Volume assessment accuracy in computed tomography: a phantom study
Why this work is in the frame
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Bibliographic record
Abstract
There is a broad push in the cancer imaging community to eventually replace linear tumor measurements with three-dimensional evaluation of tumor volume. To evaluate the potential accuracy of volume measurement in tumors by CT, a gelatin phantom consisting of 55 polymethylmethacrylate (PMMA) spheres spanning diameters from 1.6 mm to 25.4 mm was fabricated and scanned using thin slice (0.625 mm) CT (GE LightSpeed 16). Nine different reconstruction combinations of field of view dimension (FOV = 20, 30, 40 cm) and CT kernel (standard, lung, bone) were analyzed. Contiguous thin-slice images were averaged to produce CT images with greater thicknesses (1.25, 2.50, 5.0 mm). Simple grayscale thresholding techniques were used to segment the PMMA spheres from the gelatin background, where a total of 1800 spherical volumes were evaluated across the permutations studied. The geometric simplicity of the phantom established upper limits on measurement accuracy. In general, smaller slice thickness and larger sphere diameters produced more accurate volume assessment than larger slice thickness and smaller sphere diameter. The measured volumes were smaller than the actual volumes by a common factor depending on slice thickness; overall, 0.625 mm slices produced on average 18%, 1.25 mm slices produced 22%, 2.5 mm CT slices produced 29%, and 5.0 mm slices produced 39% underestimates of volume (mm3). Field of view did not have a significant effect on volume accuracy. Reconstruction algorithm significantly affected volume accuracy (p < 0.0001), with the lung kernel having the smallest error, followed by the bone and standard kernels. The results of this investigation provide guidance for CT protocol development and may guide the development of more advanced techniques to promote quantitatively accurate CT volumetric analysis of tumors.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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