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Tosefsky K, Rebchuk AD, Martin KC, Chen DW, Yip S, Makarenko S. Preoperative Corticosteroids Reduce Diagnostic Accuracy of Stereotactic Biopsies in Primary Central Nervous System Lymphoma: A Systematic Review and Meta-Analysis. Neurosurgery 2024:00006123-990000000-01141. [PMID: 38865324 DOI: 10.1227/neu.0000000000002944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 02/12/2024] [Indexed: 06/14/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Despite general acceptance that corticosteroid therapy (CST) should be withheld before biopsy for suspected primary central nervous system lymphoma (PCNSL), there remains conflicting evidence surrounding the precise impact of preoperative CST on the histopathological diagnosis. The objective of this systematic review and meta-analysis was to describe and quantify the effects of preoperative CST on the diagnostic accuracy of biopsies for PCNSL. METHODS Primary articles were screened from Ovid MEDLINE, Embase, Web of Science, and Scopus databases. Meta-analysis was performed for immunocompetent patients with histologically confirmed PCNSL. Subgroup and regression analyses were performed to assess the effects of biopsy type, CST duration, dose, and preoperative taper on the diagnostic accuracy. In addition, the sensitivity of cerebrospinal fluid (CSF) analyses for PCNSL was assessed. RESULTS Nineteen studies, comprising 1226 patients (45% female; mean age: 60.3 years), were included. Preoperative CST increased the risk of nondiagnostic biopsy with a relative risk (RR) of 2.1 (95% CI: 1.1-4.1). In the stereotactic biopsy subgroup, the RR for nondiagnostic biopsy was 3.0 (95% CI: 1.2-7.5). CST taper, duration, and dose did not significantly influence diagnostic biopsy rates. The sensitivity of CSF cytology, including flow cytometry, for PCNSL was 8.0% (95% CI: 6.0%-10.7%). CONCLUSION Our results suggest that preoperative CST reduces the diagnostic yield of stereotactic biopsies for PCNSL. We found no evidence that tapering CST before biopsy improves diagnostic rates. CSF analysis currently has a poor sensitivity for the diagnosis of PCNSL.
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Affiliation(s)
- Kira Tosefsky
- MD Undergraduate Program, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Alexander D Rebchuk
- Division of Neurosurgery, Department of Surgery, University of British Columbia, Vancouver, British Columbia, Canada
| | - Karina Chornenka Martin
- Department of Pathology & Laboratory Medicine, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - David W Chen
- Division of Neurosurgery, Department of Surgery, University of British Columbia, Vancouver, British Columbia, Canada
| | - Stephen Yip
- Department of Pathology & Laboratory Medicine, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Serge Makarenko
- Division of Neurosurgery, Department of Surgery, University of British Columbia, Vancouver, British Columbia, Canada
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Naser PV, Maurer MC, Fischer M, Karimian-Jazi K, Ben-Salah C, Bajwa AA, Jakobs M, Jungk C, Jesser J, Bendszus M, Maier-Hein K, Krieg SM, Neher P, Neumann JO. Deep learning aided preoperative diagnosis of primary central nervous system lymphoma. iScience 2024; 27:109023. [PMID: 38352223 PMCID: PMC10863328 DOI: 10.1016/j.isci.2024.109023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 12/21/2023] [Accepted: 01/22/2024] [Indexed: 02/16/2024] Open
Abstract
The preoperative distinction between glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) can be difficult, even for experts, but is highly relevant. We aimed to develop an easy-to-use algorithm, based on a convolutional neural network (CNN) to preoperatively discern PCNSL from GBM and systematically compare its performance to experienced neurosurgeons and radiologists. To this end, a CNN-based on DenseNet169 was trained with the magnetic resonance (MR)-imaging data of 68 PCNSL and 69 GBM patients and its performance compared to six trained experts on an external test set of 10 PCNSL and 10 GBM. Our neural network predicted PCNSL with an accuracy of 80% and a negative predictive value (NPV) of 0.8, exceeding the accuracy achieved by clinicians (73%, NPV 0.77). Combining expert rating with automated diagnosis in those cases where experts dissented yielded an accuracy of 95%. Our approach has the potential to significantly augment the preoperative radiological diagnosis of PCNSL.
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Affiliation(s)
- Paul Vincent Naser
- Heidelberg University Hospital, Department of Neurosurgery, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
- Heidelberg University, Medical Faculty, Grabengasse 1, 69117 Heidelberg, Germany
- Heidelberg University Hospital, Division of Stereotactic Neurosurgery, Department of Neurosurgery, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
- AI Health Innovation Cluster, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Miriam Cindy Maurer
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Germany, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
- Department of Medical Informatics, University Medical Center Göttingen, Von-Siebold-Straße 3, 37075 Göttingen, Germany
| | - Maximilian Fischer
- Heidelberg University, Medical Faculty, Grabengasse 1, 69117 Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Germany, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK), partner site Heidelberg, Heidelberg, Germany
| | - Kianush Karimian-Jazi
- Heidelberg University, Medical Faculty, Grabengasse 1, 69117 Heidelberg, Germany
- Heidelberg University Hospital, Department of Neuroradiology, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Chiraz Ben-Salah
- Heidelberg University, Medical Faculty, Grabengasse 1, 69117 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany
| | - Awais Akbar Bajwa
- Heidelberg University Hospital, Department of Neurosurgery, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
- Heidelberg University, Medical Faculty, Grabengasse 1, 69117 Heidelberg, Germany
| | - Martin Jakobs
- Heidelberg University Hospital, Department of Neurosurgery, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
- Heidelberg University, Medical Faculty, Grabengasse 1, 69117 Heidelberg, Germany
- Heidelberg University Hospital, Division of Stereotactic Neurosurgery, Department of Neurosurgery, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Christine Jungk
- Heidelberg University Hospital, Department of Neurosurgery, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
- Heidelberg University, Medical Faculty, Grabengasse 1, 69117 Heidelberg, Germany
| | - Jessica Jesser
- Heidelberg University, Medical Faculty, Grabengasse 1, 69117 Heidelberg, Germany
- Heidelberg University Hospital, Department of Neuroradiology, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Martin Bendszus
- Heidelberg University, Medical Faculty, Grabengasse 1, 69117 Heidelberg, Germany
- Heidelberg University Hospital, Department of Neuroradiology, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Klaus Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Germany, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK), partner site Heidelberg, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and the University Medical Center Heidelberg, 69120 Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, 69120 Heidelberg, Germany
- AI Health Innovation Cluster, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sandro M. Krieg
- Heidelberg University Hospital, Department of Neurosurgery, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
- Heidelberg University, Medical Faculty, Grabengasse 1, 69117 Heidelberg, Germany
| | - Peter Neher
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Germany, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK), partner site Heidelberg, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Jan-Oliver Neumann
- Heidelberg University Hospital, Department of Neurosurgery, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
- Heidelberg University, Medical Faculty, Grabengasse 1, 69117 Heidelberg, Germany
- Heidelberg University Hospital, Division of Stereotactic Neurosurgery, Department of Neurosurgery, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
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