Vai trò của trí tuệ nhân tạo trong hình ảnh phân tử của nhiễm trùng và viêm

Johannes Schwenck1, Manfred Kneilling2, Niels P. Riksen3, Christian la Fougère1, Douwe J. Mulder4, Riemer Slart5, Erik H.J.G. Aarntzen6
1Department of Nuclear Medicine and Clinical Molecular Imaging, Eberhard Karls University, Tübingen, Germany
2Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University, Röntgenweg 13, 72076, Tübingen, Germany
3Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
4Department of Biomedical Photonic Imaging, Faculty of Science and Technology, University of Twente, Enschede, the Netherlands
5Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, The Netherlands
6Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

Tóm tắt

Tóm tắt

Sự phát hiện các nhiễm trùng tiềm ẩn và viêm nhẹ trong thực hành lâm sàng vẫn là một thách thức lớn và phụ thuộc nhiều vào trình độ chuyên môn của người đọc. Mặc dù hình ảnh phân tử, như [18F]FDG PET hoặc xạ hình bạch cầu được gán nhãn phóng xạ, cung cấp dữ liệu toàn thân định lượng và có thể tái lập về các phản ứng viêm, nhưng việc giải thích chúng vẫn bị giới hạn bởi phân tích hình ảnh. Điều này thường dẫn đến chẩn đoán và điều trị chậm trễ, cũng như các lĩnh vực tiềm năng chưa được khai thác. Trí tuệ nhân tạo (AI) cung cấp những cách tiếp cận đổi mới để khai thác khối lượng lớn dữ liệu hình ảnh và đã dẫn đến những bước đột phá mang tính cách mạng trong các lĩnh vực y tế khác. Ở đây, chúng tôi thảo luận về cách các công cụ dựa trên AI có thể cải thiện độ nhạy phát hiện của hình ảnh phân tử trong nhiễm trùng và viêm, nhưng cũng là cách mà AI có thể mở rộng phân tích dữ liệu vượt xa các ứng dụng hiện tại nhằm dự đoán kết quả và đánh giá rủi ro dài hạn.

Từ khóa


Tài liệu tham khảo

Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006

Alberts I, Hunermund JN, Prenosil G, Mingels C, Bohn KP, Viscione M et al (2021) Clinical performance of long axial field of view PET/CT: a head-to-head intra-individual comparison of the Biograph Vision Quadra with the Biograph Vision PET/CT. Eur J Nucl Med Mol Imaging 48(8):2395–2404

Arabi H, Zaidi H (2021) Non-local mean denoising using multiple PET reconstructions. Ann Nucl Med 35(2):176–186

Arts RJ, Gresnigt MS, Joosten LA, Netea MG (2017) Cellular metabolism of myeloid cells in sepsis. J Leukoc Biol 101(1):151–164

Arts RJW, Moorlag S, Novakovic B, Li Y, Wang SY, Oosting M et al (2018) BCG vaccination protects against experimental viral infection in humans through the induction of cytokines associated with trained immunity. Cell Host Microbe 23(1):89-100.e5

Badawi RD, Shi H, Hu P, Chen S, Xu T, Price PM et al (2019) First human imaging studies with the EXPLORER total-body PET scanner. J Nucl Med 60(3):299–303

Bekkering S, Blok BA, Joosten LA, Riksen NP, van Crevel R, Netea MG (2016a) In vitro experimental model of trained innate immunity in human primary monocytes. Clin Vaccine Immunol 23(12):926–933

Bekkering S, van den Munckhof I, Nielen T, Lamfers E, Dinarello C, Rutten J et al (2016b) Innate immune cell activation and epigenetic remodeling in symptomatic and asymptomatic atherosclerosis in humans in vivo. Atherosclerosis 254:228–236

Bekkering S, Arts RJW, Novakovic B, Kourtzelis I, van der Heijden C, Li Y et al (2018) Metabolic induction of trained immunity through the mevalonate pathway. Cell 172(1–2):135–46.e9

Bekkering S, Stiekema LCA, Bernelot Moens S, Verweij SL, Novakovic B, Prange K et al (2019) Treatment with statins does not revert trained immunity in patients with familial hypercholesterolemia. Cell Metab 30(1):1–2

Bergeron M, Cadorette J, Tetrault MA, Beaudoin JF, Leroux JD, Fontaine R et al (2014) Imaging performance of LabPET APD-based digital PET scanners for pre-clinical research. Phys Med Biol 59(3):661–678

Bernelot Moens SJ, Stoekenbroek RM, van der Valk FM, Verweij SL, Koelemay MJ, Verberne HJ et al (2016) Carotid arterial wall inflammation in peripheral artery disease is augmented by type 2 diabetes: a cross-sectional study. BMC Cardiovasc Disord 16(1):237

Berrevoets MAH, Kouijzer IJE, Slieker K, Aarntzen E, Kullberg BJ, Oever JT et al (2019) (18)F-FDG PET/CT-guided treatment duration in patients with high-risk Staphylococcus aureus bacteremia: a proof of principle. J Nucl Med 60(7):998–1002

Bucerius J, Mani V, Moncrieff C, Rudd JH, Machac J, Fuster V et al (2012) Impact of noninsulin-dependent type 2 diabetes on carotid wall 18F-fluorodeoxyglucose positron emission tomography uptake. J Am Coll Cardiol 59(23):2080–2088

Bucerius J, Mani V, Wong S, Moncrieff C, Izquierdo-Garcia D, Machac J et al (2014) Arterial and fat tissue inflammation are highly correlated: a prospective 18F-FDG PET/CT study. Eur J Nucl Med Mol Imaging 41(5):934–945

Buther F, Vehren T, Schafers KP, Schafers M (2016) Impact of data-driven respiratory gating in clinical PET. Radiology 281(1):229–238

Camacho DM, Collins KM, Powers RK, Costello JC, Collins JJ (2018) Next-generation machine learning for biological networks. Cell 173(7):1581–1592

Chakfe N, Diener H, Lejay A, Assadian O, Berard X, Caillon J et al (2020) Editor’s Choice—European Society for Vascular Surgery (ESVS) 2020 clinical practice guidelines on the management of vascular graft and endograft infections. Eur J Vasc Endovasc Surg 59(3):339–384

Chavakis T, Mitroulis I, Hajishengallis G (2019) Hematopoietic progenitor cells as integrative hubs for adaptation to and fine-tuning of inflammation. Nat Immunol 20(7):802–811

Cheng SC, Scicluna BP, Arts RJ, Gresnigt MS, Lachmandas E, Giamarellos-Bourboulis EJ et al (2016) Broad defects in the energy metabolism of leukocytes underlie immunoparalysis in sepsis. Nat Immunol 17(4):406–413

Chiossone L, Dumas PY, Vienne M, Vivier E (2018) Natural killer cells and other innate lymphoid cells in cancer. Nat Rev Immunol 18(11):671–688

Chirillo F (2021) New approach to managing infective endocarditis. Trends Cardiovasc Med 31(5):277–286

Choe J, Lee SM, Do KH, Lee G, Lee JG, Lee SM et al (2019) Deep learning-based image conversion of CT reconstruction kernels improves radiomics reproducibility for pulmonary nodules or masses. Radiology 292(2):365–373

Colling R, Pitman H, Oien K, Rajpoot N, Macklin P, Group CM-PAiHW et al (2019) Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice. J Pathol 249(2):143–150

Cui J, Gong K, Guo N, Wu C, Meng X, Kim K et al (2019) PET image denoising using unsupervised deep learning. Eur J Nucl Med Mol Imaging 46(13):2780–2789

Currie G, Hawk KE (2021) Ethical and legal challenges of artificial intelligence in nuclear medicine. Semin Nucl Med 51(2):120–125

Currie G, Rohren E (2021) Intelligent imaging in nuclear medicine: the principles of artificial intelligence, machine learning and deep learning. Semin Nucl Med 51(2):102–111

de Vos BD, Berendsen FF, Viergever MA, Sokooti H, Staring M, Isgum I (2019) A deep learning framework for unsupervised affine and deformable image registration. Med Image Anal 52:128–143

de Vries EF, Roca M, Jamar F, Israel O, Signore A (2010) Guidelines for the labelling of leucocytes with (99m)Tc-HMPAO Inflammation/Infection. Taskgroup of the European Association of Nuclear Medicine. Eur J Nucl Med Mol Imaging 37(4):842–848

Decuyper M, Maebe J, Van Holen R, Vandenberghe S (2021) Artificial intelligence with deep learning in nuclear medicine and radiology. EJNMMI Phys 8(1):81

Denny JC, Collins FS (2021) Precision medicine in 2030-seven ways to transform healthcare. Cell 184(6):1415–1419

Dietze MMA, Branderhorst W, Kunnen B, Viergever MA, de Jong H (2019) Accelerated SPECT image reconstruction with FBP and an image enhancement convolutional neural network. EJNMMI Phys 6(1):14

Dominguez-Andres J, Netea MG (2019) Long-term reprogramming of the innate immune system. J Leukoc Biol 105(2):329–338

Ebrahimian S, Digumarthy S, Bizzo B, Primak A, Zimmermann M, Tarbiah MM et al (2021) Artificial intelligence has similar performance to subjective assessment of emphysema severity on chest CT. Acad Radiol. https://doi.org/10.1016/j.acra.2021.09.007

Emami H, Singh P, MacNabb M, Vucic E, Lavender Z, Rudd JH et al (2015) Splenic metabolic activity predicts risk of future cardiovascular events: demonstration of a cardiosplenic axis in humans. JACC Cardiovasc Imaging 8(2):121–130

Feng T, Wang J, Dong Y, Zhao J, Li H (2019) A novel data-driven cardiac gating signal extraction method for PET. IEEE Trans Med Imaging 38(2):629–637

Filippi L, Schillaci O (2022) Total-body [(18)F]FDG PET/CT scan has stepped into the arena: the faster, the better. Is it always true? Eur J Nucl Med Mol Imaging. https://doi.org/10.1007/s00259-022-05791-z

Flint TR, Fearon DT, Janowitz T (2017) Connecting the metabolic and immune responses to cancer. Trends Mol Med 23(5):451–464

Gaber T, Strehl C, Buttgereit F (2017) Metabolic regulation of inflammation. Nat Rev Rheumatol 13(5):267–279

Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278(2):563–577

Gong K, Guan J, Liu CC, Qi J (2019) PET image denoising using a deep neural network through fine tuning. IEEE Trans Radiat Plasma Med Sci 3(2):153–161

Habib G, Lancellotti P, Antunes MJ, Bongiorni MG, Casalta JP, Del Zotti F et al (2015) ESC Guidelines for the management of infective endocarditis: the Task Force for the Management of Infective Endocarditis of the European Society of Cardiology (ESC). Endorsed by: European Association for Cardio-Thoracic Surgery (EACTS), the European Association of Nuclear Medicine (EANM). Eur Heart J 36(44):3075–3128

Haggstrom I, Schmidtlein CR, Campanella G, Fuchs TJ (2019) DeepPET: a deep encoder-decoder network for directly solving the PET image reconstruction inverse problem. Med Image Anal 54:253–262

Hatt M, Cheze Le Rest C, Antonorsi N, Tixier F, Tankyevych O, Jaouen V et al (2021) Radiomics in PET/CT: current status and future AI-based evolutions. Semin Nucl Med 51(2):126–133

He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K (2019) The practical implementation of artificial intelligence technologies in medicine. Nat Med 25(1):30–36

He W, Wang Y, Liang X, Zhou W, Zhu M, Han X et al (2021) High-performance coded aperture gamma camera based on monolithic GAGG: Ce crystal. Rev Sci Instrum 92(1):013106

Hipfl C, Mooij W, Perka C, Hardt S, Wassilew GI (2021) Unexpected low-grade infections in revision hip arthroplasty for aseptic loosening : a single-institution experience of 274 hips. Bone Joint J 103-B(6):1070–1077

Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts H (2018) Artificial intelligence in radiology. Nat Rev Cancer 18(8):500–510

Hotamisligil GS (2017) Inflammation, metaflammation and immunometabolic disorders. Nature 542(7640):177–185

Hotchkiss RS, Monneret G, Payen D (2013) Sepsis-induced immunosuppression: from cellular dysfunctions to immunotherapy. Nat Rev Immunol 13(12):862–874

Hotchkiss RS, Moldawer LL, Opal SM, Reinhart K, Turnbull IR, Vincent JL (2016) Sepsis and septic shock. Nat Rev Dis Primers 2:16045

Jaltotage B, Ali U, Dorai-Raj A, Rankin J, Sanfilippo F, Dwivedi G (2021) Q fever endocarditis: a review of local and all reported cases in the literature. Heart Lung Circ 30(10):1509–1515

Jamar F, Buscombe J, Chiti A, Christian PE, Delbeke D, Donohoe KJ et al (2013) EANM/SNMMI guideline for 18F-FDG use in inflammation and infection. J Nucl Med 54(4):647–658

Joseph P, Ishai A, Mani V, Kallend D, Rudd JH, Fayad ZA et al (2017) Short-term changes in arterial inflammation predict long-term changes in atherosclerosis progression. Eur J Nucl Med Mol Imaging 44(1):141–150

Kalafati L, Kourtzelis I, Schulte-Schrepping J, Li X, Hatzioannou A, Grinenko T et al (2020) Innate immune training of granulopoiesis promotes anti-tumor activity. Cell 183(3):771-8512.e12

Kaplan S, Zhu YM (2019) Full-dose PET image estimation from low-dose PET image using deep learning: a pilot study. J Digit Imaging 32(5):773–778

Kaufmann SHE, Dorhoi A, Hotchkiss RS, Bartenschlager R (2018) Host-directed therapies for bacterial and viral infections. Nat Rev Drug Discov 17(1):35–56

Kidd BA, Peters LA, Schadt EE, Dudley JT (2014) Unifying immunology with informatics and multiscale biology. Nat Immunol 15(2):118–127

Kouijzer IJE, van Leerdam EJ, Gompelman M, Tuinte RAM, Aarntzen E, Berrevoets MAH et al (2021) Intravenous to oral switch in complicated Staphylococcus aureus bacteremia without endovascular infection: a retrospective single-center cohort study. Clin Infect Dis 73(5):895–898

Laohapensang K, Arworn S, Orrapin S, Reanpang T, Orrapin S (2017) Management of the infected aortic endograft. Semin Vasc Surg 30(2–3):91–94

Laur O, Weaver MJ, Bridge C, Chow E, Rosenthal M, Bay C et al (2021) Computed tomography-based body composition profile as a screening tool for geriatric frailty detection. Skeletal Radiol. https://doi.org/10.1007/s00256-021-03951-0

LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

Lee YS, Wollam J, Olefsky JM (2018) An integrated view of immunometabolism. Cell 172(1–2):22–40

Leentjens J, Bekkering S, Joosten LAB, Netea MG, Burgner DP, Riksen NP (2018) Trained innate immunity as a novel mechanism linking infection and the development of atherosclerosis. Circ Res 122(5):664–669

Lempiainen H, Braenne I, Michoel T, Tragante V, Vilne B, Webb TR et al (2018) Network analysis of coronary artery disease risk genes elucidates disease mechanisms and druggable targets. Sci Rep 8(1):3434

Lercher A, Baazim H, Bergthaler A (2020) Systemic immunometabolism: challenges and opportunities. Immunity 53(3):496–509

Libby P, Buring JE, Badimon L, Hansson GK, Deanfield J, Bittencourt MS et al (2019) Atherosclerosis. Nat Rev Dis Primers 5(1):56

Lopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G (2013) The hallmarks of aging. Cell 153(6):1194–1217

Makinen VP, Civelek M, Meng Q, Zhang B, Zhu J, Levian C et al (2014) Integrative genomics reveals novel molecular pathways and gene networks for coronary artery disease. PLoS Genet 10(7):e1004502

Meijering E, Carpenter AE, Peng H, Hamprecht FA, Olivo-Marin JC (2016) Imagining the future of bioimage analysis. Nat Biotechnol 34(12):1250–1255

Minarik D, Enqvist O, Tragardh E (2020) Denoising of scintillation camera images using a deep convolutional neural network: a Monte Carlo simulation approach. J Nucl Med 61(2):298–303

Mulder WJM, Ochando J, Joosten LAB, Fayad ZA, Netea MG (2019) Therapeutic targeting of trained immunity. Nat Rev Drug Discov 18(7):553–566

Municio C, Criado G (2020) Therapies targeting trained immune cells in inflammatory and autoimmune diseases. Front Immunol 11:631743

Netea MG, Joosten LAB (2018) Trained immunity and local innate immune memory in the lung. Cell 175(6):1463–1465

Netea MG, Dominguez-Andres J, Barreiro LB, Chavakis T, Divangahi M, Fuchs E et al (2020a) Defining trained immunity and its role in health and disease. Nat Rev Immunol 20:375–388

Netea MG, Dominguez-Andres J, Barreiro LB, Chavakis T, Divangahi M, Fuchs E et al (2020b) Defining trained immunity and its role in health and disease. Nat Rev Immunol 20(6):375–388

Netea MG, Giamarellos-Bourboulis EJ, Dominguez-Andres J, Curtis N, van Crevel R, van de Veerdonk FL et al (2020c) Trained immunity: a tool for reducing susceptibility to and the severity of SARS-CoV-2 infection. Cell 181(5):969–977

Norata GD, Caligiuri G, Chavakis T, Matarese G, Netea MG, Nicoletti A et al (2015) The cellular and molecular basis of translational immunometabolism. Immunity 43(3):421–434

Noz MP, Bekkering S, Groh L, Nielen TM, Lamfers EJ, Schlitzer A et al (2020) Reprogramming of bone marrow myeloid progenitor cells in patients with severe coronary artery disease. Elife. https://doi.org/10.7554/eLife.60939

O’Neill LAJ, Netea MG (2020) BCG-induced trained immunity: can it offer protection against COVID-19? Nat Rev Immunol 20(6):335–337

Orlhac F, Boughdad S, Philippe C, Stalla-Bourdillon H, Nioche C, Champion L et al (2018) A postreconstruction harmonization method for multicenter radiomic studies in PET. J Nucl Med 59(8):1321–1328

Parmar C, Barry JD, Hosny A, Quackenbush J, Aerts H (2018) Data analysis strategies in medical imaging. Clin Cancer Res 24(15):3492–3499

Pober JS, Sessa WC (2007) Evolving functions of endothelial cells in inflammation. Nat Rev Immunol 7(10):803–815

Priem B, van Leent MMT, Teunissen AJP, Sofias AM, Mourits VP, Willemsen L et al (2020) Trained immunity-promoting nanobiologic therapy suppresses tumor growth and potentiates checkpoint inhibition. Cell 183(3):786-801.e19

Ringel AE, Drijvers JM, Baker GJ, Catozzi A, Garcia-Canaveras JC, Gassaway BM et al (2020) Obesity shapes metabolism in the tumor microenvironment to suppress anti-tumor immunity. Cell 183(7):1848–66.e26

Roca M, de Vries EF, Jamar F, Israel O, Signore A (2010) Guidelines for the labelling of leucocytes with (111)In-oxine Inflammation/Infection. Taskgroup of the European Association of Nuclear Medicine. Eur J Nucl Med Mol Imaging 37(4):835–841

Rubeaux M, Joshi NV, Dweck MR, Fletcher A, Motwani M, Thomson LE et al (2016) Motion correction of 18F-NaF PET for imaging coronary atherosclerotic plaques. J Nucl Med 57(1):54–59

Saeedan MB, Wang TKM, Cremer P, Wahadat AR, Budde RPJ, Unai S et al (2021) Role of cardiac CT in infective endocarditis: current evidence, opportunities, and challenges. Radiol Cardiothorac Imaging 3(1):e200378

Schleyer PJ, O’Doherty MJ, Marsden PK (2011) Extension of a data-driven gating technique to 3D, whole body PET studies. Phys Med Biol 56(13):3953–3965

Schultze JL, consortium S, Rosenstiel P (2018) Systems medicine in chronic inflammatory diseases. Immunity 48(4):608–613

Schwenck J, Schorg B, Fiz F, Sonanini D, Forschner A, Eigentler T et al (2020) Cancer immunotherapy is accompanied by distinct metabolic patterns in primary and secondary lymphoid organs observed by non-invasive in vivo (18)F-FDG-PET. Theranostics 10(2):925–937

Seban RD, Champion L, Muneer I, Synn S, Schwartz LH, Dercle L (2021) Potential theranostic role of bone marrow glucose metabolism on baseline [18F]-FDG PET/CT in metastatic melanoma. J Nucl Med. https://doi.org/10.2967/jnumed.121.262361

Seith F, Forschner A, Weide B, Guckel B, Schwartz M, Schwenck J et al (2020) Is there a link between very early changes of primary and secondary lymphoid organs in (18)F-FDG-PET/MRI and treatment response to checkpoint inhibitor therapy? J Immunother Cancer. https://doi.org/10.1136/jitc-2020-000656

Shu L, Chan KHK, Zhang G, Huan T, Kurt Z, Zhao Y et al (2017) Shared genetic regulatory networks for cardiovascular disease and type 2 diabetes in multiple populations of diverse ethnicities in the United States. PLoS Genet 13(9):e1007040

Signore A, Sconfienza LM, Borens O, Glaudemans A, Cassar-Pullicino V, Trampuz A et al (2019) Consensus document for the diagnosis of prosthetic joint infections: a joint paper by the EANM, EBJIS, and ESR (with ESCMID endorsement). Eur J Nucl Med Mol Imaging 46(4):971–988

Slart R, Glaudemans A, Lancellotti P, Hyafil F, Blankstein R, Schwartz RG et al (2018a) A joint procedural position statement on imaging in cardiac sarcoidosis: from the Cardiovascular and Inflammation & Infection Committees of the European Association of Nuclear Medicine, the European Association of Cardiovascular Imaging, and the American Society of Nuclear Cardiology. J Nucl Cardiol 25(1):298–319

Slart R, Writing g, Reviewer g, Members of EC, Members of EI, Inflammation et al (2018b) FDG-PET/CT(A) imaging in large vessel vasculitis and polymyalgia rheumatica: joint procedural recommendation of the EANM, SNMMI, and the PET Interest Group (PIG), and endorsed by the ASNC. Eur J Nucl Med Mol Imaging 45(7):1250–1269

Slart R, Williams MC, Juarez-Orozco LE, Rischpler C, Dweck MR, Glaudemans A et al (2021) Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT. Eur J Nucl Med Mol Imaging 48(5):1399–1413

Spitzer MH, Carmi Y, Reticker-Flynn NE, Kwek SS, Madhireddy D, Martins MM et al (2017) Systemic immunity is required for effective cancer immunotherapy. Cell 168(3):487-502.e15

Stiekema LCA, Stroes ESG, Verweij SL, Kassahun H, Chen L, Wasserman SM et al (2019) Persistent arterial wall inflammation in patients with elevated lipoprotein(a) despite strong low-density lipoprotein cholesterol reduction by proprotein convertase subtilisin/kexin type 9 antibody treatment. Eur Heart J 40(33):2775–2781

Sugimoto MA, Vago JP, Perretti M, Teixeira MM (2019) Mediators of the resolution of the inflammatory response. Trends Immunol 40(3):212–227

Talha KM, DeSimone DC, Sohail MR, Baddour LM (2020) Pathogen influence on epidemiology, diagnostic evaluation and management of infective endocarditis. Heart 106(24):1878–1882

Ten Hove D, Slart R, Sinha B, Glaudemans A, Budde RPJ (2021) (18)F-FDG PET/CT in infective endocarditis: indications and approaches for standardization. Curr Cardiol Rep 23(9):130

Thaiss WM, Gatidis S, Sartorius T, Machann J, Peter A, Eigentler TK et al (2021) Noninvasive, longitudinal imaging-based analysis of body adipose tissue and water composition in a melanoma mouse model and in immune checkpoint inhibitor-treated metastatic melanoma patients. Cancer Immunol Immunother 70(5):1263–1275

Ungar B, Pavel AB, Robson PM, Kaufman A, Pruzan A, Brunner P et al (2020) A preliminary (18)F-FDG-PET/MRI study shows increased vascular inflammation in moderate-to-severe atopic dermatitis. J Allergy Clin Immunol Pract 8:3500–3506

Uribe CF, Mathotaarachchi S, Gaudet V, Smith KC, Rosa-Neto P, Benard F et al (2019) Machine learning in nuclear medicine: part 1—introduction. J Nucl Med 60(4):451–458

van der Heijden C, Smeets EMM, Aarntzen E, Noz MP, Monajemi H, Kersten S et al (2020) Arterial wall inflammation and increased hematopoietic activity in patients with primary aldosteronism. J Clin Endocrinol Metab. https://doi.org/10.1210/clinem/dgz306

van der Laak J, Litjens G, Ciompi F (2021) Deep learning in histopathology: the path to the clinic. Nat Med 27(5):775–784

van der Valk FM, Verweij SL, Zwinderman KA, Strang AC, Kaiser Y, Marquering HA et al (2016) Thresholds for arterial wall inflammation quantified by (18)F-FDG PET imaging: implications for vascular interventional studies. JACC Cardiovasc Imaging 9(10):1198–1207

van der Valk FM, Kuijk C, Verweij SL, Stiekema LCA, Kaiser Y, Zeerleder S et al (2017) Increased haematopoietic activity in patients with atherosclerosis. Eur Heart J 38(6):425–432

Yang X (2020) Multitissue multiomics systems biology to dissect complex diseases. Trends Mol Med 26(8):718–728

Yang J, Sohn JH, Behr SC, Gullberg GT, Seo Y (2021) CT-less direct correction of attenuation and scatter in the image space using deep learning for whole-body FDG PET: potential benefits and pitfalls. Radiol Artif Intell 3(2):e200137

Zatcepin A, Pizzichemi M, Polesel A, Paganoni M, Auffray E, Ziegler SI et al (2020) Improving depth-of-interaction resolution in pixellated PET detectors using neural networks. Phys Med Biol 65(17):175017

Zukotynski K, Gaudet V, Uribe CF, Mathotaarachchi S, Smith KC, Rosa-Neto P et al (2021) Machine learning in nuclear medicine: part 2—neural networks and clinical aspects. J Nucl Med 62(1):22–29

Zwanenburg A (2019) Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis. Eur J Nucl Med Mol Imaging 46(13):2638–2655