1
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Schmauch B, Elsoukkary SS, Moro A, Raj R, Wehrle CJ, Sasaki K, Calderaro J, Sin-Chan P, Aucejo F, Roberts DE. Combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgery. J Pathol Inform 2024; 15:100360. [PMID: 38292073 PMCID: PMC10825615 DOI: 10.1016/j.jpi.2023.100360] [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: 10/26/2023] [Revised: 12/10/2023] [Accepted: 12/23/2023] [Indexed: 02/01/2024] Open
Abstract
Hepatocellular carcinoma (HCC) is among the most common cancers worldwide, and tumor recurrence following liver resection or transplantation is one of the highest contributors to mortality in HCC patients after surgery. Using artificial intelligence (AI), we developed an interdisciplinary model to predict HCC recurrence and patient survival following surgery. We collected whole-slide H&E images, clinical variables, and follow-up data from 300 patients with HCC who underwent transplant and 169 patients who underwent resection at the Cleveland Clinic. A deep learning model was trained to predict recurrence-free survival (RFS) and disease-specific survival (DSS) from the H&E-stained slides. Repeated cross-validation splits were used to compute robust C-index estimates, and the results were compared to those obtained by fitting a Cox proportional hazard model using only clinical variables. While the deep learning model alone was predictive of recurrence and survival among patients in both cohorts, integrating the clinical and histologic models significantly increased the C-index in each cohort. In every subgroup analyzed, we found that a combined clinical and deep learning model better predicted post-surgical outcome in HCC patients compared to either approach independently.
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Affiliation(s)
| | - Sarah S. Elsoukkary
- Owkin Lab, Owkin, Inc., New York, NY, USA
- Department of Pathology, Cleveland Clinic, Cleveland, OH, USA
| | - Amika Moro
- Department of Surgery, Cleveland Clinic, Cleveland, OH, USA
| | - Roma Raj
- Department of Surgery, Cleveland Clinic, Cleveland, OH, USA
| | | | - Kazunari Sasaki
- Department of Surgery, Stanford University, Palo Alto, CA, USA
| | - Julien Calderaro
- Department of Pathology, Henri Mondor University Hospital, Créteil, France
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2
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Chatzipanagiotou OP, Loukas C, Vailas M, Machairas N, Kykalos S, Charalampopoulos G, Filippiadis D, Felekouras E, Schizas D. Artificial intelligence in hepatocellular carcinoma diagnosis: a comprehensive review of current literature. J Gastroenterol Hepatol 2024. [PMID: 38923550 DOI: 10.1111/jgh.16663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 04/26/2024] [Accepted: 06/07/2024] [Indexed: 06/28/2024]
Abstract
BACKGROUND AND AIM Hepatocellular carcinoma (HCC) diagnosis mainly relies on its pathognomonic radiological profile, obviating the need for biopsy. The project of incorporating artificial intelligence (AI) techniques in HCC aims to improve the performance of image recognition. Herein, we thoroughly analyze and evaluate proposed AI models in the field of HCC diagnosis. METHODS A comprehensive review of the literature was performed utilizing MEDLINE/PubMed and Web of Science databases with the end of search date being the 30th of September 2023. The MESH terms "Artificial Intelligence," "Liver Cancer," "Hepatocellular Carcinoma," "Machine Learning," and "Deep Learning" were searched in the title and/or abstract. All references of the obtained articles were also evaluated for any additional information. RESULTS Our search resulted in 183 studies meeting our inclusion criteria. Across all diagnostic modalities, reported area under the curve (AUC) of most developed models surpassed 0.900. A B-mode US and a contrast-enhanced US model achieved AUCs of 0.947 and 0.957, respectively. Regarding the more challenging task of HCC diagnosis, a 2021 deep learning model, trained with CT scans, classified hepatic malignant lesions with an AUC of 0.986. Finally, a MRI machine learning model developed in 2021 displayed an AUC of 0.975 when differentiating small HCCs from benign lesions, while another MRI-based model achieved HCC diagnosis with an AUC of 0.970. CONCLUSIONS AI tools may lead to significant improvement in diagnostic management of HCC. Many models fared better or comparable to experienced radiologists while proving capable of elevating radiologists' accuracy, demonstrating promising results for AI implementation in HCC-related diagnostic tasks.
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Affiliation(s)
- Odysseas P Chatzipanagiotou
- First Department of Surgery, National and Kapodistrian University of Athens, Laikon General Hospital, Athens, Greece
| | - Constantinos Loukas
- Laboratory of Medical Physics, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Michail Vailas
- First Department of Surgery, National and Kapodistrian University of Athens, Laikon General Hospital, Athens, Greece
| | - Nikolaos Machairas
- Second Department of Propaedeutic Surgery, National and Kapodistrian University of Athens, Laikon General Hospital, Athens, Greece
| | - Stylianos Kykalos
- Second Department of Propaedeutic Surgery, National and Kapodistrian University of Athens, Laikon General Hospital, Athens, Greece
| | - Georgios Charalampopoulos
- Second Department of Radiology, National and Kapodistrian University of Athens, Attikon University Hospital, Athens, Greece
| | - Dimitrios Filippiadis
- Second Department of Radiology, National and Kapodistrian University of Athens, Attikon University Hospital, Athens, Greece
| | - Evangellos Felekouras
- First Department of Surgery, National and Kapodistrian University of Athens, Laikon General Hospital, Athens, Greece
| | - Dimitrios Schizas
- First Department of Surgery, National and Kapodistrian University of Athens, Laikon General Hospital, Athens, Greece
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Lu F, Meng Y, Song X, Li X, Liu Z, Gu C, Zheng X, Jing Y, Cai W, Pinyopornpanish K, Mancuso A, Romeiro FG, Méndez-Sánchez N, Qi X. Artificial Intelligence in Liver Diseases: Recent Advances. Adv Ther 2024; 41:967-990. [PMID: 38286960 DOI: 10.1007/s12325-024-02781-5] [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: 09/12/2023] [Accepted: 01/03/2024] [Indexed: 01/31/2024]
Abstract
Liver diseases cause a significant burden on public health worldwide. In spite of great advances during recent years, there are still many challenges in the diagnosis and treatment of liver diseases. During recent years, artificial intelligence (AI) has been widely used for the diagnosis, risk stratification, and prognostic prediction of various diseases based on clinical datasets and medical images. Accumulative studies have shown its performance for diagnosing patients with nonalcoholic fatty liver disease and liver fibrosis and assessing their severity, and for predicting treatment response and recurrence of hepatocellular carcinoma, outcomes of liver transplantation recipients, and risk of drug-induced liver injury. Herein, we aim to comprehensively summarize the current evidence regarding diagnostic, prognostic, and/or therapeutic role of AI in these common liver diseases.
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Affiliation(s)
- Feifei Lu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
| | - Yao Meng
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, Dalian Medical University, Dalian, China
| | - Xiaoting Song
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, Dalian Medical University, Dalian, China
| | - Xiaotong Li
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Zhuang Liu
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Chunru Gu
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Xiaojie Zheng
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Yi Jing
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Wei Cai
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Kanokwan Pinyopornpanish
- Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Andrea Mancuso
- Medicina Interna 1, Azienda di Rilievo Nazionale Ad Alta Specializzazione Civico-Di Cristina-Benfratelli, Palermo, Italy.
| | | | - Nahum Méndez-Sánchez
- Liver Research Unit, Medica Sur Clinic and Foundation, National Autonomous University of Mexico, Mexico City, Mexico.
| | - Xingshun Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China.
- Postgraduate College, Dalian Medical University, Dalian, China.
- Postgraduate College, China Medical University, Shenyang, China.
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4
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Oh JH, Sinn DH. Multidisciplinary approach for hepatocellular carcinoma patients: current evidence and future perspectives. JOURNAL OF LIVER CANCER 2024; 24:47-56. [PMID: 38527905 PMCID: PMC10990674 DOI: 10.17998/jlc.2024.02.27] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Revised: 02/22/2024] [Accepted: 02/26/2024] [Indexed: 03/27/2024]
Abstract
Management of hepatocellular carcinoma (HCC) is challenging due to the complex relationship between underlying liver disease, tumor burden, and liver function. HCC is also notorious for its high recurrence rate even after curative treatment for early-stage tumor. Liver transplantation can substantially alter patient prognosis, but donor availability varies by each patient which further complicates treatment decision. Recent advancements in HCC treatments have introduced numerous potentially efficacious treatment modalities. However, high level evidence comparing the risks and benefits of these options is limited. In this complex situation, multidisciplinary approach or multidisciplinary team care has been suggested as a valuable strategy to help cope with escalating complexity in HCC management. Multidisciplinary approach involves collaboration among medical and health care professionals from various academic disciplines to provide comprehensive care. Although evidence suggests that multidisciplinary care can enhance outcomes of HCC patients, robust data from randomized controlled trials are currently lacking. Moreover, the implementation of a multidisciplinary approach necessitates increased medical resources compared to conventional cancer care. This review summarizes the current evidence on the role of multidisciplinary approach in HCC management and explores potential future directions.
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Affiliation(s)
- Joo Hyun Oh
- Department of Medicine, Nowon Eulji Medical Center, Eulji University, Eulji University School of Medicine, Seoul, Korea
| | - Dong Hyun Sinn
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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5
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Wang DD, Zhang JF, Zhang LH, Niu M, Jiang HJ, Jia FC, Feng ST. Clinical-radiomics predictors to identify the suitability of transarterial chemoembolization treatment in intermediate-stage hepatocellular carcinoma: A multicenter study. Hepatobiliary Pancreat Dis Int 2023; 22:594-604. [PMID: 36456428 DOI: 10.1016/j.hbpd.2022.11.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 11/09/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND Although transarterial chemoembolization (TACE) is the first-line therapy for intermediate-stage hepatocellular carcinoma (HCC), it is not suitable for all patients. This study aimed to determine how to select patients who are not suitable for TACE as the first treatment choice. METHODS A total of 243 intermediate-stage HCC patients treated with TACE at three centers were retrospectively enrolled, of which 171 were used for model training and 72 for testing. Radiomics features were screened using the Spearman correlation analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. Subsequently, a radiomics model was established using extreme gradient boosting (XGBoost) with 5-fold cross-validation. The Shapley additive explanations (SHAP) method was used to visualize the radiomics model. A clinical model was constructed using univariate and multivariate logistic regression. The combined model comprising the radiomics signature and clinical factors was then established. This model's performance was evaluated by discrimination, calibration, and clinical application. Generalization ability was evaluated by the testing cohort. Finally, the model was used to analyze overall and progression-free survival of different groups. RESULTS A third of the patients (81/243) were unsuitable for TACE treatment. The combined model had a high degree of accuracy as it identified TACE-unsuitable cases, at a sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of 0.759, 0.885, 0.906 [95% confidence interval (CI): 0.859-0.953] in the training cohort and 0.826, 0.776, and 0.894 (95% CI: 0.815-0.972) in the testing cohort, respectively. CONCLUSIONS The high degree of accuracy of our clinical-radiomics model makes it clinically useful in identifying intermediate-stage HCC patients who are unsuitable for TACE treatment.
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Affiliation(s)
- Dan-Dan Wang
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jin-Feng Zhang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin 150081, China
| | - Lin-Han Zhang
- Department of PET/CT, the First Affiliated Hospital of Harbin Medical University, Harbin 150007, China
| | - Meng Niu
- Department of Interventional Therapy, the First Affiliated Hospital of China Medical University, Shenyang 110001, China
| | - Hui-Jie Jiang
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China.
| | - Fu-Cang Jia
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
| | - Shi-Ting Feng
- Department of Radiology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China.
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6
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Ansari G, Mirza-Aghazadeh-Attari M, Mohseni A, Madani SP, Shahbazian H, Pawlik TM, Kamel IR. Response Assessment of Primary Liver Tumors to Novel Therapies: an Imaging Perspective. J Gastrointest Surg 2023; 27:2245-2259. [PMID: 37464140 DOI: 10.1007/s11605-023-05762-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 06/11/2023] [Indexed: 07/20/2023]
Abstract
The latest developments in cancer immunotherapy, namely the introduction of immune checkpoint inhibitors, have led to a fundamental change in advanced cancer treatments. Imaging is crucial to identify tumor response accurately and delineate prognosis in immunotherapy-treated patients. Simultaneously, advances in image acquisition techniques, notably functional and molecular imaging, have facilitated more accurate pretreatment evaluation, assessment of response to therapy, and monitoring for tumor recurrence. Traditional approaches to assessing tumor progression, such as RECIST, rely on changes in tumor size, while new strategies for evaluating tumor response to therapy, such as the mRECIST and the EASL, rely on tumor enhancement. Moreover, the assessment of tumor volume, enhancement, cellularity, and perfusion are some novel techniques that have been investigated. Validation of these novel approaches should rely on comparing their results with those of standard evaluation methods (EASL, mRECIST) while considering the ultimate outcome, which is patient survival. More recently, immunotherapy has been used in the management of primary liver tumors. However, little is known about its efficacy. This article reviews imaging modalities and techniques for assessing tumor response and survival in immunotherapy-treated patients with primary hepatic malignancies.
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Affiliation(s)
- Golnoosh Ansari
- Russell H. Morgan Department of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Mohammad Mirza-Aghazadeh-Attari
- Russell H. Morgan Department of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Alireza Mohseni
- Russell H. Morgan Department of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Seyedeh Panid Madani
- Russell H. Morgan Department of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Haneyeh Shahbazian
- Russell H. Morgan Department of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Timothy M Pawlik
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center, James Comprehensive Cancer Center, Columbus, OH, USA
| | - Ihab R Kamel
- Russell H. Morgan Department of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA.
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7
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Sariyar E, Firtina Karagonlar Z. Modelling the Sorafenib-resistant Liver Cancer Microenvironment by Using 3-D Spheroids. Altern Lab Anim 2023; 51:301-312. [PMID: 37555318 DOI: 10.1177/02611929231193421] [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] [Indexed: 08/10/2023]
Abstract
Liver cancer is the third leading cause of cancer-related mortality, and hepatocellular carcinoma (HCC) is the most common form of liver cancer, and it usually occurs in the setting of chronic liver disease and cirrhosis. For patients with advanced HCC, systemic treatment is the first choice - however, resistance occurs frequently. Sorafenib was the first tyrosine kinase inhibitor approved for advanced HCC, and resistance to the therapy is a serious concern. When sorafenib therapy fails in a patient, it can be challenging to decide whether they can undergo a second-line therapy, and to determine which therapy they will be able to tolerate. Thus, physiologically relevant in vitro preclinical models are crucial for screening potential therapies, and 3-D tumour spheroids permit studies of tumour pathobiology. In this study, a drug-resistant 3-D tumour spheroid model was developed, based on sorafenib-resistant hepatocellular carcinoma cells, LX2 stellate cells and THP-1 monocytes. Model tumour spheroids that were formed with the sorafenib-resistant cells demonstrated lower diffusion of doxorubicin and exhibited increased resistance to regorafenib. Moreover, in the sorafenib-resistant spheroids, there was increased presence of CD68-positive cells and a reduction in inflammatory marker secretion. The sorafenib-resistant cell line-derived spheroids also showed a higher expression of FGF-19, PDGF-AA and GDF-15, which are known to be involved in malignancies. This multi-cell type spheroid model represents a potentially useful system to test drug candidates in a microenvironment that mimics the drug-resistant tumour microenvironment in HCC.
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Affiliation(s)
- Ece Sariyar
- Department of Genetics and Bioengineering, İzmir University of Economics, Izmir, Turkey
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8
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Dai Y, Liu D, Xin Y, Li Y, Wang D, He B, Zeng X, Li J, Jia F, Jiang H. Efficacy and Interpretability Analysis of Noninvasive Imaging Based on Computed Tomography in Patients with Hepatocellular Carcinoma After Initial Transarterial Chemoembolization. Acad Radiol 2023; 30 Suppl 1:S61-S72. [PMID: 37393179 DOI: 10.1016/j.acra.2023.05.027] [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: 04/25/2023] [Revised: 05/19/2023] [Accepted: 05/24/2023] [Indexed: 07/03/2023]
Abstract
RATIONALE AND OBJECTIVES The objective of this study is to accurately and timely assess the efficacy of patients with hepatocellular carcinoma (HCC) after the initial transarterial chemoembolization (TACE). MATERIALS AND METHODS This retrospective study consisted of 279 patients with HCC in Center 1, who were split into training and validation cohorts in the ratio of 4:1, and 72 patients in Center 2 as an external testing cohort. Radiomics signatures both in the arterial phase and venous phase of contrast-enhanced computed tomography images were selected by univariate analysis, correlation analysis, and least absolute shrinkage and selection operator regression to build the predicting models. The clinical model and combined model were constructed by independent risk factors after univariate and multivariate logistic regression analysis. The biological interpretability of radiomics signatures correlating transcriptome sequencing data was explored using publicly available data sets. RESULTS A total of 31 radiomics signatures in the arterial phase and 13 radiomics signatures in the venous phase were selected to construct Radscore_arterial and Radscore_venous, respectively, which were independent risk factors. After constructing the combined model, the area under the receiver operating characteristic curve in three cohorts was 0.865, 0.800, and 0.745, respectively. Through correlation analysis, 11 radiomics signatures in the arterial phase and 4 radiomics signatures in the venous phase were associated with 8 and 5 gene modules, respectively (All P < .05), which enriched some pathways closely related to tumor development and proliferation. CONCLUSION Noninvasive imaging has considerable value in predicting the efficacy of patients with HCC after initial TACE. The biological interpretability of the radiological signatures can be mapped at the micro level.
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Affiliation(s)
- Yanmei Dai
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150086, China (Y.D., D.L., Y.X., D.W., X.Z., H.J.)
| | - Dongmin Liu
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150086, China (Y.D., D.L., Y.X., D.W., X.Z., H.J.)
| | - Yanjie Xin
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150086, China (Y.D., D.L., Y.X., D.W., X.Z., H.J.)
| | - Yuchong Li
- Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China (Y.L., B.H., F.J.)
| | - Dandan Wang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150086, China (Y.D., D.L., Y.X., D.W., X.Z., H.J.)
| | - Baochun He
- Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China (Y.L., B.H., F.J.)
| | - Xu Zeng
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150086, China (Y.D., D.L., Y.X., D.W., X.Z., H.J.)
| | - Jiaping Li
- Department of Interventional Oncology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China (J.L.)
| | - Fucang Jia
- Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China (Y.L., B.H., F.J.); Pazhou Lab, Guangzhou, China (F.J.)
| | - Huijie Jiang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150086, China (Y.D., D.L., Y.X., D.W., X.Z., H.J.).
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9
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Shams MY, El-kenawy ESM, Ibrahim A, Elshewey AM. A hybrid dipper throated optimization algorithm and particle swarm optimization (DTPSO) model for hepatocellular carcinoma (HCC) prediction. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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10
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Lakshmipriya B, Pottakkat B, Ramkumar G. Deep learning techniques in liver tumour diagnosis using CT and MR imaging - A systematic review. Artif Intell Med 2023; 141:102557. [PMID: 37295904 DOI: 10.1016/j.artmed.2023.102557] [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: 04/04/2022] [Revised: 04/15/2023] [Accepted: 04/18/2023] [Indexed: 06/12/2023]
Abstract
Deep learning has become a thriving force in the computer aided diagnosis of liver cancer, as it solves extremely complicated challenges with high accuracy over time and facilitates medical experts in their diagnostic and treatment procedures. This paper presents a comprehensive systematic review on deep learning techniques applied for various applications pertaining to liver images, challenges faced by the clinicians in liver tumour diagnosis and how deep learning bridges the gap between clinical practice and technological solutions with an in-depth summary of 113 articles. Since, deep learning is an emerging revolutionary technology, recent state-of-the-art research implemented on liver images are reviewed with more focus on classification, segmentation and clinical applications in the management of liver diseases. Additionally, similar review articles in literature are reviewed and compared. The review is concluded by presenting the contemporary trends and unaddressed research issues in the field of liver tumour diagnosis, offering directions for future research in this field.
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Affiliation(s)
- B Lakshmipriya
- Department of Surgical Gastroenterology, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - Biju Pottakkat
- Department of Surgical Gastroenterology, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India.
| | - G Ramkumar
- Department of Radio Diagnosis, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
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11
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Sarıyar E, Karpat O, Sezan S, Baylan SM, Kıpçak A, Guven K, Erdal E, Fırtına Karagonlar Z. EGFR and Lyn inhibition augments regorafenib induced cell death in sorafenib resistant 3D tumor spheroid model. Cell Signal 2023; 105:110608. [PMID: 36693455 DOI: 10.1016/j.cellsig.2023.110608] [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: 10/11/2022] [Revised: 01/18/2023] [Accepted: 01/19/2023] [Indexed: 01/22/2023]
Abstract
Hepatocellular carcinoma (HCC) is the most common primary cancer of the liver and the third most lethal malignancy worldwide. Patients with unresectable HCC receive systemic therapies, traditionally sorafenib or lenvatinib as first line therapy. Despite its poor therapeutic response and high rates of resistance, in most countries, sorafenib still remains the globally used first-line treatment for advanced HCC. Thus, preclinical models demonstrating sorafenib resistance are crucial. 3D tumor spheroid models are becoming extremely important as screening platforms for drug therapies. In this paper, we utilized sorafenib resistant Huh7 cell line and LX2 hepatic stellate cell line to establish a sorafenib resistant 3D tumor spheroid model which can be used to test second-line treatment options. Our analysis demonstrated that sorafenib resistant 3D tumor spheroids are also more resistant to regorafenib and exhibit diverse features compared to parental tumor spheroids. Sorafenib resistant spheroids had higher CD24 and EpCAM positive cancer stem cell populations. In addition, several oncogenic kinases are upregulated in the sorafenib resistant spheroids. Importantly, combined inhibition of EGFR and Lyn kinase in sorafenib resistant tumor spheroids are effective in inducing cell death. Our model proved to be an affordable and useful model to mimic drug resistant tumor microenvironment in HCC and provided novel insights into candidates for new combinational therapies.
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Affiliation(s)
- Ece Sarıyar
- Division of Bioengineering, Graduate School, İzmir University of Economics, Sakarya Cad., İzmir 35330, Turkey; Izmir International Biomedicine and Genome Institute (IBG-Izmir), Dokuz Eylul University, Izmir, Turkey
| | - Ozum Karpat
- Department of Genetics and Bioengineering, İzmir University of Economics, Sakarya Cad., İzmir 35330, Turkey
| | - Sıla Sezan
- Department of Genetics and Bioengineering, İzmir University of Economics, Sakarya Cad., İzmir 35330, Turkey
| | - Sude Mısra Baylan
- Department of Genetics and Bioengineering, İzmir University of Economics, Sakarya Cad., İzmir 35330, Turkey
| | - Arda Kıpçak
- Department of Genetics and Bioengineering, İzmir University of Economics, Sakarya Cad., İzmir 35330, Turkey; Department of Psychology, University of Virginia, Charlottesville, VA 22903, USA
| | - Kadriye Guven
- Department of Medical Biology and Genetics, Faculty of Medicine, Dokuz Eylul University, Izmir 35340, Turkey
| | - Esra Erdal
- Izmir International Biomedicine and Genome Institute (IBG-Izmir), Dokuz Eylul University, Izmir, Turkey; Department of Medical Biology and Genetics, Faculty of Medicine, Dokuz Eylul University, Izmir 35340, Turkey; Izmir Biomedicine and Genome Center, Izmir 35340, Turkey
| | - Zeynep Fırtına Karagonlar
- Department of Genetics and Bioengineering, İzmir University of Economics, Sakarya Cad., İzmir 35330, Turkey.
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12
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Richburg CE, Dossett LA, Hughes TM. Cognitive Bias and Dissonance in Surgical Practice: A Narrative Review. Surg Clin North Am 2023; 103:271-285. [PMID: 36948718 DOI: 10.1016/j.suc.2022.11.003] [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: 03/24/2023]
Abstract
A cognitive bias describes "shortcuts" subconsciously applied to new scenarios to simplify decision-making. Unintentional introduction of cognitive bias in surgery may result in surgical diagnostic error that leads to delayed surgical care, unnecessary procedures, intraoperative complications, and delayed recognition of postoperative complications. Data suggest that surgical error secondary to the introduction of cognitive bias results in significant harm. Thus, debiasing is a growing area of research which urges practitioners to deliberately slow decision-making to reduce the effects of cognitive bias.
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Affiliation(s)
- Caroline E Richburg
- University of Michigan Medical School, 1500 East Medical Center Drive, Ann Arbor, MI, USA. https://twitter.com/cerichburg
| | - Lesly A Dossett
- Department of Surgery, Michigan Medicine, 2101 Taubman Center, 1500 East Medical Center Drive, Ann Arbor, MI, USA. https://twitter.com/leslydossett
| | - Tasha M Hughes
- Department of Surgery, Michigan Medicine, 2101 Taubman Center, 1500 East Medical Center Drive, Ann Arbor, MI, USA.
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13
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Shen X, Wu J, Su J, Yao Z, Huang W, Zhang L, Jiang Y, Yu W, Li Z. Revisiting artificial intelligence diagnosis of hepatocellular carcinoma with DIKWH framework. Front Genet 2023; 14:1004481. [PMID: 37007970 PMCID: PMC10064216 DOI: 10.3389/fgene.2023.1004481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 02/14/2023] [Indexed: 03/09/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common type of liver cancer with a high morbidity and fatality rate. Traditional diagnostic methods for HCC are primarily based on clinical presentation, imaging features, and histopathology. With the rapid development of artificial intelligence (AI), which is increasingly used in the diagnosis, treatment, and prognosis prediction of HCC, an automated approach to HCC status classification is promising. AI integrates labeled clinical data, trains on new data of the same type, and performs interpretation tasks. Several studies have shown that AI techniques can help clinicians and radiologists be more efficient and reduce the misdiagnosis rate. However, the coverage of AI technologies leads to difficulty in which the type of AI technology is preferred to choose for a given problem and situation. Solving this concern, it can significantly reduce the time required to determine the required healthcare approach and provide more precise and personalized solutions for different problems. In our review of research work, we summarize existing research works, compare and classify the main results of these according to the specified data, information, knowledge, wisdom (DIKW) framework.
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Affiliation(s)
- Xiaomin Shen
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Jinxin Wu
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Junwei Su
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Zhenyu Yao
- School of Computer Science, King’s College London, London, United Kingdom
| | - Wei Huang
- Department of Gastroenterology II, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Li Zhang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Yiheng Jiang
- Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Wei Yu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Zhao Li
- School of Computer Science, Zhejiang University, Hangzhou, China
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14
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Miranda J, Horvat N, Fonseca GM, Araujo-Filho JDAB, Fernandes MC, Charbel C, Chakraborty J, Coelho FF, Nomura CH, Herman P. Current status and future perspectives of radiomics in hepatocellular carcinoma. World J Gastroenterol 2023; 29:43-60. [PMID: 36683711 PMCID: PMC9850949 DOI: 10.3748/wjg.v29.i1.43] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/27/2022] [Accepted: 12/14/2022] [Indexed: 01/04/2023] Open
Abstract
Given the frequent co-existence of an aggressive tumor and underlying chronic liver disease, the management of hepatocellular carcinoma (HCC) patients requires experienced multidisciplinary team discussion. Moreover, imaging plays a key role in the diagnosis, staging, restaging, and surveillance of HCC. Currently, imaging assessment of HCC entails the assessment of qualitative characteristics which are prone to inter-reader variability. Radiomics is an emerging field that extracts high-dimensional mineable quantitative features that cannot be assessed visually with the naked eye from medical imaging. The main potential applications of radiomic models in HCC are to predict histology, response to treatment, genetic signature, recurrence, and survival. Despite the encouraging results to date, there are challenges and limitations that need to be overcome before radiomics implementation in clinical practice. The purpose of this article is to review the main concepts and challenges pertaining to radiomics, and to review recent studies and potential applications of radiomics in HCC.
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Affiliation(s)
- Joao Miranda
- Department of Radiology, University of Sao Paulo, Sao Paulo 05403-010, Brazil
| | - Natally Horvat
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | | | | | - Maria Clara Fernandes
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | - Charlotte Charbel
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | - Jayasree Chakraborty
- Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | | | - Cesar Higa Nomura
- Department of Radiology, University of Sao Paulo, Sao Paulo 05403-000, Brazil
| | - Paulo Herman
- Department of Gastroenterology, University of Sao Paulo, Sao Paulo 05403-000, Brazil
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15
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Eresen A, Sun C, Zhou K, Shangguan J, Wang B, Pan L, Hu S, Ma Q, Yang J, Zhang Z, Yaghmai V. Early Differentiation of Irreversible Electroporation Ablation Regions With Radiomics Features of Conventional MRI. Acad Radiol 2022; 29:1378-1386. [PMID: 34933803 PMCID: PMC10029937 DOI: 10.1016/j.acra.2021.11.020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 01/05/2023]
Abstract
RATIONALE AND OBJECTIVES Irreversible electroporation (IRE) is a promising non-thermal ablation technique for the treatment of patients with hepatocellular carcinoma. Early differentiation of the IRE zone from surrounding reversibly electroporated (RE) penumbra is vital for the evaluation of treatment response. In this study, an advanced statistical learning framework was developed by evaluating standard MRI data to differentiate IRE ablation zones, and to correlate with histological tumor biomarkers. MATERIALS AND METHODS Fourteen rabbits with VX2 liver tumors were scanned following IRE ablation and forty-six features were extracted from T1w and T2w MRI. Following identification of key imaging variables through two-step feature analysis, multivariable classification and regression models were generated for differentiation of IRE ablation zones, and correlation with histological markers reflecting viable tumor cells, microvessel density, and apoptosis rate. The performance of the multivariable models was assessed by measuring accuracy, receiver operating characteristics curve analysis, and Spearman correlation coefficients. RESULTS The classifiers integrating four radiomics features of T1w, T2w, and T1w+T2w MRI data distinguished IRE from RE zones with an accuracy of 97%, 80%, and 97%, respectively. Also, pixelwise classification models of T1w, T2w, and T1w+T2w MRI labeled each voxel with an accuracy of 82.8%, 66.5%, and 82.9%, respectively. Regression models obtained a strong correlation with behavior of viable tumor cells (0.62 ≤ r2 ≤ 0.85, p < 0.01), apoptosis (0.40 ≤ r2 ≤ 0.82, p < 0.01), and microvessel density (0.48 ≤ r2 ≤ 0.58, p < 0.01). CONCLUSION MRI radiomics features provide descriptive power for early differentiation of IRE and RE zones while observing strong correlations among multivariable MRI regression models and histological tumor biomarkers.
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Affiliation(s)
- Aydin Eresen
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiological Sciences, University of California Irvine, Irvine, California
| | - Chong Sun
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Orthopedics, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Kang Zhou
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiology, Peking Union Medical College Hospital, Beijing, China
| | - Junjie Shangguan
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Bin Wang
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Guangzhou, China
| | - Liang Pan
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiology, Third Affiliated Hospital of Suzhou University, Changzhou, Jiangsu, China
| | - Su Hu
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Quanhong Ma
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiological Sciences, University of California Irvine, Irvine, California
| | - Jia Yang
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Zhuoli Zhang
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiological Sciences, University of California Irvine, Irvine, California; Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, Illinois; Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, California
| | - Vahid Yaghmai
- Department of Radiological Sciences, University of California Irvine, Irvine, California; Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, California.
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16
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Tonini V, Vigutto G, Donati R. Liver surgery for colorectal metastasis: New paths and new goals with the help of artificial intelligence. Artif Intell Gastroenterol 2022; 3:28-35. [DOI: 10.35712/aig.v3.i2.28] [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: 12/20/2021] [Revised: 03/28/2022] [Accepted: 04/19/2022] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer is one of the most common neoplasia with an high risk to metastatic spread. Improving medical and surgical treatment is moving along with improving the precision of diagnosis and patient's assessment, the latter two aided more and more with the use of artificial intelligence (AI). The management of colorectal liver metastasis is multidisciplinary, and surgery is the main option. After the diagnosis, a surgical assessment of the patient is fundamental. Reaching a R0 resection with a proper remnant liver volume can be done using new techniques involving also artificial intelligence. Considering the recent application of artificial intelligence as a valid substitute for liver biopsy in chronic liver diseases, several authors tried to apply similar techniques to pre-operative imaging of liver metastasis. Radiomics showed good results in identifying structural changes in a unhealthy liver and in evaluating the prognosis after a liver resection. Recently deep learning has been successfully applied in estimating the remnant liver volume before surgery. Moreover AI techniques can help surgeons to perform an early diagnosis of neoplastic relapse or a better differentiation between a colorectal metastasis and a benign lesion. AI could be applied also in the histopathological diagnostic tool. Although AI implementation is still partially automatized, it appears faster and more precise than the usual diagnostic tools and, in the short future, could become the new gold standard in liver surgery.
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Affiliation(s)
- Valeria Tonini
- Department of Medical and Surgical Sciences, Sant' Orsola Hospital University of Bologna, Bologna 40138, Italy
| | - Gabriele Vigutto
- Department of Medical and Surgical Sciences, St Orsola Hospital, University of Bologna, Bologna 40138, Italy
| | - Riccardo Donati
- Department of Electrical, Electronic and Information Engineering ”Guglielmo Marconi” (DEI), University of Bologna, Bologna 40138, Italy
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17
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Dai Y, Jiang H, Feng ST, Xia Y, Li J, Zhao S, Wang D, Zeng X, Chen Y, Xin Y, Liu D. Noninvasive Imaging Evaluation Based on Computed Tomography of the Efficacy of Initial Transarterial Chemoembolization to Predict Outcome in Patients with Hepatocellular Carcinoma. J Hepatocell Carcinoma 2022; 9:273-288. [PMID: 35411303 PMCID: PMC8994626 DOI: 10.2147/jhc.s351077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 03/18/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose This study aims to develop a new model to more comprehensively and accurately predict the survival of patients with HCC after initial TACE. Patients and Methods The whole cohort (n = 102) was randomly divided into a training cohort and a validation cohort in the ratio of 8:2. The optimal radiomics signatures were screened using the least absolute shrinkage and selection operator algorithm (LASSO) regression for constructing the radscore to predict overall survival (OS). The C-index (95% confidence interval, CI), calibration curve, and decision curve analysis (DCA) were used to evaluate the performance of the models. The independent risk factors (hazard ratio, HR) for predicting OS were stratified by Kaplan–Meier (K-M) analysis and the Log rank test. Results The median OS was 439 days (95% CI: 215.795–662.205) in whole cohort, and in the training cohort and validation cohort, the median OS was 552 days (95% CI: 171.172–932.828), 395 days (95% CI: 309.415–480.585), respectively (P = 0.889). After multivariate cox regression, the combined radscore-clinical model was consisted of radscore (HR: 2.065, 95% CI: 1.285–3.316; P = 0.0029) and post-response (HR: 1.880, 95% CI: 1.310–2.697; P = 0.0007), both of which were independent risk factors for the OS. In the validation cohort, the efficacy of both the radscore (C-index: 0.769, 95% CI: 0.496–1.000) and combined model (C-index: 0.770, 95% CI: 0.581–0.806) were higher than that of the clinical model (C-index: 0.655, 95% CI: 0.508–0.802). The calibration curve of the combined model for predicting OS presented good consistency between observations and predictions in both the training cohort and validation cohort. Conclusion Noninvasive imaging has a good prediction performance of survival after initial TACE in patients with HCC. The combined model consisting of post-response and radscore may be able to better predict outcome.
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Affiliation(s)
- Yanmei Dai
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, 150086, People’s Republic of China
| | - Huijie Jiang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, 150086, People’s Republic of China
- Correspondence: Huijie Jiang; Shi-Ting Feng, Tel +86 86605576, Email ;
| | - Shi-Ting Feng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, 510030, People’s Republic of China
| | - Yuwei Xia
- Huiying Medical Technology Co., Ltd, Beijing City, 100192, People’s Republic of China
| | - Jinping Li
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, 150086, People’s Republic of China
| | - Sheng Zhao
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, 150086, People’s Republic of China
| | - Dandan Wang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, 150086, People’s Republic of China
| | - Xu Zeng
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, 150086, People’s Republic of China
| | - Yusi Chen
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, 150086, People’s Republic of China
| | - Yanjie Xin
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, 150086, People’s Republic of China
| | - Dongmin Liu
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, 150086, People’s Republic of China
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18
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Taha A, Ochs V, Kayhan LN, Enodien B, Frey DM, Krähenbühl L, Taha-Mehlitz S. Advancements of Artificial Intelligence in Liver-Associated Diseases and Surgery. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58040459. [PMID: 35454298 PMCID: PMC9029673 DOI: 10.3390/medicina58040459] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/14/2022] [Accepted: 03/18/2022] [Indexed: 02/06/2023]
Abstract
Background and Objectives: The advancement of artificial intelligence (AI) based technologies in medicine is progressing rapidly, but the majority of its real-world applications has not been implemented. The establishment of an accurate diagnosis with treatment has now transitioned into an artificial intelligence era, which has continued to provide an amplified understanding of liver cancer as a disease and helped to proceed better with the method of procurement. This article focuses on reviewing the AI in liver-associated diseases and surgical procedures, highlighting its development, use, and related counterparts. Materials and Methods: We searched for articles regarding AI in liver-related ailments and surgery, using the keywords (mentioned below) on PubMed, Google Scholar, Scopus, MEDLINE, and Cochrane Library. Choosing only the common studies suggested by these libraries, we segregated the matter based on disease. Finally, we compiled the essence of these articles under the various sub-headings. Results: After thorough review of articles, it was observed that there was a surge in the occurrence of liver-related surgeries, diagnoses, and treatments. Parallelly, advanced computer technologies governed by AI continue to prove their efficacy in the accurate screening, analysis, prediction, treatment, and recuperation of liver-related cases. Conclusions: The continual developments and high-order precision of AI is expanding its roots in all directions of applications. Despite being novel and lacking research, AI has shown its intrinsic worth for procedures in liver surgery while providing enhanced healing opportunities and personalized treatment for liver surgery patients.
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Affiliation(s)
- Anas Taha
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, 4123 Allschwil, Switzerland
- Correspondence:
| | - Vincent Ochs
- Roche Innovation Center Basel, Department of Pharma Research & Early Development, 4070 Basel, Switzerland;
| | - Leos N. Kayhan
- Department of Surgery, Canntonal Hospital Luzern, 6004 Luzern, Switzerland;
| | - Bassey Enodien
- Department of Surgery, Wetzikon Hospital, 8620 Wetzikon, Switzerland; (B.E.); (D.M.F.)
| | - Daniel M. Frey
- Department of Surgery, Wetzikon Hospital, 8620 Wetzikon, Switzerland; (B.E.); (D.M.F.)
| | | | - Stephanie Taha-Mehlitz
- Clarunis, University Centre for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital Basel, 4002 Basel, Switzerland;
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Ginghina O, Hudita A, Zamfir M, Spanu A, Mardare M, Bondoc I, Buburuzan L, Georgescu SE, Costache M, Negrei C, Nitipir C, Galateanu B. Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient's Stratification. Front Oncol 2022; 12:856575. [PMID: 35356214 PMCID: PMC8959149 DOI: 10.3389/fonc.2022.856575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 02/16/2022] [Indexed: 01/19/2023] Open
Abstract
Colorectal cancer (CRC) is the second most frequently diagnosed type of cancer and a major worldwide public health concern. Despite the global efforts in the development of modern therapeutic strategies, CRC prognosis is strongly correlated with the stage of the disease at diagnosis. Early detection of CRC has a huge impact in decreasing mortality while pre-lesion detection significantly reduces the incidence of the pathology. Even though the management of CRC patients is based on robust diagnostic methods such as serum tumor markers analysis, colonoscopy, histopathological analysis of tumor tissue, and imaging methods (computer tomography or magnetic resonance), these strategies still have many limitations and do not fully satisfy clinical needs due to their lack of sensitivity and/or specificity. Therefore, improvements of the current practice would substantially impact the management of CRC patients. In this view, liquid biopsy is a promising approach that could help clinicians screen for disease, stratify patients to the best treatment, and monitor treatment response and resistance mechanisms in the tumor in a regular and minimally invasive manner. Liquid biopsies allow the detection and analysis of different tumor-derived circulating markers such as cell-free nucleic acids (cfNA), circulating tumor cells (CTCs), and extracellular vesicles (EVs) in the bloodstream. The major advantage of this approach is its ability to trace and monitor the molecular profile of the patient's tumor and to predict personalized treatment in real-time. On the other hand, the prospective use of artificial intelligence (AI) in medicine holds great promise in oncology, for the diagnosis, treatment, and prognosis prediction of disease. AI has two main branches in the medical field: (i) a virtual branch that includes medical imaging, clinical assisted diagnosis, and treatment, as well as drug research, and (ii) a physical branch that includes surgical robots. This review summarizes findings relevant to liquid biopsy and AI in CRC for better management and stratification of CRC patients.
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Affiliation(s)
- Octav Ginghina
- Department II, University of Medicine and Pharmacy “Carol Davila” Bucharest, Bucharest, Romania
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | - Ariana Hudita
- Department of Biochemistry and Molecular Biology, University of Bucharest, Bucharest, Romania
| | - Marius Zamfir
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | - Andrada Spanu
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | - Mara Mardare
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | - Irina Bondoc
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | | | - Sergiu Emil Georgescu
- Department of Biochemistry and Molecular Biology, University of Bucharest, Bucharest, Romania
| | - Marieta Costache
- Department of Biochemistry and Molecular Biology, University of Bucharest, Bucharest, Romania
| | - Carolina Negrei
- Department of Toxicology, University of Medicine and Pharmacy “Carol Davila” Bucharest, Bucharest, Romania
| | - Cornelia Nitipir
- Department II, University of Medicine and Pharmacy “Carol Davila” Bucharest, Bucharest, Romania
- Department of Oncology, Elias University Emergency Hospital, Bucharest, Romania
| | - Bianca Galateanu
- Department of Biochemistry and Molecular Biology, University of Bucharest, Bucharest, Romania
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Duc VT, Chien PC, Huyen LDM, Chau TLM, Chanh NDT, Soan DTM, Huyen HC, Thanh HM, Hy LNG, Nam NH, Uyen MTT, Nhi LHH, Minh LHN. Deep Learning Model With Convolutional Neural Network for Detecting and Segmenting Hepatocellular Carcinoma in CT: A Preliminary Study. Cureus 2022; 14:e21347. [PMID: 35186603 PMCID: PMC8849436 DOI: 10.7759/cureus.21347] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/16/2022] [Indexed: 12/27/2022] Open
Abstract
Introduction Hepatocellular carcinoma (HCC) is one of the most common malignancies in the world. Early detection and accurate diagnosis of HCC play an important role in patient management. This study aimed to develop a convolutional neural network-based model to identify and segment HCC lesions utilizing dynamic contrast agent-enhanced computed tomography (CT). Methods This retrospective study used CT image sets of histopathology-confirmed hepatocellular carcinoma over three phases (arterial, venous, and delayed). The proposed convolutional neural network (CNN) segmentation method was based on the U-Net architecture and trained using the domain adaptation technique. The proposed method was evaluated using 115 liver masses of 110 patients (87 men and 23 women; mean age, 56.9 years ± 11.9 (SD); mean mass size, 6.0 cm ± 3.6). The sensitivity for identifying HCC of the model and Dice score for segmentation of liver masses between radiologists and the CNN model were calculated for the test set. Results The sensitivity for HCC identification of the model was 100%. The median Dice score for HCC segmenting between radiologists and the CNN model was 0.81 for the test set. Conclusion Deep learning with CNN had high performance in the identification and segmentation of HCC on dynamic CT.
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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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22
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Li LS, Guo XY, Sun K. Recent advances in blood-based and artificial intelligence-enhanced approaches for gastrointestinal cancer diagnosis. World J Gastroenterol 2021; 27:5666-5681. [PMID: 34629793 PMCID: PMC8473600 DOI: 10.3748/wjg.v27.i34.5666] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 05/14/2021] [Accepted: 08/03/2021] [Indexed: 02/06/2023] Open
Abstract
Gastrointestinal (GI) cancers are among the most common cancer types and leading causes of cancer-related deaths worldwide. There is a tremendous clinical need for effective early diagnosis for better healthcare of GI cancer patients. In this article, we provide a short overview of the recent advances in GI cancer diagnosis. In the first part, we discuss the applications of blood-based biomarkers, such as plasma circulating cell-free DNA, circulating tumor cells, extracellular vesicles, and circulating cell-free RNA, for cancer liquid biopsies. In the second part, we review the current trends of artificial intelligence (AI) for pathology image and tissue biopsy analysis for GI cancer, as well as deep learning-based approaches for purity assessment of tissue biopsies. We further provide our opinions on the future directions in blood-based and AI-enhanced approaches for GI cancer diagnosis, and we think that these fields will have more intensive integrations with clinical needs in the near future.
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Affiliation(s)
- Li-Shi Li
- School of Chemical Biology and Biotechnology, Shenzhen Graduate School, Peking University, Shenzhen 518055, Guangdong Province, China
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen 518132, Guangdong Province, China
| | - Xiang-Yu Guo
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen 518132, Guangdong Province, China
| | - Kun Sun
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen 518132, Guangdong Province, China
- BGI-Shenzhen, Shenzhen 518083, Guangdong Province, China
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Sabottke CF, Spieler BM, Moawad AW, Elsayes KM. Artificial Intelligence in Imaging of Chronic Liver Diseases: Current Update and Future Perspectives. Magn Reson Imaging Clin N Am 2021; 29:451-463. [PMID: 34243929 DOI: 10.1016/j.mric.2021.05.011] [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] [Indexed: 11/17/2022]
Abstract
Here we review artificial intelligence (AI) models which aim to assess various aspects of chronic liver disease. Despite the clinical importance of hepatocellular carcinoma in the setting of chronic liver disease, we focus this review on AI models which are not lesion-specific and instead review models developed for liver parenchyma segmentation, evaluation of portal circulation, assessment of hepatic fibrosis, and identification of hepatic steatosis. Optimization of these models offers the opportunity to potentially reduce the need for invasive procedures such as catheterization to measure hepatic venous pressure gradient or biopsy to assess fibrosis and steatosis. We compare the performance of these AI models amongst themselves as well as to radiomics approaches and alternate modality assessments. We conclude that these models show promising performance and merit larger-scale evaluation. We review artificial intelligence models that aim to assess various aspects of chronic liver disease aside from hepatocellular carcinoma. We focus this review on models for liver parenchyma segmentation, evaluation of portal circulation, assessment of hepatic fibrosis, and identification of hepatic steatosis. We conclude that these models show promising performance and merit a larger scale evaluation.
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Affiliation(s)
- Carl F Sabottke
- Department of Medical Imaging, University of Arizona College of Medicine, 1501 N. Campbell, P.O. Box 245067, Tucson, AZ 85724-5067, USA.
| | - Bradley M Spieler
- Department of Radiology, Louisiana State University Health Sciences Center, 1542 Tulane Avenue, Rm 343, New Orleans, LA 70112, USA
| | - Ahmed W Moawad
- Department of Imaging Physics, The University of Texas, MD Anderson Cancer Center, Unit 1472, P.O. Box 301402, Houston, TX 77230-1402, USA
| | - Khaled M Elsayes
- Department of Abdominal Imaging, The University of Texas, MD Anderson Cancer Center, 1400 Pressler St, Houston, TX 77030, USA
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Spieler B, Sabottke C, Moawad AW, Gabr AM, Bashir MR, Do RKG, Yaghmai V, Rozenberg R, Gerena M, Yacoub J, Elsayes KM. Artificial intelligence in assessment of hepatocellular carcinoma treatment response. Abdom Radiol (NY) 2021; 46:3660-3671. [PMID: 33786653 DOI: 10.1007/s00261-021-03056-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 03/03/2021] [Accepted: 03/09/2021] [Indexed: 02/08/2023]
Abstract
Artificial Intelligence (AI) continues to shape the practice of radiology, with imaging of hepatocellular carcinoma (HCC) being of no exception. This article prepared by members of the LI-RADS Treatment Response (TR LI-RADS) work group and associates, presents recent trends in the utility of AI applications for the volumetric evaluation and assessment of HCC treatment response. Various topics including radiomics, prognostic imaging findings, and locoregional therapy (LRT) specific issues will be discussed in the framework of HCC treatment response classification systems with focus on the Liver Reporting and Data System treatment response algorithm (LI-RADS TRA).
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25
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Blidisel A, Marcovici I, Coricovac D, Hut F, Dehelean CA, Cretu OM. Experimental Models of Hepatocellular Carcinoma-A Preclinical Perspective. Cancers (Basel) 2021; 13:3651. [PMID: 34359553 PMCID: PMC8344976 DOI: 10.3390/cancers13153651] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 07/16/2021] [Accepted: 07/17/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC), the most frequent form of primary liver carcinoma, is a heterogenous and complex tumor type with increased incidence, poor prognosis, and high mortality. The actual therapeutic arsenal is narrow and poorly effective, rendering this disease a global health concern. Although considerable progress has been made in terms of understanding the pathogenesis, molecular mechanisms, genetics, and therapeutical approaches, several facets of human HCC remain undiscovered. A valuable and prompt approach to acquire further knowledge about the unrevealed aspects of HCC and novel therapeutic candidates is represented by the application of experimental models. Experimental models (in vivo and in vitro 2D and 3D models) are considered reliable tools to gather data for clinical usability. This review offers an overview of the currently available preclinical models frequently applied for the study of hepatocellular carcinoma in terms of initiation, development, and progression, as well as for the discovery of efficient treatments, highlighting the advantages and the limitations of each model. Furthermore, we also focus on the role played by computational studies (in silico models and artificial intelligence-based prediction models) as promising novel tools in liver cancer research.
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Affiliation(s)
- Alexandru Blidisel
- Faculty of Medicine, “Victor Babeș” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania; (A.B.); (F.H.); (O.M.C.)
| | - Iasmina Marcovici
- Faculty of Pharmacy, “Victor Babeș” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania;
- Research Center for Pharmaco-Toxicological Evaluations, Faculty of Pharmacy, “Victor Babes” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania
| | - Dorina Coricovac
- Faculty of Pharmacy, “Victor Babeș” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania;
- Research Center for Pharmaco-Toxicological Evaluations, Faculty of Pharmacy, “Victor Babes” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania
| | - Florin Hut
- Faculty of Medicine, “Victor Babeș” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania; (A.B.); (F.H.); (O.M.C.)
| | - Cristina Adriana Dehelean
- Faculty of Pharmacy, “Victor Babeș” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania;
- Research Center for Pharmaco-Toxicological Evaluations, Faculty of Pharmacy, “Victor Babes” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania
| | - Octavian Marius Cretu
- Faculty of Medicine, “Victor Babeș” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania; (A.B.); (F.H.); (O.M.C.)
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Castaldo A, De Lucia DR, Pontillo G, Gatti M, Cocozza S, Ugga L, Cuocolo R. State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma. Diagnostics (Basel) 2021; 11:1194. [PMID: 34209197 PMCID: PMC8307071 DOI: 10.3390/diagnostics11071194] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/24/2021] [Accepted: 06/24/2021] [Indexed: 12/12/2022] Open
Abstract
The most common liver malignancy is hepatocellular carcinoma (HCC), which is also associated with high mortality. Often HCC develops in a chronic liver disease setting, and early diagnosis as well as accurate screening of high-risk patients is crucial for appropriate and effective management of these patients. While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy. Radiomics and machine learning (ML) offer new tools to address these issues and may lead to scientific breakthroughs with the potential to impact clinical practice and improve patient outcomes. In this review, we will present an overview of these technologies in the setting of HCC imaging across different modalities and a range of applications. These include lesion segmentation, diagnosis, prognostic modeling and prediction of treatment response. Finally, limitations preventing clinical application of radiomics and ML at the present time are discussed, together with necessary future developments to bring the field forward and outside of a purely academic endeavor.
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Affiliation(s)
- Anna Castaldo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Davide Raffaele De Lucia
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Giuseppe Pontillo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Marco Gatti
- Radiology Unit, Department of Surgical Sciences, University of Turin, 10124 Turin, Italy;
| | - Sirio Cocozza
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Renato Cuocolo
- Department of Clinical Medicine and Surgery, University of Naples “Federico II”, 80131 Naples, Italy
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