Novel model to predict hcc recurrence after liver transplantation obtained using deep learning: A multicenter study
- Authors
- Nam, J.Y.[Nam, J.Y.]; Lee, J.-H.[Lee, J.-H.]; Bae, J.[Bae, J.]; Chang, Y.[Chang, Y.]; Cho, Y.[Cho, Y.]; Sinn, D.H.[Sinn, D.H.]; Kim, B.H.[Kim, B.H.]; Kim, S.H.[Kim, S.H.]; Yi, N.-J.[Yi, N.-J.]; Lee, K.-W.[Lee, K.-W.]; Kim, J.M.[Kim, J.M.]; Park, J.-W.[Park, J.-W.]; Kim, Y.J.[Kim, Y.J.]; Yoon, J.-H.[Yoon, J.-H.]; Joh, J.-W.[Joh, J.-W.]; Suh, K.-S.[Suh, K.-S.]
- Issue Date
- Oct-2020
- Publisher
- MDPI AG
- Keywords
- Deep learning; Hepatocellular carcinoma; Liver transplantation; Milan criteria
- Citation
- Cancers, v.12, no.10, pp.1 - 10
- Indexed
- SCIE
SCOPUS
- Journal Title
- Cancers
- Volume
- 12
- Number
- 10
- Start Page
- 1
- End Page
- 10
- URI
- https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/6906
- DOI
- 10.3390/cancers12102791
- ISSN
- 2072-6694
- Abstract
- Several models have been developed using conventional regression approaches to extend the criteria for liver transplantation (LT) in hepatocellular carcinoma (HCC) beyond the Milan criteria. We aimed to develop a novel model to predict tumor recurrence after LT by adopting artificial intelligence (MoRAL-AI). This study included 563 patients who underwent LT for HCC at three large LT centers in Korea. Derivation (n = 349) and validation (n = 214) cohorts were independently established. The primary outcome was time-to-recurrence after LT. A MoRAL-AI was derived from the derivation cohort with a residual block-based deep neural network. The median follow-up duration was 74.7 months (interquartile-range, 18.5–107.4); 204 patients (36.2%) had HCC beyond the Milan criteria. The optimal model consisted of seven layers including two residual blocks. In the validation cohort, the MoRAL-AI showed significantly better discrimination function (c-index = 0.75) than the Milan (c-index = 0.64), MoRAL (c-index = 0.69), University of California San Francisco (c-index = 0.62), up-to-seven (c-index = 0.50), and Kyoto (c-index = 0.50) criteria (all p < 0.001). The largest weighted parameter in the MoRAL-AI was tumor diameter, followed by alpha-fetoprotein, age, and protein induced by vitamin K absence-II. The MoRAL-AI had better predictability of tumor recurrence after LT than conventional models. The MoRAL-AI can also evolve with further data. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - Medicine > Department of Medicine > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.