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Nong HY, Cen YY, Qin M, Qin WQ, Xie YX, Li L, Liu MR, Ding K. Application of texture signatures based on multiparameter-magnetic resonance imaging for predicting microvascular invasion in hepatocellular carcinoma: Retrospective study. World J Gastrointest Oncol 2024; 16:1309-1318. [PMID: 38660663 PMCID: PMC11037072 DOI: 10.4251/wjgo.v16.i4.1309] [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: 12/18/2023] [Accepted: 02/05/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Despite continuous changes in treatment methods, the survival rate for advanced hepatocellular carcinoma (HCC) patients remains low, highlighting the importance of diagnostic methods for HCC. AIM To explore the efficacy of texture analysis based on multi-parametric magnetic resonance (MR) imaging (MRI) in predicting microvascular invasion (MVI) in preoperative HCC. METHODS This study included 105 patients with pathologically confirmed HCC, categorized into MVI-positive and MVI-negative groups. We employed Original Data Analysis, Principal Component Analysis, Linear Discriminant Analysis (LDA), and Non-LDA (NDA) for texture analysis using multi-parametric MR images to predict preoperative MVI. The effectiveness of texture analysis was determined using the B11 program of the MaZda4.6 software, with results expressed as the misjudgment rate (MCR). RESULTS Texture analysis using multi-parametric MRI, particularly the MI + PA + F dimensionality reduction method combined with NDA discrimination, demonstrated the most effective prediction of MVI in HCC. Prediction accuracy in the pulse and equilibrium phases was 83.81%. MCRs for the combination of T2-weighted imaging (T2WI), arterial phase, portal venous phase, and equilibrium phase were 22.86%, 16.19%, 20.95%, and 20.95%, respectively. The area under the curve for predicting MVI positivity was 0.844, with a sensitivity of 77.19% and specificity of 91.67%. CONCLUSION Texture analysis of arterial phase images demonstrated superior predictive efficacy for MVI in HCC compared to T2WI, portal venous, and equilibrium phases. This study provides an objective, non-invasive method for preoperative prediction of MVI, offering a theoretical foundation for the selection of clinical therapy.
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Affiliation(s)
- Hai-Yang Nong
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
- Department of Radiology, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise 533000, Guangxi Zhuang Autonomous Region, China
| | - Yong-Yi Cen
- Department of Radiology, The First Affiliated Hospital of Guangxi University of Traditional Chinese Medicine, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Mi Qin
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Wen-Qi Qin
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - You-Xiang Xie
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Lin Li
- Department of Hepatobiliary Surgery, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Man-Rong Liu
- Department of Ultrasound, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Ke Ding
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
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钟 伟, 梁 芳, 杨 蕊, 甄 鑫. [Prediction of microvascular invasion in hepatocellular carcinoma based on multi-phase dynamic enhanced CT radiomics feature and multi-classifier hierarchical fusion model]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2024; 44:260-269. [PMID: 38501411 PMCID: PMC10954521 DOI: 10.12122/j.issn.1673-4254.2024.02.08] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Indexed: 03/20/2024]
Abstract
OBJECTIVE To predict microvascular invasion (MVI) in hepatocellular carcinoma (HCC) using a model based on multiphase dynamic-enhanced CT (DCE-CT) radiomics feature and hierarchical fusion of multiple classifiers. METHODS We retrospectively collected preoperative DCE-CT images from 111 patients with pathologically confirmed HCC in Guangzhou First People's Hospital between January, 2016 and April, 2020. The volume of interest was outlined in the early arterial phase, late arterial phase, portal venous phase and equilibrium phase, and radiomics features of these 4 phases were extracted. Seven classifiers based on different algorithms were trained using the filtered feature subsets to obtain multiple base classifiers under each phase. According to the hierarchical fusion strategy, a multi-criteria decision-making-based weight assignment algorithm was used for fusing each base classifier under the same phase with the model after extracting the phase information to obtain the prediction model. The proposed model was evaluated using a 5-fold cross-validation and assessed for area under the ROC curve (AUC), accuracy, sensitivity, and specificity. The prediction model was also compared with the fusion models using a single phase or multiple phases, models based on a single phase with a single classifier, models with different base classifier diversities, and 8 classifier models based on other ensemble methods. RESULTS The experimental results showed that the performance of the proposed model for predicting HCCMVI was optimal after incorporating the 4 phases and 7 classifiers, with AUC, accuracy, sensitivity, and specificity of 0.828, 0.766, 0.877, and 0.648, respectively. Comparative experiments showed that this prediction model outperformed the models based on a single phase with a single classifier and other ensemble models. CONCLUSION The proposed prediction model is effective for predicting MVI in HCC with superior performance to other models.
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Affiliation(s)
- 伟雄 钟
- 南方医科大学生物医学工程学院,广东 广州 510515School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - 芳蓉 梁
- 华南理工大学医学院,广东 广州 510006School of Medicine, South China University of Technology, Guangzhou 510006, China
| | - 蕊梦 杨
- 华南理工大学附属第二医院(广州市第一人民医院)放射科,广东 广州 510180Department of Radiology, Second Affiliated Hospital of South China University of Technology (Guangzhou First People's Hospital), Guangzhou 510180, China
- 华南理工大学医学院,广东 广州 510006School of Medicine, South China University of Technology, Guangzhou 510006, China
| | - 鑫 甄
- 南方医科大学生物医学工程学院,广东 广州 510515School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
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Zhou Z, Xia T, Zhang T, Du M, Zhong J, Huang Y, Xuan K, Xu G, Wan Z, Ju S, Xu J. Prediction of preoperative microvascular invasion by dynamic radiomic analysis based on contrast-enhanced computed tomography. Abdom Radiol (NY) 2024; 49:611-624. [PMID: 38051358 DOI: 10.1007/s00261-023-04102-w] [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: 08/16/2023] [Revised: 10/10/2023] [Accepted: 10/18/2023] [Indexed: 12/07/2023]
Abstract
PURPOSE Microvascular invasion (MVI) is a common complication of hepatocellular carcinoma (HCC) surgery, which is an important predictor of reduced surgical prognosis. This study aimed to develop a fully automated diagnostic model to predict pre-surgical MVI based on four-phase dynamic CT images. METHODS A total of 140 patients with HCC from two centers were retrospectively included (training set, n = 98; testing set, n = 42). All CT phases were aligned to the portal venous phase, and were then used to train a deep-learning model for liver tumor segmentation. Radiomics features were extracted from the tumor areas of original CT phases and pairwise subtraction images, as well as peritumoral features. Lastly, linear discriminant analysis (LDA) models were trained based on clinical features, radiomics features, and hybrid features, respectively. Models were evaluated by area under curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values (PPV and NPV). RESULTS Overall, 86 and 54 patients with MVI- (age, 55.92 ± 9.62 years; 68 men) and MVI+ (age, 53.59 ± 11.47 years; 43 men) were included. Average dice coefficients of liver tumor segmentation were 0.89 and 0.82 in training and testing sets, respectively. The model based on radiomics (AUC = 0.865, 95% CI: 0.725-0.951) showed slightly better performance than that based on clinical features (AUC = 0.841, 95% CI: 0.696-0.936). The classification model based on hybrid features achieved better performance in both training (AUC = 0.955, 95% CI: 0.893-0.987) and testing sets (AUC = 0.913, 95% CI: 0.785-0.978), compared with models based on clinical and radiomics features (p-value < 0.05). Moreover, the hybrid model also provided the best accuracy (0.857), sensitivity (0.875), and NPV (0.917). CONCLUSION The classification model based on multimodal intra- and peri-tumoral radiomics features can well predict HCC patients with MVI.
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Affiliation(s)
- Zhenghao Zhou
- School of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Tianyi Xia
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, School of Medicine, Zhongda Hospital, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, China
| | - Teng Zhang
- School of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Mingyang Du
- Cerebrovascular Disease Treatment Center, Nanjing Brain Hospital Affiliated to Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Jiarui Zhong
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, School of Medicine, Zhongda Hospital, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, China
| | - Yunzhi Huang
- School of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Kai Xuan
- School of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Geyang Xu
- Information School, University of Washington, Seattle, WA, 98195, USA
| | - Zhuo Wan
- School of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Shenghong Ju
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, School of Medicine, Zhongda Hospital, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, China.
| | - Jun Xu
- School of Artificial Intelligence, Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
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Xia TY, Zhou ZH, Meng XP, Zha JH, Yu Q, Wang WL, Song Y, Wang YC, Tang TY, Xu J, Zhang T, Long XY, Liang Y, Xiao WB, Ju SH. Predicting Microvascular Invasion in Hepatocellular Carcinoma Using CT-based Radiomics Model. Radiology 2023; 307:e222729. [PMID: 37097141 DOI: 10.1148/radiol.222729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Abstract
Background Prediction of microvascular invasion (MVI) may help determine treatment strategies for hepatocellular carcinoma (HCC). Purpose To develop a radiomics approach for predicting MVI status based on preoperative multiphase CT images and to identify MVI-associated differentially expressed genes. Materials and Methods Patients with pathologically proven HCC from May 2012 to September 2020 were retrospectively included from four medical centers. Radiomics features were extracted from tumors and peritumor regions on preoperative registration or subtraction CT images. In the training set, these features were used to build five radiomics models via logistic regression after feature reduction. The models were tested using internal and external test sets against a pathologic reference standard to calculate area under the receiver operating characteristic curve (AUC). The optimal AUC radiomics model and clinical-radiologic characteristics were combined to build the hybrid model. The log-rank test was used in the outcome cohort (Kunming center) to analyze early recurrence-free survival and overall survival based on high versus low model-derived score. RNA sequencing data from The Cancer Image Archive were used for gene expression analysis. Results A total of 773 patients (median age, 59 years; IQR, 49-64 years; 633 men) were divided into the training set (n = 334), internal test set (n = 142), external test set (n = 141), outcome cohort (n = 121), and RNA sequencing analysis set (n = 35). The AUCs from the radiomics and hybrid models, respectively, were 0.76 and 0.86 for the internal test set and 0.72 and 0.84 for the external test set. Early recurrence-free survival (P < .01) and overall survival (P < .007) can be categorized using the hybrid model. Differentially expressed genes in patients with findings positive for MVI were involved in glucose metabolism. Conclusion The hybrid model showed the best performance in prediction of MVI. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Summers in this issue.
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Affiliation(s)
- Tian-Yi Xia
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, China 210009 (T.Y.X., X.P.M., J.H.Z., Q.Y., W.L.W., Y.C.W., T.Y.T., S.H.J.); Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China (Z.H.Z., J.X.); MR Scientific Marketing, Siemens Healthineers, Shanghai, China (Y.S.); Department of Radiology, The Third Affiliated Hospital of Nantong University, Nantong, China (T.Z.); Department of Radiology, The Xiangya Hospital of Central South University, Changsha, China (X.Y.L.); Department of Radiology, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, China (Y.L.); and Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China (W.B.X.)
| | - Zheng-Hao Zhou
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, China 210009 (T.Y.X., X.P.M., J.H.Z., Q.Y., W.L.W., Y.C.W., T.Y.T., S.H.J.); Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China (Z.H.Z., J.X.); MR Scientific Marketing, Siemens Healthineers, Shanghai, China (Y.S.); Department of Radiology, The Third Affiliated Hospital of Nantong University, Nantong, China (T.Z.); Department of Radiology, The Xiangya Hospital of Central South University, Changsha, China (X.Y.L.); Department of Radiology, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, China (Y.L.); and Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China (W.B.X.)
| | - Xiang-Pan Meng
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, China 210009 (T.Y.X., X.P.M., J.H.Z., Q.Y., W.L.W., Y.C.W., T.Y.T., S.H.J.); Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China (Z.H.Z., J.X.); MR Scientific Marketing, Siemens Healthineers, Shanghai, China (Y.S.); Department of Radiology, The Third Affiliated Hospital of Nantong University, Nantong, China (T.Z.); Department of Radiology, The Xiangya Hospital of Central South University, Changsha, China (X.Y.L.); Department of Radiology, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, China (Y.L.); and Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China (W.B.X.)
| | - Jun-Hao Zha
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, China 210009 (T.Y.X., X.P.M., J.H.Z., Q.Y., W.L.W., Y.C.W., T.Y.T., S.H.J.); Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China (Z.H.Z., J.X.); MR Scientific Marketing, Siemens Healthineers, Shanghai, China (Y.S.); Department of Radiology, The Third Affiliated Hospital of Nantong University, Nantong, China (T.Z.); Department of Radiology, The Xiangya Hospital of Central South University, Changsha, China (X.Y.L.); Department of Radiology, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, China (Y.L.); and Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China (W.B.X.)
| | - Qian Yu
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, China 210009 (T.Y.X., X.P.M., J.H.Z., Q.Y., W.L.W., Y.C.W., T.Y.T., S.H.J.); Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China (Z.H.Z., J.X.); MR Scientific Marketing, Siemens Healthineers, Shanghai, China (Y.S.); Department of Radiology, The Third Affiliated Hospital of Nantong University, Nantong, China (T.Z.); Department of Radiology, The Xiangya Hospital of Central South University, Changsha, China (X.Y.L.); Department of Radiology, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, China (Y.L.); and Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China (W.B.X.)
| | - Wei-Lang Wang
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, China 210009 (T.Y.X., X.P.M., J.H.Z., Q.Y., W.L.W., Y.C.W., T.Y.T., S.H.J.); Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China (Z.H.Z., J.X.); MR Scientific Marketing, Siemens Healthineers, Shanghai, China (Y.S.); Department of Radiology, The Third Affiliated Hospital of Nantong University, Nantong, China (T.Z.); Department of Radiology, The Xiangya Hospital of Central South University, Changsha, China (X.Y.L.); Department of Radiology, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, China (Y.L.); and Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China (W.B.X.)
| | - Yang Song
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, China 210009 (T.Y.X., X.P.M., J.H.Z., Q.Y., W.L.W., Y.C.W., T.Y.T., S.H.J.); Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China (Z.H.Z., J.X.); MR Scientific Marketing, Siemens Healthineers, Shanghai, China (Y.S.); Department of Radiology, The Third Affiliated Hospital of Nantong University, Nantong, China (T.Z.); Department of Radiology, The Xiangya Hospital of Central South University, Changsha, China (X.Y.L.); Department of Radiology, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, China (Y.L.); and Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China (W.B.X.)
| | - Yuan-Cheng Wang
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, China 210009 (T.Y.X., X.P.M., J.H.Z., Q.Y., W.L.W., Y.C.W., T.Y.T., S.H.J.); Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China (Z.H.Z., J.X.); MR Scientific Marketing, Siemens Healthineers, Shanghai, China (Y.S.); Department of Radiology, The Third Affiliated Hospital of Nantong University, Nantong, China (T.Z.); Department of Radiology, The Xiangya Hospital of Central South University, Changsha, China (X.Y.L.); Department of Radiology, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, China (Y.L.); and Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China (W.B.X.)
| | - Tian-Yu Tang
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, China 210009 (T.Y.X., X.P.M., J.H.Z., Q.Y., W.L.W., Y.C.W., T.Y.T., S.H.J.); Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China (Z.H.Z., J.X.); MR Scientific Marketing, Siemens Healthineers, Shanghai, China (Y.S.); Department of Radiology, The Third Affiliated Hospital of Nantong University, Nantong, China (T.Z.); Department of Radiology, The Xiangya Hospital of Central South University, Changsha, China (X.Y.L.); Department of Radiology, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, China (Y.L.); and Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China (W.B.X.)
| | - Jun Xu
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, China 210009 (T.Y.X., X.P.M., J.H.Z., Q.Y., W.L.W., Y.C.W., T.Y.T., S.H.J.); Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China (Z.H.Z., J.X.); MR Scientific Marketing, Siemens Healthineers, Shanghai, China (Y.S.); Department of Radiology, The Third Affiliated Hospital of Nantong University, Nantong, China (T.Z.); Department of Radiology, The Xiangya Hospital of Central South University, Changsha, China (X.Y.L.); Department of Radiology, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, China (Y.L.); and Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China (W.B.X.)
| | - Tao Zhang
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, China 210009 (T.Y.X., X.P.M., J.H.Z., Q.Y., W.L.W., Y.C.W., T.Y.T., S.H.J.); Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China (Z.H.Z., J.X.); MR Scientific Marketing, Siemens Healthineers, Shanghai, China (Y.S.); Department of Radiology, The Third Affiliated Hospital of Nantong University, Nantong, China (T.Z.); Department of Radiology, The Xiangya Hospital of Central South University, Changsha, China (X.Y.L.); Department of Radiology, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, China (Y.L.); and Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China (W.B.X.)
| | - Xue-Ying Long
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, China 210009 (T.Y.X., X.P.M., J.H.Z., Q.Y., W.L.W., Y.C.W., T.Y.T., S.H.J.); Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China (Z.H.Z., J.X.); MR Scientific Marketing, Siemens Healthineers, Shanghai, China (Y.S.); Department of Radiology, The Third Affiliated Hospital of Nantong University, Nantong, China (T.Z.); Department of Radiology, The Xiangya Hospital of Central South University, Changsha, China (X.Y.L.); Department of Radiology, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, China (Y.L.); and Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China (W.B.X.)
| | - Yun Liang
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, China 210009 (T.Y.X., X.P.M., J.H.Z., Q.Y., W.L.W., Y.C.W., T.Y.T., S.H.J.); Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China (Z.H.Z., J.X.); MR Scientific Marketing, Siemens Healthineers, Shanghai, China (Y.S.); Department of Radiology, The Third Affiliated Hospital of Nantong University, Nantong, China (T.Z.); Department of Radiology, The Xiangya Hospital of Central South University, Changsha, China (X.Y.L.); Department of Radiology, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, China (Y.L.); and Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China (W.B.X.)
| | - Wen-Bo Xiao
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, China 210009 (T.Y.X., X.P.M., J.H.Z., Q.Y., W.L.W., Y.C.W., T.Y.T., S.H.J.); Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China (Z.H.Z., J.X.); MR Scientific Marketing, Siemens Healthineers, Shanghai, China (Y.S.); Department of Radiology, The Third Affiliated Hospital of Nantong University, Nantong, China (T.Z.); Department of Radiology, The Xiangya Hospital of Central South University, Changsha, China (X.Y.L.); Department of Radiology, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, China (Y.L.); and Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China (W.B.X.)
| | - Sheng-Hong Ju
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, China 210009 (T.Y.X., X.P.M., J.H.Z., Q.Y., W.L.W., Y.C.W., T.Y.T., S.H.J.); Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China (Z.H.Z., J.X.); MR Scientific Marketing, Siemens Healthineers, Shanghai, China (Y.S.); Department of Radiology, The Third Affiliated Hospital of Nantong University, Nantong, China (T.Z.); Department of Radiology, The Xiangya Hospital of Central South University, Changsha, China (X.Y.L.); Department of Radiology, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, China (Y.L.); and Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China (W.B.X.)
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Xue G, Liu H, Cai X, Zhang Z, Zhang S, Liu L, Hu B, Wang G. Impact of deep learning image reconstruction algorithms on CT radiomic features in patients with liver tumors. Front Oncol 2023; 13:1167745. [PMID: 37091167 PMCID: PMC10113560 DOI: 10.3389/fonc.2023.1167745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 03/21/2023] [Indexed: 04/08/2023] Open
Abstract
ObjectiveTo evaluate the impact of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V) on abdominal CT radiomic features acquired in portal venous phase in liver tumor patients.MethodsSixty patients with liver tumors who underwent contrast-enhanced abdominal CT were retrospectively enrolled. Six groups including filtered back projection (FBP), ASIR-V (30%, 70%) and DLIR at low (DLIR-L), medium (DLIR-M and high (DLIR-H), were reconstructed using portal venous phase data. CT-based radiomic features (first-order, texture and wavelet features) were extracted from 2D and 3D liver tumors, peritumor and liver parenchyma. All features were analyzed for comparison. P < 0.05 indicated statistically different. The consistency of 3D lesion feature extraction was assessed by calculating intraclass correlation coefficient (ICC).ResultsDifferent reconstruction algorithms influenced most radiomic features. The percentages of first-order, texture and wavelet features without statistical difference among 2D and 3D lesions, peritumor and liver parenchyma for all six groups were 27.78% (5/18), 5.33% (4/75) and 5.56% (1/18), respectively (all p > 0.05), and they decreased while the level of reconstruction strengthened for both ASIR-V and DLIR. Compared with FBP, the features of ASIR-V30% and 70% without statistical difference decreased from 71.31% to 23.95%, and DLIR-L, DLIR-M, and DLIR-H decreased from 31.65% to 27.11% and 23.73%. Among texture features, unaffected features of peritumor were larger than those of lesions and liver parenchyma, and unaffected 3D lesions features were larger than those of 2D lesions. The consistency of 3D lesion first-order features was excellent, with intra- and inter-observer ICCs ranging from 0.891 to 0.999 and 0.880 to 0.998.ConclusionsBoth ASIR-V and DLIR algorithms with different strengths influenced the radiomic features of abdominal CT images in portal venous phase, and the influences aggravated as reconstruction strength increased.
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Affiliation(s)
- Gongbo Xue
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
- Graduate School, Dalian Medical University, Dalian, China
| | - Hongyan Liu
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
| | - Xiaoyi Cai
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
- Graduate School, Dalian Medical University, Dalian, China
| | - Zhen Zhang
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
| | - Shuai Zhang
- CT Imaging Research Center, GE Healthcare China, Shanghai, China
| | - Ling Liu
- CT Imaging Research Center, GE Healthcare China, Shanghai, China
| | - Bin Hu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
- *Correspondence: Guohua Wang, ; Bin Hu,
| | - Guohua Wang
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
- *Correspondence: Guohua Wang, ; Bin Hu,
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Nagami N, Arimura H, Nojiri J, Yunhao C, Ninomiya K, Ogata M, Oishi M, Ohira K, Kitamura S, Irie H. Dual segmentation models for poorly and well-differentiated hepatocellular carcinoma using two-step transfer deep learning on dynamic contrast-enhanced CT images. Phys Eng Sci Med 2023; 46:83-97. [PMID: 36469246 DOI: 10.1007/s13246-022-01202-7] [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: 06/12/2022] [Accepted: 11/17/2022] [Indexed: 12/12/2022]
Abstract
The aim of this study was to develop dual segmentation models for poorly and well-differentiated hepatocellular carcinoma (HCC), using two-step transfer learning (TSTL) based on dynamic contrast-enhanced (DCE) computed tomography (CT) images. From 2013 to 2019, DCE-CT images of 128 patients with 80 poorly differentiated and 48 well-differentiated HCCs were selected at our hospital. In the first transfer learning (TL) step, a pre-trained segmentation model with 192 CT images of lung cancer patients was retrained as a poorly differentiated HCC model. In the second TL step, a well-differentiated HCC model was built from a poorly differentiated HCC model. The average three-dimensional Dice's similarity coefficient (3D-DSC) and 95th-percentile of the Hausdorff distance (95% HD) were mainly employed to evaluate the segmentation accuracy, based on a nested fourfold cross-validation test. The DSC denotes the degree of regional similarity between the HCC reference regions and the regions estimated using the proposed models. The 95% HD is defined as the 95th-percentile of the maximum measures of how far two subsets of a metric space are from each other. The average 3D-DSC and 95% HD were 0.849 ± 0.078 and 1.98 ± 0.71 mm, respectively, for poorly differentiated HCC regions, and 0.811 ± 0.089 and 2.01 ± 0.84 mm, respectively, for well-differentiated HCC regions. The average 3D-DSC for both regions was 1.2 times superior to that calculated without the TSTL. The proposed model using TSTL from the lung cancer dataset showed the potential to segment poorly and well-differentiated HCC regions on DCE-CT images.
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Affiliation(s)
- Noriyuki Nagami
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-Ku, Fukuoka City, Fukuoka, 812-8582, Japan
- Department of Radiology, Saga University Hospital, 5-1-1, Nabeshima, Saga City, Saga, 849-8501, Japan
| | - Hidetaka Arimura
- Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-Ku, Fukuoka City, Fukuoka, 812-8582, Japan.
| | - Junichi Nojiri
- Medical Corporation Kouhoukai, Takagi Hospital, 141-11, Sakemi, Okawa City, Fukuoka, 831-0016, Japan
- Department of Radiology, Faculty of Medicine, Saga University, 5-1-1, Nabeshima, Saga City , Saga, 849-8501, Japan
| | - Cui Yunhao
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-Ku, Fukuoka City, Fukuoka, 812-8582, Japan
| | - Kenta Ninomiya
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-Ku, Fukuoka City, Fukuoka, 812-8582, Japan
| | - Manabu Ogata
- Department of Radiology, Saga University Hospital, 5-1-1, Nabeshima, Saga City, Saga, 849-8501, Japan
| | - Mitsutoshi Oishi
- Department of Radiology, Faculty of Medicine, Saga University, 5-1-1, Nabeshima, Saga City , Saga, 849-8501, Japan
| | - Keiichi Ohira
- Department of Radiology, Faculty of Medicine, Saga University, 5-1-1, Nabeshima, Saga City , Saga, 849-8501, Japan
| | - Shigetoshi Kitamura
- Department of Radiology, Saga University Hospital, 5-1-1, Nabeshima, Saga City, Saga, 849-8501, Japan
| | - Hiroyuki Irie
- Department of Radiology, Faculty of Medicine, Saga University, 5-1-1, Nabeshima, Saga City , Saga, 849-8501, Japan
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7
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Quality of radiomics for predicting microvascular invasion in hepatocellular carcinoma: a systematic review. Eur Radiol 2023; 33:3467-3477. [PMID: 36749371 DOI: 10.1007/s00330-023-09414-5] [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: 03/08/2022] [Revised: 11/06/2022] [Accepted: 01/01/2023] [Indexed: 02/08/2023]
Abstract
OBJECTIVES To comprehensively evaluate the reporting quality, risk of bias, and radiomics methodology quality of radiomics models for predicting microvascular invasion in hepatocellular carcinoma. METHODS A systematic search of available literature was performed in PubMed, Embase, Web of Science, Scopus, and the Cochrane Library up to January 21, 2022. Studies that developed and/or validated machine learning models based on radiomics data to predict microvascular invasion in hepatocellular carcinoma were included. These studies were reviewed by two investigators and the consensus data were used for analyzing. The reporting quality, risk of bias, and radiomics methodological quality were evaluated by Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD), Prediction model Risk of Bias Assessment Tool, and Radiomics Quality Score (RQS), respectively. RESULTS A total of 30 studies met eligibility criteria with 24 model developing studies and 6 model developing and external validation studies. The median overall TRIPOD adherence was 75.4% (range 56.7-94.3%). All studies were at high risk of bias with at least 2 of 20 sources of bias. Furthermore, 28 studies showed unclear risks of bias in up to 5 signaling questions because of the lack of specified reports. The median RQS score was 37.5% (range 25-61.1%). CONCLUSION Current radiomic models for MVI-status prediction have moderate to good reporting quality, moderate radiomics methodology quality, and high risk of bias in model development and validation. KEY POINTS • Current microvascular invasion prediction radiomics studies have moderate to good reporting quality, moderate radiomics methodology quality, and high risk of bias in model development and validation. • Data representativeness, feature robustness, events-per-variable ratio, evaluation metrics, and appropriate validation are five main aspects futures studies should focus more on to improve the quality of radiomics. • Both Radiomics Quality Score and Prediction model Risk of Bias Assessment Tool are needed to comprehensively evaluate a radiomics study.
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8
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Jiang Y, Wang K, Wang YR, Xiang YJ, Liu ZH, Feng JK, Cheng SQ. Preoperative and Prognostic Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Review Based on Artificial Intelligence. Technol Cancer Res Treat 2023; 22:15330338231212726. [PMID: 37933176 PMCID: PMC10631353 DOI: 10.1177/15330338231212726] [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: 07/26/2023] [Revised: 10/01/2023] [Accepted: 10/11/2023] [Indexed: 11/08/2023] Open
Abstract
Microvascular invasion of hepatocellular carcinoma is an important factor affecting tumor recurrence after liver resection and liver transplantation. There are many ways to classify microvascular invasion, however, an international consensus is urgently needed. Recently, artificial intelligence has emerged as an important tool for improving the clinical management of hepatocellular carcinoma. Many studies about microvascular invasion currently focus on preoperative and prognosis prediction of microvascular invasion using artificial intelligence. In this paper, we review the definition and staging of microvascular invasion, especially the diagnosis of it by using artificial intelligence. In preoperative prediction, deep learning based on multimodal data modeling of radiomics-screened features, clinical features, and medical images is currently the most effective means. In prognostic prediction, pathology is the gold standard, and the techniques used should more effectively utilize the global features of the pathology images.
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Affiliation(s)
- Yu Jiang
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Kang Wang
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Yu-Ran Wang
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Yan-Jun Xiang
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Zong-Han Liu
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Jin-Kai Feng
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Shu-Qun Cheng
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
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9
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Gao L, Xiong M, Chen X, Han Z, Yan C, Ye R, Zhou L, Li Y. Multi-Region Radiomic Analysis Based on Multi-Sequence MRI Can Preoperatively Predict Microvascular Invasion in Hepatocellular Carcinoma. Front Oncol 2022; 12:818681. [PMID: 35574328 PMCID: PMC9094629 DOI: 10.3389/fonc.2022.818681] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 03/21/2022] [Indexed: 01/27/2023] Open
Abstract
Objectives Microvascular invasion (MVI) affects the postoperative prognosis in hepatocellular carcinoma (HCC) patients; however, there remains a lack of reliable and effective tools for preoperative prediction of MVI. Radiomics has shown great potential in providing valuable information for tumor pathophysiology. We constructed and validated radiomics models with and without clinico-radiological factors to predict MVI. Methods One hundred and fifteen patients with pathologically confirmed HCC (training set: n = 80; validation set: n = 35) who underwent preoperative MRI were retrospectively recruited. Radiomics models based on multi-sequence MRI across various regions (including intratumoral and/or peritumoral areas) were built using four classification algorithms. A clinico-radiological model was constructed individually and combined with a radiomics model to generate a fusion model by multivariable logistic regression. Results Among the radiomics models, the model based on T2WI and arterial phase (T2WI-AP model) in the volume of the liver-HCC interface (VOIinterface) exhibited the best predictive power, with AUCs of 0.866 in the training group and 0.855 in the validation group. The clinico-radiological model exhibited good efficacy (AUC: 0.819 and 0.717, respectively). The fusion model showed excellent predictive ability (AUC: 0.915 and 0.868, respectively), outperforming both the clinico-radiological and the T2WI-AP models in the training and validation sets. Conclusion The fusion model of multi-region radiomics achieves an enhanced prediction of the individualized risk estimation of MVI in HCC patients. This may be a beneficial tool for clinicians to improve decision-making in personalized medicine.
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Affiliation(s)
- Lanmei Gao
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Meilian Xiong
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Xiaojie Chen
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Zewen Han
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,The School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, Fujian, China
| | - Chuan Yan
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Rongping Ye
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Lili Zhou
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Yueming Li
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Key Laboratory of Radiation Biology (Fujian Medical University), Fujian Province University, Fuzhou, Fujian, China
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10
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Li L, Wu C, Huang Y, Chen J, Ye D, Su Z. Radiomics for the Preoperative Evaluation of Microvascular Invasion in Hepatocellular Carcinoma: A Meta-Analysis. Front Oncol 2022; 12:831996. [PMID: 35463303 PMCID: PMC9021380 DOI: 10.3389/fonc.2022.831996] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 03/11/2022] [Indexed: 12/12/2022] Open
Abstract
Background Microvascular invasion (MVI) is an independent risk factor for postoperative recurrence of hepatocellular carcinoma (HCC). To perform a meta-analysis to investigate the diagnostic performance of radiomics for the preoperative evaluation of MVI in HCC and the effect of potential factors. Materials and Methods A systematic literature search was performed in PubMed, Embase, and the Cochrane Library for studies focusing on the preoperative evaluation of MVI in HCC with radiomics methods. Data extraction and quality assessment of the retrieved studies were performed. Statistical analysis included data pooling, heterogeneity testing and forest plot construction. Meta-regression and subgroup analyses were performed to reveal the effect of potential explanatory factors [design, combination of clinical factors, imaging modality, number of participants, and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) applicability risk] on the diagnostic performance. Results Twenty-two studies with 4,129 patients focusing on radiomics for the preoperative prediction of MVI in HCC were included. The pooled sensitivity, specificity and area under the receiver operating characteristic curve (AUC) were 84% (95% CI: 81, 87), 83% (95% CI: 78, 87) and 0.90 (95% CI: 0.87, 0.92). Substantial heterogeneity was observed among the studies (I²=94%, 95% CI: 88, 99). Meta-regression showed that all investigative covariates contributed to the heterogeneity in the sensitivity analysis (P < 0.05). Combined clinical factors, MRI, CT and number of participants contributed to the heterogeneity in the specificity analysis (P < 0.05). Subgroup analysis showed that the pooled sensitivity, specificity and AUC estimates were similar among studies with CT or MRI. Conclusion Radiomics is a promising noninvasive method that has high preoperative diagnostic performance for MVI status. Radiomics based on CT and MRI had a comparable predictive performance for MVI in HCC. Prospective, large-scale and multicenter studies with radiomics methods will improve the diagnostic power for MVI in the future. Systematic Review Registration https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=259363, identifier CRD42021259363.
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Affiliation(s)
- Liujun Li
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China.,Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Chaoqun Wu
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China.,Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Yongquan Huang
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China.,Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Jiaxin Chen
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China
| | - Dalin Ye
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China
| | - Zhongzhen Su
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China.,Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
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11
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Han S, Liu Y, Li X, Jiang X, Li B, Zhang C, Zhang J. Development and Validation of a Preoperative Nomogram for Predicting Benign and Malignant Gallbladder Polypoid Lesions. Front Oncol 2022; 12:800449. [PMID: 35402267 PMCID: PMC8990775 DOI: 10.3389/fonc.2022.800449] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 02/23/2022] [Indexed: 11/15/2022] Open
Abstract
Purpose The purpose of this study was to develop and validate a preoperative nomogram of differentiating benign and malignant gallbladder polypoid lesions (GPs) combining clinical and radiomics features. Methods The clinical and imaging data of 195 GPs patients which were confirmed by pathology from April 2014 to May 2021 were reviewed. All patients were randomly divided into the training and testing cohorts. Radiomics features based on 3 sequences of contrast-enhanced computed tomography were extracted by the Pyradiomics package in python, and the nomogram further combined with clinical parameters was established by multiple logistic regression. The performance of the nomogram was evaluated by discrimination and calibration. Results Among 195 GPs patients, 132 patients were pathologically benign, and 63 patients were malignant. To differentiate benign and malignant GPs, the combined model achieved an area under the curve (AUC) of 0.950 as compared to the radiomics model and clinical model with AUC of 0.929 and 0.925 in the training cohort, respectively. Further validation showed that the combined model contributes to better sensitivity and specificity in the training and testing cohorts by the same cutoff value, although the clinical model had an AUC of 0.943, which was higher than 0.942 of the combined model in the testing cohort. Conclusion This study develops a nomogram based on the clinical and radiomics features for the highly effective differentiation and prediction of benign and malignant GPs before surgery.
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Affiliation(s)
- Shuai Han
- Department of Hepatobiliary Surgery, The First Hospital of China Medical University, Shenyang, China
| | - Yu Liu
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Xiaohang Li
- Department of Hepatobiliary Surgery, The First Hospital of China Medical University, Shenyang, China
| | - Xiao Jiang
- Department of Endocrinology and Metabolism, The Second Hospital of Dalian Medical University, Dalian, China
| | - Baifeng Li
- Department of Hepatobiliary Surgery, The First Hospital of China Medical University, Shenyang, China
| | - Chengshuo Zhang
- Department of Hepatobiliary Surgery, The First Hospital of China Medical University, Shenyang, China
| | - Jialin Zhang
- Department of Hepatobiliary Surgery, The First Hospital of China Medical University, Shenyang, China
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12
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Yao W, Yang S, Ge Y, Fan W, Xiang L, Wan Y, Gu K, Zhao Y, Zha R, Bu J. Computed Tomography Radiomics-Based Prediction of Microvascular Invasion in Hepatocellular Carcinoma. Front Med (Lausanne) 2022; 9:819670. [PMID: 35402463 PMCID: PMC8987588 DOI: 10.3389/fmed.2022.819670] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 02/18/2022] [Indexed: 12/12/2022] Open
Abstract
Background Due to the high recurrence rate in hepatocellular carcinoma (HCC) after resection, preoperative prognostic prediction of HCC is important for appropriate patient management. Exploring and developing preoperative diagnostic methods has great clinical value in treating patients with HCC. This study sought to develop and evaluate a novel combined clinical predictive model based on standard triphasic computed tomography (CT) to discriminate microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Methods The preoperative findings of 82 patients with HCC, including conventional clinical factors, CT imaging findings, and CT texture analysis (TA), were analyzed retrospectively. All included cases were divided into MVI-negative (n = 33; no MVI) and MVI-positive (n = 49; low or high risk of MVI) groups. TA parameters were extracted from non-enhanced, arterial, portal venous, and equilibrium phase images and subsequently calculated using the Artificial Intelligence Kit. After statistical analyses, a clinical model comprising conventional clinical and CT image risk factors, radiomics signature models, and a novel combined model (fused radiomic signature) was constructed. The area under the curve (AUC) of the receiver operating characteristics (ROC) curve was used to assess the performance of the various models in discriminating MVI. Results We found that tumor diameter and pathological grade were effective clinical predictors in clinical model and 12 radiomics features were effective for MVI prediction of each CT phase. The AUCs of the clinical, plain, artery, venous, and delay models were 0.77 (95% CI: 0.67–0.88), 0.75 (95% CI: 0.64–0.87), 0.79 (95% CI: 0.69–0.89), 0.73 (95% CI: 0.61–0.85), and 0.80 (95% CI: 0.70–0.91), respectively. The novel combined model exhibited the best performance, with an AUC of 0.83 (95% CI: 0.74–0.93). Conclusions Models derived from triphasic CT can preoperatively predict MVI in patients with HCC. Of the models tested here, the novel combined model was most predictive and could become a useful tool to guide subsequent personalized treatment of HCC.
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Affiliation(s)
- Wenjun Yao
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Shuo Yang
- Department of Radiology, Anhui Mental Health Center, Hefei, China
| | | | - Wenlong Fan
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Li Xiang
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yang Wan
- Department of Hematological Lab, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Kangchen Gu
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yan Zhao
- School of Biomedical Engineering, Anhui Medical University, Hefei, China
| | - Rujing Zha
- Department of Radiology, Division of Life Science and Medicine, The First Affiliated Hospital of USTC, School of Life Science, University of Science and Technology of China, Hefei, China
| | - Junjie Bu
- School of Biomedical Engineering, Anhui Medical University, Hefei, China.,The Fourth Affiliated Hospital of Anhui Medical University, Hefei, China
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13
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Radiomics Models for Predicting Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Radiomics Quality Score Assessment. Cancers (Basel) 2021. [DOI: 10.3390/cancers13225864
expr 925508420 + 988274397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023] Open
Abstract
Preoperative prediction of microvascular invasion (MVI) is of importance in hepatocellular carcinoma (HCC) patient treatment management. Plenty of radiomics models for MVI prediction have been proposed. This study aimed to elucidate the role of radiomics models in the prediction of MVI and to evaluate their methodological quality. The methodological quality was assessed by the Radiomics Quality Score (RQS), and the risk of bias was evaluated by the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Twenty-two studies using CT, MRI, or PET/CT for MVI prediction were included. All were retrospective studies, and only two had an external validation cohort. The AUC values of the prediction models ranged from 0.69 to 0.94 in the test cohort. Substantial methodological heterogeneity existed, and the methodological quality was low, with an average RQS score of 10 (28% of the total). Most studies demonstrated a low or unclear risk of bias in the domains of QUADAS-2. In conclusion, a radiomics model could be an accurate and effective tool for MVI prediction in HCC patients, although the methodological quality has so far been insufficient. Future prospective studies with an external validation cohort in accordance with a standardized radiomics workflow are expected to supply a reliable model that translates into clinical utilization.
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14
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Wang Q, Li C, Zhang J, Hu X, Fan Y, Ma K, Sparrelid E, Brismar TB. Radiomics Models for Predicting Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Radiomics Quality Score Assessment. Cancers (Basel) 2021; 13:cancers13225864. [PMID: 34831018 PMCID: PMC8616379 DOI: 10.3390/cancers13225864] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/13/2021] [Accepted: 11/17/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Microvascular invasion (MVI) is regarded as a sign of early metastasis in liver cancer and can be only diagnosed by a histopathology exam in the resected specimen. Preoperative prediction of MVI status may exert an effect on patient treatment management, for instance, to expand the resection margin. Radiomics can identify delicate imaging features from routinely used radiological images that are invisible to the naked eye and has been increasingly adopted to predict MVI. We reviewed the available radiomics models to evaluate their role in the prediction of MVI. The discriminative capacity of the models ranged from 0.69 to 0.94. Even though the studies were preliminary and the methodologic quality was suboptimal, radiomics models hold promise for the accurate and non-invasive prediction of MVI. In accordance with a standardized radiomics workflow, future prospective studies with external validation are expected to provide a reliable and robust prediction tool for clinical implementation. Abstract Preoperative prediction of microvascular invasion (MVI) is of importance in hepatocellular carcinoma (HCC) patient treatment management. Plenty of radiomics models for MVI prediction have been proposed. This study aimed to elucidate the role of radiomics models in the prediction of MVI and to evaluate their methodological quality. The methodological quality was assessed by the Radiomics Quality Score (RQS), and the risk of bias was evaluated by the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Twenty-two studies using CT, MRI, or PET/CT for MVI prediction were included. All were retrospective studies, and only two had an external validation cohort. The AUC values of the prediction models ranged from 0.69 to 0.94 in the test cohort. Substantial methodological heterogeneity existed, and the methodological quality was low, with an average RQS score of 10 (28% of the total). Most studies demonstrated a low or unclear risk of bias in the domains of QUADAS-2. In conclusion, a radiomics model could be an accurate and effective tool for MVI prediction in HCC patients, although the methodological quality has so far been insufficient. Future prospective studies with an external validation cohort in accordance with a standardized radiomics workflow are expected to supply a reliable model that translates into clinical utilization.
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Affiliation(s)
- Qiang Wang
- Division of Medical Imaging and Technology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, 14186 Stockholm, Sweden;
- Division of Radiology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Karolinska University Hospital, 14186 Stockholm, Sweden
- Correspondence: ; Tel.: +46-72-876-8983
| | - Changfeng Li
- Institute of Hepatobiliary Surgery, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing 400038, China; (C.L.); (K.M.)
| | - Jiaxing Zhang
- Department of Pharmacy, Guizhou Provincial People’s Hospital, Guiyang 550002, China;
| | - Xiaojun Hu
- Hepatobiliary Surgery, The Fifth Affiliated Hospital, Southern Medical University, Guangzhou 510999, China;
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China;
| | - Yingfang Fan
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China;
- Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China
| | - Kuansheng Ma
- Institute of Hepatobiliary Surgery, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing 400038, China; (C.L.); (K.M.)
| | - Ernesto Sparrelid
- Division of Surgery, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Karolinska University Hospital, 14186 Stockholm, Sweden;
| | - Torkel B. Brismar
- Division of Medical Imaging and Technology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, 14186 Stockholm, Sweden;
- Division of Radiology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Karolinska University Hospital, 14186 Stockholm, Sweden
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15
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Wu J, Liang F, Wei R, Lai S, Lv X, Luo S, Wu Z, Chen H, Zhang W, Zeng X, Ye X, Wu Y, Wei X, Jiang X, Zhen X, Yang R. A Multiparametric MR-Based RadioFusionOmics Model with Robust Capabilities of Differentiating Glioblastoma Multiforme from Solitary Brain Metastasis. Cancers (Basel) 2021; 13:cancers13225793. [PMID: 34830943 PMCID: PMC8616314 DOI: 10.3390/cancers13225793] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 11/13/2021] [Accepted: 11/13/2021] [Indexed: 11/24/2022] Open
Abstract
Simple Summary Glioblastoma multiforme (GBM) and solitary brain metastasis (SBM) are common brain tumors in adults. The two tumors often pose a diagnostic dilemma owing to their similar features on conventional magnetic resonance imaging (MRI). Ability to discriminate the two tumors is critical as it informs clinical treatment strategies. This pilot study attempts to employ the machine learning technique to identify GBM and SBM by fusing radiomics features of multiple MRI sequences and multiple models. A multiparametric MR-based RadioFusionOmics (RFO) model was developed and has demonstrated promising prediction accuracy for the identifications of GBM and SBM. Abstract This study aimed to evaluate the diagnostic potential of a novel RFO model in differentiating GBM and SBM with multiparametric MR sequences collected from 244 (131 GBM and 113 SBM) patients. Three basic volume of interests (VOIs) were delineated on the conventional axial MR images (T1WI, T2WI, T2_FLAIR, and CE_T1WI), including volumetric non-enhanced tumor (nET), enhanced tumor (ET), and peritumoral edema (pTE). Using the RFO model, radiomics features extracted from different multiparametric MRI sequence(s) and VOI(s) were fused and the best sequence and VOI, or possible combinations, were determined. A multi-disciplinary team (MDT)-like fusion was performed to integrate predictions from the high-performing models for the final discrimination of GBM vs. SBM. Image features extracted from the volumetric ET (VOIET) had dominant predictive performances over features from other VOI combinations. Fusion of VOIET features from the T1WI and T2_FLAIR sequences via the RFO model achieved a discrimination accuracy of AUC = 0.925, accuracy = 0.855, sensitivity = 0.856, and specificity = 0.853, on the independent testing cohort 1, and AUC = 0.859, accuracy = 0.836, sensitivity = 0.708, and specificity = 0.919 on the independent testing cohort 2, which significantly outperformed three experienced radiologists (p = 0.03, 0.01, 0.02, and 0.01, and p = 0.02, 0.01, 0.45, and 0.02, respectively) and the MDT-decision result of three experienced experts (p = 0.03, 0.02, 0.03, and 0.02, and p = 0.03, 0.02, 0.44, and 0.03, respectively).
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Affiliation(s)
- Jialiang Wu
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
- Department of Radiology, The University of Hong Kong Shenzhen Hospital, Shenzhen 518000, China
| | - Fangrong Liang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China;
| | - Ruili Wei
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
| | - Shengsheng Lai
- School of Medical Equipment, Guangdong Food and Drug Vocational College, Guangzhou 510520, China;
| | - Xiaofei Lv
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China;
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Shiwei Luo
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
| | - Zhe Wu
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
| | - Huixian Chen
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
| | - Wanli Zhang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
| | - Xiangling Zeng
- Department of Radiology, Huizhou Municipal Central Hospital, Huizhou 516001, China;
| | - Xianghua Ye
- Department of Radiation Oncology, 1st Affiliated Hospital, Zhejiang University, Hangzhou 310009, China;
| | - Yong Wu
- Department of Oncology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China;
| | - Xinhua Wei
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
| | - Xinqing Jiang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
| | - Xin Zhen
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China;
- Correspondence: (X.Z.); (R.Y.); Tel.: +86-20-62789323 (X.Z.); +86-20-81048873 (R.Y.)
| | - Ruimeng Yang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
- Correspondence: (X.Z.); (R.Y.); Tel.: +86-20-62789323 (X.Z.); +86-20-81048873 (R.Y.)
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