Skeletal muscle index by CT correlates with kinetic growth and resectability after PVE
Bibliographic record
Abstract
Introduction Techniques to increase the future liver remnant (FLR) prior to resection expand resectability of borderline resectable liver tumors. After portal vein embolization (PVE), only 60-70% of resections are feasible. Liver growth after PVE has been shown to be impacted by age, cirrhosis, diabetes and cholestasis. Malnutrition is associated with a smaller total liver volume and worse outcomes after liver resection. Skeletal muscle index (SMI) is an objective measure of malnutrition obtained from cross-sectional imaging. Aim This study investigates, if a low SMI predicts a low KGR after PVE and thereby reduces feasibility of resection. Methods All patients requiring PVE and planned for liver surgery were retrospectively analysed at 6 international centers in the DRAGON collaborative between 2010 and 2019. MRI and CT scans were used to assess liver volumetry and SMI was measured at the third lumbar vertebrae (L3) on pre-operative scans using the software OsiriX MD, Version 11.0.2. Total liver volume was calculated by Vauthey’s formula to assess standardized FLR (sFLR). Factors with impact on KGR were assessed and a multi-variate analysis was performed using stepwise regression. Results Two hundred and ninety-three patients underwent PVE and were planned for liver surgery. Overall 254 patients (87%) were included in the analysis. Mean age was 63 years. Gender distribution was 107:147 (m:f). After a median of 27 days (IQR 21-31) the sFLR1 increases from 21% (IQR 16-27) to a sFLR2 of 30% (IQR 23-40). Median degree of hypertrophy was 41 (IQR 24-66) and median KGR was 2.02 (IQR 1.2-3.5). SMI correlates with KGR (p = 0.01). In a multi-variate analysis, pre-operative creatinine elevation and low SMI are the significant risk factors for a low KGR. Patients with a SMI of more than 41.5 cm 2 /m 2 had a higher chance undergoing resection (90% vs. 72%, p < 0.001). Conclusion Of the known factors impacting on KGR, low SMI and renal dysfunction correlate with KGR and predict resectability. A prospective study is needed to analyse if nutritional intervention improves resectability of patients with borderline resectable liver tumors. Publication History Article published online: 08 September 2020 © Georg Thieme Verlag KG Stuttgart · New York
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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 | 0.002 |
| 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) | 0.003 | 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".