Evaluation of a convolution neural network for baseline total tumor metabolic volume on [18F]FDG PET in diffuse large B cell lymphoma

Objectives:New PET data-processing tools allow for automatic lesion selection and segmentation by a convolution neural network using artificial intelligence (AI) to obtain total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) routinely at the clinical workstation. Our objective was to evaluate an AI implemented in a new version of commercial software to verify reproducibility of results and time savings in a daily workflow. Methods:Using the software to obtain TMTV and TLG, two nuclear physicians applied five methods to retrospectively analyze data for 51 patients. Methods 1 and 2 were fully automated with exclusion of lesions <= 0.5 mL and <= 0.1 mL, respectively. Methods 3 and 4 were fully automated with physician review. Method 5 was semi-automated and used as reference. Time and number of clicks to complete the measurement were recorded for each method. Inter-instrument and inter-observer variation was assessed by the intra-class coefficient (ICC) and Bland-Altman plots. Results:Between methods 3 and 5, for the main user, the ICC was 0.99 for TMTV and 1.0 for TLG. Between the two users applying method 3, ICC was 0.97 for TMTV and 0.99 for TLG. Mean processing time (+/- standard deviation) was 20 s +/- 9.0 for method 1, 178 s +/- 125.7 for method 3, and 326 s +/- 188.6 for method 5 (p < 0.05). Conclusion:AI-enabled lesion detection software offers an automated, fast, reliable, and consistently performing tool for obtaining TMTV and TLG in a daily workflow.

Automated summary

New PET data-processing tools using artificial intelligence (AI) provide automated lesion selection and segmentation, allowing for the routine acquisition of total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) at the clinical workstation. This study evaluated the implementation of AI in commercial software, demonstrating its reproducibility, time savings, and reliable performance. The results showed that AI-enabled software offers an automated, fast, and consistently performing tool for obtaining TMTV and TLG.