Automated RECOMIA AI-based total metabolic tumor volume in lymphoma – a retrospective study

BACKGROUND: Increasing evidence suggests that total metabolic tumor volume (tMTV) measured before treatment in lymphoma patients undergoing [18F]fluorodeoxyglucose (FDG) PET/CT scans can predict prognosis. However, there is a lack of fast, reliable, and easy-to-perform multilesional segmentation tools with an urgent need to improve tMTV segmentation workflow in clinical practice. Here, we develop an artificial intelligence (AI)-based tool that automatically calculates tMTV in untreated lymphoma patients undergoing FDG PET/CT. The RECOMIA AI-based tool is a 3D U-Net convolutional neural network trained on a cohort of 1,500 lymphoma patients, mean age 52 years (range 10–88), 44% were female. The model was optimized to segment metabolically active tumors in the FDG PET/CT scans, enabling automated tMTV measurements. The test group consisted of all untreated Hodgkin lymphoma (HL) patients and all Diffuse large B-cell lymphoma (DLBCL) patients who underwent FDG PET/CT at Sahlgrenska University Hospital between 2017–2018 and 2019–2022, respectively. There were 117 patients with mean age 50 years (range 7–90), 39% were female. Nine nuclear medicine physicians manually segmented lesions for tMTV calculations, with each patient independently segmented by two physicians. RESULTS: The median of the manual tMTV was 321 cm3 (interquartile range [IQR]: 92–689 cm3) and the median of the difference between two tMTV values segmented by different physicians for the same patient was 26 cm3 (IQR: 9–86 cm3). In 85 of the 117 patients, one of two manual tMTV measurements was closer to the AI tMTV value than the second manual tMTV measurement made by another physician. In 15 of the remaining 32 patients, the difference between the AI tMTV and the manual tMTV was small (< 26 cm3, the median difference between two manual tMTV values made on the same patient). CONCLUSION: The results of this study show that the RECOMIA AI-based tool achieved segmentation similarity within the inter-observer variability of experienced nuclear medicine physicians in 85% (100/117) of untreated lymphoma patients. This demonstrates the feasibility of using AI to support physicians in quantifying tMTV for assessment of prognosis in clinical practice.