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Pan Y, Liu D, Liang F, Kong Z, Zhang X, Ai Q. Perfluorobutane application value in microwave ablation of Small Hepatocellular Carcinoma (<3 cm). Clin Hemorheol Microcirc 2024:CH232055. [PMID: 38277286 DOI: 10.3233/ch-232055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2024]
Abstract
BACKGROUND No studies have been retrieved comparing perfluorobutane with sulfur hexafluoride for microwave ablation (MWA) in small hepatocellular carcinoma(sHCC). OBJECTIVE To retrospective investigate the value of perfluorobutane ultrasonography contrast agent in ultrasonography (US)-guided MWA of sHCC. METHODS We conducted a retrospective clinical controlled study about US-guided percutaneous MWA in patients with sHCC, and in patients undergoing intra-operative treatment with perfluorobutane or sulfur hexafluoride. In both groups, a contrast agent was injected to clear the tumor and then a needle was inserted. A 5-point needle prick difficulty score was developed to compare needle prick difficulty in the two groups of cases. RESULTS A total of 67 patients were included: 25 patients in group perfluorobutane, aged 41-82 (60.64±9.46), tumor size 1.1-2.8 (1.78±0.45) cm. 42 patients in group sulfur hexafluoride, aged 38-78 (62.26±9.27), with tumor size of 1.1-3.0 (1.89±0.49) cm. There was no significant difference in age or tumor size in both groups (P > 0.05). Puncture difficulty score (5-point): 2.0-2.7 (2.28±0.29) in group perfluorobutane, and 2.0-4.7 (2.95±0.85) in group sulfur hexafluoride, and the difference between the two groups was statistically significant (P < 0.05). Enhanced imaging results within 3 months after surgery: complete ablation rate was 100% (25/25) in the group perfluorobutane, 95.2% (40/42 in the group sulfur hexafluoride), with no significant difference between the two groups (P > 0.05). CONCLUSION Perfluorobutane kupffer phase can make the operator accurately deploy the ablation needle and reduce the difficulty of operation.
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Affiliation(s)
- Yanghong Pan
- Department of Emergency, Hangzhou Xixi Hospital, Hangzhou, Zhejiang Province, China
| | - Delin Liu
- Department of Ultrasonography, Hangzhou Xixi Hospital, Hangzhou, Zhejiang Province, China
| | - Fei Liang
- Department of Ultrasonography, Hangzhou Xixi Hospital, Hangzhou, Zhejiang Province, China
| | - Zixiang Kong
- Department of Ultrasonography, Hangzhou Xixi Hospital, Hangzhou, Zhejiang Province, China
| | - Xu Zhang
- Department of Ultrasonography, Hangzhou Red Cross Hospital, Hangzhou, Zhejiang Province, China
| | - Qinqin Ai
- Department of Hepatology, Hangzhou Xixi Hospital, Hangzhou, Zhejiang Province, China
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Wang H, Chen JJ, Yin SY, Sheng X, Wang HX, Lau WY, Dong H, Cong WM. A Grading System of Microvascular Invasion for Patients with Hepatocellular Carcinoma Undergoing Liver Resection with Curative Intent: A Multicenter Study. J Hepatocell Carcinoma 2024; 11:191-206. [PMID: 38283692 PMCID: PMC10822140 DOI: 10.2147/jhc.s447731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/15/2024] [Indexed: 01/30/2024] Open
Abstract
Background Microvascular invasion (MVI) is closely correlated with poor clinical outcomes in patients with hepatocellular carcinoma (HCC). A grading system of MVI is needed to assist in the management of HCC patient. Methods Multicenter data of HCC patients who underwent liver resection with curative intent was analyzed. This grading system was established by detected number and distance from tumor boundary of MVI. Survival outcomes were compared among patients in each group. This system was verified by time-receiver operating characteristic curve, time-area under the curve, calibration curve, and decision curve analyses. Cox regression analysis was performed to study the associated factors of prognosis. Logistic analysis was used to study the predictive factors of MVI. Results All patients were classified into 4 groups: M0: no MVI; M1: 1~5 proximal MVIs (≤1 cm from tumor boundary); M2a: >5 proximal MVIs (≤1 cm from tumor boundary); M2b: ≥1 distal MVIs (>1 cm from tumor boundary). The recurrence-free survival (RFS), overall survival (OS), and early RFS rates among all the individual groups were significantly different. Based on the number of proximal MVI (0~5 vs >5), patients in the M2b group were further divided into two subgroups which also showed different prognosis. Multiple methods showed this grading system to be significantly better than the MVI two-tiered system in prognostic evaluation. Four multivariate models for RFS, OS, early RFS, late RFS, and a predictive model of MVI were then established and were shown to satisfactorily evaluate prognosis and have a great discriminatory power, respectively. Conclusion This MVI grading system could precisely evaluate prognosis of HCC patients after liver resection with curative intent and it could be employed in routine pathological reports. The severity of MVI from both adjacent and distant from tumor boundary should be stated. A hypothesis about two occurrence modes of distal MVI was proposed.
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Affiliation(s)
- Han Wang
- Department of Pathology, Shanghai Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, People’s Republic of China
| | - Jun-Jie Chen
- Department of Radiology, Shanghai Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, People’s Republic of China
| | - Shu-Yi Yin
- Department of Pathology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, People’s Republic of China
| | - Xia Sheng
- Department of Pathology, Minhang Hospital, Fudan University, Shanghai, People’s Republic of China
| | - Hong-Xia Wang
- Department of Pathology, Jiading District Central Hospital, Shanghai University of Medicine & Health Sciences, Shanghai, People’s Republic of China
| | - Wan Yee Lau
- Faculty of Medicine, Chinese University of Hong Kong, Hong Kong, China
| | - Hui Dong
- Department of Pathology, Shanghai Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, People’s Republic of China
| | - Wen-Ming Cong
- Department of Pathology, Shanghai Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, People’s Republic of China
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Zhang H, Meng Z, Ru J, Meng Y, Wang K. Application and prospects of AI-based radiomics in ultrasound diagnosis. Vis Comput Ind Biomed Art 2023; 6:20. [PMID: 37828411 PMCID: PMC10570254 DOI: 10.1186/s42492-023-00147-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 09/20/2023] [Indexed: 10/14/2023] Open
Abstract
Artificial intelligence (AI)-based radiomics has attracted considerable research attention in the field of medical imaging, including ultrasound diagnosis. Ultrasound imaging has unique advantages such as high temporal resolution, low cost, and no radiation exposure. This renders it a preferred imaging modality for several clinical scenarios. This review includes a detailed introduction to imaging modalities, including Brightness-mode ultrasound, color Doppler flow imaging, ultrasound elastography, contrast-enhanced ultrasound, and multi-modal fusion analysis. It provides an overview of the current status and prospects of AI-based radiomics in ultrasound diagnosis, highlighting the application of AI-based radiomics to static ultrasound images, dynamic ultrasound videos, and multi-modal ultrasound fusion analysis.
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Affiliation(s)
- Haoyan Zhang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Zheling Meng
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Jinyu Ru
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Yaqing Meng
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China.
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Li J, Su X, Xu X, Zhao C, Liu A, Yang L, Song B, Song H, Li Z, Hao X. Preoperative prediction and risk assessment of microvascular invasion in hepatocellular carcinoma. Crit Rev Oncol Hematol 2023; 190:104107. [PMID: 37633349 DOI: 10.1016/j.critrevonc.2023.104107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 08/22/2023] [Indexed: 08/28/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is one of the most common and highly lethal tumors worldwide. Microvascular invasion (MVI) is a significant risk factor for recurrence and poor prognosis after surgical resection for HCC patients. Accurately predicting the status of MVI preoperatively is critical for clinicians to select treatment modalities and improve overall survival. However, MVI can only be diagnosed by pathological analysis of postoperative specimens. Currently, numerous indicators in serology (including liquid biopsies) and imaging have been identified to effective in predicting the occurrence of MVI, and the multi-indicator model based on deep learning greatly improves accuracy of prediction. Moreover, several genes and proteins have been identified as risk factors that are strictly associated with the occurrence of MVI. Therefore, this review evaluates various predictors and risk factors, and provides guidance for subsequent efforts to explore more accurate predictive methods and to facilitate the conversion of risk factors into reliable predictors.
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Affiliation(s)
- Jian Li
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China; Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Xin Su
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China; Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Xiao Xu
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China; Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Changchun Zhao
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China; Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Ang Liu
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China; Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Liwen Yang
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China
| | - Baoling Song
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China
| | - Hao Song
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China
| | - Zihan Li
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China
| | - Xiangyong Hao
- Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, China.
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Zhong X, Long H, Chen L, Xie Y, Shi Y, Peng J, Zheng R, Su L, Duan Y, Xie X, Lin M. Stiffness on shear wave elastography as a potential microenvironment biomarker for predicting tumor recurrence in HBV-related hepatocellular carcinoma. Insights Imaging 2023; 14:147. [PMID: 37697029 PMCID: PMC10495298 DOI: 10.1186/s13244-023-01505-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 08/16/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND To explore the pathologic basis and prognostic value of tumor and liver stiffness measured pre-operatively by two-dimensional shear wave elastography (2D-SWE) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients who undergo hepatic resection. METHODS A total of 191 HBV-infected patients with solitary resectable HCC were prospectively enrolled. The stiffness of intratumoral tissue, peritumoral tissue, adjacent liver tissue, and distant liver tissue was evaluated by 2D-SWE. The correlations between stiffness and pathological characteristics were analyzed in 114 patients. The predictive value of stiffness for recurrence-free survival (RFS) was evaluated, and Cutoff Finder was used for determining optimal cut-off stiffness values. Cox proportional hazards analysis was used to identify independent predictors of RFS. RESULTS Pathologically, intratumoral stiffness was associated with stroma proportion and microvascular invasion (MVI) while peritumoral stiffness was associated with tumor size, capsule, and MVI. Adjacent liver stiffness was correlated with capsule and liver fibrosis stage while distant liver stiffness was correlated with liver fibrosis stage. Peritumoral stiffness, adjacent liver stiffness, and distant liver stiffness were all correlated to RFS (all p < 0.05). Higher peritumoral stiffness (> 49.4 kPa) (HR = 1.822, p = 0.023) and higher adjacent liver stiffness (> 24.1 kPa) (HR = 1.792, p = 0.048) were significant independent predictors of worse RFS, along with tumor size and MVI. The nomogram based on these variables showed a C-index of 0.77 for RFS prediction. CONCLUSIONS Stiffness measured by 2D-SWE could be a tumor microenvironment and tumor invasiveness biomarker. Peritumoral stiffness and adjacent liver stiffness showed important values in predicting tumor recurrence after curative resection in HBV-related HCC. CLINICAL RELEVANCE STATEMENT Tumor and liver stiffness measured by two-dimensional shear wave elastography serve as imaging biomarkers for predicting hepatocellular carcinoma recurrence, reflecting biological behavior and tumor microenvironment. KEY POINTS • Stiffness measured by two-dimensional shear wave elastography is a useful biomarker of tumor microenvironment and invasiveness. • Higher stiffness indicated more aggressive behavior of hepatocellular carcinoma. • The study showed the prognostic value of peritumoral stiffness and adjacent liver stiffness for recurrence-free survival. • The nomogram integrating peritumoral stiffness, adjacent liver stiffness, tumor size, and microvascular invasion showed a C-index of 0.77.
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Affiliation(s)
- Xian Zhong
- Department of Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Second Road, Guangzhou, 510080, China
| | - Haiyi Long
- Department of Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Second Road, Guangzhou, 510080, China
| | - Lili Chen
- Department of Pathology, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Second Road, Guangzhou, 510080, China
| | - Yuhua Xie
- Department of Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Second Road, Guangzhou, 510080, China
| | - Yifan Shi
- Department of Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Second Road, Guangzhou, 510080, China
| | - Jianyun Peng
- Department of Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Second Road, Guangzhou, 510080, China
| | - Ruiying Zheng
- Department of Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Second Road, Guangzhou, 510080, China
| | - Liya Su
- Department of Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Second Road, Guangzhou, 510080, China
| | - Yu Duan
- Department of Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Second Road, Guangzhou, 510080, China
| | - Xiaoyan Xie
- Department of Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Second Road, Guangzhou, 510080, China
| | - Manxia Lin
- Department of Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Second Road, Guangzhou, 510080, China.
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Zheng R, Zhang X, Liu B, Zhang Y, Shen H, Xie X, Li S, Huang G. Comparison of non-radiomics imaging features and radiomics models based on contrast-enhanced ultrasound and Gd-EOB-DTPA-enhanced MRI for predicting microvascular invasion in hepatocellular carcinoma within 5 cm. Eur Radiol 2023; 33:6462-6472. [PMID: 37338553 DOI: 10.1007/s00330-023-09789-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 03/15/2023] [Accepted: 03/30/2023] [Indexed: 06/21/2023]
Abstract
OBJECTIVES The purpose of this study is to establish microvascular invasion (MVI) prediction models based on preoperative contrast-enhanced ultrasound (CEUS) and ethoxybenzyl-enhanced magnetic resonance imaging (EOB-MRI) in patients with a single hepatocellular carcinoma (HCC) ≤ 5 cm. METHODS Patients with a single HCC ≤ 5 cm and accepting CEUS and EOB-MRI before surgery were enrolled in this study. Totally, 85 patients were randomly divided into the training and validation cohorts in a ratio of 7:3. Non-radiomics imaging features, the CEUS and EOB-MRI radiomics scores were extracted from the arterial phase, portal phase and delayed phase images of CEUS and the hepatobiliary phase images of EOB-MRI. Different MVI predicting models based on CEUS and EOB-MRI were constructed and their predictive values were evaluated. RESULTS Since univariate analysis revealed that arterial peritumoral enhancement on the CEUS image, CEUS radiomics score, and EOB-MRI radiomics score were significantly associated with MVI, three prediction models, namely the CEUS model, the EOB-MRI model, and the CEUS-EOB model, were developed. In the validation cohort, the areas under the receiver operating characteristic curve of the CEUS model, the EOB-MRI model, and the CEUS-EOB model were 0.73, 0.79, and 0.86, respectively. CONCLUSIONS Radiomics scores based on CEUS and EOB-MRI, combined with arterial peritumoral enhancement on CEUS, show a satisfying performance of MVI predicting. There was no significant difference in the efficacy of MVI risk evaluation between radiomics models based on CEUS and EOB-MRI in patients with a single HCC ≤ 5 cm. CLINICAL RELEVANCE STATEMENT Radiomics models based on CEUS and EOB-MRI are effective for MVI predicting and conducive to pretreatment decision-making in patients with a single HCC within 5 cm. KEY POINTS • Radiomics scores based on CEUS and EOB-MRI, combined with arterial peritumoral enhancement on CEUS, show a satisfying performance of MVI predicting. • There was no significant difference in the efficacy of MVI risk evaluation between radiomics models based on CEUS and EOB-MRI in patients with a single HCC ≤ 5 cm.
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Affiliation(s)
- Ruiying Zheng
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Xiaoer Zhang
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Baoxian Liu
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Yi Zhang
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Hui Shen
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Xiaoyan Xie
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Shurong Li
- Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
| | - Guangliang Huang
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
- Department of Medical Ultrasonics, Guangxi Hospital Division of the First Affiliated Hospital, Sun Yat-Sen University, Guangxi, China.
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Ippolito D, Maino C, Gatti M, Marra P, Faletti R, Cortese F, Inchingolo R, Sironi S. Radiological findings in non-surgical recurrent hepatocellular carcinoma: From locoregional treatments to immunotherapy. World J Gastroenterol 2023; 29:1669-1684. [PMID: 37077517 PMCID: PMC10107213 DOI: 10.3748/wjg.v29.i11.1669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/10/2023] [Accepted: 03/02/2023] [Indexed: 03/17/2023] Open
Abstract
Since hepatocellular carcinoma (HCC) represents an important cause of mortality and morbidity all over the world. Currently, it is fundamental not only to achieve a curative treatment but also to manage in the best way any possible recurrence. Even if the latest update of the Barcelona Clinic Liver Cancer guidelines for HCC treatment has introduced new locoregional techniques and confirmed others as well-established clinical practices, there is still no consensus about the treatment of recurrent HCC (RHCC). Locoregional treatments and medical therapy represent two of the most widely accepted approaches for disease control, especially in the advanced stage of liver disease. Different medical treatments are now approved, and others are under investigation. On this basis, radiology plays a central role in the diagnosis of RHCC and the assessment of response to locoregional treatments and medical therapy for RHCC. This review summarized the actual clinical practice by underlining the importance of the radiological approach both in the diagnosis and treatment of RHCC.
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Affiliation(s)
- Davide Ippolito
- Department of Radiology, IRCCS San Gerardo dei Tintori, Monza 20900, Italy
- School of Medicine and Surgery, University of Milano-Bicocca, Milano 20121, Italy
| | - Cesare Maino
- Department of Radiology, IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Paolo Marra
- Department of Diagnostic and Interventional Radiology, Papa Giovanni XXIII Hospital, Bergamo 24127, Italy
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Francesco Cortese
- Interventional Radiology Unit, “F. Miulli” Regional General Hospital, Bari 70121, Italy
| | - Riccardo Inchingolo
- Interventional Radiology Unit, “F. Miulli” Regional General Hospital, Bari 70121, Italy
| | - Sandro Sironi
- School of Medicine and Surgery, University of Milano-Bicocca, Milano 20121, Italy
- Department of Diagnostic and Interventional Radiology, Papa Giovanni XXIII Hospital, Bergamo 24127, Italy
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Yang D, Zhu M, Xiong X, Su Y, Zhao F, Hu Y, Zhang G, Pei J, Ding Y. Clinical features and prognostic factors in patients with microvascular infiltration of hepatocellular carcinoma: Development and validation of a nomogram and risk stratification based on the SEER database. Front Oncol 2022; 12:987603. [PMID: 36185206 PMCID: PMC9515492 DOI: 10.3389/fonc.2022.987603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 08/29/2022] [Indexed: 11/29/2022] Open
Abstract
Background The goal is to establish and validate an innovative prognostic risk stratification and nomogram in patients of hepatocellular carcinoma (HCC) with microvascular invasion (MVI) for predicting the cancer-specific survival (CSS). Methods 1487 qualified patients were selected from the Surveillance, Epidemiology and End Results (SEER) database and randomly assigned to the training cohort and validation cohort in a ratio of 7:3. Concordance index (C-index), area under curve (AUC) and calibration plots were adopted to evaluate the discrimination and calibration of the nomogram. Decision curve analysis (DCA) was used to quantify the net benefit of the nomogram at different threshold probabilities and compare it to the American Joint Committee on Cancer (AJCC) tumor staging system. C-index, net reclassification index (NRI) and integrated discrimination improvement (IDI) were applied to evaluate the improvement of the new model over the AJCC tumor staging system. The new risk stratifications based on the nomogram and the AJCC tumor staging system were compared. Results Eight prognostic factors were used to construct the nomogram for HCC patients with MVI. The C-index for the training and validation cohorts was 0.785 and 0.776 respectively. The AUC values were higher than 0.7 both in the training cohort and validation cohort. The calibration plots showed good consistency between the actual observation and the nomogram prediction. The IDI values of 1-, 3-, 5-year CSS in the training cohort were 0.17, 0.16, 0.15, and in the validation cohort were 0.17, 0.17, 0.17 (P<0.05). The NRI values of the training cohort were 0.75 at 1-year, 0.68 at 3-year and 0.67 at 5-year. The DCA curves indicated that the new model more accurately predicted 1-year, 3-year, and 5-year CSS in both training and validation cohort, because it added more net benefit than the AJCC staging system. Furthermore, the risk stratification system showed the CSS in different groups had a good regional division. Conclusions A comprehensive risk stratification system and nomogram were established to forecast CSS for patients of HCC with MVI.
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Affiliation(s)
- Dashuai Yang
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Mingqiang Zhu
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiangyun Xiong
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yang Su
- Department of Gastrointestinal Surgery, Tongji Hospital, Tongji Medical College in Huazhong University of Science and Technology, Wuhan, China
| | - Fangrui Zhao
- Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yong Hu
- Department of Orthopedics, Renmin Hospital of Wuhan University, Wuhan, China
- *Correspondence: Youming Ding, ; Yong Hu,
| | - Guo Zhang
- Department of neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Junpeng Pei
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Youming Ding
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China
- *Correspondence: Youming Ding, ; Yong Hu,
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