ArticleJournal of gastrointestinal oncology2024
A predictive model based on radiomics, clinical features, and pathologic indicators for disease-free survival after liver transplantation for hepatocellular carcinoma: a 7-year retrospective study.
Article in Journal of gastrointestinal oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Artificial Intelligence in Post-Liver Transplantation: A Scoping Review of Comparative Model Performance.Journal of clinical medicine · 2026Review
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12 authors.
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Abstract
Background: Disease-free survival (DFS) is an essential indicator for evaluating the prognosis of liver transplantation (LT) in hepatocellular carcinoma (HCC) patients. Despite progress in the prediction of DFS by radiomics, only preoperative clinical features have been combined in most studies. The aim of this study was to construct a nomogram model (NM) using preoperative clinical features, radiomics, and postoperative pathological indicators for more effective prediction of DFS. Methods: This was a retrospective study of a single-center cohort comprising 139 HCC patients. Using the whole cohort, we constructed and assessed a clinical model (CM) based on alpha-fetoprotein (AFP) and alkaline phosphatase (ALP), a pathological model (PM) based on Ki-67 and tumor number, a radiomics model (RM) based on the radiomics score (Rad-score), and an NM based on the above five independent predictors. Results: Significant correlations between the NM and DFS were observed in the training and validation cohorts. Among the four prediction models, the C-index of the NM was the highest [(training/validation cohort) CM: 0.664/0.676, PM: 0.737/0.691, RM: 0.706/0.697, NM: 0.817/0.760], and the areas under the receiver operating characteristic curves (AUCs) of the NM prediction of 1-year, 2-year, and 3-year DFS were also the highest [(training/validation cohort) 1-year, 2-year, and 3-year CM: 0.726/0.726, 0.685/0.744, 0.645/0.686, PM: 0.789/0.780, 0.801/0.748, 0.841/0.735, RM: 0.769/0.752, 0.717/0.805, 0.748/0.765, NM: 0.882/0.854, 0.867/0.849, 0.882/0.801]. The NM also exhibited the highest net clinical benefit. Conclusions: Based on radiomics, clinical features, and pathological indicators, the NM could be used to effectively predict DFS after LT in HCC patients, guiding the follow-up and complementary treatment.
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