Diagnosis of Lymphoma by Image-Guided Needle Biopsies
Bibliographic record
Abstract
OBJECTIVE: To determine whether or not concurrent core biopsy adds to results obtained from image-guided fine needle aspiration biopsy (FNAB) in cases of lymphoma. STUDY DESIGN: Twenty-eight FNABs of lymphomas with adjuvant flow cytometry (FC) and concurrent core biopsy were evaluated retrospectively. In each case, completeness of diagnosis by FNAB, including phenotyping and grading, where appropriate, was reviewed. The contribution of core biopsy to the diagnosis in cases where FNAB did not render a complete diagnosis was assessed. Prognostic information not available from the FNAB but obtained from the core biopsy was also evaluated. RESULTS: FNAB with adjuvant FC gave a complete diagnosis, including phenotype and grade, where applicable, in 23 of 28 cases (82%). Core biopsy added to the diagnosis in 3 cases. In 1 case, large B-cell lymphoma was diagnosed on core biopsy when FNAB was unsatisfactory. In the other 2 cases, grade of follicle center cell lymphoma was higher on core biopsy than on FNAB. The addition of the information obtained by core biopsy to that obtained by FNAB raised the diagnostic accuracy to 93%. Core biopsy was used to assess nodularity, which could not be determined on FNAB. Core biopsy was also used to assess prognostic markers by immunohistochemistry (Ki-67 and p53); they were not available with FC. This was done in 11 cases when requested by the oncologist. CONCLUSION: FNAB with adjuvant FC is a useful technique for diagnosing and subtyping lymphomas. However, diagnosis and subclassification are often insufficient. Core biopsy material provides opportunity for obtaining additional diagnostic and prognostic information that may not be easily derived from the FNAB. This allows optimal treatment planning in patients for whom excisional biopsy is contraindicated.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".