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Towards Personalized Medicine for Chronic Liver Disease
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Towards Personalized Medicine for Chronic Liver Disease

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Personalized Therapy and Drug Delivery".

Deadline for manuscript submissions: closed (25 March 2023) | Viewed by 8573

Special Issue Editors

Dr. Jinzhou Zhu

E-Mail Website
Guest Editor
Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
Interests: fatty liver disease; machine learning; deep learning
Dr. Feng Zhang

E-Mail Website
Guest Editor
Department of Gastroenterology, Nanjing Drum Tower Hospital, Nanjing, China
Interests: liver cirrhosis; liver fibrosis; portal hypertension

Special Issue Information

Dear Colleagues,

Personalized medicine is a dynamic and rapidly developing approach in clinical practice that involves using innovative technologies to make decisions in the screening, prevention, diagnosis, and treatment of disease. Chronic liver disease is a progressive deterioration of hepatic functions and a continuous process of inflammation, destruction, and regeneration of liver parenchyma, resulting in fibrosis and cirrhosis. The spectrum of etiologies encompasses metabolic disorders, viral infection, toxins, alcohol abuse, and genetic and autoimmune diseases.

The Journal of Personalized Medicine aims to publish a collection of articles exploring recent findings and progress in personalized medicine for chronic liver disease. We will consider original research, systematic reviews, and analyses that report both experimental and clinical studies on integrating precision medicine in the management of chronic liver disease. 

Dr. Jinzhou Zhu
Dr. Feng Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Personalized Medicine is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • biomarker
  • cirrhosis
  • deep learning
  • fatty liver disease
  • liver cancer
  • machine learning
  • metabolism
  • personalized medicine
  • portal hypertension

Published Papers (6 papers)

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Editorial

Jump to: Research, Review

3 pages, 167 KiB  
Editorial
Towards Personalized Medicine for Chronic Liver Disease
by Jingwen Gao, Chunfang Xu and Jinzhou Zhu
J. Pers. Med. 2023, 13(10), 1432; https://doi.org/10.3390/jpm13101432 - 25 Sep 2023
Viewed by 705
Abstract
Chronic liver disease is a progressive deterioration of hepatic functions and a continuous process of inflammation, destruction, and regeneration of liver parenchyma, resulting in fibrosis and cirrhosis [...] Full article
(This article belongs to the Special Issue Towards Personalized Medicine for Chronic Liver Disease)

Research

Jump to: Editorial, Review

12 pages, 1975 KiB  
Article
Hepatic Venous Occlusion Type of Budd–Chiari Syndrome versus Pyrrolizidine Alkaloid-Induced Hepatic Sinusoidal Obstructive Syndrome: A Multi-Center Retrospective Study
by Yaru Tong, Ming Zhang, Zexue Qi, Wei Wu, Jinjun Chen, Fuliang He, Hao Han, Pengxu Ding, Guangchuan Wang and Yuzheng Zhuge
J. Pers. Med. 2023, 13(4), 603; https://doi.org/10.3390/jpm13040603 - 30 Mar 2023
Cited by 3 | Viewed by 1523
Abstract
(1) Background: Hepatic venous occlusion type of Budd–Chiari syndrome (BCS-HV) and pyrrolizidine alkaloid-induced hepatic sinusoidal obstructive syndrome (PA-HSOS), share similar clinical features, and imaging findings, leading to misdiagnoses; (2) Methods: We retrospectively analyzed 139 patients with BCS-HV and 257 with PA-HSOS admitted to [...] Read more.
(1) Background: Hepatic venous occlusion type of Budd–Chiari syndrome (BCS-HV) and pyrrolizidine alkaloid-induced hepatic sinusoidal obstructive syndrome (PA-HSOS), share similar clinical features, and imaging findings, leading to misdiagnoses; (2) Methods: We retrospectively analyzed 139 patients with BCS-HV and 257 with PA-HSOS admitted to six university-affiliated hospitals. We contrasted the two groups by clinical manifestations, laboratory tests, and imaging features for the most valuable distinguishing indicators.; (3) Results: The mean patient age in BCS-HV is younger than that in PA-HSOS (p < 0.05). In BCS-HV, the prevalence of hepatic vein collateral circulation of hepatic veins, enlarged caudate lobe of the liver, and early liver enhancement nodules were 73.90%, 47.70%, and 8.46%, respectively; none of the PA-HSOS patients exhibited these features (p < 0.05). DUS showed that 86.29% (107/124) of patients with BCS-HV showed occlusion of the hepatic vein, while CT or MRI showed that only 4.55%(5/110) patients had this manifestation (p < 0.001). Collateral circulation of hepatic veins was visible in 70.97% (88/124) of BCS-HV patients on DUS, while only 4.55% (5/110) were visible on CT or MRI (p < 0.001); (4) Conclusions: In addition to an established history of PA-containing plant exposure, local hepatic vein stenosis and the presence of collateral circulation of hepatic veins are the most important differential imaging features of these two diseases. However, these important imaging features may be missed by enhanced CT or MRI, leading to an incorrect diagnosis. Full article
(This article belongs to the Special Issue Towards Personalized Medicine for Chronic Liver Disease)
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13 pages, 1989 KiB  
Article
Nomogram for Predicting Portal Vein Thrombosis in Cirrhotic Patients: A Retrospective Cohort Study
by Jingnuo Ding, Fazhi Zhao, Youhan Miao, Yunnuo Liu, Huiting Zhang and Weifeng Zhao
J. Pers. Med. 2023, 13(1), 103; https://doi.org/10.3390/jpm13010103 - 01 Jan 2023
Cited by 2 | Viewed by 1472
Abstract
Aim: Portal vein thrombosis (PVT) is a common complication in cirrhotic patients and will aggravate portal hypertension, thus leading to a series of severe complications. The aim of this study was to develop a nomogram based on a simple and effective model to [...] Read more.
Aim: Portal vein thrombosis (PVT) is a common complication in cirrhotic patients and will aggravate portal hypertension, thus leading to a series of severe complications. The aim of this study was to develop a nomogram based on a simple and effective model to predict PVT in cirrhotic patients. Methods: Clinical data of 656 cirrhotic patients with or without PVT in the First Affiliated Hospital of Soochow University and The Third Affiliated Hospital of Nantong University from January 2017 to March 2022 were retrospectively collected, and all patients were divided into training, internal and external validation cohorts. SPSS and R software were used to identify the independent risk factors and construct a predictive model. We evaluated the predictive value of the model by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses. The feasibility of the model was further validated in the internal and external cohorts. All enrolled patients were followed up to construct the survival curves and calculate the incidence of complications. Results: The predictors of PVT included serum albumin, D-dimer, portal vein diameter, splenectomy, and esophageal and gastric varices. Based on the clinical and imaging findings, the final model served as a potential tool for predicting PVT in cirrhotic patients, with an AUC of 0.806 (0.766 in the internal validation cohort and 0.845 in the external validation cohort). The decision curve analysis revealed that the model had a high level of concordance between different medical centers. There was a significant difference between the PVT and non-PVT groups in survival analyses, with p values of 0.0477 and 0.0319 in the training and internal validation groups, respectively, along with p value of 0.0002 in the external validation group according to log-rank test; meanwhile, the median survival times of the PVT group were 54, 43, and 40 months, respectively. The incidence of recurrent esophageal and gastric variceal bleeding (EGVB) during the follow-up showed significant differences among the three cohorts (p = 0.009, 0.048, and 0.001 in the training, internal validation, and external validation cohorts, respectively). Conclusion: The nomogram based on our model provides a simple and convenient method for predicting PVT in cirrhotic patients. Cirrhotic patients with PVT had a shorter survival time and were prone to recurrent EGVB compared with those in the non-PVT group. Full article
(This article belongs to the Special Issue Towards Personalized Medicine for Chronic Liver Disease)
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10 pages, 2107 KiB  
Article
Predictive Risk Factors of Nonalcoholic Fatty Liver Disease in a Lean Chinese Population
by Lu Liu, Xiaolan Shi, Jingwen Gao, Chunfang Xu and Xiaolin Liu
J. Pers. Med. 2022, 12(12), 1958; https://doi.org/10.3390/jpm12121958 - 26 Nov 2022
Cited by 3 | Viewed by 1269
Abstract
Background: Although nonalcoholic fatty liver disease (NAFLD) is related to obesity, it may also affect lean individuals. Recent data suggest that lean NAFLD patients can develop the whole spectrum of NASH. However, the NAFLD predictive model for lean populations remains lacking. Methods: A [...] Read more.
Background: Although nonalcoholic fatty liver disease (NAFLD) is related to obesity, it may also affect lean individuals. Recent data suggest that lean NAFLD patients can develop the whole spectrum of NASH. However, the NAFLD predictive model for lean populations remains lacking. Methods: A total of 5037 lean individuals were included in this study, and the data were separated for training and validation. The logistic regression method was used, and a nomogram, a type of prediction model, was constructed according to the logistic regression analysis and the significant clinical factors. The performance of this model was evaluated based on its discrimination, calibration, and clinical utility. Results: The individuals were divided into the training (n = 4068) or validation (n = 969) cohorts at a ratio of 8 to 2. The overall prevalence of NAFLD in the lean cohort was 6.43%. The nomogram was constructed based on seven predictors: alanine aminotransferase, total cholesterol, triglycerides, low-density lipoprotein cholesterol, creatinine, uric acid, and hemoglobin A1C. The model based on these factors showed good predictive accuracy in the training set and in the internal validation set, with areas under the curve (AUCs) of 0.870 and 0.887, respectively. The calibration curves and decision curve analysis (DCA) displayed good clinical utility. Conclusion: the nomogram model provides a simple and reliable ability to predict the risk of NAFLD in lean subjects. The model can predict lean NAFLD and can help physicians screen and identify lean subjects at a high risk of NAFLD. Full article
(This article belongs to the Special Issue Towards Personalized Medicine for Chronic Liver Disease)
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10 pages, 2196 KiB  
Article
Automated Machine Learning in Predicting 30-Day Mortality in Patients with Non-Cholestatic Cirrhosis
by Chenyan Yu, Yao Li, Minyue Yin, Jingwen Gao, Liting Xi, Jiaxi Lin, Lu Liu, Huixian Zhang, Airong Wu, Chunfang Xu, Xiaolin Liu, Yue Wang and Jinzhou Zhu
J. Pers. Med. 2022, 12(11), 1930; https://doi.org/10.3390/jpm12111930 - 19 Nov 2022
Cited by 2 | Viewed by 1520
Abstract
Objective: To evaluate the feasibility of automated machine learning (AutoML) in predicting 30-day mortality in non-cholestatic cirrhosis. Methods: A total of 932 cirrhotic patients were included from the First Affiliated Hospital of Soochow University between 2014 and 2020. Participants were divided into training [...] Read more.
Objective: To evaluate the feasibility of automated machine learning (AutoML) in predicting 30-day mortality in non-cholestatic cirrhosis. Methods: A total of 932 cirrhotic patients were included from the First Affiliated Hospital of Soochow University between 2014 and 2020. Participants were divided into training and validation datasets at a ratio of 8.5:1.5. Models were developed on the H2O AutoML platform in the training dataset, and then were evaluated in the validation dataset by area under receiver operating characteristic curves (AUC). The best AutoML model was interpreted by SHapley Additive exPlanation (SHAP) Plot, Partial Dependence Plots (PDP), and Local Interpretable Model Agnostic Explanation (LIME). Results: The model, based on the extreme gradient boosting (XGBoost) algorithm, performed better (AUC 0.888) than the other AutoML models (logistic regression 0.673, gradient boost machine 0.886, random forest 0.866, deep learning 0.830, stacking 0.850), as well as the existing scorings (the model of end-stage liver disease [MELD] score 0.778, MELD-Na score 0.782, and albumin-bilirubin [ALBI] score 0.662). The most key variable in the XGBoost model was high-density lipoprotein cholesterol, followed by creatinine, white blood cell count, international normalized ratio, etc. Conclusion: The AutoML model based on the XGBoost algorithm presented better performance than the existing scoring systems for predicting 30-day mortality in patients with non-cholestatic cirrhosis. It shows the promise of AutoML in its future medical application. Full article
(This article belongs to the Special Issue Towards Personalized Medicine for Chronic Liver Disease)
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Review

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17 pages, 937 KiB  
Review
Shear Wave Dispersion in Chronic Liver Disease: From Physical Principles to Clinical Usefulness
by Matteo Garcovich, Mattia Paratore, Maria Elena Ainora, Laura Riccardi, Maurizio Pompili, Antonio Gasbarrini and Maria Assunta Zocco
J. Pers. Med. 2023, 13(6), 945; https://doi.org/10.3390/jpm13060945 - 02 Jun 2023
Cited by 4 | Viewed by 1296
Abstract
The development of new applications in ultrasound (US) imaging in recent years has strengthened the role of this imaging technique in the management of different pathologies, particularly in the setting of liver disease. Improved B-mode imaging (3D and 4D), contrast-enhanced US (CEUS) and [...] Read more.
The development of new applications in ultrasound (US) imaging in recent years has strengthened the role of this imaging technique in the management of different pathologies, particularly in the setting of liver disease. Improved B-mode imaging (3D and 4D), contrast-enhanced US (CEUS) and especially US-based elastography techniques have created the concept of multiparametric ultrasound (MP-US), a term borrowed from radiological sectional imaging. Among the new elastography techniques, shear wave dispersion is a newly developed imaging technology which enables the assessment of the shear waves’ dispersion slope. The analysis of the dispersion qualities of shear waves might be indirectly related to the tissue viscosity, thus providing biomechanical information concerning the pathologic state of the liver such as necroinflammation. Some of the most recent US devices have been embedded with software that evaluate the dispersion of shear waves/liver viscosity. In this review, the feasibility and the clinical applications of liver viscosity are reviewed based on the preliminary findings of both animal and human studies. Full article
(This article belongs to the Special Issue Towards Personalized Medicine for Chronic Liver Disease)
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