Liposome contrast agent for CT‐based detection and localization of neoplastic and inflammatory lesions in rabbits: validation with FDG‐PET and histology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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