New technology and research progress in neurosurgery diagnosis and treatment

Molecular techniques, including next-generation sequencing, genomic copy number profiling, fusion transcript detection, and genomic DNA methylation arrays, are now indispensable tools for the workup of central nervous system (CNS) tumors. Yet there remains a great deal of heterogeneity in using such biomarker testing across institutions and hospital systems. This is in large part because there is a persistent reluctance among third-party payers to cover molecular testing. The objective of this Review is to describe why comprehensive molecular biomarker testing is now required for the accurate diagnosis and grading and prognostication of CNS tumors and, in so doing, to justify more widespread use by clinicians and coverage by third-party payers. The 5th edition of the World Health Organization (WHO) classification system for CNS tumors incorporates specific molecular signatures into the essential diagnostic criteria for most tumor entities. Many CNS tumor types cannot be reliably diagnosed according to current WHO guidelines without molecular testing. The National Comprehensive Cancer Network also incorporates molecular testing into their guidelines for CNS tumors. Both sets of guidelines are maximally effective if they are implemented routinely for all patients with CNS tumors. Moreover, the cost of these tests is less than 5% of the overall average cost of caring for patients with CNS tumors and consistently improves management. This includes more accurate diagnosis and prognostication, clinical trial eligibility, and prediction of response to specific treatments. Each major group of CNS tumors in the WHO classification is evaluated and how molecular diagnostics enhances patient care is described. Routine advanced multidimensional molecular profiling is now required to provide optimal standard of care for patients with CNS tumors.

Molecular testing has become critical components in the diagnostic classification of the central nervous system (CNS) tumors. However, these methods require substantial resources, limiting accessibility for many patients. Recent advances in artificial intelligence (AI) and computer vision empower deep learning models to infer molecular features from histopathology images, which may often be more readily available than molecular testing, to classify CNS tumors. We trained pan-CNS tumor models to predict DNA methylation and gene expression from whole-slide images (WSIs). These molecular predictions were then used in a hierarchical machine learning framework, termed Neuropath-AI, to predict nine broad tumor families and 52 tumor types using a large diverse cohort of 5,715 histopathology samples, of which 2,988 had paired DNA methylation profiling and 848 had paired RNA-sequencing. We evaluated Neuropath-AI on another large independent multi-institutional cohort of 5,516 CNS tumors, for which Neuropath-AI predicted tumor class with an associated confidence score. Neuropath-AI designated 46% of test samples as predictable with high-confidence, which it then accuracy classified in 97% of samples. It made moderate-confidence (and above) predictions for 87% of test samples, for which it achieved a diagnostic accuracy of 80% for the top-1 prediction and 86% accuracy for a top-2 predictions. A comparison with human neuropathologists showed that Neuropath-AI is comparable at classifying tumors. A second test showed that human neuropathologists were more accurate when the Neuropath-AI classifier results were provided to them, testifying to the potential translational benefit of integrating AI classification tools into the pathologist’s workflow. Our model provides the basis for a clinically applicable deep learning assistant to improve human efficiency and accuracy of CNS tumor diagnosis. The model will be made publicly available and can be readily implemented to assist human pathologists in clinical workflows, in further expanded prospective studies.

Advanced molecular testing has increasingly become an integral component for accurate diagnosis of central nervous system (CNS) tumors. We sought to establish the current state of molecular testing availability and approaches for the diagnosis of CNS tumors in US hospitals that conduct high volumes of CNS tumor resections. We distributed a 16-item survey inquiring about molecular testing approaches for CNS tumors to 115 neuropathologists at US hospitals with neurosurgery residency programs. Thirty-five neuropathologists (30.4%) responded to the survey, all of whom indicated their institutions perform molecular testing on CNS tumor tissue. The most commonly offered tests were MGMT methylation profiling and next-generation sequencing. Fourteen respondents (40%) indicated that their institution is able to test for and report all of the molecular alterations included in our survey. Nine (25.7%) respondents indicated that molecular testing is performed as standard of care for all patients with resected CNS tumors. Our results suggest that even in academic hospitals with a high volume of CNS tumor resections, molecular testing for these tumors is limited. Continued initiatives are necessary to expand the availability of molecular testing for CNS tumors to ensure diagnostic accuracy and guide targeted therapy.

Current cIMPACT-NOW and WHO guidelines state that the accurate diagnosis of many central nervous system (CNS) tumors must not only incorporate traditional histological findings but also include additional molecular data derived from sequence and methylation profiling techniques. To address this clinical need, our group has adapted and clinically validated the CNS tumor classifier that was pioneered and developed by the German Cancer Research Network (DKFZ) and University Hospital Heidelberg for both adult and pediatric CNS tumors. The classifier analyzes whole genome methylation data derived from the Illumina EPIC array system with a machine learning algorithm trained on a large reference set to provide a tumor family and class (a more granular classification than family) along with calibrated scores to aid in determining confidence in the assigned classifications. Around this core algorithm, we have generated a computational and reporting pipeline to generate a mid-throughput clinical diagnostic assay. We have validated this assay using a combination of adult and pediatric CNS tumor samples and have established reliable run-level and sample-level quality control metrics with empirically defined thresholds. With these thresholds, approximately 90% of samples with a neoplastic content of 70% or greater were “classifiable”, such that a result could be returned by the CNS tumor diagnostic assay. The performance of the assay was exceptional, with a sensitivity, specificity and accuracy each greater than 98% as determined with a validation sample set of 105 specimens. Further, 4 discordant calls between the original diagnosis and the output of the new methylation-based diagnostic were identified and underwent further histopathologic review. This additional analysis led to the re-classification of 2 specimens due to molecular analysis, highlighting the clinical utility of the assay. The new assay is currently offered for clinical testing under the name JAX OncoMethyl ArrayTM and addresses a clear need in the field of CNS tumor diagnostics. Citation Format: Melissa Soucy, Nick Renzette, Xinming Zhuo, Prasanti Nuni, Kristi Herlth, Kevin Kelly, Gregory Omerza, Lei Li. Clinical validation of a methylation array-based diagnostic assay for improved classification of CNS tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2233.

Genome-wide DNA methylation signatures correlate with and distinguish central nervous system (CNS) tumor types. Since the publication of the initial CNS tumor DNA methylation classifier in 2018, this platform has been increasingly used as a diagnostic tool for CNS tumors, with multiple studies showing the value and utility of DNA methylation-based classification of CNS tumors. A Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) Working Group was therefore convened to describe the current state of the field and to provide advice based on lessons learned to date. Here, we provide recommendations for the use of DNA methylation-based classification in CNS tumor diagnostics, emphasizing the attributes and limitations of the modality. We emphasize that the methylation classifier is one diagnostic tool to be used alongside previously established diagnostic tools in a fully integrated fashion. In addition, we provide examples of the inclusion of DNA methylation data within the layered diagnostic reporting format endorsed by the World Health Organization (WHO) and the International Collaboration on Cancer Reporting. We emphasize the need for backward compatibility of future platforms to enable accumulated data to be compatible with new versions of the array. Finally, we outline the specific connections between methylation classes and CNS WHO tumor types to aid in the interpretation of classifier results. It is hoped that this update will assist the neuro-oncology community in the interpretation of DNA methylation classifier results to facilitate the accurate diagnosis of CNS tumors and thereby help guide patient management.

Pediatric CNS tumors: Overview and treatment paradigms.

The update on the WHO classification of central nervous system (CNS) tumors incorporated molecular signatures for a more accurate diagnosis. Recently, DKFZ has demonstrated the utility of DNA methylation profiling(MP) for molecular classification of CNS tumors. We performed a prospective clinical study over the last three years to evaluate the clinical utility ofDNA MP on FFPE samples of 66 challenging CNS tumor cases using online DKFZ classifier. Eleven samples were excluded due to low tumor DNA content or low calibration(predictive) scores(CS)< 0.3.DNA MP confirmed the original pathology diagnoses in 15(27%)cases. The integrated molecular diagnoses were changed in 38/55(70%) including establishment of a new diagnostic entity, change in molecular signature and subtyping. TheWHO grades were changed in 16(27%) of the tumors; about two-thirds resulted in upgrading. We detected non-canonical IDH mutations in 9 diffuse gliomas and the CNV plots revealed false positive FISH results for 1p/19q co-deletion in two diffuse gliomas. The CNV plots contributed to the final diagnosis in 40(72%) patients. The molecular subtypes of medulloblastoma, ependymoma and glioblastoma subclasses were determined in 36(65%) cases. Seventy-five percent of cases with confirmation of initial diagnosis or change in molecular diagnosis had CS > 0.5, among which 51% had a CS >0.9. The median and range CS of cases with new diagnostic entity and confirmed cases were 0.86(0.37–0.99) and 0.98(0.42–0.99), respectably. Furthermore, we detected higher CS in IDH-mutant gliomas in comparison to glioblastoma IDH-wild type(P=0.04). We also observed lower CS in mesenchymal glioblastoma in comparison to other subclasses. The MGMT promoter methylation was determined in 17/20(85%) glioblastoma cases. While the DKFZ group established CS of 0.9 as a cut-off for matching to methylation classes, our findings suggest lower threshold values in challenging CNS tumor cases. Our experience indicates clinical utility of MP of challenging CNS tumors as a reliable ancillary diagnostic tool in routine neuropathology practice.

Traditionally, treatment of spinal cord injury seemed frustrating and hopeless because of the remarkable morbidity and mortality, and restricted therapeutic options. Recent advances in neural injury and repair, and the progress towards development of neuroprotective and regenerative interventions are basis for increased optimism. Neural stem cells have opened a new arena of discovery for the field of regenerative science and medicine. Embryonic stem (ES) cells can give rise to all neural progenitors and they represent an important scientific tool for approaching neural repair. The growing number of dedicated regeneration centers worldwide exemplifies the changing perception towards the do-ability of spinal cord repair and this review was born from a presentation at one such leading center, the Kentucky Spinal Cord Injury Research Center. Current concepts of the pathophysiology, repair, and restoration of function in the damaged spinal cord are presented with an overlay of how neural stem cells, particularly ES cells, fit into the picture as important scientific tools and therapeutic targets. We focus on the use of genetically tagged and selectable ES cell lines for neural induction and transplantation. Unique features of ES cells, including indefinite replication, pluripotency, and genetic flexibility, provide strong tools to address questions of neural repair. Selective marker expression in transplanted ES cell derived neural cells is providing new insights into transplantation and repair not possible previously. These features of ES cells will produce a predictable and explosive growth in scientific tools that will translate into discoveries and rapid progress in neural repair.

BackgroundPathologic diagnosis of central nervous system (CNS) neoplasms is made by comparing light microscopic, immunohistochemical, and molecular cytogenetic findings with clinicoradiologic observations. Intraoperative frozen cytology smears can improve the diagnostic accuracy for CNS neoplasms. Here, we evaluate the diagnostic value of cytology in frozen diagnoses of CNS neoplasms.MethodsCases were selected from patients undergoing both frozen cytology and frozen sections. Diagnostic accuracy was evaluated.ResultsFour hundred and fifty-four cases were included in this retrospective single-center review study covering a span of 10 years. Five discrepant cases (1.1%) were found after excluding 53 deferred cases (31 cases of tentative diagnosis, 22 cases of inadequate frozen sampling). A total of 346 cases of complete concordance and 50 cases of partial concordance were classified as not discordant cases in the present study. Diagnostic accuracy of intraoperative frozen diagnosis was 87.2%, and the accuracy was 98.8% after excluding deferred cases. Discrepancies between frozen and permanent diagnoses (n = 5, 1.1%) were found in cases of nonrepresentative sampling (n = 2) and misinterpretation (n = 3). High concordance was observed more frequently in meningeal tumors (97/98, 99%), metastatic brain tumors (51/52, 98.1%), pituitary adenomas (86/89, 96.6%), schwannomas (45/47, 95.8%), high-grade astrocytic tumors (47/58, 81%), low grade astrocytic tumors (10/13, 76.9%), non-neoplastic lesions (23/36, 63.9%), in decreasing frequency.ConclusionsUsing intraoperative cytology and frozen sections of CNS tumors is a highly accurate diagnostic ancillary method, providing subtyping of CNS neoplasms, especially in frequently encountered entities.

Clinical feasibility of AI Doctors: Evaluating the replacement potential of large language models in outpatient settings for central nervous system tumors.

Central Nervous System (CNS) tumor comprises about 2-5% of all tumors. Quite a large number of patients with CNS tumor undergo surgical treatment in Bangladesh. Surgical treatment is greatly facilitated by the use of intraoperative diagnosis. The techniques for intraoperative diagnosis of neurosurgical biopsy specimen are frozen section & crush smear cytology. In our study, a total of 64 cases of CNS tumor was evaluated by crush smear. Among them, 54 (84.38%) cases were brain tumor & 10 (15.62%) cases were spinal tumor, with a slight male predominance (M:F = 1.2:1). The mean age of the patients was 31 years. Cerebrum was the commonest site of brain tumor & astrocytoma was the commonest malignant tumor. Among 64 cases, 61 cases were correctly diagnosed by crush cytologic technique. The accuracy of intraoperative cytologic diagnosis was determined in comparison to histologic diagnosis was 95.3%. It is concluded that crush cytologic technique may be considered as a reliable primary diagnostic tool for intraoperative evaluation of CNS tumor. JCMCTA 2015 ; 26 (2) : 40 - 43

To investigate the diagnostic accuracy of squash preparation (smears) and frozen section (FS) in the rapid intraoperative diagnosis of central nervous system (CNS) tumors. One hundred eighty-three CNS tumors were examined over a period of 18 months (January 1995-June 1996). All these were open biopsies, and the smear interpretation was compared with FS findings and paraffin section diagnosis. In 183 tumors, squash preparation was satisfactory in 156 cases (85.2%), and the diagnostic accuracy was 89.7% (140/156). The accuracy of FS diagnosis was 90.4% (141/156). The squash smear preparation is a fairly accurate and reliable tool in the rapid intraoperative diagnosis of CNS tumors. The accuracy of this technique is nearly as good as that of FS (P value = .9877). With the advent of stereotactic biopsies, the pathologist may have to depend entirely upon cytologic features for a definitive diagnosis.

Introduction: Central Nervous System (CNS) tumors, though relatively less common (approx 2% of all tumors), display a gamut of clinical and histological presentations. Frozen section analysis in CNS tumors in a known modality which can be a beneficial tool with potential to guide intra-operative management and a likelihood of bettering outcomes. Our study aims to ascertain the correlation and concordance between intra-operative frozen section diagnosis with final histopathological diagnosis in CNS tumors. Methods: We studied a total of 30 cases of CNS tumors for which frozen section was carried out and the same were compared with the diagnosis after final histopathological examination. For statistical analysis SPSS software version 20.0 (IBM, Armonk, New York, USA), Pearsons’s correlation coefficient Test and Chi square test were used. Results: Out of the 30 cases studied, 24 cases (80%) were completely concordant, 05 cases (16.7%) were partially concordant and 01 case (3.3%) was discordant. Considering the concordant and partially concordant cases, the sensitivity was estimated to be 96.7%. Conclusion: Intraoperative frozen section diagnosis encompasses certain limitations in layered diagnosis. Despite the limitations of retrospective studies, this study highlights its diagnostic value as a rapid and reliable method for accurate diagnosis of CNS tumors.

L-2-hydroxyglutaric aciduria (L-2-HGA) is a rare neurometabolic disorder characterized by accumulation of L2-hydroxyglutarate (L-2-HG) due to mutations in the L2HGDH gene. L-2-HGA patients have a significantly increased lifetime risk of central nervous system (CNS) tumors. Here, we present a 16-year-old girl with L-2-HGA who developed a tumor in the right cerebral hemisphere, which was discovered after left-sided neurological deficits of the patient. Histologically, the tumor had a high-grade diffuse glioma phenotype. DNA sequencing revealed the inactivating homozygous germline L2HGDH mutation as well as inactivating mutations in TP53, BCOR and NF1. Genome-wide DNA-methylation analysis was unable to classify the tumor with high confidence. More detailed analysis revealed that this tumor clustered amongst IDH-wildtype gliomas by methylation profiling and did not show the glioma CpG island methylator phenotype (G-CIMP) in contrast to IDH-mutant diffuse gliomas with accumulated levels of D-2-HG, the stereoisomer of L-2-HD. These findings were against all our expectations given the inhibitory potential of 2-HG on DNA-demethylation enzymes. Our final integrated histomolecular diagnosis of the tumor was diffuse pediatric-type high-grade glioma, H3-wildtype and IDH-wildtype. Due to rapid tumor progression the patient died nine months after initial diagnosis. In this manuscript, we provide extensive molecular characterization of the tumor as well as a literature review focusing on oncogenetic considerations of L-2-HGA-associated CNS tumors.

L-2-hydroxyglutaric aciduria (L-2-HGA) is a rare neurometabolic disorder characterized by accumulation of L2-hydroxyglutarate (L-2-HG) due to mutations in the L2HGDH gene. L-2-HGA patients have a significantly increased lifetime risk of central nervous system (CNS) tumors. Here, we present a 16-year-old girl with L-2-HGA who developed a tumor in the right cerebral hemisphere, which was discovered after left-sided neurological deficits of the patient. Histologically, the tumor had a high-grade diffuse glioma phenotype. DNA sequencing revealed the germline L2HGDH mutation as well as inactivating mutations in TP53, BCOR and NF1. Genome-wide DNA-methylation analysis was unable to classify the tumor with high confidence. More detailed analysis revealed that this tumor clustered amongst IDH-wildtype gliomas by methylation profiling and did not show the glioma CpG island methylator phenotype (G-CIMP) (in contrast to IDH-mutant diffuse gliomas with accumulated levels of D-2-HG). These findings were against all our expectations given the inhibitory potential of 2-HG on DNA-demethylation enzymes. Our final integrated histomolecular diagnosis was diffuse pediatric-type high-grade glioma, H3-wildtype and IDH-wildtype. Due to rapid tumor progression the patient died nine months after initial diagnosis. In this manuscript we provide extensive molecular characterization of the tumor as well as a literature review focusing on oncogenetic considerations of L-2-HGA-associated CNS tumors.