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Bitar R, Salem R, Finn R, Greten TF, Goldberg SN, Chapiro J, Atzen S. Interventional Oncology Meets Immuno-oncology: Combination Therapies for Hepatocellular Carcinoma. Radiology 2024; 313:e232875. [PMID: 39560477 PMCID: PMC11605110 DOI: 10.1148/radiol.232875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 08/15/2024] [Accepted: 08/27/2024] [Indexed: 11/20/2024]
Abstract
The management of hepatocellular carcinoma (HCC) is undergoing transformational changes due to the emergence of various novel immunotherapies and their combination with image-guided locoregional therapies. In this setting, immunotherapy is expected to become one of the standards of care in both neoadjuvant and adjuvant settings across all disease stages of HCC. Currently, more than 50 ongoing prospective clinical trials are investigating various end points for the combination of immunotherapy with both percutaneous and catheter-directed therapies. This review will outline essential tumor microenvironment mechanisms responsible for disease evolution and therapy resistance, discuss the rationale for combining locoregional therapy with immunotherapy, summarize ongoing clinical trials, and report on developing imaging end points and novel biomarkers that are relevant to both diagnostic and interventional radiologists participating in the management of HCC.
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Affiliation(s)
- Ryan Bitar
- From the Departments of Radiology (R.B., J.C.) and Digestive Diseases
(Hepatology) (J.C.), Yale University School of Medicine, New Haven, Conn;
Department of Radiology, Feinberg School of Medicine, Northwestern University,
Chicago, Ill (R.S.); Department of Medical Oncology, Geffen School of Medicine,
University of California Los Angeles, Los Angeles, Calif (R.F.); Center for
Cancer Research, National Institutes of Health, Bethesda, Md (T.F.G.);
Department of Radiology, Hadassah Hebrew University Medical Center, Hebrew
University, Jerusalem, Israel (S.N.G.); and Department of Biomedical
Engineering, Yale School of Engineering and Applied Sciences, 789 Howard Ave,
Clinic Bldg 363H, New Haven, CT 06520 (J.C.)
| | - Riad Salem
- From the Departments of Radiology (R.B., J.C.) and Digestive Diseases
(Hepatology) (J.C.), Yale University School of Medicine, New Haven, Conn;
Department of Radiology, Feinberg School of Medicine, Northwestern University,
Chicago, Ill (R.S.); Department of Medical Oncology, Geffen School of Medicine,
University of California Los Angeles, Los Angeles, Calif (R.F.); Center for
Cancer Research, National Institutes of Health, Bethesda, Md (T.F.G.);
Department of Radiology, Hadassah Hebrew University Medical Center, Hebrew
University, Jerusalem, Israel (S.N.G.); and Department of Biomedical
Engineering, Yale School of Engineering and Applied Sciences, 789 Howard Ave,
Clinic Bldg 363H, New Haven, CT 06520 (J.C.)
| | - Richard Finn
- From the Departments of Radiology (R.B., J.C.) and Digestive Diseases
(Hepatology) (J.C.), Yale University School of Medicine, New Haven, Conn;
Department of Radiology, Feinberg School of Medicine, Northwestern University,
Chicago, Ill (R.S.); Department of Medical Oncology, Geffen School of Medicine,
University of California Los Angeles, Los Angeles, Calif (R.F.); Center for
Cancer Research, National Institutes of Health, Bethesda, Md (T.F.G.);
Department of Radiology, Hadassah Hebrew University Medical Center, Hebrew
University, Jerusalem, Israel (S.N.G.); and Department of Biomedical
Engineering, Yale School of Engineering and Applied Sciences, 789 Howard Ave,
Clinic Bldg 363H, New Haven, CT 06520 (J.C.)
| | - Tim F. Greten
- From the Departments of Radiology (R.B., J.C.) and Digestive Diseases
(Hepatology) (J.C.), Yale University School of Medicine, New Haven, Conn;
Department of Radiology, Feinberg School of Medicine, Northwestern University,
Chicago, Ill (R.S.); Department of Medical Oncology, Geffen School of Medicine,
University of California Los Angeles, Los Angeles, Calif (R.F.); Center for
Cancer Research, National Institutes of Health, Bethesda, Md (T.F.G.);
Department of Radiology, Hadassah Hebrew University Medical Center, Hebrew
University, Jerusalem, Israel (S.N.G.); and Department of Biomedical
Engineering, Yale School of Engineering and Applied Sciences, 789 Howard Ave,
Clinic Bldg 363H, New Haven, CT 06520 (J.C.)
| | - S. Nahum Goldberg
- From the Departments of Radiology (R.B., J.C.) and Digestive Diseases
(Hepatology) (J.C.), Yale University School of Medicine, New Haven, Conn;
Department of Radiology, Feinberg School of Medicine, Northwestern University,
Chicago, Ill (R.S.); Department of Medical Oncology, Geffen School of Medicine,
University of California Los Angeles, Los Angeles, Calif (R.F.); Center for
Cancer Research, National Institutes of Health, Bethesda, Md (T.F.G.);
Department of Radiology, Hadassah Hebrew University Medical Center, Hebrew
University, Jerusalem, Israel (S.N.G.); and Department of Biomedical
Engineering, Yale School of Engineering and Applied Sciences, 789 Howard Ave,
Clinic Bldg 363H, New Haven, CT 06520 (J.C.)
| | - Julius Chapiro
- From the Departments of Radiology (R.B., J.C.) and Digestive Diseases
(Hepatology) (J.C.), Yale University School of Medicine, New Haven, Conn;
Department of Radiology, Feinberg School of Medicine, Northwestern University,
Chicago, Ill (R.S.); Department of Medical Oncology, Geffen School of Medicine,
University of California Los Angeles, Los Angeles, Calif (R.F.); Center for
Cancer Research, National Institutes of Health, Bethesda, Md (T.F.G.);
Department of Radiology, Hadassah Hebrew University Medical Center, Hebrew
University, Jerusalem, Israel (S.N.G.); and Department of Biomedical
Engineering, Yale School of Engineering and Applied Sciences, 789 Howard Ave,
Clinic Bldg 363H, New Haven, CT 06520 (J.C.)
| | - Sarah Atzen
- From the Departments of Radiology (R.B., J.C.) and Digestive Diseases
(Hepatology) (J.C.), Yale University School of Medicine, New Haven, Conn;
Department of Radiology, Feinberg School of Medicine, Northwestern University,
Chicago, Ill (R.S.); Department of Medical Oncology, Geffen School of Medicine,
University of California Los Angeles, Los Angeles, Calif (R.F.); Center for
Cancer Research, National Institutes of Health, Bethesda, Md (T.F.G.);
Department of Radiology, Hadassah Hebrew University Medical Center, Hebrew
University, Jerusalem, Israel (S.N.G.); and Department of Biomedical
Engineering, Yale School of Engineering and Applied Sciences, 789 Howard Ave,
Clinic Bldg 363H, New Haven, CT 06520 (J.C.)
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Li Z, Huang L, Yu C. Advanced Prediction of Hepatic Oncogenic Transformation in HBV Patients via RNA-Seq Data Analysis and Deep Learning Techniques. Int J Mol Sci 2024; 25:9827. [PMID: 39337315 PMCID: PMC11432201 DOI: 10.3390/ijms25189827] [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: 07/29/2024] [Revised: 09/09/2024] [Accepted: 09/10/2024] [Indexed: 09/30/2024] Open
Abstract
Liver cancer, recognized as a significant global health issue, is increasingly correlated with Hepatitis B virus (HBV) infection, as evidenced by numerous scientific studies. This study aims to examine the correlation between HBV infection and the development of liver cancer, focusing on using RNA sequencing (RNA-seq) to detect HBV sequences and applying deep learning techniques to estimate the likelihood of oncogenic transformation in individuals with HBV. Our study utilized RNA-seq data and employed Pathseq software and sophisticated deep learning models, including a convolutional neural network (CNN), to analyze the prevalence of HBV sequences in the samples of patients with liver cancer. Our research successfully identified the prevalence of HBV sequences and demonstrated that the CNN model achieved an exceptional Area Under the Curve (AUC) of 0.998 in predicting cancerous transformations. We observed no viral synergism that enhanced the pathogenicity of HBV. A detailed analysis of sequences misclassified by the CNN model revealed that longer sequences were more conducive to accurate recognition. The findings from this study provide critical insights into the management and prognosis of patients infected with HBV, highlighting the potential of advanced analytical techniques in understanding the complex interactions between viral infections and cancer development.
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Affiliation(s)
| | | | - Changyuan Yu
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; (Z.L.)
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Lopez-Lopez V, Morise Z, Albaladejo-González M, Gavara CG, Goh BKP, Koh YX, Paul SJ, Hilal MA, Mishima K, Krürger JAP, Herman P, Cerezuela A, Brusadin R, Kaizu T, Lujan J, Rotellar F, Monden K, Dalmau M, Gotohda N, Kudo M, Kanazawa A, Kato Y, Nitta H, Amano S, Valle RD, Giuffrida M, Ueno M, Otsuka Y, Asano D, Tanabe M, Itano O, Minagawa T, Eshmuminov D, Herrero I, Ramírez P, Ruipérez-Valiente JA, Robles-Campos R, Wakabayashi G. Explainable artificial intelligence prediction-based model in laparoscopic liver surgery for segments 7 and 8: an international multicenter study. Surg Endosc 2024; 38:2411-2422. [PMID: 38315197 PMCID: PMC11078826 DOI: 10.1007/s00464-024-10681-6] [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: 08/07/2023] [Accepted: 01/02/2024] [Indexed: 02/07/2024]
Abstract
BACKGROUND Artificial intelligence (AI) is becoming more useful as a decision-making and outcomes predictor tool. We have developed AI models to predict surgical complexity and the postoperative course in laparoscopic liver surgery for segments 7 and 8. METHODS We included patients with lesions located in segments 7 and 8 operated by minimally invasive liver surgery from an international multi-institutional database. We have employed AI models to predict surgical complexity and postoperative outcomes. Furthermore, we have applied SHapley Additive exPlanations (SHAP) to make the AI models interpretable. Finally, we analyzed the surgeries not converted to open versus those converted to open. RESULTS Overall, 585 patients and 22 variables were included. Multi-layer Perceptron (MLP) showed the highest performance for predicting surgery complexity and Random Forest (RF) for predicting postoperative outcomes. SHAP detected that MLP and RF gave the highest relevance to the variables "resection type" and "largest tumor size" for predicting surgery complexity and postoperative outcomes. In addition, we explored between surgeries converted to open and non-converted, finding statistically significant differences in the variables "tumor location," "blood loss," "complications," and "operation time." CONCLUSION We have observed how the application of SHAP allows us to understand the predictions of AI models in surgical complexity and the postoperative outcomes of laparoscopic liver surgery in segments 7 and 8.
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Affiliation(s)
- Victor Lopez-Lopez
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | - Zeniche Morise
- Department of Surgery, Fujita Health University School of Medicine Okazaki Medical Center, Okazaki, Aichi, Japan
| | | | - Concepción Gomez Gavara
- Department of HPB Surgery and Transplants, Vall d'Hebron University Hospital, Barcelona Autonomic University, Barcelona, Spain
| | - Brian K P Goh
- Department of Hepatopancreatobiliary and Transplant Surgery, Singapore General Hospital and National Cancer Centre Singapore, Singapore, Singapore
- Surgery Academic Clinical Programme, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Ye Xin Koh
- Department of Hepatopancreatobiliary and Transplant Surgery, Singapore General Hospital and National Cancer Centre Singapore, Singapore, Singapore
- Surgery Academic Clinical Programme, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Sijberden Jasper Paul
- Department of Surgery, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - Mohammed Abu Hilal
- Department of Surgery, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
- Department of Surgery, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Kohei Mishima
- Department of Surgery, Ageo Central General Hospital, Ageo, Japan
| | - Jaime Arthur Pirola Krürger
- Serviço de Cirurgia do Fígado, Divisão de Cirurgia do Aparelho Digestivo, Departamento de Gastroenterologia, Faculdade de Medicina, Hospital das Clínicas HCFMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Paulo Herman
- Serviço de Cirurgia do Fígado, Divisão de Cirurgia do Aparelho Digestivo, Departamento de Gastroenterologia, Faculdade de Medicina, Hospital das Clínicas HCFMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Alvaro Cerezuela
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | - Roberto Brusadin
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | - Takashi Kaizu
- Department of General, Pediatric and Hepatobiliary-Pancreatic Surgery, Kitasato University School of Medicine, Sagamihara, Japan
| | - Juan Lujan
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
- Department of General Surgery, School of Medicine, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain
| | - Fernando Rotellar
- Department of General Surgery, School of Medicine, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain
| | - Kazuteru Monden
- Department of Surgery, Fukuyama City Hospital, Hiroshima, Japan
| | - Mar Dalmau
- Department of HPB Surgery and Transplants, Vall d'Hebron University Hospital, Barcelona Autonomic University, Barcelona, Spain
| | - Naoto Gotohda
- Department of Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Masashi Kudo
- Department of Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Akishige Kanazawa
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka City General Hospital, Osaka, Japan
| | - Yutaro Kato
- Department of Surgery, Fujita Health University, Toyoake, Japan
| | - Hiroyuki Nitta
- Department of Surgery, Iwate Medical University, Iwate, Japan
| | - Satoshi Amano
- Department of Surgery, Iwate Medical University, Iwate, Japan
| | | | - Mario Giuffrida
- General Surgery Unit, Parma University Hospital, Parma, Italy
| | - Masaki Ueno
- Second Department of Surgery, Wakayama Medical University, 811-1 Kimiidera, Wakayama City, Wakayama, Japan
| | | | - Daisuke Asano
- Department of Hepatobiliary and Pancreatic Surgery, Graduate School of Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Minoru Tanabe
- Department of Hepatobiliary and Pancreatic Surgery, Graduate School of Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Osamu Itano
- Department of Hepato-Biliary-Pancreatic and Gastrointestinal Surgery, School of Medicine, International University of Health and Welfare, Chiba, Japan
| | - Takuya Minagawa
- Department of Hepato-Biliary-Pancreatic and Gastrointestinal Surgery, School of Medicine, International University of Health and Welfare, Chiba, Japan
| | - Dilmurodjon Eshmuminov
- Department of Surgery and Transplantation, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Irene Herrero
- Department of Surgery, Getafe University Hospital, Madrid, Spain
| | - Pablo Ramírez
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | | | - Ricardo Robles-Campos
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | - Go Wakabayashi
- Department of Surgery, Ageo Central General Hospital, Ageo, Japan
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Li Z, Lan L, Zhou Y, Li R, Chavin KD, Xu H, Li L, Shih DJH, Jim Zheng W. Developing deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records. J Biomed Inform 2024; 152:104626. [PMID: 38521180 DOI: 10.1016/j.jbi.2024.104626] [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: 11/20/2023] [Revised: 02/23/2024] [Accepted: 03/20/2024] [Indexed: 03/25/2024]
Abstract
OBJECTIVE The accuracy of deep learning models for many disease prediction problems is affected by time-varying covariates, rare incidence, covariate imbalance and delayed diagnosis when using structured electronic health records data. The situation is further exasperated when predicting the risk of one disease on condition of another disease, such as the hepatocellular carcinoma risk among patients with nonalcoholic fatty liver disease due to slow, chronic progression, the scarce of data with both disease conditions and the sex bias of the diseases. The goal of this study is to investigate the extent to which the aforementioned issues influence deep learning performance, and then devised strategies to tackle these challenges. These strategies were applied to improve hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease. METHODS We evaluated two representative deep learning models in the task of predicting the occurrence of hepatocellular carcinoma in a cohort of patients with nonalcoholic fatty liver disease (n = 220,838) from a national EHR database. The disease prediction task was carefully formulated as a classification problem while taking censorship and the length of follow-up into consideration. RESULTS We developed a novel backward masking scheme to deal with the issue of delayed diagnosis which is very common in EHR data analysis and evaluate how the length of longitudinal information after the index date affects disease prediction. We observed that modeling time-varying covariates improved the performance of the algorithms and transfer learning mitigated reduced performance caused by the lack of data. In addition, covariate imbalance, such as sex bias in data impaired performance. Deep learning models trained on one sex and evaluated in the other sex showed reduced performance, indicating the importance of assessing covariate imbalance while preparing data for model training. CONCLUSIONS The strategies developed in this work can significantly improve the performance of hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease. Furthermore, our novel strategies can be generalized to apply to other disease risk predictions using structured electronic health records, especially for disease risks on condition of another disease.
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Affiliation(s)
- Zhao Li
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin Street, Suite 600, Houston, TX 77030, USA
| | - Lan Lan
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin Street, Suite 600, Houston, TX 77030, USA
| | - Yujia Zhou
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin Street, Suite 600, Houston, TX 77030, USA
| | - Ruoxing Li
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin Street, Suite 600, Houston, TX 77030, USA
| | - Kenneth D Chavin
- Department of Surgery, Case Western Reserve University School of Medicine, 11100 Euclid Ave, Cleveland, OH 44106, USA
| | - Hua Xu
- Yale School of Medicine, USA
| | - Liang Li
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, 1400 Pressler Street, FCT4.6008, Houston, TX 77030, USA
| | - David J H Shih
- School of Biomedical Sciences, The University of Hong Kong, Hong Kong Special Administrative Region
| | - W Jim Zheng
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin Street, Suite 600, Houston, TX 77030, USA.
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Ramírez-Mejía MM, Méndez-Sánchez N. From prediction to prevention: Machine learning revolutionizes hepatocellular carcinoma recurrence monitoring. World J Gastroenterol 2024; 30:631-635. [PMID: 38515945 PMCID: PMC10950631 DOI: 10.3748/wjg.v30.i7.631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/12/2023] [Accepted: 01/22/2024] [Indexed: 02/21/2024] Open
Abstract
In this editorial, we comment on the article by Zhang et al entitled Development of a machine learning-based model for predicting the risk of early postoperative recurrence of hepatocellular carcinoma. Hepatocellular carcinoma (HCC), which is characterized by high incidence and mortality rates, remains a major global health challenge primarily due to the critical issue of postoperative recurrence. Early recurrence, defined as recurrence that occurs within 2 years posttreatment, is linked to the hidden spread of the primary tumor and significantly impacts patient survival. Traditional predictive factors, including both patient- and treatment-related factors, have limited predictive ability with respect to HCC recurrence. The integration of machine learning algorithms is fueled by the exponential growth of computational power and has revolutionized HCC research. The study by Zhang et al demonstrated the use of a groundbreaking preoperative prediction model for early postoperative HCC recurrence. Chall-enges persist, including sample size constraints, issues with handling data, and the need for further validation and interpretability. This study emphasizes the need for collaborative efforts, multicenter studies and comparative analyses to validate and refine the model. Overcoming these challenges and exploring innovative approaches, such as multi-omics integration, will enhance personalized oncology care. This study marks a significant stride toward precise, effi-cient, and personalized oncology practices, thus offering hope for improved patient outcomes in the field of HCC treatment.
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Affiliation(s)
- Mariana Michelle Ramírez-Mejía
- Plan of Combined Studies in Medicine, Faculty of Medicine, National Autonomous University of Mexico, Distrito Federal 04510, Mexico
- Liver Research Unit, Medica Sur Clinic & Foundation, Distrito Federal 14050, Mexico
| | - Nahum Méndez-Sánchez
- Liver Research Unit, Medica Sur Clinic & Foundation, Distrito Federal 14050, Mexico
- Faculty of Medicine, National Autonomous University of Mexico, Distrito Federal 04510, Mexico
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Chen Y, Shi Y, Wang R, Wang X, Lin Q, Huang Y, Shao E, Pan Y, Huang S, Lu L, Chen X. Development and Validation of Deep Learning Model for Intermediate-Stage Hepatocellular Carcinoma Survival with Transarterial Chemoembolization (MC-hccAI 002): a Retrospective, Multicenter, Cohort Study. J Cancer 2024; 15:2066-2073. [PMID: 38434985 PMCID: PMC10905396 DOI: 10.7150/jca.91501] [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: 10/25/2023] [Accepted: 02/04/2024] [Indexed: 03/05/2024] Open
Abstract
Background: There are few effective prediction models for intermediate-stage hepatocellular carcinoma (IM-HCC) patients treated with transarterial chemoembolization (TACE) to predict overall survival (OS) is available. The learning survival neural network (DeepSurv) was developed to showed a better performance than cox proportional hazards model in prediction of OS. This study aimed to develop a deep learning-based prediction model to predict individual OS. Methods: This multicenter, retrospective, cohort study examined data from the electronic medical record system of four hospitals in China between January 1, 2007, to December 31, 2016. Patients were divided into a training set(n=1075) and a test set(n=269) at a ratio of 8:2 to develop a deep learning-based algorithm (deepHAP IV). The deepHAP IV model was externally validated on an independent cohort(n=414) from the other three centers. The concordance index, the area under the receiver operator characteristic curves, and the calibration curve were used to assess the performance of the models. Results: The deepHAP IV model had a c-index of 0.74, whereas AUROC for predicting survival outcomes of 1-, 3-, and 5-year reached 0.80, 0.76, and 0.74 in the training set. Calibration graphs showed good consistency between the actual and predicted OS in the training set and the validation cohort. Compared to the other five Cox proportional-hazards models, the model this study conducted had a better performance. Patients were finally classified into three groups by X-tile plots with predicted 3-year OS rate (low: ≤ 0.11; middle: > 0.11 and ≤ 0.35; high: >0.35). Conclusion: The deepHAP IV model can effectively predict the OS of patients with IM-HCC, showing a better performance than previous Cox proportional hazards models.
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Affiliation(s)
- Yaying Chen
- Department of Oncology, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Yanhong Shi
- Department of Oncology, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Ruiqi Wang
- Department of Gastroenterology, Xiamen Humanity Hospital, Xiamen, China
| | - Xuewen Wang
- Department of Oncology, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Qin Lin
- Department of Oncology, The 900th Hospital of the People's Liberation Army Joint Service Support Force, Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China
| | - Yan Huang
- Department of Oncology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, Fujian, China
| | - Erqian Shao
- Department of Oncology, The Third Affiliated Hospital of Sun Yat-sen University Yuedong Hospital, Meizhou, Guangdong, China
| | - Yan Pan
- Department of Oncology, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Shanshan Huang
- Department of Oncology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Linbin Lu
- Department of Oncology, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Xiong Chen
- Department of Oncology, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, China
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Martínez-Blanco P, Suárez M, Gil-Rojas S, Torres AM, Martínez-García N, Blasco P, Torralba M, Mateo J. Prognostic Factors for Mortality in Hepatocellular Carcinoma at Diagnosis: Development of a Predictive Model Using Artificial Intelligence. Diagnostics (Basel) 2024; 14:406. [PMID: 38396445 PMCID: PMC10888215 DOI: 10.3390/diagnostics14040406] [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: 12/31/2023] [Revised: 01/24/2024] [Accepted: 02/09/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) accounts for 75% of primary liver tumors. Controlling risk factors associated with its development and implementing screenings in risk populations does not seem sufficient to improve the prognosis of these patients at diagnosis. The development of a predictive prognostic model for mortality at the diagnosis of HCC is proposed. METHODS In this retrospective multicenter study, the analysis of data from 191 HCC patients was conducted using machine learning (ML) techniques to analyze the prognostic factors of mortality that are significant at the time of diagnosis. Clinical and analytical data of interest in patients with HCC were gathered. RESULTS Meeting Milan criteria, Barcelona Clinic Liver Cancer (BCLC) classification and albumin levels were the variables with the greatest impact on the prognosis of HCC patients. The ML algorithm that achieved the best results was random forest (RF). CONCLUSIONS The development of a predictive prognostic model at the diagnosis is a valuable tool for patients with HCC and for application in clinical practice. RF is useful and reliable in the analysis of prognostic factors in the diagnosis of HCC. The search for new prognostic factors is still necessary in patients with HCC.
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Affiliation(s)
| | - Miguel Suárez
- Gastroenterology Department, Virgen de la Luz Hospital, 16002 Cuenca, Spain
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
| | - Sergio Gil-Rojas
- Gastroenterology Department, Virgen de la Luz Hospital, 16002 Cuenca, Spain
| | - Ana María Torres
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
| | | | - Pilar Blasco
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
| | - Miguel Torralba
- Internal Medicine Unit, Guadalajara University Hospital, 19002 Guadalajara, Spain (M.T.)
- Faculty of Medicine, Universidad de Alcalá de Henares, 28801 Alcalá de Henares, Spain
- Translational Research Group in Cellular Immunology (GITIC), Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
| | - Jorge Mateo
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
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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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Li Z, Lan L, Zhou Y, Li R, Chavin KD, Xu H, Li L, Shih DJH, Zheng WJ. Developing deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.17.23298691. [PMID: 38014193 PMCID: PMC10680899 DOI: 10.1101/2023.11.17.23298691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Background Deep learning models showed great success and potential when applied to many biomedical problems. However, the accuracy of deep learning models for many disease prediction problems is affected by time-varying covariates, rare incidence, and covariate imbalance when using structured electronic health records data. The situation is further exasperated when predicting the risk of one disease on condition of another disease, such as the hepatocellular carcinoma risk among patients with nonalcoholic fatty liver disease due to slow, chronic progression, the scarce of data with both disease conditions and the sex bias of the diseases. Objective The goal of this study is to investigate the extent to which time-varying covariates, rare incidence, and covariate imbalance influence deep learning performance, and then devised strategies to tackle these challenges. These strategies were applied to improve hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease. Methods We evaluated two representative deep learning models in the task of predicting the occurrence of hepatocellular carcinoma in a cohort of patients with nonalcoholic fatty liver disease (n = 220,838) from a national EHR database. The disease prediction task was carefully formulated as a classification problem while taking censorship and the length of follow-up into consideration. Results We developed a novel backward masking scheme to evaluate how the length of longitudinal information after the index date affects disease prediction. We observed that modeling time-varying covariates improved the performance of the algorithms and transfer learning mitigated reduced performance caused by the lack of data. In addition, covariate imbalance, such as sex bias in data impaired performance. Deep learning models trained on one sex and evaluated in the other sex showed reduced performance, indicating the importance of assessing covariate imbalance while preparing data for model training. Conclusions Devising proper strategies to address challenges from time-varying covariates, lack of data, and covariate imbalance can be key to counteracting data bias and accurately predicting disease occurrence using deep learning models. The novel strategies developed in this work can significantly improve the performance of hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease. Furthermore, our novel strategies can be generalized to apply to other disease risk predictions using structured electronic health records, especially for disease risks on condition of another disease.
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Affiliation(s)
- Zhao Li
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin Street, Suite 600, Houston, Texas, 77030
| | - Lan Lan
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin Street, Suite 600, Houston, Texas, 77030
| | - Yujia Zhou
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin Street, Suite 600, Houston, Texas, 77030
| | - Ruoxing Li
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin Street, Suite 600, Houston, Texas, 77030
| | - Kenneth D. Chavin
- Department of Surgery, Case Western Reserve University School of Medicine, 11100 Euclid Ave, Cleveland OH 44106
| | | | - Liang Li
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, 1400 Pressler Street, FCT4.6008, Houston TX 77030
| | | | - W. Jim Zheng
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin Street, Suite 600, Houston, Texas, 77030
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Hu M, Xia X, Chen L, Jin Y, Hu Z, Xia S, Yao X. Emerging biomolecules for practical theranostics of liver hepatocellular carcinoma. Ann Hepatol 2023; 28:101137. [PMID: 37451515 DOI: 10.1016/j.aohep.2023.101137] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/17/2023] [Accepted: 06/28/2023] [Indexed: 07/18/2023]
Abstract
Most cases of hepatocellular carcinoma (HCC) are able to be diagnosed through regular surveillance in an identifiable patient population with chronic hepatitis B or cirrhosis. Nevertheless, 50% of global cases might present incidentally owing to symptomatic advanced-stage HCC after worsening of liver dysfunction. A systematic search based on PUBMED was performed to identify relevant outcomes, covering newer surveillance modalities including secretory proteins, DNA methylation, miRNAs, and genome sequencing analysis which proposed molecular expression signatures as ideal tools in the early-stage HCC detection. In the face of low accuracy without harmonization on the analytical approaches and data interpretation for liquid biopsy, a more accurate incidence of HCC will be unveiled by using deep machine learning system and multiplex immunohistochemistry analysis. A combination of molecular-secretory biomarkers, high-definition imaging and bedside clinical indexes in a surveillance setting offers a comprehensive range of HCC potential indicators. In addition, the sequential use of numerous lines of systemic anti-HCC therapies will simultaneously benefit more patients in survival. This review provides an overview on the most recent developments in HCC theranostic platform.
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Affiliation(s)
- Miner Hu
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Xiaojun Xia
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Lichao Chen
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Yunpeng Jin
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Zhenhua Hu
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China; Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Fourth Affiliated Hospital, School of Medicine, Zhejiang University, Yiwu, Zhejiang, China.
| | - Shudong Xia
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Xudong Yao
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
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11
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Qiu Y, Xu J, Liao W, Wen Y, Jiang S, Wen J, Zhao C. Suppression of hepatocellular carcinoma by Ulva lactuca ulvan via gut microbiota and metabolite interactions. J Adv Res 2023; 52:103-117. [PMID: 37075862 PMCID: PMC10555771 DOI: 10.1016/j.jare.2023.04.008] [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: 11/17/2022] [Revised: 03/17/2023] [Accepted: 04/11/2023] [Indexed: 04/21/2023] Open
Abstract
INTRODUCTION Ulva lactuca polysaccharide (ULP) is green algae extract with numerous biological activities, including anticoagulant, anti-inflammatory, and antiviral effects. However, the inhibitory ability of ULP in the development of hepatocellular carcinoma warrants further studies. OBJECTIVES To elucidate the anti-tumor mechanism of ULP action and evaluate its regulatory effect on gut microbiota and metabolism in H22 hepatocellular carcinoma tumor-bearing mice. METHODS An H22 tumor-bearing mouse model was established by subcutaneously injecting H22 hepatoma cells. The gut microbiota composition in cecal feces was assessed and subjected to untargeted metabolomic sequencing. The antitumor activity of ULP was verified further by western blot, RT-qPCR, and reactive oxygen species (ROS) assays. RESULTS Administration of ULP alleviated tumor growth by modulating the compositions of the gut microbial communities (Tenericutes, Agathobacter, Ruminiclostridium, Parabacteroides, Lactobacillus, and Holdemania) and metabolites (docosahexaenoic acid, uric acid, N-Oleoyl Dopamine, and L-Kynurenine). Mechanistically, ULP promoted ROS production by inhibiting the protein levels of JNK, c-JUN, PI3K, Akt, and Bcl-6, thereby delaying the growth of HepG2 cells. CONCLUSION ULP attenuates tumor growth in H22 tumor-bearing mice by modulating gut microbial composition and metabolism. ULP inhibits tumor growth mainly by promoting ROS generation.
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Affiliation(s)
- Yinghui Qiu
- College of Marine Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China; College of Food Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Jingxiang Xu
- School of Basic Medicine, Gannan Medical University, Ganzhou 341000, China
| | - Wei Liao
- College of Food Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Yuxi Wen
- College of Marine Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China; Universidade de Vigo, Nutrition and Bromatology Group, Department of Analytical and Food Chemistry, Faculty of Sciences, 32004 Ourense, Spain
| | - Shiyue Jiang
- School of Basic Medicine, Gannan Medical University, Ganzhou 341000, China
| | - Jiahui Wen
- College of Food Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Chao Zhao
- College of Marine Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China; Key Laboratory of Marine Biotechnology of Fujian Province, Institute of Oceanology, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
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12
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Sun H, Yang H, Mao Y. Personalized treatment for hepatocellular carcinoma in the era of targeted medicine and bioengineering. Front Pharmacol 2023; 14:1150151. [PMID: 37214451 PMCID: PMC10198383 DOI: 10.3389/fphar.2023.1150151] [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: 01/23/2023] [Accepted: 04/24/2023] [Indexed: 05/24/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is a major global health burden, causing approximately 8.3 million deaths each year, and it is the third leading cause of cancer-related death worldwide, with a relative 5-year survival rate of around 18%. Due to the advanced stage of diagnosis in most patients, systemic treatment based on targeted therapy has become the only feasible option. Genomic studies have established a profile of molecular alterations in hepatocellular carcinoma with potentially actionable mutations, but these mutations have yet to be translated into clinical practice. The first targeted drug approved for systemic treatment of patients with advanced hepatocellular carcinoma was Sorafenib, which was a milestone. Subsequent clinical trials have identified multiple tyrosine kinase inhibitors, such as Lenvatinib, Cabozantinib, and Regorafenib, for the treatment of hepatocellular carcinoma, with survival benefits for the patient. Ongoing systemic therapy studies and trials include various immune-based combination therapies, with some early results showing promise and potential for new therapy plans. Systemic therapy for hepatocellular carcinoma is complicated by the significant heterogeneity of the disease and its propensity for developing drug resistance. Therefore, it is essential to choose a better, individualized treatment plan to benefit patients. Preclinical models capable of preserving in vivo tumor characteristics are urgently needed to circumvent heterogeneity and overcome drug resistance. In this review, we summarize current approaches to targeted therapy for HCC patients and the establishment of several patient-derived preclinical models of hepatocellular carcinoma. We also discuss the challenges and opportunities of targeted therapy for hepatocellular carcinoma and how to achieve personalized treatment with the continuous development of targeted therapies and bioengineering technologies.
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Affiliation(s)
| | - Huayu Yang
- *Correspondence: Huayu Yang, ; Yilei Mao,
| | - Yilei Mao
- *Correspondence: Huayu Yang, ; Yilei Mao,
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13
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Zhang J, Chen Y, Zeng P, Liu Y, Diao Y, Liu P. Ultra-Attention: Automatic Recognition of Liver Ultrasound Standard Sections Based on Visual Attention Perception Structures. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:1007-1017. [PMID: 36681610 DOI: 10.1016/j.ultrasmedbio.2022.12.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/12/2022] [Accepted: 12/22/2022] [Indexed: 06/17/2023]
Abstract
Acquisition of a standard section is a prerequisite for ultrasound diagnosis. For a long time, there has been a lack of clear definitions of standard liver views because of physician experience. The accurate automated scanning of standard liver sections, however, remains one of ultrasonography medicine's most important issues. In this article, we enrich and expand the classification criteria of liver ultrasound standard sections from clinical practice and propose an Ultra-Attention structured perception strategy to automate the recognition of these sections. Inspired by the attention mechanism in natural language processing, the standard liver ultrasound views will participate in the global attention algorithm as modular local images in computer vision of ultrasound images, which will significantly amplify small features that would otherwise go unnoticed. In addition to using the dropout mechanism, we also use a Part-Transfer Learning training approach to fine-tune the model's rate of convergence to increase its robustness. The proposed Ultra-Attention model outperforms various traditional convolutional neural network-based techniques, achieving the best known performance in the field with a classification accuracy of 93.2%. As part of the feature extraction procedure, we also illustrate and compare the convolutional structure and the Ultra-Attention approach. This analysis provides a reasonable view for future research on local modular feature capture in ultrasound images. By developing a standard scan guideline for liver ultrasound-based illness diagnosis, this work will advance the research on automated disease diagnosis that is directed by standard sections of liver ultrasound.
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Affiliation(s)
- Jiansong Zhang
- College of Medicine, Huaqiao University, Quanzhou, Fujian Province, China
| | - Yongjian Chen
- Department of Ultrasound, Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Pan Zeng
- College of Medicine, Huaqiao University, Quanzhou, Fujian Province, China
| | - Yao Liu
- College of Science and Engineering, National Quemoy University, Kinmen, Taiwan
| | - Yong Diao
- College of Medicine, Huaqiao University, Quanzhou, Fujian Province, China
| | - Peizhong Liu
- College of Medicine, Huaqiao University, Quanzhou, Fujian Province, China; College of Engineering, Huaqiao University, Quanzhou, Fujian Province, China.
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14
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Hosseiniyan Khatibi SM, Najjarian F, Homaei Rad H, Ardalan M, Teshnehlab M, Zununi Vahed S, Pirmoradi S. Key therapeutic targets implicated at the early stage of hepatocellular carcinoma identified through machine-learning approaches. Sci Rep 2023; 13:3840. [PMID: 36882466 PMCID: PMC9992672 DOI: 10.1038/s41598-023-30720-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 02/28/2023] [Indexed: 03/09/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most frequent type of primary liver cancer. Early-stage detection plays an essential role in making treatment decisions and identifying dominant molecular mechanisms. We utilized machine learning algorithms to find significant mRNAs and microRNAs (miRNAs) at the early and late stages of HCC. First, pre-processing approaches, including organization, nested cross-validation, cleaning, and normalization were applied. Next, the t-test/ANOVA methods and binary particle swarm optimization were used as a filter and wrapper method in the feature selection step, respectively. Then, classifiers, based on machine learning and deep learning algorithms were utilized to evaluate the discrimination power of selected features (mRNAs and miRNAs) in the classification step. Finally, the association rule mining algorithm was applied to selected features for identifying key mRNAs and miRNAs that can help decode dominant molecular mechanisms in HCC stages. The applied methods could identify key genes associated with the early (e.g., Vitronectin, thrombin-activatable fibrinolysis inhibitor, lactate dehydrogenase D (LDHD), miR-590) and late-stage (e.g., SPRY domain containing 4, regucalcin, miR-3199-1, miR-194-2, miR-4999) of HCC. This research could establish a clear picture of putative candidate genes, which could be the main actors at the early and late stages of HCC.
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Affiliation(s)
- Seyed Mahdi Hosseiniyan Khatibi
- Kidney Research Center, Tabriz University of Medical Sciences, Daneshgah Street, Tabriz, 51665118, Iran.,Clinical Research Development Unit of Tabriz Valiasr Hospital, Tabriz University of Medical Sciences, Niyayesh Blvd., Tabriz, Iran.,Rahat Breath and Sleep Research Center, Tabriz University of Medical Science, Tabriz, Iran
| | - Farima Najjarian
- Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Hamed Homaei Rad
- Rahat Breath and Sleep Research Center, Tabriz University of Medical Science, Tabriz, Iran
| | - Mohammadreza Ardalan
- Kidney Research Center, Tabriz University of Medical Sciences, Daneshgah Street, Tabriz, 51665118, Iran
| | - Mohammad Teshnehlab
- Department of Electric and Computer Engineering, K.N. Toosi University of Technology, Tehran, Iran
| | - Sepideh Zununi Vahed
- Kidney Research Center, Tabriz University of Medical Sciences, Daneshgah Street, Tabriz, 51665118, Iran.
| | - Saeed Pirmoradi
- Clinical Research Development Unit of Tabriz Valiasr Hospital, Tabriz University of Medical Sciences, Niyayesh Blvd., Tabriz, Iran.
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15
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Wong VCL, Wong MI, Lee VHF, Man K, Ng KTP, Cheung TT. Prognostic MicroRNA Fingerprints Predict Recurrence of Early-Stage Hepatocellular Carcinoma Following Hepatectomy. J Cancer 2023; 14:480-489. [PMID: 36860918 PMCID: PMC9969587 DOI: 10.7150/jca.79593] [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: 10/06/2022] [Accepted: 12/24/2022] [Indexed: 02/15/2023] Open
Abstract
Purpose: This study aims to develop liquid biopsy assays for early HCC diagnosis and prognosis. Methods: Twenty-three microRNAs were first consolidated as a panel (HCCseek-23 panel) based on their reported functions in HCC development. Serum samples were collected from 103 early-stage HCC patients before and after hepatectomy. Quantitative PCR and machine learning random forest models were applied to develop diagnostic and prognostic models. Results: For HCC diagnosis, HCCseek-23 panel demonstrated 81% sensitivity and 83% specificity for identifying HCC in the early-stage; it showed 93% sensitivity for identifying alpha-fetoprotein (AFP)-negative HCC. For HCC prognosis, the differential expressions of 8 microRNAs (HCCseek-8 panel: miR-145, miR-148a, miR-150, miR-221, miR-223, miR-23a, miR-374a, and miR-424) were significantly associated with disease-free survival (DFS) (Log-rank test p-value = 0.001). Further model improvement using these HCCseek-8 panel in combination with serum biomarkers (i.e. AFP, ALT, and AST) demonstrated a significant association with DFS (Log-rank p-value = 0.011 and Cox proportional hazards analyses p-value = 0.002). Conclusion: To the best of our knowledge, this is the first report to integrate circulating miRNAs, AST, ALT, AFP, and machine learning for predicting DFS in early HCC patients undergoing hepatectomy. In this setting, HCCSeek-23 panel is a promising circulating microRNA assay for diagnosis, while HCCSeek-8 panel is promising for prognosis to identify early HCC recurrence.
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Affiliation(s)
- Victor Chun-Lam Wong
- OncoSeek Limited, Hong Kong Science and Technology Parks, Hong Kong Special Administrative Region, People's Republic of China,✉ Corresponding author: Department of Surgery, The University of Hong Kong, 21 Sassoon Road, Pokfulam, Hong Kong Special Administrative Region, People's Republic of China; OncoSeek Limited, Hong Kong Science and Technology Parks, Hong Kong Special Administrative Region, People's Republic of China. E-mail addresses: (TC), (VW); Phone: (+852) 2255 3025 (TC); (+852) 3188 9335 (VW)
| | - Ming-In Wong
- OncoSeek Limited, Hong Kong Science and Technology Parks, Hong Kong Special Administrative Region, People's Republic of China
| | - Victor Ho-Fun Lee
- Department of Clinical Oncology, Queen Mary Hospital, LKS Faculty of Medicine, The Hong Kong Special Administrative Region, People's Republic of China
| | - Kwan Man
- Department of Surgery, Queen Mary Hospital, LKS Faculty of Medicine, The Hong Kong Special Administrative Region, People's Republic of China
| | - Kevin Tak-Pan Ng
- Department of Surgery, Queen Mary Hospital, LKS Faculty of Medicine, The Hong Kong Special Administrative Region, People's Republic of China
| | - Tan To Cheung
- Department of Surgery, Queen Mary Hospital, LKS Faculty of Medicine, The Hong Kong Special Administrative Region, People's Republic of China,✉ Corresponding author: Department of Surgery, The University of Hong Kong, 21 Sassoon Road, Pokfulam, Hong Kong Special Administrative Region, People's Republic of China; OncoSeek Limited, Hong Kong Science and Technology Parks, Hong Kong Special Administrative Region, People's Republic of China. E-mail addresses: (TC), (VW); Phone: (+852) 2255 3025 (TC); (+852) 3188 9335 (VW)
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16
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Bhagat N, Verma N, Singh V. HCC prediction post SVR: Many tools yet limited generalizability! J Hepatol 2022; 77:1226-1228. [PMID: 35526784 DOI: 10.1016/j.jhep.2022.04.028] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 04/14/2022] [Accepted: 04/15/2022] [Indexed: 12/14/2022]
Affiliation(s)
- Naveen Bhagat
- Department of Hepatology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Nipun Verma
- Department of Hepatology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Virendra Singh
- Department of Hepatology, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
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Sauzeau V, Beignet J, Vergoten G, Bailly C. Overexpressed or hyperactivated Rac1 as a target to treat hepatocellular carcinoma. Pharmacol Res 2022; 179:106220. [PMID: 35405309 DOI: 10.1016/j.phrs.2022.106220] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/31/2022] [Accepted: 04/05/2022] [Indexed: 12/12/2022]
Abstract
Despite novel targeted and immunotherapies, the prognosis remains bleak for patients with hepatocellular carcinoma (HCC), especially for advanced and/or metastatic forms. The rapid emergence of drug resistance is a major obstacle in the success of chemo-, targeted-, immuno-therapies of HCC. Novel targets are needed. The prominent roles of the small GTPase Rac1 in the development and progression of HCC are discussed here, together with its multiple protein partners, and the targeting of Rac1 with RNA-based regulators and small molecules. We discuss the oncogenic functions of Rac1 in HCC, including the contribution of Rac1 mutants and isoform Rac1b. Rac1 is a ubiquitous target, but the protein is frequently overexpressed and hyperactivated in HCC. It contributes to the aggressivity of the disease, with key roles in cancer cell proliferation, tumor metastasis and resistance to treatment. Small molecule targeting Rac1, indirectly or directly, have shown anticancer effects in HCC experimental models. Rac1-binding agents such as EHT 1864 and analogues offer novel opportunities to combat HCC. We discuss the different modalities to repress Rac1 overactivation in HCC with small molecules and the combination with reference drugs to promote cancer cell death and to repress cell invasion. We highlight the necessity to combine Rac1-targeted approach with appropriate biomarkers to select Rac1 activated tumors. Our analysis underlines the prominent oncogenic functions of Rac1 in HCC and discuss the modalities to target this small GTPase. Rac1 shall be considered as a valid target to limit the acquired and intrinsic resistance of HCC tumors and their metastatic potential.
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Affiliation(s)
- Vincent Sauzeau
- Université de Nantes, CHU Nantes, CNRS, INSERM, Institut du Thorax, Nantes, France.
| | - Julien Beignet
- SATT Ouest Valorisation, 30 boulevard Vincent Gâche, CS 70211, 44202 Nantes Cedex, France
| | - Gérard Vergoten
- University of Lille, Inserm, INFINITE - U1286, Institut de Chimie Pharmaceutique Albert Lespagnol (ICPAL), Faculté de Pharmacie, 3 rue du Professeur Laguesse, BP-83, 59006, Lille, France
| | - Christian Bailly
- OncoWitan, Scientific Consulting Office, Lille, Wasquehal 59290, France.
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