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12.04.2019 | Imaging Informatics and Artificial Intelligence

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

verfasst von: 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, Behzad Forghani

Erschienen in: European Radiology | Ausgabe 11/2019

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Abstract

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.

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Titel: Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture analysis with machine learning
verfasst von: 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
Behzad Forghani
Publikationsdatum: 12.04.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 11/2019
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-019-06159-y

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