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➡️ Assessing the performance of deep learning and hand-crafted radiomics models using MRI and ultrasound in predicting axillary lymph node status in breast cancer: a systematic review and meta-analysis

✍️ Iman Kiani, Niloufar Pourakbar, Amir Mohammad Azizpour, Tanya Mousavi, Saeed Mohammadzadeh, Farzad Pourghazi* & Amirali Khosravi*


📖 European Radiology

📊 Scopus Q1

📆 June 2026

🗂 Systematic Review and Meta-Analysis

📝 Abstract:

Background
Axillary lymph node metastasis (ALNM) is a critical prognostic factor in breast cancer. While sentinel lymph node biopsy remains the gold standard, conventional imaging relies on operator expertise with variable diagnostic accuracy. This meta-analysis evaluates the diagnostic performance of deep learning (DL) and hand-crafted radiomics (HCR) models using MRI and ultrasound (US) for ALNM prediction.

Methods
Literature search was conducted across four databases up to November 2024. Studies assessing DL or HCR models for ALNM prediction using MRI or US, with histopathological confirmation as the reference standard, were included. Diagnostic accuracy metrics were pooled using bivariate random-effects meta-analysis. Heterogeneity was assessed using Higgins I², and subgroup analyses explored its potential sources.

Results
Across 41 included studies, pooled sensitivity was 0.79 (95% CI: 0.74–0.84) and specificity 0.78 (95% CI: 0.75–0.81) for internal validation, with AUC 0.84. External validation demonstrated sensitivity of 0.78 and specificity of 0.74, with an AUC of 0.82. Likelihood ratio analysis (LR+ 3.0, LR− 0.33) indicated limited standalone clinical utility. Ensemble approaches combining DL and HCR showed higher diagnostic performance (AUC = 0.88 in MRI and AUC = 0.92 in US) compared to individual methods. Models incorporating both intratumoral and peritumoral regions yielded higher AUCs (0.81 vs 0.75) than intratumoral alone.

Conclusion
AI models demonstrate moderate diagnostic accuracy but limited standalone clinical utility. These tools may serve adjunctive roles in risk stratification and treatment planning. Ensemble approaches combining DL and HCR achieve superior performance. Methodological standardization and validation across diverse populations are essential before clinical implementation.

Key Points
Question Can artificial intelligence models applied to US and MRI accurately predict axillary lymph-node metastasis in breast cancer?

DOI لینک 🔗https://doi.org/10.1007/s00330-026-12682-6

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