1
|
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
Collapse
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
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Liu Z, Mao Y, Liu L, Li J, Li Q, Zhou Y. Preoperative CT features for characterization of vessels that encapsulate tumor clusters in hepatocellular carcinoma. Eur J Radiol 2024; 179:111681. [PMID: 39142009 DOI: 10.1016/j.ejrad.2024.111681] [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: 02/21/2024] [Revised: 08/03/2024] [Accepted: 08/08/2024] [Indexed: 08/16/2024]
Abstract
PURPOSE To explore the capability of preoperative CT imaging features, in combination with clinical indicators, for predicting vessels that encapsulate tumor clusters (VETC) pattern and prognosis in hepatocellular carcinoma (HCC). MATERIALS AND METHODS From January 2015 to May 2022, patients with HCC who underwent curative resection and preoperative enhanced CT were retrospectively included. Clinical indicators and imaging featuresassociated with the VETC pattern were determined by logistic regression analyses. The early recurrence (ER) rate was determined using the Kaplan-Meier survival curve. Factors associated with ER after surgical resection were identified by Cox regression analyses. RESULT A total of 243 patients with HCCwere evaluated. The total bilirubin > 17.1 μmol/L (odds ratio [OR] 3.43, 95 % Confidence Interval [CI] 1.70, 6.91, p = 0.001), serum α-fetoprotein > 100 ng/mL (OR 2.41, 95 % CI 1.25, 4.67, p = 0.009), intratumor artery (IA) (OR 2.00, 95 % CI 1.04, 3.86,p = 0.039) and arterial peritumoral enhancement (OR 2.60, 95 % CI 1.13, 5.96, p = 0.025) were independent risk factors for VETC+-HCC. The VETC+status andCT feature ofIA were associated with an increased risk of recurrence, with a shorter median RFS, compared to those without these factors (p < 0.001 and p = 0.019, respectively). In multivariable Cox regression analysis, the VETC+(hazard ratio [HR] 2.60, 95 % CI 1.66, 4.09, p < 0.001), morphological patterns of confluent multinodular growth (HR 1.79, 95 % CI 1.10, 2.91,p = 0.019), the number of the tumors (≥2) (HR 2.69, 95 % CI 1.56, 4.65, p < 0.001), and the IA (HR 1.73, 95 % CI 1.12, 2.66, p = 0.013) were independent predictors of ER in patients with HCC after surgical resection. CONCLUSION Preoperative CT features combined with clinical indicators could predict VETC pattern, and the CT features, along with VETC status, were of prognostic significance for early postoperative recurrence in patients with HCC. CLINICAL RELEVANCE STATEMENT Preoperative CT features combined with clinical indicators could predict VETC pattern, and the CT features, along with VETC status, were of prognostic significance for early recurrence in patients with HCC after surgical resection.
Collapse
Affiliation(s)
- Ziyu Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1, Youyi Road, Yuzhong District, Chongqing 400016, PR China.
| | - Yun Mao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1, Youyi Road, Yuzhong District, Chongqing 400016, PR China.
| | - Liu Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1, Youyi Road, Yuzhong District, Chongqing 400016, PR China.
| | - Junjie Li
- Department of Nuclear Medicine, The First Affiliated Hospital of Chongqing Medical University, PR China.
| | - Qingshu Li
- Department of Pathology, School of Basic Medicine, Chongqing Medical University/ Molecular Medicine Diagnostic and Testing Center, Chongqing Medical University/ Department of Clinical Pathology Laboratory of Pathology Diagnostic Center, Chongqing Medical University, No.1 Medical College Road, Yuzhong District, Chongqing 400016, PR China.
| | - Yin Zhou
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1, Youyi Road, Yuzhong District, Chongqing 400016, PR China.
| |
Collapse
|
4
|
Wang Y, Wang M, Cao L, Huang H, Cao S, Tian X, Lei J. A nomogram for preoperative prediction of vessels encapsulating tumor clusters (VETC) pattern and prognosis of hepatocellular carcinoma. Am J Surg 2024; 234:172-178. [PMID: 38755026 DOI: 10.1016/j.amjsurg.2024.05.004] [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: 02/09/2024] [Revised: 04/15/2024] [Accepted: 05/04/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND Vessels encapsulating tumor clusters (VETC) pattern of hepatocellular carcinoma (HCC) are associated with unfavorable prognosis. This study aimed to establish a nomogram model to predict VETC patterns based on preoperative CT imaging features. PATIENTS AND METHODS Patients who underwent surgical resection between January 1, 2016 and August 31, 2022 were retrospectively included. Predictors associated with VETC pattern were determined by using logistic regression analyses, and a nomogram model was constructed. Prognostic factors associated with recurrence-free survival (RFS) after surgical resection were identified by using Cox regression analyses. RESULTS A total of 84 patients were included for CT analysis. All patients underwent radical surgical resection. AST/ALT >1.07(odds ratio [OR], 4.91; 95 % CI: 1.11, 21.68; P < 0.05), intratumoral necrosis (OR, 4.99; 95 % CI: 1.25, 19.99; P < 0.05) and enhancing capsule (OR, 3.32; 95 % CI: 1.27, 8.94; P < 0.05) were independent predictors of VETC pattern. These features were used for the construction of nomogram model, which showed comparable prediction performance, with AUC value of 0.767 (95%CI [0.662, 0.852]). CK19 status (Hazard ratio [HR], 2.02; 95 % CI: 1.06, 3.86; P < 0.05), the number of tumors (HR, 3.31; 95 % CI: 1.47, 7.45; P < 0.05) and VETC pattern (HR, 2.52; 95 % CI: 1.31, 4.86; P < 0.05) were independent predictors of postoperative RFS. CONCLUSION A nomogram model based on preoperative CT imaging features could be used for the characterization of VETC pattern, and has prognostic significance for postoperative RFS in patients with HCC.
Collapse
Affiliation(s)
- Yinzhong Wang
- Department of Radiology, The First Hospital of Lanzhou University, No.1 Donggang West Road, Lanzhou City, Gansu Province, China
| | - Miaomiao Wang
- The First Clinical Medical College of Lanzhou University, No.1 Donggang West Road, Lanzhou City, Gansu Province, China
| | - Liang Cao
- Department of Radiology, The First Hospital of Lanzhou University, No.1 Donggang West Road, Lanzhou City, Gansu Province, China
| | - Hongliang Huang
- Department of Radiology, The First Hospital of Lanzhou University, No.1 Donggang West Road, Lanzhou City, Gansu Province, China
| | - Shi Cao
- Department of Pathology, The First Hospital of Lanzhou University, No.1 Donggang West Road, Lanzhou City, 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
- Department of Radiology, The First Hospital of Lanzhou University, No.1 Donggang West Road, Lanzhou City, Gansu Province, China.
| |
Collapse
|
5
|
Xu W, Zhang H, Zhang R, Zhong X, Li X, Zhou W, Xie X, Wang K, Xu M. Deep learning model based on contrast-enhanced ultrasound for predicting vessels encapsulating tumor clusters in hepatocellular carcinoma. Eur Radiol 2024:10.1007/s00330-024-10985-0. [PMID: 39066894 DOI: 10.1007/s00330-024-10985-0] [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: 02/20/2024] [Revised: 05/25/2024] [Accepted: 07/14/2024] [Indexed: 07/30/2024]
Abstract
OBJECTIVES To establish and validate a non-invasive deep learning (DL) model based on contrast-enhanced ultrasound (CEUS) to predict vessels encapsulating tumor clusters (VETC) patterns in hepatocellular carcinoma (HCC). MATERIALS AND METHODS This retrospective study included consecutive HCC patients with preoperative CEUS images and available tissue specimens. Patients were randomly allocated into the training and test cohorts. CEUS images were analyzed using the ResNet-18 convolutional neural network for the development and validation of the VETC predictive model. The predictive value for postoperative early recurrence (ER) of the proposed model was further evaluated. RESULTS A total of 242 patients were enrolled finally, including 195 in the training cohort (54.6 ± 11.2 years, 178 males) and 47 in the test cohort (55.1 ± 10.6 years, 40 males). The DL model (DL signature) achieved favorable performance in both the training cohort (area under the receiver operating characteristics curve [AUC]: 0.92, 95% confidence interval [CI]: 0.88-0.96) and test cohort (AUC: 0.90, 95% CI: 0.82-0.99). The stratified analysis demonstrated good discrimination of DL signature regardless of tumor size. Moreover, the DL signature was found independently correlated with postoperative ER (hazard ratio [HR]: 1.99, 95% CI: 1.29-3.06, p = 0.002). C-indexes of 0.70 and 0.73 were achieved when the DL signature was used to predict ER independently and combined with clinical features. CONCLUSION The proposed DL signature provides a non-invasive and practical method for VETC-HCC prediction, and contributes to the identification of patients with high risk of postoperative ER. CLINICAL RELEVANCE STATEMENT This DL model based on contrast-enhanced US displayed an important role in non-invasive diagnosis and prognostication for patients with VETC-HCC, which was helpful in individualized management. KEY POINTS Preoperative biopsy to determine VETC status in HCC patients is limited. The contrast-enhanced DL model provides a non-invasive tool for the prediction of VETC-HCC. The proposed deep-learning signature assisted in identifying patients with a high risk of postoperative ER.
Collapse
Affiliation(s)
- Wenxin Xu
- Department of Medical Ultrasonics, The First Affiliated Hospital, Institute of Diagnostic and Interventional Ultrasound, Sun Yat-Sen University, Guangzhou, China
| | - Haoyan Zhang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Rui Zhang
- Department of Medical Ultrasonics, The First Affiliated Hospital, Institute of Diagnostic and Interventional Ultrasound, Sun Yat-Sen University, Guangzhou, China
| | - Xian Zhong
- Department of Medical Ultrasonics, The First Affiliated Hospital, Institute of Diagnostic and Interventional Ultrasound, Sun Yat-Sen University, Guangzhou, China
| | - Xiaoju Li
- Department of Medical Ultrasonics, The First Affiliated Hospital, Institute of Diagnostic and Interventional Ultrasound, Sun Yat-Sen University, Guangzhou, China
| | - Wenwen Zhou
- Department of Medical Ultrasonics, The First Affiliated Hospital, Institute of Diagnostic and Interventional Ultrasound, Sun Yat-Sen University, Guangzhou, China
| | - Xiaoyan Xie
- Department of Medical Ultrasonics, The First Affiliated Hospital, Institute of Diagnostic and Interventional Ultrasound, Sun Yat-Sen University, Guangzhou, China
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Ming Xu
- Department of Medical Ultrasonics, The First Affiliated Hospital, Institute of Diagnostic and Interventional Ultrasound, Sun Yat-Sen University, Guangzhou, China.
| |
Collapse
|
6
|
Pan J, Huang H, Zhang S, Zhu Y, Zhang Y, Wang M, Zhang C, Zhao YC, Chen F. Intraindividual comparison of CT and MRI for predicting vessels encapsulating tumor clusters in hepatocellular carcinoma. Eur Radiol 2024:10.1007/s00330-024-10944-9. [PMID: 38992109 DOI: 10.1007/s00330-024-10944-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/30/2024] [Accepted: 06/19/2024] [Indexed: 07/13/2024]
Abstract
OBJECTIVES To establish and validate scoring models for predicting vessels encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC) using computed tomography (CT) and magnetic resonance imaging (MRI), and to intra-individually compare the predictive performance between the two modalities. METHODS We retrospectively included 324 patients with surgically confirmed HCC who underwent preoperative dynamic CT and MRI with extracellular contrast agent between June 2019 and August 2020. These patients were then divided into a discovery cohort (n = 227) and a validation cohort (n = 97). Imaging features and Liver Imaging Reporting and Data System (LI-RADS) categories of VETC-positive HCCs were evaluated. Logistic regression analyses were conducted on the discovery cohort to identify clinical and imaging predictors associated with VETC-positive cases. Subsequently, separate CT-based and MRI-based scoring models were developed, and their diagnostic performance was compared using generalized estimating equations. RESULTS On both CT and MRI, VETC-positive HCCs exhibited a higher frequency of size > 5.0 cm, necrosis or severe ischemia, non-smooth tumor margin, targetoid appearance, intratumor artery, and heterogeneous enhancement with septations or irregular ring-like structure compared to VETC-negative HCCs (all p < 0.05). Regarding LI-RADS categories, VETC-positive HCCs were more frequently categorized as LR-M than VETC-negative cases (all p < 0.05). In the validation cohort, the CT-based model showed similar sensitivity (76.7% vs. 86.7%, p = 0.375), specificity (83.6% vs. 74.6%, p = 0.180), and area under the curve value (0.80 vs. 0.81, p = 0.910) to the MRI-based model in predicting VETC-positive HCCs. CONCLUSION Preoperative CT and MRI demonstrated comparable performance in the identification of VETC-positive HCCs, thus displaying promising predictive capabilities. CLINICAL RELEVANCE STATEMENT Both computed tomography and magnetic resonance imaging demonstrated promise in preoperatively identifying the vessel-encapsulating tumor cluster pattern in hepatocellular carcinoma, with no statistically significant difference between the two modalities, potentially adding additional prognostic value. KEY POINTS Computed tomography (CT) and magnetic resonance imaging (MRI) show promise in the preoperative identification of vessels encapsulating tumor clusters-positive hepatocellular carcinoma (HCC). HCC with vessels encapsulating tumor cluster patterns were more frequently LR-M compared to those without. These CT and MRI models showed comparable ability in identifying vessels encapsulating tumor clusters-positive HCC.
Collapse
Affiliation(s)
- Junhan Pan
- Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, No.79 Qingchun Road, Hangzhou, 310003, China
| | - Huizhen Huang
- Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, No.79 Qingchun Road, Hangzhou, 310003, China
| | - Siying Zhang
- Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, No.79 Qingchun Road, Hangzhou, 310003, China
| | - Yanyan Zhu
- Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, No.79 Qingchun Road, Hangzhou, 310003, China
| | - Yuhao Zhang
- Department of Pathology, The First Affiliated Hospital of Zhejiang University School of Medicine, No.79 Qingchun Road, Hangzhou, 310003, China
| | - Meng Wang
- Department of Pathology, The First Affiliated Hospital of Zhejiang University School of Medicine, No.79 Qingchun Road, Hangzhou, 310003, China
| | - Cong Zhang
- Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, No.79 Qingchun Road, Hangzhou, 310003, China
| | - Yan-Ci Zhao
- Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, No.79 Qingchun Road, Hangzhou, 310003, China
| | - Feng Chen
- Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, No.79 Qingchun Road, Hangzhou, 310003, China.
| |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
Chen HL, He RL, Gu MT, Zhao XY, Song KR, Zou WJ, Jia NY, Liu WM. Nomogram prediction of vessels encapsulating tumor clusters in small hepatocellular carcinoma ≤ 3 cm based on enhanced magnetic resonance imaging. World J Gastrointest Oncol 2024; 16:1808-1820. [PMID: 38764811 PMCID: PMC11099422 DOI: 10.4251/wjgo.v16.i5.1808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 02/02/2024] [Accepted: 03/12/2024] [Indexed: 05/09/2024] Open
Abstract
BACKGROUND Vessels encapsulating tumor clusters (VETC) represent a recently discovered vascular pattern associated with novel metastasis mechanisms in hepatocellular carcinoma (HCC). However, it seems that no one have focused on predicting VETC status in small HCC (sHCC). This study aimed to develop a new nomogram for predicting VETC positivity using preoperative clinical data and image features in sHCC (≤ 3 cm) patients. AIM To construct a nomogram that combines preoperative clinical parameters and image features to predict patterns of VETC and evaluate the prognosis of sHCC patients. METHODS A total of 309 patients with sHCC, who underwent segmental resection and had their VETC status confirmed, were included in the study. These patients were recruited from three different hospitals: Hospital 1 contributed 177 patients for the training set, Hospital 2 provided 78 patients for the test set, and Hospital 3 provided 54 patients for the validation set. Independent predictors of VETC were identified through univariate and multivariate logistic analyses. These independent predictors were then used to construct a VETC prediction model for sHCC. The model's performance was evaluated using the area under the curve (AUC), calibration curve, and clinical decision curve. Additionally, Kaplan-Meier survival analysis was performed to confirm whether the predicted VETC status by the model is associated with early recurrence, just as it is with the actual VETC status and early recurrence. RESULTS Alpha-fetoprotein_lg10, carbohydrate antigen 199, irregular shape, non-smooth margin, and arterial peritumoral enhancement were identified as independent predictors of VETC. The model incorporating these predictors demonstrated strong predictive performance. The AUC was 0.811 for the training set, 0.800 for the test set, and 0.791 for the validation set. The calibration curve indicated that the predicted probability was consistent with the actual VETC status in all three sets. Furthermore, the decision curve analysis demonstrated the clinical benefits of our model for patients with sHCC. Finally, early recurrence was more likely to occur in the VETC-positive group compared to the VETC-negative group, regardless of whether considering the actual or predicted VETC status. CONCLUSION Our novel prediction model demonstrates strong performance in predicting VETC positivity in sHCC (≤ 3 cm) patients, and it holds potential for predicting early recurrence. This model equips clinicians with valuable information to make informed clinical treatment decisions.
Collapse
Affiliation(s)
- Hui-Lin Chen
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
- Department of Radiology, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai 200438, China
| | - Rui-Lin He
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Meng-Ting Gu
- Department of Radiology, Tongji University Affiliated Tongji Hospital, Shanghai 200065, China
| | - Xing-Yu Zhao
- Department of Radiology, Tongji University Affiliated Tongji Hospital, Shanghai 200065, China
| | - Kai-Rong Song
- Department of Radiology, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai 200438, China
| | - Wen-Jie Zou
- Department of Radiology, Tongji University Affiliated Tongji Hospital, Shanghai 200065, China
| | - Ning-Yang Jia
- Department of Radiology, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai 200438, China
| | - Wan-Min Liu
- Department of Radiology, Tongji University Affiliated Tongji Hospital, Shanghai 200065, China
| |
Collapse
|
9
|
Chen H, Dong H, He R, Gu M, Zhao X, Song K, Zou W, Jia N, Liu W. Optimizing predictions: improved performance of preoperative gadobenate-enhanced MRI hepatobiliary phase features in predicting vessels encapsulating tumor clusters in hepatocellular carcinoma-a multicenter study. Abdom Radiol (NY) 2024:10.1007/s00261-024-04283-y. [PMID: 38713432 DOI: 10.1007/s00261-024-04283-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 03/06/2024] [Accepted: 03/06/2024] [Indexed: 05/08/2024]
Abstract
BACKGROUND Vessels Encapsulating Tumor Clusters (VETC) are now recognized as independent indicators of recurrence and overall survival in hepatocellular carcinoma (HCC) patients. However, there has been limited investigation into predicting the VETC pattern using hepatobiliary phase (HBP) features from preoperative gadobenate-enhanced MRI. METHODS This study involved 252 HCC patients with confirmed VETC status from three different hospitals (Hospital 1: training set with 142 patients; Hospital 2: test set with 64 patients; Hospital 3: validation set with 46 patients). Independent predictive factors for VETC status were determined through univariate and multivariate logistic analyses. Subsequently, these factors were used to construct two distinct VETC prediction models. Model 1 included all independent predictive factors, while Model 2 excluded HBP features. The performance of both models was assessed using the Area Under the Curve (AUC), Decision Curve Analysis, and Calibration Curve. Prediction accuracy between the two models was compared using Net Reclassification Improvement (NRI) and Integrated Discriminant Improvement (IDI). RESULTS CA199, IBIL, shape, peritumoral hyperintensity on HBP, and arterial peritumoral enhancement were independent predictors of VETC. Model 1 showed robust predictive performance, with AUCs of 0.836 (training), 0.811 (test), and 0.802 (validation). Model 2 exhibited moderate performance, with AUCs of 0.813, 0.773, and 0.783 in the respective sets. Calibration and decision curves for both models indicated consistent predictions between predicted and actual VETC, benefiting HCC patients. NRI showed Model 1 increased by 0.326, 0.389, and 0.478 in the training, test, and validation sets compared to Model 2. IDI indicated Model 1 increased by 0.036, 0.028, and 0.025 in the training, test, and validation sets compared to Model 2. CONCLUSION HBP features from preoperative gadobenate-enhanced MRI can enhance the predictive performance of VETC in HCC.
Collapse
Affiliation(s)
- Huilin Chen
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
- Department of Radiology, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Eastern Hepatobiliary Surgery Hospital, Shanghai, China
| | - Hui Dong
- Department of Pathology, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Eastern Hepatobiliary Surgery Hospital, Shanghai, China
| | - Ruilin He
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Mengting Gu
- Department of Radiology, School of Medicine, Tongji University, Tongji Hospital, Shanghai, China
| | - Xingyu Zhao
- Department of Radiology, School of Medicine, Tongji University, Tongji Hospital, Shanghai, China
| | - Kairong Song
- Department of Radiology, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Eastern Hepatobiliary Surgery Hospital, Shanghai, China
| | - Wenjie Zou
- Department of Radiology, School of Medicine, Tongji University, Tongji Hospital, Shanghai, China
| | - Ningyang Jia
- Department of Radiology, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Eastern Hepatobiliary Surgery Hospital, Shanghai, China.
| | - Wanmin Liu
- Department of Radiology, School of Medicine, Tongji University, Tongji Hospital, Shanghai, China.
| |
Collapse
|
10
|
Yang J, Dong X, Wang F, Jin S, Zhang B, Zhang H, Pan W, Gan M, Duan S, Zhang L, Hu H, Ji W. A deep learning model based on MRI for prediction of vessels encapsulating tumour clusters and prognosis in hepatocellular carcinoma. Abdom Radiol (NY) 2024; 49:1074-1083. [PMID: 38175256 DOI: 10.1007/s00261-023-04141-3] [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: 09/19/2023] [Revised: 11/14/2023] [Accepted: 11/22/2023] [Indexed: 01/05/2024]
Abstract
PURPOSE This study aimed to build and evaluate a deep learning (DL) model to predict vessels encapsulating tumor clusters (VETC) and prognosis preoperatively in patients with hepatocellular carcinoma (HCC). METHODS 320 pathologically confirmed HCC patients (58 women and 262 men) from two hospitals were included in this retrospective study. Institution 1 (n = 219) and Institution 2 (n = 101) served as the training and external test cohorts, respectively. Tumors were evaluated three-dimensionally and regions of interest were segmented manually in the arterial, portal venous, and delayed phases (AP, PP, and DP). Three ResNet-34 DL models were developed, consisting of three models based on a single sequence. The fusion model was developed by inputting the prediction probability of the output from the three single-sequence models into logistic regression. The area under the receiver operating characteristic curve (AUC) was used to compare performance, and the Delong test was used to compare AUCs. Early recurrence (ER) was defined as recurrence within two years of surgery and early recurrence-free survival (ERFS) rate was evaluated by Kaplan-Meier survival analysis. RESULTS Among the 320 HCC patients, 227 were VETC- and 93 were VETC+ . In the external test cohort, the fusion model showed an AUC of 0.772, a sensitivity of 0.80, and a specificity of 0.61. The fusion model-based prediction of VETC high-risk and low-risk categories exhibits a significant difference in ERFS rates, akin to the outcomes observed in VETC + and VETC- confirmed through pathological analyses (p < 0.05). CONCLUSIONS A DL framework based on ResNet-34 has demonstrated potential in facilitating non-invasive prediction of VETC as well as patient prognosis.
Collapse
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, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Fang Wang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, People's Republic of China
| | - Shengze Jin
- Department of Radiology, Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou, 318000, 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
| | - Huangqi 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
| | - Meifu Gan
- Department of Pathology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, 317000, Zhejiang, China
| | - Shaofeng Duan
- GE Healthcare, Precision Health Institution, Pudong New Town, No.1, Huatuo Road, Shanghai, 210000, China
| | - Limin Zhang
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, People's Republic of China
| | - Wenbin Ji
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 150 Ximen St, Linhai, Taizhou, 317000, Zhejiang, China.
- Department of Radiology, Taizhou Hospital, Zhejiang University, Taizhou, 318000, Zhejiang, China.
| |
Collapse
|
11
|
Chernyak V. Editorial for "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; 59:120-121. [PMID: 37165916 DOI: 10.1002/jmri.28775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 04/25/2023] [Indexed: 05/12/2023] Open
Affiliation(s)
- Victoria Chernyak
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| |
Collapse
|
12
|
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.
Collapse
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
| |
Collapse
|
13
|
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.
Collapse
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
| |
Collapse
|
14
|
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.
Collapse
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
| |
Collapse
|
15
|
Bo J, Xiang F, XiaoWei F, LianHua Z, ShiChun L, YuKun L. A Nomogram Based on Contrast-Enhanced Ultrasound to Predict the Microvascular Invasion in Hepatocellular Carcinoma. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:1561-1568. [PMID: 37003955 DOI: 10.1016/j.ultrasmedbio.2023.02.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 02/13/2023] [Accepted: 02/27/2023] [Indexed: 05/17/2023]
Abstract
OBJECTIVE The aim of this study was to establish and validate a contrast-enhanced ultrasound (CEUS) nomogram for pre-operative microvascular invasion (MVI) prediction in hepatocellular carcinoma (HCC), and compare it with the nomogram based on gadopentetate dimeglumine-enhanced magnetic resonance imaging (Gd-MRI). METHODS A total of 251 patients with a single HCC were enrolled in this prospective study, including 176 patients in the training cohort and 75 patients in the validation cohort. Contrast-enhanced ultrasound (CEUS) with Sonazoid and Gd-MRI was performed pre-operatively. Post-operative histopathology was the gold standard for MVI. Univariate and multivariate logistic regression was performed to determine independent risk factors for MVI. Nomograms based on CEUS and Gd-MRI were established, and their discrimination, calibration and decision curve analysis were evaluated and compared. RESULTS Multivariate logistic regression revealed that arterial circular enhancement, non-enhancing area and thick ring-like enhancement in the post-vascular phase were independent risk factors for MVI. The areas under the receiver operating characteristic curve of the nomogram were 0.841 (0.779-0.892) and 0.914 (0.827-0.966) in the training and validation cohorts, with no significant difference compared with the Gd-MRI nomogram (p = 0.294, 0.321). The C-indexes were 0.821 and 0.870 in the training and validation cohorts. Decision curve analysis revealed that the CEUS nomogram had better clinical applicability than the Gd-MRI nomogram when the threshold probability was between 0.35 and 0.95. CONCLUSION The CEUS-based nomogram was available for predicting MVI in HCC, and its predictive performance was not inferior to that of Gd-MRI.
Collapse
Affiliation(s)
- Jiang Bo
- Department of Ultrasound, First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Fei Xiang
- Department of Ultrasound, First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Fan XiaoWei
- Department of Pathology, First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Zhu LianHua
- Department of Ultrasound, First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Lu ShiChun
- Department of Hepatobiliary Surgery, First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Luo YuKun
- Department of Ultrasound, First Medical Centre, Chinese PLA General Hospital, Beijing, China.
| |
Collapse
|