MRI-Based Deep Learning Algorithms vs. Radiologists for Lymph Node Metastasis in Colorectal Cancer: A Systematic
Fengguo Wang1, Wenjun Deng1, Zhilin Zhong1
1Department of Radiology, Wuhan Asia General Hospital, Wuhan, Hubei, China.
Purpose:
This systematic review and meta-analysis aimed to compare the diagnostic performance of MRI-based deep learning (DL) algorithms versus radiologists in detecting lymph node metastasis (LNM) in colorectal cancer (CRC).
Methods:
A comprehensive literature search was conducted in PubMed, Embase, and Web of Science up to June 30, 2025 for studies evaluating MRI-based DL algorithms for LNM diagnosis, using histopathology as the reference standard. Pooled sensitivity, specificity, and area under the curve (AUC) were calculated using a bivariate random-effects model. Risk of bias and applicability were assessed using the PROBAST+AI tool. Certainty of evidence was rated with the GRADE approach.
Results:
A total of 10 studies met inclusion criteria. Internal validation cohorts (9 studies, n=1850) showed pooled sensitivity of 0.89 (95% CI: 0.80-0.94), specificity of 0.85 (95% CI: 0.77-0.91), and AUC of 0.93 (95% CI: 0.91-0.95). Radiologists achieved lower pooled sensitivity of 0.65 (95% CI: 0.60-0.71) and specificity of 0.74 (95% CI: 0.71-0.77), with an AUC of 0.76 (95% CI: 0.73-0.80). DL algorithms in internal validation cohorts consistently outperformed junior radiologists in all metrics, and demonstrated higher sensitivity and AUC than senior radiologists(all P<0.05).
Conclusion:
MRI-based DL algorithms show promising diagnostic performance in detecting LNM in CRC, with performance generally higher than those reported for radiologists in internal validation cohorts, particularly junior-experienced readers. However, most included studies were retrospective and originated from China, limiting generalizability. Prospective, multicenter studies are warranted to validate these findings across diverse populations.
