Malignant transformation from IPMN to invasive IPMN and PDAC is characterized by distinct shifts in body composition – an AI-based body composition analysis
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».