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Balli HT, Piskin FC, Sozutok S, Erdoğan KE, Aikimbaev K. Outcomes in Patients with Macrotrabecular-Massive Subtype Hepatocellular Carcinoma Treated with Yttrium-90 Transarterial Radioembolization. J Vasc Interv Radiol 2024; 35:998-1003. [PMID: 38548131 DOI: 10.1016/j.jvir.2024.03.025] [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: 06/28/2023] [Revised: 03/03/2024] [Accepted: 03/13/2024] [Indexed: 05/26/2024] Open
Abstract
PURPOSE To compare the outcomes of yttrium-90 transarterial radioembolization (TARE) in patients with hepatocellular carcinoma (HCC) with and without macrotrabecular-massive (MTM) subtypes. MATERIALS AND METHODS Forty-one consecutive patients with HCC (male, 90.3%; mean age, 65.3 years [SD ± 10.7]) who underwent yttrium-90 TARE between September 2014 and January 2022 were grouped into the MTM-HCC (n = 17, 41.5%) and non-MTM-HCC (n = 24, 58.5%) groups based on their histopathological subtypes. Demographic, clinical, and radiological characteristics were compared. Survival, univariate, and multivariate analyses were performed, and prognostic factors were evaluated. RESULTS In MTM-HCC group, the rates of moderately to poorly differentiated tumors were significantly higher (13/17 vs 8/16, P = .007), and new intrahepatic/extrahepatic metastases were detected more frequently (12/17 vs 15/24, P = .038). Median overall survival (OS) in the cohort was 29 months (range, 17.1-40.9 months), whereas patients with MTM-HCC had a significantly shorter median OS (20 vs 44 months, P = .014). In univariate analysis, MTM-HCC subtype (hazard ratio [HR], 2.690; P = .021), the presence of satellite nodules (HR, 3.810; P = .004), and macrovascular invasion (HR, 3.321; P = .012) were identified as significant prognostic factors. In multivariate analysis, MTM-HCC subtype and macrovascular invasion were determined as independent poor prognostic factors (P = .038 and P = .012, respectively). CONCLUSIONS In patients with HCC treated with yttrium-90 TARE, both the rates of moderately to poorly differentiated histopathological classes and the development of intrahepatic or extrahepatic metastases were significantly higher in the MTM-HCC subtype. OS was worse in patients with MTM-HCC, and macrovascular invasion and MTM-HCC subtype were identified as independent poor prognostic factors.
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Affiliation(s)
| | | | | | - Kivilcim Eren Erdoğan
- Department of Pathology, Cukurova University Medical School, Balcali Hospital, Adana, Turkey
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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.
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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
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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.
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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.
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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:S0002-9610(24)00270-8. [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] [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.
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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.
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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.
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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, 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.
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Xu W, Huang B, Zhang R, Zhong X, Zhou W, Zhuang S, Xie X, Fang J, Xu M. Diagnostic and Prognostic Ability of Contrast-Enhanced Unltrasound and Biomarkers in Hepatocellular Carcinoma Subtypes. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:617-626. [PMID: 38281888 DOI: 10.1016/j.ultrasmedbio.2024.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 10/07/2023] [Accepted: 01/06/2024] [Indexed: 01/30/2024]
Abstract
OBJECTIVE To investigate the diagnostic and prognostic value of contrast-enhanced ultrasound (CEUS) and clinical indicators of the vessels encapsulating tumor clusters (VETC) pattern and macrotrabecular-massive subtype in hepatocellular carcinoma (MTM-HCC). METHODS This retrospective study included patients who underwent preoperative CEUS and hepatectomy for HCC between August 2018 and August 2021. Multivariable logistic regression was performed to select independent correlated factors of VETC-HCC and MTM-HCC to develop nomogram models. The association between model outcomes and early postoperative HCC recurrence was assessed using Kaplan-Meier curve and Cox regression analysis. RESULTS The training cohort included 182 patients (54.3 ± 11.3 years, 168 males) and the validation cohort included 91 patients (54.8 ± 10.6 years, 81 males). Multivariate logistic regression analysis revealed that α-fetoprotein (AFP) levels (odds ratio [OR]: 2.26, 95% confidence interval [CI]: 1.49-3.42, p < 0.001), intratumoral nonenhancement (OR: 2.40, 95% CI: 1.02-5.64, p = 0.044), and the perfusion pattern in the CEUS arterial phase (OR: 2.27, 95% CI: 1.05-4.91, p = 0.038) were independent predictors of VETC-HCC. Besides, the former two were also independently associated with MTM-HCC (AFP level: OR: 2.36, 95% CI: 1.36-4.09, p = 0.002; intratumoral nonenhancement: OR: 3.72, 95% CI: 1.02-13.56, p = 0.046). Nomogram models were constructed based on the aforementioned indicators. Kaplan-Meier curve analysis indicated that predicted VETC-HCC or MTM-HCC exhibited higher rates of early recurrence (log-rank p < 0.001 and p = 0.002, respectively). Cox regression analysis showed that a high risk of VETC-HCC was independently correlated with early recurrence (p = 0.011). CONCLUSION CEUS combined with AFP levels can predict VETC-HCC/MTM-HCC and prognosis preoperatively.
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Affiliation(s)
- Wenxin Xu
- Department of Medical Ultrasonics, The First Affiliated Hospital, Institute of Diagnostic and Interventional Ultrasound, Sun Yat-Sen University, Guangzhou, China
| | - Biyu Huang
- Key Laboratory of Gene Function and Regulation, School of Life Sciences, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou, 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
| | - Wenwen Zhou
- Department of Medical Ultrasonics, The First Affiliated Hospital, Institute of Diagnostic and Interventional Ultrasound, Sun Yat-Sen University, Guangzhou, China
| | - Shimei Zhuang
- Key Laboratory of Gene Function and Regulation, School of Life Sciences, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, 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
| | - Jianhong Fang
- Key Laboratory of Gene Function and Regulation, School of Life Sciences, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou, China
| | - Ming Xu
- Department of Medical Ultrasonics, The First Affiliated Hospital, Institute of Diagnostic and Interventional Ultrasound, Sun Yat-Sen University, Guangzhou, China.
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Wang M, Cao L, Wang Y, Huang H, Cao S, Tian X, Lei J. Prediction of vessels encapsulating tumor clusters pattern and prognosis of hepatocellular carcinoma based on preoperative gadolinium-ethoxybenzyl-diethylenetriaminepentaacetic acid magnetic resonance imaging. J Gastrointest Surg 2024; 28:442-450. [PMID: 38583894 DOI: 10.1016/j.gassur.2024.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 01/26/2024] [Accepted: 02/03/2024] [Indexed: 04/09/2024]
Abstract
BACKGROUND Vessels encapsulating tumor clusters (VETC) is a novel vascular pattern distinct from microvascular invasion that is significantly associated with poor prognosis in patients with hepatocellular carcinoma (HCC). This study aimed to predict the VETC pattern and prognosis of patients with HCC based on preoperative gadolinium-ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA) magnetic resonance imaging (MRI). METHODS Patients with HCC who underwent surgical resection and preoperative Gd-EOB-DTPA MRI between January 1, 2016 and August 31, 2022 were retrospectively included. The variables associated with VETC were evaluated using logistic regression. A nomogram model was constructed on the basis of independent risk factors. COX regression was used to determine the variables associated with recurrence-free survival (RFS). RESULTS A total of 98 patients with HCC were retrospectively included. Peritumoral hypointensity on the hepatobiliary phase (HBP) (odd ratio [OR], 2.58; 95% CI, 1.05-6.33; P = .04), tumor-to-liver signal intensity ratio on HBP of ≤0.75 (OR, 27.80; 95% CI, 1.53-502.91; P = .02), and tumor-to-liver apparent diffusion coefficient ratio of ≤1.23 (OR, 4.65; 95% CI, 1.01-21.38; P = .04) were independent predictors of VETC pattern. A nomogram was constructed by combining the aforementioned 3 significant variables. The accuracy, sensitivity, and specificity were 69.79%, 71.74%, and 68.00%, respectively, with an area under the receiver operating characteristic curve of 0.75 (95% CI, 0.65-0.83). The variables significantly associated with RFS of patients with HCC after surgery were Barcelona Clinic Liver Cancer stage (hazard ratio [HR], 2.15; 95% CI, 1.09-4.22; P = .03) and VETC pattern (HR, 2.28; 95% CI, 1.29-4.02; P = .004). CONCLUSION The preoperative imaging features based on Gd-EOB-DTPA MRI can be used to predict the VETC pattern, which has prognostic significance for postoperative RFS of patients with HCC.
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Affiliation(s)
- Miaomiao Wang
- The First Clinical Medical College of Lanzhou University, Lanzhou City, Gansu Province, China; Department of Radiology, The First Hospital 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
| | - Yinzhong Wang
- 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, Lanzhou City, Gansu Province, China
| | - Xiaoxue Tian
- Department of Nuclear Medicine, Second Hospital of LanZhou University, 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.
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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 2024:10.1007/s12094-024-03427-2. [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] [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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Kim TH, Woo S, Lee DH, Do RK, Chernyak V. MRI imaging features for predicting macrotrabecular-massive subtype hepatocellular carcinoma: a systematic review and meta-analysis. Eur Radiol 2024:10.1007/s00330-024-10671-1. [PMID: 38507054 DOI: 10.1007/s00330-024-10671-1] [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: 11/06/2023] [Revised: 02/06/2024] [Accepted: 02/14/2024] [Indexed: 03/22/2024]
Abstract
PURPOSE To identify significant MRI features associated with macrotrabecular-massive hepatocellular carcinoma (MTM-HCC), and to assess the distribution of Liver Imaging Radiology and Data System (LI-RADS, LR) category assignments. METHODS PubMed and EMBASE were searched up to March 28, 2023. Random-effects model was constructed to calculate pooled diagnostic odds ratios (DORs) and 95% confidence intervals (CIs) for each MRI feature for differentiating MTM-HCC from NMTM-HCC. The pooled proportions of LI-RADS category assignments in MTM-HCC and NMTM-HCC were compared using z-test. RESULTS Ten studies included 1978 patients with 2031 HCCs (426 (20.9%) MTM-HCC and 1605 (79.1%) NMTM-HCC). Six MRI features showed significant association with MTM-HCC: tumor in vein (TIV) (DOR = 2.4 [95% CI, 1.6-3.5]), rim arterial phase hyperenhancement (DOR =2.6 [95% CI, 1.4-5.0]), corona enhancement (DOR = 2.6 [95% CI, 1.4-4.5]), intratumoral arteries (DOR = 2.6 [95% CI, 1.1-6.3]), peritumoral hypointensity on hepatobiliary phase (DOR = 2.2 [95% CI, 1.5-3.3]), and necrosis (DOR = 4.2 [95% CI, 2.0-8.5]). The pooled proportions of LI-RADS categories in MTM-HCC were LR-3, 0% [95% CI, 0-2%]; LR-4, 11% [95% CI, 6-16%]; LR-5, 63% [95% CI, 55-71%]; LR-M, 12% [95% CI, 6-19%]; and LR-TIV, 13% [95% CI, 6-22%]. In NMTM-HCC, the pooled proportions of LI-RADS categories were LR-3, 1% [95% CI, 0-2%]; LR-4, 8% [95% CI, 3-15%]; LR-5, 77% [95% CI, 71-82%]; LR-M, 5% [95% CI, 3-7%]; and LR-TIV, 6% [95% CI, 2-11%]. MTM-HCC had significantly lower proportion of LR-5 and higher proportion of LR-M and LR-TIV categories. CONCLUSIONS Six MRI features showed significant association with MTM-HCC. Additionally, compared to NMTM-HCC, MTM-HCC are more likely to be categorized LR-M and LR-TIV and less likely to be categorized LR-5. CLINICAL RELEVANCE STATEMENT Several MR imaging features can suggest macrotrabecular-massive hepatocellular carcinoma subtype, which can assist in guiding treatment plans and identifying potential candidates for clinical trials of new treatment strategies. KEY POINTS • Macrotrabecular-massive hepatocellular carcinoma is a subtype of HCC characterized by its aggressive nature and unfavorable prognosis. • Tumor in vein, rim arterial phase hyperenhancement, corona enhancement, intratumoral arteries, peritumoral hypointensity on hepatobiliary phase, and necrosis on MRI are indicative of macrotrabecular-massive hepatocellular carcinoma. • Various MRI characteristics can be utilized for the diagnosis of the macrotrabecular-massive hepatocellular carcinoma subtype. This can prove beneficial in guiding treatment decisions and identifying potential candidates for clinical trials involving novel treatment approaches.
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Affiliation(s)
- Tae-Hyung Kim
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sungmin Woo
- Department of Radiology, NYU Langone Health, New York, NY, USA
| | - Dong Ho Lee
- Department of Radiology, Seoul National University Hospital, Seoul, South Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea
| | - Richard K Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Victoria Chernyak
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Zhang C, Zhong H, Zhao F, Ma ZY, Dai ZJ, Pang GD. Preoperatively predicting vessels encapsulating tumor clusters in hepatocellular carcinoma: Machine learning model based on contrast-enhanced computed tomography. World J Gastrointest Oncol 2024; 16:857-874. [PMID: 38577448 PMCID: PMC10989357 DOI: 10.4251/wjgo.v16.i3.857] [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: 10/30/2023] [Revised: 12/26/2023] [Accepted: 01/29/2024] [Indexed: 03/12/2024] Open
Abstract
BACKGROUND Recently, vessels encapsulating tumor clusters (VETC) was considered as a distinct pattern of tumor vascularization which can primarily facilitate the entry of the whole tumor cluster into the bloodstream in an invasion independent manner, and was regarded as an independent risk factor for poor prognosis in hepatocellular carcinoma (HCC). AIM To develop and validate a preoperative nomogram using contrast-enhanced computed tomography (CECT) to predict the presence of VETC+ in HCC. METHODS We retrospectively evaluated 190 patients with pathologically confirmed HCC who underwent CECT scanning and immunochemical staining for cluster of differentiation 34 at two medical centers. Radiomics analysis was conducted on intratumoral and peritumoral regions in the portal vein phase. Radiomics features, essential for identifying VETC+ HCC, were extracted and utilized to develop a radiomics model using machine learning algorithms in the training set. The model's performance was validated on two separate test sets. Receiver operating characteristic (ROC) analysis was employed to compare the identified performance of three models in predicting the VETC status of HCC on both training and test sets. The most predictive model was then used to constructed a radiomics nomogram that integrated the independent clinical-radiological features. ROC and decision curve analysis were used to assess the performance characteristics of the clinical-radiological features, the radiomics features and the radiomics nomogram. RESULTS The study included 190 individuals from two independent centers, with the majority being male (81%) and a median age of 57 years (interquartile range: 51-66). The area under the curve (AUC) for the combined radiomics features selected from the intratumoral and peritumoral areas were 0.825, 0.788, and 0.680 in the training set and the two test sets. A total of 13 features were selected to construct the Rad-score. The nomogram, combining clinical-radiological and combined radiomics features could accurately predict VETC+ in all three sets, with AUC values of 0.859, 0.848 and 0.757. Decision curve analysis revealed that the radiomics nomogram was more clinically useful than both the clinical-radiological feature and the combined radiomics models. CONCLUSION This study demonstrates the potential utility of a CECT-based radiomics nomogram, incorporating clinical-radiological features and combined radiomics features, in the identification of VETC+ HCC.
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Affiliation(s)
- Chao Zhang
- Department of Radiology, The Second Hospital of Shandong University, Jinan 250033, Shandong Province, China
| | - Hai Zhong
- Department of Radiology, The Second Hospital of Shandong University, Jinan 250033, Shandong Province, China
| | - Fang Zhao
- Department of Radiology, Qilu Hospital of Shandong University, Jinan 250014, Shandong Province, China
| | - Zhen-Yu Ma
- Department of Radiology, Linglong Yingcheng Hospital, Yantai 265499, Shandong Province, China
| | - Zheng-Jun Dai
- Department of Scientific Research, Huiying Medical Technology Co., Ltd, Beijing 100192, China
| | - Guo-Dong Pang
- Department of Radiology, The Second Hospital of Shandong University, Jinan 250033, Shandong Province, China
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11
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Xia T, Zhao B, Li B, Lei Y, Song Y, Wang Y, Tang T, Ju S. MRI-Based Radiomics and Deep Learning in Biological Characteristics and Prognosis of Hepatocellular Carcinoma: Opportunities and Challenges. J Magn Reson Imaging 2024; 59:767-783. [PMID: 37647155 DOI: 10.1002/jmri.28982] [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: 06/20/2023] [Revised: 08/15/2023] [Accepted: 08/16/2023] [Indexed: 09/01/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third leading cause of cancer-related death worldwide. HCC exhibits strong inter-tumor heterogeneity, with different biological characteristics closely associated with prognosis. In addition, patients with HCC often distribute at different stages and require diverse treatment options at each stage. Due to the variability in tumor sensitivity to different therapies, determining the optimal treatment approach can be challenging for clinicians prior to treatment. Artificial intelligence (AI) technology, including radiomics and deep learning approaches, has emerged as a unique opportunity to improve the spectrum of HCC clinical care by predicting biological characteristics and prognosis in the medical imaging field. The radiomics approach utilizes handcrafted features derived from specific mathematical formulas to construct various machine-learning models for medical applications. In terms of the deep learning approach, convolutional neural network models are developed to achieve high classification performance based on automatic feature extraction from images. Magnetic resonance imaging offers the advantage of superior tissue resolution and functional information. This comprehensive evaluation plays a vital role in the accurate assessment and effective treatment planning for HCC patients. Recent studies have applied radiomics and deep learning approaches to develop AI-enabled models to improve accuracy in predicting biological characteristics and prognosis, such as microvascular invasion and tumor recurrence. Although AI-enabled models have demonstrated promising potential in HCC with biological characteristics and prognosis prediction with high performance, one of the biggest challenges, interpretability, has hindered their implementation in clinical practice. In the future, continued research is needed to improve the interpretability of AI-enabled models, including aspects such as domain knowledge, novel algorithms, and multi-dimension data sources. Overcoming these challenges would allow AI-enabled models to significantly impact the care provided to HCC patients, ultimately leading to their deployment for clinical use. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Tianyi Xia
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ben Zhao
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Binrong Li
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ying Lei
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Yang Song
- MR Scientific Marketing, Siemens Healthineers Ltd., Shanghai, China
| | - Yuancheng Wang
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Tianyu Tang
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Shenghong Ju
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
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Chai F, Ma Y, Feng C, Jia X, Cui J, Cheng J, Hong N, Wang Y. Prediction of macrotrabecular-massive hepatocellular carcinoma by using MR-based models and their prognostic implications. Abdom Radiol (NY) 2024; 49:447-457. [PMID: 38042762 DOI: 10.1007/s00261-023-04121-7] [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: 08/09/2023] [Revised: 10/29/2023] [Accepted: 11/03/2023] [Indexed: 12/04/2023]
Abstract
PURPOSE To evaluate the efficacy of MRI-based radiomics and clinical models in predicting MTM-HCC. Additionally, to investigate the ability of the radiomics model designed for MTM-HCC identification in predicting disease-free survival (DFS) in patients with HCC. METHODS A total of 336 patients who underwent oncological resection for HCC between June 2007 and March 2021 were included. 127 patients in Cohort1 were used for MTM-HCC identification, and 209 patients in Cohort2 for prognostic analyses. Radiomics analysis was performed using volumes of interest of HCC delineated on pre-operative MRI images. Radiomics and clinical models were developed using Random Forest algorithm in Cohort1 and a radiomics probability (RP) of MTM-HCC was obtained from the radiomics model. Based on the RP, patients in Cohort2 were divided into a RAD-MTM-HCC (RAD-M) group and a RAD-non-MTM-HCC (RAD-nM) group. Univariate and multivariate Cox regression analyses were employed to identify the independent predictors for DFS of patients in Cohort2. Kaplan-Meier curves were used to compare the DFS between different groups pf patients based on the predictors. RESULTS The radiomics model for identifying MTM-HCC showed AUCs of 0.916 (95% CI: 0.858-0.960) and 0.833 (95% CI: 0.675-0.935), and the clinical model showed AUCs of 0.760 (95% CI: 0.669-0.836) and 0.704 (95% CI: 0.532-0.843) in the respective training and validation sets. Furthermore, the radiomics biomarker RP, portal or hepatic vein tumor thrombus, irregular rim-like arterial phase hyperenhancement (IRE) and AFP were independent predictors of DFS in patients with HCC. The DFS of RAD-nM group was significantly higher than that of the RAD-M group (p < .001). CONCLUSION MR-based clinical and radiomic models have the potential to accurately diagnose MTM-HCC. Moreover, the radiomics signature designed to identify MTM-HCC also can be used to predict prognosis in patients with HCC, realizing the diagnostic and prognostic aims at the same time.
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Affiliation(s)
- Fan Chai
- Department of Radiology, Peking University People's Hospital, 11 Xizhimen South St., Xicheng District, Beijing, 100044, China
| | - Yingteng Ma
- Department of Pathology, Peking University People's Hospital, Beijing, China
| | - Caizhen Feng
- Department of Radiology, Peking University People's Hospital, 11 Xizhimen South St., Xicheng District, Beijing, 100044, China
| | - Xiaoxuan Jia
- Department of Radiology, Peking University People's Hospital, 11 Xizhimen South St., Xicheng District, Beijing, 100044, China
| | - Jingjing Cui
- United Imaging Intelligence (Beijing) Co., Ltd, Beijing, China
| | - Jin Cheng
- Department of Radiology, Peking University People's Hospital, 11 Xizhimen South St., Xicheng District, Beijing, 100044, China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, 11 Xizhimen South St., Xicheng District, Beijing, 100044, China
| | - Yi Wang
- Department of Radiology, Peking University People's Hospital, 11 Xizhimen South St., Xicheng District, Beijing, 100044, China.
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13
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Cannella R, Matteini F, Dioguardi Burgio M, Sartoris R, Beaufrère A, Calderaro J, Mulé S, Reizine E, Luciani A, Laurent A, Seror O, Ganne-Carrié N, Wagner M, Scatton O, Vilgrain V, Cauchy F, Hobeika C, Ronot M. Association of LI-RADS and Histopathologic Features with Survival in Patients with Solitary Resected Hepatocellular Carcinoma. Radiology 2024; 310:e231160. [PMID: 38411519 DOI: 10.1148/radiol.231160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Background Both Liver Imaging Reporting and Data System (LI-RADS) and histopathologic features provide prognostic information in patients with hepatocellular carcinoma (HCC), but whether LI-RADS is independently associated with survival is uncertain. Purpose To assess the association of LI-RADS categories and features with survival outcomes in patients with solitary resected HCC. Materials and Methods This retrospective study included patients with solitary resected HCC from three institutions examined with preoperative contrast-enhanced CT and/or MRI between January 2008 and December 2019. Three independent readers evaluated the LI-RADS version 2018 categories and features. Histopathologic features including World Health Organization tumor grade, microvascular and macrovascular invasion, satellite nodules, and tumor capsule were recorded. Overall survival and disease-free survival were assessed with Cox regression models. Marginal effects of nontargetoid features on survival were estimated using propensity score matching. Results A total of 360 patients (median age, 64 years [IQR, 56-70 years]; 280 male patients) were included. At CT and MRI, the LI-RADS LR-M category was associated with increased risk of recurrence (CT: hazard ratio [HR] = 1.83 [95% CI: 1.26, 2.66], P = .001; MRI: HR = 2.22 [95% CI: 1.56, 3.16], P < .001) and death (CT: HR = 2.47 [95% CI: 1.72, 3.55], P < .001; MRI: HR = 1.80 [95% CI: 1.32, 2.46], P < .001) independently of histopathologic features. The presence of at least one nontargetoid feature was associated with an increased risk of recurrence (CT: HR = 1.80 [95% CI: 1.36, 2.38], P < .001; MRI: HR = 1.93 [95% CI: 1.81, 2.06], P < .001) and death (CT: HR = 1.51 [95% CI: 1.10, 2.07], P < .010) independently of histopathologic features. In matched samples, recurrence was associated with the presence of at least one nontargetoid feature at CT (HR = 2.06 [95% CI: 1.15, 3.66]; P = .02) or MRI (HR = 1.79 [95% CI: 1.01, 3.20]; P = .048). Conclusion In patients with solitary resected HCC, LR-M category and nontargetoid features were negatively associated with survival independently of histopathologic characteristics. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Kartalis and Grigoriadis in this issue.
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Affiliation(s)
- Roberto Cannella
- From the Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy (R.C., F.M.); Departments of Radiology (F.M., M.D.B., R.S., V.V., M.R.), Pathology (A.B.), and Hepatobiliary Surgery (F.C., C.H.), Hôpital Beaujon, AP-HP Nord, 100 Blvd du Général Leclerc, 92118 Clichy, France; Université Paris Cité, Paris, France (A.B., V.V., M.R.); Centre de Recherche sur l'Inflammation, INSERM UMR 1149, Paris, France (A.B.); Departments of Pathology (J.C.), Medical Imaging (S.M., E.R., A. Luciani), and Hepatobiliary and Digestive Surgery (A. Laurent), Hôpitaux Universitaires Henri-Mondor, AP-HP, Université Paris Est Créteil, Faculté de Santé, Créteil, France; INSERM U955, Team "Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers," Créteil, France (A. Laurent); Department of Radiology (O. Seror) and Liver Unit (N.G.C.), Avicenne Hospital, AP-HP, Bobigny, France; Sorbonne Paris Nord University, UFR SMBH, Bobigny, France (N.G.C.); INSERM UMR 1138, Team "Functional Genomic of Solid Tumors," Paris, France (N.G.C.); and Departments of Imaging (M.W.) and HPB and Liver Transplantation (O. Scatton), Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Sorbonne Université, Centre de Recherche Saint-Antoine INSERM, Paris, France
| | - Francesco Matteini
- From the Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy (R.C., F.M.); Departments of Radiology (F.M., M.D.B., R.S., V.V., M.R.), Pathology (A.B.), and Hepatobiliary Surgery (F.C., C.H.), Hôpital Beaujon, AP-HP Nord, 100 Blvd du Général Leclerc, 92118 Clichy, France; Université Paris Cité, Paris, France (A.B., V.V., M.R.); Centre de Recherche sur l'Inflammation, INSERM UMR 1149, Paris, France (A.B.); Departments of Pathology (J.C.), Medical Imaging (S.M., E.R., A. Luciani), and Hepatobiliary and Digestive Surgery (A. Laurent), Hôpitaux Universitaires Henri-Mondor, AP-HP, Université Paris Est Créteil, Faculté de Santé, Créteil, France; INSERM U955, Team "Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers," Créteil, France (A. Laurent); Department of Radiology (O. Seror) and Liver Unit (N.G.C.), Avicenne Hospital, AP-HP, Bobigny, France; Sorbonne Paris Nord University, UFR SMBH, Bobigny, France (N.G.C.); INSERM UMR 1138, Team "Functional Genomic of Solid Tumors," Paris, France (N.G.C.); and Departments of Imaging (M.W.) and HPB and Liver Transplantation (O. Scatton), Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Sorbonne Université, Centre de Recherche Saint-Antoine INSERM, Paris, France
| | - Marco Dioguardi Burgio
- From the Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy (R.C., F.M.); Departments of Radiology (F.M., M.D.B., R.S., V.V., M.R.), Pathology (A.B.), and Hepatobiliary Surgery (F.C., C.H.), Hôpital Beaujon, AP-HP Nord, 100 Blvd du Général Leclerc, 92118 Clichy, France; Université Paris Cité, Paris, France (A.B., V.V., M.R.); Centre de Recherche sur l'Inflammation, INSERM UMR 1149, Paris, France (A.B.); Departments of Pathology (J.C.), Medical Imaging (S.M., E.R., A. Luciani), and Hepatobiliary and Digestive Surgery (A. Laurent), Hôpitaux Universitaires Henri-Mondor, AP-HP, Université Paris Est Créteil, Faculté de Santé, Créteil, France; INSERM U955, Team "Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers," Créteil, France (A. Laurent); Department of Radiology (O. Seror) and Liver Unit (N.G.C.), Avicenne Hospital, AP-HP, Bobigny, France; Sorbonne Paris Nord University, UFR SMBH, Bobigny, France (N.G.C.); INSERM UMR 1138, Team "Functional Genomic of Solid Tumors," Paris, France (N.G.C.); and Departments of Imaging (M.W.) and HPB and Liver Transplantation (O. Scatton), Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Sorbonne Université, Centre de Recherche Saint-Antoine INSERM, Paris, France
| | - Riccardo Sartoris
- From the Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy (R.C., F.M.); Departments of Radiology (F.M., M.D.B., R.S., V.V., M.R.), Pathology (A.B.), and Hepatobiliary Surgery (F.C., C.H.), Hôpital Beaujon, AP-HP Nord, 100 Blvd du Général Leclerc, 92118 Clichy, France; Université Paris Cité, Paris, France (A.B., V.V., M.R.); Centre de Recherche sur l'Inflammation, INSERM UMR 1149, Paris, France (A.B.); Departments of Pathology (J.C.), Medical Imaging (S.M., E.R., A. Luciani), and Hepatobiliary and Digestive Surgery (A. Laurent), Hôpitaux Universitaires Henri-Mondor, AP-HP, Université Paris Est Créteil, Faculté de Santé, Créteil, France; INSERM U955, Team "Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers," Créteil, France (A. Laurent); Department of Radiology (O. Seror) and Liver Unit (N.G.C.), Avicenne Hospital, AP-HP, Bobigny, France; Sorbonne Paris Nord University, UFR SMBH, Bobigny, France (N.G.C.); INSERM UMR 1138, Team "Functional Genomic of Solid Tumors," Paris, France (N.G.C.); and Departments of Imaging (M.W.) and HPB and Liver Transplantation (O. Scatton), Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Sorbonne Université, Centre de Recherche Saint-Antoine INSERM, Paris, France
| | - Aurélie Beaufrère
- From the Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy (R.C., F.M.); Departments of Radiology (F.M., M.D.B., R.S., V.V., M.R.), Pathology (A.B.), and Hepatobiliary Surgery (F.C., C.H.), Hôpital Beaujon, AP-HP Nord, 100 Blvd du Général Leclerc, 92118 Clichy, France; Université Paris Cité, Paris, France (A.B., V.V., M.R.); Centre de Recherche sur l'Inflammation, INSERM UMR 1149, Paris, France (A.B.); Departments of Pathology (J.C.), Medical Imaging (S.M., E.R., A. Luciani), and Hepatobiliary and Digestive Surgery (A. Laurent), Hôpitaux Universitaires Henri-Mondor, AP-HP, Université Paris Est Créteil, Faculté de Santé, Créteil, France; INSERM U955, Team "Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers," Créteil, France (A. Laurent); Department of Radiology (O. Seror) and Liver Unit (N.G.C.), Avicenne Hospital, AP-HP, Bobigny, France; Sorbonne Paris Nord University, UFR SMBH, Bobigny, France (N.G.C.); INSERM UMR 1138, Team "Functional Genomic of Solid Tumors," Paris, France (N.G.C.); and Departments of Imaging (M.W.) and HPB and Liver Transplantation (O. Scatton), Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Sorbonne Université, Centre de Recherche Saint-Antoine INSERM, Paris, France
| | - Julien Calderaro
- From the Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy (R.C., F.M.); Departments of Radiology (F.M., M.D.B., R.S., V.V., M.R.), Pathology (A.B.), and Hepatobiliary Surgery (F.C., C.H.), Hôpital Beaujon, AP-HP Nord, 100 Blvd du Général Leclerc, 92118 Clichy, France; Université Paris Cité, Paris, France (A.B., V.V., M.R.); Centre de Recherche sur l'Inflammation, INSERM UMR 1149, Paris, France (A.B.); Departments of Pathology (J.C.), Medical Imaging (S.M., E.R., A. Luciani), and Hepatobiliary and Digestive Surgery (A. Laurent), Hôpitaux Universitaires Henri-Mondor, AP-HP, Université Paris Est Créteil, Faculté de Santé, Créteil, France; INSERM U955, Team "Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers," Créteil, France (A. Laurent); Department of Radiology (O. Seror) and Liver Unit (N.G.C.), Avicenne Hospital, AP-HP, Bobigny, France; Sorbonne Paris Nord University, UFR SMBH, Bobigny, France (N.G.C.); INSERM UMR 1138, Team "Functional Genomic of Solid Tumors," Paris, France (N.G.C.); and Departments of Imaging (M.W.) and HPB and Liver Transplantation (O. Scatton), Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Sorbonne Université, Centre de Recherche Saint-Antoine INSERM, Paris, France
| | - Sébastien Mulé
- From the Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy (R.C., F.M.); Departments of Radiology (F.M., M.D.B., R.S., V.V., M.R.), Pathology (A.B.), and Hepatobiliary Surgery (F.C., C.H.), Hôpital Beaujon, AP-HP Nord, 100 Blvd du Général Leclerc, 92118 Clichy, France; Université Paris Cité, Paris, France (A.B., V.V., M.R.); Centre de Recherche sur l'Inflammation, INSERM UMR 1149, Paris, France (A.B.); Departments of Pathology (J.C.), Medical Imaging (S.M., E.R., A. Luciani), and Hepatobiliary and Digestive Surgery (A. Laurent), Hôpitaux Universitaires Henri-Mondor, AP-HP, Université Paris Est Créteil, Faculté de Santé, Créteil, France; INSERM U955, Team "Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers," Créteil, France (A. Laurent); Department of Radiology (O. Seror) and Liver Unit (N.G.C.), Avicenne Hospital, AP-HP, Bobigny, France; Sorbonne Paris Nord University, UFR SMBH, Bobigny, France (N.G.C.); INSERM UMR 1138, Team "Functional Genomic of Solid Tumors," Paris, France (N.G.C.); and Departments of Imaging (M.W.) and HPB and Liver Transplantation (O. Scatton), Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Sorbonne Université, Centre de Recherche Saint-Antoine INSERM, Paris, France
| | - Edouard Reizine
- From the Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy (R.C., F.M.); Departments of Radiology (F.M., M.D.B., R.S., V.V., M.R.), Pathology (A.B.), and Hepatobiliary Surgery (F.C., C.H.), Hôpital Beaujon, AP-HP Nord, 100 Blvd du Général Leclerc, 92118 Clichy, France; Université Paris Cité, Paris, France (A.B., V.V., M.R.); Centre de Recherche sur l'Inflammation, INSERM UMR 1149, Paris, France (A.B.); Departments of Pathology (J.C.), Medical Imaging (S.M., E.R., A. Luciani), and Hepatobiliary and Digestive Surgery (A. Laurent), Hôpitaux Universitaires Henri-Mondor, AP-HP, Université Paris Est Créteil, Faculté de Santé, Créteil, France; INSERM U955, Team "Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers," Créteil, France (A. Laurent); Department of Radiology (O. Seror) and Liver Unit (N.G.C.), Avicenne Hospital, AP-HP, Bobigny, France; Sorbonne Paris Nord University, UFR SMBH, Bobigny, France (N.G.C.); INSERM UMR 1138, Team "Functional Genomic of Solid Tumors," Paris, France (N.G.C.); and Departments of Imaging (M.W.) and HPB and Liver Transplantation (O. Scatton), Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Sorbonne Université, Centre de Recherche Saint-Antoine INSERM, Paris, France
| | - Alain Luciani
- From the Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy (R.C., F.M.); Departments of Radiology (F.M., M.D.B., R.S., V.V., M.R.), Pathology (A.B.), and Hepatobiliary Surgery (F.C., C.H.), Hôpital Beaujon, AP-HP Nord, 100 Blvd du Général Leclerc, 92118 Clichy, France; Université Paris Cité, Paris, France (A.B., V.V., M.R.); Centre de Recherche sur l'Inflammation, INSERM UMR 1149, Paris, France (A.B.); Departments of Pathology (J.C.), Medical Imaging (S.M., E.R., A. Luciani), and Hepatobiliary and Digestive Surgery (A. Laurent), Hôpitaux Universitaires Henri-Mondor, AP-HP, Université Paris Est Créteil, Faculté de Santé, Créteil, France; INSERM U955, Team "Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers," Créteil, France (A. Laurent); Department of Radiology (O. Seror) and Liver Unit (N.G.C.), Avicenne Hospital, AP-HP, Bobigny, France; Sorbonne Paris Nord University, UFR SMBH, Bobigny, France (N.G.C.); INSERM UMR 1138, Team "Functional Genomic of Solid Tumors," Paris, France (N.G.C.); and Departments of Imaging (M.W.) and HPB and Liver Transplantation (O. Scatton), Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Sorbonne Université, Centre de Recherche Saint-Antoine INSERM, Paris, France
| | - Alexis Laurent
- From the Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy (R.C., F.M.); Departments of Radiology (F.M., M.D.B., R.S., V.V., M.R.), Pathology (A.B.), and Hepatobiliary Surgery (F.C., C.H.), Hôpital Beaujon, AP-HP Nord, 100 Blvd du Général Leclerc, 92118 Clichy, France; Université Paris Cité, Paris, France (A.B., V.V., M.R.); Centre de Recherche sur l'Inflammation, INSERM UMR 1149, Paris, France (A.B.); Departments of Pathology (J.C.), Medical Imaging (S.M., E.R., A. Luciani), and Hepatobiliary and Digestive Surgery (A. Laurent), Hôpitaux Universitaires Henri-Mondor, AP-HP, Université Paris Est Créteil, Faculté de Santé, Créteil, France; INSERM U955, Team "Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers," Créteil, France (A. Laurent); Department of Radiology (O. Seror) and Liver Unit (N.G.C.), Avicenne Hospital, AP-HP, Bobigny, France; Sorbonne Paris Nord University, UFR SMBH, Bobigny, France (N.G.C.); INSERM UMR 1138, Team "Functional Genomic of Solid Tumors," Paris, France (N.G.C.); and Departments of Imaging (M.W.) and HPB and Liver Transplantation (O. Scatton), Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Sorbonne Université, Centre de Recherche Saint-Antoine INSERM, Paris, France
| | - Olivier Seror
- From the Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy (R.C., F.M.); Departments of Radiology (F.M., M.D.B., R.S., V.V., M.R.), Pathology (A.B.), and Hepatobiliary Surgery (F.C., C.H.), Hôpital Beaujon, AP-HP Nord, 100 Blvd du Général Leclerc, 92118 Clichy, France; Université Paris Cité, Paris, France (A.B., V.V., M.R.); Centre de Recherche sur l'Inflammation, INSERM UMR 1149, Paris, France (A.B.); Departments of Pathology (J.C.), Medical Imaging (S.M., E.R., A. Luciani), and Hepatobiliary and Digestive Surgery (A. Laurent), Hôpitaux Universitaires Henri-Mondor, AP-HP, Université Paris Est Créteil, Faculté de Santé, Créteil, France; INSERM U955, Team "Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers," Créteil, France (A. Laurent); Department of Radiology (O. Seror) and Liver Unit (N.G.C.), Avicenne Hospital, AP-HP, Bobigny, France; Sorbonne Paris Nord University, UFR SMBH, Bobigny, France (N.G.C.); INSERM UMR 1138, Team "Functional Genomic of Solid Tumors," Paris, France (N.G.C.); and Departments of Imaging (M.W.) and HPB and Liver Transplantation (O. Scatton), Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Sorbonne Université, Centre de Recherche Saint-Antoine INSERM, Paris, France
| | - Nathalie Ganne-Carrié
- From the Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy (R.C., F.M.); Departments of Radiology (F.M., M.D.B., R.S., V.V., M.R.), Pathology (A.B.), and Hepatobiliary Surgery (F.C., C.H.), Hôpital Beaujon, AP-HP Nord, 100 Blvd du Général Leclerc, 92118 Clichy, France; Université Paris Cité, Paris, France (A.B., V.V., M.R.); Centre de Recherche sur l'Inflammation, INSERM UMR 1149, Paris, France (A.B.); Departments of Pathology (J.C.), Medical Imaging (S.M., E.R., A. Luciani), and Hepatobiliary and Digestive Surgery (A. Laurent), Hôpitaux Universitaires Henri-Mondor, AP-HP, Université Paris Est Créteil, Faculté de Santé, Créteil, France; INSERM U955, Team "Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers," Créteil, France (A. Laurent); Department of Radiology (O. Seror) and Liver Unit (N.G.C.), Avicenne Hospital, AP-HP, Bobigny, France; Sorbonne Paris Nord University, UFR SMBH, Bobigny, France (N.G.C.); INSERM UMR 1138, Team "Functional Genomic of Solid Tumors," Paris, France (N.G.C.); and Departments of Imaging (M.W.) and HPB and Liver Transplantation (O. Scatton), Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Sorbonne Université, Centre de Recherche Saint-Antoine INSERM, Paris, France
| | - Mathilde Wagner
- From the Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy (R.C., F.M.); Departments of Radiology (F.M., M.D.B., R.S., V.V., M.R.), Pathology (A.B.), and Hepatobiliary Surgery (F.C., C.H.), Hôpital Beaujon, AP-HP Nord, 100 Blvd du Général Leclerc, 92118 Clichy, France; Université Paris Cité, Paris, France (A.B., V.V., M.R.); Centre de Recherche sur l'Inflammation, INSERM UMR 1149, Paris, France (A.B.); Departments of Pathology (J.C.), Medical Imaging (S.M., E.R., A. Luciani), and Hepatobiliary and Digestive Surgery (A. Laurent), Hôpitaux Universitaires Henri-Mondor, AP-HP, Université Paris Est Créteil, Faculté de Santé, Créteil, France; INSERM U955, Team "Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers," Créteil, France (A. Laurent); Department of Radiology (O. Seror) and Liver Unit (N.G.C.), Avicenne Hospital, AP-HP, Bobigny, France; Sorbonne Paris Nord University, UFR SMBH, Bobigny, France (N.G.C.); INSERM UMR 1138, Team "Functional Genomic of Solid Tumors," Paris, France (N.G.C.); and Departments of Imaging (M.W.) and HPB and Liver Transplantation (O. Scatton), Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Sorbonne Université, Centre de Recherche Saint-Antoine INSERM, Paris, France
| | - Olivier Scatton
- From the Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy (R.C., F.M.); Departments of Radiology (F.M., M.D.B., R.S., V.V., M.R.), Pathology (A.B.), and Hepatobiliary Surgery (F.C., C.H.), Hôpital Beaujon, AP-HP Nord, 100 Blvd du Général Leclerc, 92118 Clichy, France; Université Paris Cité, Paris, France (A.B., V.V., M.R.); Centre de Recherche sur l'Inflammation, INSERM UMR 1149, Paris, France (A.B.); Departments of Pathology (J.C.), Medical Imaging (S.M., E.R., A. Luciani), and Hepatobiliary and Digestive Surgery (A. Laurent), Hôpitaux Universitaires Henri-Mondor, AP-HP, Université Paris Est Créteil, Faculté de Santé, Créteil, France; INSERM U955, Team "Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers," Créteil, France (A. Laurent); Department of Radiology (O. Seror) and Liver Unit (N.G.C.), Avicenne Hospital, AP-HP, Bobigny, France; Sorbonne Paris Nord University, UFR SMBH, Bobigny, France (N.G.C.); INSERM UMR 1138, Team "Functional Genomic of Solid Tumors," Paris, France (N.G.C.); and Departments of Imaging (M.W.) and HPB and Liver Transplantation (O. Scatton), Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Sorbonne Université, Centre de Recherche Saint-Antoine INSERM, Paris, France
| | - Valérie Vilgrain
- From the Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy (R.C., F.M.); Departments of Radiology (F.M., M.D.B., R.S., V.V., M.R.), Pathology (A.B.), and Hepatobiliary Surgery (F.C., C.H.), Hôpital Beaujon, AP-HP Nord, 100 Blvd du Général Leclerc, 92118 Clichy, France; Université Paris Cité, Paris, France (A.B., V.V., M.R.); Centre de Recherche sur l'Inflammation, INSERM UMR 1149, Paris, France (A.B.); Departments of Pathology (J.C.), Medical Imaging (S.M., E.R., A. Luciani), and Hepatobiliary and Digestive Surgery (A. Laurent), Hôpitaux Universitaires Henri-Mondor, AP-HP, Université Paris Est Créteil, Faculté de Santé, Créteil, France; INSERM U955, Team "Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers," Créteil, France (A. Laurent); Department of Radiology (O. Seror) and Liver Unit (N.G.C.), Avicenne Hospital, AP-HP, Bobigny, France; Sorbonne Paris Nord University, UFR SMBH, Bobigny, France (N.G.C.); INSERM UMR 1138, Team "Functional Genomic of Solid Tumors," Paris, France (N.G.C.); and Departments of Imaging (M.W.) and HPB and Liver Transplantation (O. Scatton), Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Sorbonne Université, Centre de Recherche Saint-Antoine INSERM, Paris, France
| | - François Cauchy
- From the Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy (R.C., F.M.); Departments of Radiology (F.M., M.D.B., R.S., V.V., M.R.), Pathology (A.B.), and Hepatobiliary Surgery (F.C., C.H.), Hôpital Beaujon, AP-HP Nord, 100 Blvd du Général Leclerc, 92118 Clichy, France; Université Paris Cité, Paris, France (A.B., V.V., M.R.); Centre de Recherche sur l'Inflammation, INSERM UMR 1149, Paris, France (A.B.); Departments of Pathology (J.C.), Medical Imaging (S.M., E.R., A. Luciani), and Hepatobiliary and Digestive Surgery (A. Laurent), Hôpitaux Universitaires Henri-Mondor, AP-HP, Université Paris Est Créteil, Faculté de Santé, Créteil, France; INSERM U955, Team "Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers," Créteil, France (A. Laurent); Department of Radiology (O. Seror) and Liver Unit (N.G.C.), Avicenne Hospital, AP-HP, Bobigny, France; Sorbonne Paris Nord University, UFR SMBH, Bobigny, France (N.G.C.); INSERM UMR 1138, Team "Functional Genomic of Solid Tumors," Paris, France (N.G.C.); and Departments of Imaging (M.W.) and HPB and Liver Transplantation (O. Scatton), Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Sorbonne Université, Centre de Recherche Saint-Antoine INSERM, Paris, France
| | - Christian Hobeika
- From the Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy (R.C., F.M.); Departments of Radiology (F.M., M.D.B., R.S., V.V., M.R.), Pathology (A.B.), and Hepatobiliary Surgery (F.C., C.H.), Hôpital Beaujon, AP-HP Nord, 100 Blvd du Général Leclerc, 92118 Clichy, France; Université Paris Cité, Paris, France (A.B., V.V., M.R.); Centre de Recherche sur l'Inflammation, INSERM UMR 1149, Paris, France (A.B.); Departments of Pathology (J.C.), Medical Imaging (S.M., E.R., A. Luciani), and Hepatobiliary and Digestive Surgery (A. Laurent), Hôpitaux Universitaires Henri-Mondor, AP-HP, Université Paris Est Créteil, Faculté de Santé, Créteil, France; INSERM U955, Team "Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers," Créteil, France (A. Laurent); Department of Radiology (O. Seror) and Liver Unit (N.G.C.), Avicenne Hospital, AP-HP, Bobigny, France; Sorbonne Paris Nord University, UFR SMBH, Bobigny, France (N.G.C.); INSERM UMR 1138, Team "Functional Genomic of Solid Tumors," Paris, France (N.G.C.); and Departments of Imaging (M.W.) and HPB and Liver Transplantation (O. Scatton), Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Sorbonne Université, Centre de Recherche Saint-Antoine INSERM, Paris, France
| | - Maxime Ronot
- From the Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy (R.C., F.M.); Departments of Radiology (F.M., M.D.B., R.S., V.V., M.R.), Pathology (A.B.), and Hepatobiliary Surgery (F.C., C.H.), Hôpital Beaujon, AP-HP Nord, 100 Blvd du Général Leclerc, 92118 Clichy, France; Université Paris Cité, Paris, France (A.B., V.V., M.R.); Centre de Recherche sur l'Inflammation, INSERM UMR 1149, Paris, France (A.B.); Departments of Pathology (J.C.), Medical Imaging (S.M., E.R., A. Luciani), and Hepatobiliary and Digestive Surgery (A. Laurent), Hôpitaux Universitaires Henri-Mondor, AP-HP, Université Paris Est Créteil, Faculté de Santé, Créteil, France; INSERM U955, Team "Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers," Créteil, France (A. Laurent); Department of Radiology (O. Seror) and Liver Unit (N.G.C.), Avicenne Hospital, AP-HP, Bobigny, France; Sorbonne Paris Nord University, UFR SMBH, Bobigny, France (N.G.C.); INSERM UMR 1138, Team "Functional Genomic of Solid Tumors," Paris, France (N.G.C.); and Departments of Imaging (M.W.) and HPB and Liver Transplantation (O. Scatton), Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Sorbonne Université, Centre de Recherche Saint-Antoine INSERM, Paris, France
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Tan XZ, Chen X, Liu P. Potential Influence of Inconsistent Arterial Phase Imaging on Macrotrabecular-massive Hepatocellular Carcinoma Prediction. Radiology 2024; 310:e232349. [PMID: 38226884 DOI: 10.1148/radiol.232349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Affiliation(s)
- Xian-Zheng Tan
- Department of Radiology, Hunan Provincial People's Hospital, the First Affiliated Hospital of Hunan Normal University, Changsha 410005, China
| | - Xiang Chen
- Department of Radiology, Hunan Provincial People's Hospital, the First Affiliated Hospital of Hunan Normal University, Changsha 410005, China
| | - Peng Liu
- Department of Radiology, Hunan Provincial People's Hospital, the First Affiliated Hospital of Hunan Normal University, Changsha 410005, China
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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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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 2023. [PMID: 38140862 DOI: 10.1002/jmri.29197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [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 2023. [PMID: 38116997 DOI: 10.1002/jmri.29187] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [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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Dioguardi Burgio M, Garzelli L, Cannella R, Ronot M, Vilgrain V. Hepatocellular Carcinoma: Optimal Radiological Evaluation before Liver Transplantation. Life (Basel) 2023; 13:2267. [PMID: 38137868 PMCID: PMC10744421 DOI: 10.3390/life13122267] [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: 09/04/2023] [Revised: 10/27/2023] [Accepted: 11/24/2023] [Indexed: 12/24/2023] Open
Abstract
Liver transplantation (LT) is the recommended curative-intent treatment for patients with early or intermediate-stage hepatocellular carcinoma (HCC) who are ineligible for resection. Imaging plays a central role in staging and for selecting the best LT candidates. This review will discuss recent developments in pre-LT imaging assessment, in particular LT eligibility criteria on imaging, the technical requirements and the diagnostic performance of imaging for the pre-LT diagnosis of HCC including the recent Liver Imaging Reporting and Data System (LI-RADS) criteria, the evaluation of the response to locoregional therapy, as well as the non-invasive prediction of HCC aggressiveness and its impact on the outcome of LT. We will also briefly discuss the role of nuclear medicine in the pre-LT evaluation and the emerging role of artificial intelligence models in patients with HCC.
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Affiliation(s)
- Marco Dioguardi Burgio
- Department of Radiology, Hôpital Beaujon, AP-HP. Nord, 100 Boulevard du Général Leclerc, 92110 Clichy, France (V.V.)
- Centre de Recherche sur l’Inflammation, UMR1149, Université Paris Cité, 75018 Paris, France
| | - Lorenzo Garzelli
- Service d’Imagerie Medicale, Centre Hospitalier de Cayenne, Avenue des Flamboyants, Cayenne 97306, French Guiana
| | - Roberto Cannella
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University Hospital “Paolo Giaccone”, Via del Vespro 129, 90127 Palermo, Italy
| | - Maxime Ronot
- Department of Radiology, Hôpital Beaujon, AP-HP. Nord, 100 Boulevard du Général Leclerc, 92110 Clichy, France (V.V.)
- Centre de Recherche sur l’Inflammation, UMR1149, Université Paris Cité, 75018 Paris, France
| | - Valérie Vilgrain
- Department of Radiology, Hôpital Beaujon, AP-HP. Nord, 100 Boulevard du Général Leclerc, 92110 Clichy, France (V.V.)
- Centre de Recherche sur l’Inflammation, UMR1149, Université Paris Cité, 75018 Paris, France
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Auer TA, Halskov S, Fehrenbach U, Nevermann NF, Pelzer U, Mohr R, Hamm B, Schöning W, Horst D, Ihlow J, Geisel D. Gd-EOB MRI for HCC subtype differentiation in a western population according to the 5 th edition of the World Health Organization classification. Eur Radiol 2023; 33:6902-6915. [PMID: 37115216 PMCID: PMC10511376 DOI: 10.1007/s00330-023-09669-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/29/2023] [Accepted: 04/10/2023] [Indexed: 04/29/2023]
Abstract
OBJECTIVES To investigate the value of gadoxetic acid (Gd-EOB)-enhanced magnetic resonance imaging (MRI) for noninvasive subtype differentiation of HCCs according to the 5th edition of the WHO Classification of Digestive System Tumors in a western population. METHODS This retrospective study included 262 resected lesions in 240 patients with preoperative Gd-EOB-enhanced MRI. Subtypes were assigned by two pathologists. Gd-EOB-enhanced MRI datasets were assessed by two radiologists for qualitative and quantitative imaging features, including imaging features defined in LI-RADS v2018 and area of hepatobiliary phase (HBP) iso- to hyperintensity. RESULTS The combination of non-rim arterial phase hyperenhancement with non-peripheral portal venous washout was more common in "not otherwise specified" (nos-ST) (88/168, 52%) than other subtypes, in particular macrotrabecular massive (mt-ST) (3/15, 20%), chromophobe (ch-ST) (1/8, 13%), and scirrhous subtypes (sc-ST) (2/9, 22%) (p = 0.035). Macrovascular invasion was associated with mt-ST (5/16, p = 0.033) and intralesional steatosis with steatohepatitic subtype (sh-ST) (28/32, p < 0.001). Predominant iso- to hyperintensity in the HBP was only present in nos-ST (16/174), sh-ST (3/33), and clear cell subtypes (cc-ST) (3/13) (p = 0.031). Associations were found for the following non-imaging parameters: age and sex, as patients with fibrolamellar subtype (fib-ST) were younger (median 44 years (19-66), p < 0.001) and female (4/5, p = 0.023); logarithm of alpha-fetoprotein (AFP) was elevated in the mt-ST (median 397 µg/l (74-5370), p < 0.001); type II diabetes mellitus was more frequent in the sh-ST (20/33, p = 0.027). CONCLUSIONS Gd-EOB-MRI reproduces findings reported in the literature for extracellular contrast-enhanced MRI and CT and may be a valuable tool for noninvasive HCC subtype differentiation. CLINICAL RELEVANCE STATEMENT Better characterization of the heterogeneous phenotypes of HCC according to the revised WHO classification potentially improves both diagnostic accuracy and the precision of therapeutic stratification for HCC. KEY POINTS • Previously reported imaging features of common subtypes in CT and MRI enhanced with extracellular contrast agents are reproducible with Gd-EOB-enhanced MRI. • While uncommon, predominant iso- to hyperintensity in the HBP was observed only in NOS, clear cell, and steatohepatitic subtypes. • Gd-EOB-enhanced MRI offers imaging features that are of value for HCC subtype differentiation according to the 5th edition of the WHO Classification of Digestive System Tumors.
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Affiliation(s)
- Timo A Auer
- Department of Radiology, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany.
- Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Straße 2, 10178, Berlin, Germany.
| | - Sebastian Halskov
- Department of Radiology, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Uli Fehrenbach
- Department of Radiology, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Nora F Nevermann
- Department of Surgery - CVK/CCM, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Uwe Pelzer
- Department of Hematology, Oncology and Cancer Immunology, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Raphael Mohr
- Department of Hepatology and Gastroenterology, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Bernd Hamm
- Department of Radiology, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Wenzel Schöning
- Department of Surgery - CVK/CCM, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - David Horst
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Jana Ihlow
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Dominik Geisel
- Department of Radiology, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
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20
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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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Zhao YM, Xie SS, Wang J, Zhang YM, Li WC, Ye ZX, Shen W. Added value of CE-CT radiomics to predict high Ki-67 expression in hepatocellular carcinoma. BMC Med Imaging 2023; 23:138. [PMID: 37737166 PMCID: PMC10514983 DOI: 10.1186/s12880-023-01069-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 08/02/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND This study aimed to develop a computed tomography (CT) model to predict Ki-67 expression in hepatocellular carcinoma (HCC) and to examine the added value of radiomics to clinico-radiological features. METHODS A total of 208 patients (training set, n = 120; internal test set, n = 51; external validation set, n = 37) with pathologically confirmed HCC who underwent contrast-enhanced CT (CE-CT) within 1 month before surgery were retrospectively included from January 2014 to September 2021. Radiomics features were extracted and selected from three phases of CE-CT images, least absolute shrinkage and selection operator regression (LASSO) was used to select features, and the rad-score was calculated. CE-CT imaging and clinical features were selected using univariate and multivariate analyses, respectively. Three prediction models, including clinic-radiologic (CR) model, rad-score (R) model, and clinic-radiologic-radiomic (CRR) model, were developed and validated using logistic regression analysis. The performance of different models for predicting Ki-67 expression was evaluated using the area under the receiver operating characteristic curve (AUROC) and decision curve analysis (DCA). RESULTS HCCs with high Ki-67 expression were more likely to have high serum α-fetoprotein levels (P = 0.041, odds ratio [OR] 2.54, 95% confidence interval [CI]: 1.04-6.21), non-rim arterial phase hyperenhancement (P = 0.001, OR 15.13, 95% CI 2.87-79.76), portal vein tumor thrombus (P = 0.035, OR 3.19, 95% CI: 1.08-9.37), and two-trait predictor of venous invasion (P = 0.026, OR 14.04, 95% CI: 1.39-144.32). The CR model achieved relatively good and stable performance compared with the R model (AUC, 0.805 [95% CI: 0.683-0.926] vs. 0.678 [95% CI: 0.536-0.839], P = 0.211; and 0.805 [95% CI: 0.657-0.953] vs. 0.667 [95% CI: 0.495-0.839], P = 0.135) in the internal and external validation sets. After combining the CR model with the R model, the AUC of the CRR model increased to 0.903 (95% CI: 0.849-0.956) in the training set, which was significantly higher than that of the CR model (P = 0.0148). However, no significant differences were found between the CRR and CR models in the internal and external validation sets (P = 0.264 and P = 0.084, respectively). CONCLUSIONS Preoperative models based on clinical and CE-CT imaging features can be used to predict HCC with high Ki-67 expression accurately. However, radiomics cannot provide added value.
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Affiliation(s)
- Yu-meng Zhao
- Medical School of Nankai University, No. 94, Weijin Road, Nankai District, Tianjin, China
| | - Shuang-shuang Xie
- Department of Radiology, Tianjin First Center Hospital, Tianjin Institute of imaging medicine, School of Medicine, Nankai University, Nankai District, No. 24 Fukang Road, Tianjin, China
| | - Jian Wang
- Department of Hepatobiliary Surgery, Tianjin First Central Hospital, School of Medicine, Nankai University, Nankai District, No. 24 Fukang Road, Tianjin, China
| | - Ya-min Zhang
- Department of Hepatobiliary Surgery, Tianjin First Central Hospital, School of Medicine, Nankai University, Nankai District, No. 24 Fukang Road, Tianjin, China
| | - Wen-Cui Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, 300060 China
| | - Zhao-Xiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, 300060 China
| | - Wen Shen
- Department of Radiology, Tianjin First Center Hospital, Tianjin Institute of imaging medicine, School of Medicine, Nankai University, Nankai District, No. 24 Fukang Road, Tianjin, 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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Wang YY, Dong K, Wang K, Sun Y, Xing BC. Effect of vessels that encapsulate tumor clusters (VETC) on the prognosis of different stages of hepatocellular carcinoma after hepatectomy. Dig Liver Dis 2023; 55:1288-1294. [PMID: 37037766 DOI: 10.1016/j.dld.2023.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 03/19/2023] [Accepted: 03/23/2023] [Indexed: 04/12/2023]
Abstract
BACKGROUND Vessels that encapsulate tumor clusters (VETC) is a newly discovered vascular pattern in hepatocellular carcinoma (HCC), representing high biological aggressiveness. However, it remains unclear whether the prognostic impact of VETC differs in patients with different staged HCC. This study aimed to evaluate the effect of VETC on the prognosis of patients with HCC at different stages after hepatectomy. METHODS Patients who underwent hepatectomy for HCC between January 2005 and December 2019 were assessed, and stratified according to their Barcelona Clinic Liver Cancer (BCLC) stage. Overall survival (OS) and disease-free survival (DFS) were compared between patients with and without VETC. Independent risk factors of OS and DFS were determined by multivariable Cox regression analyses. RESULTS A total of 837 consecutive patients undergoing curative hepatectomy were enrolled, and VETC pattern was found in 339 (40.5%) patients. The incidence of VETC in patients at BCLC-0, BCLC-A, BCLC-B and BCLC-C stage was 17.8%, 40.2%, 53.7% and 66.0%, respectively. In the entire patients, VETC+ patients had significantly lower OS and DFS than VETC- patients. After stratification of patients according to BCLC stage, VETC was associated with worse OS and DFS only in patients at BCLC-A and BCLC-B stages, but not in those at BCLC-0 and BCLC-C stages. Multivariable analyses also revealed that VETC was an independent risk factor for OS and DFS in both the patients at BCLC-A and BCLC-B stages. CONCLUSIONS VETC is associated with poor OS and DFS in patients with HCC at BCLC-A and BCLC-B stage after hepatectomy, but it does not affect the survival of patients with HCC at BCLC-0 and BCLC-C stage after hepatectomy.
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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, Beijing 100142, China
| | - Kun Dong
- Pathology Department, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University School of Oncology, Beijing Cancer Hospital and Institute, Beijing 100142, China
| | - Kun 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, Beijing 100142, China
| | - Yu Sun
- Pathology Department, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University School of Oncology, Beijing Cancer Hospital and Institute, 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, Beijing 100142, China.
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Li HZ, Liu QQ, Chang DH, Li SX, Yang LT, Zhou P, Deng JB, Huang CH, Xiao YD. Identification of NOX4 as a New Biomarker in Hepatocellular Carcinoma and Its Effect on Sorafenib Therapy. Biomedicines 2023; 11:2196. [PMID: 37626693 PMCID: PMC10452076 DOI: 10.3390/biomedicines11082196] [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: 06/26/2023] [Revised: 07/23/2023] [Accepted: 07/28/2023] [Indexed: 08/27/2023] Open
Abstract
To improve the survival of patients with hepatocellular carcinoma (HCC), new biomarkers and therapeutic targets are urgently needed. In this study, the GEO and TCGA dataset were used to explore the differential co-expressed genes and their prognostic correlation between HCC and normal samples. The mRNA levels of these genes were validated by qRT-PCR in 20 paired fresh HCC samples. The results demonstrated that the eight-gene model was effective in predicting the prognosis of HCC patients in the validation cohorts. Based on qRT-PCR results, NOX4 was selected to further explore biological functions within the model and 150 cases of paraffin-embedded HCC tissues were scored for NOX4 immunohistochemical staining. We found that the NOX4 expression was significantly upregulated in HCC and was associated with poor survival. In terms of function, the knockdown of NOX4 markedly inhibited the progression of HCC in vivo and in vitro. Mechanistic studies suggested that NOX4 promotes HCC progression through the activation of the epithelial-mesenchymal transition. In addition, the sensitivity of HCC cells to sorafenib treatment was obviously decreased after NOX4 overexpression. Taken together, this study reveals NOX4 as a potential therapeutic target for HCC and a biomarker for predicting the sorafenib treatment response.
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Affiliation(s)
- Hui-Zhou Li
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China (S.-X.L.)
| | - Qing-Qing Liu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - De-Hua Chang
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, 69120 Heidelberg, Germany
| | - Shu-Xian Li
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China (S.-X.L.)
| | - Long-Tao Yang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China (S.-X.L.)
| | - Peng Zhou
- Department of Pathology, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Jiang-Bei Deng
- Department of Intervention, Changsha Central Hospital, University of South Chian, Changsha 410011, China
| | - Chang-Hao Huang
- The Hunan Provincial Key Laboratory of Precision Diagnosis and Treatment for Gastrointestinal Tumor, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Yu-Dong Xiao
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China (S.-X.L.)
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25
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Chu LC, Fishman EK. AI as Virtual Biopsy Tool to Predict Macrotrabecular-Massive Hepatocellular Carcinoma. Radiology 2023; 308:e231663. [PMID: 37606567 DOI: 10.1148/radiol.231663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/23/2023]
Affiliation(s)
- Linda C Chu
- From the Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 600 N Wolfe St, Hal B168, Baltimore, MD 21287
| | - Elliot K Fishman
- From the Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 600 N Wolfe St, Hal B168, Baltimore, MD 21287
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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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Li M, Fan Y, You H, Li C, Luo M, Zhou J, Li A, Zhang L, Yu X, Deng W, Zhou J, Zhang D, Zhang Z, Chen H, Xiao Y, Huang B, Wang J. Dual-Energy CT Deep Learning Radiomics to Predict Macrotrabecular-Massive Hepatocellular Carcinoma. Radiology 2023; 308:e230255. [PMID: 37606573 DOI: 10.1148/radiol.230255] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/23/2023]
Abstract
Background It is unknown whether the additional information provided by multiparametric dual-energy CT (DECT) could improve the noninvasive diagnosis of the aggressive macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC). Purpose To evaluate the diagnostic performance of dual-phase contrast-enhanced multiparametric DECT for predicting MTM HCC. Materials and Methods Patients with histopathologic examination-confirmed HCC who underwent contrast-enhanced DECT between June 2019 and June 2022 were retrospectively recruited from three independent centers (center 1, training and internal test data set; centers 2 and 3, external test data set). Radiologic features were visually analyzed and combined with clinical information to establish a clinical-radiologic model. Deep learning (DL) radiomics models were based on DL features and handcrafted features extracted from virtual monoenergetic images and material composition images on dual phase using binary least absolute shrinkage and selection operators. A DL radiomics nomogram was developed using multivariable logistic regression analysis. Model performance was evaluated with the area under the receiver operating characteristic curve (AUC), and the log-rank test was used to analyze recurrence-free survival. Results A total of 262 patients were included (mean age, 54 years ± 12 [SD]; 225 men [86%]; training data set, n = 146 [56%]; internal test data set, n = 35 [13%]; external test data set, n = 81 [31%]). The DL radiomics nomogram better predicted MTM than the clinical-radiologic model (AUC = 0.91 vs 0.77, respectively, for the training set [P < .001], 0.87 vs 0.72 for the internal test data set [P = .04], and 0.89 vs 0.79 for the external test data set [P = .02]), with similar sensitivity (80% vs 87%, respectively; P = .63) and higher specificity (90% vs 63%; P < .001) in the external test data set. The predicted positive MTM groups based on the DL radiomics nomogram had shorter recurrence-free survival than predicted negative MTM groups in all three data sets (training data set, P = .04; internal test data set, P = .01; and external test data set, P = .03). Conclusion A DL radiomics nomogram derived from multiparametric DECT accurately predicted the MTM subtype in patients with HCC. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Chu and Fishman in this issue.
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Affiliation(s)
- Mengsi Li
- From the Departments of Radiology (M. Li, C.L., A.L., L.Z., Jinhui Zhou, D.Z., H.C., Y.X., J.W.) and Pathology (Jing Zhou), The Third Affiliated Hospital, Sun Yat-Sen University, No. 600 Tianhe Rd, Guangzhou, Guangdong, 510630, People's Republic of China; Medical AI Laboratory, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, People's Republic of China (Y.F., B.H.); Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China (H.Y.); Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China (M. Luo); and Department of Clinical Science, Philips Healthcare China, Shanghai, People's Republic of China (X.Y., W.D., Z.Z.)
| | - Yaheng Fan
- From the Departments of Radiology (M. Li, C.L., A.L., L.Z., Jinhui Zhou, D.Z., H.C., Y.X., J.W.) and Pathology (Jing Zhou), The Third Affiliated Hospital, Sun Yat-Sen University, No. 600 Tianhe Rd, Guangzhou, Guangdong, 510630, People's Republic of China; Medical AI Laboratory, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, People's Republic of China (Y.F., B.H.); Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China (H.Y.); Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China (M. Luo); and Department of Clinical Science, Philips Healthcare China, Shanghai, People's Republic of China (X.Y., W.D., Z.Z.)
| | - Huayu You
- From the Departments of Radiology (M. Li, C.L., A.L., L.Z., Jinhui Zhou, D.Z., H.C., Y.X., J.W.) and Pathology (Jing Zhou), The Third Affiliated Hospital, Sun Yat-Sen University, No. 600 Tianhe Rd, Guangzhou, Guangdong, 510630, People's Republic of China; Medical AI Laboratory, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, People's Republic of China (Y.F., B.H.); Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China (H.Y.); Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China (M. Luo); and Department of Clinical Science, Philips Healthcare China, Shanghai, People's Republic of China (X.Y., W.D., Z.Z.)
| | - Chao Li
- From the Departments of Radiology (M. Li, C.L., A.L., L.Z., Jinhui Zhou, D.Z., H.C., Y.X., J.W.) and Pathology (Jing Zhou), The Third Affiliated Hospital, Sun Yat-Sen University, No. 600 Tianhe Rd, Guangzhou, Guangdong, 510630, People's Republic of China; Medical AI Laboratory, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, People's Republic of China (Y.F., B.H.); Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China (H.Y.); Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China (M. Luo); and Department of Clinical Science, Philips Healthcare China, Shanghai, People's Republic of China (X.Y., W.D., Z.Z.)
| | - Ma Luo
- From the Departments of Radiology (M. Li, C.L., A.L., L.Z., Jinhui Zhou, D.Z., H.C., Y.X., J.W.) and Pathology (Jing Zhou), The Third Affiliated Hospital, Sun Yat-Sen University, No. 600 Tianhe Rd, Guangzhou, Guangdong, 510630, People's Republic of China; Medical AI Laboratory, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, People's Republic of China (Y.F., B.H.); Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China (H.Y.); Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China (M. Luo); and Department of Clinical Science, Philips Healthcare China, Shanghai, People's Republic of China (X.Y., W.D., Z.Z.)
| | - Jing Zhou
- From the Departments of Radiology (M. Li, C.L., A.L., L.Z., Jinhui Zhou, D.Z., H.C., Y.X., J.W.) and Pathology (Jing Zhou), The Third Affiliated Hospital, Sun Yat-Sen University, No. 600 Tianhe Rd, Guangzhou, Guangdong, 510630, People's Republic of China; Medical AI Laboratory, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, People's Republic of China (Y.F., B.H.); Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China (H.Y.); Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China (M. Luo); and Department of Clinical Science, Philips Healthcare China, Shanghai, People's Republic of China (X.Y., W.D., Z.Z.)
| | - Anqi Li
- From the Departments of Radiology (M. Li, C.L., A.L., L.Z., Jinhui Zhou, D.Z., H.C., Y.X., J.W.) and Pathology (Jing Zhou), The Third Affiliated Hospital, Sun Yat-Sen University, No. 600 Tianhe Rd, Guangzhou, Guangdong, 510630, People's Republic of China; Medical AI Laboratory, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, People's Republic of China (Y.F., B.H.); Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China (H.Y.); Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China (M. Luo); and Department of Clinical Science, Philips Healthcare China, Shanghai, People's Republic of China (X.Y., W.D., Z.Z.)
| | - Lina Zhang
- From the Departments of Radiology (M. Li, C.L., A.L., L.Z., Jinhui Zhou, D.Z., H.C., Y.X., J.W.) and Pathology (Jing Zhou), The Third Affiliated Hospital, Sun Yat-Sen University, No. 600 Tianhe Rd, Guangzhou, Guangdong, 510630, People's Republic of China; Medical AI Laboratory, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, People's Republic of China (Y.F., B.H.); Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China (H.Y.); Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China (M. Luo); and Department of Clinical Science, Philips Healthcare China, Shanghai, People's Republic of China (X.Y., W.D., Z.Z.)
| | - Xiao Yu
- From the Departments of Radiology (M. Li, C.L., A.L., L.Z., Jinhui Zhou, D.Z., H.C., Y.X., J.W.) and Pathology (Jing Zhou), The Third Affiliated Hospital, Sun Yat-Sen University, No. 600 Tianhe Rd, Guangzhou, Guangdong, 510630, People's Republic of China; Medical AI Laboratory, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, People's Republic of China (Y.F., B.H.); Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China (H.Y.); Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China (M. Luo); and Department of Clinical Science, Philips Healthcare China, Shanghai, People's Republic of China (X.Y., W.D., Z.Z.)
| | - Weiwei Deng
- From the Departments of Radiology (M. Li, C.L., A.L., L.Z., Jinhui Zhou, D.Z., H.C., Y.X., J.W.) and Pathology (Jing Zhou), The Third Affiliated Hospital, Sun Yat-Sen University, No. 600 Tianhe Rd, Guangzhou, Guangdong, 510630, People's Republic of China; Medical AI Laboratory, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, People's Republic of China (Y.F., B.H.); Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China (H.Y.); Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China (M. Luo); and Department of Clinical Science, Philips Healthcare China, Shanghai, People's Republic of China (X.Y., W.D., Z.Z.)
| | - Jinhui Zhou
- From the Departments of Radiology (M. Li, C.L., A.L., L.Z., Jinhui Zhou, D.Z., H.C., Y.X., J.W.) and Pathology (Jing Zhou), The Third Affiliated Hospital, Sun Yat-Sen University, No. 600 Tianhe Rd, Guangzhou, Guangdong, 510630, People's Republic of China; Medical AI Laboratory, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, People's Republic of China (Y.F., B.H.); Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China (H.Y.); Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China (M. Luo); and Department of Clinical Science, Philips Healthcare China, Shanghai, People's Republic of China (X.Y., W.D., Z.Z.)
| | - Dingyue Zhang
- From the Departments of Radiology (M. Li, C.L., A.L., L.Z., Jinhui Zhou, D.Z., H.C., Y.X., J.W.) and Pathology (Jing Zhou), The Third Affiliated Hospital, Sun Yat-Sen University, No. 600 Tianhe Rd, Guangzhou, Guangdong, 510630, People's Republic of China; Medical AI Laboratory, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, People's Republic of China (Y.F., B.H.); Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China (H.Y.); Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China (M. Luo); and Department of Clinical Science, Philips Healthcare China, Shanghai, People's Republic of China (X.Y., W.D., Z.Z.)
| | - Zhongping Zhang
- From the Departments of Radiology (M. Li, C.L., A.L., L.Z., Jinhui Zhou, D.Z., H.C., Y.X., J.W.) and Pathology (Jing Zhou), The Third Affiliated Hospital, Sun Yat-Sen University, No. 600 Tianhe Rd, Guangzhou, Guangdong, 510630, People's Republic of China; Medical AI Laboratory, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, People's Republic of China (Y.F., B.H.); Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China (H.Y.); Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China (M. Luo); and Department of Clinical Science, Philips Healthcare China, Shanghai, People's Republic of China (X.Y., W.D., Z.Z.)
| | - Haimei Chen
- From the Departments of Radiology (M. Li, C.L., A.L., L.Z., Jinhui Zhou, D.Z., H.C., Y.X., J.W.) and Pathology (Jing Zhou), The Third Affiliated Hospital, Sun Yat-Sen University, No. 600 Tianhe Rd, Guangzhou, Guangdong, 510630, People's Republic of China; Medical AI Laboratory, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, People's Republic of China (Y.F., B.H.); Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China (H.Y.); Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China (M. Luo); and Department of Clinical Science, Philips Healthcare China, Shanghai, People's Republic of China (X.Y., W.D., Z.Z.)
| | - Yuanqiang Xiao
- From the Departments of Radiology (M. Li, C.L., A.L., L.Z., Jinhui Zhou, D.Z., H.C., Y.X., J.W.) and Pathology (Jing Zhou), The Third Affiliated Hospital, Sun Yat-Sen University, No. 600 Tianhe Rd, Guangzhou, Guangdong, 510630, People's Republic of China; Medical AI Laboratory, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, People's Republic of China (Y.F., B.H.); Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China (H.Y.); Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China (M. Luo); and Department of Clinical Science, Philips Healthcare China, Shanghai, People's Republic of China (X.Y., W.D., Z.Z.)
| | - Bingsheng Huang
- From the Departments of Radiology (M. Li, C.L., A.L., L.Z., Jinhui Zhou, D.Z., H.C., Y.X., J.W.) and Pathology (Jing Zhou), The Third Affiliated Hospital, Sun Yat-Sen University, No. 600 Tianhe Rd, Guangzhou, Guangdong, 510630, People's Republic of China; Medical AI Laboratory, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, People's Republic of China (Y.F., B.H.); Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China (H.Y.); Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China (M. Luo); and Department of Clinical Science, Philips Healthcare China, Shanghai, People's Republic of China (X.Y., W.D., Z.Z.)
| | - Jin Wang
- From the Departments of Radiology (M. Li, C.L., A.L., L.Z., Jinhui Zhou, D.Z., H.C., Y.X., J.W.) and Pathology (Jing Zhou), The Third Affiliated Hospital, Sun Yat-Sen University, No. 600 Tianhe Rd, Guangzhou, Guangdong, 510630, People's Republic of China; Medical AI Laboratory, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, People's Republic of China (Y.F., B.H.); Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China (H.Y.); Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, People's Republic of China (M. Luo); and Department of Clinical Science, Philips Healthcare China, Shanghai, People's Republic of China (X.Y., W.D., Z.Z.)
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Li C, Wen Y, Xie J, Chen Q, Dang Y, Zhang H, Guo H, Long L. Preoperative prediction of VETC in hepatocellular carcinoma using non-Gaussian diffusion-weighted imaging at high b values: a pilot study. Front Oncol 2023; 13:1167209. [PMID: 37305565 PMCID: PMC10248416 DOI: 10.3389/fonc.2023.1167209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 05/15/2023] [Indexed: 06/13/2023] Open
Abstract
Background Vessels encapsulating tumor clusters (VETC) have been considered an important cause of hepatocellular carcinoma (HCC) metastasis. Purpose To compare the potential of various diffusion parameters derived from the monoexponential model and four non-Gaussian models (DKI, SEM, FROC, and CTRW) in preoperatively predicting the VETC of HCC. Methods 86 HCC patients (40 VETC-positive and 46 VETC-negative) were prospectively enrolled. Diffusion-weighted images were acquired using six b-values (range from 0 to 3000 s/mm2). Various diffusion parameters derived from diffusion kurtosis (DK), stretched-exponential (SE), fractional-order calculus (FROC), and continuous-time random walk (CTRW) models, together with the conventional apparent diffusion coefficient (ADC) derived from the monoexponential model were calculated. All parameters were compared between VETC-positive and VETC-negative groups using an independent sample t-test or Mann-Whitney U test, and then the parameters with significant differences between the two groups were combined to establish a predictive model by binary logistic regression. Receiver operating characteristic (ROC) analyses were used to assess diagnostic performance. Results Among all studied diffusion parameters, only DKI_K and CTRW_α significantly differed between groups (P=0.002 and 0.004, respectively). For predicting the presence of VETC in HCC patients, the combination of DKI_K and CTRW_α had the larger area under the ROC curve (AUC) than the two parameters individually (AUC=0.747 vs. 0.678 and 0.672, respectively). Conclusion DKI_K and CTRW_α outperformed traditional ADC for predicting the VETC of HCC.
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Affiliation(s)
- Chenhui Li
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Yan Wen
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Jinhuan Xie
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Qianjuan Chen
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Yiwu Dang
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Huiting Zhang
- MR Scientific Marketing, Siemens Healthcare Ltd., Wuhan, Hubei, China
| | - Hu Guo
- MR Application, Siemens Healthcare Ltd., Changsha, Hunan, China
| | - Liling Long
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Gaungxi Medical University, Ministry of Education, Nanning, Guangxi, China
- Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
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Zhang Y, He D, Liu J, Wei YG, Shi LL. Preoperative prediction of macrotrabecular-massive hepatocellular carcinoma through dynamic contrast-enhanced magnetic resonance imaging-based radiomics. World J Gastroenterol 2023; 29:2001-2014. [PMID: 37155523 PMCID: PMC10122786 DOI: 10.3748/wjg.v29.i13.2001] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 02/01/2023] [Accepted: 03/20/2023] [Indexed: 04/06/2023] Open
Abstract
BACKGROUND Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) is closely related to aggressive phenotype, gene mutation, carcinogenic pathway, and immunohistochemical markers and is a strong independent predictor of early recurrence and poor prognosis. With the development of imaging technology, successful applications of contrast-enhanced magnetic resonance imaging (MRI) have been reported in identifying the MTM-HCC subtype. Radiomics, as an objective and beneficial method for tumour evaluation, is used to convert medical images into high-throughput quantification features that greatly push the development of precision medicine.
AIM To establish and verify a nomogram for preoperatively identifying MTM-HCC by comparing different machine learning algorithms.
METHODS This retrospective study enrolled 232 (training set, 162; test set, 70) hepatocellular carcinoma patients from April 2018 to September 2021. A total of 3111 radiomics features were extracted from dynamic contrast-enhanced MRI, followed by dimension reduction of these features. Logistic regression (LR), K-nearest neighbour (KNN), Bayes, Tree, and support vector machine (SVM) algorithms were used to select the best radiomics signature. We used the relative standard deviation (RSD) and bootstrap methods to quantify the stability of these five algorithms. The algorithm with the lowest RSD represented the best stability, and it was used to construct the best radiomics model. Multivariable logistic analysis was used to select the useful clinical and radiological features, and different predictive models were established. Finally, the predictive performances of the different models were assessed by evaluating the area under the curve (AUC).
RESULTS The RSD values based on LR, KNN, Bayes, Tree, and SVM were 3.8%, 8.6%, 4.3%, 17.7%, and 17.4%, respectively. Therefore, the LR machine learning algorithm was selected to construct the best radiomics signature, which performed well with AUCs of 0.766 and 0.739 in the training and test sets, respectively. In the multivariable analysis, age [odds ratio (OR) = 0.956, P = 0.034], alpha-fetoprotein (OR = 10.066, P < 0.001), tumour size (OR = 3.316, P = 0.002), tumour-to-liver apparent diffusion coefficient (ADC) ratio (OR = 0.156, P = 0.037), and radiomics score (OR = 2.923, P < 0.001) were independent predictors of MTM-HCC. Among the different models, the predictive performances of the clinical-radiomics model and radiological-radiomics model were significantly improved compared to those of the clinical model (AUCs: 0.888 vs 0.836, P = 0.046) and radiological model (AUCs: 0.796 vs 0.688, P = 0.012), respectively, in the training set, highlighting the improved predictive performance of radiomics. The nomogram performed best, with AUCs of 0.896 and 0.805 in the training and test sets, respectively.
CONCLUSION The nomogram containing radiomics, age, alpha-fetoprotein, tumour size, and tumour-to-liver ADC ratio revealed excellent predictive ability in preoperatively identifying the MTM-HCC subtype.
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Affiliation(s)
- Yang Zhang
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou 310014, Zhejiang Province, China
| | - Dong He
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou 310014, Zhejiang Province, China
| | - Jing Liu
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou 310014, Zhejiang Province, China
| | - Yu-Guo Wei
- Precision Health Institution, General Electric Healthcare, Hangzhou 310014, Zhejiang Province, China
| | - Lin-Lin Shi
- Department of Gastroenterology, Zhejiang Hospital of Integrated Traditional Chinese and Western Medicine, Hangzhou 310005, Zhejiang Province, China
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Kinoshita M, Ueda D, Matsumoto T, Shinkawa H, Yamamoto A, Shiba M, Okada T, Tani N, Tanaka S, Kimura K, Ohira G, Nishio K, Tauchi J, Kubo S, Ishizawa T. Deep Learning Model Based on Contrast-Enhanced Computed Tomography Imaging to Predict Postoperative Early Recurrence after the Curative Resection of a Solitary Hepatocellular Carcinoma. Cancers (Basel) 2023; 15:cancers15072140. [PMID: 37046801 PMCID: PMC10092973 DOI: 10.3390/cancers15072140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/28/2023] [Accepted: 04/03/2023] [Indexed: 04/08/2023] Open
Abstract
We aimed to develop the deep learning (DL) predictive model for postoperative early recurrence (within 2 years) of hepatocellular carcinoma (HCC) based on contrast-enhanced computed tomography (CECT) imaging. This study included 543 patients who underwent initial hepatectomy for HCC and were randomly classified into training, validation, and test datasets at a ratio of 8:1:1. Several clinical variables and arterial CECT images were used to create predictive models for early recurrence. Artificial intelligence models were implemented using convolutional neural networks and multilayer perceptron as a classifier. Furthermore, the Youden index was used to discriminate between high- and low-risk groups. The importance values of each explanatory variable for early recurrence were calculated using permutation importance. The DL predictive model for postoperative early recurrence was developed with the area under the curve values of 0.71 (test datasets) and 0.73 (validation datasets). Postoperative early recurrence incidences in the high- and low-risk groups were 73% and 30%, respectively (p = 0.0057). Permutation importance demonstrated that among the explanatory variables, the variable with the highest importance value was CECT imaging analysis. We developed a DL model to predict postoperative early HCC recurrence. DL-based analysis is effective for determining the treatment strategies in patients with HCC.
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Affiliation(s)
- Masahiko Kinoshita
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Daiju Ueda
- Smart Life Science Lab, Center for Health Science Innovation, Osaka Metropolitan University, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
- Department of Diagnostic and Interventional Radiology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Toshimasa Matsumoto
- Smart Life Science Lab, Center for Health Science Innovation, Osaka Metropolitan University, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
- Department of Diagnostic and Interventional Radiology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Hiroji Shinkawa
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Akira Yamamoto
- Department of Diagnostic and Interventional Radiology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Masatsugu Shiba
- Smart Life Science Lab, Center for Health Science Innovation, Osaka Metropolitan University, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
- Department of Biofunctional Analysis, Graduate School of medicine, Osaka Metropolitan University, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Takuma Okada
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Naoki Tani
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Shogo Tanaka
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Kenjiro Kimura
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Go Ohira
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Kohei Nishio
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Jun Tauchi
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Shoji Kubo
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
| | - Takeaki Ishizawa
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan
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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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Cha H, Choi JY, Park YN, Han K, Jang M, Kim MJ, Park MS, Rhee H. Comparison of imaging findings of macrotrabecular-massive hepatocellular carcinoma using CT and gadoxetic acid-enhanced MRI. Eur Radiol 2023; 33:1364-1377. [PMID: 35999373 DOI: 10.1007/s00330-022-09105-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 06/17/2022] [Accepted: 08/09/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVES To investigate the imaging findings of macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) on CT and MRI, and examine their diagnostic performance and prognostic significance. METHODS We retrospectively enrolled 220 consecutive patients who underwent hepatic resection between June 2009 and December 2013 for single treatment-naïve HCC, who have preoperative CT and gadoxetic acid-enhanced MRI. Independent reviews of histopathology and imaging were performed by two reviewers. Previously reported imaging findings, LI-RADS category, and CT attenuation of MTM-HCC were investigated. The diagnostic performance of the MTM-HCC diagnostic criteria was compared across imaging modalities. RESULTS MTM-HCC was associated with ≥ 50% arterial phase hypovascular component, intratumoral artery, arterial phase peritumoral enhancement, and non-smooth tumor margin on CT and MRI (p < .05). Arterial phase hypovascular components were less commonly observed on MRI subtraction images than on CT or MRI, while non-rim arterial phase hyperenhancement and LR-5 were more commonly observed on MRI subtraction images than on MRI (p < .05). MTM-HCC showed lower tumor attenuation in the CT arterial phase (p = .01). Rhee's criteria, defined as ≥ 50% hypovascular component and ≥ 2 ancillary findings (intratumoral artery, arterial phase peritumoral enhancement, and non-smooth tumor margin), showed similar diagnostic performance for MRI (sensitivity, 41%; specificity, 97%) and CT (sensitivity, 31%; specificity, 94%). Rhee's criteria on CT were independent prognostic factors for overall survival. CONCLUSION The MRI diagnostic criteria for MTM-HCC are applicable on CT, showing similar diagnostic performance and prognostic significance. For MTM-HCC, arterial phase subtraction images can aid in the HCC diagnosis by depicting subtle arterial hypervascularity. KEY POINTS • MTM-HCC on CT demonstrated previously described MRI findings, including arterial phase hypovascular component, intratumoral artery, arterial phase peritumoral enhancement, and necrosis. • The MRI diagnostic criteria for MTM-HCC were also applicable to CT, showing comparable diagnostic performance and prognostic significance. • On arterial phase subtraction imaging, MTM-HCC more frequently demonstrated non-rim enhancement and LR-5 and less frequently LR-M than MRI arterial phase, which may aid in the diagnosis of HCC.
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Affiliation(s)
- Hyunho Cha
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Jin-Young Choi
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Young Nyun Park
- Department of Pathology, Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, South Korea
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Mi Jang
- Department of Pathology, National Health Insurance Service Ilsan Hospital, Goyang, South Korea
| | - Myeong-Jin Kim
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Mi-Suk Park
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Hyungjin Rhee
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, South Korea.
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Hu S, Kang Y, Xie Y, Yang T, Yang Y, Jiao J, Zou Q, Zhang H, Zhang Y. 18F-FDG PET/CT-based radiomics nomogram for preoperative prediction of macrotrabecular-massive hepatocellular carcinoma: a two-center study. Abdom Radiol (NY) 2023; 48:532-542. [PMID: 36370179 DOI: 10.1007/s00261-022-03722-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 10/18/2022] [Accepted: 10/18/2022] [Indexed: 11/13/2022]
Abstract
PURPOSE To explore the potential of β-2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) in the evaluation of macrotrabecular-massive (MTM) hepatocellular carcinoma (HCC) and to apply radiomics approach to build a radiomics nomogram for predicting MTM-HCC. METHODS This study included 140 (training cohort:101; validation cohort:39) HCC patients who underwent preoperative 18F-FDG PET/CT at two institutions. The clinical features and tumor FDG metabolism measured by the tumor-to-liver ratio (TLR) via 18F-FDG PET/CT were retrospectively collected. Radiomics features were extracted from 18F-FDG PET/CT images and a radiomics score (Rad-score) was calculated. A radiomics nomogram was then constructed by combining Rad-score and independent clinical features and was assessed with a calibration curve. The performance of the radiomics nomogram, Rad-score and TLR was evaluated by receiver operating characteristic (ROC) curves and decision curve analysis (DCA). RESULTS A total of six top weighted radiomics features were selected from PET/CT images by the least absolute shrinkage and selection operator (LASSO) regression algorithm and were used to construct a Rad-score. Multivariate analysis identified Rad-score (OR = 2.183, P = 0.004), age ≤ 50 years (OR = 3.136, P = 0.036), AST > 40U/L (OR = 0.270, P = 0.017) and TLR (OR = 1.641, P = 0.049) as independent predictors of MTM-HCC. The radiomics nomogram had a higher area under the curves (AUCs) than the Rad-score and TLR for predicting MTM-HCC in both training (0.849 [95% CI 0.774-0.924] vs. 0.764 [95% CI 0.669-0.843], 0.763 [95% CI 0.668-0.842]) and validation (0.749 [95% CI 0.584-0.873] vs. 0.690 [95% CI 0.522-0.828], 0.541 [95% CI 0.374-0.701]) cohorts. DCA showed the radiomics nomogram to be more clinically useful than Rad-score and TLR. CONCLUSIONS Tumor FDG metabolism is significantly associated with MTM-HCC. A 18F-FDG PET/CT-based radiomics nomogram may be useful for preoperatively predicting the MTM subtype in primary HCC patients, contributing to pretreatment decision-making.
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Affiliation(s)
- Siqi Hu
- Department of Nuclear Medicine, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Tianhe District, Guangzhou, 510630, China
| | - Yinqian Kang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Tianhe District, Guangzhou, 510630, China
- Department of Anesthesiology, Sun Yat-Sen University Cancer Center, No. 651 Dongfeng East Road, Yuexiu District, Guangzhou, 510060, China
| | - Yujie Xie
- Department of Nuclear Medicine, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Tianhe District, Guangzhou, 510630, China
| | - Ting Yang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Tianhe District, Guangzhou, 510630, China
| | - Yuan Yang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Tianhe District, Guangzhou, 510630, China
| | - Ju Jiao
- Department of Nuclear Medicine, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Tianhe District, Guangzhou, 510630, China
| | - Qiong Zou
- Department of Nuclear Medicine, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Tianhe District, Guangzhou, 510630, China
| | - Hong Zhang
- Department of Nuclear Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 33 Yingfeng Road, Haizhu District, Guangzhou, 510289, China.
| | - Yong Zhang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Tianhe District, Guangzhou, 510630, China.
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Yang T, Wei H, Wu Y, Qin Y, Chen J, Jiang H, Song B. Predicting histologic differentiation of solitary hepatocellular carcinoma up to 5 cm on gadoxetate disodium-enhanced MRI. Insights Imaging 2023; 14:3. [PMID: 36617583 PMCID: PMC9826771 DOI: 10.1186/s13244-022-01354-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 12/13/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND To establish a preoperative score based on gadoxetate disodium-enhanced magnetic resonance imaging (EOB-MRI) and clinical indicators for predicting histologic differentiation of solitary HCC up to 5 cm. METHODS From July 2015 to January 2022, consecutive patients with surgically proven solitary HCC measuring ≤ 5 cm at preoperative EOB-MRI were retrospectively enrolled. All MR images were independently evaluated by two radiologists who were blinded to all clinical and pathologic information. Univariate and multivariate logistic regression analyses were performed to identify significant clinicoradiological features associated with poorly differentiated (PD) HCC, which were then incorporated into the predictive score. The predictive score was validated in an independent validation set by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. RESULTS A total of 182 patients were included, 42 (23%) with PD HCC. According to the multivariate analysis, marked hepatobiliary phase hypointensity (odds ratio [OR], 9.98), LR-M category (OR, 5.60), and serum alpha-fetoprotein (AFP) level > 400 ng/mL (OR, 3.58) were incorporated into the predictive model; the predictive score achieved an AUC of 0.802 and 0.830 on the training and validation sets, respectively. The sensitivity, specificity, and accuracy of the predictive score were 66.7%, 85.7%, and 81.3%, respectively, on the training set and 66.7%, 81.0%, and 77.8%, respectively, on the validation set. CONCLUSION The proposed score integrating two EOB-MRI features and AFP level can accurately predict PD HCC in the preoperative setting.
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Affiliation(s)
- Ting Yang
- grid.13291.380000 0001 0807 1581Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041 China
| | - Hong Wei
- grid.13291.380000 0001 0807 1581Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041 China
| | - Yuanan Wu
- grid.54549.390000 0004 0369 4060Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, 610000 Sichuan China
| | - Yun Qin
- grid.13291.380000 0001 0807 1581Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041 China
| | - Jie Chen
- grid.13291.380000 0001 0807 1581Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041 China
| | - Hanyu Jiang
- grid.13291.380000 0001 0807 1581Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041 China
| | - Bin Song
- grid.13291.380000 0001 0807 1581Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041 China ,Department of Radiology, Sanya People’s Hospital, Sanya, Hainan China
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Yang L, Wang M, Zhu Y, Zhang J, Pan J, Zhao Y, Sun K, Chen F. Corona enhancement combined with microvascular invasion for prognosis prediction of macrotrabecular-massive hepatocellular carcinoma subtype. Front Oncol 2023; 13:1138848. [PMID: 36890813 PMCID: PMC9986746 DOI: 10.3389/fonc.2023.1138848] [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: 01/06/2023] [Accepted: 02/01/2023] [Indexed: 02/22/2023] Open
Abstract
Objectives The macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) is aggressive and associated with an unfavorable prognosis. This study aimed to characterize MTM-HCC features based on contrast-enhanced MRI and to evaluate the prognosis of imaging characteristics combined with pathology for predicting early recurrence and overall survival after surgery. Methods This retrospective study included 123 patients with HCC that underwent preoperative contrast-enhanced MRI and surgery, between July 2020 and October 2021. Multivariable logistic regression was performed to investigate factors associated with MTM-HCC. Predictors of early recurrence were determined with a Cox proportional hazards model and validated in a separate retrospective cohort. Results The primary cohort included 53 patients with MTM-HCC (median age 59 years; 46 male and 7 females; median BMI 23.5 kg/m2) and 70 subjects with non-MTM HCC (median age 61.5 years; 55 male and 15 females; median BMI 22.6 kg/m2) (All P>0.05). The multivariate analysis identified corona enhancement (odds ratio [OR]=2.52, 95% CI: 1.02-6.24; P=0.045) as an independent predictor of the MTM-HCC subtype. The multiple Cox regression analysis identified corona enhancement (hazard ratio [HR]=2.56, 95% CI: 1.08-6.08; P=0.033) and MVI (HR=2.45, 95% CI: 1.40-4.30; P=0.002) as independent predictors of early recurrence (area under the curve=0.790, P<0.001). The prognostic significance of these markers was confirmed by comparing results in the validation cohort to those from the primary cohort. Corona enhancement combined with MVI was significantly associated with poor outcomes after surgery. Conclusions A nomogram for predicting early recurrence based on corona enhancement and MVI could be used to characterize patients with MTM-HCC and predict their prognosis for early recurrence and overall survival after surgery.
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Affiliation(s)
- Lili Yang
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Meng Wang
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yanyan Zhu
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jiahui Zhang
- Department of Radiology, Third People's Hospital of Hangzhou, Hangzhou, Zhejiang, China
| | - Junhan Pan
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yanci Zhao
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ke Sun
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Feng Chen
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.,Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, Zhejiang, China
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He C, Zhang W, Zhao Y, Li J, Wang Y, Yao W, Wang N, Ding W, Wei X, Yang R, Jiang X. Preoperative prediction model for macrotrabecular-massive hepatocellular carcinoma based on contrast-enhanced CT and clinical characteristics: a retrospective study. Front Oncol 2023; 13:1124069. [PMID: 37197418 PMCID: PMC10183567 DOI: 10.3389/fonc.2023.1124069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 04/18/2023] [Indexed: 05/19/2023] Open
Abstract
Objective To investigate the predictive value of contrast-enhanced computed tomography (CECT) imaging features and clinical factors in identifying the macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) preoperatively. Methods This retrospective study included 101 consecutive patients with pathology-proven HCC (35 MTM subtype vs. 66 non-MTM subtype) who underwent liver surgery and preoperative CECT scans from January 2017 to November 2021. The imaging features were evaluated by two board-certified abdominal radiologists independently. The clinical characteristics and imaging findings were compared between the MTM and non-MTM subtypes. Univariate and multivariate logistic regression analyses were performed to investigate the association of clinical-radiological variables and MTM-HCCs and develop a predictive model. Subgroup analysis was also performed in BCLC 0-A stage patients. Receiver operating characteristic (ROC) curves analysis was used to determine the optimal cutoff values and the area under the curve (AUC) was employed to evaluate predictive performance. Results Intratumor hypoenhancement (odds ratio [OR] = 2.724; 95% confidence interval [CI]: 1.033, 7.467; p = .045), tumors without enhancing capsules (OR = 3.274; 95% CI: 1.209, 9.755; p = .03), high serum alpha-fetoprotein (AFP) (≥ 228 ng/mL, OR = 4.101; 95% CI: 1.523, 11.722; p = .006) and high hemoglobin (≥ 130.5 g/L; OR = 3.943; 95% CI: 1.466, 11.710; p = .009) were independent predictors for MTM-HCCs. The clinical-radiologic (CR) model showed the best predictive performance, achieving an AUC of 0.793, sensitivity of 62.9% and specificity of 81.8%. The CR model also effectively identify MTM-HCCs in early-stage (BCLC 0-A stage) patients. Conclusion Combining CECT imaging features and clinical characteristics is an effective method for preoperatively identifying MTM-HCCs, even in early-stage patients. The CR model has high predictive performance and could potentially help guide decision-making regarding aggressive therapies in MTM-HCC patients.
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Affiliation(s)
- Chutong He
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Wanli Zhang
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Yue Zhao
- Department of Radiology, Central People’s Hospital of Zhanjiang, Zhanjiang, Guangdong, China
| | - Jiamin Li
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Ye Wang
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Wang Yao
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Nianhua Wang
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Wenshuang Ding
- Department of Pathology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Xinhua Wei
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Ruimeng Yang
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
- *Correspondence: Ruimeng Yang, ; Xinqing Jiang,
| | - Xinqing Jiang
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
- *Correspondence: Ruimeng Yang, ; Xinqing Jiang,
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Feng Z, Li H, Liu Q, Duan J, Zhou W, Yu X, Chen Q, Liu Z, Wang W, Rong P. CT Radiomics to Predict Macrotrabecular-Massive Subtype and Immune Status in Hepatocellular Carcinoma. Radiology 2022; 307:e221291. [PMID: 36511807 DOI: 10.1148/radiol.221291] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Background Macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) is an aggressive variant associated with angiogenesis and immunosuppressive tumor microenvironment, which is expected to be noninvasively identified using radiomics approaches. Purpose To construct a CT radiomics model to predict the MTM subtype and to investigate the underlying immune infiltration patterns. Materials and Methods This study included five retrospective data sets and one prospective data set from three academic medical centers between January 2015 and December 2021. The preoperative liver contrast-enhanced CT studies of 365 adult patients with resected HCC were evaluated. The Third Xiangya Hospital of Central South University provided the training set and internal test set, while Yueyang Central Hospital and Hunan Cancer Hospital provided the external test sets. Radiomic features were extracted and used to develop a radiomics model with machine learning in the training set, and the performance was verified in the two test sets. The outcomes cohort, including 58 adult patients with advanced HCC undergoing transarterial chemoembolization and antiangiogenic therapy, was used to evaluate the predictive value of the radiomics model for progression-free survival (PFS). Bulk RNA sequencing of tumors from 41 patients in The Cancer Genome Atlas (TCGA) and single-cell RNA sequencing from seven prospectively enrolled participants were used to investigate the radiomics-related immune infiltration patterns. Area under the receiver operating characteristics curve of the radiomics model was calculated, and Cox proportional regression was performed to identify predictors of PFS. Results Among 365 patients (mean age, 55 years ± 10 [SD]; 319 men) used for radiomics modeling, 122 (33%) were confirmed to have the MTM subtype. The radiomics model included 11 radiomic features and showed good performance for predicting the MTM subtype, with AUCs of 0.84, 0.80, and 0.74 in the training set, internal test set, and external test set, respectively. A low radiomics model score relative to the median value in the outcomes cohort was independently associated with PFS (hazard ratio, 0.4; 95% CI: 0.2, 0.8; P = .01). The radiomics model was associated with dysregulated humoral immunity involving B-cell infiltration and immunoglobulin synthesis. Conclusion Accurate prediction of the macrotrabecular-massive subtype in patients with hepatocellular carcinoma was achieved using a CT radiomics model, which was also associated with defective humoral immunity. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Yoon and Kim in this issue.
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Affiliation(s)
- Zhichao Feng
- From the Departments of Radiology (Z.F., H.L., J.D., W.W., P.R.), Pathology (Q.C.), and Infectious Disease (Z.L.), The Third Xiangya Hospital of Central South University, No. 138 Tongzipo Rd, Changsha 410013, China; Department of Medical Imaging, Yueyang Central Hospital, Yueyang, China (Q.L., W.Z.); and Department of Diagnostic Radiology, Hunan Cancer Hospital, Changsha, China (X.Y.)
| | - Huiling Li
- From the Departments of Radiology (Z.F., H.L., J.D., W.W., P.R.), Pathology (Q.C.), and Infectious Disease (Z.L.), The Third Xiangya Hospital of Central South University, No. 138 Tongzipo Rd, Changsha 410013, China; Department of Medical Imaging, Yueyang Central Hospital, Yueyang, China (Q.L., W.Z.); and Department of Diagnostic Radiology, Hunan Cancer Hospital, Changsha, China (X.Y.)
| | - Qianyun Liu
- From the Departments of Radiology (Z.F., H.L., J.D., W.W., P.R.), Pathology (Q.C.), and Infectious Disease (Z.L.), The Third Xiangya Hospital of Central South University, No. 138 Tongzipo Rd, Changsha 410013, China; Department of Medical Imaging, Yueyang Central Hospital, Yueyang, China (Q.L., W.Z.); and Department of Diagnostic Radiology, Hunan Cancer Hospital, Changsha, China (X.Y.)
| | - Junhong Duan
- From the Departments of Radiology (Z.F., H.L., J.D., W.W., P.R.), Pathology (Q.C.), and Infectious Disease (Z.L.), The Third Xiangya Hospital of Central South University, No. 138 Tongzipo Rd, Changsha 410013, China; Department of Medical Imaging, Yueyang Central Hospital, Yueyang, China (Q.L., W.Z.); and Department of Diagnostic Radiology, Hunan Cancer Hospital, Changsha, China (X.Y.)
| | - Wenming Zhou
- From the Departments of Radiology (Z.F., H.L., J.D., W.W., P.R.), Pathology (Q.C.), and Infectious Disease (Z.L.), The Third Xiangya Hospital of Central South University, No. 138 Tongzipo Rd, Changsha 410013, China; Department of Medical Imaging, Yueyang Central Hospital, Yueyang, China (Q.L., W.Z.); and Department of Diagnostic Radiology, Hunan Cancer Hospital, Changsha, China (X.Y.)
| | - Xiaoping Yu
- From the Departments of Radiology (Z.F., H.L., J.D., W.W., P.R.), Pathology (Q.C.), and Infectious Disease (Z.L.), The Third Xiangya Hospital of Central South University, No. 138 Tongzipo Rd, Changsha 410013, China; Department of Medical Imaging, Yueyang Central Hospital, Yueyang, China (Q.L., W.Z.); and Department of Diagnostic Radiology, Hunan Cancer Hospital, Changsha, China (X.Y.)
| | - Qian Chen
- From the Departments of Radiology (Z.F., H.L., J.D., W.W., P.R.), Pathology (Q.C.), and Infectious Disease (Z.L.), The Third Xiangya Hospital of Central South University, No. 138 Tongzipo Rd, Changsha 410013, China; Department of Medical Imaging, Yueyang Central Hospital, Yueyang, China (Q.L., W.Z.); and Department of Diagnostic Radiology, Hunan Cancer Hospital, Changsha, China (X.Y.)
| | - Zhenguo Liu
- From the Departments of Radiology (Z.F., H.L., J.D., W.W., P.R.), Pathology (Q.C.), and Infectious Disease (Z.L.), The Third Xiangya Hospital of Central South University, No. 138 Tongzipo Rd, Changsha 410013, China; Department of Medical Imaging, Yueyang Central Hospital, Yueyang, China (Q.L., W.Z.); and Department of Diagnostic Radiology, Hunan Cancer Hospital, Changsha, China (X.Y.)
| | - Wei Wang
- From the Departments of Radiology (Z.F., H.L., J.D., W.W., P.R.), Pathology (Q.C.), and Infectious Disease (Z.L.), The Third Xiangya Hospital of Central South University, No. 138 Tongzipo Rd, Changsha 410013, China; Department of Medical Imaging, Yueyang Central Hospital, Yueyang, China (Q.L., W.Z.); and Department of Diagnostic Radiology, Hunan Cancer Hospital, Changsha, China (X.Y.)
| | - Pengfei Rong
- From the Departments of Radiology (Z.F., H.L., J.D., W.W., P.R.), Pathology (Q.C.), and Infectious Disease (Z.L.), The Third Xiangya Hospital of Central South University, No. 138 Tongzipo Rd, Changsha 410013, China; Department of Medical Imaging, Yueyang Central Hospital, Yueyang, China (Q.L., W.Z.); and Department of Diagnostic Radiology, Hunan Cancer Hospital, Changsha, China (X.Y.)
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Yoon JH, Kim H. CT Radiomics in Oncology: Insights into the Tumor Microenvironment of Hepatocellular Carcinoma. Radiology 2022; 307:e222988. [PMID: 36511812 DOI: 10.1148/radiol.222988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Jeong Hee Yoon
- From the Departments of Radiology (J.H.Y.) and Pathology (H.K.), Seoul National University Hospital, 101 Daehak-ro, Jongro-Gu, Seoul 03080, South Korea; and Departments of Radiology (J.H.Y.) and Pathology (H.K.), Seoul National University College of Medicine, Seoul, South Korea
| | - Haeryoung Kim
- From the Departments of Radiology (J.H.Y.) and Pathology (H.K.), Seoul National University Hospital, 101 Daehak-ro, Jongro-Gu, Seoul 03080, South Korea; and Departments of Radiology (J.H.Y.) and Pathology (H.K.), Seoul National University College of Medicine, Seoul, South Korea
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Luo M, Liu X, Yong J, Ou B, Xu X, Zhao X, Liang M, Zhao Z, Ruan J, Luo B. Preoperative prediction of macrotrabecular-massive hepatocellular carcinoma based on B-Mode US and CEUS. Eur Radiol 2022; 33:4024-4033. [PMID: 36484835 DOI: 10.1007/s00330-022-09322-0] [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: 06/24/2022] [Revised: 10/15/2022] [Accepted: 11/28/2022] [Indexed: 12/14/2022]
Abstract
OBJECTIVES To develop a preoperative prediction model to identify macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) and evaluate the model's diagnostic performance in differentiating MTM-HCC from HCC. METHODS We conducted a mono-center retrospective study in a grade A tertiary hospital in China. Consecutive patients with suspected HCC from February 2019 to December 2020 were eligible for inclusion. All consenting patients underwent CEUS examination and were histologically diagnosed. Based on the clinical and US features between the two groups, we developed a binary logistic regression model and a nomogram for predicting MTM-HCC. RESULTS A total of 161 patients (median age, 57 years; interquartile range, 48-64 years; 129 men) were included in the analysis. Twenty-seven of the HCCs (16.8%) were of the MTM subtype. Binary logistic regression analysis indicated that PVP hypoenhancement (OR = 15.497; 95% CI: 1.369, 175.451; p = 0.027), AFP > 454.6 ng/mL (OR = 8.658; 95% CI: 3.030, 24.741; p < 0.001), ALB ≤ 29.9 g/L (OR = 3.937; 95% CI: 1.017, 15.234; p = 0.047), halo sign (OR = 3.868; 95% CI: 1.314, 11.391; p = 0.016), and intratumoral artery (OR = 2.928; 95% CI: 1.039, 8.255; p = 0.042) were predictors for MTM subtype. Combining any two criteria showed a high sensitivity (100.0%); combining all five criteria showed a high specificity (99.2%); and the AUC value of the logistic regression model was 0.88 (95% CI: 0.81, 0.92). CONCLUSIONS BMUS and CEUS could be used for identifying patients suspected of having MTM-HCC. Combining clinical information, BMUS, and CEUS features could achieve a noninvasive diagnosis of MTM-HCC. KEY POINTS • Contrast-enhanced ultrasound examination helps clinicians to identify MTM-HCCs preoperatively. • PVP hypoenhancement, high AFP levels, low ALB levels, halo signs, and intratumoral arteries could be used to predict MTM-HCCs. • A logistic regression model and nomogram were built to noninvasively diagnose MTM-HCCs with an AUC value of 0.88 (95% CI: 0.81, 0.92).
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Affiliation(s)
- Man Luo
- Department of Ultrasound, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, No.33 Yingfeng Road, Guangzhou, 510289, China
| | - Xiaodi Liu
- Department of Ultrasound, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, No.33 Yingfeng Road, Guangzhou, 510289, China
- Laboratory of Ultrasound Medicine, West China Hospital, Sichuan University, No.37 Guoxue Alley, Chengdu, 610065, China
| | - Juanjuan Yong
- Department of Pathology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, No.33 Yingfeng Road, Guangzhou, 510289, China
| | - Bing Ou
- Department of Ultrasound, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, No.33 Yingfeng Road, Guangzhou, 510289, China
| | - Xiaolin Xu
- Department of Ultrasound, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, No.33 Yingfeng Road, Guangzhou, 510289, China
| | - Xinbao Zhao
- Department of Ultrasound, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, No.33 Yingfeng Road, Guangzhou, 510289, China
| | - Ming Liang
- Department of Ultrasound, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, No.33 Yingfeng Road, Guangzhou, 510289, China
| | - Zizhuo Zhao
- Department of Ultrasound, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, No.33 Yingfeng Road, Guangzhou, 510289, China
| | - Jingliang Ruan
- Department of Ultrasound, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, No.33 Yingfeng Road, Guangzhou, 510289, China.
- Department of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No.33 Yingfeng Road, Guangzhou, 510289, China.
| | - Baoming Luo
- Department of Ultrasound, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, No.33 Yingfeng Road, Guangzhou, 510289, China.
- Department of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No.33 Yingfeng Road, Guangzhou, 510289, China.
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Lee DH. Editorial for “Nomogram Predicting Vessels Encapsulating Tumor Clusters in Hepatocellular Carcinoma from Preoperative Gadoxetate‐Enhanced
MRI
: A Multicenter Study”. J Magn Reson Imaging 2022; 57:1906-1907. [PMID: 36282632 DOI: 10.1002/jmri.28489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 09/15/2022] [Indexed: 11/08/2022] Open
Affiliation(s)
- Dong Ho Lee
- Department of Radiology Seoul National University Hospital Seoul Korea
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Chen FM, Du M, Qi X, Bian L, Wu D, Zhang SL, Wang J, Zhou Y, Zhu X. Nomogram Estimating Vessels Encapsulating Tumor Clusters in Hepatocellular Carcinoma From Preoperative Gadoxetate Disodium-Enhanced MRI. J Magn Reson Imaging 2022; 57:1893-1905. [PMID: 36259347 DOI: 10.1002/jmri.28488] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 10/04/2022] [Accepted: 10/06/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Vessels encapsulating tumor clusters (VETC) pattern is a novel microvascular pattern associated with poor outcomes of hepatocellular carcinoma (HCC). Preoperative estimation of VETC has potential to improve treatment decisions. PURPOSE To develop and validate a nomogram based on gadoxetate disodium-enhanced MRI for estimating VETC in HCC and to evaluate whether the estimations are associated with recurrence after hepatic resection. STUDY TYPE Retrospective. POPULATION A total of 320 patients with HCC and histopathologic VETC pattern assessment from three centers (development cohort:validation cohort = 173:147). FIELD STRENGTH/SEQUENCE A3.0 T/turbo spin-echo T2-weighted, spin-echo echo-planar diffusion-weighted, and 3D T1-weighted gradient-echo sequences. ASSESSMENT A set of previously reported VETC- and/or prognosis-correlated qualitative and quantitative imaging features were assessed. Clinical and imaging variables were compared based on histopathologic VETC status to investigate factors indicating VETC pattern. A regression-based nomogram was then constructed using the significant factors for VETC pattern. The nomogram-estimated VETC stratification was assessed for its association with recurrence. STATISTICAL TESTS Fisher exact test, t-test or Mann-Whitney test, logistic regression analyses, Harrell's concordance index (C-index), nomogram, Kaplan-Meier curves and log-rank tests. P value < 0.05 was considered statistically significant. RESULTS Pathological VETC pattern presence was identified in 156 patients (development cohort:validation cohort = 83:73). Tumor size, presence of heterogeneous enhancement with septations or with irregular ring-like structures, and necrosis were significant factors for estimating VETC pattern. The nomogram incorporating these indicators showed good discrimination with a C-index of 0.870 (development cohort) and 0.862 (validation cohort). Significant differences in recurrence rates between the nomogram-estimated high-risk VETC group and low-risk VETC group were found (2-year recurrence rates, 50.7% vs. 30.3% and 49.6% vs. 31.8% in the development and validation cohorts, respectively). DATA CONCLUSION The nomogram integrating gadoxetate disodium-enhanced MRI features was associated with VETC pattern preoperatively and with postoperative recurrence in patients with HCC. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Fang-Ming Chen
- Department of Interventional Radiology, the First Affiliated Hospital of Soochow University, Suzhou, China.,Department of Radiology, the Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, Wuxi, China
| | - Mingzhan Du
- Department of Pathology, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiumin Qi
- Department of Pathology, the Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, Wuxi, China
| | - Linjie Bian
- Department of Radiology, the Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, Wuxi, China
| | - Danping Wu
- Department of Radiology, the Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, Wuxi, China
| | - Shuang-Lin Zhang
- Department of Radiology, the Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, Wuxi, China
| | - Jitao Wang
- Department of Hepatobiliary Surgery, Xingtai Institute of Cancer Control, the Affiliated Xingtai People's Hospital of Hebei Medical University, Xingtai, China
| | - Yongping Zhou
- Department of Hepatobiliary Surgery, the Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, Wuxi, China
| | - Xiaoli Zhu
- Department of Interventional Radiology, the First Affiliated Hospital of Soochow University, Suzhou, China
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Salehi O, Vega EA, Kutlu OC, Lunsford K, Freeman R, Ladin K, Alarcon SV, Kazakova V, Conrad C. Poorly differentiated hepatocellular carcinoma: resection is equivalent to transplantation in patients with low liver fibrosis. HPB (Oxford) 2022; 24:1100-1109. [PMID: 34969618 DOI: 10.1016/j.hpb.2021.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 11/28/2021] [Accepted: 12/08/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Organ allocation criteria for liver transplantation focus on tumor size and multifocality while tumor differentiation and existing liver damage are omitted. This study analyzes the impact of hepatocellular carcinoma (HCC) grade and liver fibrosis comparing resection (SX) to transplantation (LT). METHODS The National Cancer Database was queried between 2004 and 2016 for solitary HCC meeting Milan criteria undergoing SX vs LT. Two groups were created: low fibrosis (LF) vs high fibrosis (HF) and stratified by grade. Cox multivariable regression models, Kaplan-Meier survival analyses and log-rank tests were performed. RESULTS 1515 patients were identified; 780 had LT and 735 had SX. Median overall survival (mOS) was 39.7 months; LT mOS was 47.9 months vs SX mOS of 34.9 months (P < .001). Multivariate analysis revealed SX, no chemotherapy, longer hospital stays, and age to be associated with worse survival. However, while transplantation conferred survival benefit for well-moderately differentiated tumors, SX vs LT did not impact survival for poorly differentiated HCC in LF patients, independent of tumor size. DISCUSSION HCC differentiation and liver fibrosis, but not size, synergistically determine efficacy of SX vs LT. Therefore, current HCC transplantation criteria should incorporate tumor grade or liver fibrosis for optimal organ allocation.
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Affiliation(s)
- Omid Salehi
- Department of Surgery, St. Elizabeth's Medical Center, Tufts University School of Medicine, Boston, MA, USA
| | - Eduardo A Vega
- Department of Surgery, St. Elizabeth's Medical Center, Tufts University School of Medicine, Boston, MA, USA
| | - Onur C Kutlu
- Department of Surgery, University of Miami Health System, Miller School of Medicine, Miami, FL, USA
| | - Keri Lunsford
- Department of Surgery, Division of Transplant and Hepatobiliary Surgery, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Richard Freeman
- Department of Surgery, St. Elizabeth's Medical Center, Tufts University School of Medicine, Boston, MA, USA
| | - Keren Ladin
- Department of Occupational Therapy and Community Health, Tufts University, Boston, MA, USA
| | - Sylvia V Alarcon
- Department of Medical Oncology, Dana-Farber Cancer Institute at St. Elizabeth's Medical Center, Harvard Medical School, Boston, MA, USA
| | - Vera Kazakova
- Department of Medical Oncology, Dana-Farber Cancer Institute at St. Elizabeth's Medical Center, Harvard Medical School, Boston, MA, USA
| | - Claudius Conrad
- Department of Surgery, St. Elizabeth's Medical Center, Tufts University School of Medicine, Boston, MA, USA.
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Sessa A, Mulé S, Brustia R, Regnault H, Galletto Pregliasco A, Rhaiem R, Leroy V, Sommacale D, Luciani A, Calderaro J, Amaddeo G. Macrotrabecular-Massive Hepatocellular Carcinoma: Light and Shadow in Current Knowledge. J Hepatocell Carcinoma 2022; 9:661-670. [PMID: 35923611 PMCID: PMC9342198 DOI: 10.2147/jhc.s364703] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 06/22/2022] [Indexed: 12/11/2022] Open
Abstract
The subject of this narrative review is macrotrabecular-massive hepatocellular carcinoma (MTM‐HCC). Despite their rarity, these tumours are of general interest because of their epidemiological and clinical features and for representing a distinct model of the interaction between the angiogenetic system and neoplastic cells. The MTM‐HCC subtype is associated with various adverse biological and pathological parameters (the Alfa-foetoprotein (AFP) serum level, tumour size, vascular invasion, and satellite nodules) and is a key determinant of patient prognosis, with a strong and independent predictive value for early and overall tumour recurrence. Gene expression profiling has demonstrated that angiogenesis activation is a hallmark feature of MTM-HCC, with overexpression of both angiopoietin 2 (ANGPT2) and vascular endothelial growth factor A (VEGFA).
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Affiliation(s)
- Anna Sessa
- Hepatology Department, APHP, Henri Mondor University Hospital, Créteil, France
- Université Paris-Est Créteil, Faculté de Médecine, Créteil, France
- Inserm, U955, Team 18, Créteil, France
- Correspondence: Giuliana Amaddeo; Anna Sessa, Hepatology Department, APHP, Henri Mondor University Hospital, 1 rue Gustave Eiffel, Créteil, 94000, France, Tel +33 149812353, Email ;
| | - Sébastien Mulé
- Université Paris-Est Créteil, Faculté de Médecine, Créteil, France
- Inserm, U955, Team 18, Créteil, France
- Medical Imaging Department, AP-HP, Henri Mondor University Hospital, Créteil, France
| | - Raffaele Brustia
- Université Paris-Est Créteil, Faculté de Médecine, Créteil, France
- Inserm, U955, Team 18, Créteil, France
- Department of Digestive and Hepato-Pancreato-Biliary Surgery, AP-HP, Henri Mondor University Hospital, Créteil, France
| | - Hélène Regnault
- Hepatology Department, APHP, Henri Mondor University Hospital, Créteil, France
- Inserm, U955, Team 18, Créteil, France
| | | | - Rami Rhaiem
- Department of Hepato-Biliary Pancreatic and Digestive Oncological Surgery, Robert Debré University Hospital, Reims, France
- Reims Champagne-Ardenne University, Reims, France
| | - Vincent Leroy
- Hepatology Department, APHP, Henri Mondor University Hospital, Créteil, France
- Université Paris-Est Créteil, Faculté de Médecine, Créteil, France
- Inserm, U955, Team 18, Créteil, France
| | - Daniele Sommacale
- Université Paris-Est Créteil, Faculté de Médecine, Créteil, France
- Inserm, U955, Team 18, Créteil, France
- Department of Digestive and Hepato-Pancreato-Biliary Surgery, AP-HP, Henri Mondor University Hospital, Créteil, France
| | - Alain Luciani
- Université Paris-Est Créteil, Faculté de Médecine, Créteil, France
- Inserm, U955, Team 18, Créteil, France
- Medical Imaging Department, AP-HP, Henri Mondor University Hospital, Créteil, France
| | - Julien Calderaro
- Université Paris-Est Créteil, Faculté de Médecine, Créteil, France
- Inserm, U955, Team 18, Créteil, France
- Department of Pathology, APHP, Henri Mondor University Hospital, Créteil, France
| | - Giuliana Amaddeo
- Hepatology Department, APHP, Henri Mondor University Hospital, Créteil, France
- Université Paris-Est Créteil, Faculté de Médecine, Créteil, France
- Inserm, U955, Team 18, Créteil, France
- Correspondence: Giuliana Amaddeo; Anna Sessa, Hepatology Department, APHP, Henri Mondor University Hospital, 1 rue Gustave Eiffel, Créteil, 94000, France, Tel +33 149812353, Email ;
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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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Liang Y, Xu F, Wang Z, Tan C, Zhang N, Wei X, Jiang X, Wu H. A gadoxetic acid-enhanced MRI-based multivariable model using LI-RADS v2018 and other imaging features for preoperative prediction of macrotrabecular-massive hepatocellular carcinoma. Eur J Radiol 2022; 153:110356. [PMID: 35623312 DOI: 10.1016/j.ejrad.2022.110356] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 04/25/2022] [Accepted: 05/07/2022] [Indexed: 11/19/2022]
Abstract
PURPOSE To identify imaging features of macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) using LI-RADS v2018 and other imaging features and to develop a gadoxetic acid-enhanced MRI (EOB-MRI)-based model for pretreatment prediction of MTM-HCC. MATERIALS AND METHODS A total of 93 patients with pathologically proven HCC (39 MTM-HCC and 54 non-MTM-HCC) were retrospectively evaluated with EOB-MRI at 3 T. Imaging analysis according to LI-RADS v2018 was evaluated by two readers. Univariate and multivariate analyses were performed to determine independent predictors for MTM-HCC. Different logistic regression models were built based on MRI features, including model A (enhancing capsule, blood products in mass and ascites), model B (enhancing capsule and ascites), model C (blood products in mass and ascites), and model D (blood products in mass and enhancing capsule). Diagnostic performance was assessed by receiver operating characteristic (ROC) curves. RESULTS After multivariate analysis, absence of enhancing capsule (odds ratio = 0.102, p = 0.010), absence of blood products in mass (odds ratio = 0.073, p = 0.030), and with ascites (odds ratio = 55.677, p = 0.028) were identified as independent differential factors for the presence of MTM-HCC. Model A yielded a sensitivity, specificity, and AUC of 35.90% (21.20,52.80), 94.44% (84.60, 98.80), and 0.731 (0.629, 0.818). Model A achieved a comparable AUC than model D (0.731 vs. 0.699, p = 0.333), but a higher AUC than model B (0.731 vs. 0.644, p = 0.048) and model C (0.731 vs. 0.650, p = 0.005). CONCLUSION The EOB-MRI-based model is promising for noninvasively predicting MTM-HCC and may assist clinicians in pretreatment decisions.
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Affiliation(s)
- Yingying Liang
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University; School of Medicine, South China University of Technology, 1Panfu Road, Guangzhou, Guangdong Province 510180, China.
| | - Fan Xu
- Department of Radiology, Guangzhou Red Cross Hospital, Medical College, Jinan University, 396 Tongfu road, Guangzhou, Guangdong Province 510220, China.
| | - Zihua Wang
- Department of Radiology, Foshan Hospital of Traditional Chinese Medicine, Foshan, Guangdong Province 528000, China.
| | - Caihong Tan
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University; School of Medicine, South China University of Technology, 1Panfu Road, Guangzhou, Guangdong Province 510180, China.
| | - Nianru Zhang
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University; School of Medicine, South China University of Technology, 1Panfu Road, Guangzhou, Guangdong Province 510180, China.
| | - Xinhua Wei
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University; School of Medicine, South China University of Technology, 1Panfu Road, Guangzhou, Guangdong Province 510180, China.
| | - Xinqing Jiang
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University; School of Medicine, South China University of Technology, 1Panfu Road, Guangzhou, Guangdong Province 510180, China.
| | - Hongzhen Wu
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University; School of Medicine, South China University of Technology, 1Panfu Road, Guangzhou, Guangdong Province 510180, China.
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Li X, Yao Q, Liu C, Wang J, Zhang H, Li S, Cai P. Macrotrabecular-Massive Hepatocellular Carcinoma: What Should We Know? J Hepatocell Carcinoma 2022; 9:379-387. [PMID: 35547829 PMCID: PMC9084381 DOI: 10.2147/jhc.s364742] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 04/23/2022] [Indexed: 12/11/2022] Open
Abstract
Hepatocellular carcinoma is one of the most common malignancies globally. Recently, a newly identified histological subtype, designated as “macrotrabecular-massive hepatocellular carcinoma” (MTM-HCC), has been associated with an aggressive phenotype and has received extensive attention. MTM-HCC was a strong independent prognostic predictor of early and overall recurrence because it is closely related to tumor molecular subclass, gene mutation, carcinogenesis pathways, and immunohistochemical markers. In addition, preoperative imaging examination can potentially provide an essential clue for diagnosing MTM-HCC, intratumor necrosis or ischemia is an independent predictor for MTM-HCC on Gd-EOB-DTPA enhanced MRI or CT. Early diagnosis and appropriate treatment of MTM-HCC could prove beneficial for preventing early recurrence and could improve outcomes.
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Affiliation(s)
- Xiaoming Li
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, People’s Republic of China
- Department of Radiology, The First People’s Hospital of Zunyi, The Third Affiliated Hospital of Zunyi Medical University, Zunyi, People’s Republic of China
| | - Qiandong Yao
- Department of Radiology, Sichuan Science City Hospital, Mianyang, People’s Republic of China
| | - Chen Liu
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, People’s Republic of China
| | - Jian Wang
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, People’s Republic of China
| | - Huarong Zhang
- Institute of Pathology and Southwest Cancer Center, Third Military Medical University (Army Military Medical University), Chongqing, People’s Republic of China
| | - Shiguang Li
- Department of Radiology, The First People’s Hospital of Zunyi, The Third Affiliated Hospital of Zunyi Medical University, Zunyi, People’s Republic of China
- The Second People's Hospital of Guiyang (Jinyang Hospital), Guiyang, People's Republic of China
- Correspondence: Shiguang Li; Ping Cai, Email ;
| | - Ping Cai
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, People’s Republic of China
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Li P, Liang Y, Zeng B, Yang G, Zhu C, Zhao K, Xu Z, Wang G, Han C, Ye H, Liu Z, Zhu Y, Liang C. Preoperative prediction of intra-tumoral tertiary lymphoid structures based on CT in hepatocellular cancer. Eur J Radiol 2022; 151:110309. [DOI: 10.1016/j.ejrad.2022.110309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 04/05/2022] [Indexed: 11/03/2022]
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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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Cannella R, Dioguardi Burgio M, Beaufrère A, Trapani L, Paradis V, Hobeika C, Cauchy F, Bouattour M, Vilgrain V, Sartoris R, Ronot M. Imaging features of histological subtypes of hepatocellular carcinoma: Implication for LI-RADS. JHEP REPORTS : INNOVATION IN HEPATOLOGY 2021; 3:100380. [PMID: 34825155 PMCID: PMC8603197 DOI: 10.1016/j.jhepr.2021.100380] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 09/21/2021] [Accepted: 09/22/2021] [Indexed: 02/08/2023]
Abstract
Background & Aims The histopathological subtypes of hepatocellular carcinoma (HCC) are associated with distinct clinical features and prognoses. This study aims to report Liver Imaging Reporting and Data System (LI-RADS)-defined imaging features of different HCC subtypes in a cohort of resected tumours and to assess the influence of HCC subtypes on computed tomography (CT)/magnetic resonance imaging (MRI) LI-RADS categorisation in the subgroup of high-risk patients. Methods This retrospective institutional review board-approved study included patients with resected HCCs and available histopathological classification. Three radiologists independently reviewed preoperative CT and MRI exams. The readers evaluated the presence of imaging features according to LI-RADS v2018 definitions and provided a LI-RADS category in patients at high risk of HCC. Differences in LI-RADS features and categorisations were assessed for not otherwise specified (NOS-HCC), steatohepatitic (SH-HCC), and macrotrabecular-massive (MTM-HCC) types of HCCs. Results Two hundred and seventy-seven patients (median age 64.0 years, 215 [77.6%] men) were analysed, which involved 295 HCCs. There were 197 (66.7%) NOS-HCCs, 62 (21.0%) SH-HCCs, 23 (7.8%) MTM-HCCs, and 13 (4.5%) other rare subtypes. The following features were more frequent in MTM-HCC: elevated α-foetoprotein serum levels (p <0.001), tumour-in-vein (p <0.001 on CT, p ≤0.052 on MRI), presence of at least 1 LR-M feature (p ≤0.010 on CT), infiltrative appearance (p ≤0.032 on CT), necrosis or severe ischaemia (p ≤0.038 on CT), and larger size (p ≤0.006 on CT, p ≤0.011 on MRI). SH-HCC was associated with fat in mass (p <0.001 on CT, p ≤0.002 on MRI). The distribution of the LI-RADS major features and categories in high-risk patients did not significantly differ among the 3 main HCC subtypes. Conclusions The distribution of LI-RADS major features and categories is not different among the HCC subtypes. Nevertheless, careful analysis of tumour-in-vein, LR-M, and ancillary features as well as clinico-biological data can provide information for the non-invasive diagnosis of HCC subtypes. Lay summary In high-risk patients, the overall distribution of LI-RADS major features and categories is not different among the histological subtypes of hepatocellular carcinoma, but tumour-in-vein, presence of LR-M features, and ancillary features can provide information for the non-invasive diagnosis of hepatocellular carcinoma subtypes. The distribution of the major features and categories of LI-RADS is not different among the HCC histological subtypes. MTM-HCC was associated with TIV, ≥1 LR-M feature, infiltrative appearance, necrosis or severe ischaemia, and larger size. Steatohepatitis-related HCC was associated with fat in mass on CT and on MRI.
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Key Words
- ALT, alanine transaminase
- APHE, arterial phase hyperenhancement
- AST, aspartate aminotransferase
- CT, computed tomography
- Computed tomography
- HBP, hepatobiliary phase
- HCC, hepatocellular carcinoma
- Hepatocellular carcinoma
- Histopathological subtypes
- LI-RADS
- LI-RADS, Liver Imaging Reporting and Data System
- MRI, magnetic resonance imaging
- MTM-HCC, macrotrabecular-massive hepatocellular carcinoma
- Magnetic resonance imaging
- NOS-HCC, not otherwise specified hepatocellular carcinoma
- OS, overall survival
- RFS, recurrence-free survival
- SH-HCC, steatohepatitic hepatocellular carcinoma
- TIV, tumour-in-vein
- US, ultrasound
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Affiliation(s)
- Roberto Cannella
- Department of Radiology, Hôpital Beaujon, Clichy, France.,Section of Radiology-BiND, University Hospital 'Paolo Giaccone', Palermo, Italy.,Department of Health Promotion Sciences Maternal and Infant Care, Internal Medicine and Medical Specialties, PROMISE, University of Palermo, Palermo, Italy
| | - Marco Dioguardi Burgio
- Department of Radiology, Hôpital Beaujon, Clichy, France.,Université de Paris, INSERM U1149 'centre de recherche sur l'inflammation', CRI, Paris, France
| | | | - Loïc Trapani
- Department of Pathology, Hôpital Beaujon, Clichy, France
| | | | - Christian Hobeika
- Department of HPB Surgery and Liver Transplantation, Hôpital Beaujon, Clichy, France
| | - Francois Cauchy
- Department of HPB Surgery and Liver Transplantation, Hôpital Beaujon, Clichy, France
| | | | - Valérie Vilgrain
- Department of Radiology, Hôpital Beaujon, Clichy, France.,Université de Paris, INSERM U1149 'centre de recherche sur l'inflammation', CRI, Paris, France
| | - Riccardo Sartoris
- Department of Radiology, Hôpital Beaujon, Clichy, France.,Université de Paris, INSERM U1149 'centre de recherche sur l'inflammation', CRI, Paris, France
| | - Maxime Ronot
- Department of Radiology, Hôpital Beaujon, Clichy, France.,Université de Paris, INSERM U1149 'centre de recherche sur l'inflammation', CRI, Paris, France
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Yoon JH, Kim H. CT Characterization of Aggressive Macrotrabecular-Massive Hepatocellular Carcinoma: A Step Forward to Personalized Medicine. Radiology 2021; 300:230-232. [PMID: 33973845 DOI: 10.1148/radiol.2021210379] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Jeong Hee Yoon
- From the Departments of Radiology (J.H.Y.) and Pathology (H.K.), Seoul National University Hospital and Seoul National University College of Medicine, 101 Daehak-ro, Jongro-Gu, Seoul 03080, South Korea
| | - Haeryoung Kim
- From the Departments of Radiology (J.H.Y.) and Pathology (H.K.), Seoul National University Hospital and Seoul National University College of Medicine, 101 Daehak-ro, Jongro-Gu, Seoul 03080, South Korea
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