submucosal colorectal cancer
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2021 ◽  
Author(s):  
Jeong-ki Kim ◽  
Ye-Young Rhee ◽  
Jeong Mo Bae ◽  
Jung Ho Kim ◽  
Seong-Joon Koh ◽  
...  

Abstract Background Tumor budding is associated with lymph node (LN) metastasis in submucosal colorectal cancer (CRC). However, the rate of LN metastasis associated with the number of tumor buds is unknown. Here, we determined the optimal tumor budding cut-off number and developed a composite scoring system (CSS) for estimating LN metastasis of submucosal CRC. Methods In total, 395 patients with histologically confirmed T1N0–2M0 CRC were evaluated. The clinicopathological characteristics were subjected to univariate and multivariate analyses. The Akaike information criterion (AIC) values of the multivariate models were evaluated to identify the optimal cut-off number. A CSS for LN metastasis was developed using independent risk factors. Results The prevalence of LN metastasis was 13.2%. Histological differentiation, lymphatic or venous invasion, and tumor budding were associated with LN metastasis in univariate analyses. In multivariate models adjusted for histological differentiation and lymphatic or venous invasion, the AIC value was lowest for five tumor buds. Unfavorable differentiation (odds ratio [OR], 8.16; 95% confidence interval [CI], 1.80–36.89), lymphatic or venous invasion (OR, 5.91; 95% CI, 2.91–11.97), and five or more tumor buds (OR, 3.01; 95% CI, 1.21–7.69) were independent risk factors. In a CSS using these three risk factors, the rates of LN metastasis were 5.6%, 15.5%, 31.0%, and 52.4% for total composite scores of 0, 1, 2, and ≥ 3, respectively. Conclusions For the estimation of LN metastasis in submucosal CRC, the optimal tumor budding cut-off number was five. Our CSS can be utilized to estimate LN metastasis.


2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Soo Min Noh ◽  
Sung Wook Hwang ◽  
Sang Hyoung Park ◽  
Dong-Hoon Yang ◽  
Byong Duk Ye ◽  
...  

2021 ◽  
Author(s):  
Tamotsu Sugai ◽  
Noriyuki Yamada ◽  
Mitsumasa Osakabe ◽  
Mai Hashimoto ◽  
Noriyuki Uesugi ◽  
...  

2020 ◽  
Vol 18 (1) ◽  
pp. 96-106 ◽  
Author(s):  
Yun Sik Choi ◽  
Wan Soo Kim ◽  
Sung Wook Hwang ◽  
Sang Hyoung Park ◽  
Dong-Hoon Yang ◽  
...  

2017 ◽  
Vol 32 (4) ◽  
pp. 2123-2130 ◽  
Author(s):  
Daisuke Watanabe ◽  
Takashi Toyonaga ◽  
Makoto Ooi ◽  
Tetsuya Yoshizaki ◽  
Yoshiko Ohara ◽  
...  

2017 ◽  
Vol 152 (5) ◽  
pp. S1019-S1020
Author(s):  
Tsuyoshi Ozawa ◽  
Feng Gao ◽  
Hiroaki Nozawa ◽  
Keisuke Hata ◽  
Hiroshi Nagata ◽  
...  

2016 ◽  
Vol 27 (suppl_9) ◽  
Author(s):  
S. Fujino ◽  
N. Miyoshi ◽  
M. Ohue ◽  
M. Yasui ◽  
Y. Fujiwara ◽  
...  

2016 ◽  
Vol 27 ◽  
pp. ix53
Author(s):  
S. Fujino ◽  
N. Miyoshi ◽  
M. Ohue ◽  
M. Yasui ◽  
Y. Fujiwara ◽  
...  

Oncotarget ◽  
2016 ◽  
Vol 7 (22) ◽  
pp. 32902-32915 ◽  
Author(s):  
Chan Kwon Jung ◽  
Seung-Hyun Jung ◽  
Seon-Hee Yim ◽  
Ji-Han Jung ◽  
Hyun Joo Choi ◽  
...  

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