2021
DOI: 10.1002/jso.26615
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Risk factors for lymph node metastasis in rectal neuroendocrine tumors: A recursive partitioning analysis based on multicenter data

Abstract: Background: The well-differentiated rectal neuroendocrine tumors (RNETs) can also have lymph node metastasis (LNM). Large multicenter data were reviewed to explore the risk factors for LNM in RNETs. Further, we developed a model to predict the risk of LNM in RNETs.

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Cited by 12 publications
(12 citation statements)
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“…A previous study reported that lymphatic invasion and lymph node metastasis rates increase significantly in cases of multiple lesions in patients with rectal NETs [ 32 ]. Furthermore, lymph node metastasis of rectal NETs has been associated with tumor size, depth of invasion, vascular invasion, and WHO grade, which is consistent with our findings for GEP-NET [ 33 ]. In addition, lymph node metastasis has been reported to affect prognosis.…”
Section: Discussionsupporting
confidence: 91%
“…A previous study reported that lymphatic invasion and lymph node metastasis rates increase significantly in cases of multiple lesions in patients with rectal NETs [ 32 ]. Furthermore, lymph node metastasis of rectal NETs has been associated with tumor size, depth of invasion, vascular invasion, and WHO grade, which is consistent with our findings for GEP-NET [ 33 ]. In addition, lymph node metastasis has been reported to affect prognosis.…”
Section: Discussionsupporting
confidence: 91%
“…To predict the pCR rates of the patients with LARC after NCRT, the RPA was performed to classify patients with LARC into different risk groups. RPA was a useful statistical method for predicting patient risk in a number of cancers, including colorectal cancer, nasopharyngeal cancer, cervical cancer, and breast cancer, which could assist clinicians to determine the best medication regimen (30,(47)(48)(49). However, few studies used the RPA to forecast the NCRT response in patients with LARC.…”
Section: Discussionmentioning
confidence: 99%
“…Least absolute shrinkage and selection operator (LASSO) Cox regression model was applied to determine the ideal coefficient for each prognostic feature and estimate the likelihood deviance ( 28 , 29 ). Recursive partitioning analysis (RPA) was used to construct a decision tree that divides patients into different homogeneous risk groups by using the R project ( 30 ). Statistical significance was defined as P < 0.05.…”
Section: Discussionmentioning
confidence: 99%
“…As a statistical method of multivariate analysis, RPA divides patients into different homogeneous risk groups by constructing a decision tree and divides predictors into several groups,whose effects have been reported many times [11,12]. In every node, each predictor was examined for the best split within that variable and the optimal split it corresponds to, which has the greatest difference between patient groups.…”
Section: Recursive Partitioning Analysis (Rpa)mentioning
confidence: 99%