Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture analysis with machine learning

• Published in: European radiology Vol. 29; no. 11; pp. 6172 - 6181
• Main Authors: Forghani, Reza, Chatterjee, Avishek, Reinhold, Caroline, Pérez-Lara, Almudena, Romero-Sanchez, Griselda, Ueno, Yoshiko, Bayat, Maryam, Alexander, James W. M., Kadi, Lynda, Chankowsky, Jeffrey, Seuntjens, Jan, Forghani, Behzad
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2019; Springer Nature B.V
• Subjects: Accuracy; Artificial intelligence; Diagnostic Radiology; Diagnostic systems; Energy; Evaluation; Feature extraction; Female; Head & neck cancer; Head and Neck Neoplasms - diagnosis; Head and Neck Neoplasms - secondary; Humans; Imaging; Imaging Informatics and Artificial Intelligence; Internal Medicine; Interventional Radiology; Iodine; Learning algorithms; Lymph nodes; Lymph Nodes - diagnostic imaging; Lymphadenopathy; Lymphatic Metastasis; Machine Learning; Male; Mathematical models; Medical diagnosis; Medicine; Medicine & Public Health; Metastases; Multidetector Computed Tomography - methods; Neck; Neoplasm Staging - methods; Neuroradiology; Patients; Predictions; Radiology; Sensitivity; Sensitivity analysis; Squamous cell carcinoma; Squamous Cell Carcinoma of Head and Neck - diagnosis; Squamous Cell Carcinoma of Head and Neck - secondary; Texture; Tumors; Ultrasound; Computer-assisted diagnosis; Head and neck neoplasms; Artificial intelligence; Machine learning; Multidetector computed tomography
• ISSN: 0938-7994; 1432-1084; 1432-1084
• DOI: 10.1007/s00330-019-06159-y

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.