AI-based detection of lung lesions in [18F]FDG PET-CT from lung cancer patients
(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)
- author
- Borrelli, Pablo ; Ly, John LU ; Kaboteh, Reza ; Ulén, Johannes LU ; Enqvist, Olof ; Trägårdh, Elin LU and Edenbrandt, Lars LU
- organization
- publishing date
- 2021
- 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
-
- pmid:33768311
- scopus:85103357410
- 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
- 2024-09-07 17:22:17
@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}}, }