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Wei J, Jiang H, Zhou Y, Tian J, Furtado FS, Catalano OA. Radiomics: A radiological evidence-based artificial intelligence technique to facilitate personalized precision medicine in hepatocellular carcinoma. Dig Liver Dis 2023:S1590-8658(22)00863-5. [PMID: 36641292 DOI: 10.1016/j.dld.2022.12.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/15/2022] [Accepted: 12/19/2022] [Indexed: 01/16/2023]
Abstract
The high postoperative recurrence rates in hepatocellular carcinoma (HCC) remain a major hurdle in its management. Appropriate staging and treatment selection may alleviate the extent of fatal recurrence. However, effective methods to preoperatively evaluate pathophysiologic and molecular characteristics of HCC are lacking. Imaging plays a central role in HCC diagnosis and stratification due to the non-invasive diagnostic criteria. Vast and crucial information is hidden within image data. Other than providing a morphological sketch for lesion diagnosis, imaging could provide new insights to describe the pathophysiological and genetic landscape of HCC. Radiomics aims to facilitate diagnosis and prognosis of HCC using artificial intelligence techniques to harness the immense information contained in medical images. Radiomics produces a set of archetypal and robust imaging features that are correlated to key pathological or molecular biomarkers to preoperatively risk-stratify HCC patients. Inferred with outcome data, comprehensive combination of radiomic, clinical and/or multi-omics data could also improve direct prediction of response to treatment and prognosis. The evolution of radiomics is changing our understanding of personalized precision medicine in HCC management. Herein, we review the key techniques and clinical applications in HCC radiomics and discuss current limitations and future opportunities to improve clinical decision making.
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Affiliation(s)
- Jingwei Wei
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR. China; Beijing Key Laboratory of Molecular Imaging, Beijing 100190, PR. China.
| | - Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, PR. China
| | - Yu Zhou
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR. China; Beijing Key Laboratory of Molecular Imaging, Beijing 100190, PR. China; School of Life Science and Technology, Xidian University, Xi'an, PR. China
| | - Jie Tian
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR. China; Beijing Key Laboratory of Molecular Imaging, Beijing 100190, PR. China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, 100191, PR. China; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, PR. China.
| | - Felipe S Furtado
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States; Harvard Medical School, 25 Shattuck St, Boston, MA 02115, United States
| | - Onofrio A Catalano
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States; Harvard Medical School, 25 Shattuck St, Boston, MA 02115, United States.
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C1QC, VSIG4, and CFD as Potential Peripheral Blood Biomarkers in Atrial Fibrillation-Related Cardioembolic Stroke. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2023; 2023:5199810. [PMID: 36644582 PMCID: PMC9837713 DOI: 10.1155/2023/5199810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 11/28/2022] [Accepted: 12/09/2022] [Indexed: 01/07/2023]
Abstract
Atrial fibrillation (AF) is a major risk factor for ischemic stroke. We aimed to identify novel potential biomarkers with diagnostic value in patients with atrial fibrillation-related cardioembolic stroke (AF-CE).Publicly available gene expression profiles related to AF, cardioembolic stroke (CE), and large artery atherosclerosis (LAA) were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified and then functionally annotated. The support vector machine recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) regression analysis were conducted to identify potential diagnostic AF-CE biomarkers. Furthermore, the results were validated by using external data sets, and discriminability was measured by the area under the ROC curve (AUC). In order to verify the predictive results, the blood samples of 13 healthy controls, 20 patients with CE, and 20 patients with LAA stroke were acquired for RT-qPCR, and the correlation between biomarkers and clinical features was further explored. Lastly, a nomogram and the companion website were developed to predict the CE-risk rate. Three feature genes (C1QC, VSIG4, and CFD) were selected and validated in the training and the external datasets. The qRT-PCR evaluation showed that the levels of blood biomarkers (C1QC, VSIG4, and CFD) in patients with AF-CE can be used to differentiate patients with AF-CE from normal controls (P < 0.05) and can effectively discriminate AF-CE from LAA stroke (P < 0.05). Immune cell infiltration analysis revealed that three feature genes were correlated with immune system such as neutrophils. Clinical impact curve, calibration curves, ROC, and DCAs of the nomogram indicate that the nomogram had good performance. Our findings showed that C1QC, VSIG4, and CFD can potentially serve as diagnostic blood biomarkers of AF-CE; novel nomogram and the companion website can help clinicians to identify high-risk individuals, thus helping to guide treatment decisions for stroke patients.
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Boeken T, Feydy J, Lecler A, Soyer P, Feydy A, Barat M, Duron L. Artificial intelligence in diagnostic and interventional radiology: Where are we now? Diagn Interv Imaging 2023; 104:1-5. [PMID: 36494290 DOI: 10.1016/j.diii.2022.11.004] [Citation(s) in RCA: 51] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022]
Abstract
The emergence of massively parallel yet affordable computing devices has been a game changer for research in the field of artificial intelligence (AI). In addition, dramatic investment from the web giants has fostered the development of a high-quality software stack. Going forward, the combination of faster computers with dedicated software libraries and the widespread availability of data has opened the door to more flexibility in the design of AI models. Radiomics is a process used to discover new imaging biomarkers that has multiple applications in radiology and can be used in conjunction with AI. AI can be used throughout the various processes of diagnostic imaging, including data acquisition, reconstruction, analysis and reporting. Today, the concept of "AI-augmented" radiologists is preferred to the theory of the replacement of radiologists by AI in many indications. Current evidence bolsters the assumption that AI-assisted radiologists work better and faster. Interventional radiology becomes a data-rich specialty where the entire procedure is fully recorded in a standardized DICOM format and accessible via standard picture archiving and communication systems. No other interventional specialty can bolster such readiness. In this setting, interventional radiology could lead the development of AI-powered applications in the broader interventional community. This article provides an update on the current status of radiomics and AI research, analyzes upcoming challenges and also discusses the main applications in AI in interventional radiology to help radiologists better understand and criticize articles reporting AI in medical imaging.
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Affiliation(s)
- Tom Boeken
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Vascular and Oncological Interventional Radiology, Hôpital Européen Georges Pompidou, APHP, Paris 75015, France; HeKA team, INRIA, Paris 75012 , France.
| | | | - Augustin Lecler
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Radiology, Rothschild Foundation Hospital, Paris 75019, France
| | - Philippe Soyer
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Radiology, Hôpital Cochin, APHP, Paris 75014, France
| | - Antoine Feydy
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Radiology, Hôpital Cochin, APHP, Paris 75014, France
| | - Maxime Barat
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Radiology, Hôpital Cochin, APHP, Paris 75014, France
| | - Loïc Duron
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Radiology, Rothschild Foundation Hospital, Paris 75019, France
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Kotowski K, Kucharski D, Machura B, Adamski S, Gutierrez Becker B, Krason A, Zarudzki L, Tessier J, Nalepa J. Detecting liver cirrhosis in computed tomography scans using clinically-inspired and radiomic features. Comput Biol Med 2023; 152:106378. [PMID: 36512877 DOI: 10.1016/j.compbiomed.2022.106378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 11/21/2022] [Accepted: 11/28/2022] [Indexed: 11/30/2022]
Abstract
Hepatic cirrhosis is an increasing cause of mortality in developed countries-it is the pathological sequela of chronic liver diseases, and the final liver fibrosis stage. Since cirrhosis evolves from the asymptomatic phase, it is of paramount importance to detect it as quickly as possible, because entering the symptomatic phase commonly leads to hospitalization and can be fatal. Understanding the state of the liver based on the abdominal computed tomography (CT) scans is tedious, user-dependent and lacks reproducibility. We tackle these issues and propose an end-to-end and reproducible approach for detecting cirrhosis from CT. It benefits from the introduced clinically-inspired features that reflect the patient's characteristics which are often investigated by experienced radiologists during the screening process. Such features are coupled with the radiomic ones extracted from the liver, and from the suggested region of interest which captures the liver's boundary. The rigorous experiments, performed over two heterogeneous clinical datasets (two cohorts of 241 and 32 patients) revealed that extracting radiomic features from the liver's rectified contour is pivotal to enhance the classification abilities of the supervised learners. Also, capturing clinically-inspired image features significantly improved the performance of such models, and the proposed features were consistently selected as the important ones. Finally, we showed that selecting the most discriminative features leads to the Pareto-optimal models with enhanced feature-level interpretability, as the number of features was dramatically reduced (280×) from thousands to tens.
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Affiliation(s)
| | | | | | | | - Benjamín Gutierrez Becker
- Roche Pharma Research and Early Development, Informatics, Roche Innovation Center Basel, Basel, Switzerland
| | - Agata Krason
- Roche Pharmaceutical Research and Early Development, Early Clinical Development Oncology, Roche Innovation Center Basel, Basel, Switzerland
| | - Lukasz Zarudzki
- Department of Radiology and Diagnostic Imaging, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - Jean Tessier
- Roche Pharmaceutical Research and Early Development, Early Clinical Development Oncology, Roche Innovation Center Basel, Basel, Switzerland
| | - Jakub Nalepa
- Graylight Imaging, Gliwice, Poland; Department of Algorithmics and Software, Silesian University of Technology, Gliwice, Poland.
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Artificial intelligence: A review of current applications in hepatocellular carcinoma imaging. Diagn Interv Imaging 2023; 104:24-36. [PMID: 36272931 DOI: 10.1016/j.diii.2022.10.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 10/03/2022] [Indexed: 01/10/2023]
Abstract
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and currently the third-leading cause of cancer-related death worldwide. Recently, artificial intelligence (AI) has emerged as an important tool to improve clinical management of HCC, including for diagnosis, prognostication and evaluation of treatment response. Different AI approaches, such as machine learning and deep learning, are both based on the concept of developing prediction algorithms from large amounts of data, or big data. The era of digital medicine has led to a rapidly expanding amount of routinely collected health data which can be leveraged for the development of AI models. Various studies have constructed AI models by using features extracted from ultrasound imaging, computed tomography imaging and magnetic resonance imaging. Most of these models have used convolutional neural networks. These tools have shown promising results for HCC detection, characterization of liver lesions and liver/tumor segmentation. Regarding treatment, studies have outlined a role for AI in evaluation of treatment response and improvement of pre-treatment planning. Several challenges remain to fully integrate AI models in clinical practice. Future research is still needed to robustly evaluate AI algorithms in prospective trials, and improve interpretability, generalizability and transparency. If such challenges can be overcome, AI has the potential to profoundly change the management of patients with HCC. The purpose of this review was to sum up current evidence on AI approaches using imaging for the clinical management of HCC.
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Chan LWC, Wong SCC, Cho WCS, Huang M, Zhang F, Chui ML, Lai UNY, Chan TYK, Cheung ZHC, Cheung JCY, Tang KF, Tse ML, Wong HK, Kwok HMF, Shen X, Zhang S, Chiu KWH. Primary Tumor Radiomic Model for Identifying Extrahepatic Metastasis of Hepatocellular Carcinoma Based on Contrast Enhanced Computed Tomography. Diagnostics (Basel) 2022; 13:diagnostics13010102. [PMID: 36611394 PMCID: PMC9818425 DOI: 10.3390/diagnostics13010102] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 12/22/2022] [Accepted: 12/24/2022] [Indexed: 01/01/2023] Open
Abstract
This study aimed to identify radiomic features of primary tumor and develop a model for indicating extrahepatic metastasis of hepatocellular carcinoma (HCC). Contrast-enhanced computed tomographic (CT) images of 177 HCC cases, including 26 metastatic (MET) and 151 non-metastatic (non-MET), were retrospectively collected and analyzed. For each case, 851 radiomic features, which quantify shape, intensity, texture, and heterogeneity within the segmented volume of the largest HCC tumor in arterial phase, were extracted using Pyradiomics. The dataset was randomly split into training and test sets. Synthetic Minority Oversampling Technique (SMOTE) was performed to augment the training set to 145 MET and 145 non-MET cases. The test set consists of six MET and six non-MET cases. The external validation set is comprised of 20 MET and 25 non-MET cases collected from an independent clinical unit. Logistic regression and support vector machine (SVM) models were identified based on the features selected using the stepwise forward method while the deep convolution neural network, visual geometry group 16 (VGG16), was trained using CT images directly. Grey-level size zone matrix (GLSZM) features constitute four of eight selected predictors of metastasis due to their perceptiveness to the tumor heterogeneity. The radiomic logistic regression model yielded an area under receiver operating characteristic curve (AUROC) of 0.944 on the test set and an AUROC of 0.744 on the external validation set. Logistic regression revealed no significant difference with SVM in the performance and outperformed VGG16 significantly. As extrahepatic metastasis workups, such as chest CT and bone scintigraphy, are standard but exhaustive, radiomic model facilitates a cost-effective method for stratifying HCC patients into eligibility groups of these workups.
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Affiliation(s)
- Lawrence Wing Chi Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
- Correspondence: (L.W.C.C.); (K.W.H.C.); Tel.: +852-34008561 (L.W.C.C.)
| | - Sze Chuen Cesar Wong
- Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | | | - Mohan Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Fei Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Man Lik Chui
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Una Ngo Yin Lai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Tiffany Yuen Kwan Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Zoe Hoi Ching Cheung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Jerry Chun Yin Cheung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Kin Fu Tang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Man Long Tse
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Hung Kit Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Hugo Man Fung Kwok
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Xinping Shen
- Department of Radiology, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518053, China
| | - Sailong Zhang
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong SAR, China
| | - Keith Wan Hang Chiu
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong SAR, China
- Department of Radiology & Imaging, Queen Elizabeth Hospital, Hong Kong SAR, China
- Correspondence: (L.W.C.C.); (K.W.H.C.); Tel.: +852-34008561 (L.W.C.C.)
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Jiang ZY, Qi LS, Li JT, Cui N, Li W, Liu W, Wang KZ. Radiomics: Status quo and future challenges. Artif Intell Med Imaging 2022; 3:87-96. [DOI: 10.35711/aimi.v3.i4.87] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 12/08/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Noninvasive imaging (computed tomography, magnetic resonance imaging, endoscopic ultrasonography, and positron emission tomography) as an important part of the clinical workflow in the clinic, but it still provides limited information for diagnosis, treatment effect evaluation and prognosis prediction. In addition, judgment and diagnoses made by experts are usually based on multiple years of experience and subjective impression which lead to variable results in the same case. With accumulation of medical imaging data, radiomics emerges as a relatively new approach for analysis. Via artificial intelligence techniques, high-throughput quantitative data which is invisible to the naked eyes extracted from original images can be used in the process of patients’ management. Several studies have evaluated radiomics combined with clinical factors, pathological, or genetic information would assist in the diagnosis, particularly in the prediction of biological characteristics, risk of recurrence, and survival with encouraging results. In various clinical settings, there are limitations and challenges needing to be overcome before transformation. Therefore, we summarize the concepts and method of radiomics including image acquisition, region of interest segmentation, feature extraction and model development. We also set forth the current applications of radiomics in clinical routine. At last, the limitations and related deficiencies of radiomics are pointed out to direct the future opportunities and development.
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Affiliation(s)
- Zhi-Yun Jiang
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
| | - Li-Shuang Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, Heilongjiang Province, China
| | - Jia-Tong Li
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
| | - Nan Cui
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
| | - Wei Li
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
- Department of Interventional Vascular Surgery, The 4th Affiliated Hospital of Harbin Medical University, Harbin 150001, Heilongjiang Province, China
| | - Wei Liu
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
| | - Ke-Zheng Wang
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
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Radiomics-Based Machine Learning for Predicting the Injury Time of Rib Fractures in Gemstone Spectral Imaging Scans. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 10:bioengineering10010008. [PMID: 36671582 PMCID: PMC9855073 DOI: 10.3390/bioengineering10010008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 12/07/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022]
Abstract
This retrospective study aimed to predict the injury time of rib fractures in distinguishing fresh (30 days) or old (90 days) rib fractures. We enrolled 111 patients with chest trauma who had been scanned for rib fractures at our hospital between January 2018 and December 2018 using gemstone spectral imaging (GSI). The volume of interest of each broken end of the rib fractures was segmented using calcium-based material decomposition images derived from the GSI scans. The training and testing sets were randomly assigned in a 7:3 ratio. All cases were divided into groups distinguishing the injury time at 30 and 90 days. We constructed radiomics-based models to predict the injury time of rib fractures. The model performance was assessed by the area under the curve (AUC) obtained by the receiver operating characteristic analysis. We included 54 patients with 259 rib fracture segmentations (34 men; mean age, 52 years ± 12.02; and range, 19-72 years). Nine features were excluded by the least absolute shrinkage and selection operator logistic regression to build the radiomics signature. For distinguishing the injury time at 30 days, the Support Vector Machine (SVM) model and human-model collaboration resulted in an accuracy and AUC of 0.85 and 0.871 and 0.91 and 0.912, respectively, and 0.81 and 0.804 and 0.83 and 0.85, respectively, at 90 days in the testing set. The radiomics-based model displayed good accuracy in differentiating between the injury time of rib fractures at 30 and 90 days, and the human-model collaboration generated more accurate outcomes, which may help to add value to clinical practice and distinguish artificial injury in forensic medicine.
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Dalal S, Onyema EM, Malik A. Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy. World J Gastroenterol 2022; 28:6551-6563. [PMID: 36569269 PMCID: PMC9782838 DOI: 10.3748/wjg.v28.i46.6551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/27/2022] [Accepted: 11/21/2022] [Indexed: 12/08/2022] Open
Abstract
BACKGROUND Liver disease indicates any pathology that can harm or destroy the liver or prevent it from normal functioning. The global community has recently witnessed an increase in the mortality rate due to liver disease. This could be attributed to many factors, among which are human habits, awareness issues, poor healthcare, and late detection. To curb the growing threats from liver disease, early detection is critical to help reduce the risks and improve treatment outcome. Emerging technologies such as machine learning, as shown in this study, could be deployed to assist in enhancing its prediction and treatment.
AIM To present a more efficient system for timely prediction of liver disease using a hybrid eXtreme Gradient Boosting model with hyperparameter tuning with a view to assist in early detection, diagnosis, and reduction of risks and mortality associated with the disease.
METHODS The dataset used in this study consisted of 416 people with liver problems and 167 with no such history. The data were collected from the state of Andhra Pradesh, India, through https://www.kaggle.com/datasets/uciml/indian-liver-patient-records. The population was divided into two sets depending on the disease state of the patient. This binary information was recorded in the attribute "is_patient".
RESULTS The results indicated that the chi-square automated interaction detection and classification and regression trees models achieved an accuracy level of 71.36% and 73.24%, respectively, which was much better than the conventional method. The proposed solution would assist patients and physicians in tackling the problem of liver disease and ensuring that cases are detected early to prevent it from developing into cirrhosis (scarring) and to enhance the survival of patients. The study showed the potential of machine learning in health care, especially as it concerns disease prediction and monitoring.
CONCLUSION This study contributed to the knowledge of machine learning application to health and to the efforts toward combating the problem of liver disease. However, relevant authorities have to invest more into machine learning research and other health technologies to maximize their potential.
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Affiliation(s)
- Surjeet Dalal
- Department of CSE, Amity University, Gurugram 122413, Haryana, India
| | - Edeh Michael Onyema
- Department of Mathematics and Computer Science, Coal City University, Enugu 400102, Nigeria
| | - Amit Malik
- Department of CSE, SRM University, Delhi-NCR, Sonipat 131001, Haryana, India
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Fahmy D, Alksas A, Elnakib A, Mahmoud A, Kandil H, Khalil A, Ghazal M, van Bogaert E, Contractor S, El-Baz A. The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma. Cancers (Basel) 2022; 14:cancers14246123. [PMID: 36551606 PMCID: PMC9777232 DOI: 10.3390/cancers14246123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/08/2022] [Accepted: 12/09/2022] [Indexed: 12/15/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.
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Affiliation(s)
- Dalia Fahmy
- Diagnostic Radiology Department, Mansoura University Hospital, Mansoura 35516, Egypt
| | - Ahmed Alksas
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ahmed Elnakib
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Heba Kandil
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
- Faculty of Computer Sciences and Information, Mansoura University, Mansoura 35516, Egypt
| | - Ashraf Khalil
- College of Technological Innovation, Zayed University, Abu Dhabi 4783, United Arab Emirates
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
| | - Eric van Bogaert
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
- Correspondence:
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Xie D, Ying M, Lian J, Li X, Liu F, Yu X, Ni C. Serological indices and ultrasound variables in predicting the staging of hepatitis B liver fibrosis: A comparative study based on random forest algorithm and traditional methods. J Cancer Res Ther 2022; 18:2049-2057. [PMID: 36647969 DOI: 10.4103/jcrt.jcrt_1394_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Objective To compare the diagnostic efficacy of serological indices and ultrasound (US) variables in hepatitis B virus (HBV) liver fibrosis staging using random forest algorithm (RFA) and traditional methods. Methods The demographic and serological indices and US variables of patients with HBV liver fibrosis were retrospectively collected and divided into serology group, US group, and serology + US group according to the research content. RFA was used for training and validation. The diagnostic efficacy was compared to logistic regression analysis (LRA) and APRI and FIB-4 indices. Results For the serology group, the diagnostic performance of RFA was significantly higher than that of APRI and FIB-4 indices. The diagnostic accuracy of RFA in the four classifications (S0S1/S2/S3/S4) of the hepatic fibrosis stage was 79.17%. The diagnostic accuracy for significant fibrosis (≥S2), advanced fibrosis (≥S3), and cirrhosis (S4) was 87.99%, 90.69%, and 92.40%, respectively. The area under the curve (AUC) values were 0.945, 0.959, and 0.951, respectively. For the US group, there was no significant difference in diagnostic performance between RFA and LRA. The diagnostic performance of RFA in the serology + US group was significantly better than that of LRA. The diagnostic accuracy of the four classifications (S0S1/S2/S3/S4) of the hepatic fibrosis stage was 77.21%. The diagnostic accuracy for significant fibrosis (≥S2), advanced fibrosis (≥S3), and cirrhosis (S4) was 87.50%, 90.93%, and 93.38%, respectively. The AUC values were 0.948, 0.959, and 0.962, respectively. Conclusion RFA can significantly improve the diagnostic performance of HBV liver fibrosis staging. RFA based on serological indices has a good ability to predict liver fibrosis staging. RFA can help clinicians accurately judge liver fibrosis staging and reduce unnecessary biopsies.
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Affiliation(s)
- Daolin Xie
- Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou; Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Minghua Ying
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jingru Lian
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xin Li
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Fangyi Liu
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xiaoling Yu
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Caifang Ni
- Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
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Wang L, Wu M, Zhu C, Li R, Bao S, Yang S, Dong J. Ensemble learning based on efficient features combination can predict the outcome of recurrence-free survival in patients with hepatocellular carcinoma within three years after surgery. Front Oncol 2022; 12:1019009. [PMID: 36439437 PMCID: PMC9686395 DOI: 10.3389/fonc.2022.1019009] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 10/25/2022] [Indexed: 04/11/2024] Open
Abstract
Preoperative prediction of recurrence outcome in hepatocellular carcinoma (HCC) facilitates physicians' clinical decision-making. Preoperative imaging and related clinical baseline data of patients are valuable for evaluating prognosis. With the widespread application of machine learning techniques, the present study proposed the ensemble learning method based on efficient feature representations to predict recurrence outcomes within three years after surgery. Radiomics features during arterial phase (AP) and clinical data were selected for training the ensemble models. In order to improve the efficiency of the process, the lesion area was automatically segmented by 3D U-Net. It was found that the mIoU of the segmentation model was 0.8874, and the Light Gradient Boosting Machine (LightGBM) was the most superior, with an average accuracy of 0.7600, a recall of 0.7673, a F1 score of 0.7553, and an AUC of 0.8338 when inputting radiomics features during AP and clinical baseline indicators. Studies have shown that the proposed strategy can relatively accurately predict the recurrence outcome within three years, which is helpful for physicians to evaluate individual patients before surgery.
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Affiliation(s)
- Liyang Wang
- School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Meilong Wu
- Division of Hepatobiliary and Pancreas Surgery, Department of General Surgery, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, China
| | - Chengzhan Zhu
- Department of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Rui Li
- Department of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Shiyun Bao
- Division of Hepatobiliary and Pancreas Surgery, Department of General Surgery, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, China
| | - Shizhong Yang
- Hepato-pancreato-biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsing-hua University, Beijing, China
| | - Jiahong Dong
- School of Clinical Medicine, Tsinghua University, Beijing, China
- Hepato-pancreato-biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsing-hua University, Beijing, China
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63
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Tonon M, Moreau R. Using machine learning for predicting outcomes in ACLF. Liver Int 2022; 42:2354-2355. [PMID: 36162084 DOI: 10.1111/liv.15399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 08/13/2022] [Indexed: 01/22/2023]
Affiliation(s)
- Marta Tonon
- Unit of Internal Medicine and Hepatology, University Hospital of Padova, Padova, Italy
| | - Richard Moreau
- European Foundation for the Study of Chronic Liver Failure (EF CLIF), Barcelona, Spain.,INSERM, Université de Paris Cité, Centre de Recherche sur l'Inflammation (CRI), Paris, France.,Assistance Publique-Hôpitaux de Paris (AP-HP), and Hôpital Beaujon, Service d'Hépatologie, Clichy, France
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Mokhria RK, Singh J. Role of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma. Artif Intell Gastroenterol 2022; 3:96-104. [DOI: 10.35712/aig.v3.i4.96] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/30/2022] [Accepted: 09/14/2022] [Indexed: 02/07/2023] Open
Abstract
Artificial intelligence (AI) evolved many years ago, but it gained much advancement in recent years for its use in the medical domain. AI with its different subsidiaries, i.e. deep learning and machine learning, examine a large amount of data and performs an essential part in decision-making in addition to conquering the limitations related to human evaluation. Deep learning tries to imitate the functioning of the human brain. It utilizes much more data and intricate algorithms. Machine learning is AI based on automated learning. It utilizes earlier given data and uses algorithms to arrange and identify models. Globally, hepatocellular carcinoma is a major cause of illness and fatality. Although with substantial progress in the whole treatment strategy for hepatocellular carcinoma, managing it is still a major issue. AI in the area of gastroenterology, especially in hepatology, is particularly useful for various investigations of hepatocellular carcinoma because it is a commonly found tumor, and has specific radiological features that enable diagnostic procedures without the requirement of the histological study. However, interpreting and analyzing the resulting images is not always easy due to change of images throughout the disease process. Further, the prognostic process and response to the treatment process could be influenced by numerous components. Currently, AI is utilized in order to diagnose, curative and prediction goals. Future investigations are essential to prevent likely bias, which might subsequently influence the analysis of images and therefore restrict the consent and utilization of such models in medical practices. Moreover, experts are required to realize the real utility of such approaches, along with their associated potencies and constraints.
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Affiliation(s)
- Rajesh Kumar Mokhria
- Government Model Sanskriti Senior Secondary School, Chulkana, 132101, Panipat, Haryana, India
| | - Jasbir Singh
- Department of Biochemistry, Kurukshetra University, Kurukshetra, 136119, Haryana, India
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Quantitative Analysis of Liver Disease Using MRI-Based Radiomic Features of the Liver and Spleen. J Imaging 2022; 8:jimaging8100277. [PMID: 36286371 PMCID: PMC9605113 DOI: 10.3390/jimaging8100277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 09/26/2022] [Accepted: 09/28/2022] [Indexed: 12/03/2022] Open
Abstract
Background: Radiomics extracts quantitative image features to identify biomarkers for characterizing disease. Our aim was to characterize the ability of radiomic features extracted from magnetic resonance (MR) imaging of the liver and spleen to detect cirrhosis by comparing features from patients with cirrhosis to those without cirrhosis. Methods: This retrospective study compared MR-derived radiomic features between patients with cirrhosis undergoing hepatocellular carcinoma screening and patients without cirrhosis undergoing intraductal papillary mucinous neoplasm surveillance between 2015 and 2018 using the same imaging protocol. Secondary analyses stratified the cirrhosis cohort by liver disease severity using clinical compensation/decompensation and Model for End-Stage Liver Disease (MELD). Results: Of 167 patients, 90 had cirrhosis with 68.9% compensated and median MELD 8. Combined liver and spleen radiomic features generated an AUC 0.94 for detecting cirrhosis, with shape and texture components contributing more than size. Discrimination of cirrhosis remained high after stratification by liver disease severity. Conclusions: MR-based liver and spleen radiomic features had high accuracy in identifying cirrhosis, after stratification by clinical compensation/decompensation and MELD. Shape and texture features performed better than size features. These findings will inform radiomic-based applications for cirrhosis diagnosis and severity.
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A Multiparametric Fusion Radiomics Signature Based on Contrast-Enhanced MRI for Predicting Early Recurrence of Hepatocellular Carcinoma. JOURNAL OF ONCOLOGY 2022; 2022:3704987. [PMID: 36213823 PMCID: PMC9534653 DOI: 10.1155/2022/3704987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 07/28/2022] [Indexed: 11/18/2022]
Abstract
Objectives The postoperative early recurrence (ER) rate of hepatocellular carcinoma (HCC) is 50%, and no highly reliable predictive tool has been developed yet. The aim of this study was to develop and validate a predictive model with radiomics analysis based on multiparametric magnetic resonance (MR) images to predict early recurrence of HCC. Methods In total, 302 patients (training dataset: n = 211; validation dataset: n = 91) with pathologically confirmed HCC who underwent preoperative MR imaging were enrolled in this study. Three-dimensional regions of interest of the entire lesion were accessed by manually drawing along the tumor margins on the multiple sequences of MR images. Least absolute shrinkage and selection operator Cox regression were then applied to select ER-related radiomics features and construct radiomics signatures. Univariate analysis and multivariate Cox regression analysis were used to identify the significant clinico-radiological factors and establish a clinico-radiological model. A predictive model of ER incorporating the fusion radiomics signature and clinico-radiological risk factors was constructed. The diagnostic performance and clinical utility of this model were measured by receiver-operating characteristic (ROC), calibration curve, and decision curve analyses. Results The fusion radiomics signature consisting of 6 radiomics features achieved good prediction performance (training dataset: AUC = 0.85, validation dataset: AUC = 0.79). The predictive model of ER integrating clinico-radiological risk factors and the fusion radiomics signature improved the prediction efficacy with AUCs of 0.91 and 0.87 in the training and validation datasets, respectively. Furthermore, the nomogram and ER risk stratification system based on the predictive model demonstrated encouraging predictions of the individualized risk of ER and gave three risk groups with low, intermediate, or high risk of ER. Conclusions The proposed predictive model incorporating clinico-radiological factors and the fusion radiomics signature derived from multiparametric MR images may be an effective tool for the individualized prediction of postoperative ER in patients with HCC.
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Lafata KJ, Wang Y, Konkel B, Yin FF, Bashir MR. Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol (NY) 2022; 47:2986-3002. [PMID: 34435228 DOI: 10.1007/s00261-021-03254-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 08/15/2021] [Accepted: 08/16/2021] [Indexed: 01/18/2023]
Abstract
Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve as digital fingerprints of disease. They may also capture imaging characteristics that are difficult or impossible to characterize by the human eye. The rapid development of this field is motivated by systems biology, facilitated by data analytics, and powered by artificial intelligence. Here, as part of Abdominal Radiology's special issue on Quantitative Imaging, we provide an introduction to the field of radiomics. The technique is formally introduced as an advanced application of data analytics, with illustrating examples in abdominal radiology. Artificial intelligence is then presented as the main driving force of radiomics, and common techniques are defined and briefly compared. The complete step-by-step process of radiomic phenotyping is then broken down into five key phases. Potential pitfalls of each phase are highlighted, and recommendations are provided to reduce sources of variation, non-reproducibility, and error associated with radiomics.
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Affiliation(s)
- Kyle J Lafata
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA. .,Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA. .,Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA.
| | - Yuqi Wang
- Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA
| | - Brandon Konkel
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA
| | - Mustafa R Bashir
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA.,Department of Medicine, Gastroenterology, Duke University School of Medicine, Durham, NC, USA
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Liu C, Yang H, Feng Y, Liu C, Rui F, Cao Y, Hu X, Xu J, Fan J, Zhu Q, Li J. A K-nearest Neighbor Model to Predict Early Recurrence of Hepatocellular Carcinoma After Resection. J Clin Transl Hepatol 2022; 10:600-607. [PMID: 36062279 PMCID: PMC9396318 DOI: 10.14218/jcth.2021.00348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 09/25/2021] [Accepted: 10/10/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND AND AIMS Patients with hepatocellular carcinoma (HCC) surgically resected are at risk of recurrence; however, the risk factors of recurrence remain poorly understood. This study intended to establish a novel machine learning model based on clinical data for predicting early recurrence of HCC after resection. METHODS A total of 220 HCC patients who underwent resection were enrolled. Classification machine learning models were developed to predict HCC recurrence. The standard deviation, recall, and precision of the model were used to assess the model's accuracy and identify efficiency of the model. RESULTS Recurrent HCC developed in 89 (40.45%) patients at a median time of 14 months from primary resection. In principal component analysis, tumor size, tumor grade differentiation, portal vein tumor thrombus, alpha-fetoprotein, protein induced by vitamin K absence or antagonist-II (PIVKA-II), aspartate aminotransferase, platelet count, white blood cell count, and HBsAg were positive prognostic factors of HCC recurrence and were included in the preoperative model. After comparing different machine learning methods, including logistic regression, decision tree, naïve Bayes, deep neural networks, and k-nearest neighbor (K-NN), we choose the K-NN model as the optimal prediction model. The accuracy, recall, precision of the K-NN model were 70.6%, 51.9%, 70.1%, respectively. The standard deviation was 0.020. CONCLUSIONS The K-NN classification algorithm model performed better than the other classification models. Estimation of the recurrence rate of early HCC can help to allocate treatment, eventually achieving safe oncological outcomes.
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Affiliation(s)
- Chuanli Liu
- Department of Infectious Disease, Shandong Provincial Hospital Affiliated to Shandong Frist Medical University, Ji'nan, Shandong, China
| | - Hongli Yang
- Department of Infectious Disease, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Ji'nan, Shandong, China
| | - Yuemin Feng
- Department of Gastroenterology, Shandong Provincial Hospital Affiliated to Shandong Frist Medical University, Ji'nan, Shandong, China
| | - Cuihong Liu
- Department of Ultrasound Diagnosis and Treatment, Shandong Provincial Hospital Affiliated to Shandong Frist Medical University, Ji'nan, Shandong, China
| | - Fajuan Rui
- Department of Infectious Disease, Shandong Provincial Hospital Affiliated to Shandong Frist Medical University, Ji'nan, Shandong, China
| | - Yuankui Cao
- School of Computer Science, China University of Geosciences, Wuhan, Hubei, China
| | - Xinyu Hu
- Department of Infectious Disease, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Ji'nan, Shandong, China
| | - Jiawen Xu
- Department of Pathology, Shandong Provincial Hospital Affiliated to Shandong Frist Medical University, Ji'nan, Shandong, China
| | - Junqing Fan
- School of Computer Science, China University of Geosciences, Wuhan, Hubei, China
| | - Qiang Zhu
- Department of Gastroenterology, Shandong Provincial Hospital Affiliated to Shandong Frist Medical University, Ji'nan, Shandong, China
| | - Jie Li
- Department of Infectious Disease, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Ji'nan, Shandong, China
- Department of Infectious Diseases, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
- Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu, China
- Correspondence to: Jie Li, Department of Infectious Diseases, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu 210000, China; ORCID: https://orcid.org/0000-0003-0973-8645. Tel: +86-15863787910,
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Liu W, Song K, Zheng W, Huo L, Zhang S, Xu X, Wang P, Jia N. Hepatobiliary Phase Features of Preoperative Gadobenate-Enhanced MR can Predict Early Recurrence of Hepatocellular Carcinoma in Patients Who Underwent Anatomical Hepatectomy. Front Oncol 2022; 12:862967. [PMID: 35992871 PMCID: PMC9381876 DOI: 10.3389/fonc.2022.862967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 06/22/2022] [Indexed: 11/16/2022] Open
Abstract
Purpose The purpose of this study was to establish a model for predicting early recurrence (≤2 years) of hepatocellular carcinoma (HCC) after anatomical hepatectomy based on the hepatobiliary phase (HBP) imaging characteristics of gadobenate-enhanced MRI. Methods A total of 155 patients who underwent anatomical hepatectomy HCC therapy and gadobenate-enhanced MRI were included retrospectively. The patients were divided into the early recurrence-free group (n = 103) and the early recurrence group (n = 52). Univariate and multivariate Cox regression analysis was used to determine the independent risk factors related to early recurrence, and four models were established. The preoperative model with/without HBP imaging features (HBP-pre/No HBP-pre model) and the postoperative model with/without HBP imaging features (HBP-post/No HBP-post model). Bootstrap resampling 1,000 times was used to verify the model and displayed by nomograms. The performance of nomograms was evaluated by discrimination, calibration, and clinical utility. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to evaluate the differences between models and to select the optimal model. Results Shape, arterial peritumoral enhancement, AFP-L3, and peritumoral hypointensity on HBP were identified as independent risk factors. Prothrombin time (PT) and r-glutamyltransferase (GGT) were selected by multivariate Cox regression. These six factors construct the HBP-pre model. Removing peritumoral hypointensity on HBP was the No HBP-pre model. Adding microvascular invasion (MVI) and microscopic capsule factors were the HBP-post and No HBP-post model. The C-index was 0.766, 0.738, 0.770, and 0.742, respectively. The NRI and IDI of the HBP-pre vs. the No HBP-pre model and the HBP-post vs. the No HBP-post model significantly increased 0.258, 0.092, 0.280, and 0.086, respectively. The calibration curve and decision curve analysis (DCA) had good consistency and clinical utility. However, the NRI and IDI of the No HBP-post vs. the No HBP-pre model and the HBP-post vs. the HBP-pre model did not increase significantly. Conclusions Preoperative gadobenate-enhanced MR HBP imaging features significantly improve the model performance while the postoperative pathological factors do not. Therefore, the HBP-pre model is selected as the optimal model. The strong performance of this model may help hepatologists to assess the risk of recurrence in order to guide the selection of treatment options.
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Affiliation(s)
- Wanmin Liu
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Kairong Song
- Department of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China
| | - Wei Zheng
- Department of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China
| | - Lei Huo
- Department of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China
| | - Sisi Zhang
- Department of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China
| | - Xiaowen Xu
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Peijun Wang
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- *Correspondence: Peijun Wang, ; Ningyang Jia,
| | - Ningyang Jia
- Department of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China
- *Correspondence: Peijun Wang, ; Ningyang Jia,
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Wang L, Zhang L, Jiang B, Zhao K, Zhang Y, Xie X. Clinical application of deep learning and radiomics in hepatic disease imaging: a systematic scoping review. Br J Radiol 2022; 95:20211136. [PMID: 35816550 PMCID: PMC10162062 DOI: 10.1259/bjr.20211136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 04/26/2022] [Accepted: 07/05/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE Artificial intelligence (AI) has begun to play a pivotal role in hepatic imaging. This systematic scoping review summarizes the latest progress of AI in evaluating hepatic diseases based on computed tomography (CT) and magnetic resonance (MR) imaging. METHODS We searched PubMed and Web of Science for publications, using terms related to deep learning, radiomics, imaging methods (CT or MR), and the liver. Two reviewers independently selected articles and extracted data from each eligible article. The Quality Assessment of Diagnostic Accuracy Studies-AI (QUADAS-AI) tool was used to assess the risk of bias and concerns regarding applicability. RESULTS The screening identified 45 high-quality publications from 235 candidates, including 8 on diffuse liver diseases and 37 on focal liver lesions. Nine studies used deep learning and 36 studies used radiomics. All 45 studies were rated as low risk of bias in patient selection and workflow, but 36 (80%) were rated as high risk of bias in the index test because they lacked external validation. In terms of concerns regarding applicability, all 45 studies were rated as low concerns. These studies demonstrated that deep learning and radiomics can evaluate liver fibrosis, cirrhosis, portal hypertension, and a series of complications caused by cirrhosis, predict the prognosis of malignant hepatic tumors, and differentiate focal hepatic lesions. CONCLUSIONS The latest studies have shown that deep learning and radiomics based on hepatic CT and MR imaging have potential application value in the diagnosis, treatment evaluation, and prognosis prediction of common liver diseases. The AI methods may become useful tools to support clinical decision-making in the future. ADVANCES IN KNOWLEDGE Deep learning and radiomics have shown their potential in the diagnosis, treatment evaluation, and prognosis prediction of a series of common diffuse liver diseases and focal liver lesions.
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Affiliation(s)
- Lingyun Wang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lu Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Beibei Jiang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Keke Zhao
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yaping Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xueqian Xie
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Wu C, Chen J, Fan Y, Zhao M, He X, Wei Y, Ge W, Liu Y. Nomogram Based on CT Radiomics Features Combined With Clinical Factors to Predict Ki-67 Expression in Hepatocellular Carcinoma. Front Oncol 2022; 12:943942. [PMID: 35875154 PMCID: PMC9299359 DOI: 10.3389/fonc.2022.943942] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 06/07/2022] [Indexed: 12/24/2022] Open
Abstract
Objectives The study developed and validated a radiomics nomogram based on a combination of computed tomography (CT) radiomics signature and clinical factors and explored the ability of radiomics for individualized prediction of Ki-67 expression in hepatocellular carcinoma (HCC). Methods First-order, second-order, and high-order radiomics features were extracted from preoperative enhanced CT images of 172 HCC patients, and the radiomics features with predictive value for high Ki-67 expression were extracted to construct the radiomic signature prediction model. Based on the training group, the radiomics nomogram was constructed based on a combination of radiomic signature and clinical factors that showed an independent association with Ki-67 expression. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to verify the performance of the nomogram. Results Sixteen higher-order radiomic features that were associated with Ki-67 expression were used to construct the radiomics signature (AUC: training group, 0.854; validation group, 0.744). In multivariate logistic regression, alfa-fetoprotein (AFP) and Edmondson grades were identified as independent predictors of Ki-67 expression. Thus, the radiomics signature was combined with AFP and Edmondson grades to construct the radiomics nomogram (AUC: training group, 0.884; validation group, 0.819). The calibration curve and DCA showed good clinical application of the nomogram. Conclusion The radiomics nomogram developed in this study based on the high-order features of CT images can accurately predict high Ki-67 expression and provide individualized guidance for the treatment and clinical monitoring of HCC patients.
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Affiliation(s)
- Cuiyun Wu
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, China
| | - Junfa Chen
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, China
| | - Yuqian Fan
- Department of Clinical Pathology, Graduate School, Hebei Medical University, Shijiazhuang, China
| | - Ming Zhao
- Cancer Center, Department of Pathology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, China
| | - Xiaodong He
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, China
| | - Yuguo Wei
- Precision Health Institution, General Electrical Healthcare, Hangzhou, China
| | - Weidong Ge
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, China
| | - Yang Liu
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, China
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Wu C, Yu S, Zhang Y, Zhu L, Chen S, Liu Y. CT-Based Radiomics Nomogram Improves Risk Stratification and Prediction of Early Recurrence in Hepatocellular Carcinoma After Partial Hepatectomy. Front Oncol 2022; 12:896002. [PMID: 35875140 PMCID: PMC9302642 DOI: 10.3389/fonc.2022.896002] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 06/17/2022] [Indexed: 01/27/2023] Open
Abstract
Objectives To develop and validate an intuitive computed tomography (CT)-based radiomics nomogram for the prediction and risk stratification of early recurrence (ER) in hepatocellular carcinoma (HCC) patients after partial hepatectomy. Methods A total of 132 HCC patients treated with partial hepatectomy were retrospectively enrolled and assigned to training and test sets. Least absolute shrinkage and selection operator and gradient boosting decision tree were used to extract quantitative radiomics features from preoperative contrast-enhanced CT images of the HCC patients. The radiomics features with predictive value for ER were used, either alone or in combination with other predictive features, to construct predictive models. The best performing model was then selected to develop an intuitive, simple-to-use nomogram, and its performance in the prediction and risk stratification of ER was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). Results The radiomics model based on the radiomics score (Rad-score) achieved AUCs of 0.870 and 0.890 in the training and test sets, respectively. Among the six predictive models, the combined model based on the Rad-score, Edmondson grade, and tumor size had the highest AUCs of 0.907 in the training set and 0.948 in the test set and was used to develop an intuitive nomogram. Notably, the calibration curve and DCA for the nomogram showed good calibration and clinical application. Moreover, the risk of ER was significantly different between the high- and low-risk groups stratified by the nomogram (p <0.001). Conclusions The CT-based radiomics nomogram developed in this study exhibits outstanding performance for ER prediction and risk stratification. As such, this intuitive nomogram holds promise as a more effective and user-friendly tool in predicting ER for HCC patients after partial hepatectomy.
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Affiliation(s)
- Cuiyun Wu
- Cancer Center, Department of Radiology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), Hangzhou, China
| | - Shufeng Yu
- Cancer Center, Department of Radiology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), Hangzhou, China
| | - Yang Zhang
- Cancer Center, Department of Radiology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), Hangzhou, China
| | - Li Zhu
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), Hangzhou, China
| | - Shuangxi Chen
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), Hangzhou, China
| | - Yang Liu
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), Hangzhou, China
- Key Laboratory of Gastroenterology of Zhejiang Province, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, China
- *Correspondence: Yang Liu,
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73
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Li N, Wan X, Zhang H, Zhang Z, Guo Y, Hong D. Tumor and peritumor radiomics analysis based on contrast-enhanced CT for predicting early and late recurrence of hepatocellular carcinoma after liver resection. BMC Cancer 2022; 22:664. [PMID: 35715783 PMCID: PMC9205126 DOI: 10.1186/s12885-022-09743-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 06/02/2022] [Indexed: 01/27/2023] Open
Abstract
Background In China, liver resection has been proven to be one of the most important strategies for hepatocellular carcinoma patients, but the recurrence rate is high. This study sought to investigate the prognostic value of pretreatment tumor and peritumor contrast-enhanced CT radiomics features for early and late recurrence of BCLC stage 0-B hepatocellular carcinoma after liver resection. Methods This study involved 329 hepatocellular carcinoma patients after liver resection. A radiomics model was built by using Lasso-Cox regression model. Association between radiomics model and recurrence-free survival was explored by using Harrell’s concordance index (C-Index) and receiver operating characteristic (ROC) curves. Then, we combined the radiomics model and clinical factors to establish a nomogram whose calibration and discriminatory ability were revealed. Results Ten significant tumor and peritumor features were screened to build the radiomics model whose C-indices were 0.743 [95% CI, 0.707 to 0.778] and 0.69 [95% CI, 0.629 to 0.751] in the training and validation cohorts. Moreover, the discriminative accuracy of the radiomics model improved with peritumor features entry. The C-indices of the combined model were 0.773 [95% CI, 0.739 to 0.806] and 0.727 [95% CI, 0.667 to 0.787] in the training and validation cohorts, outperforming the radiomics model. Conclusions The tumor and peritumor contrast-enhanced CT radiomic signature is a quantitative imaging biomarker that could improve the prediction of early and late recurrence after liver resection for hepatocellular carcinoma patients when used in addition to clinical predictors. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-09743-6.
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Affiliation(s)
- Nu Li
- Department of Breast Surgery, The First Hospital of China Medical University, No.155 Nanjing Road, Heping District, Shenyang, 110000, Liaoning, China
| | - Xiaoting Wan
- Department of Nuclear Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510000, China
| | - Hong Zhang
- Department of Nuclear Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510000, China
| | - Zitian Zhang
- Department of Radiology, The First Hospital of China Medical University, No.155 Nanjing Road, Heping District, Shenyang, 110000, Liaoning, China
| | - Yan Guo
- GE Healthcare, Beijing, China
| | - Duo Hong
- Department of Interventional Radiology, The First Hospital of China Medical University, No.155 Nanjing Road, Heping District, Shenyang, 110000, Liaoning, China.
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Shi HY, Lee KT, Chiu CC, Wang JJ, Sun DP, Lee HH. 5-year recurrence prediction after hepatocellular carcinoma resection: deep learning vs. Cox regression models. Am J Cancer Res 2022; 12:2876-2890. [PMID: 35812048 PMCID: PMC9251698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/30/2022] [Indexed: 06/15/2023] Open
Abstract
Deep learning algorithms have yet to be used for predicting clinical prognosis after cancer surgery. Therefore, this study compared performance indices and permutation importance of potential confounders in three models for predicting 5-year recurrence after hepatocellular carcinoma (HCC) resection: a deep-learning deep neural network (DNN) model, a recurrent neural network (RNN) model, and a Cox proportional hazard (CPH) regression model. Data for 725 patients who had received HCC resection at three medical centers in southern Taiwan between April, 2011, and December, 2015, were randomly divided into three datasets: a training dataset containing data for 507 subjects was used for model development, a testing dataset containing data for 109 subjects was used for internal validation, and a validating dataset containing data for 109 subjects was used for external validation. Feature importance analysis was also performed to identify potential predictors of recurrence after HCC resection. Univariate Cox proportional hazards regression analyses were performed to identify potential significant predictors of 5-year recurrence after HCC resection, which were included in the forecasting models (P < 0.05). All performance indices for the DNN model were significantly higher than those for the RNN model and the conventional CPH model (P < 0.001). The most important potential predictor of 5-year recurrence after HCC resection was surgeon volume followed by, in order of importance, hospital volume, preoperative Beck Depression Scale score, preoperative Beck Anxiety Scale score, co-residence with family, tumor stage, and tumor size. The feature importance analysis performed to investigate interpretability in this study elucidated the potential use of deep learning models for predicting recurrence after HCC resection and for identifying predictors of recurrence. Further experiments using the proposed DNN model would clarify its potential uses for developing, promoting, and improving health policies for treating HCC patients after surgery.
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Affiliation(s)
- Hon-Yi Shi
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical UniversityKaohsiung 80708, Taiwan
- Department of Business Management, National Sun Yat-sen UniversityKaohsiung 80420, Taiwan
- Department of Medical Research, Kaohsiung Medical University HospitalKaohsiung 80708, Taiwan
- Department of Medical Research, China Medical University Hospital, China Medical UniversityTaichung 40402, Taiwan
| | - King-The Lee
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical UniversityKaohsiung 80708, Taiwan
- Hepatobiliary-Pancreatic Surgery, Park One International HospitalKaohsiung 81357, Taiwan
| | - Chong-Chi Chiu
- Department of General Surgery, E-Da Cancer HospitalKaohsiung 82445, Taiwan
- School of Medicine, College of Medicine, I-Shou UniversityKaohsiung 82445, Taiwan
| | - Jhi-Joung Wang
- Department of Medical Research, Chi Mei Medical CenterYongkang, Tainan 71004, Taiwan
- Allied AI Biomed Center, Southern Taiwan University of Science and TechnologyTainan 71005, Taiwan
| | - Ding-Ping Sun
- Department of Surgery, Chi Mei Medical CenterYongkang, Tainan 71004, Taiwan
| | - Hao-Hsien Lee
- Department of Surgery, Chi Mei Medical CenterLiouying, Tainan 73658, Taiwan
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Tran J, Sharma D, Gotlieb N, Xu W, Bhat M. Application of machine learning in liver transplantation: a review. Hepatol Int 2022; 16:495-508. [PMID: 35020154 DOI: 10.1007/s12072-021-10291-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 12/15/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Machine learning (ML) has been increasingly applied in the health-care and liver transplant setting. The demand for liver transplantation continues to expand on an international scale, and with advanced aging and complex comorbidities, many challenges throughout the transplantation decision-making process must be better addressed. There exist massive datasets with hidden, non-linear relationships between demographic, clinical, laboratory, genetic, and imaging parameters that conventional methods fail to capitalize on when reviewing their predictive potential. Pre-transplant challenges include addressing efficacies of liver segmentation, hepatic steatosis assessment, and graft allocation. Post-transplant applications include predicting patient survival, graft rejection and failure, and post-operative morbidity risk. AIM In this review, we describe a comprehensive summary of ML applications in liver transplantation including the clinical context and how to overcome challenges for clinical implementation. METHODS Twenty-nine articles were identified from Ovid MEDLINE, MEDLINE Epub Ahead of Print and In-Process and Other Non-Indexed Citations, Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials. CONCLUSION ML is vastly interrogated in liver transplantation with promising applications in pre- and post-transplant settings. Although challenges exist including site-specific training requirements, the demand for more multi-center studies, and optimization hurdles for clinical interpretability, the powerful potential of ML merits further exploration to enhance patient care.
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Affiliation(s)
- Jason Tran
- Department of Medicine, University of Ottawa, Ottawa, Canada
| | - Divya Sharma
- Department of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Department of Biostatistics, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
| | - Neta Gotlieb
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Wei Xu
- Department of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Department of Biostatistics, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
| | - Mamatha Bhat
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.
- Division of Gastroenterology, Department of Medicine, University of Toronto, 585 University Avenue, Toronto, ON, M5G 2N2, Canada.
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Calderaro J, Seraphin TP, Luedde T, Simon TG. Artificial intelligence for the prevention and clinical management of hepatocellular carcinoma. J Hepatol 2022; 76:1348-1361. [PMID: 35589255 PMCID: PMC9126418 DOI: 10.1016/j.jhep.2022.01.014] [Citation(s) in RCA: 72] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 12/26/2021] [Accepted: 01/14/2022] [Indexed: 12/13/2022]
Abstract
Hepatocellular carcinoma (HCC) currently represents the fifth most common malignancy and the third-leading cause of cancer-related death worldwide, with incidence and mortality rates that are increasing. Recently, artificial intelligence (AI) has emerged as a unique opportunity to improve the full spectrum of HCC clinical care, by improving HCC risk prediction, diagnosis, and prognostication. AI approaches include computational search algorithms, machine learning (ML) and deep learning (DL) models. ML consists of a computer running repeated iterations of models, in order to progressively improve performance of a specific task, such as classifying an outcome. DL models are a subtype of ML, based on neural network structures that are inspired by the neuroanatomy of the human brain. A growing body of recent data now apply DL models to diverse data sources - including electronic health record data, imaging modalities, histopathology and molecular biomarkers - to improve the accuracy of HCC risk prediction, detection and prediction of treatment response. Despite the promise of these early results, future research is still needed to standardise AI data, and to improve both the generalisability and interpretability of results. If such challenges can be overcome, AI has the potential to profoundly change the way in which care is provided to patients with or at risk of HCC.
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Affiliation(s)
- Julien Calderaro
- Assistance Publique-Hôpitaux de Paris, Henri Mondor University Hospital, Department of Pathology, Créteil, France; Inserm U955 and Univ Paris Est Creteil, INSERM, IMRB, 94010, Creteil, France
| | - Tobias Paul Seraphin
- Department of Gastroenterology, Hepatology and Infectious Diseases, University Hospital Duesseldorf, Medical Faculty at Heinrich-Heine-University Duesseldorf, Duesseldorf, Germany
| | - Tom Luedde
- Department of Gastroenterology, Hepatology and Infectious Diseases, University Hospital Duesseldorf, Medical Faculty at Heinrich-Heine-University Duesseldorf, Duesseldorf, Germany
| | - Tracey G. Simon
- Liver Center, Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.,Clinical and Translational Epidemiology Unit (CTEU), Massachusetts General Hospital, Boston, MA, USA
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Edeh MO, Dalal S, Dhaou IB, Agubosim CC, Umoke CC, Richard-Nnabu NE, Dahiya N. Artificial Intelligence-Based Ensemble Learning Model for Prediction of Hepatitis C Disease. Front Public Health 2022; 10:892371. [PMID: 35570979 PMCID: PMC9092454 DOI: 10.3389/fpubh.2022.892371] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 03/17/2022] [Indexed: 12/15/2022] Open
Abstract
Machine learning algorithms are excellent techniques to develop prediction models to enhance response and efficiency in the health sector. It is the greatest approach to avoid the spread of hepatitis C, especially injecting drugs, is to avoid these behaviors. Treatments for hepatitis C can cure most patients within 8 to 12 weeks, so being tested is critical. After examining multiple types of machine learning approaches to construct the classification models, we built an AI-based ensemble model for predicting Hepatitis C disease in patients with the capacity to predict advanced fibrosis by integrating clinical data and blood biomarkers. The dataset included a variety of factors related to Hepatitis C disease. The training data set was subjected to three machine-learning approaches and the validated data was then used to evaluate the ensemble learning-based prediction model. The results demonstrated that the proposed ensemble learning model has been observed ad more accurate compared to the existing Machine learning algorithms. The Multi-layer perceptron (MLP) technique was the most precise learning approach (94.1% accuracy). The Bayesian network was the second-most accurate learning algorithm (94.47% accuracy). The accuracy improved to the level of 95.59%. Hepatitis C has a significant frequency globally, and the disease's development can result in irreparable damage to the liver, as well as death. As a result, utilizing AI-based ensemble learning model for its prediction is advantageous in curbing the risks and improving treatment outcome. The study demonstrated that the use of ensemble model presents more precision or accuracy in predicting Hepatitis C disease instead of using individual algorithms. It also shows how an AI-based ensemble model could be used to diagnose Hepatitis C disease with greater accuracy.
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Affiliation(s)
- Michael Onyema Edeh
- Department of Mathematics and Computer Science, Coal City University, Enugu, Nigeria
| | - Surjeet Dalal
- College of Computing Science & Information Technology, Teerthanker Mahaveer University, Moradabad, India
| | - Imed Ben Dhaou
- Department of Computer Science, Hekma School of Engineering, Computing and Informatics, Dar Al-Hekma University, Jeddah, Saudi Arabia
| | | | - Chukwudum Collins Umoke
- Department of Vocational and Technical Education, Alex Ekwueme Federal University Ndufu Alike Ikwo (AE- FUNAI), Abakaliki, Nigeria
| | - Nneka Ernestina Richard-Nnabu
- Department of Computer Science/Informatics, Alex Ekwueme Federal University Ndufu Alike Ikwo (AE-FUNAI), Abakaliki, Nigeria
| | - Neeraj Dahiya
- Department of Computer Science and Engineering, SRM University Delhi-NCR, Sonipat, India
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Luo Y, Xue Y, Song H, Tang G, Liu W, Bai H, Yuan X, Tong S, Wang F, Cai Y, Sun Z. Machine learning based on routine laboratory indicators promoting the discrimination between active tuberculosis and latent tuberculosis infection. J Infect 2022; 84:648-657. [PMID: 34995637 DOI: 10.1016/j.jinf.2021.12.046] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 12/18/2021] [Accepted: 12/26/2021] [Indexed: 12/26/2022]
Abstract
BACKGROUND Discriminating active tuberculosis (ATB) from latent tuberculosis infection (LTBI) remains challenging. The present study aims to evaluate the performance of diagnostic models established using machine learning based on routine laboratory indicators in differentiating ATB from LTBI. METHODS Participants were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Diagnostic models were established based on routine laboratory indicators using machine learning. RESULTS A total of 2619 participants (1025 ATB and 1594 LTBI) were enrolled in discovery cohort and another 942 subjects (388 ATB and 554 LTBI) were recruited in validation cohort. ATB patients had significantly higher levels of tuberculosis-specific antigen/phytohemagglutinin ratio and coefficient variation of red blood cell volume distribution width, and lower levels of albumin and lymphocyte count than those of LTBI individuals. Six models were built and the optimal performance was obtained from GBM model. GBM model derived from training set (n = 1965) differentiated ATB from LTBI in the test set (n = 654) with a sensitivity of 84.38% (95% CI, 79.42%-88.31%) and a specificity of 92.71% (95% CI, 89.73%-94.88%). Further validation by an independent cohort confirmed its encouraging value with a sensitivity of 87.63% (95% CI, 83.98%-90.54%) and specificity of 91.34% (95% CI, 88.70%-93.40%), respectively. CONCLUSIONS We successfully developed a model with promising diagnostic value based on machine learning for the first time. Our study proposed that GBM model may be of great benefit served as a tool for the accurate identification of ATB.
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Affiliation(s)
- Ying Luo
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China.
| | - Ying Xue
- Department of Immunology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Huijuan Song
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China
| | - Guoxing Tang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China
| | - Wei Liu
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China
| | - Huan Bai
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China
| | - Xu Yuan
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China
| | - Shutao Tong
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China.
| | - Feng Wang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China.
| | - Yimin Cai
- Department of Epidemiology and Biostatistics, Key Laboratory of Environmental Health of Ministry of Education, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong road 13, Wuhan, China.
| | - Ziyong Sun
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China.
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Wang F, Chen Q, Zhang Y, Chen Y, Zhu Y, Zhou W, Liang X, Yang Y, Hu H. CT-Based Radiomics for the Recurrence Prediction of Hepatocellular Carcinoma After Surgical Resection. J Hepatocell Carcinoma 2022; 9:453-465. [PMID: 35646748 PMCID: PMC9139347 DOI: 10.2147/jhc.s362772] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 05/09/2022] [Indexed: 01/27/2023] Open
Abstract
Purpose To explore the effectiveness of radiomics signature in predicting the recurrence of hepatocellular carcinoma (HCC) and the benefit of postoperative adjuvant transcatheter arterial chemoembolization (PA-TACE). Patients and Methods In this multicenter retrospective study, 364 consecutive patients with multi-phase computed tomography (CT) images were included. Recurrence-related radiomics features of intra- and peritumoral regions were extracted from the pre-contrast, arterial and portal venous phase, respectively. The radiomics model was established in the training cohort (n = 187) using random survival forests analysis to output prediction probability as “Rad-score” and validated by the internal (n = 92) and external validation cohorts (n = 85). Besides, the Clinical nomogram was developed by clinical-radiologic-pathologic characteristics, and the Combined nomogram was further constructed to evaluate the added value of the Rad-score for individualized recurrence-free survival (RFS) prediction, which is our primary and only endpoint. The performance of the three models was assessed by the concordance index (C-index). Furthermore, all the patients were stratified into high- and low-risk groups of recurrence by the median value of the Rad-score to analyze the benefit of PA-TACE. Results The model built using radiomics signature demonstrated favorable prediction of HCC recurrence across all datasets, with C-index of 0.892, 0.812, 0.809, separately in the training, the internal and external validation cohorts. Univariate and multivariate analysis revealed that the Rad-score was an independent prognostic factor. Significant differences were found between the high- and low-risk group in RFS prediction in all three cohorts. Further analysis showed that compared with the low-risk group, patients with the high-risk received more benefits from PA-TACE. Conclusion The newly developed Rad-score was not only a powerful biomarker in predicting the RFS of HCC but also a strong stratification basis to explore the high-risk patients who could benefit from PA-TACE.
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Affiliation(s)
- Fang Wang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, People’s Republic of China
| | - Qingqing Chen
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, People’s Republic of China
| | - Yuanyuan Zhang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, People’s Republic of China
- Medical College, Shaoxing University, Shaoxing, 312000, People’s Republic of China
| | - Yinan Chen
- SenseTime Research, Shanghai, 200030, People’s Republic of China
| | - Yajing Zhu
- SenseTime Research, Shanghai, 200030, People’s Republic of China
| | - Wei Zhou
- Department of Radiology, Huzhou Central Hospital, Affiliated to Huzhou University, Huzhou, 313000, People’s Republic of China
| | - Xiao Liang
- Department of General Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, People’s Republic of China
| | - Yunjun Yang
- Department of Radiology, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, People’s Republic of China
- Yunjun Yang, Department of Radiology, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, People’s Republic of China, Email
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, People’s Republic of China
- Correspondence: Hongjie Hu, Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, People’s Republic of China, Tel/Fax +86-0571-86044817, Email
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Brancato V, Garbino N, Salvatore M, Cavaliere C. MRI-Based Radiomic Features Help Identify Lesions and Predict Histopathological Grade of Hepatocellular Carcinoma. Diagnostics (Basel) 2022; 12:diagnostics12051085. [PMID: 35626241 PMCID: PMC9139902 DOI: 10.3390/diagnostics12051085] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 04/06/2022] [Accepted: 04/23/2022] [Indexed: 02/04/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common form of liver cancer. Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) in the management of HCC. The purpose of our study is to develop an MRI-based radiomics approach to preoperatively detect HCC and predict its histological grade. Thirty-eight HCC patients at staging who underwent axial T2-weighted and dynamic contrast-enhanced MRI (DCE-MRI) were considered. Three-dimensional volumes of interest (VOIs) were manually placed on HCC lesions and normal hepatic tissue (HT) on arterial phase post-contrast images. Radiomic features from T2 images and arterial, portal and tardive post-contrast images from DCE-MRI were extracted by using Pyradiomics. Feature selection was performed using correlation filter, Wilcoxon-rank sum test and mutual information. Predictive models were constructed for HCC differentiation with respect to HT and HCC histopathologic grading used at each step an imbalance-adjusted bootstrap resampling (IABR) on 1000 samples. Promising results were obtained from radiomic prediction models, with best AUCs ranging from 71% to 96%. Radiomics MRI based on T2 and DCE-MRI revealed promising results concerning both HCC detection and grading. It may be a suitable tool for personalized treatment of HCC patients and could also be used to develop new prognostic biomarkers useful for HCC assessment without the need for invasive procedures.
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Wu JP, Ding WZ, Wang YL, Liu S, Zhang XQ, Yang Q, Cai WJ, Yu XL, Liu FY, Kong D, Zhong H, Yu J, Liang P. Radiomics analysis of ultrasound to predict recurrence of hepatocellular carcinoma after microwave ablation. Int J Hyperthermia 2022; 39:595-604. [PMID: 35435082 DOI: 10.1080/02656736.2022.2062463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Affiliation(s)
- Jia-peng Wu
- School of Medicine, Nankai University, Tianjin, China
- Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, China
| | - Wen-zhen Ding
- Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, China
| | - Yu-ling Wang
- Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, China
| | - Sisi Liu
- Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, China
| | - Xiao-qian Zhang
- School of Mathematical Sciences, Zhejiang University, Hangzhou, China
| | - Qi Yang
- Department of Medical Ultrasound, Peking University Shenzhen Hospital, Shenzhen, China
| | - Wen-jia Cai
- Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, China
| | - Xiao-ling Yu
- Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, China
| | - Fang-yi Liu
- Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, China
| | - Dexing Kong
- School of Mathematical Sciences, Zhejiang University, Hangzhou, China
| | - Hui Zhong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi' an Jiaotong University, Xi' an, China
| | - Jie Yu
- Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, China
| | - Ping Liang
- School of Medicine, Nankai University, Tianjin, China
- Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, China
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Christou CD, Tsoulfas G. Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities. World J Gastrointest Oncol 2022; 14:765-793. [PMID: 35582107 PMCID: PMC9048537 DOI: 10.4251/wjgo.v14.i4.765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 08/24/2021] [Accepted: 03/27/2022] [Indexed: 02/06/2023] Open
Abstract
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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Affiliation(s)
- Chrysanthos D Christou
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
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Lin Y, Dai P, Lin Q, Chen J. A Predictive Nomogram for Atypical Meningioma Based on Preoperative MRI and Routine Blood Tests. World Neurosurg 2022; 163:e610-e616. [DOI: 10.1016/j.wneu.2022.04.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 04/06/2022] [Accepted: 04/07/2022] [Indexed: 11/30/2022]
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The Value of Color Doppler Ultrasound and CT Combined with Serum AFP Examination in the Diagnosis of Hepatocellular Carcinoma. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:4147753. [PMID: 35378937 PMCID: PMC8976622 DOI: 10.1155/2022/4147753] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 03/03/2022] [Accepted: 03/07/2022] [Indexed: 12/24/2022]
Abstract
Objective To evaluate the value of the combination of color Doppler ultrasound, computed tomography (CT), and serum tumor marker alpha-fetoprotein (AFP) examination in the diagnosis of hepatocellular carcinoma (HCC). Methods 98 patients with HCC (malignant tumor group) and 50 liver lesion patients (benign control group), were selected for the study, and retrospective statistical methods were used to evaluate the diagnostic values of the three examinations on hepatocellular carcinoma. Results (1) When comparing color Doppler ultrasound blood flow parameters, the hepatic artery diameter, peak flow velocity, minimum flow velocity, and resistance index (RI) of hepatocellular carcinoma were significantly higher than those of the benign control group (P < 0.05), while the portal vein flow velocity was significantly lower than that of the control group (P < 0.05). (2) Enhanced CT imaging of hepatocellular carcinoma lesions showed mostly outflow-type enhancement changes, with high- or slightly high-density shadowing and uneven enhancement in the arterial phase, relatively low density and withdrawal of enhancement in the portal vein phase and delayed phase. (3) The serum AFP level of hepatocellular carcinoma patients was significantly higher than that of the benign control group (P < 0.01). (4) The sensitivity of color Doppler ultrasound, CT, and serum AFP alone for the diagnosis of HCC was 79.59%, 85.71%, and 66.33%, and the accuracy was 83.78%, 87.16%, and 74.32%, respectively, while the combination of the three tests could significantly increase the sensitivity to 96.94% and the accuracy to 93.92%, compared with each individual test (P < 0.01). Conclusion Color Doppler ultrasound and CT combined with serum AFP examination could significantly improve the sensitivity and accuracy of hepatocellular carcinoma diagnosis, reduce misdiagnosis, and facilitate early diagnosis and clinical early intervention.
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Wu L, Ning B, Yang J, Chen Y, Zhang C, Yan Y. Diagnosis of Liver Cirrhosis and Liver Fibrosis by Artificial Intelligence Algorithm-Based Multislice Spiral Computed Tomography. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:1217003. [PMID: 35341007 PMCID: PMC8941514 DOI: 10.1155/2022/1217003] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 02/19/2022] [Accepted: 02/22/2022] [Indexed: 12/12/2022]
Abstract
This research was aimed at investigating the artificial intelligence (AI) segmentation algorithm-based multislice spiral computed tomography (MSCT) in the diagnosis of liver cirrhosis and liver fibrosis. Besides, it was aimed at providing new methods for the diagnosis of liver cirrhosis and liver fibrosis. All patients were divided into the control group, mild liver fibrosis group, and significant liver fibrosis group. A total of 112 patients were included, with 40 cases in the mild liver fibrosis group, 48 cases in the significant liver fibrosis group, and 24 cases who underwent computed tomography (CT) examination in the control group. In the research, deconvolution algorithm of AI segmentation algorithm was adopted to process the images. The average hepatic arterial fraction (HAF) values of patients in the control group, mild liver fibrosis group, and severe liver fibrosis group were 17.59 ± 10.03%, 18.23 ± 5.57%, and 20.98 ± 6.63%, respectively. The average MTT values of patients in the control group, mild liver fibrosis group, and severe liver fibrosis group were 12.69 ± 1.78S, 12.53 ± 2.05S, and 12.04 ± 1.57S, respectively. The average blood flow (BF) values of patients in the control group, mild liver fibrosis group, and severe liver fibrosis group were 105.68 ± 15.57 mL 100 g-1·min-1, 116.07 ± 16.5 mL·100 g-1·min-1, and 110.39 ± 16.32 mL·100 g-1·min-1, respectively. Besides, the average blood volume (BV) values of patients in the control group, mild liver fibrosis group, and significant liver fibrosis group were 15.69 ± 4.35 mL·log-1, 16.97 ± 2.68 mL·log-1, and 16.11 ± 4.87 mL·100 g-1, respectively. According to statistics, the differences among the average HAF, MTT, BF, and BV values showed no statistical meaning. AI segmentation algorithm-based MSCT imaging could promote the diagnosis of liver cirrhosis and liver fibrosis effectively and offer new methods to clinical diagnosis of liver cirrhosis and liver fibrosis.
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Affiliation(s)
- Liexiu Wu
- Department of Infectious Disease, Baoji Central Hospital, Baoji, 721008 Shaanxi, China
| | - Bo Ning
- Department of Infectious Disease, Baoji Central Hospital, Baoji, 721008 Shaanxi, China
| | - Jianjun Yang
- Department of Infectious Disease, Baoji Central Hospital, Baoji, 721008 Shaanxi, China
| | - Yanni Chen
- Department of Immunization Plan, Disease Control and Prevention of Yulin Center, Yulin, 719000 Shaanxi, China
| | - Caihong Zhang
- Department of Health, Disease Control and Prevention of Yulin Center, Yulin, 719000 Shaanxi, China
| | - Yun Yan
- Department of Chronic Disease Control, Yulin City Center for Disease Control and Prevention, Yulin, 719000 Shaanxi, China
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Liu W, Zhang L, Xin Z, Zhang H, You L, Bai L, Zhou J, Ying B. A Promising Preoperative Prediction Model for Microvascular Invasion in Hepatocellular Carcinoma Based on an Extreme Gradient Boosting Algorithm. Front Oncol 2022; 12:852736. [PMID: 35311094 PMCID: PMC8931027 DOI: 10.3389/fonc.2022.852736] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 02/11/2022] [Indexed: 01/27/2023] Open
Abstract
BackgroundThe non-invasive preoperative diagnosis of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is vital for precise surgical decision-making and patient prognosis. Herein, we aimed to develop an MVI prediction model with valid performance and clinical interpretability.MethodsA total of 2160 patients with HCC without macroscopic invasion who underwent hepatectomy for the first time in West China Hospital from January 2015 to June 2019 were retrospectively included, and randomly divided into training and a validation cohort at a ratio of 8:2. Preoperative demographic features, imaging characteristics, and laboratory indexes of the patients were collected. Five machine learning algorithms were used: logistic regression, random forest, support vector machine, extreme gradient boosting (XGBoost), and multilayer perception. Performance was evaluated using the area under the receiver operating characteristic curve (AUC). We also determined the Shapley Additive exPlanation value to explain the influence of each feature on the MVI prediction model.ResultsThe top six important preoperative factors associated with MVI were the maximum image diameter, protein induced by vitamin K absence or antagonist-II, α-fetoprotein level, satellite nodules, alanine aminotransferase (AST)/aspartate aminotransferase (ALT) ratio, and AST level, according to the XGBoost model. The XGBoost model for preoperative prediction of MVI exhibited a better AUC (0.8, 95% confidence interval: 0.74–0.83) than the other prediction models. Furthermore, to facilitate use of the model in clinical settings, we developed a user-friendly online calculator for MVI risk prediction based on the XGBoost model.ConclusionsThe XGBoost model achieved outstanding performance for non-invasive preoperative prediction of MVI based on big data. Moreover, the MVI risk calculator would assist clinicians in conveniently determining the optimal therapeutic remedy and ameliorating the prognosis of patients with HCC.
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Affiliation(s)
- Weiwei Liu
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Lifan Zhang
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, China
| | - Zhaodan Xin
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Haili Zhang
- Department of Liver Surgery & Liver Transplantation Center, West China Hospital, Sichuan University, Chengdu, China
| | - Liting You
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Ling Bai
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Juan Zhou
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Juan Zhou, ; Binwu Ying,
| | - Binwu Ying
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Juan Zhou, ; Binwu Ying,
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Balsano C, Alisi A, Brunetto MR, Invernizzi P, Burra P, Piscaglia F. The application of artificial intelligence in hepatology: A systematic review. Dig Liver Dis 2022; 54:299-308. [PMID: 34266794 DOI: 10.1016/j.dld.2021.06.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 06/04/2021] [Accepted: 06/07/2021] [Indexed: 02/06/2023]
Abstract
The integration of human and artificial intelligence (AI) in medicine has only recently begun but it has already become obvious that intelligent systems can dramatically improve the management of liver diseases. Big data made it possible to envisage transformative developments of the use of AI for diagnosing, predicting prognosis and treating liver diseases, but there is still a lot of work to do. If we want to achieve the 21st century digital revolution, there is an urgent need for specific national and international rules, and to adhere to bioethical parameters when collecting data. Avoiding misleading results is essential for the effective use of AI. A crucial question is whether it is possible to sustain, technically and morally, the process of integration between man and machine. We present a systematic review on the applications of AI to hepatology, highlighting the current challenges and crucial issues related to the use of such technologies.
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Affiliation(s)
- Clara Balsano
- Dept. of Life, Health and Environmental Sciences MESVA, University of L'Aquila, Piazza S. Salvatore Tommasi 1, 67100, Coppito, L'Aquila. Italy; Francesco Balsano Foundation, Via Giovanni Battista Martini 6, 00198, Rome, Italy.
| | - Anna Alisi
- Research Unit of Molecular Genetics of Complex Phenotypes, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Maurizia R Brunetto
- Hepatology Unit and Laboratory of Molecular Genetics and Pathology of Hepatitis Viruses, University Hospital of Pisa, Pisa, Italy
| | - Pietro Invernizzi
- Division of Gastroenterology and Center of Autoimmune Liver Diseases, Department of Medicine and Surgery, San Gerardo Hospital, University of Milano, Bicocca, Italy
| | - Patrizia Burra
- Multivisceral Transplant Unit, Department of Surgery, Oncology, Gastroenterology, Padua University Hospital, Padua, Italy
| | - Fabio Piscaglia
- Division of Internal Medicine, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
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Tang X, Liang J, Xiang B, Yuan C, Wang L, Zhu B, Ge X, Fang M, Ding Z. Positron Emission Tomography/Magnetic Resonance Imaging Radiomics in Predicting Lung Adenocarcinoma and Squamous Cell Carcinoma. Front Oncol 2022; 12:803824. [PMID: 35186742 PMCID: PMC8850839 DOI: 10.3389/fonc.2022.803824] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 01/03/2022] [Indexed: 02/01/2023] Open
Abstract
Objective To investigate the diagnostic value of positron emission tomography (PET)/magnetic resonance imaging (MRI) radiomics in predicting the histological classification of lung adenocarcinoma and lung squamous cell carcinoma. Methods PET/MRI radiomics and clinical data were retrospectively collected from 61 patients with lung cancer. According to the pathological results of surgery or fiberscope, patients were divided into two groups, lung adenocarcinoma and squamous cell carcinoma group, which were set as positive for adenocarcinoma (40 cases) and negative for squamous cell carcinoma (21 cases). The radiomics characteristics most related to lung cancer classification were calculated and selected using radiomics software, and the two lung cancer groups were randomly assigned into a training set (70%) and a test set (30%). Maximum relevance and minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods in the uAI Research Portal software (United Imaging Intelligence, China) were used to select the desired characteristics from 2600 features extracted from MRI and PET. Eight optimal features were finally retained through 5-fold cross-validation, and a PET/MRI fusion model was constructed. The predictive ability of this model was evaluated by the difference in area under the curve (AUC) obtained from the receiver operating characteristic (ROC) curve. Results AUC of PET/MRI model for the training group and test group were 0.886 (0.787-0.985) and 0.847 (0.648-1.000), respectively. PET/MRI radiomics features revealed different degrees of correlation with the classification of lung adenocarcinoma and squamous cell carcinoma, with significant differences. Conclusion The prediction model constructed based on PET/MRI radiomics features can predict the preoperative histological classification of lung adenocarcinoma and squamous cell carcinoma without seminality and repeatability. It can also provide an objective basis for accurate clinical diagnosis and individualized treatment, thus having important guiding significance for clinical treatment.
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Affiliation(s)
- Xin Tang
- The Fourth Clinical College, Zhejiang Chinese Medical University, Hangzhou, China.,Department of Radiology, Hangzhou Wuyunshan Hospital (Hangzhou Health Promotion Research Institute), Hangzhou, China
| | - Jiangtao Liang
- Department of Radiology, Hangzhou Universal Medical Imaging Diagnostic Center, Hangzhou, China
| | - Bolin Xiang
- Department of Radiology, Zhejiang Quhua Hospital, Quzhou, China
| | - Changfeng Yuan
- Department of Radiology, Hangzhou Wuyunshan Hospital (Hangzhou Health Promotion Research Institute), Hangzhou, China
| | - Luoyu Wang
- Centre for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China
| | - Bin Zhu
- Department of Radiology, Zhejiang Quhua Hospital, Quzhou, China
| | - Xiuhong Ge
- Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Cancer Center, Zhejiang University School of Medicine, Hangzhou, China
| | - Min Fang
- Department of Radiology, Zhejiang Quhua Hospital, Quzhou, China
| | - Zhongxiang Ding
- Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Cancer Center, Zhejiang University School of Medicine, Hangzhou, China
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Papaconstantinou D, Hewitt DB, Brown ZJ, Schizas D, Tsilimigras DI, Pawlik TM. Patient stratification in hepatocellular carcinoma: impact on choice of therapy. Expert Rev Anticancer Ther 2022; 22:297-306. [PMID: 35157530 DOI: 10.1080/14737140.2022.2041415] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
INTRODUCTION HCC comprises around 60 to 80% of all primary liver cancers and exhibits wide geographical variability. Appropriate treatment allocation needs to include both patient and tumor characteristics. AREAS COVERED Current HCC classification systems to guide therapy are either liver function-centric and evaluate physiologic liver function to guide therapy or prognostic stratification classification systems broadly based on tumor morphologic parameters, patient performance status, and liver reserve assessment. This review focuses on different classification systems for HCC, their strengths, and weaknesses as well as the use of artificial intelligence in improving prognostication in HCC. EXPERT OPINION Future HCC classification systems will need to incorporate clinic-pathologic data from a multitude of sources and emerging therapies to develop patient-specific treatment plans targeting a patient's unique tumor profile.
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Affiliation(s)
- Dimitrios Papaconstantinou
- Third Department of Surgery, Attikon University Hospital, National and Kapodistrian University of Athens, Medical School, Greece
| | - D Brock Hewitt
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio
| | - Zachary J Brown
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio
| | - Dimitrios Schizas
- First Department of Surgery, Laikon General Hospital, National and Kapodistrian University of Athens, Medical School, Greece
| | - Diamantis I Tsilimigras
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio
| | - Timothy M Pawlik
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio
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Huang H, Ruan SM, Xian MF, Li MD, Cheng MQ, Li W, Huang Y, Xie XY, Lu MD, Kuang M, Wang W, Hu HT, Chen LD. Contrast-enhanced ultrasound-based ultrasomics score: a potential biomarker for predicting early recurrence of hepatocellular carcinoma after resection or ablation. Br J Radiol 2022; 95:20210748. [PMID: 34797687 PMCID: PMC8822579 DOI: 10.1259/bjr.20210748] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
OBJECTIVES This study aimed to construct a prediction model based on contrast-enhanced ultrasound (CEUS) ultrasomics features and investigate its efficacy in predicting early recurrence (ER) of primary hepatocellular carcinoma (HCC) after resection or ablation. METHODS This study retrospectively included 215 patients with primary HCC, who were divided into a developmental cohort (n = 139) and a test cohort (n = 76). Four representative images-grayscale ultrasound, arterial phase, portal venous phase and delayed phase-were extracted from each CEUS video. Ultrasomics features were extracted from tumoral and peritumoral area inside the region of interest. Logistic regression was used to establish models, including a tumoral model, a peritumoral model and a combined model with additional clinical risk factors. The performance of the three models in predicting recurrence within 2 years was verified. RESULTS The combined model performed best in predicting recurrence within 2 years, with an area under the curve (AUC) of 0.845, while the tumoral model had an AUC of 0.810 and the peritumoral model one of 0.808. For prediction of recurrence-free survival, the 2-year cumulative recurrence rate was significant higher in the high-risk group (76.5%) than in the low-risk group (9.5%; p < 0.0001). CONCLUSION These CEUS ultrasomics models, especially the combined model, had good efficacy in predicting early recurrence of HCC. The combined model has potential for individual survival assessment for HCC patients undergoing resection or ablation. ADVANCES IN KNOWLEDGE CEUS ultrasomics had high sensitivity, specificity and PPV in diagnosing early recurrence of HCC, and high efficacy in predicting early recurrence of HCC (AUC > 0.8). The combined model performed better than the tumoral ultrasomics model and peritumoral ultrasomics model in predicting recurrence within 2 years. Recurrence was more likely to occur in the high-risk group than in the low-risk group, with 2-year cumulative recurrence rates, respectively, 76.5% and 9.5% (p < 0.0001).
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Affiliation(s)
- Hui Huang
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Si-min Ruan
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Meng-fei Xian
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Ming-de Li
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Mei-qing Cheng
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Wei Li
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Yang Huang
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Xiao-yan Xie
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | | | | | - Wei Wang
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Hang-tong Hu
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Li-Da Chen
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
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Liu QP, Yang KL, Xu X, Liu XS, Qu JR, Zhang YD. Radiomics analysis of pretreatment MRI in predicting tumor response and outcome in hepatocellular carcinoma with transarterial chemoembolization: a two-center collaborative study. Abdom Radiol (NY) 2022; 47:651-663. [PMID: 34918174 DOI: 10.1007/s00261-021-03375-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 11/26/2021] [Accepted: 11/27/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND AND OBJECTIVE To develop a machine-learning model by integrating clinical and imaging modalities for predicting tumor response and survival of hepatocellular carcinoma (HCC) with transarterial chemoembolization (TACE). METHODS 140 HCC patients with TACE were retrospectively included from two centers. Tumor response were evaluated using modified Response Evaluation Criteria in Solid Tumors (mRECIST) criteria. Response-related radiomics scores (Rad-scores) were constructed on T2-weighted images (T2WI) and dynamic contrast-enhanced (DCE) imaging separately, and then integrated with conventional clinic-radiological variables into a logistic regression (LR) model for predicting tumor response. LR model was trained in 94 patients in center 1 and independently tested in 46 patients in center 2. RESULTS Among 4 MRI sequences, T2WI achieved better performance than DCE (area under the curve [AUC] 0.754 vs 0.602 to 0.752). LR model by combining Rad-score on T2WI with Barcelona Clinic Liver Cancer (BCLC) stage and albumin-bilirubin (ALBI) grade resulted in an AUC of 0.813 in training and 0.781 in test for predicting tumor response. In survival analysis, progression-free survival (PFS) and overall survival (OS) presented significant difference between LR-predicted responders and non-responders. The ALBI grade and BCLC stage were independent predictors of PFS; and LR-predicted response, ALBI grade, satellite node, and BCLC stage were independent predictors of OS. The resulting Cox model produced concordance-indexes of 0.705 and 0.736 for predicting PFS and OS, respectively. CONCLUSIONS The model combined MRI radiomics with clinical factors demonstrated favorable performance for predicting tumor response and clinical outcomes, thus may help personalized clinical management.
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Affiliation(s)
- Qiu-Ping Liu
- Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, No. 300, Guangzhou Road, Nanjing, 210029, Jiangsu Province, China
| | - Kai-Lan Yang
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127, Dongming Road, Zhengzhou, 450008, Henan Province, China
| | - Xun Xu
- Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, No. 300, Guangzhou Road, Nanjing, 210029, Jiangsu Province, China
| | - Xi-Sheng Liu
- Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, No. 300, Guangzhou Road, Nanjing, 210029, Jiangsu Province, China.
| | - Jin-Rong Qu
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127, Dongming Road, Zhengzhou, 450008, Henan Province, China.
| | - Yu-Dong Zhang
- Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, No. 300, Guangzhou Road, Nanjing, 210029, Jiangsu Province, China.
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Veerankutty FH, Jayan G, Yadav MK, Manoj KS, Yadav A, Nair SRS, Shabeerali TU, Yeldho V, Sasidharan M, Rather SA. Artificial Intelligence in hepatology, liver surgery and transplantation: Emerging applications and frontiers of research. World J Hepatol 2021; 13:1977-1990. [PMID: 35070002 PMCID: PMC8727218 DOI: 10.4254/wjh.v13.i12.1977] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 05/09/2021] [Accepted: 11/25/2021] [Indexed: 02/06/2023] Open
Abstract
The integration of artificial intelligence (AI) and augmented realities into the medical field is being attempted by various researchers across the globe. As a matter of fact, most of the advanced technologies utilized by medical providers today have been borrowed and extrapolated from other industries. The introduction of AI into the field of hepatology and liver surgery is relatively a recent phenomenon. The purpose of this narrative review is to highlight the different AI concepts which are currently being tried to improve the care of patients with liver diseases. We end with summarizing emerging trends and major challenges in the future development of AI in hepatology and liver surgery.
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Affiliation(s)
- Fadl H Veerankutty
- Comprehensive Liver Care, VPS Lakeshore Hospital, Cochin 682040, Kerala, India
| | - Govind Jayan
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Manish Kumar Yadav
- Department of Radiodiagnosis, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Krishnan Sarojam Manoj
- Department of Radiodiagnosis, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Abhishek Yadav
- Comprehensive Liver Care, VPS Lakeshore Hospital, Cochin 682040, Kerala, India
| | - Sindhu Radha Sadasivan Nair
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - T U Shabeerali
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Varghese Yeldho
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Madhu Sasidharan
- Gastroenterology and Hepatology, Kerala Institute of Medical Sciences, Thiruvananthapuram 695029, India
| | - Shiraz Ahmad Rather
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
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93
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Kezer CA, Shah VH, Simonetto DA. Advances in Predictive Modeling Using Machine Learning in the Field of Hepatology. Clin Liver Dis (Hoboken) 2021; 18:288-291. [PMID: 34976373 PMCID: PMC8688898 DOI: 10.1002/cld.1148] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/09/2021] [Accepted: 06/10/2021] [Indexed: 02/04/2023] Open
Abstract
Content available: Author Interview and Audio Recording.
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Affiliation(s)
| | - Vijay H. Shah
- Department of MedicineDivision of Gastroenterology and HepatologyMayo ClinicRochesterMN
| | - Douglas A. Simonetto
- Department of MedicineDivision of Gastroenterology and HepatologyMayo ClinicRochesterMN
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94
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Zhang C, Gu J, Zhu Y, Meng Z, Tong T, Li D, Liu Z, Du Y, Wang K, Tian J. AI in spotting high-risk characteristics of medical imaging and molecular pathology. PRECISION CLINICAL MEDICINE 2021; 4:271-286. [PMID: 35692858 PMCID: PMC8982528 DOI: 10.1093/pcmedi/pbab026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 11/26/2021] [Accepted: 11/29/2021] [Indexed: 02/07/2023] Open
Abstract
Medical imaging provides a comprehensive perspective and rich information for disease diagnosis. Combined with artificial intelligence technology, medical imaging can be further mined for detailed pathological information. Many studies have shown that the macroscopic imaging characteristics of tumors are closely related to microscopic gene, protein and molecular changes. In order to explore the function of artificial intelligence algorithms in in-depth analysis of medical imaging information, this paper reviews the articles published in recent years from three perspectives: medical imaging analysis method, clinical applications and the development of medical imaging in the direction of pathological molecular prediction. We believe that AI-aided medical imaging analysis will be extensively contributing to precise and efficient clinical decision.
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Affiliation(s)
- Chong Zhang
- Department of Big Data Management and Application, School of International Economics and Management, Beijing Technology and Business University, Beijing 100048, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Jionghui Gu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yangyang Zhu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zheling Meng
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tong Tong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dongyang Li
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yang Du
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing 100191, China
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95
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Yao S, Ye Z, Wei Y, Jiang HY, Song B. Radiomics in hepatocellular carcinoma: A state-of-the-art review. World J Gastrointest Oncol 2021; 13:1599-1615. [PMID: 34853638 PMCID: PMC8603458 DOI: 10.4251/wjgo.v13.i11.1599] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/02/2021] [Accepted: 08/20/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common cancer and the second major contributor to cancer-related mortality. Radiomics, a burgeoning technology that can provide invisible high-dimensional quantitative and mineable data derived from routine-acquired images, has enormous potential for HCC management from diagnosis to prognosis as well as providing contributions to the rapidly developing deep learning methodology. This article aims to review the radiomics approach and its current state-of-the-art clinical application scenario in HCC. The limitations, challenges, and thoughts on future directions are also summarized.
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Affiliation(s)
- Shan Yao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Zheng Ye
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Yi Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Han-Yu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
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96
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Lee S, Summers RM. Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen. Radiol Clin North Am 2021; 59:987-1002. [PMID: 34689882 DOI: 10.1016/j.rcl.2021.07.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Organ segmentation, chest radiograph classification, and lung and liver nodule detections are some of the popular artificial intelligence (AI) tasks in chest and abdominal radiology due to the wide availability of public datasets. AI algorithms have achieved performance comparable to humans in less time for several organ segmentation tasks, and some lesion detection and classification tasks. This article introduces the current published articles of AI applied to chest and abdominal radiology, including organ segmentation, lesion detection, classification, and predicting prognosis.
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Affiliation(s)
- Sungwon Lee
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10, Room 1C224D, 10 Center Drive, Bethesda, MD 20892-1182, USA
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10, Room 1C224D, 10 Center Drive, Bethesda, MD 20892-1182, USA.
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97
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Machine learning-based multiparametric traditional multislice computed tomography radiomics for improving the discrimination of parotid neoplasms. Mol Clin Oncol 2021; 15:245. [PMID: 34650812 DOI: 10.3892/mco.2021.2407] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 07/28/2021] [Indexed: 11/05/2022] Open
Abstract
Characterization of parotid tumors is important for treatment planning and prognosis, and parotid tumor discrimination has recently been developed at the molecular level. The aim of the present study was to establish a machine learning (ML) predictive model based on multiparametric traditional multislice CT (MSCT) radiomic and clinical data analysis to improve the accuracy of differentiation among pleomorphic adenoma (PA), Warthin tumor (WT) and parotid carcinoma (PCa). A total of 345 patients (200 with WT, 91 with PA and 54 with PCa) with pathologically confirmed parotid tumors were retrospectively enrolled from five independent institutions between January 2010 and May 2019. A total of 273 patients recruited from institutions 1, 2 and 3 were randomly assigned to the training model; the independent validation set consisted of 72 patients treated at institutions 1, 4 and 5. Data were investigated using a linear discriminant analysis-based ML classifier. Feature selection and dimension reduction were conducted using reproducibility testing and a wrapper method. The diagnostic accuracy of the predictive model was compared with histopathological findings as reference results. This classifier achieved a satisfactory performance for the discrimination of PA, WT and PCa, with a total accuracy of 82.1% in the training cohort and 80.5% in the validation cohort. In conclusion, ML-based multiparametric traditional MSCT radiomics can improve the accuracy of differentiation among PA, WT and PCa. The findings of the present study should be validated by multicenter prospective studies using completely independent external data.
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98
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Machine Learning-Based Radiomics of the Optic Chiasm Predict Visual Outcome Following Pituitary Adenoma Surgery. J Pers Med 2021; 11:jpm11100991. [PMID: 34683132 PMCID: PMC8541242 DOI: 10.3390/jpm11100991] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/25/2021] [Accepted: 09/27/2021] [Indexed: 02/05/2023] Open
Abstract
Preoperative prediction of visual recovery after pituitary adenoma surgery remains a challenge. We aimed to investigate the value of MRI-based radiomics of the optic chiasm in predicting postoperative visual field outcome using machine learning technology. A total of 131 pituitary adenoma patients were retrospectively enrolled and divided into the recovery group (N = 79) and the non-recovery group (N = 52) according to visual field outcome following surgical chiasmal decompression. Radiomic features were extracted from the optic chiasm on preoperative coronal T2-weighted imaging. Least absolute shrinkage and selection operator regression were first used to select optimal features. Then, three machine learning algorithms were employed to develop radiomic models to predict visual recovery, including support vector machine (SVM), random forest and linear discriminant analysis. The prognostic performances of models were evaluated via five-fold cross-validation. The results showed that radiomic models using different machine learning algorithms all achieved area under the curve (AUC) over 0.750. The SVM-based model represented the best predictive performance for visual field recovery, with the highest AUC of 0.824. In conclusion, machine learning-based radiomics of the optic chiasm on routine MR imaging could potentially serve as a novel approach to preoperatively predict visual recovery and allow personalized counseling for individual pituitary adenoma patients.
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99
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Kong J, Zhu S, Shi G, Liu Z, Zhang J, Ren J. Prediction of Locoregional Recurrence-Free Survival of Oesophageal Squamous Cell Carcinoma After Chemoradiotherapy Based on an Enhanced CT-Based Radiomics Model. Front Oncol 2021; 11:739933. [PMID: 34631575 PMCID: PMC8499696 DOI: 10.3389/fonc.2021.739933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 09/06/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND AND PURPOSE Chemoradiotherapy is the standard treatment for moderate and advanced oesophageal cancer. The aim of this study was to establish a predictive model based on enhanced computed tomography examination, and to evaluate its clinical value for detecting locoregional recurrence-free survival (LRFS) in cases of oesophageal squamous cell carcinoma after radiotherapy. MATERIALS AND METHODS In total, 218 patients with pathologically diagnosed oesophageal squamous cell carcinoma who received radical chemoradiotherapy from July 2016 to December 2017 were collected in this study. Patients were randomly divided into either a training group (n=153) or a validation group (n=65) in a 7:3 ratio. Clinical patient information was then recorded. The enhanced computed tomography scan images of the patients were imported into 3D-slicer software (version 4.8.1), and the radiomic features were extracted by the Python programme package. In the training group, the dimensionality reduction of the radiomic features was implemented by Lasso regression, and then a radiological label, the model of predicting LRFS, was established and evaluated. To achieve a better prediction performance, the radiological label was combined with clinical risk factor information to construct a radiomics nomogram. A receiver operating characteristic curve was used to evaluate the efficacy of different models. Calibration curves were used to assess the consistency between the predicted and observed recurrence risk, and the Hosmer-Lemeshow method was used to test model fitness. The C-index evaluated the discriminating ability of the prediction model. Decision curve analysis was used to determine the clinical value of the constructed prediction model. RESULTS Of the 218 patients followed up in this study, 44 patients (28.8%) in the training group and 21 patients (32.3%) in the validation group experienced recurrence. There was no difference in LRFS between the two groups (χ2 = 0.525, P=0.405). Lasso regression was used in the training group to select six significant radiomic features. The radiological label established using these six features had a satisfactory prediction performance. The C-index was 0.716 (95% CI: 0.645-0.787) in the training group and 0.718 (95% CI: 0.612-0.825) in the validation group. The radiomics nomogram, which included the radiological label and clinical risk factors, achieved a better prediction than the radiological label alone. The C-index was 0.742 (95% CI: 0.674-0.810) in the training group and 0.715 (95% CI: 0.609-0.820) in the validation group. The results of the calibration curve and decision curve analyses indicated that the radiomics nomogram was superior in predicting LRFS of oesophageal carcinoma after radiotherapy. CONCLUSIONS A radiological label was successfully established to predict the LRFS of oesophageal squamous cell carcinoma after radiotherapy. The radiomics nomogram was complementary to the clinical prognostic features and could improve the prediction of the LRFS after radiotherapy for oesophageal cancer.
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Affiliation(s)
- Jie Kong
- Department of Radiation Oncology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Shuchai Zhu
- Department of Radiation Oncology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Gaofeng Shi
- Department of Computed Tomography and Magnetic Resonance, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Zhikun Liu
- Department of Radiation Oncology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jun Zhang
- Department of Radiation Oncology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jialiang Ren
- Pharmaceutical Diagnosis, GE Healthcare, Beijing, China
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100
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Li X, Zhang X, Li Z, Xie C, Qin S, Yan M, Ke Q, Jin X, Lin T, Zhou M, Liang W, Qi Z, Geng Z, Quan X. Two-Trait Predictor of Venous Invasion on Contrast-Enhanced CT as a Preoperative Predictor of Outcomes for Early-Stage Hepatocellular Carcinoma After Hepatectomy. Front Oncol 2021; 11:688087. [PMID: 34540664 PMCID: PMC8442625 DOI: 10.3389/fonc.2021.688087] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 08/10/2021] [Indexed: 12/24/2022] Open
Abstract
Objectives This study aimed to assess the effectiveness of the two-trait predictor of venous invasion (TTPVI) on contrast-enhanced computed tomography (CECT) for the preoperative prediction of clinical outcomes in patients with early-stage hepatocellular carcinoma (HCC) after hepatectomy. Methods This retrospective study included 280 patients with surgically resected HCC who underwent preoperative CECT between 2012 and 2013. CT imaging features of HCC were assessed, and univariate and multivariate Cox regression analyses were used to evaluate the CT features associated with disease-free survival (DFS) and overall survival (OS). Subgroup analyses were used to summarized the hazard ratios (HRs) between patients in whom TTPVI was present and those in whom TTPVI was absent using a forest plot. Results Capsule appearance [HR, 0.504; 95% confidence interval (CI), 0.341–0.745; p < 0.001], TTPVI (HR, 1.842; 95% CI, 1.319–2.572; p < 0.001) and high level of alanine aminotransferase (HR, 1.620; 95% CI, 1.180–2.225, p = 0.003) were independent risk factors for DFS, and TTPVI (HR, 2.509; 95% CI, 1.518–4.147; p < 0.001), high level of alpha-fetoprotein (HR, 1.722; 95% CI, 1.067–2.788; p = 0.026), and gamma-glutamyl transpeptidase (HR, 1.787; 95% CI, 1.134–2.814; p = 0.026) were independent risk factors for OS. A forest plot revealed that the TTPVI present group had lower DFS and OS rates in most subgroups. Patients in whom TTPVI was present in stages I and II had a lower DFS and OS than those in whom TTPVI was absent. Moreover, there were significant differences in DFS (p < 0.001) and OS (p < 0.001) between patients classified as Barcelona Clinic Liver Cancer stage A in whom TTPVI was absent and in whom TTPVI was present. Conclusions TTPVI may be used as a preoperative biomarker for predicting postoperative outcomes for patients with early-stage HCC.
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Affiliation(s)
- Xinming Li
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Xuchang Zhang
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Zhipeng Li
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Chuanmiao Xie
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Shuping Qin
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Meng Yan
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Qiying Ke
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Xuan Jin
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Ting Lin
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Muyao Zhou
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Wen Liang
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Zhendong Qi
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Zhijun Geng
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xianyue Quan
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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