External validation of deep learning-derived 18F-FDG PET/CT delta biomarkers for loco-regional control in head and neck cancer.

Journal: Acta oncologica (Stockholm, Sweden)
Published Date: Aug 30, 2025

Abstract

BACKGROUND AND PURPOSE: Delta biomarkers that reflect changes in tumour burden over time can support personalised follow-up in head and neck cancer. However, their clinical use can be limited by the need for manual image segmentation. This study externally evaluates a deep learning model for automatic determination of volume change from serial 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) scans to stratify patients by loco-regional outcome. Patient/material and methods: An externally developed deep learning algorithm for tumour segmentation was applied to pre- and post-radiotherapy (RT, with or without concomitant chemoradiotherapy) PET/CT scans of 50 consecutive head and neck cancer patients from The Christie NHS Foundation Trust, UK. The model, originally trained on pre-treatment scans from a different institution, was deployed to derive tumour volumes at both time points. The AI-derived change in tumour volume (ΔPET-Gross tumour volume (GTV)) was calculated for each patient. Kaplan-Meier analysis assessed loco-regional control based on ΔPET-GTV, dichotomised at the cohort median. In a separate secondary analysis confined to the pre‑treatment scans, a radiation oncologist qualitatively evaluated the AI‑generated PET‑GTV contours.

Authors

  • David Gergely Kovacs
    Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark. dkov0001@regionh.dk.
  • Marianne Aznar
    Division of Cancer Sciences, University of Manchester, Manchester, the United Kingdom of Great Britain and Northern Ireland; The Christie NHS Foundation Trust, Manchester, the United Kingdom of Great Britain and Northern Ireland.
  • Marcel van Herk
    Manchester Cancer Research Centre, Division of Molecular and Clinical Cancer Science, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Sciences Centre, UK; The Christie NHS Foundation Trust, Manchester Academic Health Sciences Centre, UK.
  • Iskandar Mohamed
    The Christie NHS Foundation Trust, Manchester, the United Kingdom of Great Britain and Northern Ireland.
  • James Price
    The Christie NHS Foundation Trust, Manchester, the United Kingdom of Great Britain and Northern Ireland.
  • Claes Nøhr Ladefoged
    Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen, Denmark.
  • Barbara Malene Fischer
    Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
  • Flemming Littrup Andersen
    Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen, Denmark.
  • Andrew McPartlin
    Department of Radiation Oncology, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada.
  • Eliana M Vásquez Osorio
    Radiotherapy-Related Research Group, Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
  • Azadeh Abravan
    Division of Cancer Sciences, University of Manchester, Manchester, the United Kingdom of Great Britain and Northern Ireland; The Christie NHS Foundation Trust, Manchester, the United Kingdom of Great Britain and Northern Ireland.

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