Malignant transformation from IPMN to invasive IPMN and PDAC is characterized by distinct shifts in body composition – an AI-based body composition analysis
Bibliographic record
Abstract
Introduction: Intraductal papillary mucinous neoplasms (IPMN) are cystic lesions of the pancreas that may undergo malignant transformation. A comprehensive characterization of body composition has not been performed in patients with non-invasive IPMN as compared to invasive IPMN or pancreatic ductal adenocarcinoma (PDAC) yet. Patients and Methods: Patients with IPMN, invasive IPMN and PDAC were identified from our prospectively maintained institutional database. Analyzing patients’ routine CT scans at the time of diagnosis, body compartments were automatically segmented with a validated AI-based body composition algorithm (BCA), and body composition parameters including adipose tissue compartments, muscle and bone were quantified. Body composition measures were compared between patients with IPMN, invasive IPMN and PDAC. Results: A total of 181 patients were identified, 53 (29.3%) had IPMN, 16 (8.8%) had invasive IPMN, and 112 (61.9%) had PDAC. Median age was 68 (range 39-87) and 51.9% (n=94) of the patients were female. Mean BMI in all patients was 25.3 kg/m2, BMI values were comparable for patients with IPMN (24.6 kg/m2), invasive IPMN (24.7 kg/m2), and PDAC (25.5kg/m2). Comparing patients with IPMN and invasive IPMN, there was a trend for more pronounced visceral obesity (mean 0.31 vs. 0.33, p=0.055). In contrast, subcutaneous obesity was more common among IPMN patients (0.59 vs. 0.58, p=0.045). There was no difference regarding sarcopenia measures between the two groups. Patients with PDAC as opposed to IPMN had considerably higher rates of visceral obesity (0.68 vs. 0.59, p=0.015). In contrast, IPMN patients displayed higher rates of subcutaneous obesity than PDAC (0.58 vs. 0.56, p=0.013). There was a trend for more pronounced sarcopenia in PDAC patients as compared to IPMN (1.63 vs. 1.71, p=0.083). Comparing body composition parameters between PDAC and invasive IPMN, no statistically significant differences were detected for the adipose tissue, muscle and bone compartments. Conclusion: We performed the first analysis systematically by comparing objectively derived body composition measures between IPMN, invasive IPMN, and PDAC. IPMN as compared to malignant lesions was characterized by distinct body composition profiles. Invasive IPMN and even more so PDAC was associated with cancer body composition markers visceral obesity and sarcopenia. Body composition parameters may therefore be an important tool for early detection of malignancy in IPMN. Publication History Article published online: 04 September 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".