Commentary on "The Role of MRI Pelvimetry in Predicting Technical Difficulty and Outcomes of Open and Minimally Invasive Total Mesorectal Excision: a Systematic Review"

Overview

Journal Tech Coloproctol

Date 2020 Sep 11

PMID 32915336

Citations 2

Authors

A Martinez-Perez

E Espin

S Pucciarelli

F Ris

N deAngelis

Affiliations

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Citing Articles

Development and validation of machine learning models and nomograms for predicting the surgical difficulty of laparoscopic resection in rectal cancer.

Li X, Zhou Z, Zhu B, Wu Y, Xing C World J Surg Oncol. 2024; 22(1):111.

PMID: 38664824 PMC: 11044303. DOI: 10.1186/s12957-024-03389-3.

Author's reply to commentary on "The role of MRI pelvimetry in predicting technical difficulty and outcomes of open and minimally invasive total mesorectal excision: a systematic review".

Hong J, Brown K, Waller J, Young C, Solomon M Tech Coloproctol. 2021; 25(8):983.

PMID: 34181153 DOI: 10.1007/s10151-021-02478-9.

References

Hong J, Brown K, Waller J, Young C, Solomon M . The role of MRI pelvimetry in predicting technical difficulty and outcomes of open and minimally invasive total mesorectal excision: a systematic review. Tech Coloproctol. 2020; 24(10):991-1000. DOI: 10.1007/s10151-020-02274-x. View