ArticleEuropean journal of hybrid imaging2023
AI-based quantification of whole-body tumour burden on somatostatin receptor PET/CT.
Article in European journal of hybrid imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- PET/CT contouring techniques in neuroendocrine neoplasms: a state-of-the-art scoping review.EJNMMI reports · 2026Review
- coPERCIST: AI-assisted PET-CT response assessment.European journal of nuclear medicine and molecular imaging · 2026Article
- Future of radiotheranostics.BMJ oncology · 2026Review
- Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review.Cancers · 2024Review
- Retrospective evaluation of the predictive value of tumour burden at baseline [EJNMMI reports · 2024Article
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Authors and funding
6 authors.
Funding
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Abstract
backgroundSegmenting the whole-body somatostatin receptor-expressing tumour volume (SRETVwb) on positron emission tomography/computed tomography (PET/CT) images is highly time-consuming but has shown value as an independent prognostic factor for survival. An automatic method to measure SRETVwb could improve disease status assessment and provide a tool for prognostication. This study aimed to develop an artificial intelligence (AI)-based method to detect and quantify SRETVwb and total lesion somatostatin receptor expression (TLSREwb) from [
methodsA UNet3D convolutional neural network (CNN) was used to train an AI model with [
resultsThere were good correlations between the segmented SRETVwb and TLSREwb by the AI model and the physicians, with Spearman rank correlation coefficients of r = 0.78 and r = 0.73, respectively, for SRETVwb and r = 0.83 and r = 0.81, respectively, for TLSREwb. The sensitivity on a lesion detection level was 80% and 79%, and the positive predictive value was 83% and 84% when comparing the AI model with the two physicians.
conclusionIt was possible to develop an AI model to segment SRETVwb and TLSREwb with high performance. A fully automated method makes quantification of tumour burden achievable and has the potential to be more widely used when assessing PET/CT images.
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