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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

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

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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Abbreviations

AUC:

Area under the receiver operating curve

CI:

Confidence intervals

DECT:

Dual-energy CT

HNSCC:

Head and neck squamous cell carcinoma

NPV:

Negative predictive value

PPV:

Positive predictive value

RF:

Random forests

ROI:

Region of interest

VMI:

Virtual monochromatic image

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Funding

This work was partly supported by a grant from the Rossy Cancer Network. R.F. is a clinical research scholar supported by the FRQS (Fonds de recherche en santé du Québec).

Author information

Authors and Affiliations

  1. 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, Caroline Reinhold & Behzad Forghani

  2. Segal Cancer Centre and Lady Davis Institute for Medical Research, Jewish General Hospital, Room C-212.1, 3755 Cote Ste-Catherine Road, Montreal, QC, H3T 1E2, Canada

    Reza Forghani, Almudena Pérez-Lara, Griselda Romero-Sanchez, Maryam Bayat, James W. M. Alexander & Lynda Kadi

  3. Department of Radiology, Royal Victoria Hospital, McGill University Health Centre, 1001 Decarie Blvd, Montreal, QC, H4A 3J1, Canada

    Reza Forghani, Caroline Reinhold, Yoshiko Ueno & Jeffrey Chankowsky

  4. Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada

    Reza Forghani, Jan Seuntjens & Behzad Forghani

  5. Medical Physics Unit, Cedars Cancer Centre, McGill University Health Centre, 1001 Decarie Blvd, Montreal, QC, H4A 3J1, Canada

    Avishek Chatterjee & Jan Seuntjens

  6. Department of Radiology, Hospital Regional Universitario de Málaga, Avenida Carlos Haya, S/N, 29010, Málaga, Spain

    Almudena Pérez-Lara

  7. Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2, Kusunoki-cho, Chuo-ku, Kobe, Hyogo, 650-0017, Japan

    Yoshiko Ueno

  8. Faculty of Medicine, Université de Montréal, Montreal, QC, Canada

    Lynda Kadi

Authors
  1. Reza Forghani
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  6. Yoshiko Ueno
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  7. Maryam Bayat
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  8. James W. M. Alexander
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  12. Behzad Forghani
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Corresponding author

Correspondence to Reza Forghani.

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Guarantor

The scientific guarantor of this publication is R. Forghani.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: R.F. has acted as consultant and speaker for GE Healthcare and is a founding partner and stockholder of 4Intel Inc. B.F. is a founding partner and stockholder of 4Intel Inc.

Statistics and biometry

B.F. has significant statistical and informatics expertise and performed the mathematical and statistical analyses.

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Ethics approval was obtained by the Institutional Review Board of the Jewish General Hospital (CIUSSS West-Central Montreal).

Ethical approval

Written informed consent was waived by the Institutional Review Board.

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Forghani, R., Chatterjee, A., Reinhold, C. et al. Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture analysis with machine learning. Eur Radiol 29, 6172–6181 (2019). https://doi.org/10.1007/s00330-019-06159-y

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  • DOI: https://doi.org/10.1007/s00330-019-06159-y

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