Universität zu Köln

Lennartz, Simon, Mager, Alina, Grosse Hokamp, Nils, Schafer, Sebastian, Zopfs, David, Maintz, David, Reinhardt, Hans Christian, Thomas, Roman K., Caldeira, Liliana ORCID: 0000-0002-9530-5899 and Persigehl, Thorsten (2021). Texture analysis of iodine maps and conventional images for k-nearest neighbor classification of benign and metastatic lung nodules. Cancer Imaging, 21 (1). LONDON: BMC. ISSN 1470-7330

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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, F-18-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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Lennartz, Simon UNSPECIFIED UNSPECIFIED UNSPECIFIED Mager, Alina UNSPECIFIED UNSPECIFIED UNSPECIFIED Grosse Hokamp, Nils UNSPECIFIED UNSPECIFIED UNSPECIFIED Schafer, Sebastian UNSPECIFIED UNSPECIFIED UNSPECIFIED Zopfs, David UNSPECIFIED UNSPECIFIED UNSPECIFIED Maintz, David UNSPECIFIED UNSPECIFIED UNSPECIFIED Reinhardt, Hans Christian UNSPECIFIED UNSPECIFIED UNSPECIFIED Thomas, Roman K. UNSPECIFIED UNSPECIFIED UNSPECIFIED Caldeira, Liliana UNSPECIFIED orcid.org/0000-0002-9530-5899 UNSPECIFIED Persigehl, Thorsten UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-592943
DOI:	10.1186/s40644-020-00374-3
Journal or Publication Title:	Cancer Imaging
Volume:	21
Number:	1
Date:	2021
Publisher:	BMC
Place of Publication:	LONDON
ISSN:	1470-7330
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language Oncology; Radiology, Nuclear Medicine & Medical Imaging Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/59294