• Reza Forghani, Avishek Chatterjee, Caroline Reinhold, Almudena Pérez-Lara, Griselda Romero-Sanchez, Yoshiko Ueno, Maryam Bayat, James W M Alexander, Lynda Kadi, Jeffrey Chankowsky, Jan Seuntjens, and Behzad Forghani.
• Department 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.
• Eur Radiol. 2019 Nov 1; 29 (11): 6172-6181.

ObjectivesThis 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.MethodsEighty-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.ResultsDepending 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.ConclusionsMulti-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.

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