Using the World Health Organization Classification of Thymic Epithelial Neoplasms to Describe CT Findings
Bibliographic record
Abstract
OBJECTIVE: Our purpose was to assess the CT features of various subtypes of thymic epithelial neoplasms on the basis of the 1999 World Health Organization classification. MATERIALS AND METHODS: Thymic epithelial neoplasms in 53 patients who underwent thymectomy were retrospectively assessed histologically according to the 1999 World Health Organization classification. Type A and B neoplasms correspond to thymomas and type C, to thymic carcinoma. The study included four patients with type A, 14 with type AB, nine with type B1, 14 with type B2, four with type B3, and eight with type C epithelial tumors. Two observers independently assessed the CT scans without knowledge of the histologic findings. RESULTS: Type A tumors were more likely to have smooth contours on CT (4/4, 100%) and round shapes (3.5/4, 88%) than any other type of thymic epithelial tumor (all, p < 0.05). Type C tumors had a higher prevalence of irregular contours (6/8, 75%) than any other type of thymic epithelial tumor (all, p < 0.05). Calcification was more frequently seen in type B1 (4/9, 44%), type B2 (8.5/14, 61%), and type B3 (3/4, 75%) tumors than in type AB (2/14, 14%) and type C (0.5/8, 6%) tumors (all, p < 0.05). CONCLUSION: Smooth contours and a round shape are most suggestive of type A thymic epithelial tumor, whereas irregular contours are most suggestive of type C tumor. Calcification is suggestive of type B tumors. CT is of limited value, however, in differentiating type AB, B1, B2, and B3 tumors.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".