Development of a multi-modal liver phantom with flow for the validation and training of focal ablation procedures
Bibliographic record
Abstract
Percutaneous ablation is becoming a viable treatment option for patients with early-stage hepatocellular carcinoma (HCC) who are not candidates for surgical resection or liver transplantation.1 The success of the treatment is measured by the complete coverage, plus a positive margin, of the tumor being contained within the ablation lesion.2 In this project, a multi-modality anthropomorphic phantom with simulated tumor and vascular flow was developed. The phantom consists of five different parts: the left and right lobes, internal and external vasculature (part of the Inferior Vena Cava), and the tumors. The geometry of these anatomical features are based on patient-specific CT data. Our anthropomorphic liver phantom is made with PolyVinyl Alcohol cryogel (PVA-c) to serve as an Ultrasound-, MRI-, and CT-compatible tissue-mimicking material. Talcum powder was added to the PVA-c to provide realistic speckle under ultrasound (US) imaging, with the optimal concentration being determined by experiment. The Talcum concentration of the tumors was evaluated by US and CT imaging. To create the closed-loop vasculature flow, positive silicone vasculature molds were inserted into the liver body mold prior PVA-c filling. After the freeze-thaw cycles, the silicone vasculature molds are extracted from the liver body creating a network of canals. To recreate the blood flow, a water pump was connected to the liver phantom vasculature to allow the flowing through the internal canals. Differentiation between the liver tissue, vessels, and simulated tumors was clearly visualized in US and CT imaging. Color Doppler was acquired to test the flow of the closed-loop vasculature. The antropomorphic characteristics and the manufacturing technique makes our liver phantom customizable to work as a sandbox environment for needle puncture procedures (i.e. focal ablation) as well as training and validation of surgical navigation systems for these interventions.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".