Three-dimensional morphometric analysis for hepatectomy of centrally located hepatocellular carcinoma: A pilot study
Bibliographic record
Abstract
AIM: To describe a three-dimensional model (3DM) to accurately reconstruct anatomic relationships of centrally located hepatocellular carcinomas (HCCs). METHODS: From March 2013 to July 2014, reconstructions and visual simulations of centrally located HCCs were performed in 39 patients using a 3D subject-based computed tomography (CT) model with custom-developed software. CT images were used for the 3D reconstruction of Couinaud's pedicles and hepatic veins, and the calculation of corresponding tumor territories and hepatic segments was performed using Yorktal DMIT software. The respective volume, surgical margin, and simulated virtual resection of tumors were also estimated by this model preoperatively. All patients were treated surgically and the results were retrospectively assessed. Clinical characteristics, imaging data, procedure variables, pathologic features, and postoperative data were recorded and compared to determine the reliability of the model. RESULTS: 3D reconstruction allowed stereoscopic identification of the spatial relationships between physiologic and pathologic structures, and offered quantifiable liver resection proposals based on individualized liver anatomy. The predicted values were consistent with the actual values for tumor mass volume (82.4 ± 109.1 mL vs 84.1 ± 108.9 mL, P = 0.910), surgical margin (10.1 ± 6.2 mm vs 9.1 ± 5.9 mm, P = 0.488), and maximum tumor diameter (4.61 ± 2.16 cm vs 4.53 ± 2.14 cm, P = 0.871). In addition, the number and extent of portal venous ramifications, as well as their relation to hepatic veins, were visualized. Preoperative planning based on simulated resection facilitated complete resection of large tumors located in the confluence of major vessels. And most of the predicted data were correlated with intraoperative findings. CONCLUSION: This 3DM provides quantitative morphometry of tumor masses and a stereo-relationship with adjacent structures, thus providing a promising technique for the management of centrally located HCCs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".