Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof of Concept Study Using Artificial Intelligence for Risk Assessment
| Autor: | Christoph Düber, Lisa-Katharina Heuft, Arndt Weinmann, Simon Johannes Gairing, Fabian Bartsch, J. Baumgart, Friedrich Foerster, Aline Mähringer-Kunz, Lukas Müller, Felix Hahn, Roman Kloeckner |
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| Rok vydání: | 2021 |
| Předmět: |
Scoring system
Tertiary care Article 03 medical and health sciences 0302 clinical medicine intrahepatic cholangiocarcinoma Medicine survival prediction Intrahepatic Cholangiocarcinoma risk scoring Training set Fudan score Artificial neural network business.industry External validation General Medicine artificial intelligence machine learning Cholangiocellular carcinoma 030220 oncology & carcinogenesis 030211 gastroenterology & hepatology Artificial intelligence business Risk assessment artificial neural network |
| Zdroj: | Journal of Clinical Medicine, Vol 10, Iss 2071, p 2071 (2021) Journal of Clinical Medicine Volume 10 Issue 10 |
| ISSN: | 2077-0383 |
| Popis: | Several scoring systems have been devised to objectively predict survival for patients with intrahepatic cholangiocellular carcinoma (ICC) and support treatment stratification, but they have failed external validation. The aim of the present study was to improve prognostication using an artificial intelligence-based approach. We retrospectively identified 417 patients with ICC who were referred to our tertiary care center between 1997 and 2018. Of these, 293 met the inclusion criteria. Established risk factors served as input nodes for an artificial neural network (ANN). We compared the performance of the trained model to the most widely used conventional scoring system, the Fudan score. Predicting 1-year survival, the ANN reached an area under the ROC curve (AUC) of 0.89 for the training set and 0.80 for the validation set. The AUC of the Fudan score was significantly lower in the validation set (0.77, p < 0.001). In the training set, the Fudan score yielded a lower AUC (0.74) without reaching significance (p = 0.24). Thus, ANNs incorporating a multitude of known risk factors can outperform conventional risk scores, which typically consist of a limited number of parameters. In the future, such artificial intelligence-based approaches have the potential to improve treatment stratification when models trained on large multicenter data are openly available. |
| Databáze: | OpenAIRE |
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