
| | - Robert J Spinner
- Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, USA
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Pediatric Sarcomas: The Next Generation of Molecular Studies. Cancers (Basel) 2022; 14:cancers14102515. [PMID: 35626119 PMCID: PMC9139929 DOI: 10.3390/cancers14102515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/13/2022] [Accepted: 05/13/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary There has been an incredible amount of discovery in pediatric sarcomas, but much remains to be accomplished. Clinical challenges include diagnostic heterogeneity and the poor outcome of patients with high risk, metastatic, and relapsed disease. The emergence of single cell sequencing has allowed the ability to document tumor cell heterogeneity in amazing detail, but it does not allow the ability to visualize spatial orientation. This problem has been solved by spatial multi-omics, which can be used to map tumors and visualize the distribution of critical transcripts, mutations, and proteins. However, these tools only offer observational data. High-throughput functional genomics provides a powerful way to highlight oncogenic drivers and potential therapy opportunities. Research has been hamstrung by a need for annotated specimens, particularly in post-therapy, relapsed, and metastatic disease, and initial biopsies offer only limited data opportunities. Data complexity, variability, and inconsistency present problems best approached with AI/machine learning. We stand on the threshold of a revolution in cancer cell biology that has the potential for translation into more effective and more directed therapies, particularly for previously recalcitrant diseases. Abstract Pediatric sarcomas constitute one of the largest groups of childhood cancers, following hematopoietic, neural, and renal lesions. Partly because of their diversity, they continue to offer challenges in diagnosis and treatment. In spite of the diagnostic, nosologic, and therapeutic gains made with genetic technology, newer means for investigation are needed. This article reviews emerging technology being used to study human neoplasia and how these methods might be applicable to pediatric sarcomas. Methods reviewed include single cell RNA sequencing (scRNAseq), spatial multi-omics, high-throughput functional genomics, and clustered regularly interspersed short palindromic sequence-Cas9 (CRISPR-Cas9) technology. In spite of these advances, the field continues to be challenged by a dearth of properly annotated materials, particularly from recurrences and metastases and pre- and post-treatment samples.
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Zhang M, Tong E, Wong S, Hamrick F, Mohammadzadeh M, Rao V, Pendleton C, Smith BW, Hug NF, Biswal S, Seekins J, Napel S, Spinner RJ, Mahan MA, Yeom KW, Wilson TJ. Machine Learning Approach to Differentiation of Peripheral Schwannomas and Neurofibromas: A Multi-Center Study. Neuro Oncol 2021; 24:601-609. [PMID: 34487172 DOI: 10.1093/neuonc/noab211] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Non-invasive differentiation between schwannomas and neurofibromas is important for appropriate management, preoperative counseling, and surgical planning, but has proven difficult using conventional imaging. The objective of this study was to develop and evaluate machine learning approaches for differentiating peripheral schwannomas from neurofibromas. METHODS We assembled a cohort of schwannomas and neurofibromas from 3 independent institutions and extracted high-dimensional radiomic features from gadolinium-enhanced, T1-weighted MRI using the PyRadiomics package on Quantitative Imaging Feature Pipeline. Age, sex, neurogenetic syndrome, spontaneous pain, and motor deficit were recorded. We evaluated the performance of 6 radiomics-based classifier models with and without clinical features and compared model performance against human expert evaluators. RESULTS 107 schwannomas and 59 neurofibroma were included. The primary models included both clinical and imaging data. The accuracy of the human evaluators (0.765) did not significantly exceed the no-information rate (NIR), whereas the Support Vector Machine (0.929), Logistic Regression (0.929), and Random Forest (0.905) classifiers exceeded the NIR. Using the method of DeLong, the AUC for the Logistic Regression (AUC=0.923) and K Nearest Neighbor (AUC=0.923) classifiers was significantly greater than the human evaluators (AUC=0.766; p = 0.041). CONCLUSIONS The radiomics-based classifiers developed here proved to be more accurate and had a higher AUC on the ROC curve than expert human evaluators. This demonstrates that radiomics using routine MRI sequences and clinical features can aid in differentiation of peripheral schwannomas and neurofibromas.
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Affiliation(s)
- Michael Zhang
- Department of Neurosurgery, Stanford University, Stanford, California, USA
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Elizabeth Tong
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Sam Wong
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Forrest Hamrick
- Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
| | | | - Vaishnavi Rao
- Stanford School of Medicine, Stanford University, Stanford, California, USA
| | | | - Brandon W Smith
- Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota, USA
| | - Nicholas F Hug
- Stanford School of Medicine, Stanford University, Stanford, California, USA
| | - Sandip Biswal
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Jayne Seekins
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Sandy Napel
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Robert J Spinner
- Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark A Mahan
- Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
| | - Kristen W Yeom
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Thomas J Wilson
- Department of Neurosurgery, Stanford University, Stanford, California, USA
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Prabhu VC, Pappu S, Borys E, Ormston L, Lomasney LM. Commentary: Machine-Learning Approach to Differentiation of Benign and Malignant Peripheral Nerve Sheath Tumors: A Multicenter Study. Neurosurgery 2021; 89:E156-E157. [PMID: 34131751 DOI: 10.1093/neuros/nyab224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 05/02/2021] [Indexed: 11/14/2022] Open
Affiliation(s)
- Vikram C Prabhu
- Department of Neurological Surgery, Loyola University Medical Center, Maywood, Illinois, USA
| | - Suguna Pappu
- Section of Neurosurgery, Department of Surgery, Edward Hines Veterans Administration Hospital, Hines, Illinois, USA
| | - Ewa Borys
- Department of Pathology, Loyola University Medical Center, Maywood, Illinois, USA
| | - Leighanne Ormston
- Department of Oncology, Loyola University Medical Center, Maywood, Illinois, USA
| | - Laurie M Lomasney
- Department of Radiology, Loyola University Medical Center, Maywood, Illinois, USA
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