Differentiation of Benign From Malignant Liver Masses With Acoustic Radiation Force Impulse Technique
Bibliographic record
Abstract
OBJECTIVE: The objective of the study was to determine the performance of Acoustic Radiation Force Impulse (ARFI) imaging to differentiate benign from malignant liver masses, both of hepatocellular origin and metastases, by quantification of their stiffness. METHODS: This study has institutional review board approval and informed consent. Eighty-nine patients (42 female and 47 male patients) with 105 liver masses had ARFI evaluation on ultrasound, S2000 (Siemens, Mountain View, Calif). Mean age of the patients was 53.67 years (range, 27-83 years). Mean diameter of the masses was 2.77 cm (range, 1.0-13.0 cm). Final diagnoses, confirmed by imaging on contrast-enhanced computed tomography, magnetic resonance, or ultrasound or biopsy, include hepatocellular carcinoma (n = 28), metastasis (n = 13), hemangioma (n = 35), focal nodular hyperplasia (n = 15), focal fat sparing (n = 8), focal fat deposit (n = 4), and adenoma (n = 2). Receiver operating characteristic analysis was performed to evaluate the diagnostic accuracy of the ARFI measurement and to extract the optimal cutoff values in the differentiation of benign from malignant disease. RESULTS: Acoustic Radiation Force Impulse values showed a statistically significant difference between benign (1.73 [SD, 0.8] m/sec) and malignant masses (2.57 [SD, 1.01] m/sec) (P < 0.001). However, the area under the receiver operating characteristic curve was 0.744, suggesting only fair accuracy. For differentiation of malignant from benign masses, the sensitivity, specificity, positive predictive value, and negative predictive value were 68% (28/41), 69% (44/64), 58% (28/48), and 77% (44/57), respectively, when 1.9 m/sec was chosen as a cutoff value, reflective of a wide variation of ARFI values in each diagnosis. For differentiation of metastasis from benign masses, sensitivity, specificity, positive predictive value, and NPV were 69% (9/13), 89% (57/64), 56% (9/16), and 93% (57/61), respectively, when 2.72 m/sec was chosen as a cutoff value. CONCLUSIONS: Acoustic Radiation Force Impulse measurement may be helpful to differentiate benign masses from metastases, in particular. Otherwise, ARFI measurements alone do not differentiate benign and malignant masses because of variations in stiffness of all types of masses.
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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 | 0.007 |
| 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".