Three-dimensional High-Frequency Ultrasound Imaging for Longitudinal Evaluation of Liver Metastases in Preclinical Models
Bibliographic record
Abstract
Liver metastasis is a clinically significant contributor to the mortality associated with melanoma, colon, and breast cancer. Preclinical mouse models are essential to the study of liver metastasis, yet their utility has been limited by the inability to study this dynamic process in a noninvasive and longitudinal manner. This study shows that three-dimensional high-frequency ultrasound can be used to noninvasively track the growth of liver metastases and evaluate potential chemotherapeutics in experimental liver metastasis models. Liver metastases produced by mesenteric vein injection of B16F1 (murine melanoma), PAP2 (murine H-ras-transformed fibroblast), HT-29 (human colon carcinoma), and MDA-MB-435/HAL (human breast carcinoma) cells were identified and tracked longitudinally. Tumor size and location were verified by histologic evaluation. Tumor volumes were calculated from the three-dimensional volumetric data, with individual liver metastases showing exponential growth. The importance of volumetric imaging to reduce uncertainty in tumor volume measurement was shown by comparing three-dimensional segmented volumes with volumes estimated from diameter measurements and the assumption of an ellipsoid shape. The utility of high-frequency ultrasound imaging in the evaluation of therapeutic interventions was established with a doxorubicin treatment trial. These results show that three-dimensional high-frequency ultrasound imaging may be particularly well suited for the quantitative assessment of metastatic progression and the evaluation of chemotherapeutics in preclinical liver metastasis models.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".