WE‐D‐I‐609‐07: Analysis of Growth Dynamics of Treated Murine Liver Metastases Using Volumetric Ultrasound Micro‐Imaging
Bibliographic record
Abstract
Purpose: Two methods were compared for analyzing therapeutic responses in an experimental murine liver metastasis model using longitudinal high‐frequency three‐dimensional (3D) ultrasound imaging. Method and Materials: B16F1 mouse melanoma cells were injected into the mesenteric vein of C57BL/6 mice to produce liver metastases. Treated mice received doxorubicin every second day starting seven days post injection and continuing until day 17. Untreated mice received saline injections on the same schedule. Imaging began eight days post injection and was performed every one to two days until day 19. Three‐dimensional images were acquired using a VisualSonics Vevo 660 ultrasound system with a 40 MHz transducer. Metastasis volumes were measured in 3D images by manual segmentation. Two growth curves were constructed for each metastasis by computing least‐squares fits of an exponential function and a Gompertz function, which is an exponential with a time‐varying rate parameter, to the volume data. Results: Eight untreated and 24 treated metastases were monitored. A significant (p < 0.05 in Wilcoxon rank‐sum tests) difference in untreated and treated tumor volumes was observed as early as day 12. The growth of untreated metastases was described equally well by exponential (mean ± standard deviation of ) or Gompertz functions. However, Gompertz functions modeled the growth of treated metastases more precisely than exponential functions . The difference between exponential and Gompertz r2 values for treated growth curves was significant (p = 0.040). Conclusion: High‐frequency 3D ultrasound imaging is sensitive to changes in murine liver metastasis growth rate produced by a chemotherapeutic agent, and may be capable of characterizing temporal variations in the growth rates of treated tumors. Conflict of Interest: VisualSonics has licensed 3D reconstruction, visualization, and segmentation software from our laboratory.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".