dc.contributor.author
Fehrenbach, Uli
dc.contributor.author
Xin, Siyi
dc.contributor.author
Hartenstein, Alexander
dc.contributor.author
Auer, Timo Alexander
dc.contributor.author
Dräger, Franziska
dc.contributor.author
Froböse, Konrad
dc.contributor.author
Jann, Henning
dc.contributor.author
Mogl, Martina
dc.contributor.author
Amthauer, Holger
dc.contributor.author
Geisel, Dominik
dc.contributor.author
Denecke, Timm
dc.contributor.author
Wiedenmann, Bertram
dc.contributor.author
Penzkofer, Tobias
dc.date.accessioned
2021-11-09T14:12:30Z
dc.date.available
2021-11-09T14:12:30Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/32641
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-32365
dc.description.abstract
Background: Rapid quantification of liver metastasis for diagnosis and follow-up is an unmet medical need in patients with secondary liver malignancies. We present a 3D-quantification model of neuroendocrine liver metastases (NELM) using gadoxetic-acid (Gd-EOB)-enhanced MRI as a useful tool for multidisciplinary cancer conferences (MCC).
Methods: Manual 3D-segmentations of NELM and livers (149 patients in 278 Gd-EOB MRI scans) were used to train a neural network (U-Net architecture). Clinical usefulness was evaluated in another 33 patients who were discussed in our MCC and received a Gd-EOB MRI both at baseline and follow-up examination (n = 66) over 12 months. Model measurements (NELM volume; hepatic tumor load (HTL)) with corresponding absolute (ΔabsNELM; ΔabsHTL) and relative changes (ΔrelNELM; ΔrelHTL) between baseline and follow-up were compared to MCC decisions (therapy success/failure).
Results: Internal validation of the model's accuracy showed a high overlap for NELM and livers (Matthew's correlation coefficient (φ): 0.76/0.95, respectively) with higher φ in larger NELM volume (φ = 0.80 vs. 0.71; p = 0.003). External validation confirmed the high accuracy for NELM (φ = 0.86) and livers (φ = 0.96). MCC decisions were significantly differentiated by all response variables (ΔabsNELM; ΔabsHTL; ΔrelNELM; ΔrelHTL) (p < 0.001). ΔrelNELM and ΔrelHTL showed optimal discrimination between therapy success or failure (AUC: 1.000; p < 0.001).
Conclusion: The model shows high accuracy in 3D-quantification of NELM and HTL in Gd-EOB-MRI. The model's measurements correlated well with MCC's evaluation of therapeutic response.
en
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
neuroendocrine neoplasms
en
dc.subject
liver metastases
en
dc.subject
automatized quantification
en
dc.subject
deep learning
en
dc.subject
multidisciplinary cancer conference
en
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
dc.title
Automatized Hepatic Tumor Volume Analysis of Neuroendocrine Liver Metastases by Gd-EOB MRI—A Deep-Learning Model to Support Multidisciplinary Cancer Conference Decision-Making
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
2726
dcterms.bibliographicCitation.doi
10.3390/cancers13112726
dcterms.bibliographicCitation.journaltitle
Cancers
dcterms.bibliographicCitation.number
11
dcterms.bibliographicCitation.originalpublishername
MDPI AG
dcterms.bibliographicCitation.volume
13
refubium.affiliation
Charité - Universitätsmedizin Berlin
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.bibliographicCitation.pmid
34072865
dcterms.isPartOf.eissn
2072-6694