Universität zu Köln

Juenger, Stephanie T., Hoyer, Ulrike Cornelia Isabel, Schaufler, Diana, Laukamp, Kai Roman, Goertz, Lukas, Thiele, Frank, Grunz, Jan-Peter, Schlamann, Marc, Perkuhn, Michael, Kabbasch, Christoph, Persigehl, Thorsten, Grau, Stefan ORCID: 0000-0002-9742-527X, Borggrefe, Jan ORCID: 0000-0003-2908-7560, Scheffler, Matthias, Shahzad, Rahil and Pennig, Lenhard (2021). Fully Automated MR Detection and Segmentation of Brain Metastases in Non-small Cell Lung Cancer Using Deep Learning. J. Magn. Reson. Imaging, 54 (5). S. 1608 - 1623. HOBOKEN: WILEY. ISSN 1522-2586

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Abstract

Background Non-small cell lung cancer (NSCLC) is the most common tumor entity spreading to the brain and up to 50% of patients develop brain metastases (BMs). Detection of BMs on MRI is challenging with an inherent risk of missed diagnosis. Purpose To train and evaluate a deep learning model (DLM) for fully automated detection and 3D segmentation of BMs in NSCLC on clinical routine MRI. Study Type Retrospective. Population Ninety-eight NSCLC patients with 315 BMs on pretreatment MRI, divided into training (66 patients, 248 BMs) and independent test (17 patients, 67 BMs) and control (15 patients, 0 BMs) cohorts. Field Strength/Sequence T-1-/T-2-weighted, T-1-weighted contrast-enhanced (T1CE; gradient-echo and spin-echo sequences), and FLAIR at 1.0, 1.5, and 3.0 T from various vendors and study centers. Assessment A 3D convolutional neural network (DeepMedic) was trained on the training cohort using 5-fold cross-validation and evaluated on the independent test and control sets. Three-dimensional voxel-wise manual segmentations of BMs by a neurosurgeon and a radiologist on T1CE served as the reference standard. Statistical Tests Sensitivity (recall) and false positive (FP) findings per scan, dice similarity coefficient (DSC) to compare the spatial overlap between manual and automated segmentations, Pearson's correlation coefficient (r) to evaluate the relationship between quantitative volumetric measurements of segmentations, and Wilcoxon rank-sum test to compare the volumes of BMs. A P value In the test set, the DLM detected 57 of the 67 BMs (mean volume: 0.99 +/- 4.24 cm(3)), resulting in a sensitivity of 85.1%, while FP findings of 1.5 per scan were observed. Missed BMs had a significantly smaller volume (0.05 +/- 0.04 cm(3)) than detected BMs (0.96 +/- 2.4 cm(3)). Compared with the reference standard, automated segmentations achieved a median DSC of 0.72 and a good volumetric correlation (r = 0.95). In the control set, 1.8 FPs/scan were observed. Data Conclusion Deep learning provided a high detection sensitivity and good segmentation performance for BMs in NSCLC on heterogeneous scanner data while yielding a low number of FP findings. 3 2 Level of Evidence Technical Efficacy Stage

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Juenger, Stephanie T. UNSPECIFIED UNSPECIFIED UNSPECIFIED Hoyer, Ulrike Cornelia Isabel UNSPECIFIED UNSPECIFIED UNSPECIFIED Schaufler, Diana UNSPECIFIED UNSPECIFIED UNSPECIFIED Laukamp, Kai Roman UNSPECIFIED UNSPECIFIED UNSPECIFIED Goertz, Lukas UNSPECIFIED UNSPECIFIED UNSPECIFIED Thiele, Frank UNSPECIFIED UNSPECIFIED UNSPECIFIED Grunz, Jan-Peter UNSPECIFIED UNSPECIFIED UNSPECIFIED Schlamann, Marc UNSPECIFIED UNSPECIFIED UNSPECIFIED Perkuhn, Michael UNSPECIFIED UNSPECIFIED UNSPECIFIED Kabbasch, Christoph UNSPECIFIED UNSPECIFIED UNSPECIFIED Persigehl, Thorsten UNSPECIFIED UNSPECIFIED UNSPECIFIED Grau, Stefan UNSPECIFIED orcid.org/0000-0002-9742-527X UNSPECIFIED Borggrefe, Jan UNSPECIFIED orcid.org/0000-0003-2908-7560 UNSPECIFIED Scheffler, Matthias UNSPECIFIED UNSPECIFIED UNSPECIFIED Shahzad, Rahil UNSPECIFIED UNSPECIFIED UNSPECIFIED Pennig, Lenhard UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-588659
DOI:	10.1002/jmri.27741
Journal or Publication Title:	J. Magn. Reson. Imaging
Volume:	54
Number:	5
Page Range:	S. 1608 - 1623
Date:	2021
Publisher:	WILEY
Place of Publication:	HOBOKEN
ISSN:	1522-2586
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/58865