Classifying B‐cell non‐Hodgkin lymphoma by using MIB‐1 proliferative index in fine‐needle aspirates
Bibliographic record
Abstract
BACKGROUND: MIB-1 proliferation index (PI) has proven helpful for diagnosis and prognosis in non-Hodgkin lymphomas (NHLs). However, validated cutoff values for use in fine-needle aspiration (FNA) samples are not available. We investigated MIB-1 immunocytochemistry as an ancillary technique for stratifying NHL and attempted to establish PI cutpoints in cytologic samples. METHODS: B-cell NHL FNA cases with available cytospins (CS) MIB-1 immunocytochemistry results were included. Demographic, molecular, immunophenotyping and MIB-1 PI data were collected from cytologic reports. Cases were subtyped according to the current World Health Organization classification and separated into indolent, aggressive, and highly aggressive groups. Statistical analysis was performed with pairwise Wilcoxon rank sum test and linear discriminant analysis to suggest appropriate PI cutpoints. RESULTS: Ninety-one NHL cases were subdivided in 56 (61.5%) indolent, 30 (33%) aggressive, and 5 (5.5%) highly aggressive lymphomas. The 3 groups had significantly different MIB-1 PIs from each other. Cutpoints were established for separating indolent (<38%), aggressive (> or =38% to < or =80.1%) and highly aggressive (>80.1%). The groups were adequately predicted in 76 cases (83.5%) using the cutpoints and 15 cases showed discrepant PIs. CONCLUSIONS: MIB-1 immunohistochemistry on CS can help to stratify B-cell NHL and showed a significant increase in PI with tumor aggressiveness. Six misclassified cases had PIs close to the cutpoints. Discrepant MIB-1 PIs were related to dilution of positive cells by non-neoplastic lymphocytes and to the overlapping continuum of features between diffuse large B-cell lymphoma and Burkitt lymphoma. Validation of our approach in an unrelated, prospective dataset is required.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".