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Correlation between 18F-FDG PET/CT intra-tumor metabolic heterogeneity parameters and KRAS mutation in colorectal cancer

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Abstract

Purpose

The study aimed to evaluate the relationship between intra-tumor metabolic heterogeneity parameters of 18F-FDG and KRAS mutation status in colorectal cancer (CRC) patients and which threshold heterogeneity parameters could better reflect the heterogeneity characteristics of colorectal cancer.

Methods

Medical data of 101 CRC patients who underwent 18F-FDG PET/CT and KRAS mutation analysis were selected. On PET scans, 18F-FDG traditional indices maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and heterogeneity parameters coefficient of variation with a threshold of 2.5 (CV2.5), CV40%, heterogeneity index-1 (HI-1), and HI-2 of the primary lesions were obtained. We inferred correlations between these 18F-FDG parameters and KRAS mutation status.

Results

41 patients (40.6%) had KRAS gene mutation. Assessment of FDG parameters showed that SUVmax (19.00 vs. 13.16, p < 0.001), MTV (11.64 vs. 8.83, p = 0.001), and TLG (102.85 vs. 69.76, p < 0.001), CV2.5 (0.55 vs. 0.46, p = 0.006), and HI-2 (14.03 vs. 7.59, p < 0.001) of KRAS mutation were higher compared to wild-type (WT) KRAS. CV40% (0.22 vs. 0.24, p = 0.001) was lower in the KRAS mutation group, while HI-1 had no significant difference between the two groups. Multivariate analysis showed that MTV (OR = 4.97, 1.04–23.83, p = 0.045) was the only significant predictor in KRAS mutation, using a cut-off of 7.62 (AUC = 0.695), and MTV showed a sensitivity of 90.2% and specificity of 45.0%. However, the PET parameters were not independent predictors in KRAS mutation.

Conclusion

KRAS gene mutant CRC patients had more 18F-FDG uptake (SUVmax, MTV, TLG) and heterogeneity (CV2.5, HI-2) than WT KRAS. MTV was the only independent predictor of KRAS gene mutation in colorectal cancer patients.

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References

  1. Sung H, Ferlay J, Siegel RL, et al (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71(3):209-249. https://doi.org/10.3322/caac.21660

    Article  Google Scholar 

  2. Stewart BC, Wild CP (2019) World cancer report 2014. Available at: http://publications.iarc.fr/Non-Series-Publications/World-Cancer-Reports/World-Cancer-Report-2014. Accessed 8 June 2019

  3. National Health Commission Of The People’s Republic Of China (2020) National guidelines for diagnosis and treatment of colorectal cancer 2020 in China (English version). Chin J Cancer Res 32(4):415-445. https://doi.org/10.21147/j.issn.1000-9604.2020.04.01

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Cook AD, Single R, McCahill LE (2005) Surgical resection of primary tumors in patients who present with stage IV colorectal cancer: an analysis of surveillance, epidemiology, and end results data, 1988 to 2000. Ann Surg Oncol 12(8):637-645. https://doi.org/10.1245/ASO.2005.06.012

    Article  PubMed  Google Scholar 

  5. Karapetis CS, Khambata-Ford S, Jonker DJ et al (2008) K-ras mutations and benefit from cetuximab in advanced colorectal cancer. N Engl J Med 359(17):1757-1765. https://doi.org/10.1056/NEJMoa0804385

    Article  CAS  PubMed  Google Scholar 

  6. Send T, Kreppel B, Gaertner FC, et al (2017) PET-CT bei Karzinomen im Kopf-Hals-Bereich [PET-CT in head and neck cancer]. HNO 65(6):504-513. https://doi.org/10.1007/s00106-017-0355-7

    Article  CAS  PubMed  Google Scholar 

  7. Zaucha JM, Chauvie S, Zaucha R, et al (2019) The role of PET/CT in the modern treatment of Hodgkin lymphoma. Cancer Treat Rev 77:44-56. https://doi.org/10.1016/j.ctrv.2019.06.002

    Article  PubMed  Google Scholar 

  8. Kitajima K, Nakajo M, Kaida H, et al (2017) Present and future roles of FDG-PET/CT imaging in the management of gastrointestinal cancer: an update. Nagoya J Med Sci 79(4):527-543. https://doi.org/10.18999/nagjms.79.4.527

    Article  PubMed  PubMed Central  Google Scholar 

  9. Chen SW, Lin CY, Ho CM, et al (2015) Genetic alterations in colorectal cancer have different patterns on 18F-FDG PET/CT. Clin Nucl Med 40(8):621-626. https://doi.org/10.1097/RLU.0000000000000830

    Article  PubMed  Google Scholar 

  10. Lovinfosse P, Koopmansch B, Lambert F, et al (2016) (18)F-FDG PET/CT imaging in rectal cancer: relationship with the RAS mutational status. Br J Radiol 89(1063):20160212. https://doi.org/10.1259/bjr.20160212

    Article  PubMed  PubMed Central  Google Scholar 

  11. Cho A, Jo K, Hwang SH, et al (2017) Correlation between KRAS mutation and 18F-FDG uptake in stage IV colorectal cancer. Abdom Radiol (NY) 42(6):1621-1626. https://doi.org/10.1007/s00261-017-1054-2

    Article  Google Scholar 

  12. Kim YI, Kim YJ, Paeng JC, et al (2017) Heterogeneity index evaluated by slope of linear regression on 18F-FDG PET/CT as a prognostic marker for predicting tumor recurrence in pancreatic ductal adenocarcinoma. Eur J Nucl Med Mol Imaging 44(12):1995-2003. https://doi.org/10.1007/s00259-017-3755-8

    Article  PubMed  Google Scholar 

  13. Lee M, Lee H, Cheon GJ, et al (2017) Prognostic value of preoperative intratumoral FDG uptake heterogeneity in patients with epithelial ovarian cancer. Eur Radiol 27(1):16-23. https://doi.org/10.1007/s00330-016-4368-5

    Article  PubMed  Google Scholar 

  14. Chung HH, Kang SY, Ha S, et al (2016) Prognostic value of preoperative intratumoral FDG uptake heterogeneity in early stage uterine cervical cancer. J Gynecol Oncol 27(2):e15. https://doi.org/10.3802/jgo.2016.27.e15

    Article  CAS  PubMed  Google Scholar 

  15. Kimura M, Kato I, Ishibashi K, et al (2019) The prognostic significance of intratumoral heterogeneity of 18F-FDG uptake in patients with oral cavity squamous cell carcinoma. Eur J Radiol 114:99-104. https://doi.org/10.1016/j.ejrad.2019.03.004

    Article  PubMed  Google Scholar 

  16. Liu G, Yin H, Cheng X, et al (2021) Intra-tumor metabolic heterogeneity of gastric cancer on 18F-FDG PETCT indicates patient survival outcomes. Clin Exp Med 21(1):129-138. https://doi.org/10.1007/s10238-020-00659-8

    Article  CAS  PubMed  Google Scholar 

  17. Lièvre A, Bachet JB, Boige V, et al (2008) KRAS mutations as an independent prognostic factor in patients with advanced colorectal cancer treated with cetuximab. J Clin Oncol 26(3):374-379. https://doi.org/10.1200/JCO.2007.12.5906

    Article  CAS  PubMed  Google Scholar 

  18. Arslan E, Aksoy T, Gürsu RU, et al (2020) The prognostic value of 18F-FDG PET/CT and KRAS mutation in colorectal cancers. Mol Imaging Radionucl Ther 29(1):17-24. https://doi.org/10.4274/mirt.galenos.2019.33866

    Article  PubMed  PubMed Central  Google Scholar 

  19. Lv Y, Wang X, Liang L, et al (2019) SUVmax and metabolic tumor volume: surrogate image biomarkers of KRAS mutation status in colorectal cancer. Onco Targets Ther 12:2115-2121. https://doi.org/10.2147/OTT.S196725

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Iwamoto M, Kawada K, Nakamoto Y, et al (2014) Regulation of 18F-FDG accumulation in colorectal cancer cells with mutated KRAS. J Nucl Med 55(12):2038-2044. https://doi.org/10.2967/jnumed.114.142927

    Article  CAS  PubMed  Google Scholar 

  21. Marusyk A, Polyak K (2010) Tumor heterogeneity: causes and consequences. Biochim Biophys Acta 1805(1):105-117. https://doi.org/10.1016/j.bbcan.2009.11.002

    CAS  PubMed  Google Scholar 

  22. Paulino AC, Koshy M, Howell R, et al (2005) Comparison of CT- and FDG-PET-defined gross tumor volume in intensity-modulated radiotherapy for head-and-neck cancer. Int J Radiat Oncol Biol Phys 61(5):1385-1392. https://doi.org/10.1016/j.ijrobp.2004.08.037

    Article  PubMed  Google Scholar 

  23. Devic S, Tomic N, Faria S, et al (2010) Defining radiotherapy target volumes using 18F-fluoro-deoxy-glucose positron emission tomography/computed tomography: still a Pandora’s box?. Int J Radiat Oncol Biol Phys 78(5):1555-1562. https://doi.org/10.1016/j.ijrobp.2010.02.015

    Article  PubMed  Google Scholar 

  24. Asselin MC, O’Connor JP, Boellaard R, et al (2012) Quantifying heterogeneity in human tumours using MRI and PET. Eur J Cancer 48(4):447-455. https://doi.org/10.1016/j.ejca.2011.12.025

    Article  PubMed  Google Scholar 

  25. Gerlinger M, Rowan AJ, Horswell S, et al (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing [published correction appears in N Engl J Med. 2012 Sep 6;367(10):976]. N Engl J Med 366(10):883-892. https://doi.org/10.1056/NEJMoa1113205

  26. Dagogo-Jack I, Shaw AT (2018) Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol 15(2):81-94. https://doi.org/10.1038/nrclinonc.2017.166

    Article  CAS  PubMed  Google Scholar 

  27. Lambin P, Rios-Velazquez E, Leijenaar R, et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48(4):441-446. https://doi.org/10.1016/j.ejca.2011.11.036

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

This study was supported by the National and provincial key specialty construction plan (Grant number: Z155080000004), Anhui Provincial Key Research and Development Program Project, China (1804h08020294), and Anhui Provincial Natural Science Foundation for Youths, China (1908085QH364).

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Authors and Affiliations

  1. Department of Nuclear Medicine, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, Anhui, China

    Xin Liu, Shi-Cun Wang, Ming Ni, Qiang Xie & Yi-Fan Zhang

  2. Department of Radiology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, Anhui, China

    Wei-Fu Lv

  3. Department of General Surgery, the Fourth Affiliated Hospital of Anhui Medical University, 100 Huaihai Road, Hefei, 230012, Anhui, China

    Guang-Yong Geng

Authors
  1. Xin Liu
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  2. Shi-Cun Wang
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  3. Ming Ni
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  4. Qiang Xie
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Contributions

GYG and WFL conceived of the presented idea. XL wrote the original draft. SCW performed the computations. MN and YFZ collected data. MN and QX performed manuscript editing. All authors discussed the results and contributed to the final manuscript.

Corresponding authors

Correspondence to Wei-Fu Lv or Guang-Yong Geng.

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The authors declare that they have no conflict of interest.

Ethical approval

The article does not contain any studies with human participants or animals performed by any of the authors. The research protocol was approved by the Medical Ethics Committee of the First Affiliated Hospital of USTC (2021-RE-125).

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Liu, X., Wang, SC., Ni, M. et al. Correlation between 18F-FDG PET/CT intra-tumor metabolic heterogeneity parameters and KRAS mutation in colorectal cancer. Abdom Radiol 47, 1255–1264 (2022). https://doi.org/10.1007/s00261-022-03432-5

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