Three-dimensional High-Frequency Ultrasound Imaging for Longitudinal Evaluation of Liver Metastases in Preclinical Models
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
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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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 | 0.002 | 0.000 |
| 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) | 0.000 | 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