Systematic review of the use of pre-operative simulation and navigation for hepatectomy: current status and future perspectives
Bibliographic record
Abstract
Pre-operative simulation using three-dimensional (3D) reconstructions have been suggested to enhance surgical planning of hepatectomy. Evidence on its benefits for hepatectomy patients remains limited. This systematic review examined the use and impact of pre-operative simulation and intraoperative navigation on hepatectomy outcomes. A systematical searched electronic databases for studies reporting on the use and results of simulation and navigation for hepatectomy was performed. The primary outcome was change in operative plan based on simulation. Secondary outcomes included operating time (min), estimated blood loss, surgical margins, 30-day postoperative morbidity and mortality, and study-specific outcomes. From 222 citations, we included 11 studies including 497 patients. All were observational cohort studies. No study compared hepatectomy with and without simulation. All studies performed 3D reconstruction and segmentation, most commonly with volumetrics measurements. In six studies reporting intraoperative navigation, five relied on ultrasound, and one on a resection map. Of two studies reporting on it, the resection line was changed intraoperatively in one third of patients, based on simulation. Virtually predicted liver volumes (Pearson correlation r = 0.917 to 0.995) and surgical margins (r = 0.84 to 0.967) correlated highly with actual ones in eight studies. Heterogeneity of the included studies precluded meta-analysis. Pre-operative simulation seems accurate in measuring volumetrics and surgical margins. Current studies lack intraoperative transposition of simulation for direct navigation. Simulation appears useful planning of hepatectomies, but further work is warranted focusing on the development of improved tools and appraisal of their clinical impact compared to traditional resection.
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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 | 0.016 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".