2
|
Tarui T, Venkatesan C, Gano D, Lemmon ME, Mulkey SB, Pardo AC, Emrick L, Scher M, Agarwal S. Fetal Neurology Practice Survey: Current Practice and the Future Directions. Pediatr Neurol 2023; 145:74-79. [PMID: 37290231 DOI: 10.1016/j.pediatrneurol.2023.04.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/17/2023] [Accepted: 04/20/2023] [Indexed: 06/10/2023]
Abstract
BACKGROUND Fetal neurology is a rapidly evolving field. Consultations aim to diagnose, prognosticate, and coordinate prenatal and perinatal management along with other specialists and counsel expectant parents. Practice parameters and guidelines are limited. METHODS A 48-question online survey was administered to child neurologists. Questions targeted current care practices and perceived priorities for the field. RESULTS Representatives from 43 institutions in the United States responded; 83% had prenatal diagnosis centers, and the majority performed on-site neuroimaging. The earliest gestational age for fetal magnetic resonance imaging was variable. Annual consultations ranged from <20 to >100 patients. Fewer than half (n = 17.40%) were subspecialty trained. Most respondents (n = 39.91%) were interested in participating in a collaborative registry and educational initiatives. CONCLUSIONS The survey highlights heterogeneity in clinical practice. Large multisite and multidisciplinary collaborations are essential to gather data that inform outcomes for fetuses evaluated across institutions through registries as well as creation of guidelines and educational material.
Collapse
Affiliation(s)
- Tomo Tarui
- Division of Pediatric Neurology, Hasbro Children's Hospital, Warren Alpert Medical School of Brown University, Providence, Rhode Island
| | - Charu Venkatesan
- Division of Neurology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Dawn Gano
- Department of Neurology & Pediatrics, University of California San Francisco, San Francisco, California
| | - Monica E Lemmon
- Department of Pediatrics and Population Health Sciences, Duke University School of Medicine
| | - Sarah B Mulkey
- Prenatal Pediatrics Institute, Children's National Hospital, Washington, District of Columbia; Departments of Neurology and Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, District of Columbia
| | - Andrea C Pardo
- Division of Neurology, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago Illinois
| | - Lisa Emrick
- Department of Pediatrics, Neurology and Developmental Neuroscience, Baylor College of Medicine, Texas Children's Hospital, Houston, Texas
| | - Mark Scher
- Emeritus Full Professor Pediatrics and Neurology, Case Western Reserve University School of Medicine, Cleveland, Ohio
| | - Sonika Agarwal
- Division of Neurology & Pediatrics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Division of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
| |
Collapse
|
3
|
Payette K, Li HB, de Dumast P, Licandro R, Ji H, Siddiquee MMR, Xu D, Myronenko A, Liu H, Pei Y, Wang L, Peng Y, Xie J, Zhang H, Dong G, Fu H, Wang G, Rieu Z, Kim D, Kim HG, Karimi D, Gholipour A, Torres HR, Oliveira B, Vilaça JL, Lin Y, Avisdris N, Ben-Zvi O, Bashat DB, Fidon L, Aertsen M, Vercauteren T, Sobotka D, Langs G, Alenyà M, Villanueva MI, Camara O, Fadida BS, Joskowicz L, Weibin L, Yi L, Xuesong L, Mazher M, Qayyum A, Puig D, Kebiri H, Zhang Z, Xu X, Wu D, Liao K, Wu Y, Chen J, Xu Y, Zhao L, Vasung L, Menze B, Cuadra MB, Jakab A. Fetal brain tissue annotation and segmentation challenge results. Med Image Anal 2023; 88:102833. [PMID: 37267773 DOI: 10.1016/j.media.2023.102833] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 03/16/2023] [Accepted: 04/20/2023] [Indexed: 06/04/2023]
Abstract
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.
Collapse
Affiliation(s)
- Kelly Payette
- Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland.
| | - Hongwei Bran Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland; Department of Informatics, Technical University of Munich, Munich, Germany
| | - Priscille de Dumast
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; CIBM, Center for Biomedical Imaging, Lausanne, Switzerland
| | - Roxane Licandro
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, United States; Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab (CIR), Medical University of Vienna, Vienna, Austria
| | - Hui Ji
- Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
| | | | | | | | - Hao Liu
- Shanghai Jiaotong University, China
| | | | | | - Ying Peng
- School of Computer Science, Shaanxi Normal University, Xi'an 710119, China
| | - Juanying Xie
- School of Computer Science, Shaanxi Normal University, Xi'an 710119, China
| | - Huiquan Zhang
- School of Computer Science, Shaanxi Normal University, Xi'an 710119, China
| | - Guiming Dong
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Fu
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Guotai Wang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - ZunHyan Rieu
- Research Institute, NEUROPHET Inc., Seoul 06247, South Korea
| | - Donghyeon Kim
- Research Institute, NEUROPHET Inc., Seoul 06247, South Korea
| | - Hyun Gi Kim
- Department of Radiology, The Catholic University of Korea, Eunpyeong St. Mary's Hospital, Seoul 06247, South Korea
| | - Davood Karimi
- Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
| | - Ali Gholipour
- Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
| | - Helena R Torres
- Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga Guimarães, Portugal
| | - Bruno Oliveira
- Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga Guimarães, Portugal
| | - João L Vilaça
- 2Ai - School of Technology, IPCA, Barcelos, Portugal
| | - Yang Lin
- Department of Computer Science, Hong Kong University of Science and Technology, China
| | - Netanell Avisdris
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel; Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Israel
| | - Ori Ben-Zvi
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Israel; Sagol School of Neuroscience, Tel Aviv University, Israel
| | - Dafna Ben Bashat
- Sagol School of Neuroscience, Tel Aviv University, Israel; Sackler Faculty of Medicine, Tel Aviv University, Israel
| | - Lucas Fidon
- School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, United Kingdom
| | - Michael Aertsen
- Department of Radiology, University Hospitals Leuven, Leuven 3000, Belgium
| | - Tom Vercauteren
- School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, United Kingdom
| | - Daniel Sobotka
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Mireia Alenyà
- BCN-MedTech, Department of Information and Communications Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Maria Inmaculada Villanueva
- Department of Information and Communications Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
| | - Oscar Camara
- BCN-MedTech, Department of Information and Communications Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Bella Specktor Fadida
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel
| | - Leo Joskowicz
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel
| | - Liao Weibin
- School of Computer Science, Beijing Institute of Technology, China
| | - Lv Yi
- School of Computer Science, Beijing Institute of Technology, China
| | - Li Xuesong
- School of Computer Science, Beijing Institute of Technology, China
| | - Moona Mazher
- Department of Computer Engineering and Mathematics, University Rovira i Virgili,Spain
| | | | - Domenec Puig
- Department of Computer Engineering and Mathematics, University Rovira i Virgili,Spain
| | - Hamza Kebiri
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; CIBM, Center for Biomedical Imaging, Lausanne, Switzerland
| | - Zelin Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | - Xinyi Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | | | - Yixuan Wu
- Zhejiang University, Hangzhou, China
| | | | - Yunzhi Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | - Li Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | - Lana Vasung
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, United States; Department of Pediatrics, Harvard Medical School, United States
| | - Bjoern Menze
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Meritxell Bach Cuadra
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; CIBM, Center for Biomedical Imaging, Lausanne, Switzerland
| | - Andras Jakab
- Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland; University Research Priority Project Adaptive Brain Circuits in Development and Learning (AdaBD), University of Zürich, Zurich, Switzerland
| |
Collapse
|
4
|
Gumayan RLF, Klamer B, Ream M. Institutional Evaluation of Fetal Neurology Consults and Postnatal Outcomes: A 10-Year Retrospective Cohort Review. Neurol Clin Pract 2023; 13:e200100. [PMID: 36865645 PMCID: PMC9973289 DOI: 10.1212/cpj.0000000000200100] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 10/03/2022] [Indexed: 01/20/2023]
Abstract
Background and Objectives An increasing number of centers are offering fetal neurology consultation services; however, there is limited information available in overall institutional experiences. Data are lacking on the fetal characteristics, pregnancy course, and the influence of fetal consultation on perinatal outcomes. The aim of this study is to provide insight on the institutional fetal neurology consult process and areas of strengths and weaknesses. Methods We performed a retrospective electronic chart review of fetal consults from April 2, 2009, to August 8, 2019, at Nationwide Children's Hospital. The objectives were to summarize clinical characteristics, agreement of prenatal and postnatal diagnoses based on best available imaging, and postnatal outcomes. Results Of the 174 maternal-fetal neurology consults placed, 130 qualified for inclusion based on data available for review. Of the 131 anticipated fetuses, 5 experienced fetal demise, 7 underwent elective termination, and 10 died in the postnatal period. The majority were admitted to the neonatal intensive care unit; 34 (31%) required supportive intervention for feeding, breathing, or hydrocephalus, and 10 (8%) experienced seizures during their neonatal intensive care unit (NICU) stay. Imaging results from 113 babies who had prenatal and postnatal imaging of the brain were analyzed based on the primary diagnosis. The most common malformations were as follows (prenatal % vs postnatal %): midline anomalies (37% vs 29%), posterior fossa abnormalities (26% vs 18%), and ventriculomegaly (14% vs 8%). Additional disorders of neuronal migration were not seen on fetal imaging but were present in 9% of the postnatal studies. Analysis of agreement between prenatal and postnatal diagnostic imaging for the 95 babies who had MRIs at both time points found moderate concordance (Cohen kappa: 0.62, 95% CI 0.5-0.73; percent agreement: 69%, 95% CI 60%-78%). Consult recommendations for neonatal blood tests affected postnatal care in 64 of 73 cases in which the infant survived and data were available. Discussion Establishing a multidisciplinary fetal clinic can provide timely counseling and create rapport with families to have continuity of care for birth planning and postnatal management. Prognosis based on radiographic prenatal diagnosis requires caution as some neonatal outcomes may vary considerably.
Collapse
Affiliation(s)
- Rae Leonor Ferrer Gumayan
- Division of Neurology (RLFG, MR), Department of Pediatrics, Nationwide Children's Hospital; and Center for Biostatistics (BK), The Ohio State University
| | - Brett Klamer
- Division of Neurology (RLFG, MR), Department of Pediatrics, Nationwide Children's Hospital; and Center for Biostatistics (BK), The Ohio State University
| | - Margie Ream
- Division of Neurology (RLFG, MR), Department of Pediatrics, Nationwide Children's Hospital; and Center for Biostatistics (BK), The Ohio State University
| |
Collapse
|
5
|
Simmons R, Martinez AB, Barkovich J, Numis AL, Cilio MR, Glenn OA, Gano D, Rogers EE, Glass HC. Disorders of Neuronal Migration/Organization Convey the Highest Risk of Neonatal Onset Epilepsy Compared With Other Congenital Brain Malformations. Pediatr Neurol 2022; 127:20-27. [PMID: 34933271 DOI: 10.1016/j.pediatrneurol.2021.11.005] [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: 07/12/2021] [Revised: 11/02/2021] [Accepted: 11/06/2021] [Indexed: 10/19/2022]
Abstract
BACKGROUND Although seizures in neonates are common and often due to acute brain injury, 10-15% are unprovoked from congenital brain malformations. A better understanding of the risk of neonatal-onset epilepsy by the type of brain malformation is essential for counseling and monitoring. METHODS In this retrospective cohort study, we evaluated 132 neonates with congenital brain malformations and their risk of neonatal-onset epilepsy. Malformations were classified into one of five categories based on imaging patterns on prenatal or postnatal imaging. Infants were monitored with continuous video EEG (cEEG) for encephalopathy and paroxysmal events in addition to abnormal neuroimaging. RESULTS Seventy-four of 132 (56%) neonates underwent EEG monitoring, and 18 of 132 (14%) were diagnosed with neonatal-onset epilepsy. The highest prevalence of epilepsy was in neonates with disorders of neuronal migration/organization (9/34, 26%; 95% confidence interval [CI] = 13-44%), followed by disorders of early prosencephalic development (6/38, 16%; 95% CI = 6-31%), complex total brain malformations (2/16, 13%; 95% CI = 2-38%), and disorders of midbrain/hindbrain malformations (1/30, 3%; 95% CI = 0-17%). Of neonates with epilepsy, 5 of 18 (28%) had only electrographic seizures, 13 of 18 (72%) required treatment with two or more antiseizure medicines (ASMs), and 7 of 18 (39%) died within the neonatal period. CONCLUSION Our results demonstrate that disorders of neuronal migration/organization represent the highest-risk group for early-onset epilepsy. Seizures are frequently electrographic only, require treatment with multiple ASMs, and portend a high mortality rate. These results support American Clinical Neurophysiology Society recommendations for EEG monitoring during the neonatal period for infants with congenital brain malformations.
Collapse
Affiliation(s)
- Roxanne Simmons
- Department of Neurology and Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, California
| | | | - James Barkovich
- Department of Radiology and Biomedical Engineering, University of California, San Francisco, San Francisco, California
| | - Adam L Numis
- Department of Neurology and Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, California
| | - Maria Roberta Cilio
- Department of Pediatrics, Saint-Luc University Hospital, Catholic University of Louvain, Brussels, Belgium
| | - Orit A Glenn
- Department of Radiology and Biomedical Engineering, University of California, San Francisco, San Francisco, California
| | - Dawn Gano
- Department of Neurology and Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, California; Department of Pediatrics, Benioff Children's Hospital, University of California, San Francisco, San Francisco, California
| | - Elizabeth E Rogers
- Department of Pediatrics, Benioff Children's Hospital, University of California, San Francisco, San Francisco, California
| | - Hannah C Glass
- Department of Neurology and Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, California; Department of Pediatrics, Benioff Children's Hospital, University of California, San Francisco, San Francisco, California; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California.
| |
Collapse
|