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PET-CT imaging of neuroendocrine tumours - Beyond diagnostics

Gålne, Anni LU orcid (2024) In Lund University, Faculty of Medicine Doctoral Dissertation Series
Abstract
Background: Neuroendocrine tumours (NETs) typically overexpress somatostatin receptors. By radiolabelling somatostatin analogues with a positron-emitting radionuclide (68Ga-DOTATOC or 68Ga-DOTATATE), these tumours can be detected with high sensitivity and specificity using somatostatin receptor positron emission tomography-computed tomography (PET-CT).
Aim: To enhance knowledge about NETs imaged with PET-CT, beyond the diagnostic work-up.
Methods: Paper I, based on a clinical trial (Gapetto), evaluated whether treatment with long-acting somatostatin analogues affected the uptake of 68Ga-DOTATATE in the normal liver or tumours. Changes in uptake, measured as SUVmax, were assessed in patients before and after... (More)
Background: Neuroendocrine tumours (NETs) typically overexpress somatostatin receptors. By radiolabelling somatostatin analogues with a positron-emitting radionuclide (68Ga-DOTATOC or 68Ga-DOTATATE), these tumours can be detected with high sensitivity and specificity using somatostatin receptor positron emission tomography-computed tomography (PET-CT).
Aim: To enhance knowledge about NETs imaged with PET-CT, beyond the diagnostic work-up.
Methods: Paper I, based on a clinical trial (Gapetto), evaluated whether treatment with long-acting somatostatin analogues affected the uptake of 68Ga-DOTATATE in the normal liver or tumours. Changes in uptake, measured as SUVmax, were assessed in patients before and after treatment initiation. Paper II, based on a cohort study, explored whether the total somatostatin receptor-expressing tumour volume measured by PET-CT imaging, correlated with health-related quality of life or specific NET symptoms in patients with metastatic gastroenteropancreatic NET (GEP-NET). Paper III was a developmental study aimed at constructing an Artificial intelligence (AI) model to automatically detect and quantify somatostatin receptor-expressing tumour volume using a UNet3D convolutional neural network. The AI model’s tumour segmentation was compared with that of two reference physicians. Paper IV, a retrospective study, assessed whether the total somatostatin receptor-expressing tumour volume at baseline PET-CT could predict treatment outcomes in GEP-NET patients post 177Lu-DOTATATE treatment. Tumour volumes were quantified from baseline and follow-up PET-CT images for these patients.
Results: Paper I revealed that treatment with long-acting somatostatin analogues significantly reduced the uptake of 68Ga-DOTATATE in normal liver tissue, while the tumour uptake remained unchanged. Paper II did not find a correlation between total tumour volume and health-related quality of life, although a weak positive correlation was observed between specific NET-associated symptoms (such as dyspnoea, diarrhoea, and flushing) and larger tumour volume. Paper III demonstrated that an AI model for tumour segmentation could be developed, displaying a strong correlation with the physicians’ reference segmentation. Paper IV indicated that baseline tumour volume did not predict treatment outcomes, but an increase in tumour volume at the first follow-up predicted worse outcomes.
Conclusions: Evaluating NETs with somatostatin receptor PET-CT is feasible after initiating treatment with long-acting somatostatin analogues. Factors other than tumour volume likely have a greater impact on the health-related quality of life in patients with metastasised GEP-NET. AI models can be developed to segment tumour volume from somatostatin receptor PET-CT. Baseline tumour volume was not a predictive factor for outcomes following treatment with 177Lu-DOTATATE.
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author
supervisor
opponent
  • Professor Verburg, Frederik A, Erasmus Medical Center, Rotterdam, The Netherlands
organization
publishing date
type
Thesis
publication status
published
subject
keywords
PET-CT, Neuroendocrine tumor (NET), Quantification, Treatment, AI, Quality of life
in
Lund University, Faculty of Medicine Doctoral Dissertation Series
issue
2024:111
pages
130 pages
publisher
Lund University, Faculty of Medicine
defense location
Föreläsningssal 2, Centralblocket, Entrégatan 7, Skånes Universitetssjukhus i Lund. Join by Zoom: https://lu-se.zoom.us/j/66998337668?pwd=5hJxg6osDCGrwabihu19P6ltLyLpsH.1
defense date
2024-10-10 09:00:00
ISSN
1652-8220
ISBN
978-91-8021-607-4
language
English
LU publication?
yes
id
e2ff3ce6-389a-47a2-9d67-3e5104323531
date added to LUP
2024-09-03 14:53:38
date last changed
2024-10-09 13:25:49
@phdthesis{e2ff3ce6-389a-47a2-9d67-3e5104323531,
  abstract     = {{<b>Background</b>: Neuroendocrine tumours (NETs) typically overexpress somatostatin receptors. By radiolabelling somatostatin analogues with a positron-emitting radionuclide (68Ga-DOTATOC or 68Ga-DOTATATE), these tumours can be detected with high sensitivity and specificity using somatostatin receptor positron emission tomography-computed tomography (PET-CT).<br/><b>Aim</b>: To enhance knowledge about NETs imaged with PET-CT, beyond the diagnostic work-up.<br/><b>Methods</b>: Paper I, based on a clinical trial (Gapetto), evaluated whether treatment with long-acting somatostatin analogues affected the uptake of 68Ga-DOTATATE in the normal liver or tumours. Changes in uptake, measured as SUVmax, were assessed in patients before and after treatment initiation. Paper II, based on a cohort study, explored whether the total somatostatin receptor-expressing tumour volume measured by PET-CT imaging, correlated with health-related quality of life or specific NET symptoms in patients with metastatic gastroenteropancreatic NET (GEP-NET). Paper III was a developmental study aimed at constructing an Artificial intelligence (AI) model to automatically detect and quantify somatostatin receptor-expressing tumour volume using a UNet3D convolutional neural network. The AI model’s tumour segmentation was compared with that of two reference physicians. Paper IV, a retrospective study, assessed whether the total somatostatin receptor-expressing tumour volume at baseline PET-CT could predict treatment outcomes in GEP-NET patients post 177Lu-DOTATATE treatment. Tumour volumes were quantified from baseline and follow-up PET-CT images for these patients.<br/><b>Results</b>: Paper I revealed that treatment with long-acting somatostatin analogues significantly reduced the uptake of 68Ga-DOTATATE in normal liver tissue, while the tumour uptake remained unchanged. Paper II did not find a correlation between total tumour volume and health-related quality of life, although a weak positive correlation was observed between specific NET-associated symptoms (such as dyspnoea, diarrhoea, and flushing) and larger tumour volume. Paper III demonstrated that an AI model for tumour segmentation could be developed, displaying a strong correlation with the physicians’ reference segmentation. Paper IV indicated that baseline tumour volume did not predict treatment outcomes, but an increase in tumour volume at the first follow-up predicted worse outcomes.<br/><b>Conclusions</b>: Evaluating NETs with somatostatin receptor PET-CT is feasible after initiating treatment with long-acting somatostatin analogues. Factors other than tumour volume likely have a greater impact on the health-related quality of life in patients with metastasised GEP-NET. AI models can be developed to segment tumour volume from somatostatin receptor PET-CT. Baseline tumour volume was not a predictive factor for outcomes following treatment with 177Lu-DOTATATE.<br/>}},
  author       = {{Gålne, Anni}},
  isbn         = {{978-91-8021-607-4}},
  issn         = {{1652-8220}},
  keywords     = {{PET-CT; Neuroendocrine tumor (NET); Quantification; Treatment; AI; Quality of life}},
  language     = {{eng}},
  number       = {{2024:111}},
  publisher    = {{Lund University, Faculty of Medicine}},
  school       = {{Lund University}},
  series       = {{Lund University, Faculty of Medicine Doctoral Dissertation Series}},
  title        = {{PET-CT imaging of neuroendocrine tumours - Beyond diagnostics}},
  url          = {{https://lup.lub.lu.se/search/files/194450899/Thesis_PET-CT_imaging_of_neuroendocrine_tumours_e-pub.pdf}},
  year         = {{2024}},
}