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

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

doi:10.1186/s40644-020-00374-3

Abstract

BackgroundThe 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.Methods183 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.ResultsDifferentiation 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).ConclusionsFirst-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.

Highlights

The purpose of this study was to analyze if the use of texture analysis on spectral detector computed tomography (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
Our hypothesis was that texture analysis and dualenergy CT-derived iodine maps may work synergistically to facilitate lung nodule differentiation; we focused on first order texture analysis which has been described to be more reproducible than higher-order features, which we assumed to be favorable when applying it to Dual energy CT (DECT) data [23]
Study cohort 183 cancer patients (96 men and 87 women, mean age 63.2 ± 13.0) who underwent spectral detector CT (SDCT) of the chest were included: 85 patients with 161 benign lung nodules and 98 patients with 425 lung metastases

Summary

Introduction

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. Lung nodules are one of the most common incidental findings in chest computed tomography (CT) [1]. Different imaging features depicted in CT of the chest can be used to facilitate prediction of malignancy, especially large nodule size, part-solid appearance and/or spiculation [2,3,4]. For cancer patients, Fleischner criteria are not applicable [6]. Cancer patients with ambiguous lung nodules often undergo either additional follow-up to detect size increase or biopsy of the referring lesions [7, 8]. Uncertainty regarding metastatic status may even alter therapy [10]

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