Machine Learning Prediction Models for Diagnosing Hepatocellular Carcinoma with HCV-related Chronic Liver Disease

Hashem, Somaya; ElHefnawi, Mahmoud; Habashy, Shahira M.; Eladawy, Mohamed; Esmat, Gamal; Elakel, Wafaa; Abdelazziz, Ashraf Omar; Nabeel, Mohamed Mahmoud; Abdelmaksoud, Ahmed Hosni; Elbaz, Tamer; Shousha, Hend Ibrahim

doi:10.1016/j.cmpb.2020.105551

Cited by 57 publications

(24 citation statements)

References 15 publications

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“…Regarding the development of HCC in HCV cirrhosis, several AI/ML-based tools were developed in a new study using routinely collected data to predict HCC development in patients with HCV infection[ 51 ]. In the same spirit, in a recent study, several AI/ML-based models were developed that employ laboratory results and clinicopathological parameters that predict HCC development in patients with HCV before and after achieving SVR[ 52 ].…”

Section: Applications Of Ai In Hcc Managementmentioning

confidence: 99%

Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities

Christou¹,

Tsoulfas²

2022

WJGO

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Hepatocellular carcinoma (HCC) constitutes the fifth most frequent malignancy worldwide and the third most frequent cause of cancer-related deaths. Currently, treatment selection is based on the stage of the disease. Emerging fields such as three-dimensional (3D) printing, 3D bioprinting, artificial intelligence (AI), and machine learning (ML) could lead to evidence-based, individualized management of HCC. In this review, we comprehensively report the current applications of 3D printing, 3D bioprinting, and AI/ML-based models in HCC management; we outline the significant challenges to the broad use of these novel technologies in the clinical setting with the goal of identifying means to overcome them, and finally, we discuss the opportunities that arise from these applications. Notably, regarding 3D printing and bioprinting-related challenges, we elaborate on cost and cost-effectiveness, cell sourcing, cell viability, safety, accessibility, regulation, and legal and ethical concerns. Similarly, regarding AI/ML-related challenges, we elaborate on intellectual property, liability, intrinsic biases, data protection, cybersecurity, ethical challenges, and transparency. Our findings show that AI and 3D printing applications in HCC management and healthcare, in general, are steadily expanding; thus, these technologies will be integrated into the clinical setting sooner or later. Therefore, we believe that physicians need to become familiar with these technologies and prepare to engage with them constructively.

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Section: Applications Of Ai In Hcc Managementmentioning

confidence: 99%

Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities

Christou¹,

Tsoulfas²

2022

WJGO

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“…Hashem et al [19] presents ML approaches to predict Hepatocellular Carcinoma with HCV-related Chronic Liver Disease. They present a set of input variables that are filtered to get the optimal variable subset based on LR, DT and Classification and Regression Tree (CART).…”

Section: Related Workmentioning

confidence: 99%

Hepatitis C Virus Prediction Based on Machine Learning Framework: a Real-world Case Study in Egypt

Mamdouh

Shams

El‐Hafeez

2022

Preprint

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Prediction and classification of the diseases are essential in medical science, as it attempts to immune the spread of the disease and discover the infected regions from the early stages. Machine Learning (ML) approaches are commonly used for predicting and classifying the diseases that precisely utilized as an efficient tool for the doctors and specialists. To forecast Hepatitis C Virus (HCV) among Egyptian healthcare workers (HCWs), a prediction system based on machine learning methodologies is developed. We used data from the National Liver Institute (NLI), which was formed at Menoufiya University (Menoufiya, Egypt). The dataset includes 859 patients with 12 distinct characteristics. To test the proposed framework's robustness and dependability, we ran two scenarios, one without feature selection and the other with feature selection based on Sequential Forward Selection (SFS). In addition, a feature subset evaluation based on SFS-generated features is carried out. Induction algorithms and classifiers used for model evaluation include Nave Bayes (NB), Random Forest (RF), K Nearest Neighbor (KNN), and Logistic Regression (LR). Then, the effect of parameter tuning on learning techniques is measured. The experimental results indicated that the proposed framework achieved higher accuracies after SFS selection than without feature selection. Moreover, the RF classifier achieved 94.06% accuracy with minimum learning elapsed time 0.54 sec. Finally, after adjusting the hyperparameter values of the RF classifier, the classification accuracy is improved to 94.88% using only four features.

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“…In fact, EHRs play nowadays a major role as a data source, not only for standard shallow computational intelligence approaches but also for the growing literature trend associated with the quick development of novel deep learning algorithms [58], applied also imaging data [59], [60]. Although more classical data sources are still used (for example, insurance claims [61]), EHRs represent now a solid base for building learning models upon, both for diagnostic purposes in chronic hepatitis C [9], [62]- [64] and in derived diseases such as liver cancer [65]. Notably, in the last few years several research groups worldwide have been actively involved in assessing and predicting the progression of chronic hepatitis C into fibrosis first and then cirrhosis, proposing several methodological alternatives to carefully stage such a progressively deteriorating condition, both by classic shallow models [61], [66]- [69] as well as by more advanced deep learning approaches such as Recurrent Neural Networks [70], marking another domain where Artificial Intelligence will definitely have an impact in the near future.…”

Section: Machine Learning Studiesmentioning

confidence: 99%

An Ensemble Learning Approach for Enhanced Classification of Patients With Hepatitis and Cirrhosis

Chicco

Jurman

2021

IEEE Access

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Hepatitis C is an infectious disease that affects more than 70 million people worldwide, even killing 400 thousand of them annually. To better understand this disease and its prognosis, medical doctors can take advantage of the electronic health records (EHRs) of patients, which contain data that computerbased approaches built on statistics and computational intelligence can process to unveil new discoveries and trends otherwise unnoticeable by physicians. In this study, we analyze EHRs of 540 healthy controls and 75 patients diagnosed with hepatitis C, and use machine learning classifiers to predict their diagnosis. We employ the top classifier (Random Forests) to detect the most diagnostic variables for hepatitis C, that result being aspartate aminotransferase (AST) and alanine aminotransferase (ALT). These two enzyme levels are also employed by physicians in the AST/ALT ratio, a traditional measure commonly employed in gastroenterology and hepatology. We apply the same approach to a validation dataset of 123 patients with hepatitis C and cirrhosis, and the same two variables arose as most relevant. We therefore compared our approach with the AST/ALT ratio, and noticed that our two-features ensemble learning model outperforms the traditional AST/ALT ratio on both datasets. Our results confirm the usefulness of ensemble machine learning for hepatitis C and cirrhosis diagnosis prediction. Moreover, our discoveries can have an impact on clinical practice, helping physicians predict diagnoses of patients at risk of hepatitis C and cirrhosis more precisely.INDEX TERMS AST/ALT ratio, cirrhosis, electronic health records, clinical feature ranking, fibrosis, hepatitis C, minimal health records, aspartate aminotransferase, alanine aminotransferase.

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Machine Learning Prediction Models for Diagnosing Hepatocellular Carcinoma with HCV-related Chronic Liver Disease

Cited by 57 publications

References 15 publications

Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities

Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities

Hepatitis C Virus Prediction Based on Machine Learning Framework: a Real-world Case Study in Egypt

An Ensemble Learning Approach for Enhanced Classification of Patients With Hepatitis and Cirrhosis

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