CMET-19. QUESTIONING THE PARADIGM: DO CSF CYTOLOGY AND MRI SCANNING IDENTIFY THE SAME DISEASE IN PATIENTS WITH NEOPLASTIC MENINGITIS (NM) – DATA FROM AN INTERNATIONAL NM REGISTRY AND A META-ANALYSIS
Bibliographic record
Abstract
Abstract INTRODUCTION CSF cytology and neuraxis MRI scanning are complementary examinations for diagnosing NM. Positive cytology is the current gold standard despite relatively low sensitivity. Although MRI is an accepted alternative diagnostic tool for both treatment and clinical trial inclusion, its sensitivity and specificity are poorly understood. METHODS We conducted an exhaustive, PRISMA-compliant systematic review of the literature from January 1990 to September 2016 comparing MRI to CSF cytology (the gold standard) for the diagnosis of NM. Relevant studies were assigned a level of evidence using AAN criteria. Pre-specified data was extracted, and summary statistics were calculated using the inverse variance method and random effects model. Survival of patients diagnosed with various combinations of CSF cytology and MRI was also extracted and summary statistics were calculated. RESULTS Thirteen studies (2 class-I, 5 class-II, 5 class-III, 1 class-IV, 1646 solid and hematologic tumor patients) provided data sufficient to calculate the diagnostic characteristics of MRI. Sensitivity and specificity were low (63.4% [56.5–71.2] and 40.1% [30.2–53.1] respectively). False positive and false negative rates were high (59.9% [46.9–69.8] and 36.6% [28.8–43.5] respectively). Various sensitivity analyses (recent studies, class I/II studies, only class I studies, specific tumor histologies) yielded very similar results. Overall survival in patients with MRI (+)/CSF (+), MRI (+)/CSF (-), and MRI (-)/CSF (+) disease (3 studies, 347 patients) were 50.8 [39.8–64.8], 115.4 [70.4–189.4] and 228.7 [130.5–400.7] days respectively (p < 0.001). CONCLUSIONS MRI scanning can diagnose leptomeningeal metastases, but is a very poor surrogate for CSF cytology. Patients with NM diagnosed by various combinations CSF cytology and MRI positivity have dramatically different survivals, suggesting that these categories define subsets of patients with NM with different natural histories. This finding has important implications for patient care and clinical trial design and interpretation. Novel diagnostic strategies for NM should be subjected to similar analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.039 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".