Volume measurement variability in three-dimensional high-frequency ultrasound images of murine liver metastases
Bibliographic record
Abstract
The identification and quantification of tumour volume measurement variability is imperative for proper study design of longitudinal non-invasive imaging of pre-clinical mouse models of cancer. Measurement variability will dictate the minimum detectable volume change, which in turn influences the scheduling of imaging sessions and the interpretation of observed changes in tumour volume. In this paper, variability is quantified for tumour volume measurements from 3D high-frequency ultrasound images of murine liver metastases. Experimental B16F1 liver metastases were analysed in different size ranges including less than 1 mm3, 1-4 mm3, 4-8 mm3 and 8-70 mm3. The intra- and inter-observer repeatability was high over a large range of tumour volumes, but the coefficients of variation (COV) varied over the volume ranges. The minimum and maximum intra-observer COV were 4% and 14% for the 1-4 mm3 and <1 mm3 tumours, respectively. For tumour volumes measured by segmenting parallel planes, the maximum inter-slice distance that maintained acceptable measurement variability increased from 100 to 600 microm as tumour volume increased. Comparison of free breathing versus ventilated animals demonstrated that respiratory motion did not significantly change the measured volume. These results enable design of more efficient imaging studies by using the measured variability to estimate the time required to observe a significant change in tumour volume.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".