Clinical radiology · 2025
Artificial intelligence in CT for predicting lymph node metastasis in rectal cancer patients: a meta-analysis.
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Hou D, Her H, Han W, Ge X
Аннотация
AIM: This meta-analysis evaluates the diagnostic accuracy of computed tomography (CT)-based artificial intelligence (AI) in detecting lymph node metastasis in rectal cancer patients.
MATERIALS AND METHODS: A thorough literature search was conducted in PubMed, Embase, and Web of Science to identify studies on the diagnostic accuracy of CT-based AI in detecting lymph node metastasis in rectal cancer patients, with a deadline of September 2024. The analysis assessed the performance of AI algorithms in detection through CT imaging. Pooled sensitivity and specificity were calculated using a bivariate random-effects model, reported with 95% confidence intervals. Study heterogeneity was evaluated using the I2 statistic to account for heterogeneity among the included studies.
RESULTS: Seven studies were included in this meta-analysis. The internal test set contained 23 contingency tables, while the radiologists' data included 19 contingency tables. AI's pooled sensitivity, specificity, and area under the curve (AUC) were 0.84 (0.79-0.87), 0.77 (0.66-0.85), and 0.88 (0.16-1.00), respectively. Radiologists had pooled sensitivity, specificity, and AUC of 0.80 (0.72-0.86), 0.54 (0.46-0.63), and 0.72 (0.11-0.98). Funnel plot analysis showed P values of 0.13 for AI and 0.40 for radiologists, indicating that no publication bias was observed.
CONCLUSION: CT-based AI demonstrates superior specificity over radiologists for LNM detection in rectal cancer. However, significant heterogeneity across studies and limited external tests of existing models warrant further multicenter test studies before clinical implementation.
MATERIALS AND METHODS: A thorough literature search was conducted in PubMed, Embase, and Web of Science to identify studies on the diagnostic accuracy of CT-based AI in detecting lymph node metastasis in rectal cancer patients, with a deadline of September 2024. The analysis assessed the performance of AI algorithms in detection through CT imaging. Pooled sensitivity and specificity were calculated using a bivariate random-effects model, reported with 95% confidence intervals. Study heterogeneity was evaluated using the I2 statistic to account for heterogeneity among the included studies.
RESULTS: Seven studies were included in this meta-analysis. The internal test set contained 23 contingency tables, while the radiologists' data included 19 contingency tables. AI's pooled sensitivity, specificity, and area under the curve (AUC) were 0.84 (0.79-0.87), 0.77 (0.66-0.85), and 0.88 (0.16-1.00), respectively. Radiologists had pooled sensitivity, specificity, and AUC of 0.80 (0.72-0.86), 0.54 (0.46-0.63), and 0.72 (0.11-0.98). Funnel plot analysis showed P values of 0.13 for AI and 0.40 for radiologists, indicating that no publication bias was observed.
CONCLUSION: CT-based AI demonstrates superior specificity over radiologists for LNM detection in rectal cancer. However, significant heterogeneity across studies and limited external tests of existing models warrant further multicenter test studies before clinical implementation.