Skip to main content

Advertisement

SpringerLink
Log in

Combination of clinical and spectral-CT iodine concentration for predicting liver metastasis in gastric cancer: a preliminary study

  • Hepatobiliary
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

This study aimed to determine the diagnostic efficacy of various indicators and models for the prediction of gastric cancer with liver metastasis.

Methods

Clinical and spectral computed tomography (CT) data from 80 patients with gastric adenocarcinoma who underwent surgical resection were retrospectively analyzed. Patients were divided into metastatic and non-metastatic groups based on whether or not to occur liver metastasis, and the region of interest (ROI) was measured manually on each phase iodine map at the largest level of the tumor. Iodine concentration (IC), normalized iodine concentration (nIC), and clinical data of the primary gastric lesions were analyzed. Logistic regression analysis was used to construct the clinical indicator (CI) and clinical indicator-spectral CT iodine concentration (CI-Spectral CT-IC) Models, which contained all of the parameters with statistically significant differences between the groups. Receiver operating characteristic (ROC) curves were constructed to evaluate the accuracy of the models.

Results

The metastatic group showed significantly higher levels of Cancer antigen125 (CA125), carcinoembryonic antigen (CEA), IC, and nIC in the arterial phase, venous phase, and delayed phase than the non-metastatic group (all p < 0.05). Normalized iodine concentration Venous Phase (nICVP) exhibited a favorable performance among all IC and nIC parameters for forecasting gastric cancer with liver metastasis (area under the curve (AUC), 0.846). The combination model of clinical data with significant differences and nICVP showed the best diagnostic accuracy for predicting liver metastasis from gastric cancer, with an AUC of 0.897.

Conclusion

nICVP showed the best diagnostic efficacy for predicting gastric cancer with liver metastasis. Clinical Indicators-normalized ICVP model can improve the prediction accuracy for this condition.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209–249. https://doi.org/10.3322/caac.21660.

    Article  CAS  PubMed  Google Scholar 

  2. Smyth EC, Nilsson M, Grabsch HI, et al. Gastric cancer. Lancet. 2020;396(10251):635–648. https://doi.org/10.1016/S0140-6736(20)31288-5.

    Article  CAS  PubMed  Google Scholar 

  3. Verstegen MH, Harker M, van de Water C, et al. Metastatic pattern in esophageal and gastric cancer: Influenced by site and histology. World J Gastroenterol. 2020;26(39):6037–6046. https://doi.org/10.3748/wjg.v26.i39.6037.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Milette S, Sicklick JK, Lowy AM, Brodt P. Molecular Pathways: Targeting the Microenvironment of Liver Metastases. Clin Cancer Res. 2017 Jun;23(21):6390–6399. https://doi.org/10.1158/1078-0432.

  5. Okano K, Maeba T, Ishimura K, et al. Hepatic resection for metastatic tumors from gastric cancer. Ann Surg. 2002;235(1):86–91. https://doi.org/10.1097/00000658-200201000-00011.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Marrelli D, Roviello F, De Stefano A, et al. Risk factors for liver metastases after curative surgical procedures for gastric cancer: a prospective study of 208 patients treated with surgical resection. J Am Coll Surg. 2004;198(1):51–8. https://doi.org/10.1016/j.jamcollsurg.

    Article  PubMed  Google Scholar 

  7. Guner A, Son T, Cho I, et al. Liver-directed treatments for liver metastasis from gastric adenocarcinoma: comparison between liver resection and radiofrequency ablation. Gastric Cancer. 2016 Jul;19(3): 951–60. https://doi.org/10.1007/s10120-015-0522-z.

    Article  CAS  PubMed  Google Scholar 

  8. Kitakata H, Nemoto-Sasaki Y, Takahashi Y, et al. Essential roles of tumor necrosis factor receptor p55 in liver metastasis of intrasplenic administration of colon 26 cells. Cancer Res. 2002;62(22):668–7. PMID: 12438267.

    Google Scholar 

  9. Wen SW, Ager EI, Christophi C. Bimodal role of Kupffer cells during colorectal cancer liver metastasis. Cancer Biol Ther. 2013;14(7):606–13. https://doi.org/10.4161/cbt.24593.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ham B, Wang N, D’Costa Z, et al. TNF Receptor-2 Facilitates an Immunosuppressive Microenvironment in the Liver to Promote the Colonization and Growth of Hepatic Metastases. Cancer Res. 2015;75(24):5235–47. https://doi.org/10.1158/0008-5472.

    Article  PubMed  Google Scholar 

  11. Song JC, Ding XL, Zhang Y, et al. Prospective and prognostic factors for hepatic metastasis of gastric carcinoma: A retrospective analysis. J Cancer Res Ther. 2019;15(2):298–304. https://doi.org/10.4103/jcrt.

    Article  PubMed  Google Scholar 

  12. Talebi A, Celis-Morales CA, Borumandnia N, et al.Predicting metastasis in gastric cancer patients: machine learning-based approaches. Sci Rep. 2023;13(1):4163. https://doi.org/10.1038/s41598-023-31272-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Tsurumaru D, Nishimuta Y, Muraki T, et al. Gastric cancer with synchronous and metachronous hepatic metastasis predicted by enhancement pattern on multiphasic contrast-enhanced CT. Eur J Radiol. 2018 Nov;108: 165–171. https://doi.org/10.1016/j.ejrad.2018.09.030.

    Article  PubMed  Google Scholar 

  14. Yang H, Sun J, Liu H, et al. Clinico-radiological nomogram for preoperatively predicting post-resection hepatic metastasis in patients with gastric adenocarcinoma. Br J Radiol. 2022 Dec;95(1140):20220488. https://doi.org/10.1259/bjr.20220488.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Wang FH, Zhang XT, Li YF, et al. The Chinese Society of Clinical Oncology (CSCO): Clinical guidelines for the diagnosis and treatment of gastric cancer, 2021. Cancer Commun (Lond). 2021;41(8):747–795. https://doi.org/10.1002/cac2.12193.

    Article  PubMed  Google Scholar 

  16. So A, Nicolaou S. Spectral Computed Tomography: Fundamental Principles and Recent Developments. Korean J Radiol. 2021;22(1):86–96. https://doi.org/10.3348/kjr.2020.0144.

    Article  PubMed  Google Scholar 

  17. Li R, Li J, Wang X, et al. Detection of gastric cancer and its histological type based on iodine concentration in spectral CT. Cancer Imaging. 2018;18(1):42. https://doi.org/10.1186/s40644-018-0176-2.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Zhou Z, Liu Y, Meng K, et al. Application of spectral CT imaging in evaluating lymph node metastasis in patients with gastric cancers: initial findings. Acta Radiol. 2019;60(4):415–424. https://doi.org/10.1177/0284185118786076.

    Article  PubMed  Google Scholar 

  19. Ren T, Zhang W, Li S, et al. Combination of clinical and spectral-CT parameters for predicting lymphovascular and perineural invasion in gastric cancer. Diagn Interv Imaging. 2022;103(12):584–593. https://doi.org/10.1016/j.diii.2022.07.004.

    Article  PubMed  Google Scholar 

  20. Liang P, Ren XC, Gao JB, Chen KS, Xu X. Iodine Concentration in Spectral CT: Assessment of Prognostic Determinants in Patients With Gastric Adenocarcinoma. AJR Am J Roentgenol. 2017;209(5):1033–1038. https://doi.org/10.2214/AJR.16.16895.

    Article  PubMed  Google Scholar 

  21. Tang L, Li ZY, Li ZW, et al. Evaluating the response of gastric carcinomas to neoadjuvant chemotherapy using iodine concentration on spectral CT: a comparison with pathological regression. Clin Radiol. 2015;70(11):1198–204. https://doi.org/10.1016/j.crad.2015.06.083.

    Article  CAS  PubMed  Google Scholar 

  22. Liu J, Chai Y, Blinded for anonymity, et al. Spectral Computed Tomography Imaging of Gastric Schwannoma and Gastric Stromal Tumor. J Comput Assist Tomogr. 2017 May/Jun;41(3):417–421. https://doi.org/10.1097/RCT.0000000000000548.

  23. Chen XH, Ren K, Liang P, et al. Spectral computed tomography in advanced gastric cancer: Can iodine concentration non-invasively assess angiogenesis?. World J Gastroenterol. 2017;23(9):1666–1675. https://doi.org/10.3748/wjg.v23.i9.1666.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Greffier J, Hamard A, Pereira F, et al. Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study. 2020;30(7):3951–3959. https://doi.org/10.1007/s00330-020-06724-w.

  25. Dabli D, Frandon J, Belaouni A, et al. Optimization of image quality and accuracy of low iodine concentration quantification as function of dose level and reconstruction algorithm for abdominal imaging using dual-source CT: A phantom study. Diagn Interv Imaging. 2022;103(1):31–40. https://doi.org/10.1016/j.diii.2021.08.004.

    Article  PubMed  Google Scholar 

  26. Benchoufi M, Matzner-Lober E, Molinari N, et al. Interobserver agreement issues in radiology. Diagn Interv Imaging. 2020;101(10):639–641. https://doi.org/10.1016/j.diii.2020.09.001.

    Article  CAS  PubMed  Google Scholar 

  27. Gong W, Su Y, Liu A, et al. Clinical characteristics and treatments of patients with alpha-fetoprotein producing gastric carcinoma. Neoplasma. 2018;65(3):326–330. https://doi.org/10.4149/neo2018_170.

    Article  CAS  PubMed  Google Scholar 

  28. Dochez V, Caillon H, Vaucel E, et al. Biomarkers and algorithms for diagnosis of ovarian cancer: CA125, HE4, RMI and ROMA, a review. J Ovarian Res. 2019;12(1):28. https//doi:10.1186/s 13048-019-0503-7.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Rump A, Morikawa Y, Tanaka M, et al. Binding of ovarian cancer antigen CA125/MUC16 to mesothelin mediates cell adhesion. J Biol Chem. 2004;279(10):9190–8. https://doi.org/10.1074/jbc.M312372200.

    Article  CAS  PubMed  Google Scholar 

  30. Chen SH, Hung WC, Wang P, et al. Mesothelin binding to CA125/MUC16 promotes pancreatic cancer cell motility and invasion via MMP-7 activation. Sci Rep. 2013 May;3:1870. https://doi.org/10.1038/srep01870.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Gubbels JA, Belisle J, Onda M, et al. Mesothelin-MUC16 binding is a high affinity, N-glycan dependent interaction that facilitates peritoneal metastasis of ovarian tumors. Mol Cancer. 2006 Oct;26;5(1):50. https://doi.org/10.1186/1476-4598-5-50.

  32. Lee JH, Lee SW. The Roles of Carcinoembryonic Antigen in Liver Metastasis and Therapeutic Approaches. Gastroenterol Res Pract. 2017 May;2017:7521987. https://doi.org/10.1155/2017/7521987.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Kim CW, Roh SA, Tak KH, et al. ZKSCAN3 Facilitates Liver Metastasis of Colorectal Cancer Associated with CEA-expressing Tumor. Anticancer Res. 2016 May;36(5): 2397–2406. PMID: 27127149.

  34. Wang W, Chen XL, Zhao SY, et al. Prognostic significance of preoperative serum CA125, CA19-9 and CEA in gastric carcinoma. Oncotarget. 2016;7(23):35423–36https://doi.org/10.18632/onco-.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Li R, Li J, Wang X, et al. Detection of gastric cancer and its histological type based on iodine concentration in spectral CT. Cancer Imaging. 2018;18(1):42https://doi.org/10.1186/s40644-018-0176-2.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Hong WG, Ko YS, Pyo JS. Clinicopathological significance and prognostic role of microvessel density in gastric cancer: A meta-analysis. Pathol Res Pract. 2017;213(12):1459–1463. https://doi.org/10.1016/j.prp.2017.11.001.

    Article  PubMed  Google Scholar 

  37. Liu Y, Cui H, Xu X, et al. Prognostic value of lymph node density on cancer staging system for gastric cancer without distal metastasis: a population-based analysis of SEER database. World J Surg Oncol. 2022;20(1):325. https://doi.org/10.1186/s12957-022-02795-9.

    Article  PubMed  PubMed Central  Google Scholar 

  38. You MW, Park S, Kang HJ, et al. Radiologic serosal invasion sign as a new criterion of T4a gastric cancer on computed tomography: diagnostic performance and prognostic significance in patients with advanced gastric cancer. Abdom Radiol (NY). 2020;45(10):2950–2959. https://doi.org/10.1007/s00261-019-02156-3.

    Article  PubMed  Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China (82102151). Lanzhou Youth Science and Technology Talent Innovation Project (2023-2-44); Lanzhou University Second Hospital Cuiying Project (CY2023-QN-B09).

Author information

Author notes

  1. Yingxia She and Xianwang Liu contributed equally to this work.

Authors and Affiliations

  1. Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, People’s Republic of China

    Yingxia She, Xianwang Liu, Hong Liu, Haiting Yang, Wenjuan Zhang, Yinping Han & Junlin Zhou

  2. Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People’s Republic of China

    Yingxia She, Xianwang Liu, Hong Liu, Haiting Yang, Wenjuan Zhang, Yinping Han & Junlin Zhou

  3. Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People’s Republic of China

    Yingxia She, Xianwang Liu, Hong Liu, Haiting Yang, Wenjuan Zhang, Yinping Han & Junlin Zhou

Authors
  1. Yingxia She
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xianwang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Haiting Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wenjuan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yinping Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Junlin Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Yingxia She: Conceptualization, Methodology, Data Curation, Writing - original draft. Xianwang Liu: Statistical analysis, Resources, Original draft, Revision. Hong Liu: Methodology, Revision. Haiting Yang: Resources, Revision, Funding acquisition. Wenjuan Zhang: Revision, Funding acquisition. Yinping Han: Resources, Revision. Junlin Zhou: Conceptualization, Methodology, Supervision, Funding acquisition.

Corresponding author

Correspondence to Junlin Zhou.

Ethics declarations

Informed consent

The study was approved by the Institutional Review Board, and informed consent was waived due to retrospective analysis of the study.

Conflict of interest

All authors have no conflicts of interest to declare.

Additional information

Publisher’s Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) 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

She, Y., Liu, X., Liu, H. et al. Combination of clinical and spectral-CT iodine concentration for predicting liver metastasis in gastric cancer: a preliminary study. Abdom Radiol (2024). https://doi.org/10.1007/s00261-024-04346-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00261-024-04346-0

Keywords

Search

Navigation