Diagnostic Accuracy of Radiomics and Artificial Intelligence Models in Diagnosing Lymph Node Metastasis in Head and Neck Cancers: a Systematic Review and Meta-analysis

Overview

Journal Neuroradiology

Specialties Neurology
Radiology

Date 2024 Nov 11

PMID 39527265

Authors

Parya Valizadeh

Payam Jannatdoust

Mohammad-Taha Pahlevan-Fallahy

Amir Hassankhani

Melika Amoukhteh

Sara Bagherieh

Delaram J Ghadimi

Ali Gholamrezanezhad

Affiliations

Soon will be listed here.

Abstract

Introduction: Head and neck cancers are the seventh most common globally, with lymph node metastasis (LNM) being a critical prognostic factor, significantly reducing survival rates. Traditional imaging methods have limitations in accurately diagnosing LNM. This meta-analysis aims to estimate the diagnostic accuracy of Artificial Intelligence (AI) models in detecting LNM in head and neck cancers.

Methods: A systematic search was performed on four databases, looking for studies reporting the diagnostic accuracy of AI models in detecting LNM in head and neck cancers. Methodological quality was assessed using the METRICS tool and meta-analysis was performed using bivariate model in R environment.

Results: 23 articles met the inclusion criteria. Due to the absence of external validation in most studies, all analyses were confined to internal validation sets. The meta-analysis revealed a pooled AUC of 91% for CT-based radiomics, 84% for MRI-based radiomics, and 92% for PET/CT-based radiomics. Sensitivity and specificity were highest for PET/CT-based models. The pooled AUC was 92% for deep learning models and 91% for hand-crafted radiomics models. Models based on lymph node features had a pooled AUC of 92%, while those based on primary tumor features had an AUC of 89%. No significant differences were found between deep learning and hand-crafted radiomics models or between lymph node and primary tumor feature-based models.

Conclusion: Radiomics and deep learning models exhibit promising accuracy in diagnosing LNM in head and neck cancers, particularly with PET/CT. Future research should prioritize multicenter studies with external validation to confirm these results and enhance clinical applicability.

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