prikaz prve stranice dokumenta Use of machine learning models for identification of predictors of survival and tumor recurrence in patients undergoing liver transplantation for hepatocellular carcinoma
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Use of machine learning models for identification of predictors of survival and tumor recurrence in patients undergoing liver transplantation for hepatocellular carcinoma
At: Transplantation 2022;106 Suppl.8:60/-2022 Joint International Congress of ILTS, ELITA & LICAGE. Istanbul, Turkey, 2022. https://doi.org/10.1097/01.tp.0000872804.80721.17
Bezjak, Miran; Kocman, Branislav; Jadrijević, Stipislav; Filipec Kanižaj, Tajana; Dalbelo Bašić, Bojana; Antonijević, Miro; Mikulić, Danko

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Bezjak, M., Kocman, B., Jadrijević, S., Filipec Kanižaj, T., Dalbelo Bašić, B., Antonijević, M. & Mikulić, D. Use of machine learning models for identification of predictors of survival and tumor recurrence in patients undergoing liver transplantation for hepatocellular carcinoma [PowerPoint slides]. Retrieved from https://urn.nsk.hr/urn:nbn:hr:264:740580

Bezjak, Miran, et al. "Use of machine learning models for identification of predictors of survival and tumor recurrence in patients undergoing liver transplantation for hepatocellular carcinoma." Transplantation 2022;106 Suppl.8:60/-2022 Joint International Congress of ILTS, ELITA & LICAGE, 04.05.2022.-07.05.2022., Istanbul, Turkey. Presentation. https://urn.nsk.hr/urn:nbn:hr:264:740580

Bezjak, Miran, Branislav Kocman, Stipislav Jadrijević, Tajana Filipec Kanižaj, Bojana Dalbelo Bašić, Miro Antonijević and Danko Mikulić. "Use of machine learning models for identification of predictors of survival and tumor recurrence in patients undergoing liver transplantation for hepatocellular carcinoma." Presentation at Transplantation 2022;106 Suppl.8:60/-2022 Joint International Congress of ILTS, ELITA & LICAGE, Istanbul, Turkey, 04.05.2022.-07.05.2022. https://urn.nsk.hr/urn:nbn:hr:264:740580

Bezjak, M., et al. 'Use of machine learning models for identification of predictors of survival and tumor recurrence in patients undergoing liver transplantation for hepatocellular carcinoma' [Presentation], Transplantation 2022;106 Suppl.8:60/-2022 Joint International Congress of ILTS, ELITA & LICAGE. 04.05.2022.-07.05.2022. Available at: https://urn.nsk.hr/urn:nbn:hr:264:740580

Bezjak M, Kocman B, Jadrijević S, Filipec Kanižaj T, Dalbelo Bašić B, Antonijević M, and sur.. Use of machine learning models for identification of predictors of survival and tumor recurrence in patients undergoing liver transplantation for hepatocellular carcinoma. [Presentation] [cited 2024 December 13] Available at: https://urn.nsk.hr/urn:nbn:hr:264:740580

M. Bezjak, et al., "Use of machine learning models for identification of predictors of survival and tumor recurrence in patients undergoing liver transplantation for hepatocellular carcinoma", presented at: Transplantation 2022;106 Suppl.8:60/-2022 Joint International Congress of ILTS, ELITA & LICAGE, Istanbul, 04.05.2022.-07.05.2022. Available at: https://urn.nsk.hr/urn:nbn:hr:264:740580

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Title (english)Use of machine learning models for identification of predictors of survival and tumor recurrence in patients undergoing liver transplantation for hepatocellular carcinoma
AuthorMiran Bezjak
AuthorBranislav Kocman
AuthorStipislav Jadrijević
AuthorTajana Filipec Kanižaj
AuthorBojana Dalbelo Bašić
AuthorMiro Antonijević
AuthorDanko Mikulić
Author's institution(Merkur University Hospital)
Scientific / art field,
discipline and subdiscipline		BIOMEDICINE AND HEALTHCARE
Clinical Medical Sciences
Surgery
Abstract (english)
Background: Hepatocellular carcinoma (HCC) is one of the leading indications for liver transplantation, however, selection criteria remain controversial. We aimed to identify survival factors and predictors for tumor recurrence using machine learning methods. We also compared a machine learning model to Cox regression model. Methods: 32 donor and recipient general and tumor specific parameters were analyzed from 170 patients who underwent liver transplantation for HCC between March 2013 and December 2018 at the University Hospital Merkur. Survival rates were calculated using the Kaplan-Meier method, and multivariate analysis was performed using the Cox proportional hazards regression model. Data was also processed through machine learning Random Forest (RF) method, which included preprocessing, variable selection, Random Forest variable significance, resampling, training and cross-validation of the RF model. Accuracy and concordance index were used for evaluation metrics. Results: Two year recipient and graft survival was 78% and 75%, respectively. The best predictive accuracy of our RF model was 0.75 while the best concordance index was 0.80. RF analysis yielded several relevant predictors of survival: donor CRP, bilirubin and sodium levels, recipient MELD and recipient age. Most significant predictors of HCC recurrence were recipient AFP level and donor CRP and sodium levels. Some of the analyzed parameters were shown to be detrimental for survival both in Cox multivariate analysis and in the RF models. In contrast to the RF model, Cox analysis showed an association between donor age and recipient and graft survival, while donor BMI and donor male sex were identified as risk factors for HCC recurrence. Conclusions: The purpose of a machine learning model for prediction of post transplant HCC recurrence is to identify the patients that would benefit from liver transplantation. Further research including prospectively collected data and additional parameters is needed to confirm our results and improve the existing model.
Keywords (english)
Languageenglish
Peer reviewPeer review - international
DOIhttps://doi.org/10.1097/01.tp.0000872804.80721.17
URN:NBNurn:nbn:hr:264:740580
Presentation date2022-05-05
ConferenceTitle: Transplantation 2022;106 Suppl.8:60/-2022 Joint International Congress of ILTS, ELITA & LICAGE
Location: Istanbul, Turkey
Start date: 2022-05-04
End date: 2022-05-07
Assembly type: Lecture
Document URLhttps://ilts2022.abstractserver.com/program/#/details/presentations/633
Type of resourceText
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-02-22 11:16:56