Survival Prediction Model for Patients with Hepatocellular Carcinoma and Extrahepatic Metastasis Based on XGBoost Algorithm

Lim, Jihye; Jeon, Hyeon-Gi; Seo, Yeonjoo; Kim, Moonjin; Moon, Ja Un; Cho, Se Hyun

doi:10.2147/jhc.s429903

Cited by 1 publication

(1 citation statement)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tabular data is used as input after calculating Min-Max or standard scaling, and normalization is replaced by the Batch Normalization layer. Normalized input is passed to the feature transformer block ( 17 – 20 ). The feature transformer block is composed as shown in Figure 3 .…”

Section: Methodsmentioning

confidence: 99%

Development of a deep learning model for predicting recurrence of hepatocellular carcinoma after liver transplantation

Ko,

Cao,

Yang

et al. 2024

Front. Med.

View full text Add to dashboard Cite

BackgroundLiver transplantation (LT) is one of the main curative treatments for hepatocellular carcinoma (HCC). Milan criteria has long been applied to candidate LT patients with HCC. However, the application of Milan criteria failed to precisely predict patients at risk of recurrence. As a result, we aimed to establish and validate a deep learning model comparing with Milan criteria and better guide post-LT treatment.MethodsA total of 356 HCC patients who received LT with complete follow-up data were evaluated. The entire cohort was randomly divided into training set (n = 286) and validation set (n = 70). Multi-layer-perceptron model provided by pycox library was first used to construct the recurrence prediction model. Then tabular neural network (TabNet) that combines elements of deep learning and tabular data processing techniques was utilized to compare with Milan criteria and verify the performance of the model we proposed.ResultsPatients with larger tumor size over 7 cm, poorer differentiation of tumor grade and multiple tumor numbers were first classified as high risk of recurrence. We trained a classification model with TabNet and our proposed model performed better than the Milan criteria in terms of accuracy (0.95 vs. 0.86, p < 0.05). In addition, our model showed better performance results with improved AUC, NRI and hazard ratio, proving the robustness of the model.ConclusionA prognostic model had been proposed based on the use of TabNet on various parameters from HCC patients. The model performed well in post-LT recurrence prediction and the identification of high-risk subgroups.

show abstract

Section: Methodsmentioning

confidence: 99%