Evaluation of a convolution neural network for baseline total tumor metabolic volume on [F]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.

Impact of 18F-FDG PET/MR based tumor delineation in radiotherapy planning for cholangiocarcinoma.

Purpose: Radiation therapy (RT) is an effective treatment for unresectable cholangiocarcinoma (CC). Accurate tumor volume delineation is critical in achieving high rates of local control while minimizing treatment-related toxicity. This study compares F-FDG PET/MR to MR and CT for target volume delineation for RT planning.

Prognostic value of the metabolic bulk volume in patients with diffuse large B-cell lymphoma on baseline F-FDG PET-CT.

We retrospectively investigated the prognostic value of the metabolic bulk volume (MBV), defined as the metabolic volume of the largest lesion, in 106 patients with diffuse large B-cell lymphoma who underwent baseline FDG PET-CT. Semi-automatically segmented (41% SUV) total metabolic tumor volume (TMTV) and MBV underwent receiver operating characteristic analysis, identifying optimal thresholds of 147 cm for the TMTV and 41.5 cm for the MBV.