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Computer-aided diagnosis in predicting the invasion depth of early colorectal cancer: a systematic review and meta-analysis of diagnostic test accuracy

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Abstract

Background

Endoscopic resection (ER) is widely applied to treat early colorectal cancer (CRC). Predicting the invasion depth of early CRC is critical in determining treatment strategies. The use of computer-aided diagnosis (CAD) algorithms could theoretically make accurate and objective predictions regarding the suitability of lesions for ER indication based on invasion depth. This study aimed to assess diagnostic test accuracy of CAD algorithms in predicting the invasion depth of early CRC and to compare the performance between the CAD algorithms and endoscopists.

Methods

Multiple databases were searched until June 30, 2022 for studies that evaluated the diagnostic performance of CAD algorithms for invasion depth of CRC. Meta-analysis of diagnostic test accuracy using a bivariate mixed-effects model was performed.

Results

Ten studies consisting of 13 arms (13,918 images from 1472 lesions) were included. Due to significant heterogeneity, studies were stratified into Japan/Korea-based or China-based studies. For the former, the area under the curve (AUC), sensitivity, and specificity of the CAD algorithms were 0.89 (95% CI 0.86–0.91), 62% (95% CI 50–72%), and 96% (95% CI 93–98%), respectively. For the latter, AUC, sensitivity, and specificity were 0.94 (95% CI 0.92–0.96), 88% (95% CI 78–94%), and 88% (95% CI 80–93%), respectively. The performance of the CAD algorithms in Japan/Korea-based studies was not significantly different from that of all endoscopists (0.88 vs. 0.91, P = 0.10) but was inferior to that of expert endoscopists (0.88 vs. 0.92, P = 0.03). The performance of the CAD algorithms in China-based studies was better than that of all endoscopists (0.94 vs. 0.90, P = 0.01).

Conclusion

The CAD algorithms showed comparable accuracy for prediction of invasion depth of early CRC compared to all endoscopists, which was still lower than expert endoscopists in diagnostic accuracy; more improvements should be achieved before it can be extensively applied to clinical practice.

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Abbreviations

AUC:

Area under the curve

CAD:

Computer-aided diagnosis

CI:

Confidence interval

CRC:

Colorectal cancer

DOR:

Diagnostic odds ratio

FN:

False negative

FP:

False positive

IEE:

Image-enhanced endoscopy

JNET:

Japan NBI Expert Team

ME-NBI:

Magnifying endoscopy with narrow-band imaging

NBI:

Narrow-band imaging

NICE:

NBI international colorectal endoscopic

NLR:

Negative likelihood ratio

PLR:

Positive likelihood ratio

SM:

Submucosa

TN:

True negative

TP:

True positive

WLI:

White-light imaging

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Funding

This work was supported by the Key Research and Development Program of Shaanxi Province (Program No.2023-ZDLSF-36).

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Author notes

  1. Jiawei Bai and Kai Liu have contributed equally to this paper.

Authors and Affiliations

  1. Xijing Hospital of Digestive Diseases, Air Force Medical University (Fourth Military Medical University), 127 Changle West Road, Xi’an, 710032, Shaanxi, China

    Jiawei Bai, Kai Liu, Li Gao, Xin Zhao, Shaohua Zhu, Ying Han & Zhiguo Liu

  2. School of Medicine, Yan’an University, Yan’an, China

    Jiawei Bai

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  1. Jiawei Bai
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Contributions

JB and KL participated in the conception and design, manuscript preparation, statistical analysis of data, and interpretation of results; LG and JB participated in the study search and review; XZ and SZ participated in the data collection and quality assessment; ZL and YH participated in the manuscript revision and study supervision.

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Correspondence to Ying Han or Zhiguo Liu.

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Jiawei Bai, Kai Liu, Li Gao, Xin Zhao, Shaohua Zhu, Ying Han, and Zhiguo Liu have no conflicts of interest or financial ties to disclose.

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Bai, J., Liu, K., Gao, L. et al. Computer-aided diagnosis in predicting the invasion depth of early colorectal cancer: a systematic review and meta-analysis of diagnostic test accuracy. Surg Endosc 37, 6627–6639 (2023). https://doi.org/10.1007/s00464-023-10223-6

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