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Mostafa G, Mahmoud H, Abd El-Hafeez T, E ElAraby M. The power of deep learning in simplifying feature selection for hepatocellular carcinoma: a review. BMC Med Inform Decis Mak 2024; 24:287. [PMID: 39367397 PMCID: PMC11452940 DOI: 10.1186/s12911-024-02682-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 09/13/2024] [Indexed: 10/06/2024] Open
Abstract
BACKGROUND Hepatocellular Carcinoma (HCC) is a highly aggressive, prevalent, and deadly type of liver cancer. With the advent of deep learning techniques, significant advancements have been made in simplifying and optimizing the feature selection process. OBJECTIVE Our scoping review presents an overview of the various deep learning models and algorithms utilized to address feature selection for HCC. The paper highlights the strengths and limitations of each approach, along with their potential applications in clinical practice. Additionally, it discusses the benefits of using deep learning to identify relevant features and their impact on the accuracy and efficiency of diagnosis, prognosis, and treatment of HCC. DESIGN The review encompasses a comprehensive analysis of the research conducted in the past few years, focusing on the methodologies, datasets, and evaluation metrics adopted by different studies. The paper aims to identify the key trends and advancements in the field, shedding light on the promising areas for future research and development. RESULTS The findings of this review indicate that deep learning techniques have shown promising results in simplifying feature selection for HCC. By leveraging large-scale datasets and advanced neural network architectures, these methods have demonstrated improved accuracy and robustness in identifying predictive features. CONCLUSIONS We analyze published studies to reveal the state-of-the-art HCC prediction and showcase how deep learning can boost accuracy and decrease false positives. But we also acknowledge the challenges that remain in translating this potential into clinical reality.
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Affiliation(s)
- Ghada Mostafa
- Computer Science Department, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt.
- Computer Science Unit, Deraya University, EL-Minia, Egypt.
| | - Hamdi Mahmoud
- Computer Science Department, Faculty of Computers and Artificial Intelligence, Beni-Suef National University, Beni-Suef, Egypt.
| | - Tarek Abd El-Hafeez
- Department of Computer Science, Faculty of Science, Minia University, EL-Minia, Egypt.
- Computer Science Unit, Deraya University, EL-Minia, Egypt.
| | - Mohamed E ElAraby
- Computer Science Department, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt.
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Xia Y, Zhou J, Xun X, Zhang J, Wei T, Gao R, Reddy B, Liu C, Kim G, Yu Z. CT-based multimodal deep learning for non-invasive overall survival prediction in advanced hepatocellular carcinoma patients treated with immunotherapy. Insights Imaging 2024; 15:214. [PMID: 39186192 PMCID: PMC11347550 DOI: 10.1186/s13244-024-01784-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Accepted: 07/23/2024] [Indexed: 08/27/2024] Open
Abstract
OBJECTIVES To develop a deep learning model combining CT scans and clinical information to predict overall survival in advanced hepatocellular carcinoma (HCC). METHODS This retrospective study included immunotherapy-treated advanced HCC patients from 52 multi-national in-house centers between 2018 and 2022. A multi-modal prognostic model using baseline and the first follow-up CT images and 7 clinical variables was proposed. A convolutional-recurrent neural network (CRNN) was developed to extract spatial-temporal information from automatically selected representative 2D CT slices to provide a radiological score, then fused with a Cox-based clinical score to provide the survival risk. The model's effectiveness was assessed using a time-dependent area under the receiver operating curve (AUC), and risk group stratification using the log-rank test. Prognostic performances of multi-modal inputs were compared to models of missing modality, and the size-based RECIST criteria. RESULTS Two-hundred seven patients (mean age, 61 years ± 12 [SD], 180 men) were included. The multi-modal CRNN model reached the AUC of 0.777 and 0.704 of 1-year overall survival predictions in the validation and test sets. The model achieved significant risk stratification in validation (hazard ratio [HR] = 3.330, p = 0.008), and test sets (HR = 2.024, p = 0.047) based on the median risk score of the training set. Models with missing modalities (the single-modal imaging-based model and the model incorporating only baseline scans) can still achieve favorable risk stratification performance (all p < 0.05, except for one, p = 0.053). Moreover, results proved the superiority of the deep learning-based model to the RECIST criteria. CONCLUSION Deep learning analysis of CT scans and clinical data can offer significant prognostic insights for patients with advanced HCC. CRITICAL RELEVANCE STATEMENT The established model can help monitor patients' disease statuses and identify those with poor prognosis at the time of first follow-up, helping clinicians make informed treatment decisions, as well as early and timely interventions. KEY POINTS An AI-based prognostic model was developed for advanced HCC using multi-national patients. The model extracts spatial-temporal information from CT scans and integrates it with clinical variables to prognosticate. The model demonstrated superior prognostic ability compared to the conventional size-based RECIST method.
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Affiliation(s)
- Yujia Xia
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Jie Zhou
- SJTU-Yale Joint Center for Biostatistics and Data Science, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- Department of Statistics, School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaolei Xun
- Statistics in Global Statistics and Data Science, Beigene, Shanghai, China
| | | | - Ting Wei
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Ruitian Gao
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | | | - Chao Liu
- PiHealth USA, Cambridge, MA, USA
| | | | - Zhangsheng Yu
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
- SJTU-Yale Joint Center for Biostatistics and Data Science, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
- Department of Statistics, School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China.
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Heo S, Park HJ, Lee SS. Prognostication of Hepatocellular Carcinoma Using Artificial Intelligence. Korean J Radiol 2024; 25:550-558. [PMID: 38807336 PMCID: PMC11136947 DOI: 10.3348/kjr.2024.0070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/13/2024] [Accepted: 03/31/2024] [Indexed: 05/30/2024] Open
Abstract
Hepatocellular carcinoma (HCC) is a biologically heterogeneous tumor characterized by varying degrees of aggressiveness. The current treatment strategy for HCC is predominantly determined by the overall tumor burden, and does not address the diverse prognoses of patients with HCC owing to its heterogeneity. Therefore, the prognostication of HCC using imaging data is crucial for optimizing patient management. Although some radiologic features have been demonstrated to be indicative of the biologic behavior of HCC, traditional radiologic methods for HCC prognostication are based on visually-assessed prognostic findings, and are limited by subjectivity and inter-observer variability. Consequently, artificial intelligence has emerged as a promising method for image-based prognostication of HCC. Unlike traditional radiologic image analysis, artificial intelligence based on radiomics or deep learning utilizes numerous image-derived quantitative features, potentially offering an objective, detailed, and comprehensive analysis of the tumor phenotypes. Artificial intelligence, particularly radiomics has displayed potential in a variety of applications, including the prediction of microvascular invasion, recurrence risk after locoregional treatment, and response to systemic therapy. This review highlights the potential value of artificial intelligence in the prognostication of HCC as well as its limitations and future prospects.
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Affiliation(s)
- Subin Heo
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hyo Jung Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Seung Soo Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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Haghshomar M, Rodrigues D, Kalyan A, Velichko Y, Borhani A. Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies. Front Oncol 2024; 14:1362737. [PMID: 38779098 PMCID: PMC11109422 DOI: 10.3389/fonc.2024.1362737] [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: 12/28/2023] [Accepted: 04/12/2024] [Indexed: 05/25/2024] Open
Abstract
Liver tumors, whether primary or metastatic, have emerged as a growing concern with substantial global health implications. Timely identification and characterization of liver tumors are pivotal factors in order to provide optimum treatment. Imaging is a crucial part of the detection of liver tumors; however, conventional imaging has shortcomings in the proper characterization of these tumors which leads to the need for tissue biopsy. Artificial intelligence (AI) and radiomics have recently emerged as investigational opportunities with the potential to enhance the detection and characterization of liver lesions. These advancements offer opportunities for better diagnostic accuracy, prognostication, and thereby improving patient care. In particular, these techniques have the potential to predict the histopathology, genotype, and immunophenotype of tumors based on imaging data, hence providing guidance for personalized treatment of such tumors. In this review, we outline the progression and potential of AI in the field of liver oncology imaging, specifically emphasizing manual radiomic techniques and deep learning-based representations. We discuss how these tools can aid in clinical decision-making challenges. These challenges encompass a broad range of tasks, from prognosticating patient outcomes, differentiating benign treatment-related factors and actual disease progression, recognizing uncommon response patterns, and even predicting the genetic and molecular characteristics of the tumors. Lastly, we discuss the pitfalls, technical limitations and future direction of these AI-based techniques.
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Affiliation(s)
| | | | | | | | - Amir Borhani
- Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
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Mendiratta-Lala M, Aslam A, Bai HX, Chapiro J, De Baere T, Miyayama S, Chernyak V, Matsui O, Vilgrain V, Fidelman N. Ethiodized oil as an imaging biomarker after conventional transarterial chemoembolization. Eur Radiol 2024; 34:3284-3297. [PMID: 37930412 PMCID: PMC11126446 DOI: 10.1007/s00330-023-10326-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 08/10/2023] [Accepted: 08/20/2023] [Indexed: 11/07/2023]
Abstract
Conventional transarterial chemoembolization (cTACE) utilizing ethiodized oil as a chemotherapy carrier has become a standard treatment for intermediate-stage hepatocellular carcinoma (HCC) and has been adopted as a bridging and downstaging therapy for liver transplantation. Water-in-oil emulsion made up of ethiodized oil and chemotherapy solution is retained in tumor vasculature resulting in high tissue drug concentration and low systemic chemotherapy doses. The density and distribution pattern of ethiodized oil within the tumor on post-treatment imaging are predictive of the extent of tumor necrosis and duration of response to treatment. This review describes the multiple roles of ethiodized oil, particularly in its role as a biomarker of tumor response to cTACE. CLINICAL RELEVANCE: With the increasing complexity of locoregional therapy options, including the use of combination therapies, treatment response assessment has become challenging; Ethiodized oil deposition patterns can serve as an imaging biomarker for the prediction of treatment response, and perhaps predict post-treatment prognosis. KEY POINTS: • Treatment response assessment after locoregional therapy to hepatocellular carcinoma is fraught with multiple challenges given the varied post-treatment imaging appearance. • Ethiodized oil is unique in that its' radiopacity can serve as an imaging biomarker to help predict treatment response. • The pattern of deposition of ethiodozed oil has served as a mechanism to detect portions of tumor that are undertreated and can serve as an adjunct to enhancement in order to improve management in patients treated with intraarterial embolization with ethiodized oil.
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Affiliation(s)
- Mishal Mendiratta-Lala
- Department of Radiology, University of Michigan Medicine, 1500 E Medical Center Dr., UH B2 A209R, Ann Arbor, MI, 48109, USA.
| | - Anum Aslam
- Department of Radiology, University of Michigan Medicine, 1500 E Medical Center Dr., UH B2 A209R, Ann Arbor, MI, 48109, USA
| | - Harrison X Bai
- Department of Radiology and Radiological Sciences, John Hopkins University, 601 N Caroline St, Baltimore, MD, 21287, USA
| | - Julius Chapiro
- Department of Radiology & Biomedical Imaging Yale University School of Medicine, 300 Cedar Street - TAC N312A, New Haven, CT, 06520, USA
| | - Thiery De Baere
- Gustave Roussy University of Paris Saclay, Villejuif, France
- Interventional Radiology, Gustave Roussy Cancer Center, Villejuif, France
- Département d'Anesthésie, Chirurgie et Imagerie Interventionnelle, Gustave Roussy Cancer Center, Villejuif, France
| | - Shiro Miyayama
- Department of Diagnostic Radiology, Fukui-ken Saiseikai Hospital 7-1, Funabashi, Wadanaka-cho, Fukui, 918-8503, Japan
| | - Victoria Chernyak
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Osamu Matsui
- Department of Radiology, Kananzawa University, Japan, 2-21-9 Asahi-machi, Kanazawa, 920-0941, Japan
| | - Valerie Vilgrain
- Department of Radiology, Hospital Beaujon APHP.Nord, Université Paris Cité, CRI INSERM 1149, Paris, France
| | - Nicholas Fidelman
- University of California San Francisco, 505 Parnassus Avenue, Room M-361, San Francisco, CA, 94143, USA
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Nakao Y, Nishihara T, Sasaki R, Fukushima M, Miuma S, Miyaaki H, Akazawa Y, Nakao K. Investigation of deep learning model for predicting immune checkpoint inhibitor treatment efficacy on contrast-enhanced computed tomography images of hepatocellular carcinoma. Sci Rep 2024; 14:6576. [PMID: 38503827 PMCID: PMC10951210 DOI: 10.1038/s41598-024-57078-y] [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: 08/18/2023] [Accepted: 03/14/2024] [Indexed: 03/21/2024] Open
Abstract
Although the use of immune checkpoint inhibitors (ICIs)-targeted agents for unresectable hepatocellular carcinoma (HCC) is promising, individual response variability exists. Therefore, we developed an artificial intelligence (AI)-based model to predict treatment efficacy using pre-ICIs contrast-enhanced computed tomography (CT) imaging characteristics. We evaluated the efficacy of atezolizumab and bevacizumab in 43 patients at the Nagasaki University Hospital from 2020 to 2022 using the modified Response Evaluation Criteria in Solid Tumors. A total of 197 Progressive Disease (PD), 271 Partial Response (PR), and 342 Stable Disease (SD) contrast CT images of HCC were used for training. We used ResNet-18 as the Convolutional Neural Network (CNN) model and YOLOv5, YOLOv7, YOLOv8 as the You Only Look Once (YOLO) model with precision-recall curves and class activation maps (CAMs) for diagnostic performance evaluation and model interpretation, respectively. The 3D t-distributed Stochastic Neighbor Embedding was used for image feature analysis. The YOLOv7 model demonstrated Precision 53.7%, Recall 100%, F1 score 69.8%, mAP@0.5 99.5% for PD, providing accurate and clinically versatile predictions by identifying decisive points. The ResNet-18 model had Precision 100% and Recall 100% for PD. However, the CAMs sites did not align with the tumors, suggesting the CNN model is not predicting that a given CT slice is PD, PR, or SD, but that it accurately predicts Individual Patient's CT slices. Preparing substantial training data for tumor drug effect prediction models is challenging compared to general tumor diagnosis models; hence, large-scale validation using an efficient YOLO model is warranted.
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Affiliation(s)
- Yasuhiko Nakao
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki City, Nagasaki, Japan.
| | - Takahito Nishihara
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki City, Nagasaki, Japan
- Department of Gastroenterology and Hepatology, Isahaya General Hospital, 24-1 Eishohigashimachi, Isahaya, Nagasaki, Japan
| | - Ryu Sasaki
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki City, Nagasaki, Japan
| | - Masanori Fukushima
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki City, Nagasaki, Japan
| | - Satoshi Miuma
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki City, Nagasaki, Japan
| | - Hisamitsu Miyaaki
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki City, Nagasaki, Japan
| | - Yuko Akazawa
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki City, Nagasaki, Japan
- Department of Histology and Cell Biology, Nagasaki University Graduate School of Biomedical Sciences, 1-12-4 Sakamoto, Nagasaki City, Nagasaki, Japan
| | - Kazuhiko Nakao
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki City, Nagasaki, Japan
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Lee H, Chang W, Kim HY, Sung P, Cho J, Lee YJ, Kim YH. Improving radiomics reproducibility using deep learning-based image conversion of CT reconstruction algorithms in hepatocellular carcinoma patients. Eur Radiol 2024; 34:2036-2047. [PMID: 37656175 DOI: 10.1007/s00330-023-10135-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 07/06/2023] [Accepted: 07/14/2023] [Indexed: 09/02/2023]
Abstract
OBJECTIVES CT reconstruction algorithms affect radiomics reproducibility. In this study, we evaluate the effect of deep learning-based image conversion on CT reconstruction algorithms. METHODS This study included 78 hepatocellular carcinoma (HCC) patients who underwent four-phase liver CTs comprising non-contrast, late arterial (LAP), portal venous (PVP), and delayed phase (DP), reconstructed using both filtered back projection (FBP) and advanced modeled iterative reconstruction (ADMIRE). PVP images were used to train a convolutional neural network (CNN) model to convert images from FBP to ADMIRE and vice versa. LAP, PVP, and DP images were used for validation and testing. Radiomic features were extracted for each patient with a semi-automatic segmentation tool. We used concordance correlation coefficients (CCCs) to evaluate the radiomics reproducibility for original FBP (oFBP) vs. original ADMIRE (oADMIRE), oFBP vs. converted FBP (cFBP), and oADMIRE vs. converted ADMIRE (cADMIRE). RESULTS In the test group including 30 patients, the CCC and proportion of reproducible features (CCC ≥ 0.85) for oFBP vs. oADMIRE were 0.65 and 32.9% (524/1595) for LAP, 0.65 and 35.9% (573/1595) for PVP, and 0.69 and 43.8% (699/1595) for DP. For oFBP vs. cFBP, the values increased to 0.92 and 83.9% (1339/1595) for LAP, 0.89 and 71.0% (1133/1595) for PVP, and 0.90 and 79.7% (1271/1595) for DP. Similarly, for oADMIRE vs. cADMIRE, the values increased to 0.87 and 68.1% (1086/1595) for LAP, 0.91 and 82.1% (1309/1595) for PVP, and 0.89 and 76.2% (1216/1595) for DP. CONCLUSIONS CNN-based image conversion between CT reconstruction algorithms improved the radiomics reproducibility of HCCs. CLINICAL RELEVANCE STATEMENT This study demonstrates that using a CNN-based image conversion technique significantly improves the reproducibility of radiomic features in HCCs, highlighting its potential for enhancing radiomics research in HCC patients. KEY POINTS Radiomics reproducibility of HCC was improved via CNN-based image conversion between two different CT reconstruction algorithms. This is the first clinical study to demonstrate improvements across a range of radiomic features in HCC patients. This study promotes the reproducibility and generalizability of different CT reconstruction algorithms in radiomics research.
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Affiliation(s)
- Heejin Lee
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Won Chang
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea.
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Hae Young Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Pamela Sung
- Department of Radiology, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, Republic of Korea
| | - Jungheum Cho
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Yoon Jin Lee
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Young Hoon Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
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Schön F, Kieslich A, Nebelung H, Riediger C, Hoffmann RT, Zwanenburg A, Löck S, Kühn JP. Comparative analysis of radiomics and deep-learning algorithms for survival prediction in hepatocellular carcinoma. Sci Rep 2024; 14:590. [PMID: 38182664 PMCID: PMC10770355 DOI: 10.1038/s41598-023-50451-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 12/20/2023] [Indexed: 01/07/2024] Open
Abstract
To examine the comparative robustness of computed tomography (CT)-based conventional radiomics and deep-learning convolutional neural networks (CNN) to predict overall survival (OS) in HCC patients. Retrospectively, 114 HCC patients with pretherapeutic CT of the liver were randomized into a development (n = 85) and a validation (n = 29) cohort, including patients of all tumor stages and several applied therapies. In addition to clinical parameters, image annotations of the liver parenchyma and of tumor findings on CT were available. Cox-regression based on radiomics features and CNN models were established and combined with clinical parameters to predict OS. Model performance was assessed using the concordance index (C-index). Log-rank tests were used to test model-based patient stratification into high/low-risk groups. The clinical Cox-regression model achieved the best validation performance for OS (C-index [95% confidence interval (CI)] 0.74 [0.57-0.86]) with a significant difference between the risk groups (p = 0.03). In image analysis, the CNN models (lowest C-index [CI] 0.63 [0.39-0.83]; highest C-index [CI] 0.71 [0.49-0.88]) were superior to the corresponding radiomics models (lowest C-index [CI] 0.51 [0.30-0.73]; highest C-index [CI] 0.66 [0.48-0.79]). A significant risk stratification was not possible (p > 0.05). Under clinical conditions, CNN-algorithms demonstrate superior prognostic potential to predict OS in HCC patients compared to conventional radiomics approaches and could therefore provide important information in the clinical setting, especially when clinical data is limited.
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Affiliation(s)
- Felix Schön
- Institute and Polyclinic for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany.
| | - Aaron Kieslich
- OncoRay‑National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.
| | - Heiner Nebelung
- Institute and Polyclinic for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Carina Riediger
- Department of Visceral, Thoracic and Vascular Surgery, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Ralf-Thorsten Hoffmann
- Institute and Polyclinic for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Alex Zwanenburg
- OncoRay‑National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
- National Center for Tumor Diseases (NCT/UCC) Dresden, Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Steffen Löck
- OncoRay‑National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Jens-Peter Kühn
- Institute and Polyclinic for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
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Ahn JC, Shah VH. Artificial intelligence in gastroenterology and hepatology. ARTIFICIAL INTELLIGENCE IN CLINICAL PRACTICE 2024:443-464. [DOI: 10.1016/b978-0-443-15688-5.00016-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
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10
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Radiya K, Joakimsen HL, Mikalsen KØ, Aahlin EK, Lindsetmo RO, Mortensen KE. Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review. Eur Radiol 2023; 33:6689-6717. [PMID: 37171491 PMCID: PMC10511359 DOI: 10.1007/s00330-023-09609-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 02/02/2023] [Accepted: 02/06/2023] [Indexed: 05/13/2023]
Abstract
OBJECTIVES Machine learning (ML) for medical imaging is emerging for several organs and image modalities. Our objectives were to provide clinicians with an overview of this field by answering the following questions: (1) How is ML applied in liver computed tomography (CT) imaging? (2) How well do ML systems perform in liver CT imaging? (3) What are the clinical applications of ML in liver CT imaging? METHODS A systematic review was carried out according to the guidelines from the PRISMA-P statement. The search string focused on studies containing content relating to artificial intelligence, liver, and computed tomography. RESULTS One hundred ninety-one studies were included in the study. ML was applied to CT liver imaging by image analysis without clinicians' intervention in majority of studies while in newer studies the fusion of ML method with clinical intervention have been identified. Several were documented to perform very accurately on reliable but small data. Most models identified were deep learning-based, mainly using convolutional neural networks. Potentially many clinical applications of ML to CT liver imaging have been identified through our review including liver and its lesion segmentation and classification, segmentation of vascular structure inside the liver, fibrosis and cirrhosis staging, metastasis prediction, and evaluation of chemotherapy. CONCLUSION Several studies attempted to provide transparent result of the model. To make the model convenient for a clinical application, prospective clinical validation studies are in urgent call. Computer scientists and engineers should seek to cooperate with health professionals to ensure this. KEY POINTS • ML shows great potential for CT liver image tasks such as pixel-wise segmentation and classification of liver and liver lesions, fibrosis staging, metastasis prediction, and retrieval of relevant liver lesions from similar cases of other patients. • Despite presenting the result is not standardized, many studies have attempted to provide transparent results to interpret the machine learning method performance in the literature. • Prospective studies are in urgent call for clinical validation of ML method, preferably carried out by cooperation between clinicians and computer scientists.
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Affiliation(s)
- Keyur Radiya
- Department of Gastroenterological Surgery at University Hospital of North Norway (UNN), Tromso, Norway.
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway.
| | - Henrik Lykke Joakimsen
- Institute of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
- Centre for Clinical Artificial Intelligence (SPKI), University Hospital of North Norway, Tromso, Norway
| | - Karl Øyvind Mikalsen
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
- Centre for Clinical Artificial Intelligence (SPKI), University Hospital of North Norway, Tromso, Norway
- UiT Machine Learning Group, Department of Physics and Technology, UiT the Arctic University of Norway, Tromso, Norway
| | - Eirik Kjus Aahlin
- Department of Gastroenterological Surgery at University Hospital of North Norway (UNN), Tromso, Norway
| | - Rolv-Ole Lindsetmo
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
- Head Clinic of Surgery, Oncology and Women Health, University Hospital of North Norway, Tromso, Norway
| | - Kim Erlend Mortensen
- Department of Gastroenterological Surgery at University Hospital of North Norway (UNN), Tromso, Norway
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
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11
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Yan M, Zhang X, Zhang B, Geng Z, Xie C, Yang W, Zhang S, Qi Z, Lin T, Ke Q, Li X, Wang S, Quan X. Deep learning nomogram based on Gd-EOB-DTPA MRI for predicting early recurrence in hepatocellular carcinoma after hepatectomy. Eur Radiol 2023; 33:4949-4961. [PMID: 36786905 PMCID: PMC10289921 DOI: 10.1007/s00330-023-09419-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 12/26/2022] [Accepted: 01/01/2023] [Indexed: 02/15/2023]
Abstract
OBJECTIVES The accurate prediction of post-hepatectomy early recurrence in patients with hepatocellular carcinoma (HCC) is crucial for decision-making regarding postoperative adjuvant treatment and monitoring. We aimed to explore the feasibility of deep learning (DL) features derived from gadoxetate disodium (Gd-EOB-DTPA) MRI, qualitative features, and clinical variables for predicting early recurrence. METHODS In this bicentric study, 285 patients with HCC who underwent Gd-EOB-DTPA MRI before resection were divided into training (n = 195) and validation (n = 90) sets. DL features were extracted from contrast-enhanced MRI images using VGGNet-19. Three feature selection methods and five classification methods were combined for DL signature construction. Subsequently, an mp-MR DL signature fused with multiphase DL signatures of contrast-enhanced images was constructed. Univariate and multivariate logistic regression analyses were used to identify early recurrence risk factors including mp-MR DL signature, microvascular invasion (MVI), and tumor number. A DL nomogram was built by incorporating deep features and significant clinical variables to achieve early recurrence prediction. RESULTS MVI (p = 0.039), tumor number (p = 0.001), and mp-MR DL signature (p < 0.001) were independent risk factors for early recurrence. The DL nomogram outperformed the clinical nomogram in the training set (AUC: 0.949 vs. 0.751; p < 0.001) and validation set (AUC: 0.909 vs. 0.715; p = 0.002). Excellent DL nomogram calibration was achieved in both training and validation sets. Decision curve analysis confirmed the clinical usefulness of DL nomogram. CONCLUSION The proposed DL nomogram was superior to the clinical nomogram in predicting early recurrence for HCC patients after hepatectomy. KEY POINTS • Deep learning signature based on Gd-EOB-DTPA MRI was the predominant independent predictor of early recurrence for hepatocellular carcinoma (HCC) after hepatectomy. • Deep learning nomogram based on clinical factors and Gd-EOB-DTPA MRI features is promising for predicting early recurrence of HCC. • Deep learning nomogram outperformed the conventional clinical nomogram in predicting early recurrence.
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Affiliation(s)
- Meng Yan
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China
| | - Xiao Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Artificial Intelligence and Clinical Innovation Research, Guangzhou, 510000, Guangdong, People's Republic of China
| | - Bin Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China
| | - Zhijun Geng
- Department of Medical Imaging, Sun Yat-Sen University Cancer Center, No. 651, Dongfeng East Road, Yuexiu District, Guangzhou, 510060, People's Republic of China
| | - Chuanmiao Xie
- Department of Medical Imaging, Sun Yat-Sen University Cancer Center, No. 651, Dongfeng East Road, Yuexiu District, Guangzhou, 510060, People's Republic of China
| | - Wei Yang
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, No. 1023, Shatai Road, Baiyun District, Guangzhou, 510515, Guangdong, People's Republic of China
| | - Shuixing Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China
| | - Zhendong Qi
- Department of Radiology, Zhujiang Hospital, Southern Medical University, No. 253, Industrial Road, Haizhu District, Guangzhou, 510282, People's Republic of China
| | - Ting Lin
- Department of Radiology, Zhujiang Hospital, Southern Medical University, No. 253, Industrial Road, Haizhu District, Guangzhou, 510282, People's Republic of China
| | - Qiying Ke
- Medical Imaging Center, the First Affiliated Hospital of Guangzhou University of Chinese Medicine, No. 16, Airport Road, Baiyun District, Guangzhou, 510405, Guangdong, People's Republic of China
| | - Xinming Li
- Department of Radiology, Zhujiang Hospital, Southern Medical University, No. 253, Industrial Road, Haizhu District, Guangzhou, 510282, People's Republic of China.
| | - Shutong Wang
- Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-Sen University, No. 58, Zhong Shan Road 2, Yuexiu District, Guangzhou, 510080, Guangdong, People's Republic of China.
| | - Xianyue Quan
- Department of Radiology, Zhujiang Hospital, Southern Medical University, No. 253, Industrial Road, Haizhu District, Guangzhou, 510282, People's Republic of China.
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12
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Nakaura T, Kobayashi N, Yoshida N, Shiraishi K, Uetani H, Nagayama Y, Kidoh M, Hirai T. Update on the Use of Artificial Intelligence in Hepatobiliary MR Imaging. Magn Reson Med Sci 2023; 22:147-156. [PMID: 36697024 PMCID: PMC10086394 DOI: 10.2463/mrms.rev.2022-0102] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 11/08/2022] [Indexed: 01/26/2023] Open
Abstract
The application of machine learning (ML) and deep learning (DL) in radiology has expanded exponentially. In recent years, an extremely large number of studies have reported about the hepatobiliary domain. Its applications range from differential diagnosis to the diagnosis of tumor invasion and prediction of treatment response and prognosis. Moreover, it has been utilized to improve the image quality of DL reconstruction. However, most clinicians are not familiar with ML and DL, and previous studies about these concepts are relatively challenging to understand. In this review article, we aimed to explain the concepts behind ML and DL and to summarize recent achievements in their use in the hepatobiliary region.
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Affiliation(s)
- Takeshi Nakaura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Kumamoto, Japan
| | - Naoki Kobayashi
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Kumamoto, Japan
| | - Naofumi Yoshida
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Kumamoto, Japan
| | - Kaori Shiraishi
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Kumamoto, Japan
| | - Hiroyuki Uetani
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Kumamoto, Japan
| | - Yasunori Nagayama
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Kumamoto, Japan
| | - Masafumi Kidoh
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Kumamoto, Japan
| | - Toshinori Hirai
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Kumamoto, Japan
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13
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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: 6] [Impact Index Per Article: 2.0] [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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14
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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: 119] [Impact Index Per Article: 39.7] [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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15
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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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16
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Li Y, Xu Z, An C, Chen H, Li X. Multi-Task Deep Learning Approach for Simultaneous Objective Response Prediction and Tumor Segmentation in HCC Patients with Transarterial Chemoembolization. J Pers Med 2022; 12:jpm12020248. [PMID: 35207736 PMCID: PMC8875107 DOI: 10.3390/jpm12020248] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 02/01/2022] [Accepted: 02/08/2022] [Indexed: 12/24/2022] Open
Abstract
This study aimed to develop a deep learning-based model to simultaneously perform the objective response (OR) and tumor segmentation for hepatocellular carcinoma (HCC) patients who underwent transarterial chemoembolization (TACE) treatment. A total of 248 patients from two hospitals were retrospectively included and divided into the training, internal validation, and external testing cohort. A network consisting of an encoder pathway, a prediction pathway, and a segmentation pathway was developed, and named multi-DL (multi-task deep learning), using contrast-enhanced CT images as input. We compared multi-DL with other deep learning-based OR prediction and tumor segmentation methods to explore the incremental value of introducing the interconnected task into a unified network. Additionally, the clinical model was developed using multivariate logistic regression to predict OR. Results showed that multi-DL could achieve the highest AUC of 0.871 in OR prediction and the highest dice coefficient of 73.6% in tumor segmentation. Furthermore, multi-DL can successfully perform the risk stratification that the low-risk and high-risk patients showed a significant difference in survival (p = 0.006). In conclusion, the proposed method may provide a useful tool for therapeutic regime selection in clinical practice.
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Affiliation(s)
- Yuze Li
- Center for Biomedical Imaging Research, School of Medicine, Tsinghua University, Beijing 100084, China; (Y.L.); (Z.X.)
| | - Ziming Xu
- Center for Biomedical Imaging Research, School of Medicine, Tsinghua University, Beijing 100084, China; (Y.L.); (Z.X.)
| | - Chao An
- Department of Minimal Invasive Intervention, Sun Yat-sen University Cancer Center, Guangzhou 510060, China;
| | - Huijun Chen
- Center for Biomedical Imaging Research, School of Medicine, Tsinghua University, Beijing 100084, China; (Y.L.); (Z.X.)
- Correspondence: (H.C.); (X.L.)
| | - Xiao Li
- Department of Interventional Therapy, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
- Correspondence: (H.C.); (X.L.)
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17
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Cao F, Yang Y, Si T, Luo J, Zeng H, Zhang Z, Feng D, Chen Y, Zheng J. The Efficacy of TACE Combined With Lenvatinib Plus Sintilimab in Unresectable Hepatocellular Carcinoma: A Multicenter Retrospective Study. Front Oncol 2022; 11:783480. [PMID: 34988019 PMCID: PMC8721033 DOI: 10.3389/fonc.2021.783480] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 11/15/2021] [Indexed: 12/24/2022] Open
Abstract
Objective To assess the efficacy and safety of transarterial Chemoembolization (TACE) combined with lenvatinib plus sintilimab in unresectable hepatocellular carcinoma (HCC). Patients and Methods The data of patients with unresectable HCC administered a combination therapy with TACE and lenvatinib plus sintilimab were retrospectively assessed. Patients received lenvatinib orally once daily 2 weeks before TACE, followed by sintilimab administration at 200 mg intravenously on day 1 of a 21-day therapeutic cycle after TACE. The primary endpoints were objective response rate (ORR) and duration of response (DOR) by the modified RECIST criteria. Results Median duration of follow-up was 12.5 months (95%CI 9.1 to 14.8 months). ORR was 46.7% (28/60). Median DOR in confirmed responders was 10.0 months (95%CI 9.0-11.0 months). Median progression-free survival (PFS) was 13.3 months (95%CI 11.9-14.7 months). Median overall survival (OS) was 23.6 months (95%CI 22.2-25.0 months). Conclusions TACE combined with lenvatinib plus sintilimab is a promising therapeutic regimen in unresectable hepatocellular carcinoma.
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Affiliation(s)
- Fei Cao
- Department of Interventional Radiology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Yi Yang
- Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Key Laboratory of Gene Editing Screening and Research and Development (R&D) of Digestive System Tumor Drugs, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Tongguo Si
- Department of Interventional Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Jun Luo
- Department of Interventional Radiology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Hui Zeng
- Department of Interventional Radiology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Zhewei Zhang
- Department of Interventional Radiology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Duiping Feng
- Department of Interventional Radiology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Yi Chen
- Department of Interventional Radiology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Jiaping Zheng
- Department of Interventional Radiology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
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18
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Ahn JC, Qureshi TA, Singal AG, Li D, Yang JD. Deep learning in hepatocellular carcinoma: Current status and future perspectives. World J Hepatol 2021; 13:2039-2051. [PMID: 35070007 PMCID: PMC8727204 DOI: 10.4254/wjh.v13.i12.2039] [Citation(s) in RCA: 17] [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: 05/28/2021] [Revised: 07/19/2021] [Accepted: 11/15/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is among the leading causes of cancer incidence and death. Despite decades of research and development of new treatment options, the overall outcomes of patients with HCC continue to remain poor. There are areas of unmet need in risk prediction, early diagnosis, accurate prognostication, and individualized treatments for patients with HCC. Recent years have seen an explosive growth in the application of artificial intelligence (AI) technology in medical research, with the field of HCC being no exception. Among the various AI-based machine learning algorithms, deep learning algorithms are considered state-of-the-art techniques for handling and processing complex multimodal data ranging from routine clinical variables to high-resolution medical images. This article will provide a comprehensive review of the recently published studies that have applied deep learning for risk prediction, diagnosis, prognostication, and treatment planning for patients with HCC.
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Affiliation(s)
- Joseph C Ahn
- Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55904, United States
| | - Touseef Ahmad Qureshi
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States
| | - Amit G Singal
- Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States
| | - Ju-Dong Yang
- Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States
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19
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Dong Z, Lin Y, Lin F, Luo X, Lin Z, Zhang Y, Li L, Li ZP, Feng ST, Cai H, Peng Z. Prediction of Early Treatment Response to Initial Conventional Transarterial Chemoembolization Therapy for Hepatocellular Carcinoma by Machine-Learning Model Based on Computed Tomography. J Hepatocell Carcinoma 2021; 8:1473-1484. [PMID: 34877267 PMCID: PMC8643205 DOI: 10.2147/jhc.s334674] [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: 08/17/2021] [Accepted: 11/05/2021] [Indexed: 12/20/2022] Open
Abstract
Purpose The treatment response to initial conventional transarterial chemoembolization (cTACE) is essential for the prognosis of patients with hepatocellular carcinoma (HCC). This study explored and verified the feasibility of machine-learning models based on clinical data and contrast-enhanced computed tomography (CT) image findings to predict early responses of HCC patients after initial cTACE treatment. Patients and Methods Overall, 110 consecutive unresectable HCC patients who were treated with cTACE for the first time were retrospectively enrolled. Clinical data and imaging features based on contrast-enhanced CT were collected for the selection of characteristics. Treatment responses were evaluated based on the modified Response Evaluation Criteria in Solid Tumors (mRECIST) by postoperative CT examination within 2 months after the procedure. Python (version 3.70) was used to develop machine learning models. Least absolute shrinkage and selection operator (LASSO) algorithm was applied to select features with the impact on predicting treatment response after the first TACE procedure. Six machine learning algorithms were used to build predictive models, including XGBoost, decision tree, support vector machine, random forest, k-nearest neighbor, and fully convolutional networks, and their performances were compared using receiver operator characteristic (ROC) curves to determine the best performing model. Results Following TACE, 31 patients (28.2%) were described as responsive to TACE, while 72 patients (71.8%) were nonresponsive to TACE. Portal vein tumor thrombosis type, albumin level, and distribution of tumors within the liver were selected for predictive model building. Among the models, the RF model showed the best performance, with area under the curve (AUC), accuracy, sensitivity, and specificity of 0.802, 0.784, 0.904, and 0.480, respectively. Conclusion Machine learning models can provide an accurate prediction of the early response of initial TACE treatment for HCC, which can help in individualizing clinical decision-making and modification of further treatment strategies for patients with unresectable HCC.
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Affiliation(s)
- Zhi Dong
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Yingyu Lin
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Fangzeng Lin
- Department of Interventional Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Xuyi Luo
- Department of Emergency, Guangzhou First People's Hospital, Guangzhou, Guangdong, 510180, People's Republic of China
| | - Zhi Lin
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Yinhong Zhang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Lujie Li
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Zi-Ping Li
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Shi-Ting Feng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Huasong Cai
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Zhenpeng Peng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People's Republic of China
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20
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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: 3.3] [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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21
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Peng J, Huang J, Huang G, Zhang J. Predicting the Initial Treatment Response to Transarterial Chemoembolization in Intermediate-Stage Hepatocellular Carcinoma by the Integration of Radiomics and Deep Learning. Front Oncol 2021; 11:730282. [PMID: 34745952 PMCID: PMC8566880 DOI: 10.3389/fonc.2021.730282] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 10/01/2021] [Indexed: 12/12/2022] Open
Abstract
Objectives We aimed to develop radiology-based models for the preoperative prediction of the initial treatment response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC) since the integration of radiomics and deep learning (DL) has not been reported for TACE. Methods Three hundred and ten intermediate-stage HCC patients who underwent TACE were recruited from three independent medical centers. Based on computed tomography (CT) images, recursive feature elimination (RFE) was used to select the most useful radiomics features. Five radiomics conventional machine learning (cML) models and a DL model were used for training and validation. Mutual correlations between each model were analyzed. The accuracies of integrating clinical variables, cML, and DL models were then evaluated. Results Good predictive accuracies were showed across the two cohorts in the five cML models, especially the random forest algorithm (AUC = 0.967 and 0.964, respectively). DL showed high accuracies in the training and validation cohorts (AUC = 0.981 and 0.972, respectively). Significant mutual correlations were revealed between tumor size and the five cML models and DL model (each P < 0.001). The highest accuracies were achieved by integrating DL and the random forest algorithm in the training and validation cohorts (AUC = 0.995 and 0.994, respectively). Conclusion The radiomics cML models and DL model showed notable accuracy for predicting the initial response to TACE treatment. Moreover, the integrated model could serve as a novel and accurate method for prediction in intermediate-stage HCC.
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Affiliation(s)
- Jie Peng
- Department of Oncology, The Second Affiliated Hospital, Guizhou Medical University, Kaili, China
| | - Jinhua Huang
- Department of Minimal Invasive Interventional Therapy, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, China
| | - Guijia Huang
- Department of Oncology, The Second Affiliated Hospital, Guizhou Medical University, Kaili, China
| | - Jing Zhang
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
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22
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Gong XQ, Tao YY, Wu Y, Liu N, Yu X, Wang R, Zheng J, Liu N, Huang XH, Li JD, Yang G, Wei XQ, Yang L, Zhang XM. Progress of MRI Radiomics in Hepatocellular Carcinoma. Front Oncol 2021; 11:698373. [PMID: 34616673 PMCID: PMC8488263 DOI: 10.3389/fonc.2021.698373] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 08/31/2021] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is the sixth most common cancer in the world and the third leading cause of cancer-related death. Although the diagnostic scheme of HCC is currently undergoing refinement, the prognosis of HCC is still not satisfactory. In addition to certain factors, such as tumor size and number and vascular invasion displayed on traditional imaging, some histopathological features and gene expression parameters are also important for the prognosis of HCC patients. However, most parameters are based on postoperative pathological examinations, which cannot help with preoperative decision-making. As a new field, radiomics extracts high-throughput imaging data from different types of images to build models and predict clinical outcomes noninvasively before surgery, rendering it a powerful aid for making personalized treatment decisions preoperatively. OBJECTIVE This study reviewed the workflow of radiomics and the research progress on magnetic resonance imaging (MRI) radiomics in the diagnosis and treatment of HCC. METHODS A literature review was conducted by searching PubMed for search of relevant peer-reviewed articles published from May 2017 to June 2021.The search keywords included HCC, MRI, radiomics, deep learning, artificial intelligence, machine learning, neural network, texture analysis, diagnosis, histopathology, microvascular invasion, surgical resection, radiofrequency, recurrence, relapse, transarterial chemoembolization, targeted therapy, immunotherapy, therapeutic response, and prognosis. RESULTS Radiomics features on MRI can be used as biomarkers to determine the differential diagnosis, histological grade, microvascular invasion status, gene expression status, local and systemic therapeutic responses, and prognosis of HCC patients. CONCLUSION Radiomics is a promising new imaging method. MRI radiomics has high application value in the diagnosis and treatment of HCC.
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Affiliation(s)
- Xue-Qin Gong
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Yun-Yun Tao
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Yao–Kun Wu
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Ning Liu
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xi Yu
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Ran Wang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Jing Zheng
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Nian Liu
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiao-Hua Huang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Jing-Dong Li
- Department of Hepatocellular Surgery, Institute of Hepato-Biliary-Intestinal Disease, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Gang Yang
- Department of Hepatocellular Surgery, Institute of Hepato-Biliary-Intestinal Disease, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiao-Qin Wei
- School of Medical Imaging, North Sichuan Medical College, Nanchong, China
| | - Lin Yang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiao-Ming Zhang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
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23
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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: 2.5] [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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24
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Jin ZC, Zhong BY. Application of radiomics in hepatocellular carcinoma: A review. Artif Intell Med Imaging 2021; 2:64-72. [DOI: 10.35711/aimi.v2.i3.64] [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: 05/13/2021] [Revised: 06/19/2021] [Accepted: 06/30/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer with low 5-year survival rate. The high molecular heterogeneity in HCC poses huge challenges for clinical practice or trial design and has become a major barrier to improving the management of HCC. However, current clinical practice based on single bioptic or archived tumor tissue has been deficient in identifying useful biomarkers. The concept of radiomics was first proposed in 2012 and is different from the traditional imaging analysis based on the qualitative or semi-quantitative analysis by radiologists. Radiomics refers to high-throughput extraction of large amounts number of high-dimensional quantitative features from medical images through machine learning or deep learning algorithms. Using the radiomics method could quantify tumoral phenotypes and heterogeneity, which may provide benefits in clinical decision-making at a lower cost. Here, we review the workflow and application of radiomics in HCC.
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Affiliation(s)
- Zhi-Cheng Jin
- Center of Interventional Radiology and Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Bin-Yan Zhong
- Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou 215006, Jiangsu Province, China
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25
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Zhang L, Yan ZP, Hou ZH, Huang P, Yang MJ, Zhang S, Zhang S, Zhang SH, Zhu XL, Ni CF, Li Q. Neutrophil-to-Lymphocyte and Platelet-to-Lymphocyte Ratios as Predictors of Outcomes in Patients With Unresectable Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization Plus Sorafenib. Front Mol Biosci 2021; 8:624366. [PMID: 34124139 PMCID: PMC8194392 DOI: 10.3389/fmolb.2021.624366] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 01/15/2021] [Indexed: 12/26/2022] Open
Abstract
Objectives: To investigate the predictive value of inflammatory biomarkers in patients with unresectable hepatocellular carcinoma (HCC) for outcomes following the combination treatment of transarterial chemoembolization (TACE) plus sorafenib. Materials and Methods: A total of 314 (270 male and 44 female) treatment-naïve patients with unresectable HCC treated by TACE plus sorafenib between January 2011 and December 2018 were enrolled in the retrospective study. The primary outcome was overall survival (OS). The secondary outcome was progression-free survival (PFS). Neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were obtained within 3–7 days before the initial TACE and the median value of the NLR and PLR was considered as the cut-off value. Results: The median value of NLR and PLR was 2.42 and 100, respectively. The median OS and PFS of the entire cohort were 18.7 months (95% CI: 16.8–20.6) and 9.1 months (95% CI: 8.5–9.8), respectively. The low NLR and PLR group showed improved OS and PFS compared with the high NLR and PLR group [21.8 months (95% CI: 15.2–28.5) vs. 15.4 months (95% CI: 12.4–18.3), p < 0.0001; 21.6 months (95% CI: 15.8–27.5) vs. 14.9 months (95% CI: 11.9–17.8), p = 0.00027, respectively]. In addition, the low NLR and PLR group also provided a longer PFS than the high NLR and PLR group [10.4 months (95% CI: 8.9–12.0) vs. 8.1 months (95% CI: 7.1–9.2), p = 0.00022; 10.3 months (95% CI: 8.6–11.9) vs. 8.2 months (95% CI: 7.2–9.2), p < 0.0001, respectively]. High NLR and PLR at baseline were predictive factors of poor OS (p = 0.02 and p = 0.004) and PFS (p = 0.045 and p = 0.005). Conclusion: This study showed the prognostic value of quantitative inflammatory biomarkers in correlation with OS and PFS in unresectable HCC patients undergoing TACE plus sorafenib treatment.
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Affiliation(s)
- Lei Zhang
- Department of Radiology, The Affiliated People's Hospital of Ningbo University, Ningbo, China.,Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Zhi-Ping Yan
- Department of Interventional Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Institution of Medical Imaging, Shanghai, China.,National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Zhong-Heng Hou
- Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Peng Huang
- Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Min-Jie Yang
- Department of Interventional Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Institution of Medical Imaging, Shanghai, China.,National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Shuai Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Shen Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Shao-Hua Zhang
- Institute of Urology, The Affiliated Luohu Hospital of Shenzhen University, Shenzhen, China
| | - Xiao-Li Zhu
- Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Cai-Fang Ni
- Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.,Department of Interventional Radiology and Vascular Surgery, The Dushu Lake Affiliated Hospital of Soochow University, Suzhou, China
| | - Qiang Li
- Department of Radiology, The Affiliated People's Hospital of Ningbo University, Ningbo, China
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