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European Radiology

, Volume 29, Issue 12, pp 6469–6476 | Cite as

Rectal cancer: can T2WI histogram of the primary tumor help predict the existence of lymph node metastasis?

  • Lanqing Yang
  • Dan Liu
  • Xin Fang
  • Ziqiang Wang
  • Yue Xing
  • Ling Ma
  • Bing WuEmail author
Gastrointestinal

Abstract

Objectives

To explore if there is a correlation between T2WI histogram features of the primary tumor and the existence of regional lymph node (LN) metastasis in rectal cancer.

Methods

Eighty-eight patients with pathologically proven rectal adenocarcinoma, who received direct surgical resection and underwent preoperative rectal MRIs, were enrolled retrospectively. Based on pathological analysis of surgical specimen, patients were classified into negative LN (LN−) and positive LN (LN+) groups. The degree of differentiation and pathological T stage were recorded. Clinical T stage, tumor location, and maximum diameter of tumor were evaluated of each patient. Whole-tumor texture analysis was independently performed by two radiologists on axial T2WI, including skewness, kurtosis, energy, and entropy.

Results

The interobserver agreement was overall good for texture analysis between two radiologists, with intraclass correlation coefficients (ICCs) ranging from 0.626 to 0.826. The LN− group had a significantly higher skewness (p < 0.001), kurtosis (p < 0.001), and energy (p = 0.004) than the LN+ group, and a lower entropy (p = 0.028). These four parameters showed moderate to good diagnostic power in predicting LN metastasis with respective AUC of 0.750, 0.733, 0.669, and 0.648. In addition, they were both correlated with LN metastasis (rs = − 0.413, − 0.385, − 0.28, and 0.245, respectively). The multivariate analysis showed that lower skewness was an independent risk factor of LN metastasis (odds ratio, OR = 9.832; 95%CI, 1.171–56.295; p = 0.01).

Conclusions

Signal intensity histogram parameters of primary tumor on T2WI were associated with regional LN status in rectal cancer, which may help improve the prediction of nodal stage.

Key Points

• Histogram parameters of tumor on T2WI may help to reduce uncertainty when assessing LN status in rectal cancer.

• Histogram parameters of tumor on T2WI showed a significant difference between different regional LN status groups in rectal cancer.

• Skewness was an independent risk factor of regional LN metastasis in rectal cancer.

Keywords

Rectal neoplasms Lymphatic metastasis Magnetic resonance imaging Computer-assisted image analysis 

Abbreviations

ICCs

Intraclass correlation coefficients

LN−

Lymph node negative

LN

Lymph node

LN+

Lymph node positive

OR

Odds ratio

Notes

Funding

The authors state that this work has not received any funding.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Prof. Wu Bing.

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

One of the authors has significant statistical expertise.

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

References

  1. 1.
    Glynne-Jones R, Wyrwicz L, Tiret E et al (2017) Rectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 4:iv22–iv40CrossRefGoogle Scholar
  2. 2.
    Beets GL, Figueiredo NF, Beets-Tan RGH (2017) Management of rectal cancer without radical resection. Annu Rev Med 1:169–182CrossRefGoogle Scholar
  3. 3.
    Valentini V, van Stiphout RG, Lammering G et al (2011) Nomograms for predicting local recurrence, distant metastases, and overall survival for patients with locally advanced rectal cancer on the basis of European randomized clinical trials. J Clin Oncol 23:3163–3172CrossRefGoogle Scholar
  4. 4.
    Brouwer NPM, Stijns RCH, Lemmens VEPP et al (2018) Clinical lymph node staging in colorectal cancer; a flip of the coin? Eur J Surg Oncol 8:1241–1246CrossRefGoogle Scholar
  5. 5.
    Beets-Tan RGH, Lambregts DMJ, Maas M et al (2018) Magnetic resonance imaging for clinical management of rectal cancer: updated recommendations from the 2016 European Society of Gastrointestinal and Abdominal Radiology (ESGAR) consensus meeting. Eur Radiol 4:1465–1475CrossRefGoogle Scholar
  6. 6.
    Yu J, Dai X, Zou HH et al (2018) Diffusion kurtosis imaging in identifying the malignancy of lymph nodes during the primary staging of rectal cancer. Colorectal Dis 2:116–125CrossRefGoogle Scholar
  7. 7.
    Qiu L, Liu XL, Liu SR et al (2016) Role of quantitative intravoxel incoherent motion parameters in the preoperative diagnosis of nodal metastasis in patients with rectal carcinoma. J Magn Reson Imaging 4:1031–1039CrossRefGoogle Scholar
  8. 8.
    Armbruster M, D'Anastasi M, Holzner V et al (2018) Improved detection of a tumorous involvement of the mesorectal fascia and locoregional lymph nodes in locally advanced rectal cancer using DCE-MRI. Int J Colorectal Dis 7:901–909CrossRefGoogle Scholar
  9. 9.
    Zhang H, Zhang C, Zheng Z et al (2017) Chemical shift effect predicting lymph node status in rectal cancer using high-resolution MR imaging with node-for-node matched histopathological validation. Eur Radiol 9:3845–3855CrossRefGoogle Scholar
  10. 10.
    Chang HC, Huang SC, Chen JS et al (2012) Risk factors for lymph node metastasis in pT1 and pT2 rectal cancer: a single-institute experience in 943 patients and literature review. Ann Surg Oncol 8:2477–2484CrossRefGoogle Scholar
  11. 11.
    Kajiwara Y, Ueno H, Hashiguchi Y, Mochizuki H, Hase K (2010) Risk factors of nodal involvement in T2 colorectal cancer. Dis Colon Rectum 10:1393–1399CrossRefGoogle Scholar
  12. 12.
    Suh JH, Han KS, Kim BC et al (2012) Predictors for lymph node metastasis in T1 colorectal cancer. Endoscopy 6:590–595Google Scholar
  13. 13.
    Grovik E, Redalen KR, Storas TH et al (2017) Dynamic multi-echo DCE- and DSC-MRI in rectal cancer: low primary tumor K (trans) and DeltaR2* peak are significantly associated with lymph node metastasis. J Magn Reson Imaging 1:194–206CrossRefGoogle Scholar
  14. 14.
    Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ (2017) CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics 5:1483–1503CrossRefGoogle Scholar
  15. 15.
    Gourtsoyianni S, Doumou G, Prezzi D et al (2017) Primary rectal cancer: repeatability of global and local-regional MR imaging texture features. Radiology 2:552–561CrossRefGoogle Scholar
  16. 16.
    De Cecco CN, Ganeshan B, Ciolina M et al (2015) Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance. Invest Radiol 4:239–245CrossRefGoogle Scholar
  17. 17.
    Horvat N, Veeraraghavan H, Khan M et al (2018) MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology 3:833–843CrossRefGoogle Scholar
  18. 18.
    Jalil O, Afaq A, Ganeshan B et al (2017) Magnetic resonance based texture parameters as potential imaging biomarkers for predicting long-term survival in locally advanced rectal cancer treated by chemoradiotherapy. Colorectal Dis 4:349–362CrossRefGoogle Scholar
  19. 19.
    Al-Sukhni E, Milot L, Fruitman M et al (2012) Diagnostic accuracy of MRI for assessment of T category, lymph node metastases, and circumferential resection margin involvement in patients with rectal cancer: a systematic review and meta-analysis. Ann Surg Oncol 7:2212–2223CrossRefGoogle Scholar
  20. 20.
    Liu S, Shi H, Ji C et al (2018) Preoperative CT texture analysis of gastric cancer: correlations with postoperative TNM staging. Clin Radiol 8:756.e751–756.e759Google Scholar
  21. 21.
    Gu Y, She Y, Xie D et al (2018) A texture analysis-based prediction model for lymph node metastasis in stage IA lung adenocarcinoma. Ann Thorac Surg 1:214–220CrossRefGoogle Scholar
  22. 22.
    Liu L, Liu Y, Xu L et al (2017) Application of texture analysis based on apparent diffusion coefficient maps in discriminating different stages of rectal cancer. J Magn Reson Imaging 6:1798–1808CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Lanqing Yang
    • 1
  • Dan Liu
    • 1
  • Xin Fang
    • 1
  • Ziqiang Wang
    • 2
  • Yue Xing
    • 3
  • Ling Ma
    • 4
  • Bing Wu
    • 1
    Email author
  1. 1.Department of Radiology, West China HospitalSichuan UniversityChengduPeople’s Republic of China
  2. 2.Department of Colorectal Surgery, West China HospitalSichuan UniversityChengduPeople’s Republic of China
  3. 3.Sir Peter Mansfield Imaging Center, School of MedicineNottinghamUK
  4. 4.GE HealthcareShanghaiPeople’s Republic of China

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