Universität zu Köln

Euler, Andre ORCID: 0000-0001-5019-4585, Laqua, Fabian Christopher, Cester, Davide, Lohaus, Niklas ORCID: 0000-0002-4869-5257, Sartoretti, Thomas ORCID: 0000-0002-4812-987X, dos Santos, Daniel Pinto, Alkadhi, Hatem ORCID: 0000-0002-2581-2166 and Baessler, Bettina (2021). Virtual Monoenergetic Images of Dual-Energy CT-Impact on Repeatability, Reproducibility, and Classification in Radiomics. Cancers, 13 (18). BASEL: MDPI. ISSN 2072-6694

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Abstract

Simple Summary Virtual monoenergetic images from dual-energy CT are incrementally used in routine clinical practice. Thus, radiomic analysis will be more often performed on these images in the future. This study characterized the test-retest repeatability and reproducibility of radiomic features from virtual monoenergetic images and their impact on machine-learning-based lesion classification. The results of this study provide a basis to improve radiomic analyses and identify the role of feature stability in classification tasks when using virtual monoenergetic imaging with different scan or reconstruction parameters in multicenter clinical studies. The purpose of this study was to (i) evaluate the test-retest repeatability and reproducibility of radiomic features in virtual monoenergetic images (VMI) from dual-energy CT (DECT) depending on VMI energy (40, 50, 75, 120, 190 keV), radiation dose (5 and 15 mGy), and DECT approach (dual-source and split-filter DECT) in a phantom (ex vivo), and (ii) to assess the impact of VMI energy and feature repeatability on machine-learning-based classification in vivo in 72 patients with 72 hypodense liver lesions. Feature repeatability and reproducibility were determined by concordance-correlation-coefficient (CCC) and dynamic range (DR) >= 0.9. Test-retest repeatability was high within the same VMI energies and scan conditions (percentage of repeatable features ranging from 74% for SFDE mode at 40 keV and 15 mGy to 86% for DSDE at 190 keV and 15 mGy), while reproducibility varied substantially across different VMI energies and DECTs (percentage of reproducible features ranging from 32.8% for SFDE at 5 mGy comparing 40 with 190 keV to 99.2% for DSDE at 15 mGy comparing 40 with 50 keV). No major differences were observed between the two radiation doses (<10%) in all pair-wise comparisons. In vivo, machine learning classification using penalized regression and random forests resulted in the best discrimination of hemangiomas and metastases at low-energy VMI (40 keV), and for cysts at high-energy VMI (120 keV). Feature selection based on feature repeatability did not improve classification performance. Our results demonstrate the high repeatability of radiomics features when keeping scan and reconstruction conditions constant. Reproducibility diminished when using different VMI energies or DECT approaches. The choice of optimal VMI energy improved lesion classification in vivo and should hence be adapted to the specific task.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Euler, Andre UNSPECIFIED orcid.org/0000-0001-5019-4585 UNSPECIFIED Laqua, Fabian Christopher UNSPECIFIED UNSPECIFIED UNSPECIFIED Cester, Davide UNSPECIFIED UNSPECIFIED UNSPECIFIED Lohaus, Niklas UNSPECIFIED orcid.org/0000-0002-4869-5257 UNSPECIFIED Sartoretti, Thomas UNSPECIFIED orcid.org/0000-0002-4812-987X UNSPECIFIED dos Santos, Daniel Pinto UNSPECIFIED UNSPECIFIED UNSPECIFIED Alkadhi, Hatem UNSPECIFIED orcid.org/0000-0002-2581-2166 UNSPECIFIED Baessler, Bettina UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-571399
DOI:	10.3390/cancers13184710
Journal or Publication Title:	Cancers
Volume:	13
Number:	18
Date:	2021
Publisher:	MDPI
Place of Publication:	BASEL
ISSN:	2072-6694
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language TEXTURE ANALYSIS; FEATURES; RECURRENCE Multiple languages Oncology Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/57139