CT perfusion imaging and interventional therapy of genetically engineered mouse prostate cancer
Bibliographic record
Abstract
Objective To evaluate the blood perfusion in tumors of genetically engineered mouse prostate cancer models and report the results of interventional therapy.Methods:CT perfusion was performed in six genetically engineered mouse prostate cancer models and five normal mouse.One genetically engineered mouse prostate cancer model was performed transcatheter arterial chemo-embolization(TACE) with Lipiodo1.Results:(1) The blood flow(BF) and blood volume(BV) value in the peripheral regions of the tumor were lager than those in the center of tumor and normal prostate tissue(P0.01).However,there were no statistic difference between of the BF and BV value in the center of tumor and normal prostate tissue(P0.05).(2) For the permeability surface(PS),the value in the center of tumor were larger than those in the peripheral regions of the tumor and normal prostate(P0.01).There were no statistic difference between the peripheral regions of the tumor and normal prostate.(P0.05).The interventional therapy was successfully performed in one genetically engineered mouse prostate cancer model.CT scan showed that lipiodol deposited in the tumors.Immunochemistry displayed that the necrosis in the tumors after sacrificed.Conclusion:CT perfusion is suitable to evaluate the change of hemodynamics in the genetically engineered mouse prostate cancer model.There are potential clinical applicational value of TAE in the prostatic carcinoma.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".