Synthesis and characterisation of a cancerous liver for presurgical planning and training applications
Bibliographic record
Abstract
OBJECTIVES: Oncology surgeons use animals and cadavers in training because of a lack of alternatives. The aim of this work was to develop a design methodology to create synthetic liver models familiar to surgeons, and to help plan, teach and rehearse patient-specific cancerous liver resection surgery. DESIGN: Synthetic gels were selected and processed to recreate accurate anthropomorphic qualities. Organic and synthetic materials were mechanically tested with the same equipment and standards to determine physical properties like hardness, elastic modulus and viscoelasticity. Collected data were compared with published data on the human liver. Patient-specific CT data were segmented and reconstructed and additive manufactured models were made of the liver vasculature, parenchyma and lesion. Using toolmaking and dissolvable scaffolds, models were transformed into tactile duplicates that could mimic liver tissue behaviour. RESULTS: Porcine liver tissue hardness was found to be 23 H00 (±0.1) and synthetic liver was 10 H00 (±2.3), while human parenchyma was reported as 15.06 H00 (±2.64). Average elastic Young's modulus of human liver was reported as 0.012 MPa, and synthetic liver was 0.012 MPa, but warmed porcine parenchyma was 0.28 MPa. The final liver model demonstrated a time-dependant viscoelastic response to cyclic loading. CONCLUSION: Synthetic liver was better than porcine liver at recreating the mechanical properties of living human liver. Warmed porcine liver was more brittle, less extensible and stiffer than both human and synthetic tissues. Qualitative surgical assessment of the model by a consultant liver surgeon showed vasculature was explorable and that bimanual palpation, organ delivery, transposition and organ slumping were analogous to human liver behaviour.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".