Strain elastography in the characterization of renal cell carcinoma and angiomyolipoma
Bibliographic record
Abstract
INTRODUCTION: We evaluate the diagnostic performance of strain elastography to differentiate renal cell carcinoma (RCC) from angiomyolipoma (AML). METHODS: Strain elastography was performed in 65 patients (mean age 55.5 years; range: 32-81) who had renal lesions (24 AMLs and 41 RCCs) prospectively. Lesions were classified according to lesion size and histological subtypes. The strain ratios of the RCCs and AMLs were evaluated by a radiologist. The area under the curve and the cut-off point were used to assess diagnostic performance. Sensitivity, specificity, and positive and negative predictive values were obtained. RESULTS: In assessing the mean strain ratio, we divided the groups in 3 according to size: (1) <20-mm lesions; (2) 20- to 40-mm lesions; and (3) >40-mm lesions; the respective mean strain ratios were: 1.5 ± 0.5 (range: 0.06-5.92), 2.8 ± 0.4 (range: 0.17-9.92), 2.7 ± 0.3 (range: 0.08-6.15). When RCCs and AMLs were compared, there was a statistically significant difference in the strain ratio among the 3 groups divided per lesion size (p < 0.01). For the strain ratio, the mean ± standard deviation was 1.1 ± 0.1 for AMLs and 3.4 ± 0.3 for RCCs (p < 0.01). When lesion subtypes were compared, there was a statistically significant difference in the strain ratio between the AML and clear cell RCC (p < 0.01). CONCLUSIONS: For assessing renal lesions, strain elastography and strain ratio values may be useful in differentiating RCCs from AMLs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".