AI-based quantification of whole-body tumour burden on somatostatin receptor PET/CT

Abstract Background Segmenting 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 [68Ga]Ga-DOTA-TOC/TATE PET/CT images. Methods A UNet3D convolutional neural network (CNN) was used to train an AI model with [68Ga]Ga-DOTA-TOC/TATE PET/CT images, where all tumours were manually segmented with a semi-automatic method. The training set consisted of 148 patients, of which 108 had PET-positive tumours. The test group consisted of 30 patients, of which 25 had PET-positive tumours. Two physicians segmented tumours in the test group for comparison with the AI model. Results There 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. Conclusion It 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

Relationship between somatostatin receptor expressing tumour volume and health-related quality of life in patients with metastatic GEP-NET

For patients with gastroenteropancreatic neuroendocrine tumours (GEP-NET), health-related quality of life (HRQoL) is important. Meanwhile, whether tumour volume is associated with HRQoL is unknown. Hence, the aim of this study was to assess if total somatostatin receptor expressing tumour volume is correlated with HRQoL in patients with metastatic GEP-NET. Some 71 patients were included in the study. HRQoL and NET-specific symptoms were assessed with EORTC QLQ-C30 and EORTC GI.NET21. A summary score was calculated from the output of the QLQ-C30. Total somatostatin receptor expressing tumour volume was retrospectively evaluated on somatostatin receptor imaging with positron emission tomography-computed tomography (68Ga-DOTA-TATE/TOC PET-CT) in each patient. Simple and multiple linear regression were used to evaluate the correlation between tumour volume and HRQoL, controlling for potential confounders. No correlation was found between total somatostatin receptor expressing tumour volume and QLQ-C30 summary score. Weak positive correlations were found between total tumour volume and the specific symptoms dyspnoea, diarrhoea and flushing. To the best of our knowledge, this is the first study to evaluate the association between total somatostatin expressing tumour volume and HRQoL. Our results indicate that, while tumour volume is weakly associated with symptom severity of the carcinoid syndrome, other factors might impact more on overall HRQoL