Artificial intelligence for the diagnosis of lymph node metastases in patients with abdominopelvic malignancy: a systematic review and meta-analysis
Date
2021
Authors
Bedrikovetski, S.
Dudi-Venkata, N.
Maicas, G.
Kroon, H.
Seow, W.
Carneiro, G.
Moore, J.
Sammour, T.
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Journal article
Citation
European Journal of Surgical Oncology, 2021; 47(2):e19-e20
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Sergei Bedrikovetski, Nagendra Dudi-Venkata, Gabriel Maicas, Hidde Kroon, Warren Seow, Gustavo Carneiro, James Moore, Tarik Sammour
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Abstract
Background: Accurate clinical diagnosis of lymph node metastases is of paramount importance in the treatment of patients with abdominopelvic malignancy. This review assesses the diagnostic performance of deep learning algorithms and radiomics models for lymph node metastases in abdominopelvic malignancies. Materials and Methods: The Embase (PubMed, MEDLINE), Science Direct and IEEE Xplore Digital Library databases were searched to identify eligible studies published between Jan 2009 and March 2019. Studies that reported on the accuracy of deep learning algorithms and radiomics models for abdominopelvic malignancy by CT or MRI were selected. Study characteristics and diagnostic measures were extracted from each article. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed using the QUADAS-2 tool. Results: In total, 498 potentially eligible studies were identified, of which 21 were included and 17 offered enough information for a quantitative analysis. Studies were heterogeneous and substantial risk of bias was found in 18 studies. Almost all studies employed radiomics models (n¼20). The single published deep-learning model out-performed radiomics models with a higher AUROC (0.912 vs 0.895), but both radiomics and deep-learning models outperformed the radiologist’s interpretation in isolation (0.774). Pooled results for radiomics nomograms amongst tumour subtypes demonstrated the highest AUC 0.895 (95%CI, 0.810 - 0.980) for urological malignancy, and the lowest AUC 0.798 (95%CI, 0.744 - 0.852) for colorectal malignancy. Conclusions: Radiomics models improve the diagnostic accuracy of lymph node staging for abdominopelvic malignancies in comparison with radiologist’s assessment. Deep learning models may further improve on this, but data remain limited.
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© 2020 Published by Elsevier Ltd.