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

Pablo Borrelli; John Ly; Reza Kaboteh; Johannes Ulén; Olof Enqvist; Elin Trägårdh; Lars Edenbrandt

doi:10.1186/s40658-021-00376-5

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

EJNMMI Phys. 2021 Mar 25;8(1):32. doi: 10.1186/s40658-021-00376-5.

Authors

Pablo Borrelli^#¹, John Ly^#^{2

3}, Reza Kaboteh¹, Johannes Ulén⁴, Olof Enqvist^{4

5}, Elin Trägårdh^{6

7}, Lars Edenbrandt^{1

8}

Affiliations

¹ Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
² Department of Radiology, Kristianstad Hospital, Kristianstad, Sweden. john.ly@med.lu.se.
³ Department of Translational Medicine and Wallenberg Center for Molecular Medicine, Lund University, Malmö, Sweden. john.ly@med.lu.se.
⁴ Eigenvision AB, Malmö, Sweden.
⁵ Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden.
⁶ Department of Translational Medicine and Wallenberg Center for Molecular Medicine, Lund University, Malmö, Sweden.
⁷ Department of Clinical Physiology and Nuclear Medicine, Skåne University Hospital, Malmö, Sweden.
⁸ Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.

^# Contributed equally.

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 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.

Keywords: AI; Automatic; FDG; Lung cancer; PET-CT; Segmentation; Total lesion glycolysis.