Automated quantification of reference levels in liver and mediastinal blood pool for the Deauville therapy response classification using FDG-PET/CT in Hodgkin and non-Hodgkin lymphomas

Research output: Contribution to journalArticle


Background: 18F-FDG-PET/CT has become a standard for assessing treatment response in patients with lymphoma. A subjective interpretation of the scan based on the Deauville 5-point scale has been widely adopted. However, inter-observer variability due to the subjectivity of the interpretation is a limitation. Our main goal is to develop an objective and automated method for evaluating response. The first step is to develop and validate an artificial intelligence (AI)-based method, for the automated quantification of reference levels in the liver and mediastinal blood pool in patients with lymphoma. Methods: The AI-based method was trained to segment the liver and the mediastinal blood pool in CT images from 80 lymphoma patients, who had undergone 18F-FDG-PET/CT, and apply this to a validation group of six lymphoma patients. CT segmentations were transferred to the PET images to obtain automatic standardized uptake values (SUV). The AI-based analysis was compared to corresponding manual segmentations performed by two radiologists. Results: The mean difference for the comparison between the AI-based liver SUV quantifications and those of the two radiologists in the validation group was 0·02 and 0·02, respectively, and 0·02 and 0·02 for mediastinal blood pool respectively. Conclusions: An AI-based method for the automated quantification of reference levels in the liver and mediastinal blood pool shows good agreement with results obtained by experienced radiologists who had manually segmented the CT images. This is a first, promising step towards objective treatment response evaluation in patients with lymphoma based on 18F-FDG-PET/CT.


  • May Sadik
  • Erica Lind
  • Eirini Polymeri
  • Olof Enqvist
  • Johannes Ulén
  • Elin Trägårdh
External organisations
  • Sahlgrenska University Hospital
  • Chalmers University of Technology
  • Eigenvision AB
  • Skåne University Hospital
  • University of Gothenburg
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Radiology, Nuclear Medicine and Medical Imaging
  • Cancer and Oncology


  • artificial intelligence, convolutional neural network, objective, segmentation
Original languageEnglish
Pages (from-to)78-84
JournalClinical Physiology and Functional Imaging
Issue number1
Early online date2018 Oct 3
Publication statusPublished - 2019 Jan
Publication categoryResearch