Comparative diagnostic accuracy of deep learning and hand-crafted radiomics models for detecting lymph node metastases in head and neck cancers: A meta-analysis.

e18023 Background: Accurate detection of lymph node metastases (LNM) is crucial in the management of head and neck cancers. Artificial intelligence (AI) techniques, including deep learning (DL) and hand-crafted radiomics (HCR) models, have shown potential in improving diagnostic accuracy. This study aims to compare the performance of DL and HCR models for detecting LNM. Methods: Studies employing DL and HCR models for LNM detection were systematically analyzed. Internal validation datasets were utilized due to limited external validation in the literature. Diagnostic performance metrics, including sensitivity, specificity, and area under the curve (AUC), were evaluated using summary receiver operating characteristic (SROC) curves and paired forest plots. Heterogeneity was assessed using the I² statistic, and leave-one-out sensitivity analyses were performed to identify outliers. Data analysis was conducted using R software environment (version 4.2.1, R Foundation for Statistical Computing, Vienna, Austria). Results: The pooled AUCs were 92.1% (95% CI: 84.9–94.7%) for DL models and 90.5% (95% CI: 82.9–91.8%) for HCR models, with no statistically significant difference in diagnostic accuracy (p=0.978). Sensitivities and specificities for DL models were 83.9% (95% CI: 77.6–88.7%) and 87.0% (95% CI: 81.6–91.1%), while HCR models achieved 82.7% (95% CI: 76.9–87.2%) and 86.2% (95% CI: 81.1–90.5%), respectively. Substantial heterogeneity was observed (DL: I² = 72.5–93.1%; HCR: I² = 31.3–65.9%). However, the exclusion of outliers did not significantly alter the results (p=0.981). Conclusions: DL and HCR models exhibit comparable diagnostic accuracy for detecting LNM in head and neck cancers. While both approaches show promise, significant heterogeneity highlighted the need for external validation to confirm their reliability across diverse clinical settings.