Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture
Reza Forghani1,2,3,4, Avishek Chatterjee5, Caroline Reinhold6,7
1Department of Radiology and Research Institute of the McGill University Health Centre, McGill University, Room C02.5821, 1001 Decarie Blvd, Montreal, QC, H4A 3J1, Canada. reza.forghani@mcgill.ca.
Objectives:
This study was conducted in order to evaluate a novel risk stratification model using dual-energy CT (DECT) texture analysis of head and neck squamous cell carcinoma (HNSCC) with machine learning to (1) predict associated cervical lymphadenopathy and (2) compare the accuracy of spectral versus single-energy (65 keV) texture evaluation for endpoint prediction.
Methods:
Eighty-seven patients with HNSCC were evaluated. Texture feature extraction was performed on virtual monochromatic images (VMIs) at 65 keV alone or different sets of multi-energy VMIs ranging from 40 to 140 keV, in addition to iodine material decomposition maps and other clinical information. Random forests (RF) models were constructed for outcome prediction with internal cross-validation in addition to the use of separate randomly selected training (70%) and testing (30%) sets. Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were determined for predicting positive versus negative nodal status in the neck.
Results:
Depending on the model used and subset of patients evaluated, an accuracy, sensitivity, specificity, PPV, and NPV of up to 88, 100, 67, 83, and 100%, respectively, could be achieved using multi-energy texture analysis. Texture evaluation of VMIs at 65 keV alone or in combination with only iodine maps had a much lower accuracy.
Conclusions:
Multi-energy DECT texture analysis of HNSCC is superior to texture analysis of 65 keV VMIs and iodine maps alone and can be used to predict cervical nodal metastases with relatively high accuracy, providing information not currently available by expert evaluation of the primary tumor alone.
Key Points:
• Texture features of HNSCC tumor are predictive of nodal status. • Multi-energy texture analysis is superior to analysis of datasets at a single energy. • Dual-energy CT texture analysis with machine learning can enhance noninvasive diagnostic tumor evaluation.
Related Concept Videos
01:26Arteries of the Head and Neck
The internal carotid arteries supply blood to the anterior portion of the cerebrum. They enter the...
01:19Veins of Head and Neck
On the other hand, the vertebral veins, unlike their arterial counterparts, are not primarily responsible for brain drainage. Instead, they drain the cervical vertebrae, spinal cord, and some small...
01:23Detailed Structure and Function of Lymph Nodes
From a histological perspective, lymph nodes can be split into two main areas: the superficial cortex and the deep medulla. The outer cortex is populated by dendritic cells, macrophages, and B lymphocytes, which are densely packed into follicles. When these B-lymphocytes are presented...
01:14Node Analysis for AC Circuits
To unravel the complexities of this system, nodal analysis is employed, a powerful technique founded on Kirchhoff's current law (KCL), which remains valid for phasors. AC circuits can effectively be...
02:58Cell Potential and Free Energy
Thermodynamics is the branch of physics dealing with the relationship between heat and other forms of energy. In an electrochemical cell, chemical energy is converted into electrical energy.
Thus, a link can be predicted between cell potential, free energy change, and the equilibrium constant for the reaction. Cell potential can also be measured as the oxidant or the reducing strength, and similar acid-base strength measures are reflected in equilibrium...
01:25Shape and Texture of Coarse Aggregate