World Journal of Surgery

, 32:2089 | Cite as

Risk Factors for Lymph Node Metastasis in Submucosal Invasive Colorectal Cancer

  • Pyong W. Choi
  • Chang Sik YuEmail author
  • Se J. Jang
  • Sang H. Jung
  • Hee C. Kim
  • Jin C. Kim



Recent studies have shown a 7–15% lymph node (LN) metastasis rate in submucosal invasive colorectal cancer (SICC). Identifying risk factors for LN metastasis is crucial in selecting therapeutic modalities for SICC. We assessed the possibility of and the risk factors for LN metastasis in SICC.


We performed a retrospective study on 168 SICC patients who underwent curative resection between June 1989 and December 2004 at Asan Medical Center. The level of submucosal invasion was classified into upper third (sm1), middle third (sm2), and lower third (sm3). The following carcinoma-related variables were assessed: tumor size, tumor location, depth of submucosal invasion, cell differentiation, lymphovascular invasion, neural invasion, and tumor cell dissociation (TCD).


The overall LN metastasis rate was 14.3%. Significant predictors of LN metastasis both univariately and multivariately were sm3 (p = 0.039), poorly differentiated cancer (p = 0.028), and TCD (p = 0.045). Lymphovascular invasion was a risk factor for LN metastasis in univariate analysis (p = 0.019); however, in multivariate analysis, lymphovascular invasion could not predict LN metastasis. No statistical difference was observed in the risk of LN metastasis with regard to tumor location, size, and neural invasion.


The depth of submucosal invasion, cell differentiation, and tumor cell dissociation were significant pathologic predictors of LN metastasis in SICC. Because SICC is associated with a considerable risk of LN metastasis, local excision may be performed carefully in SICC without adverse features.


Lymph Node Metastasis Local Excision Lymphovascular Invasion Invasive Front Submucosal Invasion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Société Internationale de Chirurgie 2008

Authors and Affiliations

  • Pyong W. Choi
    • 1
  • Chang Sik Yu
    • 2
    Email author
  • Se J. Jang
    • 3
  • Sang H. Jung
    • 2
  • Hee C. Kim
    • 2
  • Jin C. Kim
    • 2
  1. 1.Department of Surgery, Ilsan Paik HospitalInje University College of MedicineGoyangSouth Korea
  2. 2.Colorectal Clinic, Department of SurgeryUniversity of Ulsan College of Medicine and Asan Medical CenterSeoulSouth Korea
  3. 3.Diagnostic PathologyUniversity of Ulsan College of Medicine and Asan Medical CenterSeoulSouth Korea

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