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Record W2551095427 · doi:10.1093/neuonc/now245

Atypical meningioma—is it time to standardize surgical sampling techniques?

2016· article· en· W2551095427 on OpenAlexafffund
Michael D. Jenkinson, Thomas Santarius, Gelareh Zadeh, Kenneth Aldape

Bibliographic record

VenueNeuro-Oncology · 2016
Typearticle
Languageen
FieldMedicine
TopicMeningioma and schwannoma management
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoNational Institute for Health and Care Research
KeywordsMeningiomaSampling (signal processing)MedicineComputer scienceMedical physicsRadiologyComputer vision

Abstract

fetched live from OpenAlex

The World Health Organization (WHO) Classification of Tumours of the Central Nervous System has recently been updated.1 While dramatic changes have been made to gliomas with the inclusion of molecular markers, a more subtle change has been made to meningiomas that may have implications for clinical trials. The brain tumor community is focusing collaborative research on grades II and III meningiomas through an international consortium (http://www.soc-neuro-onc.org/events/172/). There are several international clinical trials for atypical meningiomas, including ROAM/EORTC 1308,2 EORTC 1320 (https://clinicaltrials.gov/ct2/show/NCT02234050), RTOG 0593 (https://www.rtog.org/ClinicalTrials/ProtocolTable/StudyDetails.aspx?study=0539), and NRG-BN003 (personal communication, L. Rogers). Trial entry is contingent upon accurate histopathological diagnosis. The updated WHO classification includes an important change, namely that brain invasion in addition to mitotic count of 4–20 mitoses per 10 high power microscopic fields is now diagnostic for atypical meningioma. While the new WHO change is unlikely to lead to increased reporting, as previously observed,3 it has potential implications for neurosurgeons. The surgical technique for meningioma resection is internal tumor decompression or “piecemeal” resection, followed by microsurgical dissection of the tumor–brain interface. The process of tumor debulking leads to sample loss in the suction and only rarely can the neurosurgeon perform en bloc resection and provide the neuropathologist with the “perfect” specimen. The impact of surgical sampling on glioma grading is well recognized but perhaps underappreciated and little discussed in meningiomas.4 Although neuropathologists often work with limited surgical samples, the neurosurgeon should provide the best possible specimens for diagnosis and research. Accurate assessment of brain invasion is important for meningioma prognostication,5,6 and sampling limitations may miss a brain-invasive meningioma, leading to undergrading and a potentially different management course than the one most suitable, including participation in clinical trials. The updated WHO classification places an emphasis on accurate assessment of brain invasion. Meningiomas broadly fall into 2 categories: the minority that do not invade the pial surface and can be resected without disruption of the brain, and the majority where parenchymal disruption occurs during surgery.4 In the former, sampling of the resection cavity would not be appropriate; however, in the latter, the neurosurgeon may observe macroscopic brain invasion, and this raises an important question: “should sampling of the tumor–brain interface be made to specifically address the issue of microscopic brain invasion?” This would involve a paradigm shift in surgical practice, but one that should be considered. As a corollary, an absence of brain tissue in the surgical specimen precludes the possibility of neuropathological assessment of invasion and leads to a second question: “should pathologists report the presence/absence of brain tissue with which to assess invasion?” Previous studies have shown that extensive and systematic surgical sampling in combination with thorough histopathology assessment increases reporting of brain invasion.4 Here we propose a possible paradigm. Neurosurgeon’s role: Label samples known or likely to contain brain tissue as “tumor–brain interface.” Neuropathologist’ role: Inspect highlighted samples for the presence of brain tissue. Based on the above, the following statements can be made: Brain invasion present Brain invasion absent A more pertinent question is whether the time is ripe to move away from histopathology definitions and rely on identifying molecular markers of recurrence and response to therapy—so-called molecular oncology. Although this is possible for gliomas, our molecular understanding of meningiomas is insufficiently developed at the present time. Accurate grading and systematic tissue collection for research as part of an international collaboration are required due to the rarity of atypical (and anaplastic) meningiomas. G.Z. and K.D.A. founded the International Consortium for Meningiomas. Disclosures or potential conflicts of interest. M.D.J. is the recipient of a grant from the National Institute of Health Research Health Technology Assessment program for the ROAM trial (NIHR HTA: 12/173/14).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.350
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations25
Published2016
Admission routes2
Has abstractyes

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