Skip to main content

Advertisement

Log in

Whole-body tumor burden in PET/CT expert review

  • Mini-Review
  • Published:
Clinical and Translational Imaging Aims and scope Submit manuscript

Abstract

Introduction

PET/CT whole-body tumor burden (WBTB), as a measure for overall burden of cancer, has been shown bear a strong correlation with prognosis. In the last decade, there has been significant progress in WBTB determination because of software advances and the increasing availability of positron-emitting radiopharmaceuticals. However, the determination of tumor burden with PET/CT is still a challenge especially in widespread metastatic disease.

Methods

In this non-systematic review, we will discuss the current role of determination of WBTB in cancer such as non-small cell lung cancer, lymphoma, breast cancer, among others and with a variety of radiotracers. Furthermore, we will address imaging techniques and quantification methods available and challenges.

Results

Many types of segmentation methods and different thresholds according to tumor types and radiotracers can be applied. These variations may show different WBTB results, but in general, despite variations, WBTB determination for staging purposes in lung cancer, breast cancer, lymphoma, melanoma, prostate cancer and neuroendocrine tumors have shown to bear a strong correlation with patient prognosis.

Conclusion

PET/CT whole-body tumor burden has an invaluable potential to assess prognosis. The accelerated radiopharmaceutical development will provide molecules and mechanisms to determine WBTB with advanced imaging qualification tools to further adjust radiotherapeutic doses in oncology. WBTB will most likely only become routinely accessible in clinical practice when fully automated programs become available and standardized.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Vivanti R, Szeskin A, Lev-Cohain N, Sosna J, Joskowicz L (2017) Automatic detection of new tumors and tumor burden evaluation in longitudinal liver CT scan studies. Int J Comput Assist Radiol Surg 12(11):1945–1957. https://doi.org/10.1007/s11548-017-1660-z

    Article  CAS  Google Scholar 

  2. Fletcher JW, Djulbegovic B, Soares HP, Siegel BA, Lowe VJ, Lyman GH et al (2008) Recommendations on the use of 18F-FDG PET in oncology. J Nucl Med 49(3):480–508. https://doi.org/10.2967/jnumed.107.047787

    Article  Google Scholar 

  3. Tosi D, Pieropan S, Cattoni M, Bonitta G, Franzi S, Mendogni P et al (2021) Prognostic value of 18F-FDG PET/CT metabolic parameters in surgically treated stage i lung adenocarcinoma patients. Clin Nucl Med 46(8):621–626. https://doi.org/10.1097/RLU.0000000000003714

    Article  Google Scholar 

  4. Davison J, Mercier G, Russo G, Subramaniam RM (2013) PET-Based primary tumor volumetric parameters and survival of patients with non—small cell lung carcinoma. Am J Roentgenol 200(3):635–640. https://doi.org/10.2214/AJR.12.9138

    Article  Google Scholar 

  5. Kim J, Yoo SW, Kang S-R, Cho S-G, Oh J-R, Chong A et al (2012) Prognostic significance of metabolic tumor volume measured by (18)F-FDG PET/CT in operable primary breast cancer. Nucl Med Mol Imaging 46(4):278–285. https://doi.org/10.1007/s13139-012-0161-9

    Article  Google Scholar 

  6. An Y-S, Kang DK, Jung Y, Kim TH (2017) Volume-based metabolic parameter of breast cancer on preoperative 18F-FDG PET/CT could predict axillary lymph node metastasis. Medicine. 96(45):e8557. https://doi.org/10.1097/MD.0000000000008557

    Article  Google Scholar 

  7. Elimova E, Wang X, Etchebehere E, Shiozaki H, Shimodaira Y, Wadhwa R et al (2015) 18-fluorodeoxy-glucose positron emission computed tomography as predictive of response after chemoradiation in oesophageal cancer patients. Eur J Cancer 51(17):2545–2552. https://doi.org/10.1016/j.ejca.2015.07.044

    Article  Google Scholar 

  8. Aksu A, Vural Topuz Ö, Yılmaz G, Çapa Kaya G, Yılmaz B (2022) Dual time point imaging of staging PSMA PET/CT quantification; spread and radiomic analyses. Ann Nucl Med 36(3):310–318. https://doi.org/10.1007/s12149-021-01705-5

    Article  CAS  Google Scholar 

  9. Wang H, Amiel T, Würnschimmel C, Langbein T, Steiger K, Rauscher I et al (2021) PSMA-ligand uptake can serve as a novel biomarker in primary prostate cancer to predict outcome after radical prostatectomy. EJNMMI Res 11(1):76. https://doi.org/10.1186/s13550-021-00818-2

    Article  CAS  Google Scholar 

  10. Vos JL, Zuur CL, Smit LA, de Boer JP, Al-Mamgani A, van den Brekel MWM et al (2021) [18F]FDG-PET accurately identifies pathological response early upon neoadjuvant immune checkpoint blockade in head and neck squamous cell carcinoma. Eur J Nucl Med Mol Imaging. https://doi.org/10.1007/s00259-021-05610-x

    Article  Google Scholar 

  11. Dall’Olio FG, Calabrò D, Conci N, Argalia G, Marchese PV, Fabbri F et al (2021) Baseline total metabolic tumour volume on 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography-computed tomography as a promising biomarker in patients with advanced non–small cell lung cancer treated with first-line pembrolizumab. Eur J Cancer 150:99–107. https://doi.org/10.1016/j.ejca.2021.03.020

    Article  CAS  Google Scholar 

  12. Chung HW, Lee KY, Kim HJ, Kim WS, So Y (2014) FDG PET/CT metabolic tumor volume and total lesion glycolysis predict prognosis in patients with advanced lung adenocarcinoma. J Cancer Res Clin Oncol 140(1):89–98. https://doi.org/10.1007/s00432-013-1545-7

    Article  CAS  Google Scholar 

  13. Oliveira FRA, Santos AdO, de Lima MdCL, Toro IFC, de Souza TF, Amorim BJ et al (2021) The ratio between the whole-body and primary tumor burden, measured on (18)F-FDG PET/CT studies, as a prognostic indicator in advanced non-small cell lung cancer. Radiologia brasileira. 54(5):289–94. https://doi.org/10.1590/0100-3984.2020.0054

    Article  Google Scholar 

  14. Mathew B, Vijayasekharan K, Shah S, Purandare NC, Agrawal A, Puranik A et al (2020) Prognostic value of 18F-FDG PET/CT—metabolic parameters at baseline and interim assessment in pediatric anaplastic large cell lymphoma. Clin Nucl Med 45(3):182–186. https://doi.org/10.1097/rlu.0000000000002927

    Article  Google Scholar 

  15. Meignan M, Cottereau AS, Versari A, Chartier L, Dupuis J, Boussetta S et al (2016) Baseline metabolic tumor volume predicts outcome in high–tumor-burden follicular lymphoma: a pooled analysis of three multicenter studies. J Clin Oncol 34(30):3618–3626. https://doi.org/10.1200/jco.2016.66.9440

    Article  Google Scholar 

  16. Song M-K, Chung J-S, Lee J-J, Jeong SY, Lee S-M, Hong J-S et al (2013) Metabolic tumor volume by positron emission tomography/computed tomography as a clinical parameter to determine therapeutic modality for early stage Hodgkin’s lymphoma. Cancer Sci 104(12):1656–1661. https://doi.org/10.1111/cas.12282

    Article  CAS  Google Scholar 

  17. Song M-K, Chung J-S, Shin H-J, Lee S-M, Lee S-E, Lee H-S et al (2012) Clinical significance of metabolic tumor volume by PET/CT in stages II and III of diffuse large B cell lymphoma without extranodal site involvement. Ann Hematol 91(5):697–703. https://doi.org/10.1007/s00277-011-1357-2

    Article  CAS  Google Scholar 

  18. Tatsumi M, Isohashi K, Matsunaga K, Watabe T, Kato H, Kanakura Y et al (2019) Volumetric and texture analysis on FDG PET in evaluating and predicting treatment response and recurrence after chemotherapy in follicular lymphoma. Int J Clin Oncol 24(10):1292–1300. https://doi.org/10.1007/s10147-019-01482-2

    Article  Google Scholar 

  19. Brito AE, Santos A, Sasse AD, Cabello C, Oliveira P, Mosci C et al (2017) 18F-Fluoride PET/CT tumor burden quantification predicts survival in breast cancer. Oncotarget 8(22):36001–36011. https://doi.org/10.18632/oncotarget.16418

    Article  Google Scholar 

  20. Zou Q, Jiao J, Zou M-h, Li M-z, Yang T, Xu L et al (2020) Semi-automatic evaluation of baseline whole-body tumor burden as an imaging biomarker of 68Ga-PSMA-11 PET/CT in newly diagnosed prostate cancer. Abdomin Radiol 45(12):4202–13. https://doi.org/10.1007/s00261-020-02745-7

    Article  Google Scholar 

  21. Lee J, Sato MM, Coel MN, Lee K-H, Kwee SA (2016) Prediction of PSA progression in castration-resistant prostate cancer based on treatment-associated change in tumor burden quantified by <sup>18</sup>F-Fluorocholine PET/CT. J Nucl Med 57(7):1058. https://doi.org/10.2967/jnumed.115.169177

    Article  CAS  Google Scholar 

  22. Mikhaeel NG, Smith D, Dunn JT, Phillips M, Møller H, Fields PA et al (2016) Combination of baseline metabolic tumour volume and early response on PET/CT improves progression-free survival prediction in DLBCL. Eur J Nucl Med Mol Imaging 43(7):1209–1219. https://doi.org/10.1007/s00259-016-3315-7

    Article  CAS  Google Scholar 

  23. Son SH, Lee S-W, Jeong SY, Song B-I, Chae YS, Ahn B-C et al (2015) Whole-Body metabolic tumor volume, as determined by 18F-FDG PET/CT, as a prognostic factor of outcome for patients with breast cancer who have distant metastasis. Am J Roentgenol 205(4):878–885. https://doi.org/10.2214/AJR.14.13906

    Article  Google Scholar 

  24. Takahashi N, Umezawa R, Takanami K, Yamamoto T, Ishikawa Y, Kozumi M et al (2018) Whole-body total lesion glycolysis is an independent predictor in patients with esophageal cancer treated with definitive chemoradiotherapy. Radiother Oncol 129(1):161–165. https://doi.org/10.1016/j.radonc.2017.10.019

    Article  CAS  Google Scholar 

  25. Brito AET, Mourato FA, de Oliveira RPM, Leal ALG, Filho PJA, de Filho JLL (2019) Evaluation of whole-body tumor burden with 68Ga-PSMA PET/CT in the biochemical recurrence of prostate cancer. Ann Nucl Med 33(5):344–350. https://doi.org/10.1007/s12149-019-01342-z

    Article  CAS  Google Scholar 

  26. Quaquarini E, D’Ambrosio D, Sottotetti F, Gallivanone F, Hodolic M, Baiardi P et al (2019) Prognostic Value of (18)F-Fluorocholine PET parameters in metastatic castrate-resistant prostate cancer patients treated with docetaxel. Contrast Media Mol Imaging 2019:4325946. https://doi.org/10.1155/2019/4325946

    Article  CAS  Google Scholar 

  27. Santos A, Mattiolli A, Carvalheira JBC, Ferreira U, Camacho M, Silva C et al (2021) PSMA whole-body tumor burden in primary staging and biochemical recurrence of prostate cancer. Eur J Nucl Med Mol Imaging 48(2):493–500. https://doi.org/10.1007/s00259-020-04981-x

    Article  CAS  Google Scholar 

  28. Chen S-W, Hsieh T-C, Yen K-Y, Liang J-A, Kao C-H (2014) Pretreatment 18F-FDG PET/CT in whole-body total lesion glycolysis to predict survival in patients with pharyngeal cancer treated with definitive radiotherapy. Clin Nuclear Med 39(5):e296

    Article  Google Scholar 

  29. Kanoun S, Rossi C, Berriolo-Riedinger A, Dygai-Cochet I, Cochet A, Humbert O et al (2014) Baseline metabolic tumour volume is an independent prognostic factor in Hodgkin lymphoma. Eur J Nucl Med Mol Imaging 41(9):1735–1743. https://doi.org/10.1007/s00259-014-2783-x

    Article  Google Scholar 

  30. Capobianco N, Meignan M, Cottereau A, Vercellino L, Sibille L, Spottiswoode B et al (2021) Deep-Learning F-18-FDG uptake classification enables total metabolic tumor volume estimation in diffuse large B-Cell lymphoma. J Nucl Med 62(1):30–36. https://doi.org/10.2967/jnumed.120.242412

    Article  CAS  Google Scholar 

  31. Malek E, Sendilnathan A, Yellu M, Petersen A, Fernandez-Ulloa M, Driscoll JJ (2015) Metabolic tumor volume on interim PET is a better predictor of outcome in diffuse large B-cell lymphoma than semiquantitative methods. Blood Cancer J 5(7):e326. https://doi.org/10.1038/bcj.2015.51

    Article  CAS  Google Scholar 

  32. Kim TM, Paeng JC, Chun IK, Keam B, Jeon YK, Lee S-H et al (2013) Total lesion glycolysis in positron emission tomography is a better predictor of outcome than the International Prognostic Index for patients with diffuse large B cell lymphoma. Cancer 119(6):1195–1202. https://doi.org/10.1002/cncr.27855

    Article  CAS  Google Scholar 

  33. Ito K, Schöder H, Teng R, Humm JL, Ni A, Wolchok JD et al (2019) Prognostic value of baseline metabolic tumor volume measured on 18F-fluorodeoxyglucose positron emission tomography/computed tomography in melanoma patients treated with ipilimumab therapy. Eur J Nucl Med Mol Imaging 46(4):930–939. https://doi.org/10.1007/s00259-018-4211-0

    Article  CAS  Google Scholar 

  34. Nakamoto R, Zaba LC, Rosenberg J, Reddy SA, Nobashi TW, Davidzon G et al (2020) Prognostic value of volumetric PET parameters at early response evaluation in melanoma patients treated with immunotherapy. Eur J Nucl Med Mol Imaging 47(12):2787–2795. https://doi.org/10.1007/s00259-020-04792-0

    Article  CAS  Google Scholar 

  35. Sullivan D, Obuchowski N, Kessler L, Raunig D, Gatsonis C, Huang E et al (2015) Metrology standards for quantitative imaging biomarkers. Radiology 277(3):813–825. https://doi.org/10.1148/radiol.2015142202

    Article  Google Scholar 

  36. Adams M, Turkington T, Wilson J, Wong T (2010) A Systematic review of the factors affecting accuracy of SUV measurements. Am J Roentgenol 195(2):310–320. https://doi.org/10.2214/AJR.10.4923

    Article  Google Scholar 

  37. Zhao J, Xue Q, Chen X, You Z, Wang Z, Yuan J et al (2021) Evaluation of SUVlean consistency in FDG and PSMA PET/MR with Dixon-, James-, and Janma-based lean body mass correction. EJNMMI Physics 8(1):17. https://doi.org/10.1186/s40658-021-00363-w

    Article  Google Scholar 

  38. Kim CK, Gupta NC, Chandramouli B, Alavi A (1994) Standardized uptake values of FDG: body surface area correction is preferable to body weight correction. J Nucl Med 35(1):164–167

    CAS  Google Scholar 

  39. Haghighat Jahromi A, Barkauskas DA, Zabel M, Goodman AM, Frampton G, Nikanjam M et al (2020) Relationship between tumor mutational burden and maximum standardized uptake value in 2-[18F]FDG PET (positron emission tomography) scan in cancer patients. EJNMMI Res 10(1):150. https://doi.org/10.1186/s13550-020-00732-z

    Article  CAS  Google Scholar 

  40. Siminiak N, Wojciechowska K, Miechowicz I, Cholewinski W, Ruchala M, Czepczynski R (2019) 18F-choline positron emission tomography/computed tomography for the detection of prostate cancer relapse: assessment of maximum standardized uptake value correlation with prostate-specific antigen levels. Nuclear Med Commun 40(12):1263

    Article  CAS  Google Scholar 

  41. Zhu D, Wang Y, Wang L, Chen J, Byanju S, Zhang H et al (2017) Prognostic value of the maximum standardized uptake value of pre-treatment primary lesions in small-cell lung cancer on 18F-FDG PET/CT: a meta-analysis. Acta Radiol 59(9):1082–1090. https://doi.org/10.1177/0284185117745907

    Article  Google Scholar 

  42. Kim SJ, Chang S (2015) Limited prognostic value of SUV<sub>max</sub> measured by F-18 FDG PET/CT in newly diagnosed small cell lung cancer patients. Oncol Res Treatment 38(11):577–585. https://doi.org/10.1159/000441289

    Article  CAS  Google Scholar 

  43. Im H-J, Bradshaw T, Solaiyappan M, Cho SY (2018) Current methods to define metabolic tumor volume in positron emission tomography: which one is better? Nucl Med Mol Imaging 52(1):5–15

    Article  Google Scholar 

  44. Sher A, Lacoeuille F, Fosse P, Vervueren L, Cahouet-Vannier A, Dabli D et al (2016) For avid glucose tumors, the SUV peak is the most reliable parameter for [(18)F]FDG-PET/CT quantification, regardless of acquisition time. EJNMMI Research 6(1):21. https://doi.org/10.1186/s13550-016-0177-8

    Article  CAS  Google Scholar 

  45. Takahashi MES, Mosci C, Souza EM, Brunetto SQ, de Souza C, Pericole FV et al (2020) Computed tomography–based skeletal segmentation for quantitative PET metrics of bone involvement in multiple myeloma. Nucl Med Commun 41(4):377

    Article  CAS  Google Scholar 

  46. Oh J-R, Seo J-H, Chong A, Min J-J, Song H-C, Kim Y-C et al (2012) Whole-body metabolic tumour volume of 18F-FDG PET/CT improves the prediction of prognosis in small cell lung cancer. Eur J Nucl Med Mol Imaging 39(6):925–935. https://doi.org/10.1007/s00259-011-2059-7

    Article  Google Scholar 

  47. Soussan M, Chouahnia K, Maisonobe J-A, Boubaya M, Eder V, Morère J-F et al (2013) Prognostic implications of volume-based measurements on FDG PET/CT in stage III non-small-cell lung cancer after induction chemotherapy. Eur J Nucl Med Mol Imaging 40(5):668–676. https://doi.org/10.1007/s00259-012-2321-7

    Article  CAS  Google Scholar 

  48. Pellegrino S, Fonti R, Mazziotti E, Piccin L, Mozzillo E, Damiano V et al (2019) Total metabolic tumor volume by 18F-FDG PET/CT for the prediction of outcome in patients with non-small cell lung cancer. Ann Nucl Med 33(12):937–944. https://doi.org/10.1007/s12149-019-01407-z

    Article  CAS  Google Scholar 

  49. Larson SM, Erdi Y, Akhurst T, Mazumdar M, Macapinlac HA, Finn RD et al (1999) Tumor treatment response based on visual and quantitative changes in global tumor glycolysis using PET-FDG imaging: the visual response score and the change in total lesion glycolysis. Clin Positron Imaging 2(3):159–171

    Article  Google Scholar 

  50. Duarte PS, Sapienza MT (2021) Letter to the Editor: It is time for the nuclear medicine community to define a unit for the total lesion glycolysis (TLG) and similar metrics. Eur J Nucl Med Mol Imaging 48:2312

    Article  Google Scholar 

  51. Takahashi MES, Mosci C, Souza EM, Brunetto SQ, Etchebehere E, Santos AO et al (2019) Proposal for a quantitative 18F-FDG PET/CT metabolic parameter to assess the intensity of bone involvement in multiple myeloma. Sci Rep 9(1):16429. https://doi.org/10.1038/s41598-019-52740-2

    Article  CAS  Google Scholar 

  52. Seifert R, Sandach P, Kersting D, Fendler WP, Hadaschik B, Herrmann K et al (2021) Repeatability of 68Ga-PSMA-HBED-CC PET/CT-derived total molecular tumor volume. J Nuclear Med. https://doi.org/10.2967/jnumed.121.262528

    Article  Google Scholar 

  53. Brito AE, Mourato F, Santos A, Mosci C, Ramos C, Etchebehere E (2018) Validation of the semiautomatic quantification of <sup>18</sup>F-Fluoride PET/CT whole-body skeletal tumor burden. J Nucl Med Technol 46(4):378. https://doi.org/10.2967/jnmt.118.211474

    Article  Google Scholar 

  54. Foster B, Bagci U, Mansoor A, Xu Z, Mollura DJ (2014) A review on segmentation of positron emission tomography images. Comput Biol Med 50:76–96. https://doi.org/10.1016/j.compbiomed.2014.04.014

    Article  Google Scholar 

  55. Zirakchian Zadeh M, Raynor WY, Østergaard B, Hess S, Yellanki DP, Ayubcha C et al (2020) Correlation of whole-bone marrow dual-time-point (18)F-FDG, as measured by a CT-based method of PET/CT quantification, with response to treatment in newly diagnosed multiple myeloma patients. Am J Nuclear Medicine Mol Imaging 10(5):257–264

    Google Scholar 

  56. Kothekar E, Yellanki D, Borja AJ, Al-zaghal A, Werner TJ, Revheim M-E et al (2020) 18F-FDG-PET/CT in measuring volume and global metabolic activity of thigh muscles: a novel CT-based tissue segmentation methodology. Nuclear Med Commun 41(2):162

    Article  Google Scholar 

  57. Al-Zaghal A, Yellanki DP, Ayubcha C, Hon AAMD (2018) CT-based tissue segmentation to assess knee joint inflammation and reactive bone formation assessed by 18 18 F-FDG and F-NaF PET/CT: Effects of age and BMI. Hell J Nucl Med 21(2):102–107

    Google Scholar 

  58. Starmans MPA, van der Voort SR, Tovar JMC, Veenland JF, Klein S, Niessen WJ (2020) Radiomics: Data mining using quantitative medical image features. In: Zhou K, Rueckert D, Fichtinger G (eds) Handbook of medical image computing and computer assisted intervention. Elsevier, Amsterdam, pp 429–56

    Chapter  Google Scholar 

  59. Pfaehler E, Mesotten L, Kramer G, Thomeer M, Vanhove K, de Jong J et al (2021) Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET. EJNMMI Res 11(1):4. https://doi.org/10.1186/s13550-020-00744-9

    Article  Google Scholar 

  60. van Sluis J, de Heer EC, Boellaard M, Jalving M, Brouwers AH, Boellaard R (2021) Clinically feasible semi-automatic workflows for measuring metabolically active tumour volume in metastatic melanoma. Eur J Nucl Med Mol Imaging 48(5):1498–1510. https://doi.org/10.1007/s00259-020-05068-3

    Article  Google Scholar 

  61. Wehrend J, Silosky M, Xing F, Chin BB (2021) Automated liver lesion detection in. EJNMMI Res 11(1):98. https://doi.org/10.1186/s13550-021-00839-x

    Article  CAS  Google Scholar 

  62. Georgi TW, Zieschank A, Kornrumpf K, Kurch L, Sabri O, Körholz D et al (2022) Automatic classification of lymphoma lesions in FDG-PET-Differentiation between tumor and non-tumor uptake. PLoS ONE 17(4):e0267275. https://doi.org/10.1371/journal.pone.0267275

    Article  CAS  Google Scholar 

  63. Gallivanone F, Fazio F, Presotto L, Gilardi MC, Canevari C, Castiglioni I. Adaptive threshold method based on PET measured lesion-to-background ratio for the estimation of Metabolic Target Volume from <sup>18</sup>F-FDG PET images. 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC)2013. p. 1–7.

  64. Schaefer A, Kim YJ, Kremp S, Mai S, Fleckenstein J, Bohnenberger H et al (2013) PET-based delineation of tumour volumes in lung cancer: comparison with pathological findings. Eur J Nucl Med Mol Imaging 40(8):1233–1244. https://doi.org/10.1007/s00259-013-2407-x

    Article  CAS  Google Scholar 

  65. Schaefer A, Kremp S, Hellwig D, Rübe C, Kirsch C-M, Nestle U (2008) A contrast-oriented algorithm for FDG-PET-based delineation of tumour volumes for the radiotherapy of lung cancer: derivation from phantom measurements and validation in patient data. Eur J Nucl Med Mol Imaging 35(11):1989–1999. https://doi.org/10.1007/s00259-008-0875-1

    Article  Google Scholar 

  66. Black QC, Grills IS, Kestin LL, Wong YO, Wong JW, Martinez AA et al (2004) Defining a radiotherapy target with positron emission tomography. Int J Radiation Phys 60(4):1272–82. https://doi.org/10.1016/j.ijrobp.2004.06.254

    Article  Google Scholar 

  67. Nestle U, Kremp S, Schaefer-Schuler A, Sebastian-Welsch C, Hellwig D, Rübe C et al (2005) Comparison of different methods for delineation of <sup>18</sup>F-FDG PET–positive tissue for target volume definition in radiotherapy of patients with non-small cell lung cancer. J Nucl Med 46(8):1342

    Google Scholar 

  68. Drever L, Robinson DM, McEwan A, Roa W (2006) A local contrast based approach to threshold segmentation for PET target volume delineation. Med Phys 33:1583–94. https://doi.org/10.1118/1.2198308

    Article  Google Scholar 

  69. Riegel AC, Bucci MK, Mawlawi OR, Johnson V, Ahmad M, Sun X et al (2010) Target definition of moving lung tumors in positron emission tomography: correlation of optimal activity concentration thresholds with object size, motion extent, and source-to-background ratio. Med Phys 37(4):1742–1752. https://doi.org/10.1118/1.3315369

    Article  Google Scholar 

  70. Matheoud R, Della Monica P, Secco C, Loi G, Krengli M, Inglese E et al (2011) Influence of different contributions of scatter and attenuation on the threshold values in contrast-based algorithms for volume segmentation. Physica Med 27(1):44–51. https://doi.org/10.1016/j.ejmp.2010.02.003

    Article  Google Scholar 

  71. Jentzen W, Freudenberg L, Eising EG, Heinze M, Brandau W, Bockisch A (2007) Segmentation of PET volumes by iterative image thresholding. J Nucl Med 48(1):108–114

    CAS  Google Scholar 

  72. Hirata K, Kobayashi K, Wong K-P, Manabe O, Surmak A, Tamaki N et al (2014) A semi-automated technique determining the liver standardized uptake value reference for tumor delineation in FDG PET-CT. PloS One 9(8):e105682. https://doi.org/10.1371/journal.pone.0105682

    Article  CAS  Google Scholar 

  73. Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T et al (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9(7):676–682. https://doi.org/10.1038/nmeth.2019

    Article  CAS  Google Scholar 

  74. Fonti R, Larobina M, Del Vecchio S, De Luca S, Fabbricini R, Catalano L et al (2012) Metabolic tumor volume assessed by 18F-FDG PET/CT for the prediction of outcome in patients with multiple myeloma. J Nucl Med 53(12):1829–1835

    Article  CAS  Google Scholar 

  75. Im H-J, Pak K, Cheon GJ, Kang KW, Kim S-J, Kim I-J et al (2015) Prognostic value of volumetric parameters of 18F-FDG PET in non-small-cell lung cancer: a meta-analysis. Eur J Nucl Med Mol Imaging 42(2):241–251. https://doi.org/10.1007/s00259-014-2903-7

    Article  CAS  Google Scholar 

  76. Rohren EM, Etchebehere EC, Araujo JC, Hobbs BP, Swanston NM, Everding M et al (2015) Determination of skeletal tumor burden on 18F-Fluoride PET/CT. J Nuclear Med: Official Publication, Society of Nuclear Med 56(10):1507–1512. https://doi.org/10.2967/jnumed.115.156026

    Article  CAS  Google Scholar 

  77. Eude F, Toledano MN, Vera P, Tilly H, Mihailescu S-D, Becker S (2021) Reproducibility of baseline tumour metabolic volume measurements in diffuse large B-Cell lymphoma: is there a superior method? Metabolites 11(2):72

    Article  CAS  Google Scholar 

  78. Boellaard R, Delgado-Bolton R, Oyen WJG, Giammarile F, Tatsch K, Eschner W et al (2015) FDG PET/CT: EANM procedure guidelines for tumour imaging: version 20. Eur J Nuclear Med Mol Imaging 42(2):328–54

    Article  CAS  Google Scholar 

  79. Meignan M, Sasanelli M, Casasnovas RO, Luminari S, Fioroni F, Coriani C et al (2014) Metabolic tumour volumes measured at staging in lymphoma: methodological evaluation on phantom experiments and patients. Eur J Nucl Med Mol Imaging 41(6):1113–1122

    Article  CAS  Google Scholar 

  80. Xu W, Yu S, Ma Y, Liu C, Xin J (2017) Effect of different segmentation algorithms on metabolic tumor volume measured on 18F-FDG PET/CT of cervical primary squamous cell carcinoma. Nucl Med Commun 38(3):259–265. https://doi.org/10.1097/MNM.0000000000000641

    Article  Google Scholar 

  81. Ramos CD, Erdi YE, Gonen M, Riedel E, Yeung HWD, Macapinlac HA et al (2001) FDG-PET standardized uptake values in normal anatomical structures using iterative reconstruction segmented attenuation correction and filtered back-projection. Eur J Nucl Med 28(2):155–164

    Article  CAS  Google Scholar 

  82. Gafita A, Bieth M, Kroenke M, Tetteh G, Guenther E, Menze B et al (2019) qPSMA: a semi-automatic software for whole-body tumor burden assessment in prostate cancer using 68Ga-PSMA11 PET/CT. J Nuclear Med. https://doi.org/10.2967/jnumed.118.224055

    Article  Google Scholar 

  83. Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25(1):44–56. https://doi.org/10.1038/s41591-018-0300-7

    Article  CAS  Google Scholar 

  84. Seifert R, Weber M, Kocakavuk E, Rischpler C, Kersting D. Artificial intelligence and machine learning in nuclear medicine: future perspectives. Elsevier: Amsterdam. p. 170–7.

  85. Sollini M, Antunovic L, Chiti A, Kirienko M (2019) Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics. Eur J Nucl Med Mol Imaging 46(13):2656–2672. https://doi.org/10.1007/s00259-019-04372-x

    Article  Google Scholar 

  86. Zaidi H, El Naqa I (2021) Quantitative molecular positron emission tomography imaging using advanced deep learning techniques. Annu Rev Biomed Eng 23(1):249–276. https://doi.org/10.1146/annurev-bioeng-082420-020343

    Article  CAS  Google Scholar 

  87. Porenta G (2019) Is there value for artificial intelligence applications in molecular imaging and nuclear medicine? J Nucl Med 60(10):1347. https://doi.org/10.2967/jnumed.119.227702

    Article  Google Scholar 

  88. Arabi H, AkhavanAllaf A, Sanaat A, Shiri I, Zaidi H (2021) The promise of artificial intelligence and deep learning in PET and SPECT imaging. Physica Med 83:122–137. https://doi.org/10.1016/j.ejmp.2021.03.008

    Article  Google Scholar 

  89. Castiglioni I, Gallivanone F, Soda P, Avanzo M, Stancanello J, Aiello M et al (2019) AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics. Eur J Nucl Med Mol Imaging 46(13):2673–2699. https://doi.org/10.1007/s00259-019-04414-4

    Article  Google Scholar 

  90. Xu L, Tetteh G, Lipkova J, Zhao Y, Li H, Christ P et al (2018) Automated whole-body bone lesion detection for multiple myeloma on 68Ga-Pentixafor PET/CT imaging using deep learning methods. Contrast Media Mol Imaging 2018:2391925

    Article  Google Scholar 

  91. Capobianco N, Sibille L, Chantadisai M, Gafita A, Langbein T, Platsch G et al (2022) Whole-body uptake classification and prostate cancer staging in 68Ga-PSMA-11 PET/CT using dual-tracer learning. Eur J Nucl Med Mol Imaging 49(2):517–526. https://doi.org/10.1007/s00259-021-05473-2

    Article  CAS  Google Scholar 

  92. Blanc-Durand P, Jégou S, Kanoun S, Berriolo-Riedinger A, Bodet-Milin C, Kraeber-Bodéré F et al (2021) Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET/CT for total metabolic tumour volume prediction using a convolutional neural network. Eur J Nucl Med Mol Imaging 48(5):1362–1370. https://doi.org/10.1007/s00259-020-05080-7

    Article  CAS  Google Scholar 

  93. Jemaa S, Fredrickson J, Carano RAD, Nielsen T, de Crespigny A, Bengtsson T (2020) Tumor Segmentation and feature extraction from whole-body FDG-PET/CT using cascaded 2D and 3D convolutional neural networks. J Digit Imaging 33(4):888–894. https://doi.org/10.1007/s10278-020-00341-1

    Article  Google Scholar 

  94. Sibille L, Seifert R, Avramovic N, Vehren T, Spottiswoode B, Zuehlsdorff S et al (2020) 18F-FDG PET/CT uptake classification in lymphoma and lung cancer by using deep convolutional neural networks. Radiology 294(2):445–452. https://doi.org/10.1148/radiol.2019191114

    Article  Google Scholar 

  95. Sollini M, Bartoli F, Marciano A, Zanca R, Slart RHJA, Erba PA (2020) Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology. Eur J Hybrid Imaging 4(1):24. https://doi.org/10.1186/s41824-020-00094-8

    Article  Google Scholar 

  96. Pauwels EKJ, Ribeiro MJ, Stoot JHMB, McCready VR, Bourguignon M, Mazière B (1998) FDG accumulation and tumor biology. Nucl Med Biol 25(4):317–322. https://doi.org/10.1016/S0969-8051(97)00226-6

    Article  CAS  Google Scholar 

  97. Erol M, Önner H, Eren Karanis Mİ (2021) Evaluation of the histopathological features of early-stage invasive ductal breast carcinoma by (18)Fluoride-fluorodeoxyglucose positron emission tomography/computed tomography. Mol Imaging Radionuclide Therapy 30(3):129–136. https://doi.org/10.4274/mirt.galenos.2021.25582

    Article  Google Scholar 

  98. Chang C-C, Cho S-F, Chen Y-W, Tu H-P, Lin C-Y, Chang C-S (2012) SUV on Dual-Phase FDG PET/CT correlates with the Ki-67 proliferation index in patients with newly diagnosed non-hodgkin lymphoma. Clinical Nuclear Med 37(8):e189

    Article  Google Scholar 

  99. Özdemir S, Sılan F, Akgün MY, Aracı N, Çırpan İ, Koç Öztürk F et al (2021) Prognostic prediction of BRCA mutations by (18)F-FDG PET/CT SUV(max) in breast cancer. Mol Imaging Radionuclide Therapy 30(3):158–168. https://doi.org/10.4274/mirt.galenos.2021.82584

    Article  Google Scholar 

  100. Kostakoglu L, Chauvie S (2018) Metabolic tumor volume metrics in lymphoma. Semin Nucl Med 48(1):50–66. https://doi.org/10.1053/j.semnuclmed.2017.09.005

    Article  Google Scholar 

  101. Albano D, Mazzoletti A, Spallino M, Muzi C, Zilioli VR, Pagani C et al (2020) Prognostic role of baseline 18F-FDG PET/CT metabolic parameters in elderly HL: a two-center experience in 123 patients. Ann Hematol 99(6):1321–1330. https://doi.org/10.1007/s00277-020-04039-w

    Article  Google Scholar 

  102. Herraez I, Bento L, Daumal J, Repetto A, Del Campo R, Perez S et al (2021) Total lesion glycolysis improves tumor burden evaluation and risk assessment at diagnosis in hodgkin lymphoma. J Clin Med 10(19):4396. https://doi.org/10.3390/jcm10194396

    Article  CAS  Google Scholar 

  103. Krarup MMK, Krokos G, Subesinghe M, Nair A, Fischer BM (2021) Artificial intelligence for the characterization of pulmonary nodules, lung tumors and mediastinal nodes on PET/CT. Semin Nucl Med 51(2):143–156. https://doi.org/10.1053/j.semnuclmed.2020.09.001

    Article  Google Scholar 

  104. Gupta N, Singh N (2020) To evaluate prognostic significance of metabolic-derived tumour volume at staging 18-flurodeoxyglucose PET-CT scan and to compare it with standardized uptake value-based response evaluation on interim 18-flurodeoxyglucose PET-CT scan in patients of non-Hodgkin’s lymphoma (diffuse large B-cell lymphoma). Nuclear Med Commun 41(4):395

    Article  CAS  Google Scholar 

  105. Organization WH: breast cancer. https://www.who.int/news-room/fact-sheets/detail/breast-cancer (2021). Accessed 03 Dec 2022.

  106. Groheux D, Espié M, Giacchetti S, Hindié E (2013) Performance of FDG PET/CT in the clinical management of breast cancer. Radiology 266(2):388–405. https://doi.org/10.1148/radiol.12110853

    Article  Google Scholar 

  107. Wang L, Zhang S, Wang X (2021) The metabolic mechanisms of breast cancer metastasis. Front Oncol. https://doi.org/10.3389/fonc.2020.602416

    Article  Google Scholar 

  108. Iqbal R, Mammatas LH, Aras T, Vogel WV, van de Brug T, Oprea-Lager DE et al (2021) Diagnostic Performance of [(18)F]FDG PET in Staging Grade 1–2, Estrogen Receptor Positive Breast Cancer. Diagnostics (Basel, Switzerland) 11(11):1954. https://doi.org/10.3390/diagnostics11111954

    Article  CAS  Google Scholar 

  109. Chen X, Zhang C, Guo D, Wang Y, Hu J, Hu J et al (2022) Distant metastasis and prognostic factors in patients with invasive ductal carcinoma of the breast. Eur J Clin Invest 52(4):e13704. https://doi.org/10.1111/eci.13704

    Article  CAS  Google Scholar 

  110. Evangelista L, Cervino AR, Ghiotto C, Saibene T, Michieletto S, Fernando B et al (2015) Could semiquantitative FDG analysis add information to the prognosis in patients with stage II/III breast cancer undergoing neoadjuvant treatment? Eur J Nucl Med Mol Imaging 42(11):1648–1655. https://doi.org/10.1007/s00259-015-3088-4

    Article  CAS  Google Scholar 

  111. Reinert CP, Gatidis S, Sekler J, Dittmann H, Pfannenberg C, la Fougère C et al (2020) Clinical and prognostic value of tumor volumetric parameters in melanoma patients undergoing 18F-FDG-PET/CT: a comparison with serologic markers of tumor burden and inflammation. Cancer Imaging 20(1):44. https://doi.org/10.1186/s40644-020-00322-1

    Article  Google Scholar 

  112. Kitajima K, Watabe T, Nakajo M, Ishibashi M, Daisaki H, Soeda F et al (2022) Tumor response evaluation in patients with malignant melanoma undergoing immune checkpoint inhibitor therapy and prognosis prediction using (18)F-FDG PET/CT: multicenter study for comparison of EORTC, PERCIST, and imPERCIST. Jpn J Radiol 40(1):75–85. https://doi.org/10.1007/s11604-021-01174-w

    Article  CAS  Google Scholar 

  113. Prigent K, Lasnon C, Ezine E, Janson M, Coudrais N, Joly E et al (2021) Assessing immune organs on 18F-FDG PET/CT imaging for therapy monitoring of immune checkpoint inhibitors: inter-observer variability, prognostic value and evolution during the treatment course of melanoma patients. Eur J Nucl Med Mol Imaging 48(8):2573–2585. https://doi.org/10.1007/s00259-020-05103-3

    Article  CAS  Google Scholar 

  114. Liberini V, Rubatto M, Mimmo R, Passera R, Ceci F, Fava P et al (2021) Predictive value of baseline [18F]FDG PET/CT for response to systemic therapy in patients with advanced melanoma. J Clin Med. https://doi.org/10.3390/jcm10214994

    Article  Google Scholar 

  115. Robert C, Long GV, Brady B, Dutriaux C, Maio M, Mortier L et al (2014) Nivolumab in previously untreated melanoma without BRAF mutation. N Engl J Med 372(4):320–330. https://doi.org/10.1056/NEJMoa1412082

    Article  CAS  Google Scholar 

  116. Hodi FS, O’Day SJ, McDermott DF, Weber RW, Sosman JA, Haanen JB et al (2010) Improved survival with Ipilimumab in patients with metastatic melanoma. N Engl J Med 363(8):711–723. https://doi.org/10.1056/NEJMoa1003466

    Article  CAS  Google Scholar 

  117. Wolchok JD, Chiarion-Sileni V, Gonzalez R, Rutkowski P, Grob J-J, Cowey CL et al (2017) Overall survival with combined nivolumab and Ipilimumab in advanced melanoma. N Engl J Med 377(14):1345–1356. https://doi.org/10.1056/NEJMoa1709684

    Article  CAS  Google Scholar 

  118. Mathew B, Purandare NC, Puranik A, Shah S, Agrawal A, Pramesh CS et al (2020) Prognostic value of metabolic parameters measured by 18F-fluorodeoxyglucose positron emission tomography-computed tomography in surgically resected non-small cell lung cancer patients. World J Nuclear Med 19(01):8–14

    Article  Google Scholar 

  119. Dashevsky BZ, Zhang C, Yan L, Yuan C, Xiong L, Liu Y et al (2017) Whole body metabolic tumor volume is a prognostic marker in patients with newly diagnosed stage 3B non-small cell lung cancer, confirmed with external validation. Eur J Hybrid Imag. https://doi.org/10.1186/s41824-017-0013-z

    Article  Google Scholar 

  120. Borrelli P, Ly J, Kaboteh R, Ulén J, Enqvist O, Trägårdh E et al (2021) AI-based detection of lung lesions in [18F]FDG PET-CT from lung cancer patients. EJNMMI Physics 8(1):32. https://doi.org/10.1186/s40658-021-00376-5

    Article  Google Scholar 

  121. Bortot DC, Amorim BJ, Oki GC, Gapski SB, Santos AO, Lima MCL et al (2012) 18F-Fluoride PET/CT is highly effective for excluding bone metastases even in patients with equivocal bone scintigraphy. Eur J Nucl Med Mol Imaging 39(11):1730–1736. https://doi.org/10.1007/s00259-012-2195-8

    Article  Google Scholar 

  122. Even-Sapir E, Metser U, Mishani E, Lievshitz G, Lerman H, Leibovitch I (2006) The Detection of Bone Metastases in Patients with High-Risk Prostate Cancer: <sup>99m</sup>Tc-MDP Planar Bone Scintigraphy, Single- and Multi-Field-of-View SPECT, <sup>18</sup>F-Fluoride PET, and <sup>18</sup>F-Fluoride PET/CT. J Nucl Med 47(2):287

    Google Scholar 

  123. Etchebehere EC, Araujo JC, Fox PS, Swanston NM, Macapinlac HA, Rohren EM (2015) Prognostic Factors in Patients Treated with <sup>223</sup>Ra: the role of skeletal tumor burden on baseline <sup>18</sup>F-Fluoride PET/CT in predicting overall survival. J Nucl Med 56(8):1177. https://doi.org/10.2967/jnumed.115.158626

    Article  CAS  Google Scholar 

  124. Etchebehere EC, Araujo JC, Milton DR, Erwin WD, Wendt RE III, Swanston NM et al (2016) Skeletal tumor burden on baseline 18F-Fluoride PET/CT predicts bone marrow failure after 223Ra therapy. Clin Nuclear Med 41(4):268

    Article  Google Scholar 

  125. Can C, Gündoğan C, Yildirim OA, Poyraz K, Güzel Y, Kömek H (2021) Role of 68Ga-PSMA PET/CT parameters in treatment evaluation and survival prediction in prostate cancer patients compared with biochemical response assessment. Hell J Nucl Med 24(1):25–35. https://doi.org/10.1967/s002449912303

    Article  Google Scholar 

  126. Schmidkonz C, Cordes M, Schmidt D, Bäuerle T, Goetz TI, Beck M et al (2018) 68Ga-PSMA-11 PET/CT-derived metabolic parameters for determination of whole-body tumor burden and treatment response in prostate cancer. Eur J Nucl Med Mol Imaging 45(11):1862–1872. https://doi.org/10.1007/s00259-018-4042-z

    Article  Google Scholar 

  127. Schmuck S, von Klot CA, Henkenberens C, Sohns JM, Christiansen H, Wester H-J et al (2017) Initial experience with volumetric <sup>68</sup>Ga-PSMA I&T PET/CT for assessment of whole-body tumor burden as a quantitative imaging biomarker in patients with prostate cancer. J Nucl Med 58(12):1962. https://doi.org/10.2967/jnumed.117.193581

    Article  CAS  Google Scholar 

  128. Waldum HL, Arnestad JS, Brenna E, Eide I, Syversen U, Sandvik AK (1996) Marked increase in gastric acid secretory capacity after omeprazole treatment. Gut 39(5):649–653. https://doi.org/10.1136/gut.39.5.649

    Article  CAS  Google Scholar 

  129. Malaguarnera M, Cristaldi E, Cammalleri L, Colonna V, Lipari H, Capici A et al (2009) Elevated chromogranin A (CgA) serum levels in the patients with advanced pancreatic cancer. Arch Gerontol Geriatr 48(2):213–217. https://doi.org/10.1016/j.archger.2008.01.014

    Article  CAS  Google Scholar 

  130. Marotta V, Zatelli MC, Sciammarella C, Ambrosio MR, Bondanelli M, Colao A et al (2018) Chromogranin A as circulating marker for diagnosis and management of neuroendocrine neoplasms: more flaws than fame. Endocr Relat Cancer 25(1):R11–R29. https://doi.org/10.1530/ERC-17-0269

    Article  CAS  Google Scholar 

  131. Sciola V, Massironi S, Conte D, Caprioli F, Ferrero S, Ciafardini C et al (2009) Plasma Chromogranin A in Patients with Inflammatory Bowel Disease. Inflamm Bowel Dis 15(6):867–871. https://doi.org/10.1002/ibd.20851

    Article  Google Scholar 

  132. Falconi M, Bartsch DK, Eriksson B, Klöppel G, Lopes JM, O’Connor JM et al (2012) ENETS Consensus Guidelines for the management of patients with digestive neuroendocrine neoplasms of the digestive system: well-differentiated pancreatic non-functioning tumors. Neuroendocrinology 95(2):120–134. https://doi.org/10.1159/000335587

    Article  CAS  Google Scholar 

  133. Thuillier P, Liberini V, Grimaldi S, Rampado O, Gallio E, De Santi B et al (2021) Prognostic value of whole-body PET volumetric parameters extracted from 68Ga-DOTATOC-PET/CT in well-differentiated neuroendocrine tumors. J Nuclear Med. https://doi.org/10.2967/jnumed.121.262652

    Article  Google Scholar 

  134. Ohlendorf F, Henkenberens C, Brunkhorst T, Ross TL, Christiansen H, Bengel FM et al (2020) Volumetric 68Ga-DOTA-TATE PET/CT for assessment of whole-body tumor burden as a quantitative imaging biomarker in patients with metastatic gastroenteropancreatic neuroendocrine tumors. Q J Nucl Med Mol Imaging. https://doi.org/10.23736/S1824-4785.20.03238-0

    Article  Google Scholar 

  135. Abdulrezzak U, Kurt YK, Kula M, Tutus A (2016) Combined imaging with 68Ga-DOTA-TATE and 18F-FDG PET/CT on the basis of volumetric parameters in neuroendocrine tumors. Nuclear Med Commun 37(8):874

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Contributions

DFS: literature review and writing. MET: literature review and writing. MC: writing and editing. MCLL: writing and editing. BJA: writing and editing. EMR: writing and editing. EE: content planning, editing and writing.

Corresponding author

Correspondence to Elba Etchebehere.

Ethics declarations

Conflict of interest

The authors Dihego F. Santos, Maria Emilia Seren Takahashi, Mariana Camacho, Mariana da Cunha Lopes de Lima, Bárbara Juarez Amorim, Eric M. Rohren, and Elba Etchebehere, declare no conflict of interest.

Human and animal rights

Futhermore, this article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Santos, D.F., Takahashi, M.E., Camacho, M. et al. Whole-body tumor burden in PET/CT expert review. Clin Transl Imaging 11, 5–22 (2023). https://doi.org/10.1007/s40336-022-00517-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40336-022-00517-5

Keywords

Navigation