Negative Predictive Value of Contrast-Enhanced Ultrasound of Liver and Kidney Thermal Ablation Sites for Local Tumour Progression during Long-term Follow-up: A Retrospective Consecutive Study
Bibliographic record
Abstract
PURPOSE: To determine negative predictive value (NPV) of contrast-enhanced ultrasound (CEUS) to demonstrate local tumour progression (LTP) at thermal ablation (TA) sites. METHODS: Our institutional review board approved this retrospective study; acquisition of consent was waived. Consecutive CEUS examinations performed between 2004-2014 for TA site evaluation on patients who could not undergo enhanced computed tomography (CT) or magnetic resonance imaging (MRI), or had inconclusive CT or MRI, were retrospectively reviewed. Those reported as no abnormal enhancement in or surrounding TA site were included. CEUS examination was considered true-negative based on stability or lack of enhancement/washout on follow-up imaging for at least 1 year, and false-negative (FN), if there was an arterially enhancing focus with wash-out at or surrounding TA site on subsequent follow-up imaging. RESULTS: Study population included 56 tumours in 54 patients, 11 women, 43 men; mean age 71 years. Two patients had TA of two different hepatocellular carcinomas. Thirty-six examinations were for hepatic TA and twenty for renal TA. Lesion sizes ranged from 1 cm to 7 cm (mean 3.1 ± 1.2). Mean diameter of 7 recurrences was 13.8 mm. Overall FN rate was 12.5% (7/56). Corresponding numbers were 0% (0/20) for renal TA and 19.4% (7/36) for hepatic TA. Overall NPV of CEUS was 87.5% (49/56) (confidence interval [CI]: 78.8%-96.2%). NPV for renal TA was 100% (20/20) (CI: 100%-100%) and for hepatic TA 81.5% (29/36) (CI: 67.6 %-93.5%). CONCLUSION: In this cohort, CEUS showed high NPV for exclusion of LTP at renal TA sites. NPV for hepatic TA sites was high but lower than renal TA.
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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 | 0.006 |
| 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 | 0.001 |
| 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 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".