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Related Experiment Video

Updated: May 28, 2026

Multianimal Magnetic Resonance Imaging for Tumor Measurements in Pancreatic Cancer Mouse Models
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Multianimal Magnetic Resonance Imaging for Tumor Measurements in Pancreatic Cancer Mouse Models

Published on: February 3, 2026

Exploratory PET/CT Radiomics for Predicting Early Progression in Locally Advanced Pancreatic Cancer.

Michele Fiore1,2, Ermanno Cordelli3,4, Gian Marco Petrianni2

  • 1Research Unit of Radiation Oncology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy.

Diagnostics (Basel, Switzerland)
|May 27, 2026
PubMed

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Summary
This summary is machine-generated.

Predicting early progression in locally advanced pancreatic cancer (LAPC) is challenging. A new model using 18F-FDG PET/CT radiomics and clinical data accurately identifies high-risk patients before treatment.

Area of Science:

  • Oncology
  • Radiology
  • Medical Imaging

Background:

  • Early progression (EP) is a significant challenge in locally advanced pancreatic cancer (LAPC).
  • Predicting EP is crucial for effective treatment planning and improving patient outcomes.
  • Current prediction methods for EP in LAPC are insufficient.

Purpose of the Study:

  • To develop and validate a multiparametric predictive model for early progression in LAPC.
  • To integrate radiomic features from 18F-FDG PET/CT with clinical data.
  • To enhance risk stratification for personalized treatment decisions in LAPC.

Main Methods:

  • Extraction of 242 radiomic features (first-order, GLCM, LBP-TOP) from CT and PET scans.
  • Integration of radiomic features with PET-derived metrics and clinical variables.
Keywords:
PET/CTpancreatic cancerradiomics

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Last Updated: May 28, 2026

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  • Development of a two-level decision tree classifier with cross-validation and feature selection.

    • Main Results:

    • The multiparametric model achieved 80.7% accuracy and an AUC of 0.83.
    • Integrated CT and PET texture analysis identified patients at high risk of EP.
    • The model demonstrated effective risk stratification prior to treatment initiation.

    Conclusions:

    • 18F-FDG PET/CT radiomic biomarkers combined with clinical data can non-invasively assess tumor heterogeneity.
    • This approach improves risk stratification for LAPC patients.
    • The findings support personalized therapeutic decision-making for LAPC.