European survey on neurosurgical management of primary central nervous system lymphomas and preoperative corticosteroid therapy

Preoperative Corticosteroids Reduce Diagnostic Accuracy of Stereotactic Biopsies in Primary Central Nervous System Lymphoma: A Systematic Review and Meta-Analysis

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.

Deep learning aided preoperative diagnosis of primary central nervous system lymphoma

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.