Dual-energy CT–based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer

Abstract

Objectives

To build a dual-energy CT (DECT)–based deep learning radiomics nomogram for lymph node metastasis (LNM) prediction in gastric cancer.

Materials and methods

Preoperative DECT images were retrospectively collected from 204 pathologically confirmed cases of gastric adenocarcinoma (mean age, 58 years; range, 28–81 years; 157 men [mean age, 60 years; range, 28–81 years] and 47 women [mean age, 54 years; range, 28–79 years]) between November 2011 and October 2018, They were divided into training (n = 136) and test (n = 68) sets. Radiomics features were extracted from monochromatic images at arterial phase (AP) and venous phase (VP). Clinical information, CT parameters, and follow-up data were collected. A radiomics nomogram for LNM prediction was built using deep learning approach and evaluated in test set using ROC analysis. Its prognostic performance was determined with Harrell’s concordance index (C-index) based on patients’ outcomes.

Results

The dual-energy CT radiomics signature was associated with LNM in two sets (Mann-Whitney U test, p < 0.001) and an achieved area under the ROC curve (AUC) of 0.71 for AP and 0.76 for VP in test set. The nomogram incorporated the two radiomics signatures and CT-reported lymph node status exhibited AUCs of 0.84 in the training set and 0.82 in the test set. The C-indices of the nomogram for progression-free survival and overall survival prediction were 0.64 (p = 0.004) and 0.67 (p = 0.002).

Conclusion

The DECT-based deep learning radiomics nomogram showed good performance in predicting LNM in gastric cancer. Furthermore, it was significantly associated with patients’ prognosis.

Key Points

• This study investigated the value of deep learning dual-energy CT–based radiomics in predicting lymph node metastasis in gastric cancer.

• The dual-energy CT–based radiomics nomogram outweighed the single-energy model and the clinical model.

• The nomogram also exhibited a significant prognostic ability for patient survival and enriched radiomics studies.

This is a preview of subscription content, access via your institution.

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

Abbreviations

AP:

Arterial phase

AUC:

Area under the receiver operating characteristic curve

CI:

Confidence interval

GC:

Gastric cancer

GSI:

Gemstone spectral imaging

IC:

Iodine concentration

LNM:

Lymph node metastasis

MD:

Material deposition

OS:

Overall survival

PFS:

Progression-free survival

VP:

Venous phase

References

  1. 1.

    Ferlay J, Soerjomataram I, Dikshit R et al (2015) Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 136(5):E359–E386

    CAS  Article  Google Scholar 

  2. 2.

    GLOBOCAN (2012) Stomach cancer: estimated incidence, mortality and prevalence worldwide in 2012. Available at: http://globocan.iarc.fr/old/FactSheets/cancers/stomach-new.asp. Accessed 4 Nov 2014

  3. 3.

    Shen L, Shan YS, Hu HM et al (2013) Management of gastric cancer in Asia: resource-stratified guidelines. Lancet Oncol 14(12):e535–e547

    Article  Google Scholar 

  4. 4.

    Chen W, Zheng R, Baade PD et al (2016) Cancer statistics in China, 2015. CA Cancer J Clin 66(2):115–132

    Article  Google Scholar 

  5. 5.

    Saito H, Fukumoto Y, Osaki T et al (2007) Prognostic significance of level and number of lymph node metastases in patients with gastric cancer. Ann Surg Oncol 14(5):1688–1693

    Article  Google Scholar 

  6. 6.

    Oka S, Tanaka S, Kaneko I et al (2006) Advantage of endoscopic submucosal dissection compared with EMR for early gastric cancer. Gastrointest Endosc 64(6):877–883

    Article  Google Scholar 

  7. 7.

    Ajani JA, Bentrem DJ, Besh S et al (2013) Gastric cancer, version 2.2013: featured updates to the NCCN guidelines. J Natl Compr Canc Netw 11(5):531–546

    CAS  Article  Google Scholar 

  8. 8.

    Miyahara K, Hatta W, Nakagawa M et al (2018) The role of an undifferentiated component in submucosal invasion and submucosal invasion depth after endoscopic submucosal dissection for early gastric cancer. Digestion 98(3):161–168

    CAS  Article  Google Scholar 

  9. 9.

    Yamashita K, Hosoda K, Ema A, Watanabe M (2016) Lymph node ratio as a novel and simple prognostic factor in advanced gastric cancer. Eur J Surg Oncol 42(9):1253–1260

  10. 10.

    Persiani R, Rausei S, Biondi A, Boccia S, Cananzi F, D'Ugo D (2008) Ratio of metastatic lymph nodes: impact on staging and survival of gastric cancer. Eur J Surg Oncol 34(5):519–524

  11. 11.

    National Comprehensive Cancer Network (NCCN) guidelines. Available online: http://www.nccn.org/. Accessed on May 2018

  12. 12.

    Kwee RM, Kwee TC (2016) Imaging in local staging of gastric cancer: a systematic review. J Clin Oncol 25(15):2107–2116

    Article  Google Scholar 

  13. 13.

    Fairweather M, Jajoo K, Sainani N, Bertagnolli MM, Wang J (2015) Accuracy of EUS and CT imaging in preoperative gastric cancer staging. J Surg Oncol 111(8):1016–1020

  14. 14.

    Saito T, Kurokawa Y, Takiguchi S et al (2015) Accuracy of multidetector-row CT in diagnosing lymph node metastasis in patients with gastric cancer. Eur Radiol 25(2):368–374

    Article  Google Scholar 

  15. 15.

    Burbidge S, Mahady K, Naik K (2013) The role of CT and staging laparoscopy in the staging of gastric cancer. Clin Radiol 68(3):251–255

    CAS  Article  Google Scholar 

  16. 16.

    Limkin EJ, Sun R, Dercle L et al (2017) Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol 28(6):1191–1206

    CAS  Article  Google Scholar 

  17. 17.

    Aerts HJ (2016) The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol 2(12):1636–1642

    Article  Google Scholar 

  18. 18.

    Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14(12):749–762

    Article  Google Scholar 

  19. 19.

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

    Article  Google Scholar 

  20. 20.

    Huang Y, Liang C, He L et al (2016) Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol 34(18):2157–2164

    Article  Google Scholar 

  21. 21.

    Wu S, Zheng J, Li Y et al (2017) A radiomics nomogram for the preoperative prediction of lymph node metastasis in bladder cancer. Clin Cancer Res 23(22):6904–6911

    CAS  Article  Google Scholar 

  22. 22.

    Jiang Y, Wang W, Chen C et al (2019) Radiomics signature on computed tomography imaging: association with lymph node metastasis in patients with gastric cancer. Front Oncol 9:340

    Article  Google Scholar 

  23. 23.

    Giganti F, Antunes S, Salerno A et al (2017) Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker. Eur Radiol 27(5):1831–1839

    Article  Google Scholar 

  24. 24.

    Liu S, He J, Liu S et al (2019) Radiomics analysis using contrast-enhanced CT for preoperative prediction of occult peritoneal metastasis in advanced gastric cancer. Eur Radiol. https://doi.org/10.1007/s00330-019-06368-5

  25. 25.

    Dong D, Tang L, Li Z et al (2019) Development and validation of an individualized nomogram to identify occult peritoneal metastasis in patients with advanced gastric cancer. Ann Oncol 30(3):431–438

    CAS  Article  Google Scholar 

  26. 26.

    Tang L, Li ZY, Li ZW et al (2015) Evaluating the response of gastric carcinomas to neoadjuvant chemotherapy using iodine concentration on spectral CT: a comparison with pathological regression. Clin Radiol 70(11):1198–1204

    CAS  Article  Google Scholar 

  27. 27.

    Li J, Fang M, Wang R et al (2018) Diagnostic accuracy of dual-energy CT-based nomograms to predict lymph node metastasis in gastric cancer. Eur Radiol 28(12):5241–5249

    Article  Google Scholar 

  28. 28.

    Lakhani P, Sundaram B (2017) Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284(2):574–582

    Article  Google Scholar 

  29. 29.

    Peng H, Dong D, Fang M et al (2019) Prognostic value of deep learning PET/CT-based radiomics: potential role for future individual induction chemotherapy in advanced nasopharyngeal carcinoma. Clin Cancer Res 25(14):4271–4279

    Article  Google Scholar 

  30. 30.

    Miles KA (1999) Tumour angiogenesis and its relation to contrast enhancement on computed tomography: a review. Eur J Radiol 30(3):198–205

    CAS  Article  Google Scholar 

  31. 31.

    Ogata T, Ueguchi T, Yagi M et al (2013) Feasibility and accuracy of relative electron density determined by virtual monochromatic CT value subtraction at two different energies using the gemstone spectral imaging. Radiat Oncol 8:83

    Article  Google Scholar 

  32. 32.

    Al Ajmi E, Forghani B, Reinhold C, Bayat M, Forghani R (2018) Spectral multi-energy CT texture analysis with machine learning for tissue classification: an investigation using classification of benign parotid tumours as a testing paradigm. Eur Radiol 28(6):2604–2611

  33. 33.

    Su KH, Kuo JW, Jordan DW et al (2018) Machine learning-based dual-energy CT parametric mapping. Phys Med Biol 63(12):125001

    Article  Google Scholar 

  34. 34.

    Ozguner O, Dhanantwari A, Halliburton S, Wen G, Utrup S, Jordan D (2018) Objective image characterization of a spectral CT scanner with dual-layer detector. Phys Med Biol 63(2):025027

  35. 35.

    Jacobsen MC, Schellingerhout D, Wood CA et al (2018) Intermanufacturer comparison of dual-energy CT iodine quantification and monochromatic attenuation: a phantom study. Radiology 287(1):224–234

    Article  Google Scholar 

  36. 36.

    Pan Z, Pang L, Ding B et al (2013) Gastric cancer staging with dual energy spectral CT imaging. PLoS One 8(2):e53651

    CAS  Article  Google Scholar 

Download references

Funding

This study has received funding by National Natural Science Foundation of China (81271573, 91959130, 0, 81971776, 81771924).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Jianbo Gao.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Jianbo Gao.

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 not required for this study because this is a retrospective diagnostic study. Written informed consent was waived by the Institutional Review Board of Zhengzhou University.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

A total of 73 of the 204 patients have been previously reported. This prior article dealt with the potential of IC value for the prediction of lymph node metastasis in gastric cancer by multivariate logistic analysis, whereas in this manuscript, we report on the additional predictive value of a deep learning spectral CT-based radiomics model for the LNM prediction in gastric cancer.

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.

Electronic supplementary material

ESM 1

(DOCX 10084 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, J., Dong, D., Fang, M. et al. Dual-energy CT–based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer. Eur Radiol 30, 2324–2333 (2020). https://doi.org/10.1007/s00330-019-06621-x

Download citation

Keywords

  • Gastric cancer
  • Tomography, X-ray computed
  • Lymph node
  • Radiomics
  • Deep learning