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Tak D, Ye Z, Zapaischykova A, Zha Y, Boyd A, Vajapeyam S, Chopra R, Hayat H, Prabhu SP, Liu KX, Elhalawani H, Nabavizadeh A, Familiar A, Resnick AC, Mueller S, Aerts HJWL, Bandopadhayay P, Ligon KL, Haas-Kogan DA, Poussaint TY, Kann BH. Noninvasive Molecular Subtyping of Pediatric Low-Grade Glioma with Self-Supervised Transfer Learning. Radiol Artif Intell 2024; 6:e230333. [PMID: 38446044 DOI: 10.1148/ryai.230333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
Purpose To develop and externally test a scan-to-prediction deep learning pipeline for noninvasive, MRI-based BRAF mutational status classification for pediatric low-grade glioma. Materials and Methods This retrospective study included two pediatric low-grade glioma datasets with linked genomic and diagnostic T2-weighted MRI data of patients: Dana-Farber/Boston Children's Hospital (development dataset, n = 214 [113 (52.8%) male; 104 (48.6%) BRAF wild type, 60 (28.0%) BRAF fusion, and 50 (23.4%) BRAF V600E]) and the Children's Brain Tumor Network (external testing, n = 112 [55 (49.1%) male; 35 (31.2%) BRAF wild type, 60 (53.6%) BRAF fusion, and 17 (15.2%) BRAF V600E]). A deep learning pipeline was developed to classify BRAF mutational status (BRAF wild type vs BRAF fusion vs BRAF V600E) via a two-stage process: (a) three-dimensional tumor segmentation and extraction of axial tumor images and (b) section-wise, deep learning-based classification of mutational status. Knowledge-transfer and self-supervised approaches were investigated to prevent model overfitting, with a primary end point of the area under the receiver operating characteristic curve (AUC). To enhance model interpretability, a novel metric, center of mass distance, was developed to quantify the model attention around the tumor. Results A combination of transfer learning from a pretrained medical imaging-specific network and self-supervised label cross-training (TransferX) coupled with consensus logic yielded the highest classification performance with an AUC of 0.82 (95% CI: 0.72, 0.91), 0.87 (95% CI: 0.61, 0.97), and 0.85 (95% CI: 0.66, 0.95) for BRAF wild type, BRAF fusion, and BRAF V600E, respectively, on internal testing. On external testing, the pipeline yielded an AUC of 0.72 (95% CI: 0.64, 0.86), 0.78 (95% CI: 0.61, 0.89), and 0.72 (95% CI: 0.64, 0.88) for BRAF wild type, BRAF fusion, and BRAF V600E, respectively. Conclusion Transfer learning and self-supervised cross-training improved classification performance and generalizability for noninvasive pediatric low-grade glioma mutational status prediction in a limited data scenario. Keywords: Pediatrics, MRI, CNS, Brain/Brain Stem, Oncology, Feature Detection, Diagnosis, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2024.
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Affiliation(s)
- Divyanshu Tak
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Zezhong Ye
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Anna Zapaischykova
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Yining Zha
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Aidan Boyd
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Sridhar Vajapeyam
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Rishi Chopra
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Hasaan Hayat
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Sanjay P Prabhu
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Kevin X Liu
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Hesham Elhalawani
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Ali Nabavizadeh
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Ariana Familiar
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Adam C Resnick
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Sabine Mueller
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Hugo J W L Aerts
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Pratiti Bandopadhayay
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Keith L Ligon
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Daphne A Haas-Kogan
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Tina Y Poussaint
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Benjamin H Kann
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
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2
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Zapaishchykova A, Liu KX, Saraf A, Ye Z, Catalano PJ, Benitez V, Ravipati Y, Jain A, Huang J, Hayat H, Likitlersuang J, Vajapeyam S, Chopra RB, Familiar AM, Nabavidazeh A, Mak RH, Resnick AC, Mueller S, Cooney TM, Haas-Kogan DA, Poussaint TY, Aerts HJWL, Kann BH. Automated temporalis muscle quantification and growth charts for children through adulthood. Nat Commun 2023; 14:6863. [PMID: 37945573 PMCID: PMC10636102 DOI: 10.1038/s41467-023-42501-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 10/12/2023] [Indexed: 11/12/2023] Open
Abstract
Lean muscle mass (LMM) is an important aspect of human health. Temporalis muscle thickness is a promising LMM marker but has had limited utility due to its unknown normal growth trajectory and reference ranges and lack of standardized measurement. Here, we develop an automated deep learning pipeline to accurately measure temporalis muscle thickness (iTMT) from routine brain magnetic resonance imaging (MRI). We apply iTMT to 23,876 MRIs of healthy subjects, ages 4 through 35, and generate sex-specific iTMT normal growth charts with percentiles. We find that iTMT was associated with specific physiologic traits, including caloric intake, physical activity, sex hormone levels, and presence of malignancy. We validate iTMT across multiple demographic groups and in children with brain tumors and demonstrate feasibility for individualized longitudinal monitoring. The iTMT pipeline provides unprecedented insights into temporalis muscle growth during human development and enables the use of LMM tracking to inform clinical decision-making.
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Affiliation(s)
- Anna Zapaishchykova
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kevin X Liu
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Anurag Saraf
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Zezhong Ye
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Paul J Catalano
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Viviana Benitez
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, MA, USA
| | - Yashwanth Ravipati
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Arnav Jain
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Julia Huang
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Hasaan Hayat
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Michigan State University, East Lansing, MI, USA
| | - Jirapat Likitlersuang
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sridhar Vajapeyam
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA
| | - Rishi B Chopra
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ariana M Familiar
- Children's Hospital of Philadelphia, Philadelphia, USA
- University of Pennsylvania, Pennsylvania, USA
| | - Ali Nabavidazeh
- Children's Hospital of Philadelphia, Philadelphia, USA
- University of Pennsylvania, Pennsylvania, USA
| | - Raymond H Mak
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Adam C Resnick
- Children's Hospital of Philadelphia, Philadelphia, USA
- University of Pennsylvania, Pennsylvania, USA
| | - Sabine Mueller
- Department of Neurology, Neurosurgery and Pediatrics, University of California, San Francisco, USA
| | - Tabitha M Cooney
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, MA, USA
| | - Daphne A Haas-Kogan
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Tina Y Poussaint
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA
| | - Hugo J W L Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands
| | - Benjamin H Kann
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
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3
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Rollins CK, Calderon J, Wypij D, Taylor AM, Davalji Kanjiker TS, Rohde JS, Maiman M, Zambrano LD, Newhams MM, Rodriguez S, Hart N, Worhach J, Kucukak S, Poussaint TY, Son MBF, Friedman ML, Gertz SJ, Hobbs CV, Kong M, Maddux AB, McGuire JL, Licht PA, Staat MA, Yonker LM, Mazumdar M, Randolph AG, Campbell AP, Newburger JW. Neurological and Psychological Sequelae Associated With Multisystem Inflammatory Syndrome in Children. JAMA Netw Open 2023; 6:e2324369. [PMID: 37466939 PMCID: PMC10357334 DOI: 10.1001/jamanetworkopen.2023.24369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 06/04/2023] [Indexed: 07/20/2023] Open
Abstract
Importance Acute neurological involvement occurs in some patients with multisystem inflammatory syndrome in children (MIS-C), but few data report neurological and psychological sequelae, and no investigations include direct assessments of cognitive function 6 to 12 months after discharge. Objective To characterize neurological, psychological, and quality of life sequelae after MIS-C. Design, Setting, and Participants This cross-sectional cohort study was conducted in the US and Canada. Participants included children with MIS-C diagnosed from November 2020 through November 2021, 6 to 12 months after hospital discharge, and their sibling or community controls, when available. Data analysis was performed from August 2022 to May 2023. Exposure Diagnosis of MIS-C. Main Outcomes and Measures A central study site remotely administered a onetime neurological examination and in-depth neuropsychological assessment including measures of cognition, behavior, quality of life, and daily function. Generalized estimating equations, accounting for matching, assessed for group differences. Results Sixty-four patients with MIS-C (mean [SD] age, 11.5 [3.9] years; 20 girls [31%]) and 44 control participants (mean [SD] age, 12.6 [3.7] years; 20 girls [45%]) were enrolled. The MIS-C group exhibited abnormalities on neurological examination more frequently than controls (15 of 61 children [25%] vs 3 of 43 children [7%]; odds ratio, 4.7; 95% CI, 1.3-16.7). Although the 2 groups performed similarly on most cognitive measures, the MIS-C group scored lower on the National Institutes of Health Cognition Toolbox List Sort Working Memory Test, a measure of executive functioning (mean [SD] scores, 96.1 [14.3] vs 103.1 [10.5]). Parents reported worse psychological outcomes in cases compared with controls, particularly higher scores for depression symptoms (mean [SD] scores, 52.6 [13.1] vs 47.8 [9.4]) and somatization (mean [SD] scores, 55.5 [15.5] vs 47.0 [7.6]). Self-reported (mean [SD] scores, 79.6 [13.1] vs 85.5 [12.3]) and parent-reported (mean [SD] scores, 80.3 [15.5] vs 88.6 [13.0]) quality of life scores were also lower in cases than controls. Conclusions and Relevance In this cohort study, compared with contemporaneous sibling or community controls, patients with MIS-C had more abnormal neurologic examinations, worse working memory scores, more somatization and depression symptoms, and lower quality of life 6 to 12 months after hospital discharge. Although these findings need to be confirmed in larger studies, enhanced monitoring may be warranted for early identification and treatment of neurological and psychological symptoms.
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Affiliation(s)
- Caitlin K. Rollins
- Department of Neurology, Boston Children’s Hospital, Boston, Massachusetts
- Department of Neurology, Harvard Medical School, Boston, Massachusetts
| | - Johanna Calderon
- Department of Neurology, Boston Children’s Hospital, Boston, Massachusetts
- National Institute of Health and Medical Research INSERM U1046, PhyMedExp, Montpellier, France
- Department of Psychiatry, Boston Children’s Hospital, Boston, Massachusetts
| | - David Wypij
- Department of Cardiology, Boston Children’s Hospital, Boston, Massachusetts
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts
| | - Alex M. Taylor
- Department of Psychiatry, Boston Children’s Hospital, Boston, Massachusetts
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | | | - Julia S. Rohde
- Department of Neurology, Boston Children’s Hospital, Boston, Massachusetts
| | - Moshe Maiman
- Department of Psychiatry, Boston Children’s Hospital, Boston, Massachusetts
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Laura D. Zambrano
- COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Margaret M. Newhams
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children’s Hospital, Boston, Massachusetts
| | - Susan Rodriguez
- Department of Neurology, Boston Children’s Hospital, Boston, Massachusetts
| | - Nicholas Hart
- Department of Neurology, Boston Children’s Hospital, Boston, Massachusetts
| | - Jennifer Worhach
- Department of Neurology, Boston Children’s Hospital, Boston, Massachusetts
| | - Suden Kucukak
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children’s Hospital, Boston, Massachusetts
| | - Tina Y. Poussaint
- Department of Radiology, Boston Children’s Hospital, Boston, Massachusetts
- Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Mary Beth F. Son
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts
- Division of Immunology, Department of Pediatrics, Boston Children’s Hospital, Boston, Massachusetts
| | - Matthew L. Friedman
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, Indiana University School of Medicine and Riley Hospital for Children, Indianapolis
| | - Shira J. Gertz
- Division of Pediatric Critical Care, Department of Pediatrics, Cooperman Barnabas Medical Center, Livingston, New Jersey
| | - Charlotte V. Hobbs
- Division of Infectious Diseases, Department of Pediatrics, Department of Microbiology, University of Mississippi Medical Center, Jackson
| | - Michele Kong
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Alabama at Birmingham, Birmingham
| | - Aline B. Maddux
- Department of Pediatrics, Section of Critical Care Medicine, University of Colorado School of Medicine and Children’s Hospital Colorado, Aurora
| | - Jennifer L. McGuire
- Division of Neurology at The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
- Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Paul A. Licht
- Department of Cardiology, Boston Children’s Hospital, Boston, Massachusetts
| | - Mary Allen Staat
- Department of Pediatrics, University of Cincinnati, Division of Infectious Diseases, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
| | - Lael M. Yonker
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts
- Department of Pediatrics, Division of Pediatric Pulmonary and Mucosal Immunology and Biology Research Center, Division of Infectious Disease, Massachusetts General Hospital, Boston
| | - Maitreyi Mazumdar
- Department of Neurology, Boston Children’s Hospital, Boston, Massachusetts
- Department of Neurology, Harvard Medical School, Boston, Massachusetts
| | - Adrienne G. Randolph
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children’s Hospital, Boston, Massachusetts
- Department of Anaesthesia, Harvard Medical School, Boston, Massachusetts
| | - Angela P. Campbell
- COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Jane W. Newburger
- Department of Cardiology, Boston Children’s Hospital, Boston, Massachusetts
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts
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Rameh V, Vajapeyam S, Ziaei A, Kao P, London WB, Baker SJ, Chiang J, Lucas J, Tinkle CL, Wright KD, Poussaint TY. Correlation between Multiparametric MR Imaging and Molecular Genetics in Pontine Pediatric High-Grade Glioma. AJNR Am J Neuroradiol 2023:ajnr.A7910. [PMID: 37321859 PMCID: PMC10337620 DOI: 10.3174/ajnr.a7910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 05/22/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND PURPOSE Molecular profiling is a crucial feature in the "integrated diagnosis" of CNS tumors. We aimed to determine whether radiomics could distinguish molecular types of pontine pediatric high-grade gliomas that have similar/overlapping phenotypes on conventional anatomic MR images. MATERIALS AND METHODS Baseline MR images from children with pontine pediatric high-grade gliomas were analyzed. Retrospective imaging studies included standard precontrast and postcontrast sequences and DTI. Imaging analyses included median, mean, mode, skewness, and kurtosis of the ADC histogram of the tumor volume based on T2 FLAIR and enhancement at baseline. Histone H3 mutations were identified through immunohistochemistry and/or Sanger or next-generation DNA sequencing. The log-rank test identified imaging factors prognostic of survival from the time of diagnosis. Wilcoxon rank-sum and Fisher exact tests compared imaging predictors among groups. RESULTS Eighty-three patients had pretreatment MR imaging and evaluable tissue sampling. The median age was 6 years (range, 0.7-17 years); 50 tumors had a K27M mutation in H3-3A, and 11, in H3C2/3. Seven tumors had histone H3 K27 alteration, but the specific gene was unknown. Fifteen were H3 wild-type. Overall survival was significantly higher in H3C2/3- compared with H3-3A-mutant tumors (P = .003) and in wild-type tumors compared with any histone mutation (P = .001). Lower overall survival was observed in patients with enhancing tumors (P = .02) compared with those without enhancement. H3C2/3-mutant tumors showed higher mean, median, and mode ADC_total values (P < .001) and ADC_enhancement (P < .004), with lower ADC_total skewness and kurtosis (P < .003) relative to H3-3A-mutant tumors. CONCLUSIONS ADC histogram parameters are correlated with histone H3 mutation status in pontine pediatric high-grade glioma.
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Affiliation(s)
- V Rameh
- From the Department of Radiology (V.R., S.V., A.Z., T.Y.P.), Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
| | - S Vajapeyam
- From the Department of Radiology (V.R., S.V., A.Z., T.Y.P.), Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
| | - A Ziaei
- From the Department of Radiology (V.R., S.V., A.Z., T.Y.P.), Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
| | - P Kao
- Department of Pediatric Oncology (P.K., W.B.L., K.D.W.), Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - W B London
- Department of Pediatric Oncology (P.K., W.B.L., K.D.W.), Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - S J Baker
- Departments of Developmental Neurobiology (S.J.B.)
| | | | - J Lucas
- Radiation Oncology (J.L., C.L.T.), St. Jude Children's Research Hospital, Memphis, Tennessee
| | - C L Tinkle
- Radiation Oncology (J.L., C.L.T.), St. Jude Children's Research Hospital, Memphis, Tennessee
| | - K D Wright
- Department of Pediatric Oncology (P.K., W.B.L., K.D.W.), Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - T Y Poussaint
- From the Department of Radiology (V.R., S.V., A.Z., T.Y.P.), Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
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5
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Rogers SN, Udayasankar U, Pruthi S, Lai HA, Kadom N, Guerin J, Malinzak M, Shulkin BL, Poussaint TY, Patay Z, Bag AK. Imaging of pediatric spine and spinal cord tumors: A COG Diagnostic Imaging Committee/SPR Oncology Committee/ASPNR White Paper. Pediatr Blood Cancer 2023; 70 Suppl 4:e30150. [PMID: 36562555 PMCID: PMC10681366 DOI: 10.1002/pbc.30150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 11/24/2022] [Indexed: 12/24/2022]
Abstract
Childhood spinal tumors are rare. Tumors can involve the spinal cord, the meninges, bony spine, and the paraspinal tissue. Optimized imaging should be utilized to evaluate tumors arising from specific spinal compartments. This paper provides consensus-based recommendations for optimized imaging of tumors arising from specific spinal compartments at diagnosis, follow-up during and after therapy, and response assessment.
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Affiliation(s)
- Samuel N Rogers
- Department of Medical Imaging, University of Arizona College of Medicine. Tucson, AZ
| | - Unni Udayasankar
- Department of Medical Imaging, University of Arizona College of Medicine. Tucson, AZ
| | - Sumit Pruthi
- Department of Radiology and Radiological Sciences, Vanderbilt University. Nashville, TN
| | - Hollie A. Lai
- Department of Radiology, Children’s Health Orange County. Orange, CA
| | - Nadja Kadom
- Department of Radiology, Emory University School of Medicine. Atlanta, GA
| | - Julie Guerin
- Department of Radiology, Mayo Clinic. Rochester, MN
| | - Michael Malinzak
- Department of Radiology, Duke University School of Medicine. Durham, NC
| | - Barry L. Shulkin
- Department of Diagnostic Imaging, St. Jude Children’s Research Hospital. Memphis, TN
| | | | - Zoltan Patay
- Department of Diagnostic Imaging, St. Jude Children’s Research Hospital. Memphis, TN
| | - Asim K. Bag
- Department of Diagnostic Imaging, St. Jude Children’s Research Hospital. Memphis, TN
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Siewert B, Bruno MA, Fleishon HB, Hublall R, Slanetz PJ, Jankovic SN, Kotsenas AL, Schwartz ES, Pawley B, Mukherji SK, Bourland JD, Artunduaga M, Saif M, Poussaint TY, Scanlon MH, Kirsch J, Lexa FJ. Summary of the 2022 ACR Intersociety Meeting. J Am Coll Radiol 2023; 20:479-486. [PMID: 37121627 DOI: 10.1016/j.jacr.2023.03.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 03/06/2023] [Indexed: 05/02/2023]
Abstract
The ACR Intersociety Committee meeting of 2022 (ISC-2022) was convened around the theme of "Recovering From The Great Resignation, Moral Injury and Other Stressors: Rebuilding Radiology for a Robust Future." Representatives from 29 radiology organizations, including all radiology subspecialties, radiation oncology, and medical physics, as well as academic and private practice radiologists, met for 3 days in early August in Park City, Utah, to search for solutions to the most pressing problems facing the specialty of radiology in 2022. Of these, the mismatch between the clinical workload and the available radiologist workforce was foremost-as many other identifiable problems flowed downstream from this, including high job turnover, lack of time for teaching and research, radiologist burnout, and moral injury.
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Affiliation(s)
- Bettina Siewert
- Associate Professor of Radiology, Harvard Medical School, Boston, Massachusetts, and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Massachusetts; Vice Chair of RSNA Quality Improvement Committee.
| | - Michael A Bruno
- Professor of Radiology and Professor of Medicine, Penn State University, University Park, Pennsylvania, and Department of Radiology, Penn State Health, Hershey Medical Center, Hershey, Pennsylvania
| | - Howard B Fleishon
- Associate Professor of Radiology, Department of Radiology and Imaging Sciences, Atlanta, Georgia; President, ACR
| | - Ronald Hublall
- Central Illinois Radiological Associates, East Peoria, Illinois
| | - Priscilla J Slanetz
- Professor of Radiology, Boston University Chobanian & Avedisian School of Medicine and Department of Radiology, Boston Medical Center, Boston, Massachusetts; President-Elect, AUR; Vice Chair of Academic Affairs in the Department of Radiology and Associate Program Director of the Diagnostic Radiology Residency, Boston Medical Center; Subspecialty Chair, ACR Appropriateness Criteria Breast Imaging Panels; Chair, Mentorship Committee, Society of Breast Imaging; Co-Chair, Fellowship Committee and Breast Imaging Committee of the Massachusetts Radiological Society
| | - Stephanie N Jankovic
- Department of Radiology, Oregon Health & Science University Hospital, Portland, Oregon
| | - Amy L Kotsenas
- Professor of Radiology, Mayo Clinic School of Medicine, Rochester, Minnesota, and Department of Radiology, Mayo Clinic, Rochester, Minnesota; Board of Chancellors, ACR
| | - Erin S Schwartz
- Professor of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania and Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Barbara Pawley
- Associate Professor of Radiology, University of Kentucky, Lexington, Kentucky, and Department of Radiology, UK Albert B. Chandler Hospital, Lexington, Kentucky; Immediate Past-President, American Association for Women Radiologists
| | | | - J Daniel Bourland
- Professor of Radiation Oncology, Department of Radiation Oncology, Wake Forest University School of Medicine, Winston-Salem, North Carolina; 2022 President, American Association of Physicists in Medicine, 2023 Chair, Board of Directors, American Association of Physicists in Medicine
| | - Maddy Artunduaga
- Assistant Professor of Radiology, Department of Radiology, Pediatric Radiology Division, UT Southwestern Medical Center, Dallas, Texas
| | - Manal Saif
- Department of Radiology, Penn State Health, Hershey Medical Center, Hershey, Pennsylvania
| | - Tina Y Poussaint
- Lionel W. Young Chair in Radiology, Professor of Radiology, Harvard Medical School, Boston, Massachusetts, and Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, 1st Past President, American Society of Neyruradiology
| | - Mary H Scanlon
- Clinical Professor of Radiology, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, Pennsylvania, and Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, President, Association of Program Directors in Radiology
| | - Jacobo Kirsch
- Chair, Florida Region Imaging Institute, Cleveland Clinic Florida, Weston Hospital, Weston, Florida
| | - Frank J Lexa
- Professor and Vice Chair Faculty Affairs, Department of Radiology, University of Pittsburgh Medical Center International, Pittsburgh, Pennsylvania; Vice President, ACR; Chief Medical Officer, The Radiology Leadership Institute of the ACR
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Cogswell PM, Jack CR, Barakos JA, Barkhof F, Benzinger TS, Raji CA, Poussaint TY, Ramanan VK, Whitlow CT. Reply. AJNR Am J Neuroradiol 2023; 44:E6. [PMID: 36574316 PMCID: PMC9835908 DOI: 10.3174/ajnr.a7731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- P M Cogswell
- Department of RadiologyMayo ClinicRochester, Minnesota
| | - C R Jack
- Department of RadiologyMayo ClinicRochester, Minnesota
| | - J A Barakos
- Department of RadiologyCalifornia Pacific Medical CenterSan Francisco, California
| | - F Barkhof
- Departments of Radiology and Nuclear MedicineVU University Medical CenterAmsterdam, the NetherlandsQueen Square Institute of Neurology and Centre for Medical Image ComputingUniversity CollegeLondon, UK
| | - T S Benzinger
- Departments of Radiology and NeurosurgeryWashington University School of MedicineSt. Louis, Missouri
| | - C A Raji
- Departments of Radiology and NeurologyWashington University School of MedicineSt. Louis, Missouri
| | - T Y Poussaint
- Department of RadiologyBoston Children's HospitalBoston, Massachusetts
| | - V K Ramanan
- Department of NeurologyMayo ClinicRochester, Minnesota
| | - C T Whitlow
- Departments of Radiology and Biomedical EngineeringWake Forest School of MedicineWinston-Salem, North Carolina
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8
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LaRovere KL, Poussaint TY, Young CC, Newhams MM, Kucukak S, Irby K, Kong M, Schwartz SP, Walker TC, Bembea MM, Wellnitz K, Havlin KM, Cvijanovich NZ, Hall MW, Fitzgerald JC, Schuster JE, Hobbs CV, Halasa NB, Singh AR, Mack EH, Bradford TT, Gertz SJ, Schwarz AJ, Typpo KV, Loftis LL, Giuliano JS, Horwitz SM, Biagas KV, Clouser KN, Rowan CM, Maddux AB, Soma VL, Babbitt CJ, Aguiar CL, Kolmar AR, Heidemann SM, Harvey H, Zambrano LD, Campbell AP, Randolph AG. Changes in Distribution of Severe Neurologic Involvement in US Pediatric Inpatients With COVID-19 or Multisystem Inflammatory Syndrome in Children in 2021 vs 2020. JAMA Neurol 2023; 80:91-98. [PMID: 36342679 PMCID: PMC9641594 DOI: 10.1001/jamaneurol.2022.3881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 09/02/2022] [Indexed: 11/09/2022]
Abstract
Importance In 2020 during the COVID-19 pandemic, neurologic involvement was common in children and adolescents hospitalized in the United States for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related complications. Objective To provide an update on the spectrum of SARS-CoV-2-related neurologic involvement among children and adolescents in 2021. Design, Setting, and Participants Case series investigation of patients reported to public health surveillance hospitalized with SARS-CoV-2-related illness between December 15, 2020, and December 31, 2021, in 55 US hospitals in 31 states with follow-up at hospital discharge. A total of 2253 patients were enrolled during the investigation period. Patients suspected of having multisystem inflammatory syndrome in children (MIS-C) who did not meet criteria (n = 85) were excluded. Patients (<21 years) with positive SARS-CoV-2 test results (reverse transcriptase-polymerase chain reaction and/or antibody) meeting criteria for MIS-C or acute COVID-19 were included in the analysis. Exposure SARS-CoV-2 infection. Main Outcomes and Measures Patients with neurologic involvement had acute neurologic signs, symptoms, or diseases on presentation or during hospitalization. Life-threatening neurologic involvement was adjudicated by experts based on clinical and/or neuroradiological features. Type and severity of neurologic involvement, laboratory and imaging data, vaccination status, and hospital discharge outcomes (death or survival with new neurologic deficits). Results Of 2168 patients included (58% male; median age, 10.3 years), 1435 (66%) met criteria for MIS-C, and 476 (22%) had documented neurologic involvement. Patients with neurologic involvement vs without were older (median age, 12 vs 10 years) and more frequently had underlying neurologic disorders (107 of 476 [22%] vs 240 of 1692 [14%]). Among those with neurologic involvement, 42 (9%) developed acute SARS-CoV-2-related life-threatening conditions, including central nervous system infection/demyelination (n = 23; 15 with possible/confirmed encephalitis, 6 meningitis, 1 transverse myelitis, 1 nonhemorrhagic leukoencephalopathy), stroke (n = 11), severe encephalopathy (n = 5), acute fulminant cerebral edema (n = 2), and Guillain-Barré syndrome (n = 1). Ten of 42 (24%) survived with new neurologic deficits at discharge and 8 (19%) died. Among patients with life-threatening neurologic conditions, 15 of 16 vaccine-eligible patients (94%) were unvaccinated. Conclusions and Relevance SARS-CoV-2-related neurologic involvement persisted in US children and adolescents hospitalized for COVID-19 or MIS-C in 2021 and was again mostly transient. Central nervous system infection/demyelination accounted for a higher proportion of life-threatening conditions, and most vaccine-eligible patients were unvaccinated. COVID-19 vaccination may prevent some SARS-CoV-2-related neurologic complications and merits further study.
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Affiliation(s)
- Kerri L. LaRovere
- Department of Neurology, Boston Children’s Hospital, Boston, Massachusetts
| | - Tina Y. Poussaint
- Department of Radiology, Boston Children’s Hospital, Boston, Massachusetts
| | - Cameron C. Young
- Division of Critical Care Medicine, Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children’s Hospital, Boston, Massachusetts
| | - Margaret M. Newhams
- Division of Critical Care Medicine, Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children’s Hospital, Boston, Massachusetts
| | - Suden Kucukak
- Division of Critical Care Medicine, Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children’s Hospital, Boston, Massachusetts
| | - Katherine Irby
- Section of Pediatric Critical Care, Department of Pediatrics, Arkansas Children's Hospital, Little Rock
| | - Michele Kong
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Alabama at Birmingham
| | - Stephanie P. Schwartz
- Department of Pediatrics, University of North Carolina at Chapel Hill Children’s Hospital, Chapel Hill
| | - Tracie C. Walker
- Department of Pediatrics, University of North Carolina at Chapel Hill Children’s Hospital, Chapel Hill
| | - Melania M. Bembea
- Division of Pediatric Anesthesiology and Critical Care Medicine, Department of Anesthesiology and Critical Care Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Kari Wellnitz
- Division of Pediatric Critical Care, Stead Family Department of Pediatrics, University of Iowa Carver College of Medicine, Iowa City
| | - Kevin M. Havlin
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Louisville, Norton Children’s Hospital, Louisville, Kentucky
| | - Natalie Z. Cvijanovich
- Division of Critical Care Medicine, UCSF Benioff Children’s Hospital, Oakland, California
| | - Mark W. Hall
- Division of Critical Care Medicine, Department of Pediatrics, Nationwide Children’s Hospital, Columbus, Ohio
| | - Julie C. Fitzgerald
- Division of Critical Care, Department of Anesthesiology and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Jennifer E. Schuster
- Division of Pediatric Infectious Diseases, Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, Missouri
| | - Charlotte V. Hobbs
- Division of Infectious Diseases, Departments of Pediatrics and Microbiology, University of Mississippi Medical Center, Jackson
| | - Natasha B. Halasa
- Division of Pediatric Infectious Diseases, Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Aalok R. Singh
- Pediatric Critical Care Division, Maria Fareri Children’s Hospital at Westchester Medical Center, New York Medical College, Valhalla
| | - Elizabeth H. Mack
- Division of Pediatric Critical Care Medicine, Medical University of South Carolina, Charleston
| | - Tamara T. Bradford
- Division of Cardiology, Department of Pediatrics, Louisiana State University Health Sciences Center, Children’s Hospital of New Orleans, New Orleans
| | - Shira J. Gertz
- Division of Pediatric Critical Care, Department of Pediatrics, Cooperman Barnabas Medical Center, Livingston, New Jersey
| | - Adam J. Schwarz
- Division of Critical Care Medicine, Children’s Health Orange County (CHOC), Orange, California
| | - Katri V. Typpo
- Department of Pediatrics and Banner Children’s at Diamond Children’s Medical Center, University of Arizona, Tucson
| | - Laura L. Loftis
- Section of Critical Care Medicine, Department of Pediatrics, Texas Children’s Hospital, Houston
| | - John S. Giuliano
- Division of Critical Care, Department of Pediatrics, Yale University School of Medicine, New Haven, Connecticut
| | - Steven M. Horwitz
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey
| | - Katherine V. Biagas
- Department of Pediatrics, Stony Brook University Renaissance School of Medicine, Stony Brook, New York
| | - Katharine N. Clouser
- Department of Pediatrics, Joseph M. Sanzari Children’s Hospital at Hackensack University Medical Center, Hackensack, New Jersey
| | - Courtney M. Rowan
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, Indiana University School of Medicine, Riley Hospital for Children, Indianapolis
| | - Aline B. Maddux
- Section of Critical Care Medicine, Department of Pediatrics, University of Colorado School of Medicine and Children’s Hospital Colorado, Aurora
| | - Vijaya L. Soma
- Division of Pediatric Infectious Diseases, Department of Pediatrics, New York University Grossman School of Medicine, New York
| | | | - Cassyanne L. Aguiar
- Division of Pediatric Rheumatology, Department of Pediatrics, Eastern Virginia Medical School, Children’s Hospital of The King’s Daughters, Norfolk
| | - Amanda R. Kolmar
- Division of Critical Care, Department of Pediatrics, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Sabrina M. Heidemann
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, Central Michigan University, Detroit
| | - Helen Harvey
- Division of Pediatric Critical Care, Rady Children’s Hospital, San Diego, California
| | - Laura D. Zambrano
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Angela P. Campbell
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Adrienne G. Randolph
- Division of Critical Care Medicine, Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children’s Hospital, Boston, Massachusetts
- Departments of Anaesthesia and Pediatrics, Harvard Medical School, Boston, Massachusetts
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9
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Cogswell PM, Barakos JA, Barkhof F, Benzinger TS, Jack CR, Poussaint TY, Raji CA, Ramanan VK, Whitlow CT. Amyloid-Related Imaging Abnormalities with Emerging Alzheimer Disease Therapeutics: Detection and Reporting Recommendations for Clinical Practice. AJNR Am J Neuroradiol 2022; 43:E19-E35. [PMID: 35953274 PMCID: PMC9451628 DOI: 10.3174/ajnr.a7586] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Monoclonal antibodies are emerging disease-modifying therapies for Alzheimer disease that require brain MR imaging for eligibility assessment as well as for monitoring for amyloid-related imaging abnormalities. Amyloid-related imaging abnormalities result from treatment-related loss of vascular integrity and may occur in 2 forms. Amyloid-related imaging abnormalities with edema or effusion are transient, treatment-induced edema or sulcal effusion, identified on T2-FLAIR. Amyloid-related imaging abnormalities with hemorrhage are treatment-induced microhemorrhages or superficial siderosis identified on T2* gradient recalled-echo. As monoclonal antibodies become more widely available, treatment screening and monitoring brain MR imaging examinations may greatly increase neuroradiology practice volumes. Radiologists must become familiar with the imaging appearance of amyloid-related imaging abnormalities, how to select an appropriate imaging protocol, and report findings in clinical practice. On the basis of clinical trial literature and expert experience from clinical trial imaging, we summarize imaging findings of amyloid-related imaging abnormalities, describe potential interpretation pitfalls, and provide recommendations for a standardized imaging protocol and an amyloid-related imaging abnormalities reporting template. Standardized imaging and reporting of these findings are important because an amyloid-related imaging abnormalities severity score, derived from the imaging findings, is used along with clinical status to determine patient management and eligibility for continued monoclonal antibody dosing.
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Affiliation(s)
- P M Cogswell
- From the Departments of Radiology (P.M.C., C.R.J.)
| | - J A Barakos
- Department of Radiology (J.A.B.), California Pacific Medical Center, San Francisco, California
| | - F Barkhof
- Departments of Radiology (F.B.)
- Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam, the Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, UK
| | - T S Benzinger
- Departments of Radiology (T.S.B., C.A.R.)
- Neurosurgery (T.S.B.)
| | - C R Jack
- From the Departments of Radiology (P.M.C., C.R.J.)
| | - T Y Poussaint
- Department of Radiology (T.Y.P.), Boston Children's Hospital, Boston, Massachusetts
| | - C A Raji
- Departments of Radiology (T.S.B., C.A.R.)
- Neurology (C.A.R.),Washington University School of Medicine, St. Louis, Missouri
| | - V K Ramanan
- Neurology (V.K.R.), Mayo Clinic, Rochester, Minnesota
| | - C T Whitlow
- Departments of Radiology (C.T.W.)
- Biomedical Engineering (C.T.W.), Wake Forest School of Medicine, Winston-Salem, North Carolina
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Osborn AG, Louis DN, Poussaint TY, Linscott LL, Salzman KL. The 2021 World Health Organization Classification of Tumors of the Central Nervous System: What Neuroradiologists Need to Know. AJNR Am J Neuroradiol 2022; 43:928-937. [PMID: 35710121 DOI: 10.3174/ajnr.a7462] [Citation(s) in RCA: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 11/08/2021] [Indexed: 12/12/2022]
Abstract
Neuroradiologists play a key role in brain tumor diagnosis and management. Staying current with the latest classification systems and diagnostic markers is important to provide optimal patient care. Publication of the 2016 World Health Organization Classification of Tumors of the Central Nervous System introduced a paradigm shift in the diagnosis of CNS neoplasms. For the first time, both histologic features and genetic alterations were incorporated into the diagnostic framework, classifying and grading brain tumors. The newly published 2021 World Health Organization Classification of Tumors of the Central Nervous System, May 2021, 5th edition, has added even more molecular features and updated pathologic diagnoses. We present, summarize, and illustrate the most salient aspects of the new 5th edition. We have selected the key "must know" topics for practicing neuroradiologists.
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Affiliation(s)
- A G Osborn
- From the Department of Radiology and Imaging Sciences (A.G.O., K.L.S.), University of Utah School of Medicine, Salt Lake City, Utah
| | - D N Louis
- Department of Pathology (D.N.L.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - T Y Poussaint
- Department of Radiology (T.Y.P.), Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
| | - L L Linscott
- Intermountain Pediatric Imaging (L.L.L.), Primary Children's Hospital, University of Utah School of Medicine, Salt Lake City, Utah
| | - K L Salzman
- From the Department of Radiology and Imaging Sciences (A.G.O., K.L.S.), University of Utah School of Medicine, Salt Lake City, Utah
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11
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Zhang M, Wong S, Wright J, Toescu S, Mohammadzadeh M, Han M, Lummus S, Wagner M, Yecies DW, Lai H, Eghbal A, Radmanesh A, Nemelka J, Harward SC, Malinzak M, Laughlin S, Perreault S, Braun K, Vosough A, Poussaint TY, Goetti R, Ertl-Wagner B, Ho C, Oztekin O, Ramaswamy V, Mankad K, Vitanza N, Cheshier SH, Said M, Aquilina K, Thompson EM, Jaju A, Grant GA, Lober R, Yeom K. 507 Rational Radiomic Design for Stepwise Diagnosis of Posterior Fossa Pediatric Tumors. Neurosurgery 2022. [DOI: 10.1227/neu.0000000000001880_507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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Peng J, Kim DD, Patel JB, Zeng X, Huang J, Chang K, Xun X, Zhang C, Sollee J, Wu J, Dalal DJ, Feng X, Zhou H, Zhu C, Zou B, Jin K, Wen PY, Boxerman JL, Warren KE, Poussaint TY, States LJ, Kalpathy-Cramer J, Yang L, Huang RY, Bai HX. Deep learning-based automatic tumor burden assessment of pediatric high-grade gliomas, medulloblastomas, and other leptomeningeal seeding tumors. Neuro Oncol 2022; 24:289-299. [PMID: 34174070 PMCID: PMC8804897 DOI: 10.1093/neuonc/noab151] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Longitudinal measurement of tumor burden with magnetic resonance imaging (MRI) is an essential component of response assessment in pediatric brain tumors. We developed a fully automated pipeline for the segmentation of tumors in pediatric high-grade gliomas, medulloblastomas, and leptomeningeal seeding tumors. We further developed an algorithm for automatic 2D and volumetric size measurement of tumors. METHODS The preoperative and postoperative cohorts were randomly split into training and testing sets in a 4:1 ratio. A 3D U-Net neural network was trained to automatically segment the tumor on T1 contrast-enhanced and T2/FLAIR images. The product of the maximum bidimensional diameters according to the RAPNO (Response Assessment in Pediatric Neuro-Oncology) criteria (AutoRAPNO) was determined. Performance was compared to that of 2 expert human raters who performed assessments independently. Volumetric measurements of predicted and expert segmentations were computationally derived and compared. RESULTS A total of 794 preoperative MRIs from 794 patients and 1003 postoperative MRIs from 122 patients were included. There was excellent agreement of volumes between preoperative and postoperative predicted and manual segmentations, with intraclass correlation coefficients (ICCs) of 0.912 and 0.960 for the 2 preoperative and 0.947 and 0.896 for the 2 postoperative models. There was high agreement between AutoRAPNO scores on predicted segmentations and manually calculated scores based on manual segmentations (Rater 2 ICC = 0.909; Rater 3 ICC = 0.851). Lastly, the performance of AutoRAPNO was superior in repeatability to that of human raters for MRIs with multiple lesions. CONCLUSIONS Our automated deep learning pipeline demonstrates potential utility for response assessment in pediatric brain tumors. The tool should be further validated in prospective studies.
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Affiliation(s)
- Jian Peng
- Department of Neurology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Daniel D Kim
- Department of Diagnostic Imaging, Rhode Island Hospital and Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Jay B Patel
- Department of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Xiaowei Zeng
- Department of Neurology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Jiaer Huang
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Ken Chang
- Department of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Xinping Xun
- Department of Neurology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Chen Zhang
- Department of Neurology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - John Sollee
- Department of Diagnostic Imaging, Rhode Island Hospital and Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Jing Wu
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Deepa J Dalal
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Xue Feng
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Hao Zhou
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Chengzhang Zhu
- Department of Neurology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Beiji Zou
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Ke Jin
- Department of Radiology, Hunan Children’s Hospital, Changsha, Hunan, China
| | - Patrick Y Wen
- Center for Neuro-Oncology, Dana Farber Cancer Institute, Boston, Massachusetts, USA
| | - Jerrold L Boxerman
- Department of Diagnostic Imaging, Rhode Island Hospital and Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Katherine E Warren
- Department of Pediatrics, Dana Farber Cancer Institute, Boston, Massachusetts, USA
| | - Tina Y Poussaint
- Department of Radiology, Boston Children’s Hospital, Boston, Massachusetts, USA
| | - Lisa J States
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Jayashree Kalpathy-Cramer
- Department of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Li Yang
- Department of Neurology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Harrison X Bai
- Department of Diagnostic Imaging, Rhode Island Hospital and Alpert Medical School of Brown University, Providence, Rhode Island, USA
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13
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Poussaint TY, LaRovere KL, Newburger JW, Chou J, Nigrovic LE, Novak T, Randolph AG. Multisystem Inflammatory-like Syndrome in a Child Following COVID-19 mRNA Vaccination. Vaccines (Basel) 2021; 10:vaccines10010043. [PMID: 35062704 PMCID: PMC8781649 DOI: 10.3390/vaccines10010043] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 12/27/2021] [Accepted: 12/29/2021] [Indexed: 12/29/2022] Open
Abstract
A 12-year-old male was presented to the hospital with acute encephalopathy, headache, vomiting, diarrhea, and elevated troponin after recent COVID-19 vaccination. Two days prior to admission and before symptom onset, he received the second dose of the Pfizer-BioNTech COVID-19 vaccine. Symptoms developed within 24 h with worsening neurologic symptoms, necessitating admission to the pediatric intensive care unit. Brain magnetic resonance imaging within 16 h of admission revealed a cytotoxic splenial lesion of the corpus callosum (CLOCC). Nineteen days prior to admission, he developed erythema migrans, and completed an amoxicillin treatment course for clinical Lyme disease. However, Lyme antibody titers were negative on admission and nine days later, making active Lyme disease an unlikely explanation for his presentation to hospital. An extensive workup for other etiologies on cerebrospinal fluid and blood samples was negative, including infectious and autoimmune causes and known immune deficiencies. Three weeks after hospital discharge, all of his symptoms had dissipated, and he had a normal neurologic exam. Our report highlights a potential role of mRNA vaccine-induced immunity leading to MIS-C-like symptoms with cardiac involvement and a CLOCC in a recently vaccinated child and the complexity of establishing a causal association with vaccination. The child recovered without receipt of immune modulatory treatment.
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Affiliation(s)
- Tina Y. Poussaint
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Correspondence: ; Tel.: +1-617-355-6450
| | - Kerri L. LaRovere
- Department of Neurology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, USA;
| | - Jane W. Newburger
- Department of Cardiology, Boston Children’s Hospital, Boston, MA 02115, USA;
- Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA; (J.C.); (L.E.N.); (A.G.R.)
| | - Janet Chou
- Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA; (J.C.); (L.E.N.); (A.G.R.)
- Division of Immunology, Boston Children’s Hospital, Boston, MA 02115, USA
| | - Lise E. Nigrovic
- Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA; (J.C.); (L.E.N.); (A.G.R.)
- Divison of Emergency Medicine, Boston Children’s Hospital, Boston, MA 02115, USA
| | - Tanya Novak
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children’s Hospital and Department of Anaesthesia, Harvard Medical School, Boston, MA 02115, USA;
| | - Adrienne G. Randolph
- Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA; (J.C.); (L.E.N.); (A.G.R.)
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children’s Hospital and Department of Anaesthesia, Harvard Medical School, Boston, MA 02115, USA;
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14
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Peng J, Kim DD, Patel JB, Zeng X, Huang J, Chang K, Xun X, Zhang C, Sollee J, Wu J, Dalal DJ, Feng X, Zhou H, Zhu C, Zou B, Jin K, Wen PY, Boxerman JL, Warren KE, Poussaint TY, States LJ, Kalpathy-Cramer J, Yang L, Huang RY, Bai HX. Corrigendum to: Deep learning-based automatic tumor burden assessment of pediatric high-grade gliomas, medulloblastomas, and other leptomeningeal seeding tumors. Neuro Oncol 2021; 23:2124. [PMID: 34551090 DOI: 10.1093/neuonc/noab226] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Jian Peng
- Department of Neurology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Daniel D Kim
- Department of Diagnostic Imaging, Rhode Island Hospital and Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Jay B Patel
- Department of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Xiaowei Zeng
- Department of Neurology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Jiaer Huang
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Ken Chang
- Department of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Xinping Xun
- Department of Neurology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Chen Zhang
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - John Sollee
- Department of Diagnostic Imaging, Rhode Island Hospital and Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Jing Wu
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Deepa J Dalal
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Xue Feng
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Hao Zhou
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Chengzhang Zhu
- Department of Neurology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Beiji Zou
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Ke Jin
- Department of Radiology, Hunan Children's Hospital, Changsha, Hunan, China
| | - Patrick Y Wen
- Center for Neuro-Oncology, Dana Farber Cancer Institute, Boston, Massachusetts, USA
| | - Jerrold L Boxerman
- Department of Diagnostic Imaging, Rhode Island Hospital and Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Katherine E Warren
- Department of Pediatrics, Dana Farber Cancer Institute, Boston, Massachusetts, USA
| | - Tina Y Poussaint
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Lisa J States
- Department of Neurology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.,Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Jayashree Kalpathy-Cramer
- Department of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Li Yang
- Department of Neurology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Harrison X Bai
- Department of Diagnostic Imaging, Rhode Island Hospital and Alpert Medical School of Brown University, Providence, Rhode Island, USA
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15
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LaRovere KL, Riggs BJ, Poussaint TY, Young CC, Newhams MM, Maamari M, Walker TC, Singh AR, Dapul H, Hobbs CV, McLaughlin GE, Son MBF, Maddux AB, Clouser KN, Rowan CM, McGuire JK, Fitzgerald JC, Gertz SJ, Shein SL, Munoz AC, Thomas NJ, Irby K, Levy ER, Staat MA, Tenforde MW, Feldstein LR, Halasa NB, Giuliano JS, Hall MW, Kong M, Carroll CL, Schuster JE, Doymaz S, Loftis LL, Tarquinio KM, Babbitt CJ, Nofziger RA, Kleinman LC, Keenaghan MA, Cvijanovich NZ, Spinella PC, Hume JR, Wellnitz K, Mack EH, Michelson KN, Flori HR, Patel MM, Randolph AG. Neurologic Involvement in Children and Adolescents Hospitalized in the United States for COVID-19 or Multisystem Inflammatory Syndrome. JAMA Neurol 2021; 78:536-547. [PMID: 33666649 DOI: 10.1001/jamaneurol.2021.0504] [Citation(s) in RCA: 240] [Impact Index Per Article: 80.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Importance Coronavirus disease 2019 (COVID-19) affects the nervous system in adult patients. The spectrum of neurologic involvement in children and adolescents is unclear. Objective To understand the range and severity of neurologic involvement among children and adolescents associated with COVID-19. Setting, Design, and Participants Case series of patients (age <21 years) hospitalized between March 15, 2020, and December 15, 2020, with positive severe acute respiratory syndrome coronavirus 2 test result (reverse transcriptase-polymerase chain reaction and/or antibody) at 61 US hospitals in the Overcoming COVID-19 public health registry, including 616 (36%) meeting criteria for multisystem inflammatory syndrome in children. Patients with neurologic involvement had acute neurologic signs, symptoms, or diseases on presentation or during hospitalization. Life-threatening involvement was adjudicated by experts based on clinical and/or neuroradiologic features. Exposures Severe acute respiratory syndrome coronavirus 2. Main Outcomes and Measures Type and severity of neurologic involvement, laboratory and imaging data, and outcomes (death or survival with new neurologic deficits) at hospital discharge. Results Of 1695 patients (909 [54%] male; median [interquartile range] age, 9.1 [2.4-15.3] years), 365 (22%) from 52 sites had documented neurologic involvement. Patients with neurologic involvement were more likely to have underlying neurologic disorders (81 of 365 [22%]) compared with those without (113 of 1330 [8%]), but a similar number were previously healthy (195 [53%] vs 723 [54%]) and met criteria for multisystem inflammatory syndrome in children (126 [35%] vs 490 [37%]). Among those with neurologic involvement, 322 (88%) had transient symptoms and survived, and 43 (12%) developed life-threatening conditions clinically adjudicated to be associated with COVID-19, including severe encephalopathy (n = 15; 5 with splenial lesions), stroke (n = 12), central nervous system infection/demyelination (n = 8), Guillain-Barré syndrome/variants (n = 4), and acute fulminant cerebral edema (n = 4). Compared with those without life-threatening conditions (n = 322), those with life-threatening neurologic conditions had higher neutrophil-to-lymphocyte ratios (median, 12.2 vs 4.4) and higher reported frequency of D-dimer greater than 3 μg/mL fibrinogen equivalent units (21 [49%] vs 72 [22%]). Of 43 patients who developed COVID-19-related life-threatening neurologic involvement, 17 survivors (40%) had new neurologic deficits at hospital discharge, and 11 patients (26%) died. Conclusions and Relevance In this study, many children and adolescents hospitalized for COVID-19 or multisystem inflammatory syndrome in children had neurologic involvement, mostly transient symptoms. A range of life-threatening and fatal neurologic conditions associated with COVID-19 infrequently occurred. Effects on long-term neurodevelopmental outcomes are unknown.
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Affiliation(s)
- Kerri L LaRovere
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts
| | - Becky J Riggs
- Division of Pediatric Anesthesiology and Critical Care Medicine, Department of Anesthesiology and Critical Care Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Tina Y Poussaint
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts
| | - Cameron C Young
- Division of Critical Care Medicine, Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, Massachusetts
| | - Margaret M Newhams
- Division of Critical Care Medicine, Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, Massachusetts
| | - Mia Maamari
- Division of Critical Care Medicine, Department of Pediatrics, University of Texas Southwestern, Children's Health Medical Center Dallas
| | - Tracie C Walker
- Department of Pediatrics, University of North Carolina at Chapel Hill Children's Hospital, Chapel Hill
| | - Aalok R Singh
- Pediatric Critical Care Division, Maria Fareri Children's Hospital at Westchester Medical Center and New York Medical College, Valhalla
| | - Heda Dapul
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, New York University Grossman School of Medicine, New York
| | - Charlotte V Hobbs
- Division of Infectious Diseases, Department of Pediatrics, Department of Microbiology, University of Mississippi Medical Center, Jackson
| | - Gwenn E McLaughlin
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Miami Miller School of Medicine, Miami, Florida
| | - Mary Beth F Son
- Division of Immunology, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts
| | - Aline B Maddux
- Section of Critical Care Medicine, Department of Pediatrics, University of Colorado School of Medicine and Children's Hospital Colorado, Aurora
| | - Katharine N Clouser
- Department of Pediatrics, Joseph M. Sanzari Children's Hospital at Hackensack University Medical Center, Hackensack, New Jersey
| | - Courtney M Rowan
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, Indiana University School of Medicine, Riley Hospital for Children, Indianapolis
| | - John K McGuire
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Washington, Seattle
| | - Julie C Fitzgerald
- Division of Critical Care, Department of Anesthesiology and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Shira J Gertz
- Division of Pediatric Critical Care, Department of Pediatrics, Saint Barnabas Medical Center, Livingston, New Jersey
| | - Steven L Shein
- Division of Pediatric Critical Care Medicine, Rainbow Babies and Children's Hospital, Cleveland, Ohio
| | - Alvaro Coronado Munoz
- Pediatric Critical Care Division, Department of Pediatrics, University of Texas Health Science Center at Houston, Houston
| | - Neal J Thomas
- Department of Pediatrics, Penn State Hershey Children's Hospital, Pennsylvania State University College of Medicine, Hershey
| | - Katherine Irby
- Section of Pediatric Critical Care, Department of Pediatrics, Arkansas Children's Hospital, Little Rock
| | - Emily R Levy
- Divisions of Pediatric Infectious Diseases and Pediatric Critical Care Medicine, Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minnesota
| | - Mary A Staat
- Department of Pediatrics, University of Cincinnati, Division of Infectious Diseases, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Mark W Tenforde
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia.,Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Leora R Feldstein
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia.,Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Natasha B Halasa
- Division of Pediatric Infectious Diseases, Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - John S Giuliano
- Division of Critical Care, Yale University School of Medicine, New Haven, Connecticut
| | - Mark W Hall
- Division of Critical Care Medicine, Department of Pediatrics, Nationwide Children's Hospital, Columbus, Ohio
| | - Michele Kong
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Alabama at Birmingham
| | | | - Jennifer E Schuster
- Division of Pediatric Infectious Diseases, Department of Pediatrics, Children's Mercy Kansas City, Kansas City, Missouri
| | - Sule Doymaz
- Division of Pediatric Critical Care, Department of Pediatrics, State University of New York Downstate Health Sciences University, Brooklyn
| | - Laura L Loftis
- Section of Critical Care Medicine, Department of Pediatrics, Texas Children's Hospital, Houston
| | - Keiko M Tarquinio
- Division of Critical Care Medicine, Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia
| | | | - Ryan A Nofziger
- Division of Critical Care Medicine, Akron Children's Hospital, Akron, Ohio
| | - Lawrence C Kleinman
- Division of Population Health, Quality, and Implementation Sciences (PopQuIS), Department of Pediatrics, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey
| | - Michael A Keenaghan
- Pediatric Critical Care, New York City Health and Hospitals, Kings County Hospital, Brooklyn, New York
| | - Natalie Z Cvijanovich
- Division of Critical Care Medicine, University of California, San Francisco, Benioff Children's Hospital, Oakland
| | - Philip C Spinella
- Division of Critical Care, Department of Pediatrics, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Janet R Hume
- Division of Pediatric Critical Care, University of Minnesota Masonic Children's Hospital, Minneapolis
| | - Kari Wellnitz
- Division of Pediatric Critical Care, Stead Family Department of Pediatrics, University of Iowa Carver College of Medicine, Iowa City, Iowa
| | - Elizabeth H Mack
- Division of Pediatric Critical Care Medicine, Medical University of South Carolina, Charleston
| | - Kelly N Michelson
- Division of Critical Care Medicine, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois
| | - Heidi R Flori
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, Mott Children's Hospital and University of Michigan, Ann Arbor
| | - Manish M Patel
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia.,Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Adrienne G Randolph
- Division of Critical Care Medicine, Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, Massachusetts.,Departments of Anaesthesia and Pediatrics, Harvard Medical School, Boston, Massachusetts
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16
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Goldman S, Pollack IF, Jakacki RI, Billups CA, Poussaint TY, Adesina AM, Panigrahy A, Parsons DW, Broniscer A, Robinson GW, Robison NJ, Partap S, Kilburn LB, Onar-Thomas A, Dunkel IJ, Fouladi M. Phase II study of peginterferon alpha-2b for patients with unresectable or recurrent craniopharyngiomas: a Pediatric Brain Tumor Consortium report. Neuro Oncol 2021; 22:1696-1704. [PMID: 32393959 DOI: 10.1093/neuonc/noaa119] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Craniopharyngiomas account for approximately 1.2-4% of all CNS tumors. They are typically treated with a combination of surgical resection and focal radiotherapy. Unfortunately, treatment can lead to permanent deleterious effects on behavior, learning, and endocrine function. METHODS The Pediatric Brain Tumor Consortium performed a multicenter phase 2 study in children and young adults with unresectable or recurrent craniopharyngioma (PBTC-039). Between December 2013 and November 2017, nineteen patients (median age at enrollment, 13.1 y; range, 2-25 y) were enrolled in one of 2 strata: patients previously treated with surgery alone (stratum 1) or who received radiation (stratum 2). RESULTS Eighteen eligible patients (8 male, 10 female) were treated with weekly subcutaneous pegylated interferon alpha-2b for up to 18 courses (108 wk). Therapy was well tolerated with no grade 4 or 5 toxicities. 2 of the 7 eligible patients (28.6%) in stratum 1 had a partial response, but only one response was sustained for more than 3 months. None of the 11 stratum 2 patients had an objective radiographic response, although median progression-free survival was 19.5 months. CONCLUSIONS Pegylated interferon alpha-2b treatment, in lieu of or following radiotherapy, was well tolerated in children and young adults with recurrent craniopharyngiomas. Although objective responses were limited, progression-free survival results are encouraging, warranting further studies.
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Affiliation(s)
- Stewart Goldman
- Division of Hematology, Oncology, Neuro-Oncology, Stem Cell Transplantation, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois
| | - Ian F Pollack
- Department of Pediatric Neurosurgery, Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Regina I Jakacki
- Department of Pediatric Neurosurgery, Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Catherine A Billups
- Department of Biostatistics, St Jude's Children's Research Hospital, Memphis, Tennessee
| | - Tina Y Poussaint
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | | | - Ashok Panigrahy
- Department of Pediatric Radiology, Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania
| | - Donald W Parsons
- Texas Children's Cancer and Hematology Centers, Texas Medical Center, Houston, Texas
| | - Alberto Broniscer
- Department of Radiology, Children's Hospital of Pittsburgh, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Giles W Robinson
- Division of Neuro-Oncology, St Jude's Children's Research Hospital, Memphis, Tennessee
| | - Nathan J Robison
- Children's Center for Cancer and Blood Diseases, Children's Hospital Los Angeles, Los Angeles, California
| | - Sonia Partap
- Department of Neurology, Stanford University School of Medicine, Stanford, California
| | - Lindsay B Kilburn
- Department of Hematology and Oncology, Children's National Medical Center, Washington, DC
| | - Arzu Onar-Thomas
- Department of Biostatistics, St Jude's Children's Research Hospital, Memphis, Tennessee
| | - Ira J Dunkel
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Maryam Fouladi
- Department of Hematology and Oncology, Cincinnati Children's Hospital, Cincinnati, Ohio
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17
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Erker C, Tamrazi B, Poussaint TY, Mueller S, Mata-Mbemba D, Franceschi E, Brandes AA, Rao A, Haworth KB, Wen PY, Goldman S, Vezina G, Macdonald TJ, Dunkel IJ, Morgan PS, Jaspan T, Prados MD, Warren KE. IMG-04. RESPONSE ASSESSMENT IN PEDIATRIC HIGH-GRADE GLIOMA: RECOMMENDATIONS FROM THE RESPONSE ASSESSMENT IN PEDIATRIC NEURO-ONCOLOGY WORKING GROUP. Neuro Oncol 2020. [PMCID: PMC7715898 DOI: 10.1093/neuonc/noaa222.340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
INTRODUCTION
Response criteria for pediatric high-grade gliomas (pHGG) have varied historically and across clinical trials. Compared to adult HGG, pHGG response assessment has unique challenges. An international Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group was established to develop pHGG response assessment criteria.
METHODS
Pediatric and adult neuro-oncologists, neuro-radiologists and experts in imaging informatics developed a consensus statement and established a unified response assessment for biopsy-proven pHGG, excluding DIPG. This was achieved by identifying major challenges, reviewing existing literature and current practices, and finally developing recommendations through an iterative process.
RESULTS
Categories for response assessment include complete response, partial response, minor response, stable disease and progressive disease. Refractory disease is excluded. Criteria used to determine response assessment include quantitative evaluation of measurable disease, qualitative assessment of diffusion imaging, presence or absence of new lesions, clinical status using performance score, and vascular endothelial growth factor inhibitor and/or corticosteroid use. Response is determined over 2-time points ≥ 8 weeks apart, and when progressive disease is unclear, guidance for repeat MRI imaging and/or utility of repeat biopsy is described. A number of recommendations are also given to standardize response assessment across clinical trials including MRI protocol sequence recommendations for brain and spine, definitions for measurable and non-measurable disease, and imaging time points with post-operative considerations. In addition, guidance is given for differentiating vasogenic edema versus tumor invasion in non-enhancing disease.
CONCLUSION
Consensus recommendations and response definitions have been established and, similar to other RAPNO recommendations, prospective validation in clinical trials is warranted.
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Affiliation(s)
- Craig Erker
- Dalhousie University, Halifax, NS, Canada
- IWK Health Centre, Halifax, NS, Canada
| | - Benita Tamrazi
- Keck School of Medicine University of Southern California, Los Angeles, CA, USA
- Children’s Hospital of Los Angeles, Los Angeles, CA, USA
| | - Tina Y Poussaint
- Harvard Medical School, Boston, MA, USA
- Boston Children’s Hospital, Boston, MA, USA
| | - Sabine Mueller
- University of California San Francisco, San Francisco, CA, USA
- UCSF Benioff Children’s Hospital, San Francisco, CA, USA
| | - Daddy Mata-Mbemba
- Dalhousie University, Halifax, NS, Canada
- IWK Health Centre, Halifax, NS, Canada
| | - Enrico Franceschi
- Azienda Unità Sanitaria Locale– Istituto di Ricovero e Cura a Carattere Scientifico Istituto delle Scienze Neurologiche, Bologna, Italy
- Bellaria-Maggiore Hospital, Bologna, Italy
| | - Alba A Brandes
- Azienda Unità Sanitaria Locale– Istituto di Ricovero e Cura a Carattere Scientifico Istituto delle Scienze Neurologiche, Bologna, Italy
- Bellaria-Maggiore Hospital, AUSL Bologna, Bologna, Italy
| | - Arvind Rao
- University of Michigan, Ann Arbor, MI, USA
| | | | - Patrick Y Wen
- Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Stewart Goldman
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Ann & Robert H, Lurie Children’s Hospital of Chicago, Chicago, IL, USA
| | - Gilbert Vezina
- George Washington University School of Medicine and Health Sciences, Washington, DC, USA
- Children’s National Medical Center, Washington, DC, USA
| | - Tobey J Macdonald
- Emory University School of Medicine, Atlanta, GA, USA
- Children’s Healthcare of Atlanta, Atlanta, GA, USA
| | - Ira J Dunkel
- Weill Cornell Medical College, New York, NY, USA
- Memorial Sloan-Kettering Cancer Center and Weill Cornell Medical College, New York, NY, USA
| | - Paul S Morgan
- Nottingham University Hospitals National Health Service Trust, Nottingham, United Kingdom
- Queen’s Medical Centre, Nottingham, United Kingdom
| | - Tim Jaspan
- Nottingham University Hospitals National Health Service Trust, Nottingham, United Kingdom
- Queen’s Medical Centre, Nottingham, United Kingdom
| | | | - Katherine E Warren
- Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, Boston, MA, USA
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18
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Quon JL, Bala W, Chen LC, Wright J, Kim LH, Han M, Shpanskaya K, Lee EH, Tong E, Iv M, Seekins J, Lungren MP, Braun KRM, Poussaint TY, Laughlin S, Taylor MD, Lober RM, Vogel H, Fisher PG, Grant GA, Ramaswamy V, Vitanza NA, Ho CY, Edwards MSB, Cheshier SH, Yeom KW. Deep Learning for Pediatric Posterior Fossa Tumor Detection and Classification: A Multi-Institutional Study. AJNR Am J Neuroradiol 2020; 41:1718-1725. [PMID: 32816765 PMCID: PMC7583118 DOI: 10.3174/ajnr.a6704] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2020] [Accepted: 05/27/2020] [Indexed: 01/05/2023]
Abstract
BACKGROUND AND PURPOSE Posterior fossa tumors are the most common pediatric brain tumors. MR imaging is key to tumor detection, diagnosis, and therapy guidance. We sought to develop an MR imaging-based deep learning model for posterior fossa tumor detection and tumor pathology classification. MATERIALS AND METHODS The study cohort comprised 617 children (median age, 92 months; 56% males) from 5 pediatric institutions with posterior fossa tumors: diffuse midline glioma of the pons (n = 122), medulloblastoma (n = 272), pilocytic astrocytoma (n = 135), and ependymoma (n = 88). There were 199 controls. Tumor histology served as ground truth except for diffuse midline glioma of the pons, which was primarily diagnosed by MR imaging. A modified ResNeXt-50-32x4d architecture served as the backbone for a multitask classifier model, using T2-weighted MRIs as input to detect the presence of tumor and predict tumor class. Deep learning model performance was compared against that of 4 radiologists. RESULTS Model tumor detection accuracy exceeded an AUROC of 0.99 and was similar to that of 4 radiologists. Model tumor classification accuracy was 92% with an F1 score of 0.80. The model was most accurate at predicting diffuse midline glioma of the pons, followed by pilocytic astrocytoma and medulloblastoma. Ependymoma prediction was the least accurate. Tumor type classification accuracy and F1 score were higher than those of 2 of the 4 radiologists. CONCLUSIONS We present a multi-institutional deep learning model for pediatric posterior fossa tumor detection and classification with the potential to augment and improve the accuracy of radiologic diagnosis.
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Affiliation(s)
- J L Quon
- From the Departments of Neurosurgery (J.L.Q., G.A.G., M.S.B.E.)
| | - W Bala
- Department of Radiology (W.B., J.S., M.P.L., K.W.Y.)
| | | | - J Wright
- Department of Radiology (J.W.), Seattle Children's Hospital, University of Washington School of Medicine, Seattle, Washington
| | - L H Kim
- Stanford University School of Medicine (L.H.K., M.H., K.S.), Stanford, California
| | - M Han
- Stanford University School of Medicine (L.H.K., M.H., K.S.), Stanford, California
| | - K Shpanskaya
- Stanford University School of Medicine (L.H.K., M.H., K.S.), Stanford, California
| | - E H Lee
- Electrical Engineering (E.H.L.)
| | | | | | - J Seekins
- Department of Radiology (W.B., J.S., M.P.L., K.W.Y.)
| | - M P Lungren
- Department of Radiology (W.B., J.S., M.P.L., K.W.Y.)
| | - K R M Braun
- Departments of Clinical Radiology & Imaging Sciences (K.R.M.B., C.Y.H.), Riley Children's Hospital, Indiana University, Indianapolis, Indiana
| | - T Y Poussaint
- Departments of Radiology (T.Y.P.), Boston Children's Hospital, Boston, Massachusetts
| | - S Laughlin
- Departments of diagnostic Imaging (S.L.)
| | | | - R M Lober
- Department of Neurosurgery (R.M.L.), Dayton Children's Hospital, Wright State University Boonshoft School of Medicine, Dayton, Ohio
| | - H Vogel
- and Pathology (H.V.), Stanford University, Stanford, California
| | - P G Fisher
- Division of Child Neurology (P.G.F.), Lucile Packard Children's Hospital, Stanford University, Palo Alto, California
| | - G A Grant
- From the Departments of Neurosurgery (J.L.Q., G.A.G., M.S.B.E.)
| | - V Ramaswamy
- and Haematology/Oncology (V.R.), The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
| | - N A Vitanza
- Division of Pediatric Hematology/Oncology (N.A.V.), Department of Pediatrics, University of Washington, Seattle Children's Hospital, Seattle Washington.,Fred Hutchinson Cancer Research Center (N.A.V.), Seattle, Washington
| | - C Y Ho
- Departments of Clinical Radiology & Imaging Sciences (K.R.M.B., C.Y.H.), Riley Children's Hospital, Indiana University, Indianapolis, Indiana
| | - M S B Edwards
- From the Departments of Neurosurgery (J.L.Q., G.A.G., M.S.B.E.)
| | - S H Cheshier
- Departments of Neurosurgery (S.H.C.), University of Utah School of Medicine, Salt Lake City, Utah
| | - K W Yeom
- Department of Radiology (W.B., J.S., M.P.L., K.W.Y.)
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19
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Erker C, Tamrazi B, Poussaint TY, Mueller S, Mata-Mbemba D, Franceschi E, Brandes AA, Rao A, Haworth KB, Wen PY, Goldman S, Vezina G, MacDonald TJ, Dunkel IJ, Morgan PS, Jaspan T, Prados MD, Warren KE. Response assessment in paediatric high-grade glioma: recommendations from the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group. Lancet Oncol 2020; 21:e317-e329. [PMID: 32502458 DOI: 10.1016/s1470-2045(20)30173-x] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 03/05/2020] [Accepted: 03/12/2020] [Indexed: 12/27/2022]
Abstract
Response criteria for paediatric high-grade glioma vary historically and across different cooperative groups. The Response Assessment in Neuro-Oncology working group developed response criteria for adult high-grade glioma, but these were not created to meet the unique challenges in children with the disease. The Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group, consisting of an international panel of paediatric and adult neuro-oncologists, clinicians, radiologists, radiation oncologists, and neurosurgeons, was established to address issues and unique challenges in assessing response in children with CNS tumours. We established a subcommittee to develop response assessment criteria for paediatric high-grade glioma. Current practice and literature were reviewed to identify major challenges in assessing the response of paediatric high-grade gliomas to various treatments. For areas in which scientific investigation was scarce, consensus was reached through an iterative process. RAPNO response assessment recommendations include the use of MRI of the brain and the spine, assessment of clinical status, and the use of corticosteroids or antiangiogenics. Imaging standards for brain and spine are defined. Compared with the recommendations for the management of adult high-grade glioma, for paediatrics there is inclusion of diffusion-weighted imaging and a higher reliance on T2-weighted fluid-attenuated inversion recovery. Consensus recommendations and response definitions have been established and, similar to other RAPNO recommendations, prospective validation in clinical trials is warranted.
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Affiliation(s)
- Craig Erker
- Department of Pediatrics, Division of Pediatric Hematology/Oncology, Dalhousie University and IWK Health Centre, Halifax, NS, Canada.
| | - Benita Tamrazi
- Department of Radiology, Keck School of Medicine, University of Southern California and Children's Hospital of Los Angeles, Los Angeles, CA, USA
| | - Tina Y Poussaint
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA
| | - Sabine Mueller
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA; Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA; Department of Neurosurgery, University of California San Francisco, San Francisco, CA, USA
| | - Daddy Mata-Mbemba
- Department of Diagnostic Imaging, Dalhousie University and IWK Health Centre, Halifax, NS, Canada
| | - Enrico Franceschi
- Department of Medical Oncology, Azienda USL, Bologna, Italy; IRCCS Institute of Neurological Sciences, Bologna, Italy
| | - Alba A Brandes
- Department of Medical Oncology, Azienda USL, Bologna, Italy; IRCCS Institute of Neurological Sciences, Bologna, Italy
| | - Arvind Rao
- Departments of Computational Medicine and Bioinformatics and Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Kellie B Haworth
- Division of Neuro-Oncology, Department of Oncology, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Patrick Y Wen
- Center For Neuro-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Stewart Goldman
- Department of Haematology, Oncology, Neuro-Oncology, and Stem Cell Transplantation, Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Gilbert Vezina
- Department of Radiology, Children's National Medical Center, Washington, DC, USA
| | - Tobey J MacDonald
- Department of Pediatrics, Aflac Cancer and Blood Disorders Center, Emory University School of Medicine, Atlanta, GA, USA
| | - Ira J Dunkel
- Department of Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Paul S Morgan
- Department of Medical Physics and Clinical Engineering, Nottingham University Hospitals, Queen's Medical Centre, Nottingham, UK
| | - Tim Jaspan
- Department of Radiology, Nottingham University Hospitals, Queen's Medical Centre, Nottingham, UK
| | - Michael D Prados
- Department of Neurosurgery, University of California San Francisco, San Francisco, CA, USA
| | - Katherine E Warren
- Department of Pediatric Oncology, Dana- Farber/Boston Children's Cancer and Blood Disorders Center, Dana-Farber Cancer Institute, Boston, MA, USA
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20
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Jaimes C, Vajapeyam S, Brown D, Kao PC, Ma C, Greenspan L, Gupta N, Goumnerova L, Bandopahayay P, Dubois F, Greenwald NF, Zack T, Shapira O, Beroukhim R, Ligon KL, Chi S, Kieran MW, Wright KD, Poussaint TY. MR Imaging Correlates for Molecular and Mutational Analyses in Children with Diffuse Intrinsic Pontine Glioma. AJNR Am J Neuroradiol 2020; 41:874-881. [PMID: 32381545 DOI: 10.3174/ajnr.a6546] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Accepted: 03/16/2020] [Indexed: 01/13/2023]
Abstract
BACKGROUND AND PURPOSE Recent advances in molecular techniques have characterized distinct subtypes of diffuse intrinsic pontine gliomas. Our aim was the identification of MR imaging correlates of these subtypes. MATERIALS AND METHODS Initial MRIs from subjects with diffuse intrinsic pontine gliomas recruited for a prospective clinical trial before treatment were analyzed. Retrospective imaging analyses included FLAIR/T2 tumor volume, tumor volume enhancing, the presence of cyst and/or necrosis, median, mean, mode, skewness, kurtosis of ADC tumor volume based on FLAIR, and enhancement at baseline. Molecular subgroups based on EGFR and MGMT mutations were established. Histone mutations were also determined (H3F3A, HIST1H3B, HIST1H3C). Univariate Cox proportional hazards regression was used to test the association of imaging predictors with overall and progression-free survival. Wilcoxon rank sum, Kruskal-Wallis, and Fisher exact tests were used to compare imaging measures among groups. RESULTS Fifty patients had biopsy and MR imaging. The median age at trial registration was 6 years (range, 3.3-17.5 years); 52% were female. On the basis of immunohistochemical results, 48 patients were assigned to 1 of 4 subgroups: 28 in MGMT-/epidermal growth factor receptor (EGFR)-, 14 in MGMT-/EGFR+, 3 in MGMT+/EGFR-, and 3 in MGMT+/EGFR+. Twenty-three patients had histone mutations in H3F3A, 8 in HIST1H3B, and 3 in HIST1H3C. Enhancing tumor volume was near-significantly different across molecular subgroups (P = .04), after accounting for the false discovery rate. Tumor volume enhancing, median, mode, skewness, and kurtosis ADC T2-FLAIR/T2 were significantly different (P ≤ .048) between patients with H3F3A and HIST1H3B/C mutations. CONCLUSIONS MR imaging features including enhancement and ADC histogram parameters are correlated with molecular subgroups and mutations in children with diffuse intrinsic pontine gliomas.
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Affiliation(s)
- C Jaimes
- From the Departments of Radiology (C.J., S.V., T.Y.P.).,Fetal-Neonatal Neuroimaging and Developmental Science Center (C.J.), Division of Newborn Medicine; Boston Children's Hospital, Boston, Massachusetts.,Harvard Medical School (C.J., S.V., C.M., P.B., F.D., R.B., K.L.L., S.C., K.D.W., T.Y.P.), Boston, Massachusetts
| | - S Vajapeyam
- From the Departments of Radiology (C.J., S.V., T.Y.P.).,Harvard Medical School (C.J., S.V., C.M., P.B., F.D., R.B., K.L.L., S.C., K.D.W., T.Y.P.), Boston, Massachusetts
| | - D Brown
- Tumor Imaging Metrics Core (D.B.), Massachusetts General Hospital, Boston, Massachusetts
| | - P-C Kao
- Pediatrics, Division of Oncology (P.-C.K., C.M., L.G., P.B., R.B., S.C., K.D.W.).,Dana Farber Cancer Institute (P.-C.K., C.M., L.G., P.B., F.D., O.S., R.B., K.L.L., S.C., K.D.W.), Boston, Massachusetts
| | - C Ma
- Pediatrics, Division of Oncology (P.-C.K., C.M., L.G., P.B., R.B., S.C., K.D.W.).,Dana Farber Cancer Institute (P.-C.K., C.M., L.G., P.B., F.D., O.S., R.B., K.L.L., S.C., K.D.W.), Boston, Massachusetts.,Harvard Medical School (C.J., S.V., C.M., P.B., F.D., R.B., K.L.L., S.C., K.D.W., T.Y.P.), Boston, Massachusetts
| | - L Greenspan
- Pediatrics, Division of Oncology (P.-C.K., C.M., L.G., P.B., R.B., S.C., K.D.W.).,Dana Farber Cancer Institute (P.-C.K., C.M., L.G., P.B., F.D., O.S., R.B., K.L.L., S.C., K.D.W.), Boston, Massachusetts
| | - N Gupta
- Department of Pediatric Neurosurgery (N.G.), University of California San Francisco Benioff Children's Hospital, San Francisco, California.,University of California San Francisco School of Medicine (N.G., T.Z.), San Francisco, California
| | | | - P Bandopahayay
- Pediatrics, Division of Oncology (P.-C.K., C.M., L.G., P.B., R.B., S.C., K.D.W.).,Dana Farber Cancer Institute (P.-C.K., C.M., L.G., P.B., F.D., O.S., R.B., K.L.L., S.C., K.D.W.), Boston, Massachusetts.,Harvard Medical School (C.J., S.V., C.M., P.B., F.D., R.B., K.L.L., S.C., K.D.W., T.Y.P.), Boston, Massachusetts
| | - F Dubois
- Dana Farber Cancer Institute (P.-C.K., C.M., L.G., P.B., F.D., O.S., R.B., K.L.L., S.C., K.D.W.), Boston, Massachusetts.,Harvard Medical School (C.J., S.V., C.M., P.B., F.D., R.B., K.L.L., S.C., K.D.W., T.Y.P.), Boston, Massachusetts
| | - N F Greenwald
- Stanford University School of Medicine (N.F.G.), Palo Alto, California
| | - T Zack
- University of California San Francisco School of Medicine (N.G., T.Z.), San Francisco, California
| | - O Shapira
- Dana Farber Cancer Institute (P.-C.K., C.M., L.G., P.B., F.D., O.S., R.B., K.L.L., S.C., K.D.W.), Boston, Massachusetts.,Broad Institute of Massachusetts Institute of Technology and Harvard University (O.S.), Cambridge, Massachusetts
| | - R Beroukhim
- Pediatrics, Division of Oncology (P.-C.K., C.M., L.G., P.B., R.B., S.C., K.D.W.).,Dana Farber Cancer Institute (P.-C.K., C.M., L.G., P.B., F.D., O.S., R.B., K.L.L., S.C., K.D.W.), Boston, Massachusetts.,Harvard Medical School (C.J., S.V., C.M., P.B., F.D., R.B., K.L.L., S.C., K.D.W., T.Y.P.), Boston, Massachusetts
| | - K L Ligon
- Dana Farber Cancer Institute (P.-C.K., C.M., L.G., P.B., F.D., O.S., R.B., K.L.L., S.C., K.D.W.), Boston, Massachusetts.,Department of Pathology (K.L.L.), Brigham and Women's Hospital, Boston, Massachusetts.,Harvard Medical School (C.J., S.V., C.M., P.B., F.D., R.B., K.L.L., S.C., K.D.W., T.Y.P.), Boston, Massachusetts
| | - S Chi
- Pediatrics, Division of Oncology (P.-C.K., C.M., L.G., P.B., R.B., S.C., K.D.W.).,Dana Farber Cancer Institute (P.-C.K., C.M., L.G., P.B., F.D., O.S., R.B., K.L.L., S.C., K.D.W.), Boston, Massachusetts.,Harvard Medical School (C.J., S.V., C.M., P.B., F.D., R.B., K.L.L., S.C., K.D.W., T.Y.P.), Boston, Massachusetts
| | - M W Kieran
- Clinical Trials Division (M.W.K.), Bristol-Myers-Squibb, New York, New York
| | - K D Wright
- Pediatrics, Division of Oncology (P.-C.K., C.M., L.G., P.B., R.B., S.C., K.D.W.).,Dana Farber Cancer Institute (P.-C.K., C.M., L.G., P.B., F.D., O.S., R.B., K.L.L., S.C., K.D.W.), Boston, Massachusetts.,Harvard Medical School (C.J., S.V., C.M., P.B., F.D., R.B., K.L.L., S.C., K.D.W., T.Y.P.), Boston, Massachusetts
| | - T Y Poussaint
- From the Departments of Radiology (C.J., S.V., T.Y.P.) .,Harvard Medical School (C.J., S.V., C.M., P.B., F.D., R.B., K.L.L., S.C., K.D.W., T.Y.P.), Boston, Massachusetts
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21
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Vajapeyam S, Brown D, Billups C, Patay Z, Vezina G, Shiroishi MS, Law M, Baxter P, Onar-Thomas A, Fangusaro JR, Dunkel IJ, Poussaint TY. Advanced ADC Histogram, Perfusion, and Permeability Metrics Show an Association with Survival and Pseudoprogression in Newly Diagnosed Diffuse Intrinsic Pontine Glioma: A Report from the Pediatric Brain Tumor Consortium. AJNR Am J Neuroradiol 2020; 41:718-724. [PMID: 32241771 DOI: 10.3174/ajnr.a6499] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 02/10/2020] [Indexed: 12/23/2022]
Abstract
BACKGROUND AND PURPOSE Diffuse intrinsic pontine glioma is a lethal childhood brain cancer with dismal prognosis and MR imaging is the primary methodology used for diagnosis and monitoring. Our aim was to determine whether advanced diffusion, perfusion, and permeability MR imaging metrics predict survival and pseudoprogression in children with newly diagnosed diffuse intrinsic pontine glioma. MATERIALS AND METHODS A clinical trial using the poly (adenosine diphosphate ribose) polymerase (PARP) inhibitor veliparib concurrently with radiation therapy, followed by maintenance therapy with veliparib + temozolomide, in children with diffuse intrinsic pontine glioma was conducted by the Pediatric Brain Tumor Consortium. Standard MR imaging, DWI, dynamic contrast-enhanced perfusion, and DSC perfusion were performed at baseline and approximately every 2 months throughout treatment. ADC histogram metrics of T2-weighted FLAIR and enhancing tumor volume, dynamic contrast-enhanced permeability metrics for enhancing tumors, and tumor relative CBV from DSC perfusion MR imaging were calculated. Baseline values, post-radiation therapy changes, and longitudinal trends for all metrics were evaluated for associations with survival and pseudoprogression. RESULTS Fifty children were evaluable for survival analyses. Higher baseline relative CBV was associated with shorter progression-free survival (P = .02, Q = 0.089) and overall survival (P = .006, Q = 0.055). Associations of higher baseline mean transfer constant from the blood plasma into the extravascular extracellular space with shorter progression-free survival (P = .03, Q = 0.105) and overall survival (P = .03, Q = 0.102) trended toward significance. An increase in relative CBV with time was associated with shorter progression-free survival (P < .001, Q < 0.001) and overall survival (P = .004, Q = 0.043). Associations of longitudinal mean extravascular extracellular volume fraction with progression-free survival (P = .03, Q = 0.104) and overall survival (P = .03, Q = 0.105) and maximum transfer constant from the blood plasma into the extravascular extracellular space with progression-free survival (P = .03, Q = 0.102) trended toward significance. Greater increases with time were associated with worse outcomes. True radiologic progression showed greater post-radiation therapy decreases in mode_ADC_FLAIR compared with pseudoprogression (means, -268.15 versus -26.11, P = .01.) CONCLUSIONS: ADC histogram, perfusion, and permeability MR imaging metrics in diffuse intrinsic pontine glioma are useful in predicting survival and pseudoprogression.
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Affiliation(s)
- S Vajapeyam
- From the Radiology (S.V., T.Y.P.), Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts
| | - D Brown
- DF/HCC Tumor Imaging Metrics Core (D.B.), Massachusetts General Hospital, Boston, Massachusetts
| | | | - Z Patay
- Diagnostic Imaging (Z.P.), St. Jude Children's Research Hospital, Memphis, Tennessee
| | - G Vezina
- Radiology (G.V.), Children's National Medical Center, Washington, DC
| | - M S Shiroishi
- Radiology (M.S.S.), Keck Medical Center of USC, Los Angeles, California
| | - M Law
- Neuroscience (M.L.), Monash University, Melbourne, Australia
| | - P Baxter
- Cancer and Hematology Center (P.B.), Texas Children's Hospital, Houston, Texas
| | | | - J R Fangusaro
- Aflac Cancer and Blood Disorders Center (J.R.F.), Children's Healthcare of Atlanta, Atlanta, Georgia
| | - I J Dunkel
- Pediatrics (I.J.D.), Memorial Sloan Kettering Cancer Center, New York, New York
| | - T Y Poussaint
- From the Radiology (S.V., T.Y.P.), Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts
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22
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Warren KE, Vezina G, Poussaint TY, Warmuth-Metz M, Chamberlain MC, Packer RJ, Brandes AA, Reiss M, Goldman S, Fisher MJ, Pollack IF, Prados MD, Wen PY, Chang SM, Dufour C, Zurakowski D, Kortmann RD, Kieran MW. Response assessment in medulloblastoma and leptomeningeal seeding tumors: recommendations from the Response Assessment in Pediatric Neuro-Oncology committee. Neuro Oncol 2019; 20:13-23. [PMID: 28449033 DOI: 10.1093/neuonc/nox087] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Lack of standard response criteria in clinical trials for medulloblastoma and other seeding tumors complicates assessment of therapeutic efficacy and comparisons across studies. An international working group was established to develop consensus recommendations for response assessment. The aim is that these recommendations be prospectively evaluated in clinical trials, with the goal of achieving more reliable risk stratification and uniformity across clinical trials. Current practices and literature review were performed to identify major confounding issues and justify subsequently developed recommendations; in areas lacking scientific investigations, recommendations were based on experience of committee members and consensus was reached after discussion. Recommendations apply to both adult and pediatric patients with medulloblastoma and other seeding tumors. Response should be assessed using MR imaging (brain and spine), CSF cytology, and neurologic examination. Clinical imaging standards with minimum mandatory sequence acquisition that optimizes detection of leptomeningeal metastases are defined. We recommend central review prior to inclusion in treatment cohorts to ensure appropriate risk stratification and cohort inclusion. Consensus recommendations and response definitions for patients with medulloblastomas and other seeding tumors have been established; as with other Response Assessment in Neuro-Oncology recommendations, these need to now be prospectively validated in clinical trials.
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Affiliation(s)
- Katherine E Warren
- Pediatric Oncology Branch, National Cancer Institute, Bethesda, Maryland
| | - Gilbert Vezina
- Department of Radiology, Children's National Medical Center, Washington, DC
| | - Tina Y Poussaint
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts
| | - Monika Warmuth-Metz
- Department of Neuroradiology, University Hospital Würzburg, Würzburg, Germany
| | - Marc C Chamberlain
- Department of Neurology, Seattle Cancer Care Alliance, Seattle, Washington
| | - Roger J Packer
- Center for Neuroscience and Behavioral Medicine, Children's National Medical Center, Washington, DC
| | - Alba A Brandes
- Medical Oncology Department, AUSL-IRCCS Scienze Neurologiche, Bologna, Italy
| | - Moshe Reiss
- Division of Pediatric Neuro-Oncology, New York Medical College, Valhalla, New York
| | - Stewart Goldman
- Hematology-Oncology, Neuro-Oncology & Stem Cell Transplantation, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois
| | - Michael J Fisher
- Division of Oncology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Ian F Pollack
- Department of Neurological Surgery, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, Pennsylvania
| | - Michael D Prados
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California.,Department of Pediatrics, University of California San Francisco, San Francisco, California
| | - Patrick Y Wen
- Center for Neuro-Oncology, Dana Farber Cancer Institute, Boston, Massachusetts
| | - Susan M Chang
- Department of Neurosurgery, University of California San Francisco, San Francisco, California
| | - Christelle Dufour
- Department of Pediatric and Adolescent Oncology, Gustave Roussy, Villejuif, France
| | - David Zurakowski
- Departments of Anesthesia & Surgery, Boston Children's Hospital, Boston, Massachusetts
| | - Rolf D Kortmann
- Department of Radiation Oncology, University of Leipzig, Leipzig, Germany
| | - Mark W Kieran
- Pediatric Neuro-Oncology, Dana Farber Boston Children's Cancer and Blood Disorder's Center, Boston, Massachusetts
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23
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Warren KE, Vezina G, Poussaint TY, Warmuth-Metz M, Packer RJ, Kieran MW. Response to Harreld re: "Response assessment in medulloblastoma and leptomeningeal seeding tumors: recommendations from the Response Assessment in Pediatric Neuro-Oncology Committee". Neuro Oncol 2018; 20:144-145. [PMID: 29329453 DOI: 10.1093/neuonc/nox219] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Katherine E Warren
- Pediatric Oncology Branch, National Cancer Institute, Bethesda, Maryland, USA
| | - Gilbert Vezina
- Department of Radiology, Children's National Medical Center, Rockville, Maryland, USA
| | - Tina Y Poussaint
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Monika Warmuth-Metz
- Department of Neuroradiology, University Hospital Würzburg, Würzburg, Germany
| | - Roger J Packer
- Center for Neuroscience and Behavioral Medicine, Children's National Medical Center, Rockville, Maryland, USA
| | - Mark W Kieran
- Pediatric Neuro-Oncology, Dana-Farber Boston Children's Cancer and Blood Disorders Center, Boston, Massachusetts, USA
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24
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Vajapeyam S, Brown D, Johnston PR, Ricci KI, Kieran MW, Lidov HGW, Poussaint TY. Multiparametric Analysis of Permeability and ADC Histogram Metrics for Classification of Pediatric Brain Tumors by Tumor Grade. AJNR Am J Neuroradiol 2018; 39:552-557. [PMID: 29301780 DOI: 10.3174/ajnr.a5502] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Accepted: 10/30/2017] [Indexed: 12/22/2022]
Abstract
BACKGROUND AND PURPOSE Accurate tumor grading is essential for treatment planning of pediatric brain tumors. We hypothesized that multiparametric analyses of a combination of permeability metrics and ADC histogram metrics would differentiate high- and low-grade tumors with high accuracy. MATERIALS AND METHODS DTI and dynamic contrast-enhanced MR imaging using T1-mapping with flip angles of 2°, 5°, 10°, and 15°, followed by a 0.1-mmol/kg body weight gadolinium-based bolus was performed on all patients in addition to standard MR imaging. Permeability data were processed and transfer constant from the blood plasma into the extracellular extravascular space, rate constant from the extracellular extravascular space back into blood plasma, extravascular extracellular volume fraction, and fractional blood plasma volume were calculated from 3D tumor volumes. Apparent diffusion coefficient histogram metrics were calculated for 3 separate tumor volumes derived from T2-FLAIR sequences, T1 contrast-enhanced sequences, and permeability maps, respectively. RESULTS Results from 41 patients (0.3-16.76 years of age; mean, 6.22 years) with newly diagnosed contrast-enhancing brain tumors (16 low-grade; 25 high-grade) were included in the institutional review board-approved retrospective analysis. Wilcoxon tests showed a higher transfer constant from blood plasma into extracellular extravascular space and rate constant from extracellular extravascular space back into blood plasma, and lower extracellular extravascular volume fraction (P < .001) in high-grade tumors. The mean ADCs of FLAIR and enhancing tumor volumes were significantly lower in high-grade tumors (P < .001). ROC analysis showed that a combination of extravascular volume fraction and mean ADC of FLAIR volume differentiated high- and low-grade tumors with high accuracy (area under receiver operating characteristic curve = 0.918). CONCLUSIONS ADC histogram metrics combined with permeability metrics differentiate low- and high-grade pediatric brain tumors with high accuracy.
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Affiliation(s)
- S Vajapeyam
- From the Departments of Radiology (S.V., D.B., P.R.J., T.Y.P.) .,Harvard Medical School (S.V., M.W.K., H.G.W.L., T.Y.P.), Boston, Massachusetts
| | - D Brown
- From the Departments of Radiology (S.V., D.B., P.R.J., T.Y.P.)
| | - P R Johnston
- From the Departments of Radiology (S.V., D.B., P.R.J., T.Y.P.)
| | - K I Ricci
- Cancer Center (K.I.R.), Massachusetts General Hospital, Boston, Massachusetts
| | - M W Kieran
- Division of Pediatric Oncology (M.W.K.), Dana-Farber Boston Children's Cancer and Blood Disorders Center, Boston, Massachusetts.,Harvard Medical School (S.V., M.W.K., H.G.W.L., T.Y.P.), Boston, Massachusetts
| | - H G W Lidov
- Pathology (H.G.W.L.), Boston Children's Hospital, Boston, Massachusetts.,Harvard Medical School (S.V., M.W.K., H.G.W.L., T.Y.P.), Boston, Massachusetts
| | - T Y Poussaint
- From the Departments of Radiology (S.V., D.B., P.R.J., T.Y.P.).,Harvard Medical School (S.V., M.W.K., H.G.W.L., T.Y.P.), Boston, Massachusetts
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25
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Fangusaro J, Onar-Thomas A, Poussaint TY, Wu S, Ligon AH, Lindeman N, Banerjee A, Packer RJ, Kilburn LB, Pollack IF, Jakacki RI, Qaddoumi I, Fisher PG, Dhall G, Baxter P, Kreissman SG, Stewart CF, Pfister SM, Jones DTW, Vezina G, Stern J, Panigrahy A, Jones BV, Patay Z, Tamrazi B, Jones JY, Haque SS, Enterline DS, Cha S, Doyle LA, Smith M, Boyett JM, Dunkel IJ, Fouladi M. LGG-08. A PHASE II PROSPECTIVE STUDY OF SELUMETINIB IN CHILDREN WITH RECURRENT OR REFRACTORY LOW-GRADE GLIOMA (LGG): A PEDIATRIC BRAIN TUMOR CONSORTIUM (PBTC) STUDY. Neuro Oncol 2017. [DOI: 10.1093/neuonc/nox083.141] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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26
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Zukotynski K, Fahey F, Kocak M, Kun L, Boyett J, Fouladi M, Vajapeyam S, Treves T, Poussaint TY. 18F-FDG PET and MR imaging associations across a spectrum of pediatric brain tumors: a report from the pediatric brain tumor consortium. J Nucl Med 2014; 55:1473-80. [PMID: 25071098 DOI: 10.2967/jnumed.114.139626] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
UNLABELLED The purpose of this study was to describe (18)F-FDG uptake across a spectrum of pediatric brain tumors and correlate (18)F-FDG PET with MR imaging variables, progression-free survival (PFS), and overall survival (OS). METHODS A retrospective analysis was conducted of children enrolled in phase I/II clinical trials through the Pediatric Brain Tumor Consortium from August 2000 to June 2010. PET variables were summarized within diagnostic categories using descriptive statistics. Associations of PET with MR imaging variables and PFS and OS by tumor types were evaluated. RESULTS Baseline (18)F-FDG PET was available in 203 children; 66 had newly diagnosed brain tumors, and 137 had recurrent/refractory brain tumors before enrolling in a Pediatric Brain Tumor Consortium trial. MR imaging was performed within 2 wk of PET and before therapy in all cases. The (18)F-FDG uptake pattern and MR imaging contrast enhancement (CE) varied by tumor type. On average, glioblastoma multiforme and medulloblastoma had uniform, intense uptake throughout the tumor, whereas brain stem gliomas (BSGs) had low uptake in less than 50% of the tumor and ependymoma had low uptake throughout the tumor. For newly diagnosed BSG, correlation of (18)F-FDG uptake with CE portended reduced OS (P = 0.032); in refractory/recurrent BSG, lack of correlation between (18)F-FDG uptake and CE suggested decreased PFS (P = 0.023). In newly diagnosed BSG for which more than 50% of the tumor had (18)F-FDG uptake, there was a suggestion of lower apparent diffusion coefficient (P = 0.061) and decreased PFS (P = 0.065). CONCLUSION (18)F-FDG PET and MR imaging showed a spectrum of patterns depending on tumor type. In newly diagnosed BSG, the correlation of (18)F-FDG uptake and CE suggested decreased OS, likely related to more aggressive disease. When more than 50% of the tumor had (18)F-FDG uptake, the apparent diffusion coefficient was lower, consistent with increased cellularity. In refractory/recurrent BSG, poor correlation between (18)F-FDG uptake and CE was associated with decreased PFS, which may reflect concurrent tissue breakdown at sites of treated disease and development of new sites of (18)F-FDG-avid malignancy.
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Affiliation(s)
- Katherine Zukotynski
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Ontario, Canada Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Frederic Fahey
- Department of Radiology, Harvard Medical School, Boston, Massachusetts Department of Radiology, Boston Children's Hospital, Boston, Massachusetts
| | - Mehmet Kocak
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee
| | - Larry Kun
- Department of Radiological Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - James Boyett
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee; and
| | - Maryam Fouladi
- Department of Hematology/Oncology, Cincinnati Children's Medical Center, Cincinnati, Ohio
| | - Sridhar Vajapeyam
- Department of Radiology, Harvard Medical School, Boston, Massachusetts Department of Radiology, Boston Children's Hospital, Boston, Massachusetts
| | - Ted Treves
- Department of Radiology, Harvard Medical School, Boston, Massachusetts Department of Radiology, Boston Children's Hospital, Boston, Massachusetts
| | - Tina Y Poussaint
- Department of Radiology, Harvard Medical School, Boston, Massachusetts Department of Radiology, Boston Children's Hospital, Boston, Massachusetts
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McCann ME, Schouten ANJ, Dobija N, Munoz C, Stephenson L, Poussaint TY, Kalkman CJ, Hickey PR, de Vries LS, Tasker RC. Infantile postoperative encephalopathy: perioperative factors as a cause for concern. Pediatrics 2014; 133:e751-7. [PMID: 24515520 DOI: 10.1542/peds.2012-0973] [Citation(s) in RCA: 99] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
We report on 6 infants who underwent elective surgery and developed postoperative encephalopathy, which had features most consistent with intraoperative cerebral hypoperfusion. All infants were <48 weeks' postmenstrual age and underwent procedures lasting 120 to 185 minutes. Intraoperative records revealed that most of the measured systolic blood pressure (SBP) values were <60 mm Hg (the threshold for hypotension in awake infants according to the Pediatric Advanced Life Support guidelines) but that only 11% of the measured SBP values were <1 SD of the mean definition of hypotension (<45 mm Hg) as reported in a survey of members of the Society for Pediatric Anesthesia in 2009. Four infants also exhibited prolonged periods of mild hypocapnia (<35 mm Hg). One infant did not receive intraoperative dextrose. All infants developed new-onset seizures within 25 hours of administration of the anesthetic, with a predominant cerebral pathology of supratentorial watershed infarction in the border zone between the anterior, middle, and posterior cerebral arteries. Follow-up of these infants found that 1 died, 1 had profound developmental delays, 1 had minor motor delays, 2 were normal, and 1 was lost to follow-up. Although the precise cause of encephalopathy cannot be determined, it is important to consider the role that SBP hypotension (as well as hypoglycemia, hyperthermia, hyperoxia, and hypocapnia) plays during general anesthesia in young infants in the development of infantile postoperative encephalopathy. Our observations highlight the lack of evidence-based recommendations for the lower limits of adequate SBP and end-tidal carbon dioxide in anesthetized infants.
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Dombi E, Ardern-Holmes SL, Babovic-Vuksanovic D, Barker FG, Connor S, Evans DG, Fisher MJ, Goutagny S, Harris GJ, Jaramillo D, Karajannis MA, Korf BR, Mautner V, Plotkin SR, Poussaint TY, Robertson K, Shih CS, Widemann BC. Recommendations for imaging tumor response in neurofibromatosis clinical trials. Neurology 2013; 81:S33-40. [PMID: 24249804 PMCID: PMC3908340 DOI: 10.1212/01.wnl.0000435744.57038.af] [Citation(s) in RCA: 95] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2013] [Accepted: 08/13/2013] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE Neurofibromatosis (NF)-related benign tumors such as plexiform neurofibromas (PN) and vestibular schwannomas (VS) can cause substantial morbidity. Clinical trials directed at these tumors have become available. Due to differences in disease manifestations and the natural history of NF-related tumors, response criteria used for solid cancers (1-dimensional/RECIST [Response Evaluation Criteria in Solid Tumors] and bidimensional/World Health Organization) have limited applicability. No standardized response criteria for benign NF tumors exist. The goal of the Tumor Measurement Working Group of the REiNS (Response Evaluation in Neurofibromatosis and Schwannomatosis) committee is to propose consensus guidelines for the evaluation of imaging response in clinical trials for NF tumors. METHODS Currently used imaging endpoints, designs of NF clinical trials, and knowledge of the natural history of NF-related tumors, in particular PN and VS, were reviewed. Consensus recommendations for response evaluation for future studies were developed based on this review and the expertise of group members. RESULTS MRI with volumetric analysis is recommended to sensitively and reproducibly evaluate changes in tumor size in clinical trials. Volumetric analysis requires adherence to specific imaging recommendations. A 20% volume change was chosen to indicate a decrease or increase in tumor size. Use of these criteria in future trials will enable meaningful comparison of results across studies. CONCLUSIONS The proposed imaging response evaluation guidelines, along with validated clinical outcome measures, will maximize the ability to identify potentially active agents for patients with NF and benign tumors.
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Affiliation(s)
- Eva Dombi
- From the Pediatric Oncology Branch (E.D., B.C.W.), National Cancer Institute, Bethesda, MD; Department of Neurology (S.L.A.-H.), The Children's Hospital at Westmead, Sydney, Australia; Department of Medical Genetics (D. B.-V.), Mayo Clinic, Rochester, MN; Neurosurgical Service (F.G.B.), Department of Radiology (G.J.H.), and Department of Neurology and Cancer Center (S.R.P.), Massachusetts General Hospital, Boston, MA; Department of Neuroradiology (S.C.), King's College Hospital, London, UK; Department of Genetic Medicine (D.G.E.), MAHSC, St Mary's Hospital, Manchester, UK; Division of Oncology (M.J.F.) and Department of Radiology (D.J.), The Children's Hospital of Philadelphia; Department of Pediatrics (M.J.F.), The Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA; Department of Neurosurgery (S.G.), Hôpital Beaujon, Clichy, France; Division of Pediatric Hematology/Oncology and NYU Cancer Institute (M.A.K.), NYU Langone Medical Center, New York, NY; Department of Genetics (B.R.K.), University of Alabama at Birmingham, Birmingham, AL; Department of Neurology (V.M.), University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department of Radiology (T.Y.P.), Boston Children's Hospital, Boston, MA; and Department of Pediatrics (K.R., C.-S.S.), Riley Hospital for Children, Indianapolis, IN
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Warren KE, Poussaint TY, Vezina G, Hargrave D, Packer RJ, Goldman S, Wen PY, Pollack IF, Zurakowski D, Kun LE, Prados MD, Rutkowski S, Kieran MW. Challenges with defining response to antitumor agents in pediatric neuro-oncology: a report from the response assessment in pediatric neuro-oncology (RAPNO) working group. Pediatr Blood Cancer 2013; 60:1397-401. [PMID: 23625747 PMCID: PMC6300142 DOI: 10.1002/pbc.24562] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2013] [Accepted: 03/21/2013] [Indexed: 11/08/2022]
Abstract
Criteria for new drug approval include demonstration of efficacy. In neuro-oncology, this is determined radiographically utilizing tumor measurements on MRI scans. Limitations of this method have been identified where drug activity is not reflected in decreased tumor size. The RANO (Response Assessment in Neuro-Oncology) working group was established to address limitations in defining endpoints for clinical trials in adult neuro-oncology and to develop standardized response criteria. RAPNO was subsequently established to address unique issues in pediatric neuro-oncology. The aim of this paper is to delineate response criteria issues in pediatric clinical trials as a basis for subsequent recommendations.
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Affiliation(s)
- Katherine E. Warren
- National Cancer Institute, Bethesda, Maryland,Correspondence to: Katherine E. Warren, Pediatric Oncology Branch, National Cancer Institute, Building 10/Room 1-5750, 9000 Rockville Pike, Bethesda, MD 20892.
| | | | - Gilbert Vezina
- Children’s National Medical Center, Washington, District of Columbia
| | | | - Roger J. Packer
- Children’s National Medical Center, Washington, District of Columbia
| | | | | | - Ian F. Pollack
- Children’s Hospital of Pittsburgh, Pittsburgh, Pennsylvania
| | | | - Larry E. Kun
- St. Jude Children’s Research Hospital, Memphis, Tennessee
| | | | | | - Mark W. Kieran
- Dana-Farber/Children’s Hospital Cancer Center, Boston, Massachusetts
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Zukotynski KA, Fahey FH, Vajapeyam S, Ng SS, Kocak M, Gururangan S, Kun LE, Poussaint TY. Exploratory evaluation of MR permeability with 18F-FDG PET mapping in pediatric brain tumors: a report from the Pediatric Brain Tumor Consortium. J Nucl Med 2013; 54:1237-43. [PMID: 23801675 DOI: 10.2967/jnumed.112.115782] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
UNLABELLED The purpose of this study was to develop a method of registering (18)F-FDG PET with MR permeability images for investigating the correlation of (18)F-FDG uptake, permeability, and cerebral blood volume (CBV) in children with pediatric brain tumors and their relationship with outcome. METHODS Twenty-four children with brain tumors in a phase II study of bevacizumab and irinotecan underwent brain MR and (18)F-FDG PET within 2 wk. Tumor types included supratentorial high-grade astrocytoma (n = 7), low-grade glioma (n = 9), brain stem glioma (n = 4), medulloblastoma (n = 2), and ependymoma (n = 2). There were 33 cases (pretreatment only [n = 12], posttreatment only [n = 3], and both pretreatment [n = 9] and posttreatment [n = 9]). (18)F-FDG PET images were registered to MR images from the last time point of the T1 perfusion time series using mutual information. Three-dimensional regions of interest (ROIs) drawn on permeability images were automatically transferred to registered PET images. The quality of ROI registration was graded (1, excellent; 2, very good; 3, good; 4, fair; and 5, poor) by 3 independent experts. Spearman rank correlations were used to assess correlation of maximum tumor permeability (Kps(max)), maximum CBV (CBV(max)), and maximum (18)F-FDG uptake normalized to white matter (T/W(max)). Cox proportional hazards models were used to investigate associations of these parameters with progression-free survival (PFS). RESULTS The quality of ROI registration between PET and MR was good to excellent in 31 of 33 cases. There was no correlation of baseline Kps(max) with CBV(max) (Spearman rank correlation = 0.018 [P = 0.94]) or T/W(max) (Spearman rank correlation = 0.07 [P = 0.76]). Baseline CBV(max) was correlated with T/W(max) (Spearman rank correlation = 0.47 [P = 0.036]). Baseline Kps(max), CBV(max), and T/W(max) were not significantly associated with PFS (P = 0.42, hazard ratio [HR] = 0.97, 95% confidence interval [CI] = 0.90-1.045, and number of events [n(events)] = 15 for Kps(max); P = 0.41, HR = 0.989, 95% CI = 0.963-1.015, and n(events) = 14 for CBV(max); and P = 0.17, HR = 1.49, 95% CI = 0.856-2.378, and n(events) = 15 for T/W(max)). CONCLUSION (18)F-FDG PET and MR permeability images were successfully registered and compared across a spectrum of pediatric brain tumors. The lack of correlation between metabolism and permeability may be expected because these parameters characterize different molecular processes. The correlation of CBV and tumor metabolism may be related to an association with tumor grade. More patients are needed for a covariate analysis of these parameters and PFS by tumor histology.
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Affiliation(s)
- Katherine A Zukotynski
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
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Li Y, Estroff JA, Khwaja O, Mehta TS, Poussaint TY, Robson CD, Feldman HA, Ware J, Levine D. Callosal dysgenesis in fetuses with ventriculomegaly: levels of agreement between imaging modalities and postnatal outcome. Ultrasound Obstet Gynecol 2012; 40:522-529. [PMID: 22262510 PMCID: PMC3733468 DOI: 10.1002/uog.11098] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/20/2011] [Indexed: 05/31/2023]
Abstract
OBJECTIVE To assess neurodevelopmental outcome of fetuses diagnosed with callosal abnormalities after referral for ventriculomegaly. METHODS This sub-analysis of a prospective study of 430 fetuses, which were referred for ventriculomegaly and underwent sonography and magnetic resonance imaging (MRI), included those fetuses with a diagnosis of corpus callosal abnormalities after recruitment into the main study. Between three and six radiologists independently reviewed ultrasound and MR images and recorded central nervous system (CNS) abnormalities, with final diagnoses being decided by consensus. Postnatal outcomes of fetuses with callosal abnormalities were compared between those with and those without other abnormalities. RESULTS Callosal abnormalities were detected in 13% (58/430) of the fetuses referred with ventriculomegaly. Callosal dysgenesis was isolated in 24% (14/58) of these cases, with the remainder complicated by CNS, karyotypic or other major abnormalities. Five fetuses diagnosed prenatally as having isolated callosal abnormalities had additional CNS findings on postnatal assessment. Preconference kappa for callosal abnormalities was 0.76 for ultrasound and 0.78 for MRI, indicating that these investigations had a similar level of operator dependence. Neurodevelopmental outcome was normal or showed only mild delay that resolved in 67% (8/12) children with isolated callosal abnormalities compared to 7% (2/27) in those with non-isolated callosal abnormalities (P = 0.003). CONCLUSION Callosal abnormalities are present in a significant proportion of fetuses with a diagnosis of ventriculomegaly. Isolated callosal abnormalities are associated with normal neurodevelopmental outcome in approximately two-thirds of fetuses.
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Affiliation(s)
- Y Li
- Harvard Medical School, Boston, MA, USA
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Pier DB, Levine D, Kataoka ML, Estroff JA, Werdich XQ, Ware J, Beeghly M, Poussaint TY, DuPlessis A A, Li Y, Feldman HA. Magnetic resonance volumetric assessments of brains in fetuses with ventriculomegaly correlated to outcomes. J Ultrasound Med 2011; 30:595-603. [PMID: 21527607 PMCID: PMC3683412 DOI: 10.7863/jum.2011.30.5.595] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
OBJECTIVES The purpose of this study was to correlate 2-dimensional magnetic resonance (MR) measurements of lateral ventricular width and 3-dimensional measurements of lateral ventricular and supratentorial parenchymal volumes to postnatal outcomes in fetuses with ventriculomegaly. METHODS A total of 307 fetuses (mean gestational age, 26.0 weeks; range, 15.7-39.4 weeks) had MR volumetry after referral for ventriculomegaly. Fetuses were grouped into those with (n = 114) and without (n = 193) other central nervous system (CNS) anomalies. Pregnancy and postnatal neurodevelopmental outcomes up to 3 years of age were obtained. A subgroup analysis was performed excluding fetuses with other CNS anomalies. Logistic regression analysis was performed to assess which measurement was most predictive of outcomes. RESULTS There were 50 terminations, 2 stillbirths, and 255 live births. Seventy-five cases were lost to follow-up. Among 180 live-born neonates with follow-up, 140 had abnormal and 40 had normal outcomes. Atrial diameter (P < .0001), frontal horn diameter (P < .0001), and ventricular volume (P = .04) were predictive of live birth, with 92% specificity at 60% sensitivity. Among fetuses without other CNS anomalies, 180 of 193 pregnancies (93%) resulted in live deliveries, with atrial diameter (P < .0001), frontal horn diameter (P = .003), and ventricular volume (P = .008) associated with live birth and atrial diameter having the highest specificity (>99% at 60% sensitivity). Parenchymal volume was not associated with normal or abnormal outcomes (either live birth versus death or normal versus abnormal neurodevelopmental outcome). Among live-born neonates, no age-adjusted threshold for any of the measurements reliably distinguished between normal and abnormal neurodevelopmental outcomes. CONCLUSIONS Ventricular volume and diameter, but not parenchymal volume, correlate with live birth in fetuses with ventriculomegaly. However, once live born, neither 2- nor 3-dimensional measurements can distinguish a fetus that will have a normal outcome.
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Affiliation(s)
- Danielle B Pier
- Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA
- Harvard Medical School, Boston, MA
| | - Deborah Levine
- Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA
- Harvard Medical School, Boston, MA
| | - Miliam L Kataoka
- Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA
- Harvard Medical School, Boston, MA
| | - Judy A. Estroff
- Harvard Medical School, Boston, MA
- Department of Radiology, Children’s Hospital Boston, Boston, MA
- Advanced Fetal Care Center, Children’s Hospital Boston, Boston, MA
| | - Xiang Q. Werdich
- Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA
| | - Janice Ware
- Harvard Medical School, Boston, MA
- Division of Developmental Medicine, Children’s Hospital Boston, Boston, MA
| | - Marjorie Beeghly
- Division of Developmental Medicine, Children’s Hospital Boston, Boston, MA
- Department of Psychology, Wayne State University, Detroit, MI
| | - Tina Y Poussaint
- Harvard Medical School, Boston, MA
- Department of Radiology, Children’s Hospital Boston, Boston, MA
| | | | - Y Li
- Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA
- Harvard Medical School, Boston, MA
| | - Henry A. Feldman
- Harvard Medical School, Boston, MA
- Clinical Research Program, Children’s Hospital Boston, Boston, MA
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Senapati GM, Levine D, Smith C, Estroff JA, Barnewolt CE, Robertson RL, Poussaint TY, Mehta TS, Werdich XQ, Pier D, Feldman HA, Robson CD. Frequency and cause of disagreements in imaging diagnosis in children with ventriculomegaly diagnosed prenatally. Ultrasound Obstet Gynecol 2010; 36:582-595. [PMID: 20499405 PMCID: PMC2965802 DOI: 10.1002/uog.7680] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
OBJECTIVES To assess the frequency and cause of variability in diagnosis on cranial sonography and magnetic resonance imaging (MRI) in children referred following prenatal diagnosis of ventriculomegaly. METHODS Between 19 September 2003 and 16 March 2007, 119 infants with ultrasound and/or MRI studies performed within 13 months (median, 6 days) after birth, following prenatal referral for ventriculomegaly, were studied prospectively. There were 97 infants with ultrasound results and 53 with MRI, including 31 with both. Three sonologists and three pediatric neuroradiologists interpreted the postnatal ultrasound and MRI findings, blinded to prenatal diagnosis, and a final consensus diagnosis or group of diagnoses was obtained. Ventricular sizes as well as types of and reasons for any disagreement in diagnosis were recorded. Disagreements on a per patient basis were categorized as being major when they crossed diagnostic categories and had the potential to change patient counseling. Postnatal and prenatal diagnoses were compared. RESULTS There was prospective agreement on 42/97 (43%) ultrasound and on 9/53 (17%) MRI readings. Prospective consensus was more likely when the number of central nervous system (CNS) anomalies was lower (P < 0.001 and P = 0.002 for ultrasound and MRI, respectively). In 24/55 (44%) ultrasound and 11/44 (25%) MRI examinations with disagreement in diagnosis, there was disagreement concerning the presence of ventriculomegaly. In 22/97 (23%) ultrasound studies and 22/53 (42%) MRI studies the disagreements were potentially important. Reasons for discrepancies in the reporting of major findings included errors of observation as well as modality differences in depiction of abnormalities. In comparing prenatal with postnatal diagnoses, there were 11/97 (11%) ultrasound and 27/53 (51%) MRI examinations with newly detected major findings, the most common being migrational abnormalities, callosal dysgenesis/destruction and interval development of hemorrhage. CONCLUSION Variability in postnatal CNS diagnosis is common after a prenatal diagnosis of ventriculomegaly. This is due in part to a lack of standardization in the definition of postnatal ventriculomegaly.
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Affiliation(s)
- G M Senapati
- Tufts University School of Medicine, Boston, MA, USA
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Abstract
While back pain presents less frequently in children than in adults, it still poses a significant clinical challenge with respect to making a firm diagnosis and developing an effective treatment plan. When children have back pain and medical attention is sought, an underlying pathology is usually suspected. Pediatric patients are evaluated, first, with a complete clinical history and examination and, second, by an imaging work-up that is based on initial findings, including the child's age and size, signs and symptoms, and suspected etiology. This article describes 1) the epidemiology of back pain in children, 2) the imaging work-up used, and 3) the correlation of imaging findings with disease entities that may cause back pain in the pediatric patient. The list of diseases giving rise to back pain is not meant to be exhaustive but rather reflective of the most commonly identified pathologies and disorders among young children and adolescents, from athletic injuries to lethal cancers.
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Affiliation(s)
- D P Rodriguez
- Harvard Medical School and Division of Neuroradiology, Department of Radiology, Children's Hospital, Boston, Massachusetts 02115, USA
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Levine D, Feldman HA, Tannus JFK, Estroff JA, Magnino M, Robson CD, Poussaint TY, Barnewolt CE, Mehta TS, Robertson RL. Frequency and cause of disagreements in diagnoses for fetuses referred for ventriculomegaly. Radiology 2008; 247:516-27. [PMID: 18430880 DOI: 10.1148/radiol.2472071067] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
PURPOSE To prospectively assess the frequency and cause of disagreements in diagnoses at ultrasonography (US) and magnetic resonance (MR) imaging for fetuses referred for ventriculomegaly (VM). MATERIALS AND METHODS One hundred ninety-five women, aged 18-44 years, with 200 fetal referrals for VM, were recruited in a prospective IRB-approved, HIPAA-compliant study. Written informed consent was obtained. US scans were prospectively interpreted by three obstetric radiologists and MR examinations were read by one obstetric radiologist and three pediatric neuroradiologists. Final diagnosis was reached by consensus (198 US, 198 MR, and 196 US-MR comparisons). Gestational age, ventricular size, types of disagreements, and reasons for disagreements were recorded. Interreader agreement was assessed with kappa statistics. Ventricular diameter, gestational age, and confidence scores were analyzed by using mixed-model analysis of variance, accounting for correlation within reader and fetus. RESULTS There was prospective agreement on 118 (60%) of 198 US and 104 (53%) of 198 MR readings. Consensus was more likely when the final diagnosis was isolated VM (83 of 104, 80% at US; 82 of 109, 75% at MR) than when the final diagnosis included other anomalies as well (14 of 63, 22% at US; seven of 68, 10% at MR; P < .001). There was disagreement on 19 (10%) of 196 and 31 (16%) of 196 fetuses about the presence of VM at US and MR, respectively, and on 29 (15%) of 198 and 39 (20%) of 198 fetuses regarding the presence of major findings at US and MR, respectively. Reasons for discrepancies in reporting major findings included errors of observation, lack of real-time US scanning, lack of neuroradiology experience, as well as modality differences in helping depict abnormalities. CONCLUSION Of radiologists who read high-risk obstetric US and fetal MR images for VM, there is considerable variability in central nervous system diagnosis.
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Affiliation(s)
- Deborah Levine
- Department of Radiology, Beth Israel Deaconess Medical Center, 330 Brookline Ave, Boston, MA 02215, USA.
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Broniscer A, Gururangan S, MacDonald TJ, Goldman S, Packer RJ, Stewart CF, Wallace D, Danks MK, Friedman HS, Poussaint TY, Kun LE, Boyett JM, Gajjar A. Phase I trial of single-dose temozolomide and continuous administration of o6-benzylguanine in children with brain tumors: a pediatric brain tumor consortium report. Clin Cancer Res 2008; 13:6712-8. [PMID: 18006772 DOI: 10.1158/1078-0432.ccr-07-1016] [Citation(s) in RCA: 47] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
PURPOSE To estimate the maximum tolerated dose (MTD) and dose-limiting toxicity (DLT) of escalating doses of temozolomide combined with O(6)-benzylguanine in patients < or =21 years with recurrent brain tumors. EXPERIMENTAL DESIGN Treatment strata consisted of patients who had previously received no or local radiotherapy (Str1) and patients who had undergone craniospinal radiotherapy or myeloablative chemotherapy (Str2). One-hour i.v. administration of O(6)-benzylguanine at 120 mg/m(2) was followed by 48-h continuous infusion at 30 mg/m(2)/day. Single-dose temozolomide at five dosage levels (267, 355, 472, 628, and 835 mg/m(2)) was given at least 6 h after completion of O(6)-benzylguanine bolus. Treatment was repeated after recovery from toxicities at least 4 weeks apart for a maximum of 12 courses. Dose escalation followed the modified continual reassessment method. Pharmacokinetic analyses of temozolomide and 5-triazeno imidazole carboxamide (MTIC) were done in 28 patients. RESULTS A total of 44 and 26 eligible patients were enrolled on Str1 and Str2, respectively. Median age at study entry in each stratum was 8.6 and 11.3 years, respectively. Predominant diagnoses were high-grade/brainstem glioma in Str1 and medulloblastoma in Str2. Whereas the estimated MTDs of temozolomide for Str1 and Str2 were 562 and 407 mg/m(2), respectively, the doses recommended for phase II investigations are 472 and 355 mg/m(2), respectively. DLTs were predominantly neutropenia and thrombocytopenia. Three patients with gliomas experienced centrally confirmed partial responses to therapy. Four patients completed all planned therapy. Temozolomide and MTIC exposures were statistically associated with temozolomide dosage. CONCLUSIONS The current schedule of temozolomide and O(6)-benzylguanine is safe and showed modest activity against recurrent brain tumors in children.
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Affiliation(s)
- Alberto Broniscer
- Department of Oncology, St. Jude Children's Research Hospital, 332 North Lauderdale, Memphis, TN 38105, USA.
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Baser ME, Poussaint TY. Age associated increase in the prevalence of chromosome 22q loss of heterozygosity in histological subsets of benign meningioma. J Med Genet 2005; 43:285-7. [PMID: 15980114 PMCID: PMC2563234 DOI: 10.1136/jmg.2005.035162] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Chromosome 22q loss of heterozygosity (LOH) is the most common allelic loss in benign meningioma and is thought to be the earliest initiating event in meningioma formation. We used published data and logistic regression to evaluate the association of 22q LOH with age at diagnosis in 318 transitional, fibroblastic, and meningothelial meningiomas. After adjustment for anatomical location, the odds ratio of 22q LOH per year of age was >1 in each histological type of meningioma, and was significantly >1 in transitional and fibroblastic meningioma. This finding is compatible with involvement of the neurofibromatosis 2 tumour suppressor gene, NF2, on chromosome 22q in the high incidence of benign meningioma in the elderly.
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Sun Y, Schmidt NO, Schmidt K, Doshi S, Rubin JB, Mulkern RV, Carroll R, Ziu M, Erkmen K, Poussaint TY, Black P, Albert M, Burstein D, Kieran MW. Perfusion MRI of U87 brain tumors in a mouse model. Magn Reson Med 2004; 51:893-9. [PMID: 15122670 DOI: 10.1002/mrm.20029] [Citation(s) in RCA: 54] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Continuous arterial spin labeling (CASL) was used to obtain an index of cerebral blood flow (ICBF) in the normal mouse brain and in an orthotopic mouse model of human U87 high-grade glioma at 8.5 T. Under the assumption of a constant tissue:blood partition coefficient for water in different tissues, the mean ICBF (n = 14) was found to be 50 +/- 9 mL/100g/min for tumor core and 209 +/- 11 mL/100g/min for normal tissue. The apparent T(1) (T(1app)) was 2.01 +/- 0.06 sec for tumor core and 1.66 +/- 0.03 sec for normal tissue. The ICBF and the T(1app) values were significantly different (P < 0.001) between these two regions. The detailed changes of ICBF and T(1app) in the transition from the tumor core through the tumor periphery to surrounding tissue were studied. Immunohistochemistry indicated that tumor vascularity was not uniform, with microvessel density highest in normal brain and the tissue surrounding the tumor and lowest in the tumor core. The large difference in ICBF between the tumor core and normal tissue suggests that this index might be useful for the assessment of the efficacy of antiangiogenic therapy.
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Affiliation(s)
- Yanping Sun
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA.
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Caruso PA, Poussaint TY, Tzika AA, Zurakowski D, Astrakas LG, Elias ER, Bay C, Irons MB. MRI and 1H MRS findings in Smith-Lemli-Opitz syndrome. Neuroradiology 2003; 46:3-14. [PMID: 14605787 DOI: 10.1007/s00234-003-1110-1] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2003] [Accepted: 07/29/2003] [Indexed: 04/13/2023]
Abstract
Smith-Lemli-Opitz syndrome (SLOS) is an autosomal recessive disorder characterized by a defect in cholesterol biosynthesis, associated with mental retardation and multisystem structural abnormalities. This study investigated the prevalence of congenital CNS abnormalities by MRI in a large series of patients with SLOS and the correlation of the clinical and biochemical findings with the results of MRI and 1H MRS. Eighteen patients were studied; all underwent MRI of the brain, and 16 had 1H MRS of the cerebral white matter. The ratios choline:NAA, lipid:NAA, and lipid:choline metabolite were found to be correlated with the clinical degree of disease severity, serum total sterol ratios (cholesterol/cholesterol + 7-dehydrocholesterol + 8-dehydrocholesterol) and in two cases with the effect of cholesterol therapy. Abnormal CNS findings were noted in five patients, including callosal abnormalities (n = 4), Dandy-Walker variant (n = 1), and arachnoid cyst (n = 1). Holoprosencephaly was noted in one patient with a prevalence of 6%. Choline:NAA was elevated in seven patients. There was a statistically significant positive correlation between the lipid:choline ratio and the serum cholesterol precursor, 8-dehydrocholesterol. In two patients 1H MRS demonstrated abnormally elevated lipids prior to cholesterol therapy, which improved on therapy. The use of MRI and 1H MRS is an effective way to demonstrate brain structural abnormalities in patients with SLOS and may prove to be an effective method for the assessment of the effects of cholesterol replacement therapy in the brain.
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Affiliation(s)
- P A Caruso
- Department of Radiology, Massachusetts Eye and Ear Infirmary, Boston, USA
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Abstract
Over the past 25 years, magnetic resonance imaging (MRI) has developed into the primary imaging tool for evaluation of the central nervous system. MRI is the essential imaging study in the twenty-first century for the evaluation of the child with a brain tumor for initial preoperative diagnosis, treatment planning and image-guided therapies. This article provides an overview of the locations and MRI features of common pediatric tumors of childhood.
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Affiliation(s)
- T Y Poussaint
- Department of Radiology, Harvard Medical School, and Children's Hospital, Boston, Massachusetts 02115, USA.
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Poussaint TY. Magnetic resonance imaging (MRI) is one of the most important imaging modalities in pediatric neuroradiology. Top Magn Reson Imaging 2001; 12:359. [PMID: 11744874 DOI: 10.1097/00002142-200112000-00001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Abstract
Two twins with late infantile globoid cell leukodystrophy of Krabbe's disease were studied with conventional magnetic resonance imaging (MRI) and proton magnetic resonance spectroscopy. Brain MRI demonstrated brain atrophy with extensive bilateral symmetric abnormal T2 signal in the posterior periventricular white matter, parietal lobes, corona radiata, centrum semiovale, and splenium of the corpus callosum. Magnetic resonance imaging-guided proton magnetic resonance spectroscopy revealed prominent peaks from choline-containing compounds, total creatine, and inositols. The N-acetylaspartate peak was markedly reduced, and the choline-to-N-acetylaspartate ratio was abnormally high; in one of the twins, lactic acid was also detected. The constellation of magnetic resonance spectroscopy findings is indicative of extensive demyelination, gliosis, and loss of axons in the involved white matter; the latter two events occur in the later stages of globoid cell leukodystrophy. In conjunction with brain MRI, these magnetic resonance spectroscopy findings may alert clinicians to the possibility of leukodystrophy in children with progressive encephalopathy.
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Affiliation(s)
- M K Zarifi
- Department of Radiology, Children's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
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Tzika AA, Zurakowski D, Poussaint TY, Goumnerova L, Astrakas LG, Barnes PD, Anthony DC, Billett AL, Tarbell NJ, Scott RM, Black PM. Proton magnetic spectroscopic imaging of the child's brain: the response of tumors to treatment. Neuroradiology 2001; 43:169-77. [PMID: 11326567 DOI: 10.1007/s002340000454] [Citation(s) in RCA: 33] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
Our aim was to determine and/or predict response to treatment of brain tumors in children using proton magnetic resonance spectro-scopic imaging (MRSI). We studied 24 patients aged 10 months to 24 years, using MRI and point-resolved spectroscopy (PRESS; TR 2000 TE 65 ms) with volume preselection and phase-encoding in two dimensions on a 1.5 T imager. Multiple logistic regression was used to establish independent predictors of active tumor growth. Biologically vital cell metabolites, such as N-acetyl aspartate and choline-containing compounds (Cho), were significantly different between tumor and control tissues (P < 0.001). The eight brain tumors which responded to radiation or chemotherapy, exhibited lower Cho (P = 0.05), higher total creatine (tCr) (P = 0.02) and lower lactate and lipid (L) (P = 0.04) than 16 tumors which were not treated (except by surgery) or did not respond to treatment. The only significant independent predictor of active tumor growth was tCr (P < 0.01). We suggest that tCr is useful in assessing response of brain tumors to treatment.
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Affiliation(s)
- A A Tzika
- Department of Radiology, Children's Hospital and Harvard Medical School, Boston, MA, USA.
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Poussaint TY, Fox JW, Dobyns WB, Radtke R, Scheffer IE, Berkovic SF, Barnes PD, Huttenlocher PR, Walsh CA. Periventricular nodular heterotopia in patients with filamin-1 gene mutations: neuroimaging findings. Pediatr Radiol 2000; 30:748-55. [PMID: 11100490 DOI: 10.1007/s002470000312] [Citation(s) in RCA: 43] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
BACKGROUND The filamin-1 (FLN-1) gene is responsible for periventricular nodular heterotopia (PNH), which is an X-linked dominant neuronal migration disorder. OBJECTIVE To review the clinical and imaging findings in a series of patients with documented filamin-1 mutations. MATERIALS AND METHODS A retrospective review of the medical records and MR studies of a series of patients with PNH and confirmed FLN-1 mutations was done. There were 16 female patients (age range: .67-71 years; mean = 28.6) with filamin-1 gene mutations. RESULTS In six of the patients the same mutation was inherited in four generations in one pedigree. In a second pedigree, a distinct mutation was found in two patients in two generations. In a third pedigree, a third mutation was found in four patients in two generations. The remaining four patients had sporadic de novo mutations that were not present in the parents. Ten patients had seizures, and all patients had normal intelligence. In all 16 patients MR demonstrated bilateral near-continuous PNH. There were no consistent radiographic or clinical differences between patients carrying different mutations. CONCLUSION Patients with confirmed FLN-1 gene mutations are usually female and have a distinctive MR pattern of PNH. Other female patients with this same MR pattern probably harbor FLN-1 mutations and risk transmission to their progeny. This information is important for genetic counseling.
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Affiliation(s)
- T Y Poussaint
- Department of Radiology, Children's Hospital, Boston, Massachusetts 02115, USA.
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45
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Drubach LA, Connolly LP, Poussaint TY, Faul PN, Treves ST. Role of intraoperative skeletal scintigraphy in the localization of osteoblastomas. Clin Nucl Med 2000; 25:819-20. [PMID: 11043725 DOI: 10.1097/00003072-200010000-00015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- L A Drubach
- Department of Radiology, Children's Hospital, Boston, Massachusetts 02115, USA
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46
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Siffert J, Poussaint TY, Goumnerova LC, Scott RM, LaValley B, Tarbell NJ, Pomeroy SL. Neurological dysfunction associated with postoperative cerebellar mutism. J Neurooncol 2000; 48:75-81. [PMID: 11026700 DOI: 10.1023/a:1006483531811] [Citation(s) in RCA: 84] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
BACKGROUND AND OBJECTIVES The postoperative cerebellar mutism syndrome (CMS) is an unique acute postoperative complication characterized by transient decrease in speech output (often mutism), apathy, irritability as well as global cerebellar dysfunction. As much as 25% of patients undergoing a resection of a cerebellar or IV ventricular tumor may develop such a syndrome. In this retrospective study we characterize the clinical features of the CMS and explore potential etiologic mechanisms. METHODS We conducted a retrospective analysis of medical records and imaging tests of 8 consecutive patients with the CMS identified through the database of the Children's Hospital and Dana-Farber Cancer Institute, Boston, and compared with a control group of 8 unaffected children undergoing a comparable tumor resection. RESULTS In contrast to the control group, children in the affected group had marked decrease in speech output and comprehension, apathy and lack of initiative, inattention, persistent eye closure, flaccid hemiparesis and a severe global cerebellar dysfunction. Swallowing difficulties and bowel and bladder dysfunction were also observed. The median duration of the syndrome as judged by the persistence of the communication abnormalities was 4 weeks. The recovery was near complete with exception for a persistent global cerebellar dysfunction. A comparison of CT and MRI scans of children in both groups failed to identify distinguishing features. CONCLUSION A surgical lesion of the midline cerebellum can cause a complex neurological dysfunction such as the CMS. Thus, we postulate that the cerebellum and its connections function as a 'modulatory system' in control of both motor and non-motor functions, including attention and language.
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Affiliation(s)
- J Siffert
- The Hyman-Newman Institute for Neurology and Neurosurgery, Beth Israel Medical Center, New York, NY 10128, USA.
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Santiago Medina L, al-Orfali M, Zurakowski D, Poussaint TY, DiCanzio J, Barnes PD. Occult lumbosacral dysraphism in children and young adults: diagnostic performance of fast screening and conventional MR imaging. Radiology 1999; 211:767-71. [PMID: 10352604 DOI: 10.1148/radiology.211.3.r99jn09767] [Citation(s) in RCA: 17] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
PURPOSE To compare fast screening and conventional magnetic resonance (MR) imaging for the detection of occult dysraphic myelodysplasias in children and young adults. MATERIALS AND METHODS A retrospective case-control study included 101 patients (mean age, 4.9 years; range, 1 day to 26 years) suspected of having occult lumbosacral dysraphism. Sixty case patients had myelodysplastic lesions (19 filar lipoma, 14 syringomyelia, 10 intradural lipoma, eight dermal sinus, five diastematomyelia, five lipomyelomeningocele, two caudal regression syndrome); 41 control patients had no dysraphic lesions; 17 patients had associated renal anomalies. Two neuroradiologists reviewed MR images from conventional and fast screening protocols. Diagnostic performance parameters included sensitivity, specificity, and area under the receiver operating characteristic curve (Az value). RESULTS The sensitivity of conventional and fast screening MR studies was 97.1% and 98.5%, respectively, the specificity was 90.9% and 84.8%, respectively. The Az value was 0.973 for the fast screening and 0.976 for the conventional MR studies (P = .83). Interobserver agreement was very good for fast screening images (kappa = 0.68) and excellent for conventional images (kappa = 0.75). For renal anomalies, the Az value was 0.786 and 0.853 for fast screening and conventional MR imaging, respectively (P = .28). CONCLUSION Conventional three-plane lumbosacral MR imaging in children and young adults suspected of having occult dysraphism provides better diagnostic information than does fast screening two-plane MR imaging because of its higher specificity and interobserver agreement.
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Affiliation(s)
- L Santiago Medina
- Department of Radiology, Children's Hospital Medical Center, Cincinnati, OH 45236, USA
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48
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Korf BR, Schneider G, Poussaint TY. Structural anomalies revealed by neuroimaging studies in the brains of patients with neurofibromatosis type 1 and large deletions. Genet Med 1999; 1:136-40. [PMID: 11258348 DOI: 10.1097/00125817-199905000-00004] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
PURPOSE The basis for cognitive problems in patients with neurofibromatosis type 1 (NF1) is unknown. A subset of NF1 patients with deletion of the entire NF1 gene has severe learning problems or mental retardation. We have reviewed neuroimaging studies (CT and MRI) in five such patients to determine whether structural anomalies in the brain are present and might explain the impaired cognitive function. METHODS Five patients with NF1 and deletion of the entire gene were identified by FISH studies. A retrospective review was conducted of CT and MRI images, as well as of data from developmental assessments. RESULTS All five patients had severe developmental impairment. None had been exposed to chemotherapy or radiation therapy. All had multiple regions of bright T2 signal intensity. Structural anomalies were seen in three of the five patients and included callosal dysgenesis in one, septum cavum vergae and pellucidum in two, mega cisterna magna in one, and Chiari I malformation with severe hydrocephalus in one patient. CONCLUSION Individuals with NF1 and large gene deletions have an increased frequency of structural anomalies of the brain not usually seen in NF1 patients. This suggests that the mental retardation in these individuals is due, at least in part, to abnormal brain development rather than a defect in brain function due to haplosufficiency of the NF1 gene product.
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Affiliation(s)
- B R Korf
- Division of Genetics, Children's Hospital, Boston, MA 02115, USA.
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49
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Poussaint TY, Gudas T, Barnes PD. Imaging of neuroendocrine disorders of childhood. Neuroimaging Clin N Am 1999; 9:157-75. [PMID: 9974504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
This article focuses on the neuroendocrine disorders of childhood. The commonly associated neuroradiologic abnormalities of the hypothalamic-pituitary axis are covered in detail.
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Affiliation(s)
- T Y Poussaint
- Department of Radiology, Harvard Medical School, Children's Hospital, Boston, Massachusetts 02115, USA
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50
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Kim FM, Poussaint TY, Barnes PD. Neuroimaging of scoliosis in childhood. Neuroimaging Clin N Am 1999; 9:195-221. [PMID: 9974506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
A curvature abnormality may be the initial or major presenting feature in a child with disease of the spinal column or spinal neuraxis. A simplified classification of common spinal curvature abnormalities of childhood include idiopathic, congenital/dysraphic, skeletal dysplasia, neurofibromatosis, and painful. The great majority of childhood scoliosis falls into the idiopathic category. Atypical clinical or radiographic features in a presumed idiopathic scoliosis may indicate an otherwise occult tumor or hydrosyringomyelia, or may be a consequence of increasing curvature with disk protrusion, nerve impingement, or cord attenuation. Neuroimaging beyond plain films is commonly necessary for atypical idiopathic scoliosis and for the other categories of scoliosis listed.
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Affiliation(s)
- F M Kim
- Division of Neuroradiology, Department of Radiology, Children's Hospital, Boston, Massachusetts 02115, USA.
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