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. 2021 May 12;10(10):2071.
doi: 10.3390/jcm10102071.

Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof of Concept Study Using Artificial Intelligence for Risk Assessment

Lukas Müller  1 , Aline Mähringer-Kunz  1 , Simon Johannes Gairing  2 , Friedrich Foerster  2 , Arndt Weinmann  2 , Fabian Bartsch  3 , Lisa-Katharina Heuft  3 , Janine Baumgart  3 , Christoph Düber  1 , Felix Hahn  1 , Roman Kloeckner  1
Affiliations

Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof of Concept Study Using Artificial Intelligence for Risk Assessment

Lukas Müller et al. J Clin Med. .

Abstract

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.

Keywords: Fudan score; artificial intelligence; artificial neural network; intrahepatic cholangiocarcinoma; machine learning; risk scoring; survival prediction.

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Conflict of interest statement

A.W. has received speaker fees and travel grants from Bayer. R.K. has received consultancy fees from Boston Scientific, Bristol-Myers Squibb, Guerbet, Roche, and SIRTEX and lectures fees from BTG, EISAI, Guerbet, Ipsen, Roche, Siemens, SIRTEX, and MSD Sharp & Dohme. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Flow diagram showing the reasons for exclusion from the study. CA19-9, carbohydrate antigen 19-9. AP, alkaline phosphatase. MRI, magnetic resonance imaging. CT, computed tomography.
Figure 2
Figure 2
Calculation of the Fudan score. CA19-9, carbohydrate antigen 19-9. AP, alkaline phosphatase.
Figure 3
Figure 3
Visualization of the created artificial neural network.
Figure 4
Figure 4
Visualization of the created artificial neural network.
Figure 5
Figure 5
Visualization of the created artificial neural network. Receiver operating characteristic curves for the training (blue) and validation (red) sets.
Figure 6
Figure 6
Kaplan–Meier curves of overall survival stratified according to Fudan score.
Figure 7
Figure 7
Receiver operating characteristic curves for the training (blue) and validation (red) sets using the Fudan score.
See this image and copyright information in PMC

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