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Mulé S. Editorial for "Preoperative Gadoxetic Acid-Enhanced MRI Features for Evaluation of Vessels Encapsulating Tumor Clusters and Microvascular Invasion in Hepatocellular Carcinoma: Creating Nomograms for Risk Assessment". J Magn Reson Imaging 2024; 60:1111-1112. [PMID: 38140862 DOI: 10.1002/jmri.29197] [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: 12/04/2023] [Accepted: 12/06/2023] [Indexed: 12/24/2023] Open
Abstract
Level of Evidence5Technical Efficacy Stage1
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Affiliation(s)
- Sébastien Mulé
- Service d'Imagerie Médicale, AP-HP, Hôpitaux Universitaires Henri Mondor, Créteil Cedex, France
- Faculté de Médecine, Université Paris Est Créteil, Créteil, France
- INSERM IMRB, U 955, Equipe 18, Créteil, France
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Qu Q, Liu Z, Lu M, Xu L, Zhang J, Liu M, Jiang J, Gu C, Ma Q, Huang A, Zhang X, Zhang T. Preoperative Gadoxetic Acid-Enhanced MRI Features for Evaluation of Vessels Encapsulating Tumor Clusters and Microvascular Invasion in Hepatocellular Carcinoma: Creating Nomograms for Risk Assessment. J Magn Reson Imaging 2024; 60:1094-1110. [PMID: 38116997 DOI: 10.1002/jmri.29187] [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: 09/14/2023] [Revised: 12/01/2023] [Accepted: 12/02/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Vessels encapsulating tumor cluster (VETC) and microvascular invasion (MVI) have a synergistic effect on prognosis assessment and treatment selection of hepatocellular carcinoma (HCC). Preoperative noninvasive evaluation of VETC and MVI is important. PURPOSE To explore the diagnosis value of preoperative gadoxetic acid (GA)-enhanced magnetic resonance imaging (MRI) features for MVI, VETC, and recurrence-free survival (RFS) in HCC. STUDY TYPE Retrospective. POPULATION 240 post-surgery patients with 274 pathologically confirmed HCC (allocated to training and validation cohorts with a 7:3 ratio) and available tumor marker data from August 2014 to December 2021. FIELD STRENGTH/SEQUENCE 3-T, T1-, T2-, diffusion-weighted imaging, in/out-phase imaging, and dynamic contrast-enhanced imaging. ASSESSMENT Three radiologists subjectively reviewed preoperative MRI, evaluated clinical and conventional imaging features associated with MVI+, VETC+, and MVI+/VETC+ HCC. Regression-based nomograms were developed for HCC in the training cohort. Based on the nomograms, the RFS prognostic stratification system was further. Follow-up occurred every 3-6 months. STATISTICAL TESTS Chi-squared test or Fisher's exact test, Mann-Whitney U-test or t-test, least absolute shrinkage and selection operator-penalized, multivariable logistic regression analyses, receiver operating characteristic analysis, Harrell's concordance index (C-index), Kaplan-Meier plots. Significance level: P < 0.05. RESULTS In the training group, 44 patients with MVI+ and 74 patients with VETC+ were histologically confirmed. Three nomograms showed good performance in the training (C-indices: MVI+ vs. VETC+ vs. MVI+/VETC+, 0.892 vs. 0.848 vs. 0.910) and validation (C-indices: MVI+ vs. VETC+ vs. MVI+/VETC+, 0.839 vs. 0.810 vs. 0.855) cohorts. The median follow-up duration for the training cohort was 43.6 (95% CI, 35.0-52.2) months and 25.8 (95% CI, 16.1-35.6) months for the validation cohort. Patients with either pathologically confirmed or nomogram-estimated MVI, VETC, and MVI+/VETC+ suffered higher risk of recurrence. DATA CONCLUSION GA-enhanced MRI and clinical variables might assist in preoperative estimation of MVI, VETC, and MVI+/VETC+ in HCC. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Qi Qu
- Nantong University, Nantong, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Zixin Liu
- Nantong University, Nantong, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Mengtian Lu
- Nantong University, Nantong, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Lei Xu
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Jiyun Zhang
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Maotong Liu
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Jifeng Jiang
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Chunyan Gu
- Department of Pathology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Qinrong Ma
- Department of Pathology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Aina Huang
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Xueqin Zhang
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Tao Zhang
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
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Wang M, Cao L, Wang Y, Huang H, Tian X, Lei J. The prognostic value of vessels encapsulating tumor clusters (VETC) in patients with hepatocellular carcinoma: a systematic review and meta-analysis. Clin Transl Oncol 2024; 26:2037-2046. [PMID: 38523240 DOI: 10.1007/s12094-024-03427-2] [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: 02/08/2024] [Accepted: 02/25/2024] [Indexed: 03/26/2024]
Abstract
BACKGROUND Studies have suggested that vessels encapsulating tumor clusters (VETC) is a strong predictor of prognosis in patients with hepatocellular carcinoma (HCC). METHODS A systematic search was conducted in PubMed, Embase, Web of Science, and Scopus databases. Overall survival (OS) and tumor efficacy (TE) were two outcome measures used to evaluate the relationship between VETC and HCC prognosis. Hazard ratios (HR) and their 95% confidence intervals (CI) were used. RESULTS Thirteen studies with 4429 patients were included in the meta-analysis. The results showed that VETC was significantly associated with both OS (HR 2.00; 95% CI 1.64-2.45) and TE (HR 1.70; 95% CI 1.44-1.99) in HCC patients. Furthermore, recurrence-free survival (RFS) was a stronger indicator of tumor efficacy (HR 1.73; 95% CI 1.44-2.07) than disease-free survival (DFS) (HR 1.69; 95% CI 1.22-2.35). This suggests that VETC-positive HCC has a higher risk of recurrence and a lower survival rate. CONCLUSION In conclusion, the meta-analysis suggests that VETC is a significant predictor of overall survival and tumor efficacy in HCC patients and may be a valid prognostic indicator.
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Affiliation(s)
- Miaomiao Wang
- The First Clinical Medical College of Lanzhou University, No.1 Donggang West Road, Lanzhou City, Gansu Province, China
- Department of Radiology, The First Hospital of Lanzhou University, No.1 Donggang West Road, Lanzhou City, 730000, Gansu Province, China
| | - Liang Cao
- The First Clinical Medical College of Lanzhou University, No.1 Donggang West Road, Lanzhou City, Gansu Province, China
- Department of Radiology, The First Hospital of Lanzhou University, No.1 Donggang West Road, Lanzhou City, 730000, Gansu Province, China
| | - Yinzhong Wang
- The First Clinical Medical College of Lanzhou University, No.1 Donggang West Road, Lanzhou City, Gansu Province, China
- Department of Radiology, The First Hospital of Lanzhou University, No.1 Donggang West Road, Lanzhou City, 730000, Gansu Province, China
| | - Hongliang Huang
- The First Clinical Medical College of Lanzhou University, No.1 Donggang West Road, Lanzhou City, Gansu Province, China
- Department of Radiology, The First Hospital of Lanzhou University, No.1 Donggang West Road, Lanzhou City, 730000, Gansu Province, China
| | - Xiaoxue Tian
- Department of Nuclear Medicine, Second Hospital of LanZhou University, No.82, Cuiyingmen, Chengguan District, Lanzhou City, Gansu Province, China
| | - Junqiang Lei
- The First Clinical Medical College of Lanzhou University, No.1 Donggang West Road, Lanzhou City, Gansu Province, China.
- Department of Radiology, The First Hospital of Lanzhou University, No.1 Donggang West Road, Lanzhou City, 730000, Gansu Province, China.
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Yang J, Dong X, Jin S, Wang S, Wang Y, Zhang L, Wei Y, Wu Y, Wang L, Zhu L, Feng Y, Gan M, Hu H, Ji W. Radiomics Model of Dynamic Contrast-Enhanced MRI for Evaluating Vessels Encapsulating Tumor Clusters and Microvascular Invasion in Hepatocellular Carcinoma. Acad Radiol 2024:S1076-6332(24)00438-0. [PMID: 39025700 DOI: 10.1016/j.acra.2024.07.007] [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: 04/16/2024] [Revised: 06/26/2024] [Accepted: 07/02/2024] [Indexed: 07/20/2024]
Abstract
RATIONALE AND OBJECTIVES To develop and validate a clinical-radiomics model of dynamic contrast-enhanced MRI (DCE-MRI) for the preoperative discrimination of Vessels encapsulating tumor clusters (VETC)- microvascular invasion (MVI) and prognosis of hepatocellular carcinoma (HCC). MATERIALS AND METHODS 219 HCC patients from Institution 1 were split into internal training and validation groups, with 101 patients from Institution 2 assigned to external validation. Histologically confirmed VETC-MVI pattern categorizing HCC into VM-HCC+ (VETC+/MVI+, VETC-/MVI+, VETC+/MVI-) and VM-HCC- (VETC-/MVI-). The regions of intratumor and peritumor were segmented manually in the arterial, portal-venous and delayed phase (AP, PP, and DP, respectively) of DCE-MRI. Six radiomics models (intratumor and peritumor in AP, PP, and DP of DCE-MRI) and one clinical model were developed for assessing VM-HCC. Establishing intra-tumoral and peri-tumoral models through combining intratumor and peritumor features. The best-performing radiomics model and the clinical model were then integrated to create a Combined model. RESULTS In institution 1, pathological VM-HCC+ were confirmed in 88 patients (training set: 61, validation set: 27). In internal testing, the Combined model had an AUC of 0.85 (95% CI: 0.76-0.93), which reached an AUC of 0.75 (95% CI: 0.66-0.85) in external validation. The model's predictions were associated with early recurrence and progression-free survival in HCC patients. CONCLUSIONS The clinical-radiomics model offers a non-invasive approach to discern VM-HCC and predict HCC patients' prognosis preoperatively, which could offer clinicians valuable insights during the decision-making phase.
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Affiliation(s)
- Jiawen Yang
- Department of Radiology, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou, Zhejiang 317000, China; Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China.
| | - Xue Dong
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
| | - Shengze Jin
- Department of Radiology, Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou 318000 Zhejiang, China.
| | - Sheng Wang
- Department of Radiology, Taizhou First People's Hospital, Wenzhou Medical College, Taizhou 318020 Zhejiang, China.
| | - Yanna Wang
- Department of Radiology, Taizhou Central Hospital,Wenzhou Medical University, Taizhou 318000 Zhejiang,China.
| | - Limin Zhang
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Yuguo Wei
- Precision Health Institution, GE Healthcare, 310000 Xihu District, Hangzhou, China.
| | - Yitian Wu
- Department of Radiology, Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou 318000 Zhejiang, China.
| | - Lingxia Wang
- Department of Radiology, Taizhou Hospital, Zhejiang University, Taizhou 318000 Zhejiang, China.
| | - Lingwei Zhu
- Department of Radiology, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou, Zhejiang 317000, China.
| | - Yuyi Feng
- Department of Radiology, Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou 318000 Zhejiang, China.
| | - Meifu Gan
- Department of Pathology, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou, Zhejiang 317000, China.
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 310016 Hangzhou, People's Republic of China.
| | - Wenbin Ji
- Department of Radiology, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou, Zhejiang 317000, China; Key Laboratory of evidence-based Radiology of Taizhou, Linhai 317000, Zhejiang, China.
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Xin Z, Chen H, Xu J, Zhang H, Peng Y, Ren J, Guo Q, Song J, Jiao L, You L, Bai L, Wei Y, Zhou J, Ying B. Exosomal mRNA in plasma serves as a predictive marker for microvascular invasion in hepatocellular carcinoma. J Gastroenterol Hepatol 2024. [PMID: 38972728 DOI: 10.1111/jgh.16677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 06/03/2024] [Accepted: 06/19/2024] [Indexed: 07/09/2024]
Abstract
BACKGROUND AND AIM There is a pressing need for non-invasive preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). This study investigates the potential of exosome-derived mRNA in plasma as a biomarker for diagnosing MVI. METHODS Patients with suspected HCC undergoing hepatectomy were prospectively recruited for preoperative peripheral blood collection. Exosomal RNA profiling was conducted using RNA sequencing in the discovery cohort, followed by differential expression analysis to identify candidate targets. We employed multiplexed droplet digital PCR technology to efficiently validate them in a larger sample size cohort. RESULTS A total of 131 HCC patients were ultimately enrolled, with 37 in the discovery cohort and 94 in the validation cohort. In the validation cohort, the expression levels of RSAD2, PRPSAP1, and HOXA2 were slightly elevated while CHMP4A showed a slight decrease in patients with MVI compared with those without MVI. These trends were consistent with the findings in the discovery cohort, although they did not reach statistical significance (P > 0.05). Notably, the expression level of exosomal PRPSAP1 in plasma was significantly higher in patients with more than 5 MVI than in those without MVI (0.147 vs 0.070, P = 0.035). CONCLUSION This study unveils the potential of exosome-derived PRPSAP1 in plasma as a promising indicator for predicting MVI status preoperatively.
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Affiliation(s)
- Zhaodan Xin
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Hao Chen
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Jingtong Xu
- Department of Immunology, School of Basic Medical Sciences, Fudan University, Shanghai, China
| | - Haili Zhang
- Division of Liver Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yufu Peng
- Division of Liver Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Ren
- Department of Laboratory Medicine, Guangyuan Central Hospital, Guangyuan, China
| | - Qin Guo
- Department of Laboratory Medicine, The First People's Hospital of Ziyang, Ziyang, China
| | - Jiajia Song
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Lin Jiao
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Liting You
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Ling Bai
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Yonggang Wei
- Division of Liver Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Juan Zhou
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Binwu Ying
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
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Zhang C, Ma LD, Zhang XL, Lei C, Yuan SS, Li JP, Geng ZJ, Li XM, Quan XY, Zheng C, Geng YY, Zhang J, Zheng QL, Hou J, Xie SY, Lu LH, Xie CM. Magnetic Resonance Deep Learning Radiomic Model Based on Distinct Metastatic Vascular Patterns for Evaluating Recurrence-Free Survival in Hepatocellular Carcinoma. J Magn Reson Imaging 2024; 60:231-242. [PMID: 37888871 DOI: 10.1002/jmri.29064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/29/2023] [Accepted: 09/29/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND The metastatic vascular patterns of hepatocellular carcinoma (HCC) are mainly microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC). However, most existing VETC-related radiological studies still focus on the prediction of VETC status. PURPOSE This study aimed to build and compare VETC-MVI related models (clinical, radiomics, and deep learning) associated with recurrence-free survival of HCC patients. STUDY TYPE Retrospective. POPULATION 398 HCC patients (349 male, 49 female; median age 51.7 years, and age range: 22-80 years) who underwent resection from five hospitals in China. The patients were randomly divided into training cohort (n = 358) and test cohort (n = 40). FIELD STRENGTH/SEQUENCE 3-T, pre-contrast T1-weighted imaging spoiled gradient recalled echo (T1WI SPGR), T2-weighted imaging fast spin echo (T2WI FSE), and contrast enhanced arterial phase (AP), delay phase (DP). ASSESSMENT Two radiologists performed the segmentation of HCC on T1WI, T2WI, AP, and DP images, from which radiomic features were extracted. The RFS related clinical characteristics (VETC, MVI, Barcelona stage, tumor maximum diameter, and alpha fetoprotein) and radiomic features were used to build the clinical model, clinical-radiomic (CR) nomogram, deep learning model. The follow-up process was done 1 month after resection, and every 3 months subsequently. The RFS was defined as the date of resection to the date of recurrence confirmed by radiology or the last follow-up. Patients were followed up until December 31, 2022. STATISTICAL TESTS Univariate COX regression, least absolute shrinkage and selection operator (LASSO), Kaplan-Meier curves, log-rank test, C-index, and area under the curve (AUC). P < 0.05 was considered statistically significant. RESULTS The C-index of deep learning model achieved 0.830 in test cohort compared with CR nomogram (0.731), radiomic signature (0.707), and clinical model (0.702). The average RFS of the overall patients was 26.77 months (range 1-80 months). DATA CONCLUSION MR deep learning model based on VETC and MVI provides a potential tool for survival assessment. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Cheng Zhang
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Li-di Ma
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | | | - Cai Lei
- Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Sha-Sha Yuan
- Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Jian-Peng Li
- Department of Radiology, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China
| | - Zhi-Jun Geng
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xin-Ming Li
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Xian-Yue Quan
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Chao Zheng
- Shukun (Beijing) Technology Co, Ltd., Beijing, China
| | - Ya-Yuan Geng
- Shukun (Beijing) Technology Co, Ltd., Beijing, China
| | - Jie Zhang
- Department of Radiology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China
| | - Qiao-Li Zheng
- Department of Pathology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China
| | - Jing Hou
- Department of Radiology, Hunan Cancer Hospital, Guangzhou, China
| | - Shu-Yi Xie
- Department of Radiology, Guangzhou People's Eighth Hospital, Guangzhou, China
| | - Liang-He Lu
- Department of Hepatobiliary Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Chuan-Miao Xie
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China
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Xiong SP, Wang CH, Zhang MF, Yang X, Yun JP, Liu LL. A multi-parametric prognostic model based on clinicopathologic features: vessels encapsulating tumor clusters and hepatic plates predict overall survival in hepatocellular carcinoma patients. J Transl Med 2024; 22:472. [PMID: 38762511 PMCID: PMC11102615 DOI: 10.1186/s12967-024-05296-3] [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: 02/03/2024] [Accepted: 05/11/2024] [Indexed: 05/20/2024] Open
Abstract
BACKGROUND Vessels encapsulating tumor clusters (VETC) is a newly described vascular pattern that is distinct from microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). Despite its importance, the current pathological diagnosis report does not include information on VETC and hepatic plates (HP). We aimed to evaluate the prognostic value of integrating VETC and HP (VETC-HP model) in the assessment of HCC. METHODS A total of 1255 HCC patients who underwent radical surgery were classified into training (879 patients) and validation (376 patients) cohorts. Additionally, 37 patients treated with lenvatinib were studied, included 31 patients in high-risk group and 6 patients in low-risk group. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to establish a prognostic model for the training set. Harrell's concordance index (C-index), time-dependent receiver operating characteristics curve (tdROC), and decision curve analysis were utilized to evaluate our model's performance by comparing it to traditional tumor node metastasis (TNM) staging for individualized prognosis. RESULTS A prognostic model, VETC-HP model, based on risk scores for overall survival (OS) was established. The VETC-HP model demonstrated robust performance, with area under the curve (AUC) values of 0.832 and 0.780 for predicting 3- and 5-year OS in the training cohort, and 0.805 and 0.750 in the validation cohort, respectively. The model showed superior prediction accuracy and discrimination power compared to TNM staging, with C-index values of 0.753 and 0.672 for OS and disease-free survival (DFS) in the training cohort, and 0.728 and 0.615 in the validation cohort, respectively, compared to 0.626 and 0.573 for TNM staging in the training cohort, and 0.629 and 0.511 in the validation cohort. Thus, VETC-HP model had higher C-index than TNM stage system(p < 0.01).Furthermore, in the high-risk group, lenvatinib alone appeared to offer less clinical benefit but better disease-free survival time. CONCLUSIONS The VETC-HP model enhances DFS and OS prediction in HCC compared to traditional TNM staging systems. This model enables personalized temporal survival estimation, potentially improving clinical decision-making in surveillance management and treatment strategies.
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Affiliation(s)
- Si-Ping Xiong
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, P.R. China
- Department of Pathology, Sun Yat-Sen University Cancer Center, 651# Dong Feng Road East, Guangzhou, 510060, Guangdong, China
- Department of Pathology, The Eighth Affiliated Hospital of Sun Yat-Sen University, Shenzhen, 518033, China
| | - Chun-Hua Wang
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, P.R. China
- Department of Pathology, Sun Yat-Sen University Cancer Center, 651# Dong Feng Road East, Guangzhou, 510060, Guangdong, China
| | - Mei-Fang Zhang
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, P.R. China
- Department of Pathology, Sun Yat-Sen University Cancer Center, 651# Dong Feng Road East, Guangzhou, 510060, Guangdong, China
| | - Xia Yang
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, P.R. China
- Department of Pathology, Sun Yat-Sen University Cancer Center, 651# Dong Feng Road East, Guangzhou, 510060, Guangdong, China
| | - Jing-Ping Yun
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, P.R. China.
- Department of Pathology, Sun Yat-Sen University Cancer Center, 651# Dong Feng Road East, Guangzhou, 510060, Guangdong, China.
| | - Li-Li Liu
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, P.R. China.
- Department of Pathology, Sun Yat-Sen University Cancer Center, 651# Dong Feng Road East, Guangzhou, 510060, Guangdong, China.
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Wang H, Liu L, Gong H, Li H. Upregulation of FAM134B inhibits endoplasmic reticulum stress-related degradation protein expression and promotes hepatocellular carcinogenesis. J Cell Mol Med 2024; 28:e17964. [PMID: 37728036 PMCID: PMC10902567 DOI: 10.1111/jcmm.17964] [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: 06/17/2023] [Revised: 09/06/2023] [Accepted: 09/08/2023] [Indexed: 09/21/2023] Open
Abstract
Endoplasmic reticulum (ER) stress can stimulate the proliferation and metastasis of hepatocellular carcinoma (HCC) cells while hindering apoptosis and immune system function, but the molecular mechanism of ER stress in HCC has yet to be fully studied. We aim to investigate the molecular mechanism by which FAM134B inhibits autophagy of HCC cells by reducing the expression of ER stress-related degradation proteins. Clinical samples were collected for this study. Normal liver cell lines HL7702 and Hep3B and Huh7 HCC cell lines were cultured. Construction of FAM134B knockdown cell line. Cell proliferation was measured using the CCK-8 assay, while cell migration and invasion capabilities were detected using the plate colony formation assay. Flow cytometry was used to detect the apoptosis rate. Transmission electron microscopy was used to observe the formation of autophagosomes. qRT-PCR and WB detective expression changes related to autophagy proteins. Finally, the expression of the relevant proteins was observed by immunohistochemistry. The expression of FAM134B was significantly increased in human liver cancer tissue and HCC cell lines Hep3B and Huh7. After the lentiviral vector was transfected into Hep3B cells with sh-FAM134B, results showed that sh-FAM134B could effectively inhibit Hep3B cell proliferation and promote HCC cell apoptosis. Meanwhile, sh-FAM134B could effectively induce the autophagy of Hep3B liver cancer cells. Immunohistochemistry results showed that sh-FAM134B could effectively induce ER stress. FAM134B inhibits HCC cell autophagy and promotes the progression of liver cancer by inhibiting the expression of ER stress-related degradation factors such as DERL2, EDEM1, SEL1L and HRD1.
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Affiliation(s)
- Houhong Wang
- Department of General SurgeryThe Affiliated Bozhou Hospital of Anhui Medical UniversityBozhouChina
| | - Lu Liu
- Department of Endocrine DepartmentThe Affiliated Nantong Hospital of Shanghai Jiao Tong UniversityNantongChina
| | - Huihui Gong
- Faculty of Health and Life SciencesOxford Brookes UniversityOxfordEnglandUK
| | - Heng Li
- Department of Comprehensive Surgery, Anhui Provincial Cancer HospitalWest District of The First Affiliated Hospital of USTCHefeiChina
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Dong X, Yang J, Zhang B, Li Y, Wang G, Chen J, Wei Y, Zhang H, Chen Q, Jin S, Wang L, He H, Gan M, Ji W. Deep Learning Radiomics Model of Dynamic Contrast-Enhanced MRI for Evaluating Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular Carcinoma. J Magn Reson Imaging 2024; 59:108-119. [PMID: 37078470 DOI: 10.1002/jmri.28745] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 04/01/2023] [Accepted: 04/03/2023] [Indexed: 04/21/2023] Open
Abstract
BACKGROUND Vessels encapsulating tumor cluster (VETC) is a critical prognostic factor and therapeutic predictor of hepatocellular carcinoma (HCC). However, noninvasive evaluation of VETC remains challenging. PURPOSE To develop and validate a deep learning radiomic (DLR) model of dynamic contrast-enhanced MRI (DCE-MRI) for the preoperative discrimination of VETC and prognosis of HCC. STUDY TYPE Retrospective. POPULATION A total of 221 patients with histologically confirmed HCC and stratified this cohort into training set (n = 154) and time-independent validation set (n = 67). FIELD STRENGTH/SEQUENCE A 1.5 T and 3.0 T; DCE imaging with T1-weighted three-dimensional fast spoiled gradient echo. ASSESSMENT Histological specimens were used to evaluate VETC status. VETC+ cases had a visible pattern (≥5% tumor area), while cases without any pattern were VETC-. The regions of intratumor and peritumor were segmented manually in the arterial, portal-venous and delayed phase (AP, PP, and DP, respectively) of DCE-MRI and reproducibility of segmentation was evaluated. Deep neural network and machine learning (ML) classifiers (logistic regression, decision tree, random forest, SVM, KNN, and Bayes) were used to develop nine DLR, 54 ML and clinical-radiological (CR) models based on AP, PP, and DP of DCE-MRI for evaluating VETC status and association with recurrence. STATISTICAL TESTS The Fleiss kappa, intraclass correlation coefficient, receiver operating characteristic curve, area under the curve (AUC), Delong test and Kaplan-Meier survival analysis. P value <0.05 was considered as statistical significance. RESULTS Pathological VETC+ were confirmed in 68 patients (training set: 46, validation set: 22). In the validation set, DLR model based on peritumor PP (peri-PP) phase had the best performance (AUC: 0.844) in comparison to CR (AUC: 0.591) and ML (AUC: 0.672) models. Significant differences in recurrence rates between peri-PP DLR model-predicted VETC+ and VETC- status were found. DATA CONCLUSIONS The DLR model provides a noninvasive method to discriminate VETC status and prognosis of HCC patients preoperatively. EVIDENCE LEVEL 4. TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Xue Dong
- Department of Radiology, Taizhou Hospital, Zhejiang University, Taizhou, Zhejiang, China
| | - Jiawen Yang
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Binhao Zhang
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Yujing Li
- Department of Pathology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Guanliang Wang
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Jinyao Chen
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Yuguo Wei
- Precision Health Institution, GE Healthcare, Xihu District, Hangzhou, China
| | - Huangqi Zhang
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Qingqing Chen
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shengze Jin
- Department of Radiology, Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou, Zhejiang, China
| | - Lingxia Wang
- Department of Radiology, Taizhou Hospital, Zhejiang University, Taizhou, Zhejiang, China
| | - Haiqing He
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Meifu Gan
- Department of Pathology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Wenbin Ji
- Department of Radiology, Taizhou Hospital, Zhejiang University, Taizhou, Zhejiang, China
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Zhu Y, Yang L, Wang M, Pan J, Zhao Y, Huang H, Sun K, Chen F. Preoperative MRI features to predict vessels that encapsulate tumor clusters and microvascular invasion in hepatocellular carcinoma. Eur J Radiol 2023; 167:111089. [PMID: 37713969 DOI: 10.1016/j.ejrad.2023.111089] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/28/2023] [Accepted: 09/06/2023] [Indexed: 09/17/2023]
Abstract
OBJECTIVE To estimate the potential of preoperative MRI features in the prediction of the integration patterns of vessels that encapsulate tumor clusters (VETC) and microvascular invasion (MVI) (VM) patterns in hepatocellular carcinoma (HCC) patients after resection and to assess the prognostic value of VM patterns. MATERIALS AND METHODS Patients who underwent surgical resection for HCC between July 2019 and July 2020 were retrospectively included in the training cohort and validation cohort. In the training cohort, patients were classified into VM-positive HCC (VM-HCC) and VM-negative HCC (non-VM HCC). Predictors associated with VM-HCC were determined by using logistic regression analyses and used to build a prediction model of VM-HCC. The model was tested in the validation cohort by area under the receiver operating characteristic curve (AUC) analysis. Prognostic factors associated with early recurrence of HCC were evaluated by use of Cox logistic regression analyses. RESULTS Alpha-fetoprotein (AFP) level higher than 400 ng/mL (odds ratio [OR] = 8.0; 95% CI: 2.6-25.2; P < 0.001), non-smooth tumor margin (OR = 3.1; 95% CI: 1.4-6.0; P < 0.001) and peritumoral arterial enhancement (OR = 2.9; 95% CI: 1.4-6.2; P = 0.004) were independent predictors of VM-HCC. The AUCs of the prediction model for VM-HCC were 0.81 for the training cohort and 0.79 for the validation cohort. The high risk of VM-HCC predicted by the three preoperative predictors derived from the prediction model (hazard ratio [HR] 2.0; 95% CI: 1.3, 3.2; P = 0.003) were independently associated with early recurrence, while pathologically confirmed VM-HCC (HR 2.8; 95% CI: 1.6, 3.8; P < 0.001) and satellite nodules (HR 1.8; 95% CI: 1.1, 3.1; P = 0.025) were independently associated with early recurrence after surgical resection. CONCLUSION The predictive model can be used to predict VM patterns. VM-HCC is associated with increased risk of early recurrence after surgical resection in HCC.
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Affiliation(s)
- Yanyan Zhu
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, China.
| | - Lili Yang
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, China.
| | - Meng Wang
- Department of Pathology, the First Affiliated Hospital, Zhejiang University School of Medicine, China
| | - Junhan Pan
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, China.
| | - Yanci Zhao
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, China.
| | - Huizhen Huang
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, China.
| | - Ke Sun
- Department of Pathology, the First Affiliated Hospital, Zhejiang University School of Medicine, China.
| | - Feng Chen
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, China.
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Wang YY, Xing BC. Author's reply: Comment on ``Effect of vessels that encapsulate tumor clusters (VETC) and different stages of hepatocellular carcinoma after hepatectomy''. Dig Liver Dis 2023; 55:1441-1442. [PMID: 37666681 DOI: 10.1016/j.dld.2023.06.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 06/30/2023] [Indexed: 09/06/2023]
Affiliation(s)
- Yan-Yan Wang
- Hepatopancreatobiliary Surgery Department I, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University School of Oncology, Beijing Cancer Hospital and Institute, 52 Fucheng Road, Haidian District, Beijing 100142, China
| | - Bao-Cai Xing
- Hepatopancreatobiliary Surgery Department I, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University School of Oncology, Beijing Cancer Hospital and Institute, 52 Fucheng Road, Haidian District, Beijing 100142, China.
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Meng XP, Tang TY, Zhou Y, Xia C, Xia T, Shi Y, Long X, Liang Y, Xiao W, Wang YC, Fang X, Ju S. Predicting post-resection recurrence by integrating imaging-based surrogates of distinct vascular patterns of hepatocellular carcinoma. JHEP Rep 2023; 5:100806. [PMID: 37575884 PMCID: PMC10413153 DOI: 10.1016/j.jhepr.2023.100806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 05/08/2023] [Accepted: 05/10/2023] [Indexed: 08/15/2023] Open
Abstract
Background & Aims Distinct vascular patterns, including microvascular invasion (MVI) and vessels encapsulating tumour clusters (VETC), are associated with poor outcomes of hepatocellular carcinoma (HCC). Imaging surrogates of these vascular patterns potentially help to predict post-resection recurrence. Herein, a prognostic model integrating imaging-based surrogates of these distinct vascular patterns was developed to predict postoperative recurrence-free survival (RFS) in patients with HCC. Methods Clinico-radiological data of 1,285 patients with HCC from China undergoing surgical resection were retrospectively enrolled from seven medical centres between 2014 and 2020. A prognostic model using clinical data and imaging-based surrogates of MVI and VETC patterns was developed (n = 297) and externally validated (n = 373) to predict RFS. The surrogates (i.e. MVI and VETC scores) were individually built from preoperative computed tomography using two independent cohorts (n = 360 and 255). Whether the model's stratification was associated with postoperative recurrence following anatomic resection was also evaluated. Results The MVI and VETC scores demonstrated effective performance in their respective training and validation cohorts (AUC: 0.851-0.883 for MVI and 0.834-0.844 for VETC). The prognostic model incorporating serum alpha-foetoprotein, tumour multiplicity, MVI score, and VETC score achieved a C-index of 0.748-0.764 for the developing and external validation cohorts and generated three prognostically distinct strata. For patients at model-predicted medium risk, anatomic resection was associated with improved RFS (p <0.05). By contrast, anatomic resection had no impact on RFS in patients at model-predicted low or high risk (both p >0.05). Conclusions The proposed model integrating imaging-based surrogates of distinct vascular patterns enabled accurate prediction for RFS. It can potentially be used to identify HCC surgical candidates who may benefit from anatomic resection. Impact and implications MVI and VETC are distinct vascular patterns of HCC associated with aggressive biological behaviour and poor outcomes. Our multicentre study provided a model incorporating imaging-based surrogates of these patterns for preoperatively predicting RFS. The proposed model, which uses imaging detection to estimate the risk of MVI and VETC, offers an opportunity to help shed light on the association between tumour aggressiveness and prognosis and to support the selection of the appropriate type of surgical resection.
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Affiliation(s)
- Xiang-Pan Meng
- Department of Radiology, Jiangsu Key Laboratory of Molecular and Functional Imaging, Zhongda Hospital, Medical School of Southeast University, Nanjing, China
| | - Tian-Yu Tang
- Department of Radiology, Jiangsu Key Laboratory of Molecular and Functional Imaging, Zhongda Hospital, Medical School of Southeast University, Nanjing, China
| | - Yongping Zhou
- Department of Hepatobiliary Surgery, Jiangnan University Medical Center, Wuxi, China
| | - Cong Xia
- Department of Radiology, Jiangsu Key Laboratory of Molecular and Functional Imaging, Zhongda Hospital, Medical School of Southeast University, Nanjing, China
| | - Tianyi Xia
- Department of Radiology, Jiangsu Key Laboratory of Molecular and Functional Imaging, Zhongda Hospital, Medical School of Southeast University, Nanjing, China
| | - Yibing Shi
- Department of Radiology, The Affiliated Xuzhou Center Hospital of Southeast University, Xuzhou, China
| | - Xueying Long
- Department of Radiology, The Xiangya Hospital of Central South University, Changsha, China
| | - Yun Liang
- Department of Hepatic-Biliary-Pancreatic Center, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Wenbo Xiao
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Yuan-Cheng Wang
- Department of Radiology, Jiangsu Key Laboratory of Molecular and Functional Imaging, Zhongda Hospital, Medical School of Southeast University, Nanjing, China
| | - Xiangming Fang
- Department of Radiology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Shenghong Ju
- Department of Radiology, Jiangsu Key Laboratory of Molecular and Functional Imaging, Zhongda Hospital, Medical School of Southeast University, Nanjing, China
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Liu K, Dennis C, Prince DS, Marsh-Wakefield F, Santhakumar C, Gamble JR, Strasser SI, McCaughan GW. Vessels that encapsulate tumour clusters vascular pattern in hepatocellular carcinoma. JHEP Rep 2023; 5:100792. [PMID: 37456680 PMCID: PMC10339254 DOI: 10.1016/j.jhepr.2023.100792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 04/13/2023] [Accepted: 04/28/2023] [Indexed: 07/18/2023] Open
Abstract
Vessels that encapsulate tumour clusters (VETC) is a distinct histologic vascular pattern associated with a novel mechanism of metastasis. First described in human cancers in 2004, its prevalence and prognostic significance in hepatocellular carcinoma (HCC) has only been appreciated in the past decade with a rapidly increasing body of literature. A robust biomarker of aggressive disease, the VETC pattern is easy to recognise but relies on histologic examination of tumour tissue for its diagnosis. Radiological recognition of the VETC pattern is an area of active research and is becoming increasingly accurate. As a prognostic marker, VETC has consistently proven to be an independent predictor of disease recurrence and overall survival in patients with HCC undergoing resection and liver transplantation. It can also guide treatment by predicting response to other therapies such as transarterial chemoembolisation and sorafenib. Without prospective randomised-controlled trials or routine evaluation of VETC in clinical practice, there are currently no firm treatment recommendations for VETC-positive tumours, although some perspectives are provided in this review based on the latest knowledge of their pathogenesis - a complex interplay between tumour angiogenesis and the immune microenvironment. Nevertheless, VETC has great potential as a future biomarker that could take us one step closer to precision medicine for HCC.
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Affiliation(s)
- Ken Liu
- Australian National Liver Transplant Unit, Royal Prince Alfred Hospital, Sydney, NSW, Australia
- Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
- Liver Injury and Cancer Program, Centenary Institute, Sydney, NSW, Australia
| | - Claude Dennis
- Australian National Liver Transplant Unit, Royal Prince Alfred Hospital, Sydney, NSW, Australia
| | - David S. Prince
- Department of Gastroenterology, Liverpool Hospital, Sydney, NSW, Australia
| | - Felix Marsh-Wakefield
- Liver Injury and Cancer Program, Centenary Institute, Sydney, NSW, Australia
- Human Immunology Laboratory, The University of Sydney, Sydney, NSW, Australia
| | - Cositha Santhakumar
- Australian National Liver Transplant Unit, Royal Prince Alfred Hospital, Sydney, NSW, Australia
- Liver Injury and Cancer Program, Centenary Institute, Sydney, NSW, Australia
- Human Immunology Laboratory, The University of Sydney, Sydney, NSW, Australia
| | - Jennifer R. Gamble
- Centre for Endothelium, Vascular Biology Program, Centenary Institute, Sydney, NSW, Australia
| | - Simone I. Strasser
- Australian National Liver Transplant Unit, Royal Prince Alfred Hospital, Sydney, NSW, Australia
- Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Geoffrey W. McCaughan
- Australian National Liver Transplant Unit, Royal Prince Alfred Hospital, Sydney, NSW, Australia
- Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
- Liver Injury and Cancer Program, Centenary Institute, Sydney, NSW, Australia
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14
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Yang J, Dong X, Wang G, Chen J, Zhang B, Pan W, Zhang H, Jin S, Ji W. Preoperative MRI features for characterization of vessels encapsulating tumor clusters and microvascular invasion in hepatocellular carcinoma. Abdom Radiol (NY) 2023; 48:554-566. [PMID: 36385192 DOI: 10.1007/s00261-022-03740-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 10/31/2022] [Accepted: 11/01/2022] [Indexed: 11/17/2022]
Abstract
PURPOSE This study aimed to analyze imaging features based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the identification of vessels encapsulating tumor clusters (VETC)-microvascular invasion (MVI) in hepatocellular carcinoma (HCC), VM-HCC pattern. METHODS Patients who underwent hepatectomy and preoperative DCE-MRI between January 2015 and March 2021 were retrospectively analyzed. Clinical and imaging features related to VM-HCC (VETC + /MVI-, VETC-/MVI +, VETC + /MVI +) and Non-VM-HCC (VETC-/MVI-) were determined by multivariable logistic regression analyses. Early and overall recurrence were determined using the Kaplan-Meier survival curve. Indicators of early and overall recurrence were identified using the Cox proportional hazard regression model. RESULTS In total, 221 patients (177 men, 44 women; median age, 60 years; interquartile range, 52-66 years) were evaluated. The multivariable logistic regression analyses revealed fetoprotein > 400 ng/mL (odds ratio [OR] = 2.17, 95% confidence interval [CI] 1.07, 4.41, p = 0.033), intratumor vascularity (OR 2.15, 95% CI 1.07, 4.31, p = 0.031), and enhancement pattern (OR 2.71, 95% CI 1.17, 6.03, p = 0.019) as independent predictors of VM-HCC. In Kaplan-Meier survival analysis, intratumor vascularity was associated with early and overall recurrence (p < 0.05). CONCLUSION Based on DCE-MRI, intratumor vascularity can be used to characterize VM-HCC and is of prognostic significance for recurrence in patients with HCC.
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Affiliation(s)
- Jiawen Yang
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 150 Ximen St, Linhai, Taizhou, 317000, Zhejiang, China
| | - Xue Dong
- Department of Radiology, Taizhou Hospital, Zhejiang University, Taizhou, 318000, Zhejiang, China
| | - Guanliang Wang
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 150 Ximen St, Linhai, Taizhou, 317000, Zhejiang, China
| | - Jinyao Chen
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 150 Ximen St, Linhai, Taizhou, 317000, Zhejiang, China
| | - Binhao Zhang
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 150 Ximen St, Linhai, Taizhou, 317000, Zhejiang, China
| | - Wenting Pan
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 150 Ximen St, Linhai, Taizhou, 317000, Zhejiang, China
| | - Huangqi Zhang
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 150 Ximen St, Linhai, Taizhou, 317000, Zhejiang, China
| | - Shengze Jin
- Department of Radiology, Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou, 318000, Zhejiang, China
| | - Wenbin Ji
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 150 Ximen St, Linhai, Taizhou, 317000, Zhejiang, China.
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Zhang P, Ono A, Fujii Y, Hayes CN, Tamura Y, Miura R, Shirane Y, Nakahara H, Yamauchi M, Uchikawa S, Uchida T, Teraoka Y, Fujino H, Nakahara T, Murakami E, Miki D, Kawaoka T, Okamoto W, Makokha GN, Imamura M, Arihiro K, Kobayashi T, Ohdan H, Fujita M, Nakagawa H, Chayama K, Aikata H. The presence of Vessels Encapsulating Tumor Clusters is associated with an immunosuppressive tumor microenvironment in hepatocellular carcinoma. Int J Cancer 2022; 151:2278-2290. [DOI: 10.1002/ijc.34247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 07/22/2022] [Accepted: 08/01/2022] [Indexed: 11/11/2022]
Affiliation(s)
- Peiyi Zhang
- Department of Gastroenterology and Metabolism, Graduate School of Biomedical & Health Sciences Hiroshima University Hiroshima Japan
| | - Atsushi Ono
- Department of Gastroenterology and Metabolism, Graduate School of Biomedical & Health Sciences Hiroshima University Hiroshima Japan
| | - Yasutoshi Fujii
- Department of Gastroenterology and Metabolism, Graduate School of Biomedical & Health Sciences Hiroshima University Hiroshima Japan
| | - C. Nelson Hayes
- Department of Gastroenterology and Metabolism, Graduate School of Biomedical & Health Sciences Hiroshima University Hiroshima Japan
| | - Yosuke Tamura
- Department of Gastroenterology and Metabolism, Graduate School of Biomedical & Health Sciences Hiroshima University Hiroshima Japan
| | - Ryoichi Miura
- Department of Gastroenterology and Metabolism, Graduate School of Biomedical & Health Sciences Hiroshima University Hiroshima Japan
| | - Yuki Shirane
- Department of Gastroenterology and Metabolism, Graduate School of Biomedical & Health Sciences Hiroshima University Hiroshima Japan
| | - Hikaru Nakahara
- Department of Gastroenterology and Metabolism, Graduate School of Biomedical & Health Sciences Hiroshima University Hiroshima Japan
| | - Masami Yamauchi
- Department of Gastroenterology and Metabolism, Graduate School of Biomedical & Health Sciences Hiroshima University Hiroshima Japan
| | - Shinsuke Uchikawa
- Department of Gastroenterology and Metabolism, Graduate School of Biomedical & Health Sciences Hiroshima University Hiroshima Japan
| | - Takuro Uchida
- Department of Gastroenterology and Metabolism, Graduate School of Biomedical & Health Sciences Hiroshima University Hiroshima Japan
| | - Yuji Teraoka
- Department of Gastroenterology and Metabolism, Graduate School of Biomedical & Health Sciences Hiroshima University Hiroshima Japan
| | - Hatsue Fujino
- Department of Gastroenterology and Metabolism, Graduate School of Biomedical & Health Sciences Hiroshima University Hiroshima Japan
| | - Takashi Nakahara
- Department of Gastroenterology and Metabolism, Graduate School of Biomedical & Health Sciences Hiroshima University Hiroshima Japan
| | - Eisuke Murakami
- Department of Gastroenterology and Metabolism, Graduate School of Biomedical & Health Sciences Hiroshima University Hiroshima Japan
| | - Daiki Miki
- Department of Gastroenterology and Metabolism, Graduate School of Biomedical & Health Sciences Hiroshima University Hiroshima Japan
| | - Tomokazu Kawaoka
- Department of Gastroenterology and Metabolism, Graduate School of Biomedical & Health Sciences Hiroshima University Hiroshima Japan
| | - Wataru Okamoto
- Cancer Treatment Center Hiroshima University Hospital Hiroshima Japan
| | - Grace Naswa Makokha
- Department of Gastroenterology and Metabolism, Graduate School of Biomedical & Health Sciences Hiroshima University Hiroshima Japan
| | - Michio Imamura
- Department of Gastroenterology and Metabolism, Graduate School of Biomedical & Health Sciences Hiroshima University Hiroshima Japan
| | - Koji Arihiro
- Department of Anatomical Pathology Hiroshima University Hospital Hiroshima Japan
| | - Tsuyoshi Kobayashi
- Department of Gastroenterological and Transplant Surgery, Graduate School of Biomedical and Health Sciences Hiroshima University
| | - Hideki Ohdan
- Department of Gastroenterological and Transplant Surgery, Graduate School of Biomedical and Health Sciences Hiroshima University
| | - Masashi Fujita
- Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences Yokohama Japan
| | - Hidewaki Nakagawa
- Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences Yokohama Japan
| | - Kazuaki Chayama
- Collaborative Research Laboratory of Medical Innovation, Graduate School of Biomedical and Health Sciences Hiroshima University Hiroshima Japan
- Research Center for Hepatology and Gastroenterology Hiroshima University Hiroshima Japan
- RIKEN Center for Integrative Medical Sciences Yokohama Japan
| | - Hiroshi Aikata
- Department of Gastroenterology and Metabolism, Graduate School of Biomedical & Health Sciences Hiroshima University Hiroshima Japan
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Chu T, Zhao C, Zhang J, Duan K, Li M, Zhang T, Lv S, Liu H, Wei F. Application of a Convolutional Neural Network for Multitask Learning to Simultaneously Predict Microvascular Invasion and Vessels that Encapsulate Tumor Clusters in Hepatocellular Carcinoma. Ann Surg Oncol 2022; 29:6774-6783. [PMID: 35754067 PMCID: PMC9492610 DOI: 10.1245/s10434-022-12000-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 05/23/2022] [Indexed: 11/23/2022]
Abstract
Background Hepatocellular carcinoma (HCC) is the fourth most common cause of cancer death worldwide, and the prognosis remains dismal. In this study, two pivotal factors, microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC) were preoperatively predicted simultaneously to assess prognosis. Methods A total of 133 HCC patients who underwent surgical resection and preoperative gadolinium ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) were included. The statuses of MVI and VETC were obtained from the pathological report and CD34 immunohistochemistry, respectively. A three-dimensional convolutional neural network (3D CNN) for single-task learning aimed at MVI prediction and for multitask learning aimed at simultaneous prediction of MVI and VETC was established by using multiphase Gd-EOB-DTPA-enhanced MRI. Results The 3D CNN for single-task learning achieved an area under receiver operating characteristics curve (AUC) of 0.896 (95% CI: 0.797–0.994). Multitask learning with simultaneous extraction of MVI and VETC features improved the performance of MVI prediction, with an AUC value of 0.917 (95% CI: 0.825–1.000), and achieved an AUC value of 0.860 (95% CI: 0.728–0.993) for the VETC prediction. The multitask learning framework could stratify high- and low-risk groups regarding overall survival (p < 0.0001) and recurrence-free survival (p < 0.0001), revealing that patients with MVI+/VETC+ were associated with poor prognosis. Conclusions A deep learning framework based on 3D CNN for multitask learning to predict MVI and VETC simultaneously could improve the performance of MVI prediction while assessing the VETC status. This combined prediction can stratify prognosis and enable individualized prognostication in HCC patients before curative resection. Supplementary Information The online version contains supplementary material available at 10.1245/s10434-022-12000-6.
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Affiliation(s)
- Tongjia Chu
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Chen Zhao
- College of Computer Science and Technology, Jilin University, Changchun, People's Republic of China
| | - Jian Zhang
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Kehang Duan
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Mingyang Li
- Department of Radiology, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Tianqi Zhang
- Department of Radiology, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Shengnan Lv
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Huan Liu
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Feng Wei
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China.
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Liu W, Zhang L, Xin Z, Zhang H, You L, Bai L, Zhou J, Ying B. A Promising Preoperative Prediction Model for Microvascular Invasion in Hepatocellular Carcinoma Based on an Extreme Gradient Boosting Algorithm. Front Oncol 2022; 12:852736. [PMID: 35311094 PMCID: PMC8931027 DOI: 10.3389/fonc.2022.852736] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 02/11/2022] [Indexed: 01/27/2023] Open
Abstract
BackgroundThe non-invasive preoperative diagnosis of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is vital for precise surgical decision-making and patient prognosis. Herein, we aimed to develop an MVI prediction model with valid performance and clinical interpretability.MethodsA total of 2160 patients with HCC without macroscopic invasion who underwent hepatectomy for the first time in West China Hospital from January 2015 to June 2019 were retrospectively included, and randomly divided into training and a validation cohort at a ratio of 8:2. Preoperative demographic features, imaging characteristics, and laboratory indexes of the patients were collected. Five machine learning algorithms were used: logistic regression, random forest, support vector machine, extreme gradient boosting (XGBoost), and multilayer perception. Performance was evaluated using the area under the receiver operating characteristic curve (AUC). We also determined the Shapley Additive exPlanation value to explain the influence of each feature on the MVI prediction model.ResultsThe top six important preoperative factors associated with MVI were the maximum image diameter, protein induced by vitamin K absence or antagonist-II, α-fetoprotein level, satellite nodules, alanine aminotransferase (AST)/aspartate aminotransferase (ALT) ratio, and AST level, according to the XGBoost model. The XGBoost model for preoperative prediction of MVI exhibited a better AUC (0.8, 95% confidence interval: 0.74–0.83) than the other prediction models. Furthermore, to facilitate use of the model in clinical settings, we developed a user-friendly online calculator for MVI risk prediction based on the XGBoost model.ConclusionsThe XGBoost model achieved outstanding performance for non-invasive preoperative prediction of MVI based on big data. Moreover, the MVI risk calculator would assist clinicians in conveniently determining the optimal therapeutic remedy and ameliorating the prognosis of patients with HCC.
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Affiliation(s)
- Weiwei Liu
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Lifan Zhang
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, China
| | - Zhaodan Xin
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Haili Zhang
- Department of Liver Surgery & Liver Transplantation Center, West China Hospital, Sichuan University, Chengdu, China
| | - Liting You
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Ling Bai
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Juan Zhou
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Juan Zhou, ; Binwu Ying,
| | - Binwu Ying
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Juan Zhou, ; Binwu Ying,
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Guan R, Lin W, Zou J, Mei J, Wen Y, Lu L, Guo R. Development and Validation of a Novel Nomogram for Predicting Vessels that Encapsulate Tumor Cluster in Hepatocellular Carcinoma. Cancer Control 2022; 29:10732748221102820. [PMID: 35609265 PMCID: PMC9136459 DOI: 10.1177/10732748221102820] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 04/18/2022] [Accepted: 05/05/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Vessels that encapsulate tumor cluster (VETC) is associated with poor prognosis in hepatocellular carcinoma (HCC). Vessels that encapsulate tumor cluster estimation before initial treatment is helpful for clinical doctors. We aimed to construct a novel predictive model for VETC, using preoperatively accessible clinical parameters and imagine features. METHODS Totally, 365 HCC patients who received curative hepatectomy in the Sun Yat-Sen University Cancer Center from 2013 to 2014 were enrolled in this study. Vessels that encapsulate tumor cluster pattern was confirmed by immunochemistry staining. 243 were randomly assigned to the training cohort while the rest was assigned to the validation cohort. Independent predictive factors for VETC estimation were determined by univariate and multivariate logistic analysis. We further constructed a predictive nomogram for VETC in HCC. The performance of the nomogram was evaluated by C-index, receiver operating characteristic (ROC) curve, and calibration curve. Besides, the decision curve was plotted to evaluate the clinical usefulness. Ultimately, Kaplan-Meier survival curves were utilized to confirm the association between the nomogram and survival. RESULTS Immunochemistry staining revealed VETC in 87 patients (23.8%). lymphocyte to monocyte ratio (>7.75, OR = 4.06), neutrophil (>7, OR = 4.48), AST to ALT ratio (AAR > .86, OR = 2.16), ALT to lymphocyte ratio index (BLRI > 21.73, OR = 2.57), alpha-fetoprotein (OR = 1.1), and tumor diameter (OR = 2.65) were independent predictive factors. The nomogram incorporating these predictive factors performed well with an area under the curve (AUC) of .746 and .707 in training and validation cohorts, respectively. Calibration curves indicated the predicted probabilities closely corresponded with the actual VETC status. Moreover, the decision curve proved our nomogram could provide clinical benefits with patients. Finally, low probability of VETC group had significantly longer recurrence free survival (RFS) and overall survival (OS) than the high probability of the VETC group (all P < .001). CONCLUSION A novel predictive nomogram integrating clinical indicators and image characteristics shows strong predictive VETC performance and might provide standardized net clinical benefits.
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Affiliation(s)
- Renguo Guan
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Wenping Lin
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Jingwen Zou
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Jie Mei
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yuhua Wen
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Lianghe Lu
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Rongping Guo
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
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