SU‐GG‐BRC‐09: CT Detection of Primary and Metastatic Lesions with a Nano‐Agent in Rabbits: Validation with FDG‐PET
Bibliographic record
Abstract
Purpose: To evaluate the performance of a recently engineered nano‐sized liposome CT agent to detect primary and metastatic tumor lesions in a VX2‐sarcoma rabbit model as compared to FDG‐PET. Method and Materials: Nine New Zealand White rabbits bearing VX2‐sarcoma in their left lateral quadriceps received a single intravenous injection of 80 nm liposomes co‐encapsulating 185 ± 37 mg/kg iodine in the form of iohexol (Omnipaque®) and 7 ± 1 mg/kg gadolinium in the form of gadoteridol (Prohance®). The CT/PET (GE Discovery ST) imaging session took place twelve days after the tumor inoculation procedure, five days post liposome contrast administration and one hour post‐FDG injection (30.3 ± 5.1 MBq/kg). Following CT/PET imaging, the rabbits were sacrificed and the primary and metastatic lesions were examined by a pathologist. The measurement of tumor size was performed on the CT data set. The registration of the CT and PET images was performed using MIPAV. Results: Liposome‐CT demonstrated the same sensitivity and specificity as FDG‐PET for the detection of the 9 primary tumors (volumes = 25 – 7280 mm3, SUVmax = 1.5 – 10.9, HUmax = 173 – 596). In addition, liposome‐CT detected 13 metastatic muscle lesions (volumes = 14 – 2732 mm3, HUmax = 254 – 493) that were histologically malignant, while FDG‐PET identified 7 (volumes = 54 – 1044 mm3, SUVmax = 2.7 – 7.1). For the 16 lesions detected by both imaging modalities, there was a positive correlation between the PET SUVmax and the CT HUmax. Conclusion: In this investigation, increased contrast of primary and metastatic lesions in CT was achieved with the administration of the liposome nano‐agent. This demonstrates the feasibility of employing the liposome‐CT method for effective tumor detection and localization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".