Preoperative CT-derived body composition as predictor of postoperative pancreatic fistula risk after whipple surgery
Bibliographic record
Abstract
Pancreaticoduodenectomy (PD) is a primary treatment for pancreatic cancer but carries a high risk of postoperative pancreatic fistula (POPF). Accurate prediction of POPF is key for improving patient outcomes and guiding surgical decisions. This study analyzed body composition (tissues within the lumbar range) and clinical factors for predicting POPF, using a cohort of 777 patients. Volumes (V) and Hounsfield Unit (HU) values were extracted from Computed Tomography (CT) scans using the Data Analysis Facilitation Suite (DAFS) CT segmentation software, and logistic regression was applied to analyze high- and low-risk groups (Fistula ±). For males, Age at Surgery (p = 0.021), while for females, the Charlson Comorbidity Index (CCI) (p = 0.018) and Malignancy (p = 0.037) were significantly associated with Fistula ±. In males, Liver HU (p = 0.037), Gallbladder HU (p = 0.037), and Heart HU (p = 0.040), while in females, Lung V (p = 0.044), Lung HU (p = 0.002), Trachea V (p = 0.029), Trachea HU (p = 0.017), Heart V (p = 0.027), and Aorta V (p = 0.001), and for both males and females respectively, Pancreas V (p = 0.006 and 0.047) exhibited significant differences with Fistula ±. For tissue features, in males, SKM V (p = 0.029), while in females, SKM HU (p = 0.006), FAT HU (p = 0.006), VAT HU (p = 0.000), SAT V (p = 0.032), and SAT HU (p = 0.012), and in both genders, FAT V (p = 0.012 and 0.021), VAT V (p = 0.002 and 0.044), and the VAT/SKM V ratio (p = 0.006 and 0.032) were significantly associated with Fistula ±. For prediction, a sub-cohort of 140 scans was used, with 11 key features selected. A weighted stratified five-fold cross-validation approach was applied, with SHapley Additive exPlanations (SHAP) ranking Age at Surgery, Pancreas HU, Liver HU, Aorta V, FAT HU, and SKM HU as top predictors. The model achieved a mean Area Under the Curve (AUC) of 0.85 (training) and 0.80 (testing), with a mean Balanced Accuracy of 0.80 and 0.78, respectively. These results highlight preoperative body composition as a non-invasive, effective tool for predicting POPF risk and optimizing surgical planning, while underscoring the importance of personalized, gender-specific risk assessments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".