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Zhou L, Qu Y, Quan G, Zuo H, Liu M. Nomogram for Predicting Microvascular Invasion in Hepatocellular Carcinoma Using Gadoxetic Acid-Enhanced MRI and Intravoxel Incoherent Motion Imaging. Acad Radiol 2024; 31:457-466. [PMID: 37491178 DOI: 10.1016/j.acra.2023.06.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/19/2023] [Accepted: 06/26/2023] [Indexed: 07/27/2023]
Abstract
RATIONALE AND OBJECTIVES Microvascular invasion (MVI) is an important risk factor in hepatocellular carcinoma (HCC), but it can only be determined through histopathological results. The aim of this study was to develop and validate a nomogram for preoperative prediction MVI in HCC using gadoxetic acid-enhanced magnetic resonance imaging (MRI) and intravoxel incoherent motion imaging (IVIM). MATERIALS AND METHODS From July 2017 to September 2022, 148 patients with surgically resected HCC who underwent preoperative gadoxetic acid-enhanced MRI and IVIM were included in this retrospective study. Clinical indicators, imaging features, and diffusion parameters were compared between the MVI-positive and MVI-negative groups using the chi-square test, Mann-Whitney U test, and independent sample t test. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance in predicting MVI. Univariate and multivariate analyses were conducted to identify the significant clinical-radiological variables associated with MVI. Subsequently, a predictive nomogram that integrates clinical-radiological risk factors and diffusion parameters was developed and validated. RESULTS Serum alpha-fetoprotein level, tumor size, nonsmooth tumor margin, peritumoral hypo-intensity on hepatobiliary phase (HBP), apparent diffusion coefficient value and D value were statistically significant different between MVI-positive group and MVI-negative group. The results of multivariate analysis identified tumor size (odds ratio [OR], 0.786; 95% confidence interval [CI], 0.675-0.915; P < .01), nonsmooth tumor margin (OR, 2.299; 95% CI, 1.005-5.257; P < .05), peritumoral hypo-intensity on HBP (OR, 2.786; 95% CI, 1.141-6.802; P < .05) and D (OR, 0.293; 95% CI,0.089-0.964; P < .05) was the independent risk factor for the status of MVI. In ROC analysis, the combination of peritumoral hypo-intensity on HBP and D demonstrated the highest area under the curve value (0.902) in prediction MVI status, with sensitivity 92.8% and specificity 87.7%. The nomogram exhibited excellent predictive performance with C-index of 0.936 (95% CI 0.895-0.976) in the patient cohort, and had well-fitted calibration curve. CONCLUSION The nomogram incorporating clinical-radiological risk factors and diffusion parameters achieved satisfactory preoperative prediction of the individualized risk of MVI in patients with HCC.
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Affiliation(s)
- Lisui Zhou
- Department of Radiology, Chengdu Xinhua Hospital Affiliated to North Sichuan Medical College, Chengdu, China (L.Z., H.Z., M.L.)
| | - Yuan Qu
- Department of Radiology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China (Y.Q.)
| | - Guangnan Quan
- MR Research China, GE Healthcare China, Beijing, China (G.Q.)
| | - Houdong Zuo
- Department of Radiology, Chengdu Xinhua Hospital Affiliated to North Sichuan Medical College, Chengdu, China (L.Z., H.Z., M.L.)
| | - Mi Liu
- Department of Radiology, Chengdu Xinhua Hospital Affiliated to North Sichuan Medical College, Chengdu, China (L.Z., H.Z., M.L.).
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Deng Y, Yang D, Tan X, Xu H, Xu L, Ren A, Liu P, Yang Z. Preoperative evaluation of microvascular invasion in hepatocellular carcinoma with a radiological feature-based nomogram: a bi-centre study. BMC Med Imaging 2024; 24:29. [PMID: 38281008 PMCID: PMC10821254 DOI: 10.1186/s12880-024-01206-7] [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: 09/03/2023] [Accepted: 01/18/2024] [Indexed: 01/29/2024] Open
Abstract
PURPOSE To develop a nomogram for preoperative assessment of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) based on the radiological features of enhanced CT and to verify two imaging techniques (CT and MRI) in an external centre. METHOD A total of 346 patients were retrospectively included (training, n = 185, CT images; external testing 1, n = 90, CT images; external testing 2, n = 71, MRI images), including 229 MVI-negative patients and 117 MVI-positive patients. The radiological features and clinical information of enhanced CT images were analysed, and the independent variables associated with MVI in HCC were determined by logistic regression analysis. Then, a nomogram prediction model was constructed. External validation was performed on CT (n = 90) and MRI (n = 71) images from another centre. RESULTS Among the 23 radiological and clinical features, size, arterial peritumoral enhancement (APE), tumour margin and alpha-fetoprotein (AFP) were independent influencing factors for MVI in HCC. The nomogram integrating these risk factors had a good predictive effect, with AUC, specificity and sensitivity values of 0.834 (95% CI: 0.774-0.895), 75.0% and 83.5%, respectively. The AUC values of external verification based on CT and MRI image data were 0.794 (95% CI: 0.700-0.888) and 0.883 (95% CI: 0.807-0.959), respectively. No statistical difference in AUC values among training set and testing sets was found. CONCLUSION The proposed nomogram prediction model for MVI in HCC has high accuracy, can be used with different imaging techniques, and has good clinical applicability.
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Affiliation(s)
- Yuhui Deng
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Yongan Road 95, West District, Beijing, 100050, China
- Medical Imaging Division, Heilongjiang Provincial Hospital, Harbin Institute of Technology, Zhongshan Road 82, Xiangfang District, Harbin, 150036, China
| | - Dawei Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Yongan Road 95, West District, Beijing, 100050, China
| | - Xianzheng Tan
- Department of Radiology, Hunan Provincial People's Hospital, the First Affiliated Hospital of Hunan Normal University, Changsha, 410005, China
| | - Hui Xu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Yongan Road 95, West District, Beijing, 100050, China
| | - Lixue Xu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Yongan Road 95, West District, Beijing, 100050, China
| | - Ahong Ren
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Yongan Road 95, West District, Beijing, 100050, China
| | - Peng Liu
- Department of Radiology, Hunan Provincial People's Hospital, the First Affiliated Hospital of Hunan Normal University, Changsha, 410005, China.
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Yongan Road 95, West District, Beijing, 100050, 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] [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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Xu G, Luo N, Deng Y. Qualitative and quantitative wall enhancement associated with unstable intracranial aneurysms: a meta-analysis. Acta Radiol 2022; 64:1974-1984. [PMID: 36475308 DOI: 10.1177/02841851221141238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background Unstable intracranial aneurysms (UIAs) are more likely to rupture and cause serious consequences. Evaluating the stability of unruptured aneurysms facilitates clinical management stratification. Purpose To compare and evaluate the predictive performance of qualitative and quantitative wall enhancement (aneurysmal wall enhancement [AWE], circumferential aneurysmal wall enhancement [CAWE], wall enhancement ratio [WER]) on high-resolution magnetic resonance imaging (MRI) of the vessel wall to predict the presence of UIA. Material and Methods Original articles describing the depiction of aneurysmal wall enhancement on 3.0-T or 1.5-T high-resolution vessel wall imaging were retrieved from the Web of Science, Medline/PubMed, the Cochrane Library, and EMBASE databases up to 15 February 2022. The combined sensitivity, specificity, and summary area under the receiver operating characteristic curve (AUC) were calculated, and meta-regression analysis was performed. Results In total, 12 original articles involving 1619 intracranial aneurysms (IAs) were included. The combined sensitivity and specificity of AWE, CAWE, and WER were 91% and 67%, 59% and 83%, and 86% and 75%, respectively, in the diagnosis of UIA. The summary AUC values of these items were, in order from high to low, 0.88 (WER), 0.84 (AWE), and 0.77 (CAWE), and the differences among them were significant ( z = 2.976, P = 0.003 and z = 2.950, P = 0.003). The meta-regression analysis identified average size and 2D/3D magnetic imaging technology as possible sources of heterogeneity. Conclusion Qualitative and quantitative wall enhancement showed moderate accuracy in predicting UIA, and WER had the highest accuracy among them in this meta-analysis. Two covariates were found to explain the heterogeneity.
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Affiliation(s)
- Guizhi Xu
- Department of Neurosurgery, The Second Hospital of Heilongjiang Province, Harbin, PR China
| | - Ning Luo
- Department of Endocrinology, Chenzhou No.1 People's Hospital, Chenzhou, Hunan, PR China
| | - Yuhui Deng
- Medical Imaging Division, Heilongjiang Provincial Hospital, Harbin Institute of Technology, Harbin, PR China
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Deng Y, Jia X, Yu G, Hou J, Xu H, Ren A, Wang Z, Yang D, Yang Z. Can a proposed double branch multimodality-contribution-aware TripNet improve the prediction performance of the microvascular invasion of hepatocellular carcinoma based on small samples? Front Oncol 2022; 12:1035775. [PMID: 36387069 PMCID: PMC9640917 DOI: 10.3389/fonc.2022.1035775] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 10/10/2022] [Indexed: 05/26/2024] Open
Abstract
OBJECTIVES To evaluate the potential improvement of prediction performance of a proposed double branch multimodality-contribution-aware TripNet (MCAT) in microvascular invasion (MVI) of hepatocellular carcinoma (HCC) based on a small sample. METHODS In this retrospective study, 121 HCCs from 103 consecutive patients were included, with 44 MVI positive and 77 MVI negative, respectively. A MCAT model aiming to improve the accuracy of deep neural network and alleviate the negative effect of small sample size was proposed and the improvement of MCAT model was verified among comparisons between MCAT and other used deep neural networks including 2DCNN (two-dimentional convolutional neural network), ResNet (residual neural network) and SENet (squeeze-and-excitation network), respectively. RESULTS Through validation, the AUC value of MCAT is significantly higher than 2DCNN based on CT, MRI, and both imaging (P < 0.001 for all). The AUC value of model with single branch pretraining based on small samples is significantly higher than model with end-to-end training in CT branch and double branch (0.62 vs 0.69, p=0.016, 0.65 vs 0.83, p=0.010, respectively). The AUC value of the double branch MCAT based on both CT and MRI imaging (0.83) was significantly higher than that of the CT branch MCAT (0.69) and MRI branch MCAT (0.73) (P < 0.001, P = 0.03, respectively), which was also significantly higher than common-used ReNet (0.67) and SENet (0.70) model (P < 0.001, P = 0.005, respectively). CONCLUSION A proposed Double branch MCAT model based on a small sample can improve the effectiveness in comparison to other deep neural networks or single branch MCAT model, providing a potential solution for scenarios such as small-sample deep learning and fusion of multiple imaging modalities.
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Affiliation(s)
- Yuhui Deng
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
- Medical Imaging Division, Heilongjiang Provincial Hospital, Harbin Institute of Technology, Harbin, China
| | - Xibin Jia
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Gaoyuan Yu
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Jian Hou
- Department of Radiology, The People’s Hospital of Jimo.Qingdao, Qingdao, China
| | - Hui Xu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Ahong Ren
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zhenchang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Dawei Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
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