Liposome contrast agent for CT‐based detection and localization of neoplastic and inflammatory lesions in rabbits: validation with FDG‐PET and histology
Bibliographic record
Abstract
PURPOSE: This study was aimed at assessing the performance of a liposome-based computed tomography (CT) contrast agent to detect tumor and inflammatory lesions in a rabbit model relative to (18)F-fluorodeoxyglucose- positron emission tomography (FDG-PET). MATERIALS AND METHODS: Nine New Zealand White rabbits were inoculated with a cell suspension obtained from the tumor tissue of a donor rabbit bearing VX2 carcinoma. Spontaneously formed inflammatory lesions were identified in the skeletal muscles of six of the nine animals. The CT liposome agent (185 +/- 37 mg/kg of iodine) was administered intravenously 7 days following tumor inoculation. The PET/CT imaging session took place five days post-liposome contrast administration and 1 h post (18)F-FDG injection (30.3 +/- 5.1 MBq/kg). Approximately 20 h post-imaging, the tumor and inflammatory lesions were excised for histo-pathology assessment. RESULTS: Liposome-CT identified the same number of primary tumors as FDG-PET (nine lesions, volumes = 0.07-7.01 cm(3), SUV(max) = 1.5-10.9, HU(mean) = 103.0-140.6). It also detected 25 inflammatory lesions (volumes = 0.01-2.73 cm(3), HU(mean) = 114.5-268.6), while FDG-PET identified seven (volumes = 0.05-1.04 cm(3), SUV(max) = 2.7-7.1). Differences in the mean CT signal (HU(mean)) between the tumor and inflammatory lesions were statistically significant (p < 0.0001). Partial volume adjusted SUV(max) values for the two lesion types calculated from the FDG-PET data set did not yield a significant difference (p > 0.15). CONCLUSION: These results demonstrate that liposome-CT can be considered for effective screening of neoplastic and inflammatory diseases, as well as subsequent image-guided biopsy. Moreover, the differential accumulation of the liposomal agent at tumor and inflammatory sites highlights its potential role in increasing the specificity of image-based diagnosis.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".