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

• Published in: EJNMMI physics 2021-03, Vol.8 (1), p.32-32, Article 32
• Main Authors: Borrelli, Pablo, Ly, John, Kaboteh, Reza, Ulén, Johannes, Enqvist, Olof, Trägårdh, Elin, Edenbrandt, Lars
• Format: Article
• Language: English
• Subjects: Applied and Technical Physics; Artificial intelligence; Automatic; Clinical Medicine; Computational Mathematics and Numerical Analysis; Computed tomography; Engineering; FDG; Fluorine isotopes; Glycolysis; Imaging; Klinisk medicin; Lesions; Lung cancer; Mathematical analysis; Medical and Health Sciences; Medicin och hälsovetenskap; Medicine; Medicine & Public Health; Nuclear Medicine; Original Research; PET-CT; Positron emission; Radiologi och bildbehandling; Radiology; Radiology, Nuclear Medicine and Medical Imaging; Segmentation; Tomography; Total lesion glycolysis
• ISSN: 2197-7364; 2197-7364
• DOI: 10.1186/s40658-021-00376-5

Background [ 18 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 2 = 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.