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

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dc.contributor.authorBedrikovetski, S.
dc.contributor.authorDudi-Venkata, N.N.
dc.contributor.authorKroon, H.M.
dc.contributor.authorSeow, W.
dc.contributor.authorVather, R.
dc.contributor.authorCarneiro, G.
dc.contributor.authorMoore, J.W.
dc.contributor.authorSammour, T.
dc.date.issued2021
dc.description.abstractBackground: 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 perpatient 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.
dc.description.statementofresponsibilitySergei Bedrikovetski, Nagendra N. Dudi-Venkata, Hidde M. Kroon, Warren Seow, Ryash Vather, Gustavo Carneiro, James W. Moore, and Tarik Sammour
dc.identifier.citationBMC Cancer, 2021; 21(1):1058-1-1058-10
dc.identifier.doi10.1186/s12885-021-08773-w
dc.identifier.issn1471-2407
dc.identifier.issn1471-2407
dc.identifier.orcidBedrikovetski, S. [0000-0001-9330-625X]
dc.identifier.orcidDudi-Venkata, N.N. [0000-0002-9775-3599]
dc.identifier.orcidKroon, H.M. [0000-0002-8923-7527]
dc.identifier.orcidCarneiro, G. [0000-0002-5571-6220]
dc.identifier.orcidSammour, T. [0000-0002-4918-8871]
dc.identifier.urihttps://hdl.handle.net/2440/133876
dc.language.isoen
dc.publisherSpringer Nature
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/180103232
dc.rights© The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
dc.source.urihttps://doi.org/10.1186/s12885-021-08773-w
dc.subjectColorectal cancer
dc.subjectArtificial intelligence
dc.subjectRadiomics
dc.subjectDeep learning
dc.subjectMachine learning
dc.subjectmeta-analysis
dc.subjectLymph node metastasis
dc.subject.meshArtificial Intelligence
dc.subject.meshBias
dc.subject.meshColorectal Neoplasms
dc.subject.meshDeep Learning
dc.subject.meshHumans
dc.subject.meshLymph Nodes
dc.subject.meshLymphatic Metastasis
dc.subject.meshMagnetic Resonance Imaging
dc.subject.meshPreoperative Care
dc.subject.meshPublication Bias
dc.subject.meshROC Curve
dc.subject.meshRadiologists
dc.subject.meshRectal Neoplasms
dc.subject.meshSensitivity and Specificity
dc.subject.meshTomography, X-Ray Computed
dc.titleArtificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis
dc.typeJournal article
pubs.publication-statusPublished

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