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Han JW, Lee SK, Kwon JH, Nam SW, Yang H, Bae SH, Kim JH, Nam H, Kim CW, Lee HL, Kim HY, Lee SW, Lee A, Chang UI, Song DS, Kim SH, Song MJ, Sung PS, Choi JY, Yoon SK, Jang JW. A Machine Learning Algorithm Facilitates Prognosis Prediction and Treatment Selection for Barcelona Clinic Liver Cancer Stage C Hepatocellular Carcinoma. Clin Cancer Res 2024; 30:2812-2821. [PMID: 38639918 DOI: 10.1158/1078-0432.ccr-23-3978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/29/2024] [Accepted: 04/16/2024] [Indexed: 04/20/2024]
Abstract
PURPOSE Given its heterogeneity and diverse clinical outcomes, precise subclassification of Barcelona Clinic Liver Cancer stage C (BCLC-C) hepatocellular carcinoma (HCC) is required for appropriately determining patient prognosis and selecting treatment. EXPERIMENTAL DESIGN We recruited 2,626 patients with BCLC-C HCC from multiple centers, comprising training/test (n = 1,693) and validation cohorts (n = 933). The XGBoost model was chosen for maximum performance among the machine learning (ML) models. Patients were categorized into low-, intermediate-, high-, and very high-risk subgroups based on the estimated prognosis, and this subclassification was named the CLAssification via Machine learning of BCLC-C (CLAM-C). RESULTS The areas under the receiver operating characteristic curve of the CLAM-C for predicting the 6-, 12-, and 24-month survival of patients with BCLC-C were 0.800, 0.831, and 0.715, respectively-significantly higher than those of the conventional models, which were consistent in the validation cohort. The four subgroups had significantly different median overall survivals, and this difference was maintained among various patient subgroups and treatment modalities. Immune-checkpoint inhibitors and transarterial therapies were associated with significantly better survival than tyrosine kinase inhibitors (TKI) in the low- and intermediate-risk subgroups. In cases with first-line systemic therapy, the CLAM-C identified atezolizumab-bevacizumab as the best therapy, particularly in the high-risk group. In cases with later-line systemic therapy, nivolumab had better survival than TKIs in the low-to-intermediate-risk subgroup, whereas TKIs had better survival in the high- to very high-risk subgroup. CONCLUSIONS ML modeling effectively subclassified patients with BCLC-C HCC, potentially aiding treatment allocation. Our study underscores the potential utilization of ML modeling in terms of prognostication and treatment allocation in patients with BCLC-C HCC.
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Affiliation(s)
- Ji W Han
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Soon K Lee
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Incheon, Republic of Korea
| | - Jung H Kwon
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Incheon, Republic of Korea
| | - Soon W Nam
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Incheon, Republic of Korea
| | - Hyun Yang
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Si H Bae
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Ji H Kim
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, Republic of Korea
| | - Heechul Nam
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, Republic of Korea
| | - Chang W Kim
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, Republic of Korea
| | - Hae L Lee
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Bucheon, Republic of Korea
| | - Hee Y Kim
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Bucheon, Republic of Korea
| | - Sung W Lee
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Bucheon, Republic of Korea
| | - Ahlim Lee
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Suwon, Republic of Korea
| | - U I Chang
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Suwon, Republic of Korea
| | - Do S Song
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Suwon, Republic of Korea
| | - Seok-Hwan Kim
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Daejeon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Daejeon, Republic of Korea
| | - Myeong J Song
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Daejeon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Daejeon, Republic of Korea
| | - Pil S Sung
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jong Y Choi
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Seung K Yoon
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jeong W Jang
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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Peng G, Cao X, Huang X, Zhou X. Radiomics and machine learning based on preoperative MRI for predicting extrahepatic metastasis in hepatocellular carcinoma patients treated with transarterial chemoembolization. Eur J Radiol Open 2024; 12:100551. [PMID: 38347937 PMCID: PMC10859286 DOI: 10.1016/j.ejro.2024.100551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 01/14/2024] [Accepted: 01/15/2024] [Indexed: 02/15/2024] Open
Abstract
Purpose To develop and validate a radiomics machine learning (Rad-ML) model based on preoperative MRI to predict extrahepatic metastasis (EHM) in hepatocellular carcinoma (HCC) patients receiving transarterial chemoembolization (TACE) treatment. Methods A total of 355 HCC patients who received multiple TACE procedures were split at random into a training set and a test set at a 7:3 ratio. Radiomic features were calculated from tumor and peritumor in arterial phase and portal venous phase, and were identified using intraclass correlation coefficient, maximal relevance and minimum redundancy, and least absolute shrinkage and selection operator techniques. Cox regression analysis was employed to determine the clinical model. The best-performing algorithm among eight machine learning methods was used to construct the Rad-ML model. A nomogram combining clinical and Rad-ML parameters was used to develop a combined model. Model performance was evaluated using C-index, decision curve analysis, calibration plot, and survival analysis. Results In clinical model, elevated neutrophil to lymphocyte ratio and alpha-fetoprotein were associated with faster EHM. The XGBoost-based Rad-ML model demonstrated the best predictive performance for EHM. When compared to the clinical model, both the Rad-ML model and the combination model performed better (C-indexes of 0.61, 0.85, and 0.86 in the training set, and 0.62, 0.82, and 0.83 in the test set, respectively). However, the combined model's and the Rad-ML model's prediction performance did not differ significantly. The most influential feature was peritumoral waveletHLL_firstorder_Minimum in AP, which exhibited an inverse relationship with EHM risk. Conclusions Our study suggests that the preoperative MRI-based Rad-ML model is a valuable tool to predict EHM in HCC patients treated with TACE.
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Affiliation(s)
- Gang Peng
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaojing Cao
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoyu Huang
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiang Zhou
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Guo Q, Xie F, Zhong F, Wen W, Zhang X, Yu X, Wang X, Huang B, Li L, Wang X. Application of interpretable machine learning algorithms to predict distant metastasis in ovarian clear cell carcinoma. Cancer Med 2024; 13:e7161. [PMID: 38613173 PMCID: PMC11015070 DOI: 10.1002/cam4.7161] [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: 12/17/2023] [Revised: 03/16/2024] [Accepted: 03/26/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND Ovarian clear cell carcinoma (OCCC) represents a subtype of ovarian epithelial carcinoma (OEC) known for its limited responsiveness to chemotherapy, and the onset of distant metastasis significantly impacts patient prognoses. This study aimed to identify potential risk factors contributing to the occurrence of distant metastasis in OCCC. METHODS Utilizing the Surveillance, Epidemiology, and End Results (SEER) database, we identified patients diagnosed with OCCC between 2004 and 2015. The most influential factors were selected through the application of Gaussian Naive Bayes (GNB) and Adaboost machine learning algorithms, employing a Venn test for further refinement. Subsequently, six machine learning (ML) techniques, namely XGBoost, LightGBM, Random Forest (RF), Adaptive Boosting (Adaboost), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were employed to construct predictive models for distant metastasis. Shapley Additive Interpretation (SHAP) analysis facilitated a visual interpretation for individual patient. Model validity was assessed using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and the area under the receiver operating characteristic curve (AUC). RESULTS In the realm of predicting distant metastasis, the Random Forest (RF) model outperformed the other five machine learning algorithms. The RF model demonstrated accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and AUC (95% CI) values of 0.792 (0.762-0.823), 0.904 (0.835-0.973), 0.759 (0.731-0.787), 0.221 (0.186-0.256), 0.974 (0.967-0.982), 0.353 (0.306-0.399), and 0.834 (0.696-0.967), respectively, surpassing the performance of other models. Additionally, the calibration curve's Brier Score (95%) for the RF model reached the minimum value of 0.06256 (0.05753-0.06759). SHAP analysis provided independent explanations, reaffirming the critical clinical factors associated with the risk of metastasis in OCCC patients. CONCLUSIONS This study successfully established a precise predictive model for OCCC patient metastasis using machine learning techniques, offering valuable support to clinicians in making informed clinical decisions.
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Affiliation(s)
- Qin‐Hua Guo
- Jiangxi Province Key Laboratory of Laboratory Medicine, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated HospitalJiangxi Medical College, Nanchang UniversityNanchangJiangxiChina
- Department of Clinical LaboratoryThe First Hospital of Nanchang (The Third Affiliated Hospital of Nanchang University)NanchangJiangxiChina
- School of Public HealthNanchang UniversityNanchangJiangxiChina
| | - Feng‐Chun Xie
- Department of Clinical LaboratoryNanchang Renai Obstetrics and Gynecology HospitalNanchangJiangxiChina
| | - Fang‐Min Zhong
- Jiangxi Province Key Laboratory of Laboratory Medicine, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated HospitalJiangxi Medical College, Nanchang UniversityNanchangJiangxiChina
| | - Wen Wen
- Jiangxi Province Key Laboratory of Laboratory Medicine, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated HospitalJiangxi Medical College, Nanchang UniversityNanchangJiangxiChina
- School of Public HealthNanchang UniversityNanchangJiangxiChina
| | - Xue‐Ru Zhang
- Jiangxi Province Key Laboratory of Laboratory Medicine, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated HospitalJiangxi Medical College, Nanchang UniversityNanchangJiangxiChina
- School of Public HealthNanchang UniversityNanchangJiangxiChina
| | - Xia‐Jing Yu
- Jiangxi Province Key Laboratory of Laboratory Medicine, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated HospitalJiangxi Medical College, Nanchang UniversityNanchangJiangxiChina
- School of Public HealthNanchang UniversityNanchangJiangxiChina
| | - Xin‐Lu Wang
- Jiangxi Province Key Laboratory of Laboratory Medicine, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated HospitalJiangxi Medical College, Nanchang UniversityNanchangJiangxiChina
- School of Public HealthNanchang UniversityNanchangJiangxiChina
| | - Bo Huang
- Jiangxi Province Key Laboratory of Laboratory Medicine, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated HospitalJiangxi Medical College, Nanchang UniversityNanchangJiangxiChina
| | - Li‐Ping Li
- Department of Clinical LaboratoryThe First Hospital of Nanchang (The Third Affiliated Hospital of Nanchang University)NanchangJiangxiChina
| | - Xiao‐Zhong Wang
- Jiangxi Province Key Laboratory of Laboratory Medicine, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated HospitalJiangxi Medical College, Nanchang UniversityNanchangJiangxiChina
- Department of Clinical LaboratoryThe First Hospital of Nanchang (The Third Affiliated Hospital of Nanchang University)NanchangJiangxiChina
- School of Public HealthNanchang UniversityNanchangJiangxiChina
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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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Lee HW, Kim H, Park T, Park SY, Chon YE, Seo YS, Lee JS, Park JY, Kim DY, Ahn SH, Kim BK, Kim SU. A machine learning model for predicting hepatocellular carcinoma risk in patients with chronic hepatitis B. Liver Int 2023; 43:1813-1821. [PMID: 37452503 DOI: 10.1111/liv.15597] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 04/17/2023] [Accepted: 04/19/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND Machine learning (ML) algorithms can be used to overcome the prognostic performance limitations of conventional hepatocellular carcinoma (HCC) risk models. We established and validated an ML-based HCC predictive model optimized for patients with chronic hepatitis B (CHB) infections receiving antiviral therapy (AVT). METHODS Treatment-naïve CHB patients who were started entecavir (ETV) or tenofovir disoproxil fumarate (TDF) were enrolled. We used a training cohort (n = 960) to develop a novel ML model that predicted HCC development within 5 years and validated the model using an independent external cohort (n = 1937). ML algorithms consider all potential interactions and do not use predefined hypotheses. RESULTS The mean age of the patients in the training cohort was 48 years, and most patients (68.9%) were men. During the median 59.3 (interquartile range 45.8-72.3) months of follow-up, 69 (7.2%) patients developed HCC. Our ML-based HCC risk prediction model had an area under the receiver-operating characteristic curve (AUC) of 0.900, which was better than the AUCs of CAMD (0.778) and REAL B (0.772) (both p < .05). The better performance of our model was maintained (AUC = 0.872 vs. 0.788 for CAMD and 0.801 for REAL B) in the validation cohort. Using cut-off probabilities of 0.3 and 0.5, the cumulative incidence of HCC development differed significantly among the three risk groups (p < .001). CONCLUSIONS Our new ML model performed better than models in terms of predicting the risk of HCC development in CHB patients receiving AVT.
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Affiliation(s)
- Hye Won Lee
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
- Institute of Gastroenterology, Yonsei University College of Medicine, Seoul, Republic of Korea
- Yonsei Liver Center, Severance Hospital, Seoul, Republic of Korea
| | - Hwiyoung Kim
- Department of Biomedical Systems Informatics, Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul, Republic of Korea
- Department of Artificial Intelligence, Yonsei University, College of Medicine, Seoul, Republic of Korea
| | - Taeyun Park
- Department of Artificial Intelligence, Yonsei University, College of Medicine, Seoul, Republic of Korea
| | - Soo Young Park
- Department of Internal medicine, Kyungpook National University School of Medicine, Daegu, Republic of Korea
| | - Young Eun Chon
- Department of Internal Medicine, CHA Bundang Medical Center, CHA University, Bundang, Republic of Korea
| | - Yeon Seok Seo
- Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jae Seung Lee
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
- Institute of Gastroenterology, Yonsei University College of Medicine, Seoul, Republic of Korea
- Yonsei Liver Center, Severance Hospital, Seoul, Republic of Korea
| | - Jun Yong Park
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
- Institute of Gastroenterology, Yonsei University College of Medicine, Seoul, Republic of Korea
- Yonsei Liver Center, Severance Hospital, Seoul, Republic of Korea
| | - Do Young Kim
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
- Institute of Gastroenterology, Yonsei University College of Medicine, Seoul, Republic of Korea
- Yonsei Liver Center, Severance Hospital, Seoul, Republic of Korea
| | - Sang Hoon Ahn
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
- Institute of Gastroenterology, Yonsei University College of Medicine, Seoul, Republic of Korea
- Yonsei Liver Center, Severance Hospital, Seoul, Republic of Korea
| | - Beom Kyung Kim
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
- Institute of Gastroenterology, Yonsei University College of Medicine, Seoul, Republic of Korea
- Yonsei Liver Center, Severance Hospital, Seoul, Republic of Korea
| | - Seung Up Kim
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
- Institute of Gastroenterology, Yonsei University College of Medicine, Seoul, Republic of Korea
- Yonsei Liver Center, Severance Hospital, Seoul, Republic of Korea
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Koelsch N, Manjili MH. From Reductionistic Approach to Systems Immunology Approach for the Understanding of Tumor Microenvironment. Int J Mol Sci 2023; 24:12086. [PMID: 37569461 PMCID: PMC10419122 DOI: 10.3390/ijms241512086] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 07/23/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
The tumor microenvironment (TME) is a complex and dynamic ecosystem that includes a variety of immune cells mutually interacting with tumor cells, structural/stromal cells, and each other. The immune cells in the TME can have dual functions as pro-tumorigenic and anti-tumorigenic. To understand such paradoxical functions, the reductionistic approach classifies the immune cells into pro- and anti-tumor cells and suggests the therapeutic blockade of the pro-tumor and induction of the anti-tumor immune cells. This strategy has proven to be partially effective in prolonging patients' survival only in a fraction of patients without offering a cancer cure. Recent advances in multi-omics allow taking systems immunology approach. This essay discusses how a systems immunology approach could revolutionize our understanding of the TME by suggesting that internetwork interactions of the immune cell types create distinct collective functions independent of the function of each cellular constituent. Such collective function can be understood by the discovery of the immunological patterns in the TME and may be modulated as a therapeutic means for immunotherapy of cancer.
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Affiliation(s)
- Nicholas Koelsch
- Department of Microbiology & Immunology, Virginia Commonwealth University School of Medicine, Richmond, VA 23298, USA;
| | - Masoud H. Manjili
- Department of Microbiology & Immunology, Virginia Commonwealth University School of Medicine, Richmond, VA 23298, USA;
- VCU Massey Cancer Center, 401 College Street, Boc 980035, Richmond, VA 23298, USA
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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: 0] [Impact Index Per Article: 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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Astray G, Soria-Lopez A, Barreiro E, Mejuto JC, Cid-Samamed A. Machine Learning to Predict the Adsorption Capacity of Microplastics. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:1061. [PMID: 36985954 PMCID: PMC10051191 DOI: 10.3390/nano13061061] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/10/2023] [Accepted: 03/11/2023] [Indexed: 06/18/2023]
Abstract
Nowadays, there is an extensive production and use of plastic materials for different industrial activities. These plastics, either from their primary production sources or through their own degradation processes, can contaminate ecosystems with micro- and nanoplastics. Once in the aquatic environment, these microplastics can be the basis for the adsorption of chemical pollutants, favoring that these chemical pollutants disperse more quickly in the environment and can affect living beings. Due to the lack of information on adsorption, three machine learning models (random forest, support vector machine, and artificial neural network) were developed to predict different microplastic/water partition coefficients (log Kd) using two different approximations (based on the number of input variables). The best-selected machine learning models present, in general, correlation coefficients above 0.92 in the query phase, which indicates that these types of models could be used for the rapid estimation of the absorption of organic contaminants on microplastics.
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Affiliation(s)
- Gonzalo Astray
- Universidade de Vigo, Departamento de Química Física, Facultade de Ciencias, 32004 Ourense, Spain
| | - Anton Soria-Lopez
- Universidade de Vigo, Departamento de Química Física, Facultade de Ciencias, 32004 Ourense, Spain
| | - Enrique Barreiro
- Universidade de Vigo, Departamento de Informática, Escola Superior de Enxeñaría Informática, 32004 Ourense, Spain
| | - Juan Carlos Mejuto
- Universidade de Vigo, Departamento de Química Física, Facultade de Ciencias, 32004 Ourense, Spain
| | - Antonio Cid-Samamed
- Universidade de Vigo, Departamento de Química Física, Facultade de Ciencias, 32004 Ourense, Spain
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9
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Feng T, Wu T, Zhang Y, Zhou L, Liu S, Li L, Li M, Hu E, Wang Q, Fu X, Zhan L, Xie Z, Xie W, Huang X, Shang X, Yu G. Stemness Analysis Uncovers That The Peroxisome Proliferator-Activated Receptor Signaling Pathway Can Mediate Fatty Acid Homeostasis In Sorafenib-Resistant Hepatocellular Carcinoma Cells. Front Oncol 2022; 12:912694. [PMID: 35957896 PMCID: PMC9361019 DOI: 10.3389/fonc.2022.912694] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 06/22/2022] [Indexed: 12/24/2022] Open
Abstract
Hepatocellular carcinoma (HCC) stem cells are regarded as an important part of individualized HCC treatment and sorafenib resistance. However, there is lacking systematic assessment of stem-like indices and associations with a response of sorafenib in HCC. Our study thus aimed to evaluate the status of tumor dedifferentiation for HCC and further identify the regulatory mechanisms under the condition of resistance to sorafenib. Datasets of HCC, including messenger RNAs (mRNAs) expression, somatic mutation, and clinical information were collected. The mRNA expression-based stemness index (mRNAsi), which can represent degrees of dedifferentiation of HCC samples, was calculated to predict drug response of sorafenib therapy and prognosis. Next, unsupervised cluster analysis was conducted to distinguish mRNAsi-based subgroups, and gene/geneset functional enrichment analysis was employed to identify key sorafenib resistance-related pathways. In addition, we analyzed and confirmed the regulation of key genes discovered in this study by combining other omics data. Finally, Luciferase reporter assays were performed to validate their regulation. Our study demonstrated that the stemness index obtained from transcriptomic is a promising biomarker to predict the response of sorafenib therapy and the prognosis in HCC. We revealed the peroxisome proliferator-activated receptor signaling pathway (the PPAR signaling pathway), related to fatty acid biosynthesis, that was a potential sorafenib resistance pathway that had not been reported before. By analyzing the core regulatory genes of the PPAR signaling pathway, we identified four candidate target genes, retinoid X receptor beta (RXRB), nuclear receptor subfamily 1 group H member 3 (NR1H3), cytochrome P450 family 8 subfamily B member 1 (CYP8B1) and stearoyl-CoA desaturase (SCD), as a signature to distinguish the response of sorafenib. We proposed and validated that the RXRB and NR1H3 could directly regulate NR1H3 and SCD, respectively. Our results suggest that the combined use of SCD inhibitors and sorafenib may be a promising therapeutic approach.
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Affiliation(s)
- Tingze Feng
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Tianzhi Wu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Yanxia Zhang
- Department of Medical Genetics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Lang Zhou
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Shanshan Liu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Country Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Hepatology Unit and Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Lin Li
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Ming Li
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Erqiang Hu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Qianwen Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Xiaocong Fu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Li Zhan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Zijing Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Wenqin Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Xianying Huang
- Division of Vascular and Interventional Radiology, Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
- *Correspondence: Xianying Huang, ; Xuan Shang, ; Guangchuang Yu,
| | - Xuan Shang
- Department of Medical Genetics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- *Correspondence: Xianying Huang, ; Xuan Shang, ; Guangchuang Yu,
| | - Guangchuang Yu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Division of Vascular and Interventional Radiology, Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
- *Correspondence: Xianying Huang, ; Xuan Shang, ; Guangchuang Yu,
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10
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An C, Yang H, Yu X, Han ZY, Cheng Z, Liu F, Dou J, Li B, Li Y, Li Y, Yu J, Liang P. A Machine Learning Model Based on Health Records for Predicting Recurrence After Microwave Ablation of Hepatocellular Carcinoma. J Hepatocell Carcinoma 2022; 9:671-684. [PMID: 35923613 PMCID: PMC9342890 DOI: 10.2147/jhc.s358197] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 07/08/2022] [Indexed: 12/24/2022] Open
Abstract
Background and Aim Early recurrence (ER) presents a challenge for the survival prognosis of patients with hepatocellular carcinoma (HCC). The aim of this study was to investigate machine learning (ML) models using clinical data for predicting ER after microwave ablation (MWA). Methods Between August 2005 and December 2019, 1574 patients with early-stage HCC underwent MWA at four hospitals were reviewed. Then, 36 clinical data points per patient were collected, and the patients were assigned to the training, internal, and external validation set. Apart from traditional logistic regression (LR), three ML models—random forest, support vector machine, and eXtreme Gradient Boosting (XGBoost)—were built and validated for their predictive ability with the area under ROC curve (AUC). Algorithms such as SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) were used to realize their interpretability. Results The three ML models all outperformed LR (P < 0.001 for all) in predictive ability. When nine variables (tumor number, platelet, α-fetoprotein, comorbidity score, white blood cell, cholinesterase, prothrombin time, neutrophils, and etiology) were extracted simultaneously using recursive feature elimination with cross-validation, the XGBoost model achieved the best discrimination among all models, with an AUC value 0.75 (95% CI [confidence interval]: 0.72–0.78) in the training set, 0.74 (95% CI: 0.69–0.80) in the internal validation set, and 0.76 (95% CI: 0.70–0.82) in the external validation set, and it was interpreted depending on the visualization of risk factors by the SHAP and LIME algorithms. The predictive system of post-ablation recurrence risk stratification was provided on online (http://114.251.235.51:8001/) based on XGboost analysis. Conclusion The XGBoost model based on clinical data can effectively predict ER risk after MWA, which can contribute to surveillance, prevention, and treatment strategies for HCC.
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Affiliation(s)
- Chao An
- Department of Ultrasound, PLA Medical College & 5th Medical Center of Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China
| | - Hongcai Yang
- Department of Ultrasound, PLA Medical College & 5th Medical Center of Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China
- School of Medicine, Nankai University, Tianjin, People’s Republic of China
| | - Xiaoling Yu
- Department of Ultrasound, PLA Medical College & 5th Medical Center of Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China
| | - Zhi-Yu Han
- Department of Ultrasound, PLA Medical College & 5th Medical Center of Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China
| | - Zhigang Cheng
- Department of Ultrasound, PLA Medical College & 5th Medical Center of Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China
| | - Fangyi Liu
- Department of Ultrasound, PLA Medical College & 5th Medical Center of Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China
| | - Jianping Dou
- Department of Ultrasound, PLA Medical College & 5th Medical Center of Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China
| | - Bing Li
- National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, Beijing, People’s Republic of China
| | - Yansheng Li
- DHC Mediway Technology CO, Ltd, Beijing, People’s Republic of China
| | - Yichao Li
- DHC Mediway Technology CO, Ltd, Beijing, People’s Republic of China
| | - Jie Yu
- Department of Ultrasound, PLA Medical College & 5th Medical Center of Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China
| | - Ping Liang
- Department of Ultrasound, PLA Medical College & 5th Medical Center of Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China
- Correspondence: Ping Liang; Jie Yu, Department of Ultrasound, PLA Medical College & 5th Medical Center of Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China, Tel +86-10-66939530, Fax +86-10-68161218, Email ;
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11
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Combined transarterial iodized oil injection and computed tomography-guided thermal ablation for hepatocellular carcinoma: utility of the iodized oil retention pattern. Abdom Radiol (NY) 2022; 47:431-442. [PMID: 34642785 PMCID: PMC8776722 DOI: 10.1007/s00261-021-03305-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 09/30/2021] [Accepted: 10/01/2021] [Indexed: 11/21/2022]
Abstract
Purpose To investigate whether the iodized oil (Lipiodol, Guerbet Group, Villepinte, France) retention pattern influences the treatment efficacy of combined transarterial Lipiodol injection (TLI) and thermal ablation in patients with hepatocellular carcinoma (HCC). Methods Data of 198 patients (280 HCC lesions), who underwent TLI plus computed tomography (CT)-guided thermal ablation at three separate medical institutions between June 2014 and September 2020, were reviewed and analyzed. The Lipiodol retention pattern was classified as complete or incomplete based on non-enhanced CT at the time of ablation. The primary outcome was local recurrence-free survival (LRFS) for lesions; the secondary outcome was overall survival (OS) for patients. Propensity score matching (PSM) was performed using a caliper width of 0.1 between the two groups. Differences in LRFS and OS between the two groups were compared using the log-rank test. Results A total of 133 lesions exhibited a complete Lipiodol retention pattern, while 147 exhibited an incomplete pattern. After PSM analysis of baseline characteristics of the lesions, 121 pairs of lesions were matched. LRFS was significantly longer for lesions exhibiting complete retention than for those exhibiting incomplete retention (P = 0.030). After PSM analysis of patient baseline characteristics, 74 pairs of patients were matched. There was no significant difference in OS between the two groups (P = 0.456). Conclusion Lipiodol retention patterns may influence the treatment efficacy of combined TLI and thermal ablation for HCC lesions. However, a survival benefit for the Lipiodol retention pattern among HCC patients was not observed and needs further confirmation.
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12
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Wong GLH, Hui VWK, Tan Q, Xu J, Lee HW, Yip TCF, Yang B, Tse YK, Yin C, Lyu F, Lai JCT, Lui GCY, Chan HLY, Yuen PC, Wong VWS. Novel machine-learning models outperform risk scores in predicting hepatocellular carcinoma in patients with chronic viral hepatitis. JHEP Rep 2022; 4:100441. [PMID: 35198928 PMCID: PMC8844233 DOI: 10.1016/j.jhepr.2022.100441] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 12/20/2021] [Accepted: 12/28/2021] [Indexed: 02/08/2023] Open
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13
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Yang XY, Deng JB, An TZ, Zhou S, Li JX. Tumor enhancement ratio with unenhanced imaging is an independent prognostic factor for patients with hepatocellular carcinoma after transarterial chemoembolization. J Int Med Res 2021; 49:3000605211058367. [PMID: 34812068 PMCID: PMC8647277 DOI: 10.1177/03000605211058367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Objective To investigative whether the odds tumor enhancement ratio (OTER) on
cross-sectional imaging is a prognostic factor for hepatocellular carcinoma
after transarterial chemoembolization (TACE). Methods This study involved 126 patients who underwent TACE from May 2015 to March
2019. The signal intensity/Hounsfield units (HU) was measured by placing
regions of interest on the tumor and surrounding liver in unenhanced and
arterial-phase contrast-enhanced cross-sectional images. The OTER was
calculated as follows:
OTER = (HUTUMORart − HUTUMORun)/
(HULIVERart − HULIVERun). Univariate analysis was
performed to determine the factors associated with overall survival (OS).
Variables with a P value of <0.10 were included in the multivariate Cox
regression analysis. Results The median OS was 757 days. Tumors with a peripheral location, small size,
and low OTER had better OS than those with a central location, large size,
and high OTER. OS did not differ according to the extent of tumor
involvement or tumor enhancement pattern. The OTER, tumor location, and size
were included in the multivariate Cox regression analysis. A low OTER was
the predictor of better OS. Conclusion A high OTER is a risk factor for poor OS in patients undergoing TACE. This
should be taken into consideration before the procedure.
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Affiliation(s)
- Xi-Yuan Yang
- Department of Interventional Radiology, the Affiliated Baiyun Hospital of Guizhou Medical University, Guiyang, China
| | - Jiang-Bei Deng
- Department of Interventional Radiology, Changsha Central Hospital, University of South China, Changsha, China
| | - Tian-Zhi An
- Department of Interventional Radiology, 74720The Affiliated Hospital of Guizhou Medical University, the Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Shi Zhou
- Department of Interventional Radiology, 74720The Affiliated Hospital of Guizhou Medical University, the Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Jun-Xiang Li
- Department of Interventional Radiology, Guizhou Medical University Affiliated Cancer Hospital, Guiyang, China
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14
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Zou ZM, An TZ, Li JX, Zhang ZS, Xiao YD, Liu J. Predicting early refractoriness of transarterial chemoembolization in patients with hepatocellular carcinoma using a random forest algorithm: A pilot study. J Cancer 2021; 12:7079-7087. [PMID: 34729109 PMCID: PMC8558659 DOI: 10.7150/jca.63370] [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: 05/31/2021] [Accepted: 10/03/2021] [Indexed: 12/13/2022] Open
Abstract
Purpose: To develop and validate a random forest (RF) based predictive model of early refractoriness to transarterial chemoembolization (TACE) in patients with unresectable hepatocellular carcinoma (HCC). Methods: A total of 227 patients with unresectable HCC who initially treated with TACE from three independent institutions were retrospectively included. Following a random split, 158 patients (70%) were assigned to a training cohort and the remaining 69 patients (30%) were assigned to a validation cohort. The process of variables selection was based on the importance variable scores generated by RF algorithm. A RF predictive model incorporating the selected variables was developed, and five-fold cross-validation was performed. The discrimination and calibration of the RF model were measured by a receiver operating characteristic (ROC) curve and the Hosmer-Lemeshow test. Results: The potential variables selected by RF algorithm for developing predictive model of early TACE refractoriness included patients' age, number of tumors, tumor distribution, platelet count (PLT), and neutrophil-to-lymphocyte ratio (NLR). The results showed that the RF predictive model had good discrimination ability, with an area under curve (AUC) of 0.863 in the training cohort and 0.767 in the validation cohort, respectively. In Hosmer-Lemeshow test, the RF model had a satisfactory calibration with P values of 0.538 and 0.068 in training cohort and validation cohort, respectively. Conclusion: The RF algorithm-based model has a good predictive performance in the prediction of early TACE refractoriness, which may easily be deployed in clinical routine and help to determine the optimal patient of care.
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Affiliation(s)
- Zhi-Min Zou
- Department of Radiology, the Second Xiangya Hospital of Central South University, Changsha, 410011, China.,Department of Radiology, Hunan Children's Hospital, Changsha, 410007, China
| | - Tian-Zhi An
- Department of Interventional Radiology, the Affiliated Hospital of Guizhou Medical University, Guiyang, 550002, China
| | - Jun-Xiang Li
- Department of Interventional Radiology, Guizhou Medical University Affiliated Cancer Hospital, Guiyang, 550004, China
| | - Zi-Shu Zhang
- Department of Radiology, the Second Xiangya Hospital of Central South University, Changsha, 410011, China
| | - Yu-Dong Xiao
- Department of Radiology, the Second Xiangya Hospital of Central South University, Changsha, 410011, China.,Clinical Research Center for Medical Imaging in Hunan Province, Changsha, 410011, China.,Department of Radiology Quality Control Center, Changsha, 410011, China
| | - Jun Liu
- Department of Radiology, the Second Xiangya Hospital of Central South University, Changsha, 410011, China.,Clinical Research Center for Medical Imaging in Hunan Province, Changsha, 410011, China.,Department of Radiology Quality Control Center, Changsha, 410011, China
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15
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Blidisel A, Marcovici I, Coricovac D, Hut F, Dehelean CA, Cretu OM. Experimental Models of Hepatocellular Carcinoma-A Preclinical Perspective. Cancers (Basel) 2021; 13:3651. [PMID: 34359553 PMCID: PMC8344976 DOI: 10.3390/cancers13153651] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 07/16/2021] [Accepted: 07/17/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC), the most frequent form of primary liver carcinoma, is a heterogenous and complex tumor type with increased incidence, poor prognosis, and high mortality. The actual therapeutic arsenal is narrow and poorly effective, rendering this disease a global health concern. Although considerable progress has been made in terms of understanding the pathogenesis, molecular mechanisms, genetics, and therapeutical approaches, several facets of human HCC remain undiscovered. A valuable and prompt approach to acquire further knowledge about the unrevealed aspects of HCC and novel therapeutic candidates is represented by the application of experimental models. Experimental models (in vivo and in vitro 2D and 3D models) are considered reliable tools to gather data for clinical usability. This review offers an overview of the currently available preclinical models frequently applied for the study of hepatocellular carcinoma in terms of initiation, development, and progression, as well as for the discovery of efficient treatments, highlighting the advantages and the limitations of each model. Furthermore, we also focus on the role played by computational studies (in silico models and artificial intelligence-based prediction models) as promising novel tools in liver cancer research.
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Affiliation(s)
- Alexandru Blidisel
- Faculty of Medicine, “Victor Babeș” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania; (A.B.); (F.H.); (O.M.C.)
| | - Iasmina Marcovici
- Faculty of Pharmacy, “Victor Babeș” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania;
- Research Center for Pharmaco-Toxicological Evaluations, Faculty of Pharmacy, “Victor Babes” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania
| | - Dorina Coricovac
- Faculty of Pharmacy, “Victor Babeș” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania;
- Research Center for Pharmaco-Toxicological Evaluations, Faculty of Pharmacy, “Victor Babes” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania
| | - Florin Hut
- Faculty of Medicine, “Victor Babeș” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania; (A.B.); (F.H.); (O.M.C.)
| | - Cristina Adriana Dehelean
- Faculty of Pharmacy, “Victor Babeș” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania;
- Research Center for Pharmaco-Toxicological Evaluations, Faculty of Pharmacy, “Victor Babes” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania
| | - Octavian Marius Cretu
- Faculty of Medicine, “Victor Babeș” University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, RO-300041 Timișoara, Romania; (A.B.); (F.H.); (O.M.C.)
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16
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Zhou H, Jiang T, Li Q, Zhang C, Zhang C, Liu Y, Cao J, Sun Y, Jin P, Luo J, Pan M, Huang P. US-Based Deep Learning Model for Differentiating Hepatocellular Carcinoma (HCC) From Other Malignancy in Cirrhotic Patients. Front Oncol 2021; 11:672055. [PMID: 34168992 PMCID: PMC8217663 DOI: 10.3389/fonc.2021.672055] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 04/20/2021] [Indexed: 12/12/2022] Open
Abstract
The aim was to build a predictive model based on ultrasonography (US)-based deep learning model (US-DLM) and clinical features (Clin) for differentiating hepatocellular carcinoma (HCC) from other malignancy (OM) in cirrhotic patients. 112 patients with 120 HCCs and 60 patients with 61 OMs were included. They were randomly divided into training and test cohorts with a 4:1 ratio for developing and evaluating US-DLM model, respectively. Significant Clin predictors of OM in the training cohort were combined with US-DLM to build a nomogram predictive model (US-DLM+Clin). The diagnostic performance of US-DLM and US-DLM+Clin were compared with that of contrast enhanced magnetic resonance imaging (MRI) liver imaging and reporting system category M (MRI LR-M). US-DLM was the best independent predictor for evaluating OMs, followed by clinical information, including high cancer antigen 199 (CA199) level and female. The US-DLM achieved an AUC of 0.74 in the test cohort, which was comparable with that of MRI LR-M (AUC=0.84, p=0.232). The US-DLM+Clin for predicting OMs also had similar AUC value (0.81) compared with that of LR-M+Clin (0.83, p>0.05). US-DLM+Clin obtained a higher specificity, but a lower sensitivity, compared to that of LR-M +Clin (Specificity: 82.6% vs. 73.9%, p=0.007; Sensitivity: 78.6% vs. 92.9%, p=0.006) for evaluating OMs in the test set. The US-DLM+Clin model is valuable in differentiating HCC from OM in the setting of cirrhosis.
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Affiliation(s)
- Hang Zhou
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Jiang
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qunying Li
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chao Zhang
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cong Zhang
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yajing Liu
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jing Cao
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yu Sun
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Peile Jin
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiali Luo
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Minqiang Pan
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Pintong Huang
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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