Advertisement

European Radiology

, Volume 30, Issue 1, pp 239–246 | Cite as

Radiomics analysis using contrast-enhanced CT for preoperative prediction of occult peritoneal metastasis in advanced gastric cancer

  • Shunli Liu
  • Jian He
  • Song Liu
  • Changfeng Ji
  • Wenxian Guan
  • Ling Chen
  • Yue Guan
  • Xiaofeng YangEmail author
  • Zhengyang ZhouEmail author
Gastrointestinal

Abstract

Objectives

To evaluate the predictive value of CT radiomics features derived from the primary tumor in discriminating occult peritoneal metastasis (PM) in advanced gastric cancer (AGC).

Methods

Preoperative CT images of 233 patients with AGC were retrospectively analyzed. The region of interest (ROI) was manually drawn along the margin of the lesion on the largest slice of venous CT images, and a total of 539 quantified features were extracted automatically. The intra-class correlation coefficient (ICC) and the absolute correlation coefficient (ACC) were calculated for selecting influential features. A multivariate logistic regression model was constructed based on the training cohort, and the testing cohort validated the reliability of the model. Additionally, another model based on the preoperative clinic-pathological features was also developed. The comparison of the diagnostic performance between the two models was performed using ROC analysis and the Akaike information criterion (AIC) value.

Results

Six radiomics features (ID_Energy, LoG(0.5)_Energy, Compactness2, Max Diameter, Orientation, and Surface Area Density) differed significantly between AGCs with and without PM and performed well in distinguishing AGCs with PM from those without PM in the primary cohort (AUC = 0.618–0.658). The radiomics model showed a higher AUC value than each single radiomics feature in the primary cohort (0.741 vs. 0.618–0.658) and similar diagnosis performance in the validation cohort. The radiomics model showed slightly worse diagnostic efficacy than the clinic-pathological model (AUC, 0.724 vs. 0.762).

Conclusion

Venous CT radiomics analysis based on the primary tumor provided valuable information for predicting occult PM in AGCs.

Key Points

Venous CT radiomics analysis provided valuable information for predicting occult peritoneal metastases in advanced gastric cancer.

CT-based T stage was an independent predictive factor of occult peritoneal metastases in advanced gastric cancer.

A radiomics model showed slightly worse diagnostic efficacy than a clinic-pathological model.

Keywords

Stomach neoplasms Multidetector computed tomography Peritoneum Diagnosis Neoplasm metastasis 

Abbreviations

ACC

Absolute correlation coefficient

AGC

Advanced gastric cancer

AIC

Akaike information criterion

AUC

Area under the curve

HU

Hounsfield unit

ICC

Intra-class correlation coefficient

PM

Peritoneal metastasis

ROC

Receiver operating characteristic

ROI

Regions of interest

Notes

Funding

This study has received funding by the National Natural Science Foundation of China (ID: 81501441, 81601463, 81871410), Social Development Foundation of Jiangsu Province (BE2015605), Natural Science Foundation of Jiangsu Province (ID: BK20150109), Jiangsu Province Health and Family Planning Commission Youth Scientific Research Project (ID: Q201508), Six Talent Peaks Project of Jiangsu Province (ID: 2015-WSN-079), and Jiangsu Provincial Medical Youth Talent (ID: QNRC2016040).

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Zhengyang Zhou, MD.

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 by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

330_2019_6368_MOESM1_ESM.docx (352 kb)
ESM 1 (DOCX 351 kb)

References

  1. 1.
    Fitzmaurice C, Allen C, Barber RM et al (2017) Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 32 cancer groups, 1990 to 2015: a systematic analysis for the global burden of disease study. JAMA Oncol 3:524–548CrossRefGoogle Scholar
  2. 2.
    Thomassen I, van Gestel YR, van Ramshorst B et al (2014) Peritoneal carcinomatosis of gastric origin: a population-based study on incidence, survival and risk factors. Int J Cancer 134:622–628CrossRefGoogle Scholar
  3. 3.
    Abbasi SY, Taani HE, Saad A, Badheeb A, Addasi A (2011) Advanced gastric cancer in Jordan from 2004 to 2008: a study of epidemiology and outcomes. Gastrointest Cancer Res 4:122–127PubMedPubMedCentralGoogle Scholar
  4. 4.
    Wallace MB, Nietert PJ, Earle C et al (2002) An analysis of multiple staging management strategies for carcinoma of the esophagus: computed tomography, endoscopic ultrasound, positron emission tomography, and thoracoscopy/laparoscopy. Ann Thorac Surg 74:1026–1032CrossRefPubMedCentralGoogle Scholar
  5. 5.
    Li K, Cannon JGD, Jiang SY et al (2018) Diagnostic staging laparoscopy in gastric cancer treatment: a cost-effectiveness analysis. J Surg Oncol 117:1288–1296CrossRefGoogle Scholar
  6. 6.
    Chang DK, Kim JW, Kim BK et al (2005) Clinical significance of CT-defined minimal ascites in patients with gastric cancer. World J Gastroenterol 11:6587–6592CrossRefPubMedCentralGoogle Scholar
  7. 7.
    Gretschel S, Siegel R, Estévez-Schwarz L, Hünerbein M, Schneider U, Schlag PM (2006) Surgical strategies for gastric cancer with synchronous peritoneal carcinomatosis. Br J Surg 93:1530–1535CrossRefGoogle Scholar
  8. 8.
    Kim SJ, Kim HH, Kim YH et al (2009) Peritoneal metastasis: detection with 16- or 64-detector row CT in patients undergoing surgery for gastric cancer. Radiology 253:407–415CrossRefGoogle Scholar
  9. 9.
    Yajima K, Kanda T, Ohashi M et al (2006) Clinical and diagnostic significance of preoperative computed tomography findings of ascites in patients with advanced gastric cancer. Am J Surg 192:185–190CrossRefPubMedCentralGoogle Scholar
  10. 10.
    Yan C, Zhu ZG, Yan M et al (2010) Value of multidetector-row CT in the preoperative prediction of peritoneal metastasis from gastric cancer: a single-center and large-scale study. Zhonghua Wei Chang Wai Ke Za Zhi 13:106–110PubMedGoogle Scholar
  11. 11.
    Fujii S, Matsusue E, Kanasaki Y et al (2008) Detection of peritoneal dissemination in gynecological malignancy: evaluation by diffusion-weighted MR imaging. Eur Radiol 18:18–23CrossRefGoogle Scholar
  12. 12.
    Bozkurt M, Doganay S, Kantarci M et al (2011) Comparison of peritoneal tumor imaging using conventional MR imaging and diffusion-weighted MR imaging with different b values. Eur J Radiol 80:224–228CrossRefGoogle Scholar
  13. 13.
    Fehniger J, Thomas S, Lengyel E et al (2016) A prospective study evaluating diffusion weighted magnetic resonance imaging (DW-MRI) in the detection of peritoneal carcinomatosis in suspected gynecologic malignancies. Gynecol Oncol 142:169–175CrossRefGoogle Scholar
  14. 14.
    Wang Z, Chen JQ (2011) Imaging in assessing hepatic and peritoneal metastases of gastric cancer: a systematic review. BMC Gastroenterol 11:19CrossRefPubMedCentralGoogle Scholar
  15. 15.
    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 27:1831–1839CrossRefGoogle Scholar
  16. 16.
    Giganti F, Marra P, Ambrosi A et al (2017) Pre-treatment MDCT-based texture analysis for therapy response prediction in gastric cancer: comparison with tumour regression grade at final histology. Eur J Radiol 90:129–137CrossRefGoogle Scholar
  17. 17.
    Liu S, Liu S, Ji C et al (2017) Application of CT texture analysis in predicting histopathological characteristics of gastric cancers. Eur Radiol 27:4951–4959CrossRefGoogle Scholar
  18. 18.
    Liu S, Shi H, Ji C et al (2018) Preoperative CT texture analysis of gastric cancer: correlations with postoperative TNM staging. Clin Radiol 73:756.e751–756.e759Google Scholar
  19. 19.
    Ma Z, Fang M, Huang Y et al (2017) CT-based radiomics signature for differentiating Borrmann type IV gastric cancer from primary gastric lymphoma. Eur J Radiol 91:142–147CrossRefPubMedCentralGoogle Scholar
  20. 20.
    Hou Z, Yang Y, Li S et al (2018) Radiomic analysis using contrast-enhanced CT: predict treatment response to pulsed low dose rate radiotherapy in gastric carcinoma with abdominal cavity metastasis. Quant Imaging Med Surg 8:410–420CrossRefPubMedCentralGoogle Scholar
  21. 21.
    Kim HY, Kim YH, Yun G, Chang W, Lee YJ, Kim B (2018) Could texture features from preoperative CT image be used for predicting occult peritoneal carcinomatosis in patients with advanced gastric cancer? PLoS One 13:e0194755CrossRefPubMedCentralGoogle Scholar
  22. 22.
    Burbidge S, Mahady K, Naik K (2013) The role of CT and staging laparoscopy in the staging of gastric cancer. Clin Radiol 68:251–255CrossRefGoogle Scholar
  23. 23.
    Power DG, Schattner MA, Gerdes H et al (2009) Endoscopic ultrasound can improve the selection for laparoscopy in patients with localized gastric cancer. J Am Coll Surg 208:173–178CrossRefGoogle Scholar
  24. 24.
    Dong D, Tang L, Li ZY et al (2019) Development and validation of an individualized nomogram to identify occult peritoneal metastasis in patients with advanced gastric cancer. Ann Oncol.  https://doi.org/10.1093/annonc/mdz001 CrossRefPubMedCentralGoogle Scholar
  25. 25.
    Zhang L, Fried DV, Fave XJ, Hunter LA, Yang J, Court LE (2015) IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. Med Phys 42:1341–1353CrossRefPubMedCentralGoogle Scholar
  26. 26.
    Huang YQ, Liang CH, 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 34:2157–2164CrossRefGoogle Scholar
  27. 27.
    Kim HJ, Kim AY, Oh ST et al (2005) Gastric cancer staging at multi-detector row CT gastrography: comparison of transverse and volumetric CT scanning. Radiology 236:879–885CrossRefPubMedCentralGoogle Scholar
  28. 28.
    Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006CrossRefPubMedCentralGoogle Scholar
  29. 29.
    Nakagawa S, Nashimoto A, Yabusaki H (2007) Role of staging laparoscopy with peritoneal lavage cytology in the treatment of locally advanced gastric cancer. Gastric Cancer 10:29–34CrossRefPubMedCentralGoogle Scholar
  30. 30.
    Li Z, Li Z, Zhang L et al (2018) Staging laparoscopy for locally advanced gastric cancer in Chinese patients: a multicenter prospective registry study. BMC Cancer 18:63CrossRefPubMedCentralGoogle Scholar
  31. 31.
    Ahn SJ, Kim JH, Park SJ, Han JK (2016) Prediction of the therapeutic response after FOLFOX and FOLFIRI treatment for patients with liver metastasis from colorectal cancer using computerized CT texture analysis. Eur J Radiol 85:1867–1874CrossRefGoogle Scholar
  32. 32.
    Ng F, Kozarski R, Ganeshan B, Goh V (2013) Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis? Eur J Radiol 82:342–348CrossRefGoogle Scholar
  33. 33.
    Lubner MG, Stabo N, Lubner SJ et al (2015) CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes. Abdom Imaging 40:2331–2337CrossRefGoogle Scholar
  34. 34.
    Komori M, Asayama Y, Fujita N et al (2013) Extent of arterial tumor enhancement measured with preoperative MDCT gastrography is a prognostic factor in advanced gastric cancer after curative resection. AJR Am J Roentgenol 201:W253–W261CrossRefGoogle Scholar
  35. 35.
    Tustumi F, Bernardo WM, Dias AR et al (2016) Detection value of free cancer cells in peritoneal washing in gastric cancer: a systematic review and meta-analysis. Clinics (Sao Paulo) 71:733–745CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Shunli Liu
    • 1
  • Jian He
    • 1
  • Song Liu
    • 1
  • Changfeng Ji
    • 1
  • Wenxian Guan
    • 2
  • Ling Chen
    • 3
  • Yue Guan
    • 4
  • Xiaofeng Yang
    • 5
    Email author
  • Zhengyang Zhou
    • 1
    Email author
  1. 1.Department of Radiology, Nanjing Drum Tower HospitalThe Affiliated Hospital of Nanjing University Medical SchoolNanjingChina
  2. 2.Department of Gastrointestinal Surgery, Nanjing Drum Tower HospitalThe Affiliated Hospital of Nanjing University Medical SchoolNanjingChina
  3. 3.Department of Pathology, Nanjing Drum Tower HospitalThe Affiliated Hospital of Nanjing University Medical SchoolNanjingChina
  4. 4.School of Electronic Science and EngineeringNanjing UniversityNanjingChina
  5. 5.Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaUSA

Personalised recommendations