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Open Access 01-12-2021 | Metastasis | Research article

Texture analysis of iodine maps and conventional images for k-nearest neighbor classification of benign and metastatic lung nodules

Authors: Simon Lennartz, Alina Mager, Nils Große Hokamp, Sebastian Schäfer, David Zopfs, David Maintz, Hans Christian Reinhardt, Roman K. Thomas, Liliana Caldeira, Thorsten Persigehl

Published in: Cancer Imaging | Issue 1/2021

Abstract

Background

The purpose of this study was to analyze if the use of texture analysis on spectral detector CT (SDCT)-derived iodine maps (IM) in addition to conventional images (CI) improves lung nodule differentiation, when being applied to a k-nearest neighbor (KNN) classifier.

Methods

183 cancer patients who underwent contrast-enhanced, venous phase SDCT of the chest were included: 85 patients with 146 benign lung nodules (BLN) confirmed by either prior/follow-up CT or histopathology and 98 patients with 425 lung metastases (LM) verified by histopathology, ¹⁸F-FDG-PET-CT or unequivocal change during treatment. Semi-automatic 3D segmentation of BLN/LM was performed, and volumetric HU attenuation and iodine concentration were acquired. For conventional images and iodine maps, average, standard deviation, entropy, kurtosis, mean of the positive pixels (MPP), skewness, uniformity and uniformity of the positive pixels (UPP) within the volumes of interests were calculated. All acquired parameters were transferred to a KNN classifier.

Results

Differentiation between BLN and LM was most accurate, when using all CI-derived features combined with the most significant IM-derived feature, entropy (Accuracy:0.87; F1/Dice:0.92). However, differentiation accuracy based on the 4 most powerful CI-derived features performed only slightly inferior (Accuracy:0.84; F1/Dice:0.89, p=0.125). Mono-parametric lung nodule differentiation based on either feature alone (i.e. attenuation or iodine concentration) was poor (AUC=0.65, 0.58, respectively).

Conclusions

First-order texture feature analysis of contrast-enhanced staging SDCT scans of the chest yield accurate differentiation between benign and metastatic lung nodules. In our study cohort, the most powerful iodine map-derived feature slightly, yet insignificantly increased classification accuracy compared to classification based on conventional image features only.

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Title: Texture analysis of iodine maps and conventional images for k-nearest neighbor classification of benign and metastatic lung nodules
Authors: Simon Lennartz
Alina Mager
Nils Große Hokamp
Sebastian Schäfer
David Zopfs
David Maintz
Hans Christian Reinhardt
Roman K. Thomas
Liliana Caldeira
Thorsten Persigehl
Publication date: 01-12-2021
Publisher: BioMed Central
Keywords: Metastasis
Computed Tomography
Computed Tomography
Published in: Cancer Imaging / Issue 1/2021
Electronic ISSN: 1470-7330
DOI: https://doi.org/10.1186/s40644-020-00374-3

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