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Knaup H, Weindler J, van Heek L, Voltin CA, Fuchs M, Borchmann P, Dietlein M, Kobe C, Roth K. PET/CT Reconstruction and Its Impact on [Measures of] Metabolic Tumor Volume. Acad Radiol 2023:S1076-6332(23)00691-8. [PMID: 38155023 DOI: 10.1016/j.acra.2023.12.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 12/10/2023] [Accepted: 12/11/2023] [Indexed: 12/30/2023]
Abstract
RATIONALE AND OBJECTIVES In oncological imaging, the use of metabolic tumor volume (MTV) for further prognostic differentiation and the development of risk adapted strategies appears promising. The aim of this analysis was to evaluate ultra-high definition (UHD) and ordered subset expectation maximization (OSEM) PET/CT reconstructions for their potential impact on different methods of MTV measurement. MATERIALS AND METHODS We analyzed positron emission tomography combined with computed tomography (PET/CT) scans of 40 Hodgkin lymphoma patients before first-line treatment who had undergone fluorodeoxyglucose (FDG) PET/CT. The MTVs were determined taking an SUV of 4.0 (MTV4.0) as a fixed threshold or 41% of the single hottest voxel (MTV41%) as an adaptive threshold for automated lymphoma delineation in both UHD and OSEM reconstructions. We then compared the absolute and relative differences between MTV4.0 and MTV41% in UHD and OSEM reconstructions. The relative distribution of MTV4.0 and MTV41% in relation to the reconstruction method applied was recorded and respective differences were tested for statistical significance using the paired sample t-test. RESULTS A comparison of MTV4.0 and MTV41% showed smaller relative and absolute differences in MTV between different reconstruction settings for the MTV4.0 method. Conversely, the absolute as well as the relative differences between MTVs obtained from different reconstructions settings were significantly greater when the MTV41% method was applied (p < 0001). CONCLUSION MTV4.0 brings higher robustness between different reconstruction settings, while with MTV41% the deviation between volumes obtained with different reconstruction settings is greater. For clinical routine and for multicenter settings, the MTV4.0 therefore appears most promising.
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Affiliation(s)
- Henry Knaup
- Department of Nuclear Medicine, University Hospital of Cologne, Kerpener Str. 62, Cologne, 50937, Germany (H.K., J.W., L.V.H., C.A.V., M.D., C.K., K.R.)
| | - Jasmin Weindler
- Department of Nuclear Medicine, University Hospital of Cologne, Kerpener Str. 62, Cologne, 50937, Germany (H.K., J.W., L.V.H., C.A.V., M.D., C.K., K.R.)
| | - Lutz van Heek
- Department of Nuclear Medicine, University Hospital of Cologne, Kerpener Str. 62, Cologne, 50937, Germany (H.K., J.W., L.V.H., C.A.V., M.D., C.K., K.R.)
| | - Conrad-Amadeus Voltin
- Department of Nuclear Medicine, University Hospital of Cologne, Kerpener Str. 62, Cologne, 50937, Germany (H.K., J.W., L.V.H., C.A.V., M.D., C.K., K.R.)
| | - Michael Fuchs
- German Hodgkin Study Group, Department I of Internal Medicine, Center for Integrated Oncology Cologne Bonn, University Hospital of Cologne, Cologne, Germany (M.F., P.B.)
| | - Peter Borchmann
- German Hodgkin Study Group, Department I of Internal Medicine, Center for Integrated Oncology Cologne Bonn, University Hospital of Cologne, Cologne, Germany (M.F., P.B.)
| | - Markus Dietlein
- Department of Nuclear Medicine, University Hospital of Cologne, Kerpener Str. 62, Cologne, 50937, Germany (H.K., J.W., L.V.H., C.A.V., M.D., C.K., K.R.)
| | - Carsten Kobe
- Department of Nuclear Medicine, University Hospital of Cologne, Kerpener Str. 62, Cologne, 50937, Germany (H.K., J.W., L.V.H., C.A.V., M.D., C.K., K.R.).
| | - Katrin Roth
- Department of Nuclear Medicine, University Hospital of Cologne, Kerpener Str. 62, Cologne, 50937, Germany (H.K., J.W., L.V.H., C.A.V., M.D., C.K., K.R.)
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Wendler T, Kreissl MC, Schemmer B, Rogasch JMM, De Benetti F. Artificial Intelligence-powered automatic volume calculation in medical images - available tools, performance and challenges for nuclear medicine. Nuklearmedizin 2023; 62:343-353. [PMID: 37995707 PMCID: PMC10667065 DOI: 10.1055/a-2200-2145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 10/26/2023] [Indexed: 11/25/2023]
Abstract
Volumetry is crucial in oncology and endocrinology, for diagnosis, treatment planning, and evaluating response to therapy for several diseases. The integration of Artificial Intelligence (AI) and Deep Learning (DL) has significantly accelerated the automatization of volumetric calculations, enhancing accuracy and reducing variability and labor. In this review, we show that a high correlation has been observed between Machine Learning (ML) methods and expert assessments in tumor volumetry; Yet, it is recognized as more challenging than organ volumetry. Liver volumetry has shown progression in accuracy with a decrease in error. If a relative error below 10 % is acceptable, ML-based liver volumetry can be considered reliable for standardized imaging protocols if used in patients without major anomalies. Similarly, ML-supported automatic kidney volumetry has also shown consistency and reliability in volumetric calculations. In contrast, AI-supported thyroid volumetry has not been extensively developed, despite initial works in 3D ultrasound showing promising results in terms of accuracy and reproducibility. Despite the advancements presented in the reviewed literature, the lack of standardization limits the generalizability of ML methods across diverse scenarios. The domain gap, i. e., the difference in probability distribution of training and inference data, is of paramount importance before clinical deployment of AI, to maintain accuracy and reliability in patient care. The increasing availability of improved segmentation tools is expected to further incorporate AI methods into routine workflows where volumetry will play a more prominent role in radionuclide therapy planning and quantitative follow-up of disease evolution.
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Affiliation(s)
- Thomas Wendler
- Clinical Computational Medical Imaging Research, Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Germany
- Institute of Digital Medicine, Universitätsklinikum Augsburg, Germany
- Computer-Aided Medical Procedures and Augmented Reality School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | | | | | - Julian Manuel Michael Rogasch
- Department of Nuclear Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin,Germany
| | - Francesca De Benetti
- Computer-Aided Medical Procedures and Augmented Reality School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
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