Systematic review and meta-analysis of histopathological predictive factors for lymph node metastasis in T1 colorectal cancer

2015 ◽  
Vol 50 (7) ◽  
pp. 727-734 ◽  
Author(s):  
Hiroo Wada ◽  
Manabu Shiozawa ◽  
Kayoko Katayama ◽  
Naoyuki Okamoto ◽  
Yohei Miyagi ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Systematic Review ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Predictive Factors ◽  
Meta Analysis ◽  
Node Metastasis ◽  
T1 Colorectal Cancer

Pathologic predictive factors for lymph node metastasis in submucosal invasive (T1) colorectal cancer: a systematic review and meta-analysis

2013 ◽  
Vol 27 (8) ◽  
pp. 2692-2703 ◽  
Author(s):  
Shanshan Mou ◽  
Roy Soetikno ◽  
Tadakasu Shimoda ◽  
Robert Rouse ◽  
Tonya Kaltenbach
Keyword(s):  
Colorectal Cancer ◽  
Systematic Review ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Predictive Factors ◽  
Meta Analysis ◽  
Node Metastasis ◽  
T1 Colorectal Cancer

scholarly journals Patient gender as a factor associated with lymph node metastasis in T1 colorectal cancer: A systematic review and meta-analysis

2017 ◽  
Vol 6 (4) ◽  
pp. 517-524 ◽  
Author(s):  
Katsuro Ichimasa ◽  
Shin-Ei Kudo ◽  
Hideyuki Miyachi ◽  
Yuta Kouyama ◽  
Fumio Ishida ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Systematic Review ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Meta Analysis ◽  
Node Metastasis ◽  
Patient Gender ◽  
T1 Colorectal Cancer

ID: 3521068 DEEP SUBMUCOSAL INVASION AS INDEPENDENT RISK FACTOR FOR LYMPH NODE METASTASIS IN T1 COLORECTAL CANCER: A SYSTEMATIC REVIEW AND META-ANALYSIS

2021 ◽  
Vol 93 (6) ◽  
pp. AB81
Author(s):  
Liselotte W. Zwager ◽  
Barbara A. Bastiaansen ◽  
Nahid Mostafavi ◽  
Roel Hompes ◽  
Valeria Barresi ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Systematic Review ◽  
Risk Factor ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Independent Risk Factor ◽  
Meta Analysis ◽  
Submucosal Invasion ◽  
Node Metastasis ◽  
T1 Colorectal Cancer

scholarly journals Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sergei Bedrikovetski ◽  
Nagendra N. Dudi-Venkata ◽  
Hidde M. Kroon ◽  
Warren Seow ◽  
Ryash Vather ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Systematic Review ◽  
Deep Learning ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Meta Analysis ◽  
Cochrane Library ◽  
Learning Models ◽  
Node Metastasis

Abstract Background Artificial intelligence (AI) is increasingly being used in medical imaging analysis. We aimed to evaluate the diagnostic accuracy of AI models used for detection of lymph node metastasis on pre-operative staging imaging for colorectal cancer. Methods A systematic review was conducted according to PRISMA guidelines using a literature search of PubMed (MEDLINE), EMBASE, IEEE Xplore and the Cochrane Library for studies published from January 2010 to October 2020. Studies reporting on the accuracy of radiomics models and/or deep learning for the detection of lymph node metastasis in colorectal cancer by CT/MRI were included. Conference abstracts and studies reporting accuracy of image segmentation rather than nodal classification were excluded. The quality of the studies was assessed using a modified questionnaire of the QUADAS-2 criteria. Characteristics and diagnostic measures from each study were extracted. Pooling of area under the receiver operating characteristic curve (AUROC) was calculated in a meta-analysis. Results Seventeen eligible studies were identified for inclusion in the systematic review, of which 12 used radiomics models and five used deep learning models. High risk of bias was found in two studies and there was significant heterogeneity among radiomics papers (73.0%). In rectal cancer, there was a per-patient AUROC of 0.808 (0.739–0.876) and 0.917 (0.882–0.952) for radiomics and deep learning models, respectively. Both models performed better than the radiologists who had an AUROC of 0.688 (0.603 to 0.772). Similarly in colorectal cancer, radiomics models with a per-patient AUROC of 0.727 (0.633–0.821) outperformed the radiologist who had an AUROC of 0.676 (0.627–0.725). Conclusion AI models have the potential to predict lymph node metastasis more accurately in rectal and colorectal cancer, however, radiomics studies are heterogeneous and deep learning studies are scarce. Trial registration PROSPERO CRD42020218004.

Su1611 Does Gender Predict Lymph Node Metastasis in pT1 Colorectal Cancer? A Systematic Review and Meta-Analysis

2016 ◽  
Vol 83 (5) ◽  
pp. AB363-AB364
Author(s):  
Katsuro Ichimasa ◽  
Shin-ei Kudo ◽  
Hideyuki Miyachi ◽  
Yuta Kouyama ◽  
Toshiyuki Baba ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Systematic Review ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Meta Analysis ◽  
Node Metastasis

scholarly journals Current problems and perspectives of pathological risk factors for lymph node metastasis in T1 colorectal cancer: a systematic review

2021 ◽  
Author(s):  
Katsuro Ichimasa ◽  
Shin‐ei Kudo ◽  
Hideyuki Miyachi ◽  
Yuta Kouyama ◽  
Kenichi Mochizuki ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Systematic Review ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Node Metastasis ◽  
T1 Colorectal Cancer
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