Gutenberg Open Science: Predicting survival after transarterial chemoembolization for hepatocellular carcinoma using a neural network : a pilot study

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-6311

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DC Field	Value	Language
dc.contributor.author	Mähringer-Kunz, Aline	-
dc.contributor.author	Wagner, Franziska	-
dc.contributor.author	Hahn, Felix	-
dc.contributor.author	Weinmann, Arndt	-
dc.contributor.author	Brodehl, Sebastian	-
dc.contributor.author	Schotten, Sebastian	-
dc.contributor.author	Hinrichs, Jan B.	-
dc.contributor.author	Düber, Christoph	-
dc.contributor.author	Galle, Peter R.	-
dc.contributor.author	Pinto dos Santos, Daniel	-
dc.contributor.author	Kloeckner, Roman	-
dc.date.accessioned	2021-08-26T08:18:37Z	-
dc.date.available	2021-08-26T08:18:37Z	-
dc.date.issued	2020	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/6321	-
dc.description.abstract	BACKGROUND AND AIMS Deciding when to repeat and when to stop transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC) can be difficult even for experienced investigators. Our aim was to develop a survival prediction model for such patients undergoing TACE using novel machine learning algorithms and to compare it to conventional prediction scores, ART, ABCR and SNACOR. METHODS For this retrospective analysis, 282 patients who underwent TACE for HCC at our tertiary referral centre between January 2005 and December 2017 were included in the final analysis. We built an artificial neural network (ANN) including all parameters used by the aforementioned risk scores and other clinically meaningful parameters. Following an 80:20 split, the first 225 patients were used for training; the more recently treated 20% were used for validation. RESULTS The ANN had a promising performance at predicting 1-year survival, with an area under the ROC curve (AUC) of 0.77 ± 0.13. Internal validation yielded an AUC of 0.83 ± 0.06, a positive predictive value of 87.5% and a negative predictive value of 68.0%. The sensitivity was 77.8% and specificity 81.0%. In a head-to-head comparison, the ANN outperformed the aforementioned scoring systems, which yielded lower AUCs (SNACOR 0.73 ± 0.07, ABCR 0.70 ± 0.07 and ART 0.54 ± 0.08). This difference reached significance for ART (P < .001); for ABCR and SNACOR significance was not reached (P = .143 and P = .201). CONCLUSIONS Artificial neural networks could be better at predicting patient survival after TACE for HCC than traditional scoring systems. Once established, such prediction models could easily be deployed in clinical routine and help determine optimal patient care.	en_GB
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	610 Medizin	de_DE
dc.subject.ddc	610 Medical sciences	en_GB
dc.title	Predicting survival after transarterial chemoembolization for hepatocellular carcinoma using a neural network : a pilot study	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-6311	-
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 04 Medizin	de
jgu.organisation.number	2700	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Liver international	de
jgu.journal.volume	40	de
jgu.journal.issue	3	de
jgu.pages.start	694	de
jgu.pages.end	703	de
jgu.publisher.year	2020	-
jgu.publisher.name	Wiley-Blackwell	de
jgu.publisher.place	Oxford	de
jgu.publisher.uri	https://doi.org/10.1111/liv.14380	de
jgu.publisher.issn	1478-3231	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	610	de
jgu.publisher.doi	10.1111/liv.14380
jgu.organisation.ror	https://ror.org/023b0x485
Appears in collections:	JGU-Publikationen

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