Universität zu Köln

Iuga, Andra-Iza, Lossau, Tanja, Caldeira, Liliana Laurenco, Rinneburger, Miriam ORCID: 0000-0003-3980-931X, Lennartz, Simon ORCID: 0000-0002-3254-4809, Hokamp, Nils Grosse, Puesken, Michael, Carolus, Heike ORCID: 0000-0002-6747-6578, Maintz, David, Klinder, Tobias and Persigehl, Thorsten (2021). Automated mapping and N-Staging of thoracic lymph nodes in contrast-enhanced CT scans of the chest using a fully convolutional neural network. Eur. J. Radiol., 139. CLARE: ELSEVIER IRELAND LTD. ISSN 1872-7727

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Abstract

Purpose: To develop a deep-learning (DL)-based approach for thoracic lymph node (LN) mapping based on their anatomical location. Method: The training-and validation-dataset included 89 contrast-enhanced computed tomography (CT) scans of the chest. 4201 LNs were semi-automatically segmented and then assigned to LN levels according to their anatomical location. The LN level classification task was addressed by a multi-class segmentation procedure using a fully convolutional neural network. Mapping was performed by firstly determining potential level affiliation for each voxel and then performing majority voting over all voxels belonging to each LN. Mean classification accuracies on the validation data were calculated separately for each level and overall Top-1, Top-2 and Top-3 scores were determined, where a Top-X score describes how often the annotated class was within the top-X predictions. To demonstrate the clinical applicability of our model, we tested its N-staging capabilities in a simulated clinical use case scenario assuming a patient diseased with lung cancer. Results: The artificial intelligence(AI)-based assignment revealed mean classification accuracies of 86.36 % (Top1), 94.48 % (Top-2) and 96.10 % (Top-3). Best accuracies were achieved for LNs in the subcarinal level 7 (98.31 %) and axillary region (98.74 %). The highest misclassification rates were observed among LNs in adjacent levels. The proof-of-principle application in a simulated clinical use case scenario for automated tumor N-staging showed a mean classification accuracy of up to 96.14 % (Top-1). Conclusions: The proposed AI approach for automatic classification of LN levels in chest CT as well as the proofof-principle-experiment for automatic N-staging, revealed promising results, warranting large-scale validation for clinical application.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Iuga, Andra-Iza UNSPECIFIED UNSPECIFIED UNSPECIFIED Lossau, Tanja UNSPECIFIED UNSPECIFIED UNSPECIFIED Caldeira, Liliana Laurenco UNSPECIFIED UNSPECIFIED UNSPECIFIED Rinneburger, Miriam UNSPECIFIED orcid.org/0000-0003-3980-931X UNSPECIFIED Lennartz, Simon UNSPECIFIED orcid.org/0000-0002-3254-4809 UNSPECIFIED Hokamp, Nils Grosse UNSPECIFIED UNSPECIFIED UNSPECIFIED Puesken, Michael UNSPECIFIED UNSPECIFIED UNSPECIFIED Carolus, Heike UNSPECIFIED orcid.org/0000-0002-6747-6578 UNSPECIFIED Maintz, David UNSPECIFIED UNSPECIFIED UNSPECIFIED Klinder, Tobias UNSPECIFIED UNSPECIFIED UNSPECIFIED Persigehl, Thorsten UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-579933
DOI:	10.1016/j.ejrad.2021.109718
Journal or Publication Title:	Eur. J. Radiol.
Volume:	139
Date:	2021
Publisher:	ELSEVIER IRELAND LTD
Place of Publication:	CLARE
ISSN:	1872-7727
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language Radiology, Nuclear Medicine & Medical Imaging Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/57993