Scholarlink

Background <p>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.</p> Methods <p>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 <i>I</i>², and subgroup analyses explored its potential sources.</p> Results <p>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.</p> Conclusion <p>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.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>Can artificial intelligence models applied to US and MRI accurately predict axillary lymph-node metastasis in breast cancer</i>?</p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>Across 41 studies, AI models demonstrated good diagnostic performance for predicting nodal metastasis. DL, HCR, and combined approaches each achieved clinically meaningful accuracy, with integrated DL + HCR models showing the highest pooled diagnostic effect and lowest heterogeneity</i>.</p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>AI-enhanced imaging can assist in non-invasively stratifying nodal status and may reduce reliance on invasive procedures such as sentinel lymph-node biopsy. Consistent imaging protocols, standardized model development, and external validation are needed before clinical adoption</i>.</p> Graphical Abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 发表日期：

    2026年6月19日

查看全文
  • Iman Kiani,
  • Niloufar Pourakbar,
  • Amir Mohammad Azizpour,
  • Tanya Mousavi,
  • Saeed Mohammadzadeh,
  • Farzad Pourghazi,
  • Amirali Khosravi

摘要

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?

Findings Across 41 studies, AI models demonstrated good diagnostic performance for predicting nodal metastasis. DL, HCR, and combined approaches each achieved clinically meaningful accuracy, with integrated DL + HCR models showing the highest pooled diagnostic effect and lowest heterogeneity.

Clinical relevance AI-enhanced imaging can assist in non-invasively stratifying nodal status and may reduce reliance on invasive procedures such as sentinel lymph-node biopsy. Consistent imaging protocols, standardized model development, and external validation are needed before clinical adoption.

Graphical Abstract