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

Gålne, Anni LU orcid ; Enqvist, Olof LU ; Sundlöv, Anna LU orcid ; Valind, Kristian LU orcid ; Minarik, David LU and Trägårdh, Elin LU (2023) In European journal of hybrid imaging 7(1).
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... (More)

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.

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author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
AI, Somatostatin receptor-expressing tumour volume, [68 Ga]Ga-DOTA- TATE, [ 68 Ga]Ga-DOTA-TOC, PET/CT
in
European journal of hybrid imaging
volume
7
issue
1
article number
14
pages
16 pages
publisher
Springer
external identifiers
  • scopus:85167517530
  • pmid:37544941
ISSN
2510-3636
DOI
10.1186/s41824-023-00172-7
language
English
LU publication?
yes
additional info
© 2023. The Author(s).
id
b24fc026-5f2d-4346-9bb4-bfeee6a280be
date added to LUP
2023-08-10 21:39:03
date last changed
2024-04-19 20:07:11
@article{b24fc026-5f2d-4346-9bb4-bfeee6a280be,
  abstract     = {{<p>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 [<br>
 68Ga]Ga-DOTA-TOC/TATE PET/CT images.<br>
 </p><p>METHODS: A UNet3D convolutional neural network (CNN) was used to train an AI model with [<br>
 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.<br>
 </p><p>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.</p><p>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.</p>}},
  author       = {{Gålne, Anni and Enqvist, Olof and Sundlöv, Anna and Valind, Kristian and Minarik, David and Trägårdh, Elin}},
  issn         = {{2510-3636}},
  keywords     = {{AI; Somatostatin receptor-expressing tumour volume; [68 Ga]Ga-DOTA- TATE; [ 68 Ga]Ga-DOTA-TOC; PET/CT}},
  language     = {{eng}},
  month        = {{08}},
  number       = {{1}},
  publisher    = {{Springer}},
  series       = {{European journal of hybrid imaging}},
  title        = {{AI-based quantification of whole-body tumour burden on somatostatin receptor PET/CT}},
  url          = {{http://dx.doi.org/10.1186/s41824-023-00172-7}},
  doi          = {{10.1186/s41824-023-00172-7}},
  volume       = {{7}},
  year         = {{2023}},
}