Evaluation of Cystic and Solid Renal Lesions with Contrast-Enhanced Ultrasound: A Retrospective Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it