AI-based detection of lung lesions in [18F]FDG PET-CT from lung cancer patients
Artikel i vetenskaplig tidskrift, 2021

Background: [ 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 = 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.

AI

Total lesion glycolysis

FDG

PET-CT

Lung cancer

Automatic

Segmentation

Författare

Pablo Borrelli

Sahlgrenska universitetssjukhuset

John Ly

Centralsjukhuset Kristianstad

Lunds universitet

R. Kaboteh

Sahlgrenska universitetssjukhuset

Johannes Ulén

Eigenvision AB

Olof Enqvist

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Eigenvision AB

E. Tragardh

Lunds universitet

Skånes universitetssjukhus (SUS)

L. Edenbrandt

Göteborgs universitet

Sahlgrenska universitetssjukhuset

EJNMMI Physics

2197-7364 (eISSN)

Vol. 8 1 32

Ämneskategorier

Klinisk laboratoriemedicin

Radiologi och bildbehandling

Medicinsk bildbehandling

DOI

10.1186/s40658-021-00376-5

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Senast uppdaterat

2021-04-30