Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

AI-based detection of lung lesions in [18F]FDG PET-CT from lung cancer patients

Borrelli, Pablo ; Ly, John LU ; Kaboteh, Reza ; Ulén, Johannes LU ; Enqvist, Olof ; Trägårdh, Elin LU and Edenbrandt, Lars LU (2021) In EJNMMI Physics 8(1).
Abstract

Background: [18F]-fluorodeoxyglucose (FDG) positron emission tomography with computed tomography (PET-CT) is a well-established modality in the work-up of patients with suspected or confirmed diagnosis of lung cancer. Recent research efforts have focused on extracting theragnostic and textural information from manually indicated lung lesions. Both semi-automatic and fully automatic use of artificial intelligence (AI) to localise and classify FDG-avid foci has been demonstrated. To fully harness AI’s usefulness, we have developed a method which both automatically detects abnormal lung lesions and calculates the total lesion glycolysis (TLG) on FDG PET-CT. Methods: One hundred twelve patients (59 females and 53 males) who... (More)

Background: [18F]-fluorodeoxyglucose (FDG) positron emission tomography with computed tomography (PET-CT) is a well-established modality in the work-up of patients with suspected or confirmed diagnosis of lung cancer. Recent research efforts have focused on extracting theragnostic and textural information from manually indicated lung lesions. Both semi-automatic and fully automatic use of artificial intelligence (AI) to localise and classify FDG-avid foci has been demonstrated. To fully harness AI’s usefulness, we have developed a method which both automatically detects abnormal lung lesions and calculates the total lesion glycolysis (TLG) on FDG PET-CT. Methods: One hundred twelve patients (59 females and 53 males) who underwent FDG PET-CT due to suspected or for the management of known lung cancer were studied retrospectively. These patients were divided into a training group (59%; n = 66), a validation group (20.5%; n = 23) and a test group (20.5%; n = 23). A nuclear medicine physician manually segmented abnormal lung lesions with increased FDG-uptake in all PET-CT studies. The AI-based method was trained to segment the lesions based on the manual segmentations. TLG was then calculated from manual and AI-based measurements, respectively and analysed with Bland-Altman plots. Results: The AI-tool’s performance in detecting lesions had a sensitivity of 90%. One small lesion was missed in two patients, respectively, where both had a larger lesion which was correctly detected. The positive and negative predictive values were 88% and 100%, respectively. The correlation between manual and AI TLG measurements was strong (R2 = 0.74). Bias was 42 g and 95% limits of agreement ranged from − 736 to 819 g. Agreement was particularly high in smaller lesions. Conclusions: The AI-based method is suitable for the detection of lung lesions and automatic calculation of TLG in small- to medium-sized tumours. In a clinical setting, it will have an added value due to its capability to sort out negative examinations resulting in prioritised and focused care on patients with potentially malignant lesions.

(Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
AI, Automatic, FDG, Lung cancer, PET-CT, Segmentation, Total lesion glycolysis
in
EJNMMI Physics
volume
8
issue
1
article number
32
publisher
Springer
external identifiers
  • scopus:85103357410
  • pmid:33768311
ISSN
2197-7364
DOI
10.1186/s40658-021-00376-5
language
English
LU publication?
yes
id
64bca0a6-ae9f-46dc-81b5-2b650deb861d
date added to LUP
2021-04-06 13:44:43
date last changed
2024-06-15 09:15:41
@article{64bca0a6-ae9f-46dc-81b5-2b650deb861d,
  abstract     = {{<p>Background: [<sup>18</sup>F]-fluorodeoxyglucose (FDG) positron emission tomography with computed tomography (PET-CT) is a well-established modality in the work-up of patients with suspected or confirmed diagnosis of lung cancer. Recent research efforts have focused on extracting theragnostic and textural information from manually indicated lung lesions. Both semi-automatic and fully automatic use of artificial intelligence (AI) to localise and classify FDG-avid foci has been demonstrated. To fully harness AI’s usefulness, we have developed a method which both automatically detects abnormal lung lesions and calculates the total lesion glycolysis (TLG) on FDG PET-CT. Methods: One hundred twelve patients (59 females and 53 males) who underwent FDG PET-CT due to suspected or for the management of known lung cancer were studied retrospectively. These patients were divided into a training group (59%; n = 66), a validation group (20.5%; n = 23) and a test group (20.5%; n = 23). A nuclear medicine physician manually segmented abnormal lung lesions with increased FDG-uptake in all PET-CT studies. The AI-based method was trained to segment the lesions based on the manual segmentations. TLG was then calculated from manual and AI-based measurements, respectively and analysed with Bland-Altman plots. Results: The AI-tool’s performance in detecting lesions had a sensitivity of 90%. One small lesion was missed in two patients, respectively, where both had a larger lesion which was correctly detected. The positive and negative predictive values were 88% and 100%, respectively. The correlation between manual and AI TLG measurements was strong (R<sup>2</sup> = 0.74). Bias was 42 g and 95% limits of agreement ranged from − 736 to 819 g. Agreement was particularly high in smaller lesions. Conclusions: The AI-based method is suitable for the detection of lung lesions and automatic calculation of TLG in small- to medium-sized tumours. In a clinical setting, it will have an added value due to its capability to sort out negative examinations resulting in prioritised and focused care on patients with potentially malignant lesions.</p>}},
  author       = {{Borrelli, Pablo and Ly, John and Kaboteh, Reza and Ulén, Johannes and Enqvist, Olof and Trägårdh, Elin and Edenbrandt, Lars}},
  issn         = {{2197-7364}},
  keywords     = {{AI; Automatic; FDG; Lung cancer; PET-CT; Segmentation; Total lesion glycolysis}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{Springer}},
  series       = {{EJNMMI Physics}},
  title        = {{AI-based detection of lung lesions in [<sup>18</sup>F]FDG PET-CT from lung cancer patients}},
  url          = {{http://dx.doi.org/10.1186/s40658-021-00376-5}},
  doi          = {{10.1186/s40658-021-00376-5}},
  volume       = {{8}},
  year         = {{2021}},
}