Temporal validation of a PET-Radiomic model for distant-relapse-free-survival after radio-chemotherapy for pancreatic adenocarcinoma

Vincenzi, Monica Maria, Mori, Martina, Passoni, Paolo, Tummineri, Roberta, Slim, Najla, Midulla, Martina, Palazzo, Gabriele, Alfonso, Belardo, Spezi, Emiliano ORCID: https://orcid.org/0000-0002-1452-8813, Picchio, Maria, Reni, Michele, Chiti, Arturo, Vecchio, Antonella del, Fiorino, Claudio and Muzio, Nadia Gisella Di 2025. Temporal validation of a PET-Radiomic model for distant-relapse-free-survival after radio-chemotherapy for pancreatic adenocarcinoma. Cancers 17 (6) , 1036. 10.3390/cancers17061036

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Abstract

Background/Objectives: Pancreatic cancer is a very aggressive disease with a poor prognosis, even when diagnosed at an early stage. This study aimed to validate and refine a radiomic-based [18F]FDG-PET model to predict distant relapse-free survival (DRFS) in patients with unresectable locally advanced pancreatic cancer (LAPC). Methods: A Cox regression model incorporating two radiomic features (RFs) and cancer stage (III vs. IV) was temporally validated using a larger cohort (215 patients treated between 2005–2022). Patients received concurrent chemoradiotherapy with capecitabine and hypo-fractionated Intensity Modulated Radiotherapy (IMRT). Data were split into training (145 patients, 2005–2017) and validation (70 patients, 2017–2022) groups. Seventy-eight RFs were extracted, harmonized, and analyzed using machine learning to develop refined models. Results: The model incorporating Statistical-Percentile10, Morphological-ComShift, and stage demonstrated moderate predictive accuracy (training: C-index = 0.632; validation: C-index = 0.590). When simplified to include only Statistical-Percentile10, performance improved slightly in the validation group (C-index = 0.601). Adding GLSZM3D-grayLevelVariance to Statistical-Percentile10, while excluding Morphological-ComShift, further enhanced accuracy (training: C-index = 0.654; validation: C-index = 0.623). Despite these refinements, all versions showed similar moderate ability to stratify patients into risk classes. Conclusions: [18F]FDG-PET radiomic features are robust predictors of DRFS after chemoradiotherapy in LAPC. Despite moderate performance, these models hold promise for patient risk stratification. Further validation with external cohorts is ongoing.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Publisher:	MDPI
ISSN:	2072-6694
Date of First Compliant Deposit:	6 January 2025
Date of Acceptance:	18 December 2024
Last Modified:	01 Apr 2025 10:35
URI:	https://orca.cardiff.ac.uk/id/eprint/175007

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