A New Three-Dimensional Ultrasound Microimaging Technology for Preclinical Studies Using a Transgenic Prostate Cancer Mouse Model
Bibliographic record
Abstract
Prostate cancer is the most common cancer in adult men in North America. Preclinical studies of prostate cancer employ genetically engineered mouse models, because prostate cancer does not occur naturally in rodents. Widespread application of these models has been limited because autopsy was the only reliable method to evaluate treatment efficacy in longitudinal studies. This article reports the first use of three-dimensional ultrasound microimaging for measuring tumor progression in a genetically engineered mouse model, the 94-amino acid prostate secretory protein gene-directed transgenic prostate cancer model. Qualitative comparisons of three-dimensional ultrasound images with serial histology sections of prostate tumors show the ability of ultrasound to accurately depict the size and shape of malignant masses in live mice. Ultrasound imaging identified tumors ranging from 2.4 to 14 mm maximum diameter. The correlation coefficient of tumor diameter measurements done in vivo with three-dimensional ultrasound and at autopsy was 0.998. Prospective tumor detection sensitivity and specificity were both >90% when diagnoses were based on repeated ultrasound examinations done on separate days. Representative exponential growth curves constructed via longitudinal ultrasound imaging indicated volume doubling times of 5 and 13 days for two prostate tumors. Compared with other microimaging and molecular imaging modalities, the application of three-dimensional ultrasound imaging to prostate cancer in mice showed advantages, such as high spatial resolution and contrast in soft tissue, fast and uncomplicated protocols, and portable and economical equipment that will likely enable ultrasound to become a new microimaging modality for mouse preclinical trial studies.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".