6533b82dfe1ef96bd1290a85

RESEARCH PRODUCT

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

Christoph DüberLisa-katharina HeuftArndt WeinmannSimon Johannes GairingFabian BartschJ. BaumgartFriedrich FoersterAline Mähringer-kunzLukas MüllerFelix HahnRoman Kloeckner

subject

Scoring systemTertiary careArticle03 medical and health sciences0302 clinical medicineintrahepatic cholangiocarcinomaMedicinesurvival predictionIntrahepatic Cholangiocarcinomarisk scoringTraining setFudan scoreArtificial neural networkbusiness.industryRExternal validationGeneral Medicineartificial intelligencemachine learningCholangiocellular carcinoma030220 oncology & carcinogenesisMedicine030211 gastroenterology & hepatologyArtificial intelligencebusinessRisk assessmentartificial neural network

description

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 &lt

yearjournalcountryeditionlanguage
2021-05-12Journal of Clinical Medicine
https://doi.org/10.3390/jcm10102071