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Poster Display session

348P - Early-onset pancreatic cancer (EOPC) patients (pts) treated at an Italian third level referral center: A real-word artificial-intelligence (AI) analysis from the Gemelli Generator experience

Date

27 Jun 2024

Session

Poster Display session

Presenters

Linda DiFrancesco

Citation

Annals of Oncology (2024) 35 (suppl_1): S119-S161. 10.1016/annonc/annonc1481

Authors

L. DiFrancesco1, F. Minnozzi2, M. Bensi1, G. Caira3, A. Cosmai1, D. Barone1, A. Ceccarelli1, S. Perazzo1, L. Chiofalo1, A. Spring1, M. Chiaravalli1, G. Quero4, S. Alfieri4, C. Bagalà4, G. Tortora4, L. Salvatore4, J. Lenkowicz4, C. Iacomini2, N. Digiorgi3

Author affiliations

  • 1 Fondazione Policlinico Universitario Agostino Gemelli–IRCCS, Università Cattolica del Sacro Cuore, Rome/IT
  • 2 Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome/IT
  • 3 Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy, Rome/IT
  • 4 Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome/IT

Resources

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Abstract 348P

Background

Although PC is mainly diagnosed in pts older than 60 years, its incidence in younger pts is raising. Few data are available for EOPC (pts ≤ 50 years). We aim to evaluate clinicopathological characteristics and outcome of EOPC pts.

Methods

Real-world data were collected and analyzed by an AI tool of Gemelli GENERATOR facility within the Gemelli Science and Technology Park (G-STeP). Study population was identified from records of pts treated at our Institution from Jan-2018 to Jun-2023 matching 3 inclusion criteria: pts hospitalized with a diagnosis of PC (International Classification of Disease 9, ICD-9 codes), pts with at least one pathology report or one hospital discharge letter including PC evidence (selected using clinically validated text mining techniques from unstructured data source). Epidemiological, clinical and anatomopathological variables were extracted by SAS (SAS(R) Institute suite for ETL); statistical analyses were conducted by R software.

Results

A total of 915 pts were included; of those 83 (9%) were EOPC pts. Median age was 44 (25-50), 12/83 (14.5%) pts were ≤ 39 years, 55% were male, ECOG PS was 0-1 in 90%, median BMI was 21.3 (95% CI 20.3-22.4). 53% of pts had 0 comorbidities and 8.5% had two or more; the most frequent was diabetes (10%). Regarding tumor characteristics, 95% were adenocarcinomas, 58% were localized in pancreatic head, 22% of pts received surgery for resectable disease (RD), 33% had a locally advanced tumor (LA) and 45% had a de novo metastatic disease (MD). 72% of pts with RD received a (neo)adjuvant treatment (tx); 87% of pts with LA or MD received a first-line tx and 45% a second-line. Median OS was 35.7 months (95% CI 28-2-Not reached]) for RD pts, 18.1 (95% CI 15.8-28.3) for LA pts and 10.8 (95% CI 8.1-18.1) for MD pts. BMI, ECOG PS, number of comorbidities and sex were not associated with OS in any of these settings.

Conclusions

Our experience demonstrated the importance of an AI-based tool to evaluate EOPC pts treated at our Institution. Our data are in line with previous published results and confirmed the importance of this subgroup of pts. Molecular and DNA damage repair mutations are ongoing and will be presented at the congress.

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

Disclosure

All authors have declared no conflicts of interest.

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