Chen, M.; Jiang, Y.; Zhou, X.; Wu, D.; Xie, Q. Dual-Energy Computed Tomography in Detecting and Predicting Lymph Node Metastasis in Malignant Tumor Patients: A Comprehensive Review. Diagnostics2024, 14, 377.
Chen, M.; Jiang, Y.; Zhou, X.; Wu, D.; Xie, Q. Dual-Energy Computed Tomography in Detecting and Predicting Lymph Node Metastasis in Malignant Tumor Patients: A Comprehensive Review. Diagnostics 2024, 14, 377.
Chen, M.; Jiang, Y.; Zhou, X.; Wu, D.; Xie, Q. Dual-Energy Computed Tomography in Detecting and Predicting Lymph Node Metastasis in Malignant Tumor Patients: A Comprehensive Review. Diagnostics2024, 14, 377.
Chen, M.; Jiang, Y.; Zhou, X.; Wu, D.; Xie, Q. Dual-Energy Computed Tomography in Detecting and Predicting Lymph Node Metastasis in Malignant Tumor Patients: A Comprehensive Review. Diagnostics 2024, 14, 377.
Abstract
The accurate and timely assessment of lymph node involvement is paramount in the management of patients with malignant tumors, owing to its direct correlation with cancer staging, therapeutic strategy formulation, and prognostication. Dual-energy computed tomography (DECT), as a burgeoning imaging modality, has shown promising results in the diagnosis and prediction of preoperative metastatic lymph nodes in recent years. This article aims to explore the application of DECT in identifying metastatic lymph nodes (LNs) across various cancer types, including but not limited to thyroid carcinoma (focusing on papillary thyroid carcinoma), lung cancer, and colorectal cancer. Through this narrative review, we aim to elucidate the clinical relevance and utility of DECT in the detection and predictive assessment of lymph node metastasis in malignant tumors, thereby contributing to the broader academic discourse in oncologic radiology and diagnostic precision.
Keywords
DECT; metastatic lymph nodes; cancer; radiomics; artificial intelligence; deep learning
Subject
Medicine and Pharmacology, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
