Prediction model for hepatocellular carcinoma recurrence after hepatectomy: Machine learning-based development and interpretation study

Liu, Rongqiang; Wu, Shinan; Yu, Hao yuan; Zeng, Kaining; Liang, Zhixing; Li, Siqi; Hu, Yongwei; Yang, Yang; Ye, Linsen

doi:10.1016/j.heliyon.2023.e22458

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…Traditional Milan criteria based on morphological characteristics is unable to provide a quantification of HCC recurrence risk. Other prediction models on account of mathematical statistics analysis failed to predict tumor recurrence precisely as well ( 26 – 28 ). Therefore, a more efficient model predicting HCC recurrence after LT is urgently needed.…”

Section: Discussionmentioning

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.

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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.

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Section: Discussionmentioning

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.

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Uveal melanoma distant metastasis prediction system: A retrospective observational study based on machine learning

Wu,

Qin,

Zhu

et al. 2024

Cancer Science

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Uveal melanoma (UM) patients face a significant risk of distant metastasis, closely tied to a poor prognosis. Despite this, there is a dearth of research utilizing big data to predict UM distant metastasis. This study leveraged machine learning methods on the Surveillance, Epidemiology, and End Results (SEER) database to forecast the risk probability of distant metastasis. Therefore, the information on UM patients from the SEER database (2000–2020) was split into a 7:3 ratio training set and an internal test set based on distant metastasis presence. Univariate and multivariate logistic regression analyses assessed distant metastasis risk factors. Six machine learning methods constructed a predictive model post‐feature variable selection. The model evaluation identified the multilayer perceptron (MLP) as optimal. Shapley additive explanations (SHAP) interpreted the chosen model. A web‐based calculator personalized risk probabilities for UM patients. The results show that nine feature variables contributed to the machine learning model. The MLP model demonstrated superior predictive accuracy (Precision = 0.788; ROC AUC = 0.876; PR AUC = 0.788). Grade recode, age, primary site, time from diagnosis to treatment initiation, and total number of malignant tumors were identified as distant metastasis risk factors. Diagnostic method, laterality, rural–urban continuum code, and radiation recode emerged as protective factors. The developed web calculator utilizes the MLP model for personalized risk assessments. In conclusion, the MLP machine learning model emerges as the optimal tool for predicting distant metastasis in UM patients. This model facilitates personalized risk assessments, empowering early and tailored treatment strategies.

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A comprehensive review for chronic disease prediction using machine learning algorithms

Islam,

Sultana,

Islam

2024

Journal of Electrical Systems and Inf Technol

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The past few years have seen an emergence of interest in examining the significance of machine learning (ML) in the medical field. Diseases, health emergencies, and medical disorders may now be identified with greater accuracy because of technological advancements and advances in ML. It is essential especially to diagnose individuals with chronic diseases (CD) as early as possible. Our study has focused on analyzing ML’s applicability to predict CD, including cardiovascular disease, diabetes, cancer, liver, and neurological disorders. This study offered a high-level summary of the previous research on ML-based approaches for predicting CD and some instances of their applications. To wrap things up, we compared the results obtained by various studies and the methodologies as well as tools employed by the researchers. The factors or parameters that are responsible for improving the accuracy of the predicting model for different previous works are also identified. For identifying significant features, most of the authors employed a variety of strategies, where least absolute shrinkage and selection (LASSO), minimal-redundancy-maximum-relevance (mRMR), and RELIEF are extensively used methods. It is seen that a wide range of ML approaches, including support vector machine (SVM), random forest (RF), decision tree (DT), naïve Bayes (NB), etc., have been widely used. Also, several deep learning techniques and hybrid models are employed to create CD prediction models, resulting in efficient and reliable clinical decision-making models. For the benefit of the whole healthcare system, we have also offered our suggestions for enhancing the prediction results of CD.

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Prediction model for hepatocellular carcinoma recurrence after hepatectomy: Machine learning-based development and interpretation study

Cited by 3 publications

References 37 publications

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

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

Uveal melanoma distant metastasis prediction system: A retrospective observational study based on machine learning

A comprehensive review for chronic disease prediction using machine learning algorithms

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