Evaluation of Cystic and Solid Renal Lesions with Contrast-Enhanced Ultrasound: A Retrospective Study
Bibliographic record
Abstract
Abstract Purpose Renal lesions are frequent random findings on CT, MRI, and conventional ultrasound. Since they are usually found accidentally, the respective examinations have not been performed optimally to provide a conclusive diagnosis, making additional multiphase contrast-enhanced examinations necessary. The aim of the study is to correlate CEUS findings with the final diagnosis and to determine whether it is a suitable method for the conclusive characterization of undetermined renal lesions. Materials and Methods All CEUS examinations of focal renal lesions performed at our institute between 2007 and 2014 were retrospectively examined. 437 patients with a total of 491 lesions and 543 examinations were included. 54 patients had bilateral lesions. One patient had three lesions in one kidney. Histology was available in 49 cases and follow-ups in 124 cases. The sensitivity, specificity, positive and negative predictive value as well as positive and negative likelihood ratios were calculated. Results There were 54 malignant and 437 benign lesions. The sensitivity and specificity were 0.981/0.954 overall, 1.000/0.956 for cystic lesions, 0.977/0.906 for solid lesions, and 0.971/0.071 for the histologically confirmed lesions. Bosniak classification was consistent in 289 of 301 lesions (96%). Only 12 lesions (3.9%) were falsely assessed as malignant. Conclusion CEUS is an appropriate method for the clarification of undetermined renal lesions. The characterization of cystic lesions according to Bosniak is adequately possible, especially for potentially malignant lesions (types III and IV).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".