PET-CT imaging of neuroendocrine tumours - Beyond diagnostics
Gålne, Anni LU
(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.
(Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/e2ff3ce6-389a-47a2-9d67-3e5104323531
- author
- Gålne, Anni
LU
- supervisor
-
- Elin Trägårdh LU
- Anna Sundlöv LU
- Olof Enqvist LU
- Johan Wasselius LU
- opponent
-
- Professor Verburg, Frederik A, Erasmus Medical Center, Rotterdam, The Netherlands
- organization
- publishing date
- 2024
- 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 1, Centralblocket, Entrégatan 7, Skånes Universitetssjukhus i Lund
- 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
- 2024-09-09 13:34:26
@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}},
}