Clinical Evaluation of Deep Learning for Tumor Delineation on 18F-FDG PET/CT of Head and Neck Cancer

• Published in: The Journal of nuclear medicine (1978) Vol. 65; no. 4; p. 623
• Main Authors: Kovacs, David G, Ladefoged, Claes N, Andersen, Kim F, Brittain, Jane M, Christensen, Charlotte B, Dejanovic, Danijela, Hansen, Naja L, Loft, Annika, Petersen, Jørgen H, Reichkendler, Michala, Andersen, Flemming L, Fischer, Barbara M
• Format: Journal Article
• Language: English
• Published: New York Society of Nuclear Medicine 01.04.2024
• Subjects: Artificial intelligence; Automation; Biomarkers; Cancer; Computed tomography; Deep learning; Delineation; Fluorine isotopes; Head & neck cancer; Machine learning; Positron emission; Positron emission tomography; Radiation therapy; State-of-the-art reviews; Teaching methods; Tumors; Variability; Variance analysis
• ISSN: 0161-5505; 1535-5667
• DOI: 10.2967/jnumed.123.266574

Artificial intelligence (AI) may decrease 18F-FDG PET/CT–based gross tumor volume (GTV) delineation variability and automate tumor-volume–derived image biomarker extraction. Hence, we aimed to identify and evaluate promising state-of-the-art deep learning methods for head and neck cancer (HNC) PET GTV delineation. Methods: We trained and evaluated deep learning methods using retrospectively included scans of HNC patients referred for radiotherapy between January 2014 and December 2019 (ISRCTN16907234). We used 3 test datasets: an internal set to compare methods, another internal set to compare AI-to-expert variability and expert interobserver variability (IOV), and an external set to compare internal and external AI-to-expert variability. Expert PET GTVs were used as the reference standard. Our benchmark IOV was measured using the PET GTV of 6 experts. The primary outcome was the Dice similarity coefficient (DSC). ANOVA was used to compare methods, a paired t test was used to compare AI-to-expert variability and expert IOV, an unpaired t test was used to compare internal and external AI-to-expert variability, and post hoc Bland–Altman analysis was used to evaluate biomarker agreement. Results: In total, 1,220 18F-FDG PET/CT scans of 1,190 patients (mean age ± SD, 63 ± 10 y; 858 men) were included, and 5 deep learning methods were trained using 5-fold cross-validation (n = 805). The nnU-Net method achieved the highest similarity (DSC, 0.80 [95% CI, 0.77–0.86]; n = 196). We found no evidence of a difference between expert IOV and AI-to-expert variability (DSC, 0.78 for AI vs. 0.82 for experts; mean difference of 0.04 [95% CI, −0.01 to 0.09]; P = 0.12; n = 64). We found no evidence of a difference between the internal and external AI-to-expert variability (DSC, 0.80 internally vs. 0.81 externally; mean difference of 0.004 [95% CI, −0.05 to 0.04]; P = 0.87; n = 125). PET GTV–derived biomarkers of AI were in good agreement with experts. Conclusion: Deep learning can be used to automate 18F-FDG PET/CT tumor-volume–derived imaging biomarkers, and the deep-learning–based volumes have the potential to assist clinical tumor volume delineation in radiation oncology.