coPERCIST: AI-assisted PET-CT response assessment.
Authors
Affiliations (8)
Affiliations (8)
- Department of Clinical Physiology and Nuclear Medicine, Skåne University Hospital, Inga Marie Nilssons G 49, 205 02, Malmö, Sweden. [email protected].
- Department of Translational Medicine and Wallenberg Center for Molecular Medicine, Lund University, Malmö, Sweden. [email protected].
- Eigenvision AB, Lund, Sweden.
- Chalmers University of Technology, Gothenburg, Sweden.
- Slicevault AB, Malmö, Sweden.
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
- Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark.
- Department of Molecular and Clinical Medicine, Gothenburg University, Gothenburg, Sweden.
Abstract
The PET Response Criteria in Solid Tumours (PERCIST) 1.0 provides a standardized framework for evaluating treatment response using [<sup>18</sup>F]fluorodeoxyglucose ([<sup>18</sup>F]FDG) positron emission tomography - computed tomography (PET-CT), but its clinical use is hindered by manual complexity. This study presents coPERCIST, an artificial intelligence (AI)-assisted module integrated into the RECOMIA platform that semi-automates and streamlines PERCIST analysis. coPERCIST performs organ segmentation and automates key steps of the PERCIST workflow, including background activity quantification, lesion detection, SULpeak calculation, and longitudinal lesion comparison. A novel image alignment method using organ-specific transformations and uncertainty estimation enables accurate lesion tracking over time. The system was evaluated in 58 oncological patients, each with two PET-CT scans. Up to three measurable lesions per patient were analysed. The AI-suggested liver and aorta volume of interest for threshold calculation were correct in all baseline and follow-up studies. Follow-up studies were classified as progressive metabolic disease (PMD) in 38 cases, stable metabolic disease (SMD) in 16, and partial metabolic response (PMR) in 4. Of 130 lesions evaluated, anatomical alignment was accurate in all cases, and pairwise SULpeak quantification was accurate in 95%. Pairwise SULpeak quantification failed in seven lesion pairs due to proximity to other lesions or misclassified physiological uptake. Review time was less than one minute for most cases. This study demonstrates the feasibility of AI-assisted PERCIST evaluation for [<sup>18</sup>F]FDG PET-CT, showing promising accuracy. coPERCIST offers potential for reproducible response assessment and supports future multicentre validation. It is freely available to researchers via the RECOMIA platform.