Pre-operative radiomics model for prognostication in resectable pancreatic adenocarcinoma: multi-institutional development and external
Notice bibliographique
Résumé
Objectives: In patients with resectable pancreatic ductal adenocarcinoma (PDAC), there are few strictly pre-operative prognostic biomarkers available to guide therapy decisions. Radiomics has demonstrated potential prognostic value but it lacks external validation. We aimed to develop and externally validate a pre-operative clinical-radiomic prognostic model for PDAC. Methods: This was a retrospective international, multi-center study in patients with resectable PDAC who underwent pre-operative contrast-enhanced CT. Patients who received neoadjuvant therapy were excluded. The training cohort consisted of 352 patients who underwent CTs at five Toronto hospitals and subsequent resection at Toronto General Hospital, Toronto, Canada. The external test cohort consistent of 215 patients who underwent resection at a St Vincent’s University Hospital, Dublin, following pre-operative CTs performed at 34 Irish hospitals. Segmentation was performed using 3d Slicer v 4.11.2. Then 116 radiomic features were extracted using the PyRadiomics 3.0 library. Pre-operative Cox proportional hazard models incorporated (a) clinical factors (clinical), (b) clinical plus radiomics features (clinical-radiomic) and (c) a post-operative model incorporating pathological findings (TNM), which served as the reference standard. Outcomes were overall (OS) and disease-free survival (DFS). Model discrimination and calibration were assessed using concordance index (C-index), calibration plots and mean calibration error. A previously validated statistical tool for batch-effect correction (Combat) was used in an attempt to mitigate the impact of variation in CT scanner protocols between the multiple study sites. Results: In the validation cohort, the Radiomic signature was predictive of OS / DFS, with adjusted hazard ratios (HR) of 2.87 (95% CI: 1.40-5.87, p<0.001 / 5.28 (95% CI 2.35-11.86, p<0.001) respectively, along with age 1.02 (1.01-1.04, p=0.01) / 1.02 (1.00-1.04, p=0.03). No other clinical features were significantly associated with OS and DFS. Median OS was 22.9 versus 37 months (p=0.0092) and DFS 14.2 versus 29.8 Abstract 7 (p=0.0023) for the high versus low-risk groups in the external cohort. Calibration was moderate in the external cohort, with mean absolute error 7% and 13% for OS at 3 and 5 years respectively. The clinical-radiomic model demonstrated better discrimination for OS in the external cohort (C-index 0.545, 95%: 0.543-0.546) than the clinical model alone (0.497 95% CI 0.496-0.499, p<0.001) or the post-operative TNM model (0.525 95% CI 0.534-0.526, p<0.001). Implementation of Combat to mitigate the impact of multi-institutional variation in CT acquisition parameters did not improve discrimination results. In decision curve analysis, despite superior net benefit compared to clinical model, the clinical-radiomic model was not clinically useful for most threshold probabilities. TNM demonstrated the highest net benefit of the three models. Conclusion: A pre-operative model containing clinical variables and radiomics significantly improved prognostication of patients with resectable PDAC compared to using clinical information alone and it generalized to a large external dataset. Performance was similar to using pathological data (TNM), which are only available post-operatively. Despite superior performance compared to the clinical model, discrimination and clinical utility are suboptimal. This likely reflects inherent limitations of radiomics for PDAC prognostication, when deployed in real-world settings. Future work should focus upon standardization of CT acquisition protocols.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».