The diagnostic accuracy of deep learning-based AI models in predicting lymph node metastasis in T1 and T2 colorectal cancer: A systematic review and meta-analysis

Abstract

BACKGROUND: Colorectal cancer (CRC) continues to be a leading cause of cancer-related mortality globally, and accurately predicting lymph node metastasis (LNM) in T1 and T2 lesions is vital for informing treatment strategies. This study aimed to assess the diagnostic accuracy of artificial intelligence (AI)-based models, particularly deep learning (DL) and machine learning (ML) approaches, in predicting LNM risk in CRC. METHODS: A comprehensive literature search was conducted across PubMed, EMBASE, Web of Science, Cochrane Library, and Scopus databases, identifying relevant studies published up to April 6, 2024. A total of 6552 articles were retrieved, and after screening, 12 studies involving 8540 patients were included for qualitative analysis, with 9 studies eligible for quantitative meta-analysis. The methodological quality of the studies was evaluated using the QUADAS-2 tool. RESULTS: Meta-analysis yielded the following diagnostic parameters: a sensitivity (SEN) of 0.87 (95% CI: 0.76-0.93), specificity (SPE) of 0.69 (95% CI: 0.52-0.82), and AUC of 0.88 (95% CI: 0.84-0.90). The likelihood ratios were 2.80 (95% CI: 1.74-4.50) for positive and 0.18 (95% CI: 0.10-0.34) for negative predictions, with a diagnostic odds ratio of 15.27 (95% CI: 6.49-35.89). CONCLUSION: This meta-analysis indicates that AI-based models, particularly DL and ML techniques, demonstrate moderate SEN and good SPE in predicting LNM in T1 and T2 CRC lesions. These findings highlight the potential of AI models as noninvasive diagnostic tools in clinical practice.

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