Application of real-time contrast-enhanced ultrasound in differential diagnosis of liver malignancies
Bibliographic record
Abstract
The objective of this study was to evaluate the value of real-time contrast-enhanced ultrasound in differential diagnosis of malignant liver tumors. Sixty patients pathologically diagnosed with liver cancer from May 2016 to May 2017 were selected as the subjects. All patients received ultrasonic diagnosis followed by the observation on dynamic contrast enhancement of the image and the analysis on perfusion characteristics and difference in contrast-enhanced ultrasound results in patients with different types of liver cancer. The signal during the arterial phase of 21 patients with well-differentiated hepatocellular carcinoma increased, including 12 cases of slow withdrawal of portal venous phase. The relative height of artery of 30 patients with middle-low differentiation of hepatocellular carcinoma increased along with the speed at which the contrast media was washed out at the end of the portal phase. In 9 patients with intrahepatic bile duct cancer, the enhancement of the arteries was slightly increased in the periphery of the tumor and low enhancement was found in the portal venous phase. There were some differences in the timing and the intensity of the enhancement of the ultrasound signals with 3 different types of diseases (P < 0.05). There are obvious differences in real-time contrast-enhanced ultrasound features of patients with different types of malignant liver tumor, which may provide references for clinical 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 | 0.002 | 0.004 |
| 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 | 0.000 | 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".