Diagnostic Accuracy of MRI for Detecting Inferior Vena Cava Wall Invasion in Renal Cell Carcinoma Tumor Thrombus Using Quantitative and Subjective Analysis
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to evaluate MRI in inferior vena cava (IVC) renal cell carcinoma (RCC) tumor thrombus for the diagnosis of caval wall invasion. MATERIALS AND METHODS: This retrospective case-control study evaluated 24 consecutive patients who underwent thrombectomy for RCC IVC tumor thrombus (11 [45.8%] with invasion) seen at preoperative MRI. A blinded radiologist segmented tumor thrombus on apparent diffusion coefficient (ADC) maps and T2-weighted images for texture analysis, measured the diameter of the renal vein and IVC at the level of the renal vein ostium, and measured the craniocaudal extent and volume of the tumor thrombus. Two blinded radiologists independently evaluated the margin of the tumor thrombus (smooth vs irregular), thinning or thickening and abnormal T2-weighted signal or enhancement of the IVC wall, and overall impression of invasion. Comparisons were performed using logistic regression models and chi-square with accuracy calculated using ROC. RESULTS: ; p = 0.003) than did thrombi without invasion. The ROC AUC ranged from 0.78 to 0.83. ADC and texture parameters were not significantly different between groups (p = 0.208-0.503); however, larger entropy in invasive tumor thrombus trended toward significance (p = 0.061). A model combining volume, entropy, and overall impression achieved an AUC of 0.91 (95% CI, 0.77-1.0). CONCLUSION: The combination of tumor thrombus volume with entropy and subjective overall impression of IVC wall invasion achieved the highest accuracy for diagnosis.
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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 | 0.005 | 0.025 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".