Universität zu Köln

Maehringer-Kunz, Aline, Wagner, Franziska, Hahn, Felix, Weinmann, Arndt, Brodehl, Sebastian, Schotten, Sebastian, Hinrichs, Jan B., Dueber, Christoph, Galle, Peter R., dos Santos, Daniel Pinto and Kloeckner, Roman (2020). Predicting survival after transarterial chemoembolization for hepatocellular carcinoma using a neural network: A Pilot Study. Liver Int., 40 (3). S. 694 - 704. HOBOKEN: WILEY. ISSN 1478-3231

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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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Maehringer-Kunz, Aline UNSPECIFIED UNSPECIFIED UNSPECIFIED Wagner, Franziska UNSPECIFIED UNSPECIFIED UNSPECIFIED Hahn, Felix UNSPECIFIED UNSPECIFIED UNSPECIFIED Weinmann, Arndt UNSPECIFIED UNSPECIFIED UNSPECIFIED Brodehl, Sebastian UNSPECIFIED UNSPECIFIED UNSPECIFIED Schotten, Sebastian UNSPECIFIED UNSPECIFIED UNSPECIFIED Hinrichs, Jan B. UNSPECIFIED UNSPECIFIED UNSPECIFIED Dueber, Christoph UNSPECIFIED UNSPECIFIED UNSPECIFIED Galle, Peter R. UNSPECIFIED UNSPECIFIED UNSPECIFIED dos Santos, Daniel Pinto UNSPECIFIED UNSPECIFIED UNSPECIFIED Kloeckner, Roman UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-348258
DOI:	10.1111/liv.14380
Journal or Publication Title:	Liver Int.
Volume:	40
Number:	3
Page Range:	S. 694 - 704
Date:	2020
Publisher:	WILEY
Place of Publication:	HOBOKEN
ISSN:	1478-3231
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language ART; RETREATMENT; MODEL; PROGNOSIS; SMOKING; SCORE; ABCR Multiple languages Gastroenterology & Hepatology Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/34825