Skip to main content

Advertisement

SpringerLink
Log in

Spectral CT vs. diffusion-weighted imaging for the quantitative prediction of pathologic response to neoadjuvant chemotherapy in locally advanced gastric cancer

  • Gastrointestinal
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To compare the performance of spectral CT and diffusion-weighted imaging (DWI) for predicting pathologic response after neoadjuvant chemotherapy (NAC) in locally advanced gastric cancer (LAGC).

Materials and methods

This was a retrospective analysis drawn from a prospective dataset. Sixty-five patients who underwent baseline concurrent triple-phase enhanced spectral CT and DWI-MRI and standard NAC plus radical gastrectomy were enrolled, and those with poor images were excluded. The tumor regression grade (TRG) was the reference standard, and patients were classified as responders (TRG 0 + 1) or non-responders (TRG 2 + 3). Quantitative iodine concentration (IC), normalized IC (nIC), and apparent diffusion coefficient (ADC) were measured by placing a freehand region of interest manually on the maximal two-dimensional plane. Their differences between responders and non-responders were compared. The performances of significant parameters were evaluated by the receiver operating characteristic analysis. The correlations between parameters and TRG status were explored through Spearman correlation coefficient test. Kaplan–Meier survival analysis was adopted to analyze their relationship with patient survival.

Results

nICDP and ADC were associated with the TRG and yielded comparable performances for predicting TRG categories, with area under the curve (AUC) of 0.674 and 0.673, respectively. Their combination achieved a significantly increased AUC of 0.770 (p ; 0.05) and was associated with patient disease-free survival, with hazard ratio of 2.508 (1.043–6.029).

Conclusion

Spectral CT and DWI were equally useful imaging techniques for predicting pathologic response to NAC in LAGC. The combination of nICDP and ADC gained significant incremental benefits and was related to patient disease-free survival.

Clinical relevance statement

Spectral CT and DWI-based quantitative measurements are effective markers for predicting the pathologic regression outcomes of locally advanced gastric cancer patients after neoadjuvant chemotherapy.

Key Points

• The pathologic tumor regression grade, the standard criteria for treatment response after neoadjuvant chemotherapy in gastric cancer patients, is difficult to predict early.

• The quantitative parameters of normalized iodine concentration at delay phase and apparent diffusion coefficients were correlated with pathologic response; their combination demonstrated incremental benefits and was associated with patient disease-free survival.

• Spectral CT and DWI are equally useful imaging modalities for predicting tumor regression grade after neoadjuvant chemotherapy in patients with locally advanced gastric cancer.

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

Abbreviations

ADC:

Apparent diffusion coefficients

AJCC:

American Joint Committee on Cancer

AP:

Arterial phase

AUC:

Area under the receiver operating characteristic curve

DP:

Delay phase

DWI:

Diffusion-weighted imaging

GC:

Gastric cancer

IC:

Iodine concentration

LAGC:

Locally advanced gastric cancer

NAC:

Neoadjuvant chemotherapy

NCCN:

National Comprehensive Cancer Network

nIC:

Normalized iodine concentration

TRG:

Tumor regression grade

VP:

Venous phase

References

  1. Cunningham D, Allum WH, Stenning SP et al (2006) Perioperative chemotherapy versus surgery alone for resectable gastroesophageal cancer. N Engl J Med 355:11–20

    Article  PubMed  CAS  Google Scholar 

  2. Ajani JA, D’Amico TA, Bentrem DJ et al (2022) Gastric cancer, Version 2.2022, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 20:167–192

    Article  PubMed  CAS  Google Scholar 

  3. Shi C, MD, Berlin J, Branton PA et al (2020) Protocol for the examination of specimens from patients with carcinoma of the stomach (Version: Stomach 4.1.0.0) [EB/OL]. Northfield: College of American pathologists February. https://documents.cap.org/protocols/cp-giupper-stomach-20-4100.pdf

  4. Sinnamon AJ, Savoldy M, Mehta R et al (2023) Tumor regression grade and overall survival following gastrectomy with preoperative therapy for gastric cancer. Ann Surg Oncol 30:3580–3589

    Article  PubMed  Google Scholar 

  5. Wang ZL, Li YL, Li XT, Tang L, Li ZY, Sun YS (2021) Role of CT in the prediction of pathological complete response in gastric cancer after neoadjuvant chemotherapy. Abdom Radiol (NY) 46:3011–3018

    Article  PubMed  Google Scholar 

  6. Lv P, Lin XZ, Li J, Li W, Chen K (2011) Differentiation of small hepatic hemangioma from small hepatocellular carcinoma: recently introduced spectral CT method. Radiology 259:720–729

    Article  PubMed  Google Scholar 

  7. Li J, Fang M, Wang R et al (2018) Diagnostic accuracy of spectral CT-based nomograms to predict lymph node metastasis in gastric cancer. Eur Radiol 28:5241–5248

    Article  PubMed  Google Scholar 

  8. Chen XH, Ken K, Liang P, Chai YR, Chen KS, Gao JB (2017) Spectral computed tomography in advanced gastric cancer: can iodine concentration noninvasively assess angiogenesis? World J Gastroenterol 23:1666–1675

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Tang L, Li ZY, Li ZW et al (2015) Evaluating the response of gastric carcinomas to neoadjuvan chemotherapy using iodine concentration on spectral CT: a comparison with pathological regression. Clin Radiol 70:1198–1204

    Article  PubMed  CAS  Google Scholar 

  10. Renzulli M, Clemente A, Spinelli D et al (2020) Gastric cancer staging: is it time for magnetic resonance imaging? Cancers (Basel) 12:1402

    Article  PubMed  Google Scholar 

  11. Arslan H, Fatih Özbay M et al (2017) Contribution of diffusion weighted MRI to diagnosis and staging in gastric tumors and comparison with multi-detector computed tomography. Radiol Oncol 51:23–29

    Article  PubMed  PubMed Central  Google Scholar 

  12. Giganti F, Orsenigo E, Esposito A et al (2015) Prognostic role of diffusion-weighted MR imaging for resectable gastric cancer. Radiology 276:444–452

    Article  PubMed  Google Scholar 

  13. Suh CH, Kim HS, Jung SC et al (2018) Multiparametric MRI as a potential surrogate endpoint for decision-making in early treatment response following concurrent chemoradiotherapy in patients with newly diagnosed glioblastoma: a systematic review and meta-analysis. Eur Radiol 28:2628–2638

    Article  PubMed  Google Scholar 

  14. Li J, Yan LL, Zhang HK et al (2022) Dynamic contrast-enhanced and diffusion-weighted MR imaging in early prediction of pathologic response to neoadjuvant chemotherapy in locally advanced gastric cancer. Abdom Radiol (NY) 47:3394–3405

    Article  PubMed  Google Scholar 

  15. Smyth EC, Nilsson M, Grabsch HI, van Grieken NC, Lordick F (2020) Gastric cancer. Lancet 396:635–648

    Article  PubMed  CAS  Google Scholar 

  16. Song R, Cui Y, Ren J et al (2022) CT-based radiomics analysis in the prediction of response to neoadjuvant chemotherapy in locally advanced gastric cancer: a dual-center study. Radiother Oncol 171:155–163

    Article  PubMed  CAS  Google Scholar 

  17. Beam CA (1992) Strategies for improving power in diagnostic radiology research. AJR Am J Roentgenol 159(3):631–637

    Article  PubMed  CAS  Google Scholar 

  18. Chen M, Feng C, Wang Q et al (2021) Comparison of reduced field-of-view diffusion-weighted imaging (DWI) and conventionalDWI techniques in the assessment of Cervical carcinoma at 3.0T: Image quality and FIGO staging. Eur J Radiol 137:109557

    Article  PubMed  Google Scholar 

  19. Chen CY, Hsu JS, Wu DC et al (2007) Gastric cancer: preoperative local staging with 3D multi-detector row CT–correlation with surgical and histopathologic results. Radiology 242:472–482

    Article  PubMed  Google Scholar 

  20. Amin MB, Greene FL, Edge SB et al (2017) The Eighth Edition AJCC Cancer Staging Manual: continuing to build a bridge from a population-based to a more “personalized” approach to cancer staging. CA Cancer J Clin 67:93–99

    Article  PubMed  Google Scholar 

  21. Cuenod CA, Fournier L, Balvay D, Guinebretière JM (2006) Tumor angiogenesis: pathophysiology and implications for contrast-enhanced MRI and CT assessment. Abdom Imaging 31:188–193

    Article  PubMed  CAS  Google Scholar 

  22. Huang MT, Chen ZX, Wei B et al (2007) Preoperative growth inhibition of human gastric adenocarcinoma treated with a combination of celecoxib and octreotide. Acta Pharmacol Sin 28:1842–1850

    Article  PubMed  CAS  Google Scholar 

  23. Li J, Xu S, Wang Y et al (2023) Spectral CT-based nomogram for preoperative prediction of perineural invasion in locally advanced gastric cancer: a prospective study. Eur Radiol 33:5172–5183

    Article  PubMed  Google Scholar 

  24. Pan Z, Pang L, Ding B et al (2013) Gastric cancer staging with dual energy spectral CT imaging. PLoS One 8:e53651

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  25. De Cobelli F, Giganti F, Orsenigo E et al (2013) Apparent diffusion coefficient modifications in assessing gastro-oesophageal cancer response to neoadjuvant treatment: comparison with tumor regression grade at histology. Eur Radiol 23:2165–2174

    Article  PubMed  Google Scholar 

  26. Giganti F, De Cobelli F, Canevari C et al (2014) Response to chemotherapy in gastric adenocarcinoma with diffusion-weighted MRI and (18) F-FDG-PET/CT: correlation of apparent diffusion coefficient and partial volume corrected standardized uptake value with histological tumor regression grade. J Magn Reson Imaging 40:1147–1157

    Article  PubMed  Google Scholar 

  27. Le Bihan D (2013) Apparent diffusion coefficient and beyond: what diffusion MR imaging can tell us about tissue structure. Radiology 268:318–322

    Article  PubMed  Google Scholar 

Download references

Funding

This study has received funding from Special funding of the Henan Health Science and Technology Innovation Talent Project (YXKC2021054), and the National Natural Science Foundation of China (82202146).

Author information

Authors and Affiliations

  1. Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan, China

    Jing Li, Xuejun Chen & Jinrong Qu

  2. Department of Gastrointestinal Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan, China

    Shuning Xu

  3. Department of Pathology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan, China

    Yi Wang

  4. Department of Gastrointestinal Surgery, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan, China

    Fei Ma

Authors
  1. Jing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuning Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fei Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xuejun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jinrong Qu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jing Li or Jinrong Qu.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Jinrong Qu.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived due to the retrospective nature of the study.

Ethical approval

Ethical approval was obtained from Institutional Review Board of the Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital.

Study subjects or cohorts overlap

None.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

Additional information

Publisher's note

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

Jing Li and Jinrong Qu contributed equally to this work and are co-corresponding authors.

Supplementary information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 226 KB)

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

Li, J., Xu, S., Wang, Y. et al. Spectral CT vs. diffusion-weighted imaging for the quantitative prediction of pathologic response to neoadjuvant chemotherapy in locally advanced gastric cancer. Eur Radiol (2024). https://doi.org/10.1007/s00330-024-10642-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00330-024-10642-6

Keywords

Search

Navigation