Evaluation of diagnostic performance of CT for detection of tumor thrombus in children with Wilms tumor: A report from the Children's Oncology Group
Bibliographic record
Abstract
BACKGROUND: Pre-operative assessment of intravascular extension of Wilms tumor is essential to guide management. Our aim is to evaluate the diagnostic performance of multidetector CT in detection of tumor thrombus in Wilms tumor. PROCEDURE: The study population was drawn from the first 1,015 cases in the AREN03B2 study of the Children's Oncology Group. CT scans of children with (n = 62) and without (n = 111) tumor thrombus at nephrectomy were independently reviewed by two radiologists, blinded to patient information. Doppler sonography results were obtained from institutional radiology reports, as Doppler requires real-time evaluation. The diagnostic performance of CT and Doppler for detection of tumor thrombus was determined using nephrectomy findings as reference standard. RESULTS: In the primary nephrectomy group, tumor thrombus detection sensitivity, specificity of CT was 65.6, 84.8%, and Doppler was 45.8, 95.7%, respectively. In this group, sensitivity of CT, Doppler for detection of cavoatrial thrombus was 84.6 and 70.0%, respectively. In the secondary nephrectomy group, tumor thrombus detection sensitivity, specificity of CT was 86.7, 90.6%, and Doppler was 66.7, 100.0%, respectively. In this group, sensitivity of CT, Doppler for detection of cavoatrial thrombus was 96.0 and 68.8%, respectively. Pre-operative Doppler evaluation performed in 108/173 cases, detected 3 cases with intravenous extension (2 in renal vein, 1 in IVC at renal vein level) that were missed at CT. CONCLUSIONS: CT can accurately identify cavoatrial tumor thrombus that will impact surgical approach. Routine Doppler evaluation, after CT has already been performed, is not required in Wilms tumor.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".