Treffer: Global and regional accuracy of deep learning-based tumor segmentation from whole-body [ 18 F]fluorodeoxyglucose PET/CT images.
Title:
Global and regional accuracy of deep learning-based tumor segmentation from whole-body [ 18 F]fluorodeoxyglucose PET/CT images.
Authors:
Ciarmiello A; Nuclear Medicine Department, S. Andrea Hospital, La Spezia, Italy. ciarmiello@yahoo.com., Yosifov N; Nuclear Medicine Department, S. Andrea Hospital, La Spezia, Italy., Masciale D; Nuclear Medicine Department, S. Andrea Hospital, La Spezia, Italy., Ferrando O; Medical Physics Department, S. Andrea Hospital, La Spezia, Italy., Foppiano F; Medical Physics Department, S. Andrea Hospital, La Spezia, Italy., Milano A; Oncology Unit, S. Andrea Hospital, La Spezia, Italy., Canevari M; Biomedical Engineering Department, S. Andrea Hospital, La Spezia, Italy., Florimonte L; Nuclear Medicine Department, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy., Castellani M; Nuclear Medicine Department, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy., Giovacchini G; Nuclear Medicine Department, S. Andrea Hospital, La Spezia, Italy., Maffioli LS; IRCCS Istituto Romagnolo Per Lo Studio Dei Tumori (IRST) 'Dino Amadori', Meldola, Italy., Alfano B; Human Shape Technologies, Naples, Italy.
Source:
EJNMMI research [EJNMMI Res] 2025 Nov 28. Date of Electronic Publication: 2025 Nov 28.
Publication Model:
Ahead of Print
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Springer Berlin Country of Publication: Germany NLM ID: 101560946 Publication Model: Print-Electronic Cited Medium: Print ISSN: 2191-219X (Print) Linking ISSN: 2191219X NLM ISO Abbreviation: EJNMMI Res
Imprint Name(s):
Original Publication: Heidelberg : Springer Berlin
References:
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Shi J, Zhang K, Guo C, Yang Y, Xu Y, Wu J. A survey of label-noise deep learning for medical image analysis. Med Image Anal. 2024;95:103166. https://doi.org/10.1016/j.media.2024.103166.
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Safiri S, Kolahi AA, Naghavi M. Global, regional and national burden of bladder cancer and its attributable risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease study 2019. BMJ Glob Health. 2021. https://doi.org/10.1136/bmjgh-2020-004128.
Ebner R, Sheikh GT, Brendel M, Ricke J, Cyran CC. ESR Essentials: staging and restaging with FDG-PET/CT in oncology-practice recommendations by the European Society for Hybrid, Molecular and Translational Imaging. Eur Radiol. 2024. https://doi.org/10.1007/s00330-024-11094-8.
Ebner R, Sheikh GT, Brendel M, Ricke J, Cyran CC. ESR essentials: role of PET/CT in neuroendocrine tumors-practice recommendations by the European Society for Hybrid, Molecular and Translational Imaging. Eur Radiol. 2025;35:1903–12. https://doi.org/10.1007/s00330-024-11095-7.
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Toxopeus R, Kasalak Ö, Yakar D, Noordzij W, Dierckx R, Kwee TC. Is work overload associated with diagnostic errors on (18)F-FDG-PET/CT? Eur j nuclear medicine molecular imaging. 2024;51(4):1079–84. https://doi.org/10.1007/s00259-023-6543-3.
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Ciarmiello A, Giovannini E, Tutino F, Yosifov N, Milano A, Florimonte L. Does FDG PET-based radiomics have an added value for prediction of overall survival in non-small cell lung cancer? J Clin Med. 2024. https://doi.org/10.3390/jcm13092613.
Shiyam Sundar LK, Yu J, Muzik O, Kulterer OC, Fueger B, Kifjak D, et al. Fully automated, semantic segmentation of whole-body (18)F-FDG PET/CT images based on data-centric artificial intelligence. J Nucl Med. 2022;63:1941–8. https://doi.org/10.2967/jnumed.122.264063.
Wasserthal J, Breit HC, Meyer MT, Pradella M, Hinck D, Sauter AW, et al. Totalsegmentator: robust segmentation of 104 anatomic structures in CT images. Radiology: Artificial Intelligence. 2023;5:e230024. https://doi.org/10.1148/ryai.230024.
Hasani N, Paravastu SS, Farhadi F, Yousefirizi F, Morris MA, Rahmim A, et al. Artificial intelligence in lymphoma PET imaging: a scoping review (current trends and future directions). PET Clin. 2022;17:145–74. https://doi.org/10.1016/j.cpet.2021.09.006.
Häggström I, Leithner D, Alvén J, Campanella G, Abusamra M, Zhang H, et al. Deep learning for [ 18 F]fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysis. Lancet Digit Health. 2024;6:e114–25. https://doi.org/10.1016/s2589-7500(23)00203-0.
Gatidis S, Früh M, Fabritius MP, Gu S, Nikolaou K, Fougère CL, et al. Results from the autoPET challenge on fully automated lesion segmentation in oncologic PET/CT imaging. Nat Mach Intell. 2024;6:1396–405. https://doi.org/10.1038/s42256-024-00912-9.
Tarai S, Lundström E, Ahmad N, Strand R, Ahlström H, Kullberg J. Whole-body tumor segmentation from FDG-PET/CT: leveraging a segmentation prior from tissue-wise projections. Heliyon. 2025;11:e41038. https://doi.org/10.1016/j.heliyon.2024.e41038.
Rahman WT, Wale DJ, Viglianti BL, Townsend DM, Manganaro MS, Gross MD, et al. The impact of infection and inflammation in oncologic (18)F-FDG PET/CT imaging. Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie. 2019 https://doi.org/10.1016/j.biopha.2019.109168.
Hess S, Scholtens AM, Gormsen LC. Patient preparation and patient-related challenges with FDG-PET/CT in infectious and inflammatory disease. PET Clin. 2020;15:125–34. https://doi.org/10.1016/j.cpet.2019.11.001.
Metser U, Even-Sapir E. Increased (18)F-fluorodeoxyglucose uptake in benign, nonphysiologic lesions found on whole-body positron emission tomography/computed tomography (PET/CT): accumulated data from four years of experience with PET/CT. Semin Nucl Med. 2007;37:206–22. https://doi.org/10.1053/j.semnuclmed.2007.01.001.
Gatidis STKA. whole-body FDG-PET/CT dataset with manually annotated tumor lesions (FDG-PET-CT-Lesions). Cancer Imaging Archive. 2022. https://doi.org/10.7937/gkr0-xv29.
Gatidis S, Hepp T, Fruh M, La Fougere C, Nikolaou K, Pfannenberg C, et al. A whole-body FDG-PET/CT dataset with manually annotated tumor lesions. Sci Data. 2022;9(1):601. https://doi.org/10.1038/s41597-022-01718-3.
Boellaard R, Delgado-Bolton R, Oyen WJ, Giammarile F, Tatsch K, Eschner W, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging. 2015;42:328. https://doi.org/10.1007/s00259-014-2961-x.
Ronneberger O. Medical image computing and computer-assisted intervention–MICCAI 2015. Springer; 2015.
Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. Nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18:203–11. https://doi.org/10.1038/s41592-020-01008-z.
Yu C, Anakwenze CP, Zhao Y, Martin RM, Ludmir EB, J SN, et al. Multi-organ segmentation of abdominal structures from non-contrast and contrast enhanced CT images. Sci Rep. 2022. https://doi.org/10.1038/s41598-022-21206-3.
Xue H, Fang Q, Yao Y, Teng Y. 3D PET/CT tumor segmentation based on nnU-Net with GCN refinement. Phys Med Biol. 2023. https://doi.org/10.1088/1361-6560/acede6.
Kalisch H, Hörst F, K H, Kleesiek J, Seibold C. 2024 Autopet III challenge: Incorporating anatomical knowledge into nnUNet for lesion segmentation in PET/CT. arXiv preprint arXiv:221102701. 2024.
Astaraki M, Bendazzolie S. Lesion Segmentation in Whole-Body Multi-Tracer PET-CT Images; a Contribution to AutoPET 2024 Challenge. arXiv preprint arXiv:221102701. 2024.
Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. Scipy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17:261–72. https://doi.org/10.1038/s41592-019-0686-2.
Cardoso MJ, Li W, Brown R, Ma N, Kerfoot E, Wang Y, et al. Monai: An open-source framework for deep learning in healthcare. arXiv preprint arXiv:221102701. 2022.
Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26:297–302.
Sachpekidis C, Enqvist O, Ulen J, Kopp-Schneider A, Pan L, Jauch A, et al. Application of an artificial intelligence-based tool in [(18)F]FDG PET/CT for the assessment of bone marrow involvement in multiple myeloma. Eur J Nucl Med Mol Imaging. 2023;50:3697–708. https://doi.org/10.1007/s00259-023-06339-5.
Yousefirizi F, Klyuzhin IS, JH O, Harsini S, Tie X, Shiri I, et al. TMTV-Net: fully automated total metabolic tumor volume segmentation in lymphoma PET/CT images - a multi-center generalizability analysis. Eur J Nucl Med Mol Imaging. 2024;51:1937–54. (Epub 2024 Feb 8). https://doi.org/10.1007/s00259-024-06616-x.
Jemaa S, Fredrickson J, Carano RAD, Nielsen T, de Crespigny A, Bengtsson T. Tumor segmentation and feature extraction from whole-body FDG-PET/CT using cascaded 2D and 3D convolutional neural networks. J Digit Imaging. 2020;33:888–94. https://doi.org/10.1007/s10278-020-00341-1.
Huang B, Chen Z, Wu PM, Ye Y, Feng ST, Wong CO, et al. Fully automated delineation of gross tumor volume for head and neck cancer on PET-CT using deep learning: A Dual-center study. Contrast Media Mol Imaging. 2018;2018:8923028. https://doi.org/10.1155/2018/8923028.
Teramoto A, Fujita H, Yamamuro O, Tamaki T. Automated detection of pulmonary nodules in PET/CT images: ensemble false-positive reduction using a convolutional neural network technique. Med Phys. 2016;43:2821–7. https://doi.org/10.1118/1.4948498.
Zincirkeser S, Sahin E, Halac M, Sager S. Standardized uptake values of normal organs on 18F-fluorodeoxyglucose positron emission tomography and computed tomography imaging. J Int Med Res. 2007;35:231–6. https://doi.org/10.1177/147323000703500207.
Sharma P, Chatterjee P, Alvarado L, Dwivedi A. Standardized uptake value of normal organs on routine clinical [18F]FDG PET/CT: impact of tumor metabolism and patient-related factors. Nucl Med Rev. 2023;26(0):1–10. https://doi.org/10.5603/NMR.a2022.0036.
Lindgren Belal S, Sadik M, Kaboteh R, Enqvist O, Ulén J, Poulsen MH, et al. Deep learning for segmentation of 49 selected bones in CT scans: first step in automated PET/CT-based 3D quantification of skeletal metastases. Eur J Radiol. 2019;113:89–95.
Carles M, Kuhn D, Fechter T, Baltas D, Mix M, Nestle U, et al. Development and evaluation of two open-source nnU-Net models for automatic segmentation of lung tumors on PET and CT images with and without respiratory motion compensation. Eur Radiol. 2024;34:6701–11. https://doi.org/10.1007/s00330-024-10751-2.
Jafari E, Zarei A, Dadgar H, Keshavarz A, Manafi-Farid R, Rostami H, et al. A convolutional neural network-based system for fully automatic segmentation of whole-body [(68)Ga]Ga-PSMA PET images in prostate cancer. Eur J Nucl Med Mol Imaging. 2024;51:1476–87. https://doi.org/10.1007/s00259-023-6555-z. (Epub 2023 Dec 14).
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Wang H, Jin Q, Li S, Liu S, Wang M, Song Z. A comprehensive survey on deep active learning in medical image analysis. Med Image Anal. 2024;95:103201. https://doi.org/10.1016/j.media.2024.103201.
Shi J, Zhang K, Guo C, Yang Y, Xu Y, Wu J. A survey of label-noise deep learning for medical image analysis. Med Image Anal. 2024;95:103166. https://doi.org/10.1016/j.media.2024.103166.
Paez D, Giammarile F, Orellana P. Nuclear medicine: a global perspective. Clin Transl Imaging. 2020;8:51–3. https://doi.org/10.1007/s40336-020-00359-z.
OECD. OECD Health Statistics 2022. 2022. https://statsoecdorg/indexaspx?queryid=30160. https://doi.org/10.1787/ae3016b9.
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–49. https://doi.org/10.3322/caac.21660.
Safiri S, Kolahi AA, Naghavi M. Global, regional and national burden of bladder cancer and its attributable risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease study 2019. BMJ Glob Health. 2021. https://doi.org/10.1136/bmjgh-2020-004128.
Ebner R, Sheikh GT, Brendel M, Ricke J, Cyran CC. ESR Essentials: staging and restaging with FDG-PET/CT in oncology-practice recommendations by the European Society for Hybrid, Molecular and Translational Imaging. Eur Radiol. 2024. https://doi.org/10.1007/s00330-024-11094-8.
Ebner R, Sheikh GT, Brendel M, Ricke J, Cyran CC. ESR essentials: role of PET/CT in neuroendocrine tumors-practice recommendations by the European Society for Hybrid, Molecular and Translational Imaging. Eur Radiol. 2025;35:1903–12. https://doi.org/10.1007/s00330-024-11095-7.
Alotaibi NA, Yakar D, Glaudemans A, Kwee TC. Diagnostic errors in clinical FDG-PET/CT. Eur J Radiol. 2020;132:109296. https://doi.org/10.1016/j.ejrad.2020.109296.
Toxopeus R, Kasalak Ö, Yakar D, Noordzij W, Dierckx R, Kwee TC. Is work overload associated with diagnostic errors on (18)F-FDG-PET/CT? Eur j nuclear medicine molecular imaging. 2024;51(4):1079–84. https://doi.org/10.1007/s00259-023-6543-3.
Yang F, Zamzmi G, Angara S, Rajaraman S, Aquilina A, Xue Z, et al. Assessing inter-annotator agreement for medical image segmentation. IEEE Access. 2023;11:21300–12. https://doi.org/10.1109/access.2023.3249759.
Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med. 2021;13:152. https://doi.org/10.1186/s13073-021-00968-x.
Liao J, Li X, Gan Y, Han S, Rong P, Wang W, et al. Artificial intelligence assists precision medicine in cancer treatment. Front Oncol. 2022;12:998222. https://doi.org/10.3389/fonc.2022.998222.
Ciarmiello A, Giovannini E, Tutino F, Yosifov N, Milano A, Florimonte L. Does FDG PET-based radiomics have an added value for prediction of overall survival in non-small cell lung cancer? J Clin Med. 2024. https://doi.org/10.3390/jcm13092613.
Shiyam Sundar LK, Yu J, Muzik O, Kulterer OC, Fueger B, Kifjak D, et al. Fully automated, semantic segmentation of whole-body (18)F-FDG PET/CT images based on data-centric artificial intelligence. J Nucl Med. 2022;63:1941–8. https://doi.org/10.2967/jnumed.122.264063.
Wasserthal J, Breit HC, Meyer MT, Pradella M, Hinck D, Sauter AW, et al. Totalsegmentator: robust segmentation of 104 anatomic structures in CT images. Radiology: Artificial Intelligence. 2023;5:e230024. https://doi.org/10.1148/ryai.230024.
Hasani N, Paravastu SS, Farhadi F, Yousefirizi F, Morris MA, Rahmim A, et al. Artificial intelligence in lymphoma PET imaging: a scoping review (current trends and future directions). PET Clin. 2022;17:145–74. https://doi.org/10.1016/j.cpet.2021.09.006.
Häggström I, Leithner D, Alvén J, Campanella G, Abusamra M, Zhang H, et al. Deep learning for [ 18 F]fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysis. Lancet Digit Health. 2024;6:e114–25. https://doi.org/10.1016/s2589-7500(23)00203-0.
Gatidis S, Früh M, Fabritius MP, Gu S, Nikolaou K, Fougère CL, et al. Results from the autoPET challenge on fully automated lesion segmentation in oncologic PET/CT imaging. Nat Mach Intell. 2024;6:1396–405. https://doi.org/10.1038/s42256-024-00912-9.
Tarai S, Lundström E, Ahmad N, Strand R, Ahlström H, Kullberg J. Whole-body tumor segmentation from FDG-PET/CT: leveraging a segmentation prior from tissue-wise projections. Heliyon. 2025;11:e41038. https://doi.org/10.1016/j.heliyon.2024.e41038.
Rahman WT, Wale DJ, Viglianti BL, Townsend DM, Manganaro MS, Gross MD, et al. The impact of infection and inflammation in oncologic (18)F-FDG PET/CT imaging. Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie. 2019 https://doi.org/10.1016/j.biopha.2019.109168.
Hess S, Scholtens AM, Gormsen LC. Patient preparation and patient-related challenges with FDG-PET/CT in infectious and inflammatory disease. PET Clin. 2020;15:125–34. https://doi.org/10.1016/j.cpet.2019.11.001.
Metser U, Even-Sapir E. Increased (18)F-fluorodeoxyglucose uptake in benign, nonphysiologic lesions found on whole-body positron emission tomography/computed tomography (PET/CT): accumulated data from four years of experience with PET/CT. Semin Nucl Med. 2007;37:206–22. https://doi.org/10.1053/j.semnuclmed.2007.01.001.
Gatidis STKA. whole-body FDG-PET/CT dataset with manually annotated tumor lesions (FDG-PET-CT-Lesions). Cancer Imaging Archive. 2022. https://doi.org/10.7937/gkr0-xv29.
Gatidis S, Hepp T, Fruh M, La Fougere C, Nikolaou K, Pfannenberg C, et al. A whole-body FDG-PET/CT dataset with manually annotated tumor lesions. Sci Data. 2022;9(1):601. https://doi.org/10.1038/s41597-022-01718-3.
Boellaard R, Delgado-Bolton R, Oyen WJ, Giammarile F, Tatsch K, Eschner W, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging. 2015;42:328. https://doi.org/10.1007/s00259-014-2961-x.
Ronneberger O. Medical image computing and computer-assisted intervention–MICCAI 2015. Springer; 2015.
Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. Nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18:203–11. https://doi.org/10.1038/s41592-020-01008-z.
Yu C, Anakwenze CP, Zhao Y, Martin RM, Ludmir EB, J SN, et al. Multi-organ segmentation of abdominal structures from non-contrast and contrast enhanced CT images. Sci Rep. 2022. https://doi.org/10.1038/s41598-022-21206-3.
Xue H, Fang Q, Yao Y, Teng Y. 3D PET/CT tumor segmentation based on nnU-Net with GCN refinement. Phys Med Biol. 2023. https://doi.org/10.1088/1361-6560/acede6.
Kalisch H, Hörst F, K H, Kleesiek J, Seibold C. 2024 Autopet III challenge: Incorporating anatomical knowledge into nnUNet for lesion segmentation in PET/CT. arXiv preprint arXiv:221102701. 2024.
Astaraki M, Bendazzolie S. Lesion Segmentation in Whole-Body Multi-Tracer PET-CT Images; a Contribution to AutoPET 2024 Challenge. arXiv preprint arXiv:221102701. 2024.
Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. Scipy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17:261–72. https://doi.org/10.1038/s41592-019-0686-2.
Cardoso MJ, Li W, Brown R, Ma N, Kerfoot E, Wang Y, et al. Monai: An open-source framework for deep learning in healthcare. arXiv preprint arXiv:221102701. 2022.
Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26:297–302.
Sachpekidis C, Enqvist O, Ulen J, Kopp-Schneider A, Pan L, Jauch A, et al. Application of an artificial intelligence-based tool in [(18)F]FDG PET/CT for the assessment of bone marrow involvement in multiple myeloma. Eur J Nucl Med Mol Imaging. 2023;50:3697–708. https://doi.org/10.1007/s00259-023-06339-5.
Yousefirizi F, Klyuzhin IS, JH O, Harsini S, Tie X, Shiri I, et al. TMTV-Net: fully automated total metabolic tumor volume segmentation in lymphoma PET/CT images - a multi-center generalizability analysis. Eur J Nucl Med Mol Imaging. 2024;51:1937–54. (Epub 2024 Feb 8). https://doi.org/10.1007/s00259-024-06616-x.
Jemaa S, Fredrickson J, Carano RAD, Nielsen T, de Crespigny A, Bengtsson T. Tumor segmentation and feature extraction from whole-body FDG-PET/CT using cascaded 2D and 3D convolutional neural networks. J Digit Imaging. 2020;33:888–94. https://doi.org/10.1007/s10278-020-00341-1.
Huang B, Chen Z, Wu PM, Ye Y, Feng ST, Wong CO, et al. Fully automated delineation of gross tumor volume for head and neck cancer on PET-CT using deep learning: A Dual-center study. Contrast Media Mol Imaging. 2018;2018:8923028. https://doi.org/10.1155/2018/8923028.
Teramoto A, Fujita H, Yamamuro O, Tamaki T. Automated detection of pulmonary nodules in PET/CT images: ensemble false-positive reduction using a convolutional neural network technique. Med Phys. 2016;43:2821–7. https://doi.org/10.1118/1.4948498.
Zincirkeser S, Sahin E, Halac M, Sager S. Standardized uptake values of normal organs on 18F-fluorodeoxyglucose positron emission tomography and computed tomography imaging. J Int Med Res. 2007;35:231–6. https://doi.org/10.1177/147323000703500207.
Sharma P, Chatterjee P, Alvarado L, Dwivedi A. Standardized uptake value of normal organs on routine clinical [18F]FDG PET/CT: impact of tumor metabolism and patient-related factors. Nucl Med Rev. 2023;26(0):1–10. https://doi.org/10.5603/NMR.a2022.0036.
Lindgren Belal S, Sadik M, Kaboteh R, Enqvist O, Ulén J, Poulsen MH, et al. Deep learning for segmentation of 49 selected bones in CT scans: first step in automated PET/CT-based 3D quantification of skeletal metastases. Eur J Radiol. 2019;113:89–95.
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Contributed Indexing:
Keywords: 18F-FDG PET/CT; Deep learning; NnU -Net; Segmentation
Entry Date(s):
Date Created: 20251128 Latest Revision: 20251128
Update Code:
20251128
DOI:
10.1186/s13550-025-01333-4
PMID:
41313553
Database:
MEDLINE
Weitere Informationen
Background: The number of [¹⁸F]fluorodeoxyglucose ([¹⁸F]FDG)-PET/CT scans performed has significantly increased in the last decade in line with the increasing trend of oncological malignancies. Such images, which signal high glucose-uptake areas are key in defining the extent of the disease, staging and response to therapy. Processing and evaluation of ([¹⁸F]FDG)-PET/CT scans, however, require manual annotation by well-trained specialists and above all time. In time and resource-constrained settings meeting the increasing demand for PET/CT scans has become challenging. The main goal of our study was to test the relationship between the volumes predicted by the deep learning algorithm and the manually segmented ones. The secondary objective goal was to measure the extent at which the predictive accuracy is associated with normal background uptake.
Results: The study sample included 1334 [¹⁸F]FDG-PET/CT scans from subjects with histologically confirmed diagnoses of lung cancer, lymphoma, and melanoma. 933 (70%) [¹⁸F]FDG-PET/CT scans were used as the training dataset and 267 (20%) scans were used as an internal validation dataset. A subsample of 134 (10%) [¹⁸F]FDG-PET/CT scans not used for training was used as the test dataset. The segmentation model was implemented with the nnU-Net convolutional network available in the MONAI framework. Model performance was measured with the Dice score. Correlation between manual and predicted segmentation was assessed using linear correlation. Totalsegmentator tool was used to identify lesions location and assess the tumor-to-background ratio (TBR) for quantitative analysis. Network achieved Dice scores of 0.918 (validation) and 0.879 (test), showing strong agreement with manual segmentations. The model achieved an F1 score of 0.91 on the test set. High correlation (R=0.82, p<0.0001) was observed between predicted and ground truth volumes. Segmentation accuracy improved with higher TBRs, as lesions with TBR>2 had significantly better Dice scores than those with lower contrast (TBR ≤ 1-2 or≤1).
Conclusions: These results are consistent with previous reports on PET-based segmentation, further validating nnU-Net as a reliable approach for detecting hypermetabolic lesions and assessing global disease burden in FDG-PET imaging. Moreover, the significant relationship between TBR and segmentation accuracy suggests the possibility of further improvements by integrating metabolic profile into the predictive model.
(© 2025. The Author(s).)
Declarations. Ethical approval and consent to participate: Study was approved by the institutional ethics committee of the Medical Faculty of the University of Tübingen as well as the institutional data security and privacy review board and regional ethics committees. Study was conducted in accordance with the first revision of the Declaration of Helsinki from 1975. Informed consent was obtained from all individual participants included in the study. Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interests.