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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