Abstract
Introduction: Our aim was to evaluate the performance in clinical research and in clinical routine of a research prototype, called positron emission tomography (PET) Assisted Reporting System (PARS) (Siemens Healthineers) and based on a convolutional neural network (CNN), which is designed to detect suspected cancer sites in fluorine-18 fluorodeoxyglucose (18F-FDG) PET/computed tomography (CT).Method: We retrospectively studied two cohorts of patients. The first cohort consisted of research-based patients who underwent PET scans as part of the initial workup for diffuse large B-cell lymphoma (DLBCL). The second cohort consisted of patients who underwent PET scans as part of the evaluation of miscellaneous cancers in clinical routine. In both cohorts, we assessed the correlation between manually and automatically segmented total metabolic tumor volumes (TMTVs), and the overlap between both segmentations (Dice score). For the research cohort, we also compared the prognostic value for progression-free survival (PFS) and overall survival (OS) of manually and automatically obtained TMTVs.Results: For the first cohort (research cohort), data from 119 patients were retrospectively analyzed. The median Dice score between automatic and manual segmentations was 0.65. The intraclass correlation coefficient between automatically and manually obtained TMTVs was 0.68. Both TMTV results were predictive of PFS (hazard ratio: 2.1 and 3.3 for automatically based and manually based TMTVs, respectively) and OS (hazard ratio: 2.4 and 3.1 for automatically based and manually based TMTVs, respectively). For the second cohort (routine cohort), data from 430 patients were retrospectively analyzed. The median Dice score between automatic and manual segmentations was 0.48. The intraclass correlation coefficient between automatically and manually obtained TMTVs was 0.61.Conclusion: The TMTVs determined for the research cohort remain predictive of total and PFS for DLBCL. However, the segmentations and TMTVs determined automatically by the algorithm need to be verified and, sometimes, corrected to be similar to the manual segmentation.
Highlights
Our aim was to evaluate the performance in clinical research and in clinical routine of a research prototype, called positron emission tomography (PET) Assisted Reporting System (PARS) (Siemens Healthineers) and based on a convolutional neural network (CNN), which is designed to detect suspected cancer sites in fluorine-18 fluorodeoxyglucose (18F-FDG) Positron emission tomography (PET)/computed tomography (CT)
The second is the absence of a standard method for the segmentation of hypermetabolic lesions, which is responsible for some variability in the determination of total metabolic tumor volume (TMTV)
Two patient cohorts were analyzed: a first clinical research cohort composed of patients with diffuse large B-cell lymphoma (DLBCL), as TMTV is a well-known prognostic factor for this disease [10], and a second clinical routine cohort composed of patients selected at random and followed up for miscellaneous cancers to evaluate if an automatic measurement of TMTV can be performed in routine
Summary
Our aim was to evaluate the performance in clinical research and in clinical routine of a research prototype, called positron emission tomography (PET) Assisted Reporting System (PARS) (Siemens Healthineers) and based on a convolutional neural network (CNN), which is designed to detect suspected cancer sites in fluorine-18 fluorodeoxyglucose (18F-FDG) PET/computed tomography (CT). PET can establish an initial staging of tumor lesions [2], enable treatment optimization, and evaluate treatment effectiveness or possible relapse [3,4,5,6,7,8] It provides prognostic parameters in certain types of cancer, in particular in onco-hematology, such as the Deauville score, which evaluates the therapeutic response and is used in clinical routine, or the total metabolic tumor volume (TMTV) [9]. TMTV represents, generally on FDG PET, the volume of the entire cancerous disease It is obtained by segmenting each diagnosed lesion. A fixed threshold of SUVmax (for example 41% for lymphomas) for each lesion is frequently used [15] This may not be appropriate for all pathological foci, in the case of heterogeneous tumor fixation and adjacent physiological volume with high uptake [16]
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