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Wang QF, Li ZW, Zhou HF, Zhu KZ, Wang YJ, Wang YQ, Zhang YW. Predicting the prognosis of hepatic arterial infusion chemotherapy in hepatocellular carcinoma. World J Gastrointest Oncol 2024; 16:2380-2393. [DOI: 10.4251/wjgo.v16.i6.2380] [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: 12/18/2023] [Revised: 01/19/2024] [Accepted: 04/03/2024] [Indexed: 06/14/2024] Open
Abstract
Hepatic artery infusion chemotherapy (HAIC) has good clinical efficacy in the treatment of advanced hepatocellular carcinoma (HCC); however, its efficacy varies. This review summarized the ability of various markers to predict the efficacy of HAIC and provided a reference for clinical applications. As of October 25, 2023, 51 articles have been retrieved based on keyword predictions and HAIC. Sixteen eligible articles were selected for inclusion in this study. Comprehensive literature analysis found that methods used to predict the efficacy of HAIC include serological testing, gene testing, and imaging testing. The above indicators and their combined forms showed excellent predictive effects in retrospective studies. This review summarized the strategies currently used to predict the efficacy of HAIC in middle and advanced HCC, analyzed each marker's ability to predict HAIC efficacy, and provided a reference for the clinical application of the prediction system.
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Affiliation(s)
- Qi-Feng Wang
- Department of Hepatobiliary Pancreatic Center, Beijing Tsinghua Changgung Hospital Affiliated to Tsinghua University, Beijing 102218, China
- Department of Hepatobiliary and Pancreatic Surgery, Qinghai University Affiliated Hospital, Xining 810000, Qinghai Province, China
| | - Zong-Wei Li
- Department of Hepatobiliary and Pancreatic Surgery, Qinghai University Affiliated Hospital, Xining 810000, Qinghai Province, China
| | - Hai-Feng Zhou
- Department of Hepatobiliary and Pancreatic Surgery, Qinghai University Affiliated Hospital, Xining 810000, Qinghai Province, China
| | - Kun-Zhong Zhu
- Department of Hepatobiliary and Pancreatic Surgery, Qinghai University Affiliated Hospital, Xining 810000, Qinghai Province, China
| | - Ya-Jing Wang
- Department of Hepatobiliary Pancreatic Center, Beijing Tsinghua Changgung Hospital Affiliated to Tsinghua University, Beijing 102218, China
| | - Ya-Qin Wang
- Department of Hepatobiliary Pancreatic Center, Beijing Tsinghua Changgung Hospital Affiliated to Tsinghua University, Beijing 102218, China
| | - Yue-Wei Zhang
- Department of Hepatobiliary Pancreatic Center, Beijing Tsinghua Changgung Hospital Affiliated to Tsinghua University, Beijing 102218, China
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Wang QF, Li ZW, Zhou HF, Zhu KZ, Wang YJ, Wang YQ, Zhang YW. Predicting the prognosis of hepatic arterial infusion chemotherapy in hepatocellular carcinoma. World J Gastrointest Oncol 2024; 16:2368-2381. [DOI: 10.4251/wjgo.v16.i6.2368] [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: 12/18/2023] [Revised: 01/19/2024] [Accepted: 04/03/2024] [Indexed: 06/13/2024] Open
Abstract
Hepatic artery infusion chemotherapy (HAIC) has good clinical efficacy in the treatment of advanced hepatocellular carcinoma (HCC); however, its efficacy varies. This review summarized the ability of various markers to predict the efficacy of HAIC and provided a reference for clinical applications. As of October 25, 2023, 51 articles have been retrieved based on keyword predictions and HAIC. Sixteen eligible articles were selected for inclusion in this study. Comprehensive literature analysis found that methods used to predict the efficacy of HAIC include serological testing, gene testing, and imaging testing. The above indicators and their combined forms showed excellent predictive effects in retrospective studies. This review summarized the strategies currently used to predict the efficacy of HAIC in middle and advanced HCC, analyzed each marker's ability to predict HAIC efficacy, and provided a reference for the clinical application of the prediction system.
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Affiliation(s)
- Qi-Feng Wang
- Department of Hepatobiliary Pancreatic Center, Beijing Tsinghua Changgung Hospital Affiliated to Tsinghua University, Beijing 102218, China
- Department of Hepatobiliary and Pancreatic Surgery, Qinghai University Affiliated Hospital, Xining 810000, Qinghai Province, China
| | - Zong-Wei Li
- Department of Hepatobiliary and Pancreatic Surgery, Qinghai University Affiliated Hospital, Xining 810000, Qinghai Province, China
| | - Hai-Feng Zhou
- Department of Hepatobiliary and Pancreatic Surgery, Qinghai University Affiliated Hospital, Xining 810000, Qinghai Province, China
| | - Kun-Zhong Zhu
- Department of Hepatobiliary and Pancreatic Surgery, Qinghai University Affiliated Hospital, Xining 810000, Qinghai Province, China
| | - Ya-Jing Wang
- Department of Hepatobiliary Pancreatic Center, Beijing Tsinghua Changgung Hospital Affiliated to Tsinghua University, Beijing 102218, China
| | - Ya-Qin Wang
- Department of Hepatobiliary Pancreatic Center, Beijing Tsinghua Changgung Hospital Affiliated to Tsinghua University, Beijing 102218, China
| | - Yue-Wei Zhang
- Department of Hepatobiliary Pancreatic Center, Beijing Tsinghua Changgung Hospital Affiliated to Tsinghua University, Beijing 102218, China
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Liu BJ, Guan YY, Qiao LX, Zhang JM, Li AJ, Yang PX, Gao YX, Chen DX, Wang CX, Wu J. The mechanism and experimental verification of Ixeris sonchifolia promoting apoptosis of hepatocellular carcinoma based on network pharmacology: Ixeris sonchifolia Induces Hepatocellular Carcinoma Apoptosis via the PI3K/AKT Pathway. JOURNAL OF ETHNOPHARMACOLOGY 2024; 327:117994. [PMID: 38437889 DOI: 10.1016/j.jep.2024.117994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 02/22/2024] [Accepted: 02/26/2024] [Indexed: 03/06/2024]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Ixeris sonchifolia alias Kudiezi, it was named Ixeris sonchifolia (Bunge) Hance, a synonym for Crepidiastrum sonchifolium (Bunge) Pak & Kawano in the https://www.iplant.cn/. And it was first published in J. Linn. Soc., Bot. 13: 108 (1873), which was named Ixeris sonchifolia (Maxim.) Hance in the MPNS (http://mpns.kew.org). As a widely distributed medicinal and edible wild plant, it possesses unique bitter-cold characteristics and constituents with various pharmacological activities. Its main antitumor substances, same as artemisinin and paclitaxel, are classified as terpenoids and have become research foci in recent years. However, its specific biological activity and role in antitumor treatment remain largely unclear. AIM OF THE STUDY This study aimed to elucidate the molecular targets and potential mechanisms of hepatocellular carcinoma apoptosis induced by Ixeris sonchifolia. MATERIALS AND METHODS We used network pharmacology methods to analyze and screen the active ingredients and possible underlying mechanisms of Ixeris sonchifolia in treating liver cancer and employed integrative time- and dose-dependent toxicity, transcriptomics, and molecular biology approaches to comprehensively verify the function of Ixeris sonchifolia extract (IsE) in human hepatoblastoma cell (HepG2) apoptosis and its potential mechanism. RESULTS A total of 169 common targets were screened by network pharmacology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that IsE inhibited HepG2 cell activity in a time- and dose-dependent manner. Western blot analysis confirmed that IsE promoted HepG2 cell apoptosis by inhibiting the PI3K/AKT signaling pathway and that the PI3K/AKT inhibitor LY294002 also substantially enhanced IsE-induced apoptosis. The PI3K/AKT signaling pathway exhibited significant differences compared to that in the control group. CONCLUSION Combining network pharmacology with experimental verification, IsE inhibited mitochondrial function and the PI3K/AKT pathway while inducing hepatoma cell apoptosis. IsE may have promising potential for liver cancer treatment and chemoprevention.
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Affiliation(s)
- Bao-Jun Liu
- Department of Head, Neck and Genitourinary Oncology, Harbin Medical University Cancer Hospital, Heilongjiang, 150081, China; Department of Oncology, The Second Affiliated Hospital of Shandong First Medical University, Shandong, 271000, China
| | - Yuan-Yue Guan
- Department of Beijing Institute of Hepatology, Beijing You an Hospital, Capital Medical University, Beijing, 100069, China
| | - Lu-Xin Qiao
- Department of Beijing Institute of Hepatology, Beijing You an Hospital, Capital Medical University, Beijing, 100069, China
| | - Ji-Mei Zhang
- School of Pharmacy, Shandong First Medical University, Shandong, 271000, China
| | - Ai-Ju Li
- Department of Oncology, The Second Affiliated Hospital of Shandong First Medical University, Shandong, 271000, China
| | - Peng-Xiang Yang
- Department of Beijing Institute of Hepatology, Beijing You an Hospital, Capital Medical University, Beijing, 100069, China
| | - Yu-Xue Gao
- Department of Beijing Institute of Hepatology, Beijing You an Hospital, Capital Medical University, Beijing, 100069, China
| | - De-Xi Chen
- Department of Beijing Institute of Hepatology, Beijing You an Hospital, Capital Medical University, Beijing, 100069, China.
| | - Chun-Xiao Wang
- Department of Pharmacy, The Second Affiliated Hospital of Shandong First Medical University, Shandong, 271000, China.
| | - Jin Wu
- Department of Head, Neck and Genitourinary Oncology, Harbin Medical University Cancer Hospital, Heilongjiang, 150081, China.
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Wang X, Zhao L, Wang S, Zhao X, Chen L, Sun X, Liu Y, Liu J, Sun S. Utility of contrast-enhanced MRI radiomics features combined with clinical indicators for predicting induction chemotherapy response in primary central nervous system lymphoma. J Neurooncol 2024; 166:451-460. [PMID: 38308802 DOI: 10.1007/s11060-023-04554-6] [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: 11/27/2023] [Accepted: 12/23/2023] [Indexed: 02/05/2024]
Abstract
PURPOSE To assess the utility of combining contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics features with clinical variables in predicting the response to induction chemotherapy (IC) for primary central nervous system lymphoma (PCNSL). METHODS A total of 131 patients with PCNSL (101 in the training set and 30 in the testing set) who had undergone contrast-enhanced MRI scans were retrospectively analyzed. Pyradiomics was utilized to extract radiomics features, and the clinical variables of the patients were gathered. Radiomics prediction models were developed using different combinations of feature selection methods and machine learning models, and the best combination was ultimately chosen. We screened clinical variables associated with treatment outcomes and developed clinical prediction models. The predictive performance of radiomics model, clinical model, and combined model, which integrates the best radiomics model and clinical characteristics, was independently assessed and compared using Receiver Operating Characteristic (ROC) curves. RESULTS In total, we extracted 1598 features. The best radiomics model we selected as the best utilized T-test and Recursive Feature Elimination (RFE) for feature selection and logistic regression for model building. Serum Interleukin 2 Receptor (IL-2R) and Eastern Cooperative Oncology Group (ECOG) Score were utilized to develop a clinical predictive model for assessing the response to induction chemotherapy. The results of the testing set revealed that the combined prediction model (radiomics and IL-2R) achieved the highest area under the ROC curve at 0.868 (0.683, 0.967), followed by the radiomics model at 0.857 (0.681, 0.957), and the clinical prediction model (IL-2R and ECOG) at 0.618 (0.413, 0.797). The combined model was significantly more accurate than the clinical model, with an AUC of 0.868 compared to 0.618 (P < 0.05). While the radiomics model had slightly better predictive power than the clinical model, this difference was not statistically significant (AUC, 0.857 vs. 0.618, P > 0.05). CONCLUSIONS Our prediction model, which combines radiomics signatures from CE-MRI with serum IL-2R, can effectively stratify patients with PCNSL before high-dose methotrexate (HD-MTX) -based chemotherapy.
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Affiliation(s)
- Xiaochen Wang
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Neuroradiology, Beijing Neurosurgical Institute, Beijing, China
| | - Litao Zhao
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of China, Beijing, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Sihui Wang
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xuening Zhao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Lingxu Chen
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xuefei Sun
- Department of Hematology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yuanbo Liu
- Department of Hematology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jiangang Liu
- School of Engineering Medicine, Beihang University, Beijing, China.
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of China, Beijing, China.
- Beijing Engineering Research Center of Cardiovascular Wisdom Diagnosis and Treatment, Beijing, China.
| | - Shengjun Sun
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
- Department of Neuroradiology, Beijing Neurosurgical Institute, Beijing, China.
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Zhou J, Zhao M, Yang Z, Chen L, Liu X. Exploring the Value of MRI Measurement of Hippocampal Volume for Predicting the Occurrence and Progression of Alzheimer's Disease Based on Artificial Intelligence Deep Learning Technology and Evidence-Based Medicine Meta-Analysis. J Alzheimers Dis 2024; 97:1275-1288. [PMID: 38277290 DOI: 10.3233/jad-230733] [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] [Indexed: 01/28/2024]
Abstract
BACKGROUND Alzheimer's disease (AD), a major dementia cause, lacks effective treatment. MRI-based hippocampal volume measurement using artificial intelligence offers new insights into early diagnosis and intervention in AD progression. OBJECTIVE This study, involving 483 AD patients, 756 patients with mild cognitive impairment (MCI), and 968 normal controls (NC), investigated the predictive capability of MRI-based hippocampus volume measurements for AD risk using artificial intelligence and evidence-based medicine. METHODS Utilizing data from ADNI and OASIS-brains databases, three convolutional neural networks (InceptionResNetv2, Densenet169, and SEResNet50) were employed for automated AD classification based on structural MRI imaging. A multitask deep learning model and a densely connected 3D convolutional network were utilized. Additionally, a systematic meta-analysis explored the value of MRI-based hippocampal volume measurement in predicting AD occurrence and progression, drawing on 23 eligible articles from PubMed and Embase databases. RESULTS InceptionResNetv2 outperformed other networks, achieving 99.75% accuracy and 100% AUC for AD-NC classification and 99.16% accuracy and 100% AUC for MCI-NC classification. Notably, at a 512×512 size, InceptionResNetv2 demonstrated a classification accuracy of 94.29% and an AUC of 98% for AD-NC and 97.31% accuracy and 98% AUC for MCI-NC. CONCLUSIONS The study concludes that MRI-based hippocampal volume changes effectively predict AD onset and progression, facilitating early intervention and prevention.
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Affiliation(s)
- Jianguo Zhou
- Department of Radiology, Lianyungang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Lianyungang, China
| | - Mingli Zhao
- Department of Radiology, The Fourth People's Hospital of Lianyungang Affiliated to Nanjing Medical University Kangda, Lianyungang, China
| | - Zhou Yang
- Department of Rehabilitation, Lianyungang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Lianyungang, China
| | - Liping Chen
- Department of Rehabilitation, Lianyungang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Lianyungang, China
| | - Xiaoli Liu
- Department of Rehabilitation, Lianyungang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Lianyungang, China
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Cao X, Yang H, Luo X, Zou L, Zhang Q, Li Q, Zhang J, Li X, Shi Y, Jin C. A Cox Nomogram for Assessing Recurrence Free Survival in Hepatocellular Carcinoma Following Surgical Resection Using Dynamic Contrast-Enhanced MRI Radiomics. J Magn Reson Imaging 2023; 58:1930-1941. [PMID: 37177868 DOI: 10.1002/jmri.28725] [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: 12/12/2022] [Revised: 03/28/2023] [Accepted: 03/28/2023] [Indexed: 05/15/2023] Open
Abstract
BACKGROUND The prognosis of hepatocellular carcinoma (HCC) is difficult to predict and carries high mortality. This study utilized radiomic techniques with clinical examinations to assess recurrence in HCC. PURPOSE To develop a Cox nomogram to assess the risk of postoperative recurrence in HCC using radiomic features of three volumes of interest (VOIs) in preoperative dynamic contrast-enhanced MRI (DCE-MRI), along with clinical findings. STUDY TYPE Retrospective. SUBJECTS 249 patients with pathologically proven HCCs undergoing surgical resection at three institutions were selected. FIELD STRENGTH/SEQUENCE Fat saturated T2-weighted, Fat saturated T1-weighted, and DCE-MRI performed at 1.5 T and 3.0 T. ASSESSMENT Three VOIs were generated; the tumor VOI corresponds to the area from the tumor core to the outer perimeter of the tumor, the tumor +10 mm VOI represents the area from the tumor perimeter to 10 mm distal to the tumor in all directions, finally, the background liver parenchyma VOI represents the hepatic tissue outside the tumor. Three models were generated. The total radiomic model combined information from the three listed VOI's above. The clinical-radiological model combines physical examination findings with imaging characteristics such as tumor size, margin features, and metastasis. The combined radiomic model includes features from both models listed above and showed the highest reliability for assessing 24-month survival for HCC. STATISTICAL TESTS The least absolute shrinkage and selection operator (LASSO) Cox regression, univariable, and multivariable Cox regression, Kmeans clustering, and Kaplan-Meier analysis. The discrimination performance of each model was quantified by the C-index. A P value <0.05 was considered statistically significant. RESULTS The combined radiomic model, which included features from the radiomic VOI's and clinical imaging provided the highest performance (C-index: training cohort = 0.893, test cohort = 0.851, external cohort = 0.797) in assessing the survival of HCC. CONCLUSION The combined radiomic model provides superior ability to discern the possibility of recurrence-free survival in HCC over the total radiomic and the clinical-radiological models. EVIDENCE LEVEL 4. TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Xinshan Cao
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Department of Radiology, Affiliated Hospital of Binzhou Medical College, Binzhou, China
| | - Haoran Yang
- Department of Radiology, Affiliated Hospital of Binzhou Medical College, Binzhou, China
| | - Xin Luo
- Department of Radiology, Zibo Central Hospital, Zibo, China
| | - Linxuan Zou
- Department of Radiology, Affiliated Hospital of Binzhou Medical College, Binzhou, China
| | - Qiang Zhang
- Department of Radiology, Affiliated Hospital of Binzhou Medical College, Binzhou, China
| | - Qilin Li
- Department of Radiology, Zibo Central Hospital, Zibo, China
| | - Juntao Zhang
- GE Healthcare Precision Health Institution, Shanghai, China
| | - Xiangfeng Li
- Department of Radiology, The Fourth People Hospital of Zibo, Zibo, China
| | - Yan Shi
- Department of Medical Ultrasonics, Affiliated Hospital of Binzhou Medical College, Binzhou, China
| | - Chenwang Jin
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
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Liu HF, Lu Y, Wang Q, Lu YJ, Xing W. Machine Learning-Based CEMRI Radiomics Integrating LI-RADS Features Achieves Optimal Evaluation of Hepatocellular Carcinoma Differentiation. J Hepatocell Carcinoma 2023; 10:2103-2115. [PMID: 38050577 PMCID: PMC10693828 DOI: 10.2147/jhc.s434895] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 11/22/2023] [Indexed: 12/06/2023] Open
Abstract
Purpose To develop and compare various machine learning (ML) classifiers that employ radiomics extracted from contrast-enhanced magnetic resonance imaging (CEMRI) for diagnosing pathological differentiation of hepatocellular carcinoma (HCC), and validate the performance of the best model. Methods A total of 251 patients with HCCs (n = 262) were assigned to a training (n = 200) cohort and a validation (n = 62) cohort. A collection of 5502 radiomics signatures were extracted from the CEMRI images for each HCC nodule. To reduce redundancy and dimensionality, Spearman rank correlation, minimum redundancy maximum relevance (mRMR), and the least absolute shrinkage and selection operator (LASSO) approach were employed. Eight ML classifiers were trained to obtain the best radiomics model. The performance of each model was evaluated based on the area under the receiver operating characteristic curve (AUC). The radiomics model was integrated with liver imaging reporting and data system (LI-RADS) features to design a combined model. Results The eXtreme Gradient Boosting (XGBoost)-based radiomics model outperformed other ML classifiers in evaluating pHCC, achieving an AUC of 1.00 and accuracy of 1.00 in the training cohort. The LI-RADS model demonstrated an AUC value of 0.77 and 0.82 in the training and validation cohorts. The combined model exhibited best performance in both the training and validation cohorts, with AUCs of 1.00 and 0.86 for evaluating HCC differentiation, respectively. Conclusion CEMRI radiomics integrating LI-RADS features demonstrated excellent performance in evaluating HCC differentiation, suggesting an optimal clinical decision tool for individualized diagnosis of HCC differentiation.
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Affiliation(s)
- Hai-Feng Liu
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213000, People’s Republic of China
| | - Yang Lu
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213000, People’s Republic of China
| | - Qing Wang
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213000, People’s Republic of China
| | - Yu-Jie Lu
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213000, People’s Republic of China
| | - Wei Xing
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213000, People’s Republic of China
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Liu N, Wu Y, Tao Y, Zheng J, Huang X, Yang L, Zhang X. Differentiation of Hepatocellular Carcinoma from Intrahepatic Cholangiocarcinoma through MRI Radiomics. Cancers (Basel) 2023; 15:5373. [PMID: 38001633 PMCID: PMC10670473 DOI: 10.3390/cancers15225373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 10/25/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023] Open
Abstract
The purpose of this study was to investigate the efficacy of magnetic resonance imaging (MRI) radiomics in differentiating hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (ICC). The clinical and MRI data of 129 pathologically confirmed HCC patients and 48 ICC patients treated at the Affiliated Hospital of North Sichuan Medical College between April 2016 and December 2021 were retrospectively analyzed. The patients were randomly divided at a ratio of 7:3 into a training group of 124 patients (90 with HCC and 34 with ICC) and a validation group of 53 patients (39 with HCC and 14 with ICC). Radiomic features were extracted from axial fat suppression T2-weighted imaging (FS-T2WI) and axial arterial-phase (AP) and portal-venous-phase (PVP) dynamic-contrast-enhanced MRI (DCE-MRI) sequences, and the corresponding datasets were generated. The least absolute shrinkage and selection operator (LASSO) method was used to select the best radiomic features. Logistic regression was used to establish radiomic models for each sequence (FS-T2WI, AP and PVP models), a clinical model for optimal clinical variables (C model) and a joint radiomics model (JR model) integrating the radiomics features of all the sequences as well as a radiomics-clinical model combining optimal radiomic features and clinical risk factors (RC model). The performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC). The AUCs of the FS-T2WI, AP, PVP, JR, C and RC models for distinguishing HCC from ICC were 0.693, 0.863, 0.818, 0.914, 0.936 and 0.977 in the training group and 0.690, 0.784, 0.727, 0.802, 0.860 and 0.877 in the validation group, respectively. The results of this study suggest that MRI-based radiomics may help noninvasively differentiate HCC from ICC. The model integrating the radiomics features and clinical risk factors showed a further improvement in performance.
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Affiliation(s)
- Ning Liu
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
- Hospital of Chengdu Office of People’s Government of Tibetan Autonomous Region (Hospital. C.T.), Chengdu 610041, China
| | - Yaokun Wu
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
| | - Yunyun Tao
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
| | - Jing Zheng
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
| | - Xiaohua Huang
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
| | - Lin Yang
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
| | - Xiaoming Zhang
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
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Greten TF, Villanueva A, Korangy F, Ruf B, Yarchoan M, Ma L, Ruppin E, Wang XW. Biomarkers for immunotherapy of hepatocellular carcinoma. Nat Rev Clin Oncol 2023; 20:780-798. [PMID: 37726418 DOI: 10.1038/s41571-023-00816-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/29/2023] [Indexed: 09/21/2023]
Abstract
Immune-checkpoint inhibitors (ICIs) are now widely used for the treatment of patients with advanced-stage hepatocellular carcinoma (HCC). Two different ICI-containing regimens, atezolizumab plus bevacizumab and tremelimumab plus durvalumab, are now approved standard-of-care first-line therapies in this setting. However, and despite substantial improvements in survival outcomes relative to sorafenib, most patients with advanced-stage HCC do not derive durable benefit from these regimens. Advances in genome sequencing including the use of single-cell RNA sequencing (both of tumour material and blood samples), as well as immune cell identification strategies and other techniques such as radiomics and analysis of the microbiota, have created considerable potential for the identification of novel predictive biomarkers enabling the accurate selection of patients who are most likely to derive benefit from ICIs. In this Review, we summarize data on the immunology of HCC and the outcomes in patients receiving ICIs for the treatment of this disease. We then provide an overview of current biomarker use and developments in the past 5 years, including gene signatures, circulating tumour cells, high-dimensional flow cytometry, single-cell RNA sequencing as well as approaches involving the microbiome, radiomics and clinical markers. Novel concepts for further biomarker development in HCC are then discussed including biomarker-driven trials, spatial transcriptomics and integrated 'big data' analysis approaches. These concepts all have the potential to better identify patients who are most likely to benefit from ICIs and to promote the development of new treatment approaches.
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Affiliation(s)
- Tim F Greten
- Gastrointestinal Malignancies Section, Thoracic and Gastrointestinal Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
- Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
| | - Augusto Villanueva
- Divisions of Liver Disease and Hematology/Medical Oncology, Tisch Cancer Institute, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Firouzeh Korangy
- Gastrointestinal Malignancies Section, Thoracic and Gastrointestinal Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Benjamin Ruf
- Gastrointestinal Malignancies Section, Thoracic and Gastrointestinal Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Mark Yarchoan
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Lichun Ma
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Xin W Wang
- Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
- Liver Carcinogenesis Section, Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
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10
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Huang JL, Sun Y, Wu ZH, Zhu HJ, Xia GJ, Zhu XS, Wu JH, Zhang KH. Differential diagnosis of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on spatial and channel attention mechanisms. J Cancer Res Clin Oncol 2023; 149:10161-10168. [PMID: 37268850 DOI: 10.1007/s00432-023-04935-4] [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: 04/25/2023] [Accepted: 05/23/2023] [Indexed: 06/04/2023]
Abstract
BACKGROUND The pre-operative non-invasive differential diagnosis of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) mainly depends on imaging. However, the accuracy of conventional imaging and radiomics methods in differentiating between the two carcinomas is unsatisfactory. In this study, we aimed to establish a novel deep learning model based on computed tomography (CT) images to provide an effective and non-invasive pre-operative differential diagnosis method for HCC and ICC. MATERIALS AND METHODS We retrospectively investigated the CT images of 395 HCC patients and 99 ICC patients who were diagnosed based on pathological analysis. To differentiate between HCC and ICC we developed a deep learning model called CSAM-Net based on channel and spatial attention mechanisms. We compared the proposed CSAM-Net with conventional radiomic models such as conventional logistic regression, least absolute shrinkage and selection operator regression, support vector machine, and random forest models. RESULTS With respect to differentiating between HCC and ICC, the CSAM-Net model showed area under the receiver operating characteristic curve (AUC) values of 0.987 (accuracy = 0.939), 0.969 (accuracy = 0.914), and 0.959 (accuracy = 0.912) for the training, validation, and test sets, respectively, which were significantly higher than those of the conventional radiomics models (0.736-0.913 [accuracy = 0.735-0.912], 0.602-0.828 [accuracy = 0.647-0.818], and 0.638-0.845 [accuracy = 0.618-0.849], respectively. The decision curve analysis showed a high net benefit of the CSAM-Net model, which suggests potential efficacy in differentiating between HCC and ICC in the diagnosis of liver cancers. CONCLUSIONS The proposed CSAM-Net model based on channel and spatial attention mechanisms provides an effective and non-invasive tool for the differential diagnosis of HCC and ICC on CT images, and has potential applications in diagnosis of liver cancers.
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Affiliation(s)
- Ji-Lan Huang
- Department of Radiology, First Affiliated Hospital of Nanchang University, Nanchang, 330006, China
| | - Ying Sun
- Department of Gastroenterology, Fuzhou First General Hospital Affiliated With Fujian Medical University, Fuzhou, 350004, China
| | - Zhi-Heng Wu
- School of Information Engineering, Nanchang University, No.999, Xuefu Road, Nanchang, 330031, China
| | - Hui-Jun Zhu
- School of Information Engineering, Nanchang University, No.999, Xuefu Road, Nanchang, 330031, China
| | - Guo-Jin Xia
- Department of Radiology, First Affiliated Hospital of Nanchang University, Nanchang, 330006, China
| | - Xi-Shun Zhu
- School of Advanced Manufacturing, Nanchang University, Nanchang, 330031, China
| | - Jian-Hua Wu
- School of Information Engineering, Nanchang University, No.999, Xuefu Road, Nanchang, 330031, China.
| | - Kun-He Zhang
- Department of Gastroenterology, Jiangxi Institute of Gastroenterology and Hepatology, First Affiliated Hospital of Nanchang University, No.17, Yongwai Zheng Street, Nanchang, 330006, China.
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11
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Gong XQ, Liu N, Tao YY, Li L, Li ZM, Yang L, Zhang XM. Radiomics models based on multisequence MRI for predicting PD-1/PD-L1 expression in hepatocellular carcinoma. Sci Rep 2023; 13:7710. [PMID: 37173350 PMCID: PMC10182068 DOI: 10.1038/s41598-023-34763-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 05/07/2023] [Indexed: 05/15/2023] Open
Abstract
The purpose of this study was to explore the effectiveness of radiomics based on multisequence MRI in predicting the expression of PD-1/PD-L1 in hepatocellular carcinoma (HCC). One hundred and eight patients with HCC who underwent contrast-enhanced MRI 2 weeks before surgical resection were enrolled in this retrospective study. Corresponding paraffin sections were collected for immunohistochemistry to detect the expression of PD-1 and PD-L1. All patients were randomly divided into a training cohort and a validation cohort at a ratio of 7:3. Univariate and multivariate analyses were used to select potential clinical characteristics related to PD-1 and PD-L1 expression. Radiomics features were extracted from the axial fat-suppression T2-weighted imaging (FS-T2WI) images and the arterial phase and portal venous phase images from the axial dynamic contrast-enhanced MRI, and the corresponding feature sets were generated. The least absolute shrinkage and selection operator (LASSO) was used to select the optimal radiomics features for analysis. Logistic regression analysis was performed to construct single-sequence and multisequence radiomics and radiomic-clinical models. The predictive performance was judged by the area under the receiver operating characteristic curve (AUC) in the training and validation cohorts. In the whole cohort, PD-1 expression was positive in 43 patients, and PD-L1 expression was positive in 34 patients. The presence of satellite nodules served as an independent predictor of PD-L1 expression. The AUC values of the FS-T2WI, arterial phase, portal venous phase and multisequence models in predicting the expression of PD-1 were 0.696, 0.843, 0.863, and 0.946 in the training group and 0.669, 0.792, 0.800 and 0.815 in the validation group, respectively. The AUC values of the FS-T2WI, arterial phase, portal venous phase, multisequence and radiomic-clinical models in predicting PD-L1 expression were 0.731, 0.800, 0.800, 0.831 and 0.898 in the training group and 0.621, 0.743, 0.771, 0.810 and 0.779 in the validation group, respectively. The combined models showed better predictive performance. The results of this study suggest that a radiomics model based on multisequence MRI has the potential to predict the preoperative expression of PD-1 and PD-L1 in HCC, which could become an imaging biomarker for immune checkpoint inhibitor (ICI)-based treatment.
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Affiliation(s)
- Xue-Qin Gong
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Ning Liu
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Yun-Yun Tao
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Li Li
- Department of Pathology, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Zu-Mao Li
- Department of Pathology, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Lin Yang
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China.
| | - Xiao-Ming Zhang
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
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12
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Criss C, Nagar AM, Makary MS. Hepatocellular carcinoma: State of the art diagnostic imaging. World J Radiol 2023; 15:56-68. [PMID: 37035828 PMCID: PMC10080581 DOI: 10.4329/wjr.v15.i3.56] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 02/12/2023] [Accepted: 03/22/2023] [Indexed: 03/27/2023] Open
Abstract
Primary liver cancer is the fourth most common malignancy worldwide, with hepatocellular carcinoma (HCC) comprising up to 90% of cases. Imaging is a staple for surveillance and diagnostic criteria for HCC in current guidelines. Because early diagnosis can impact treatment approaches, utilizing new imaging methods and protocols to aid in differentiation and tumor grading provides a unique opportunity to drastically impact patient prognosis. Within this review manuscript, we provide an overview of imaging modalities used to screen and evaluate HCC. We also briefly discuss emerging uses of new imaging techniques that offer the potential for improving current paradigms for HCC characterization, management, and treatment monitoring.
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Affiliation(s)
- Cody Criss
- Heritage College of Osteopathic Medicine, Ohio University, Athens, OH 45701, United States
| | - Arpit M Nagar
- Department of Radiology, The Ohio State University Medical Center, Columbus, OH 43210, United States
| | - Mina S Makary
- Department of Radiology, The Ohio State University Medical Center, Columbus, OH 43210, United States
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13
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AmeliMojarad M, AmeliMojarad M, Cui X. Prospective role of PD-1/PD-L1 immune checkpoint inhibitors in GI cancer. Pathol Res Pract 2023; 244:154338. [PMID: 36905697 DOI: 10.1016/j.prp.2023.154338] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/16/2023] [Accepted: 01/21/2023] [Indexed: 01/24/2023]
Abstract
One of the mechanisms by which tumor cells can evade the immune system is over activation of the programmed cell death protein-1 (PD-1) / programmed death-ligand 1 (PD-L1) pathway. The binding of PD-1 to its ligand PD-L1 can trigger an inhibitory signal for reducing T-cell proliferation, inhibiting the anticancer effect of T cells, and limiting the anti-tumor immunity of effectors T cell responses to protect tissues from immune-mediated tissue damage in the tumor microenvironment (TME). PD-1/PD-L1 immune checkpoint inhibitors have created a new pattern in cancer immunotherapy and can increase T cell- surveillance; therefore, the development of better clinical application of PD-1/PD-L1 inhibitors can significantly enhance antitumor immunity and prolong survival in GI cancer patients.
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Affiliation(s)
- Mandana AmeliMojarad
- Department of Oncology, The First Affiliated Hospital of Dalian Medical University, Dalian, PR China
| | - Melika AmeliMojarad
- Department of Oncology, The First Affiliated Hospital of Dalian Medical University, Dalian, PR China
| | - Xiaonan Cui
- Department of Oncology, The First Affiliated Hospital of Dalian Medical University, Dalian, PR China.
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14
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Tao YY, Shi Y, Gong XQ, Li L, Li ZM, Yang L, Zhang XM. Radiomic Analysis Based on Magnetic Resonance Imaging for Predicting PD-L2 Expression in Hepatocellular Carcinoma. Cancers (Basel) 2023; 15:cancers15020365. [PMID: 36672315 PMCID: PMC9856314 DOI: 10.3390/cancers15020365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/01/2023] [Accepted: 01/03/2023] [Indexed: 01/09/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the sixth most common malignant tumour and the third leading cause of cancer death in the world. The emerging field of radiomics involves extracting many clinical image features that cannot be recognized by the human eye to provide information for precise treatment decision making. Radiomics has shown its importance in HCC identification, histological grading, microvascular invasion (MVI) status, treatment response, and prognosis, but there is no report on the preoperative prediction of programmed death ligand-2 (PD-L2) expression in HCC. The purpose of this study was to investigate the value of MRI radiomic features for the non-invasive prediction of immunotherapy target PD-L2 expression in hepatocellular carcinoma (HCC). A total of 108 patients with HCC confirmed by pathology were retrospectively analysed. Immunohistochemical analysis was used to evaluate the expression level of PD-L2. 3D-Slicer software was used to manually delineate volumes of interest (VOIs) and extract radiomic features on preoperative T2-weighted, arterial-phase, and portal venous-phase MR images. Least absolute shrinkage and selection operator (LASSO) was performed to find the best radiomic features. Multivariable logistic regression models were constructed and validated using fivefold cross-validation. The area under the receiver characteristic curve (AUC) was used to evaluate the predictive performance of each model. The results show that among the 108 cases of HCC, 50 cases had high PD-L2 expression, and 58 cases had low PD-L2 expression. Radiomic features correlated with PD-L2 expression. The T2-weighted, arterial-phase, and portal venous-phase and combined MRI radiomics models showed AUCs of 0.789 (95% CI: 0.702-0.875), 0.727 (95% CI: 0.632-0.823), 0.770 (95% CI: 0.682-0.875), and 0.871 (95% CI: 0.803-0.939), respectively. The combined model showed the best performance. The results of this study suggest that prediction based on the radiomic characteristics of MRI could noninvasively predict the expression of PD-L2 in HCC before surgery and provide a reference for the selection of immune checkpoint blockade therapy.
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Affiliation(s)
- Yun-Yun Tao
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
| | - Yue Shi
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
| | - Xue-Qin Gong
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
| | - Li Li
- Department of Pathology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
| | - Zu-Mao Li
- Department of Pathology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
| | - Lin Yang
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
- Correspondence:
| | - Xiao-Ming Zhang
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
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15
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Fahmy D, Alksas A, Elnakib A, Mahmoud A, Kandil H, Khalil A, Ghazal M, van Bogaert E, Contractor S, El-Baz A. The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma. Cancers (Basel) 2022; 14:cancers14246123. [PMID: 36551606 PMCID: PMC9777232 DOI: 10.3390/cancers14246123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/08/2022] [Accepted: 12/09/2022] [Indexed: 12/15/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.
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Affiliation(s)
- Dalia Fahmy
- Diagnostic Radiology Department, Mansoura University Hospital, Mansoura 35516, Egypt
| | - Ahmed Alksas
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ahmed Elnakib
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Heba Kandil
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
- Faculty of Computer Sciences and Information, Mansoura University, Mansoura 35516, Egypt
| | - Ashraf Khalil
- College of Technological Innovation, Zayed University, Abu Dhabi 4783, United Arab Emirates
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
| | - Eric van Bogaert
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
- Correspondence:
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16
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Mao Q, Zhou MT, Zhao ZP, Liu N, Yang L, Zhang XM. Role of radiomics in the diagnosis and treatment of gastrointestinal cancer. World J Gastroenterol 2022; 28:6002-6016. [PMID: 36405385 PMCID: PMC9669820 DOI: 10.3748/wjg.v28.i42.6002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 09/24/2022] [Accepted: 10/27/2022] [Indexed: 11/10/2022] Open
Abstract
Gastrointestinal cancer (GIC) has high morbidity and mortality as one of the main causes of cancer death. Preoperative risk stratification is critical to guide patient management, but traditional imaging studies have difficulty predicting its biological behavior. The emerging field of radiomics allows the conversion of potential pathophysiological information in existing medical images that cannot be visually recognized into high-dimensional quantitative image features. Tumor lesion characterization, therapeutic response evaluation, and survival prediction can be achieved by analyzing the relationships between these features and clinical and genetic data. In recent years, the clinical application of radiomics to GIC has increased dramatically. In this editorial, we describe the latest progress in the application of radiomics to GIC and discuss the value of its potential clinical applications, as well as its limitations and future directions.
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Affiliation(s)
- Qi Mao
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Mao-Ting Zhou
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Zhang-Ping Zhao
- Department of Radiology, Panzhihua Central Hospital, Panzhihua 617000, Sichuan Province, China
| | - Ning Liu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Lin Yang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Xiao-Ming Zhang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
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17
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MRI-based delta-radiomic features for prediction of local control in liver lesions treated with stereotactic body radiation therapy. Sci Rep 2022; 12:18631. [PMID: 36329116 PMCID: PMC9633752 DOI: 10.1038/s41598-022-22826-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 10/19/2022] [Indexed: 11/05/2022] Open
Abstract
Real-time magnetic resonance image guided stereotactic ablative radiotherapy (MRgSBRT) is used to treat abdominal tumors. Longitudinal data is generated from daily setup images. Our study aimed to identify delta radiomic texture features extracted from these images to predict for local control in patients with liver tumors treated with MRgSBRT. Retrospective analysis of an IRB-approved database identified patients treated with MRgSBRT for primary liver and secondary metastasis histologies. Daily low field strength (0.35 T) images were retrieved, and the gross tumor volume was identified on each image. Next, images' gray levels were equalized, and 39 s-order texture features were extracted. Delta-radiomics were calculated as the difference between feature values on the initial scan and after delivered biological effective doses (BED, α/β = 10) of 20 Gy and 40 Gy. Then, features were ranked by the Gini Index during training of a random forest model. Finally, the area under the receiver operating characteristic curve (AUC) was estimated using a bootstrapped logistic regression with the top two features. We identified 22 patients for analysis. The median dose delivered was 50 Gy in 5 fractions. The top two features identified after delivery of BED 20 Gy were gray level co-occurrence matrix features energy and gray level size zone matrix based large zone emphasis. The model generated an AUC = 0.9011 (0.752-1.0) during bootstrapped logistic regression. The same two features were selected after delivery of a BED 40 Gy, with an AUC = 0.716 (0.600-0.786). Delta-radiomic features after a single fraction of SBRT predicted local control in this exploratory cohort. If confirmed in larger studies, these features may identify patients with radioresistant disease and provide an opportunity for physicians to alter management much sooner than standard restaging after 3 months. Expansion of the patient database is warranted for further analysis of delta-radiomic features.
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18
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Tang Q, Chen Y, Li X, Long S, Shi Y, Yu Y, Wu W, Han L, Wang S. The role of PD-1/PD-L1 and application of immune-checkpoint inhibitors in human cancers. Front Immunol 2022; 13:964442. [PMID: 36177034 PMCID: PMC9513184 DOI: 10.3389/fimmu.2022.964442] [Citation(s) in RCA: 96] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 08/23/2022] [Indexed: 11/13/2022] Open
Abstract
Programmed cell death protein-1 (PD-1) is a checkpoint receptor expressed on the surface of various immune cells. PD-L1, the natural receptor for PD-1, is mainly expressed in tumor cells. Studies have indicated that PD-1 and PD-L1 are closely associated with the progression of human cancers and are promising biomarkers for cancer therapy. Moreover, the interaction of PD-1 and PD-L1 is one of the important mechanism by which human tumors generate immune escape. This article provides a review on the role of PD-L1/PD-1, mechanisms of immune response and resistance, as well as immune-related adverse events in the treatment of anti-PD-1/PD-L1 immunotherapy in human cancers. Moreover, we summarized a large number of clinical trials to successfully reveal that PD-1/PD-L1 Immune-checkpoint inhibitors have manifested promising therapeutic effects, which have been evaluated from different perspectives, including overall survival, objective effective rate and medium progression-free survival. Finally, we pointed out the current problems faced by PD-1/PD-L1 Immune-checkpoint inhibitors and its future prospects. Although PD-1/PD-L1 immune checkpoint inhibitors have been widely used in the treatment of human cancers, tough challenges still remain. Combination therapy and predictive models based on integrated biomarker determination theory may be the future directions for the application of PD-1/PD-L1 Immune-checkpoint inhibitors in treating human cancers.
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Affiliation(s)
- Qing Tang
- Guangdong-Hong Kong-Macau Joint Lab on Chinese Medicine and Immune Disease Research, Clinical and Basic Research Team of Traditional Chinese Medicine (TCM) Prevention and Treatment of Non small cell lung cancer (NSCLC), Department of Oncology, The Second Clinical College of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yun Chen
- Department of Organ Transplantation, Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiaojuan Li
- Institute of Rehabilitation Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Shunqin Long
- Guangdong-Hong Kong-Macau Joint Lab on Chinese Medicine and Immune Disease Research, Clinical and Basic Research Team of Traditional Chinese Medicine (TCM) Prevention and Treatment of Non small cell lung cancer (NSCLC), Department of Oncology, The Second Clinical College of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yao Shi
- Department of Cerebrovascular Disease, Guangdong Provincial Hospital of Chinese Medicine, The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yaya Yu
- Department of Oncology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Wanyin Wu
- Guangdong-Hong Kong-Macau Joint Lab on Chinese Medicine and Immune Disease Research, Clinical and Basic Research Team of Traditional Chinese Medicine (TCM) Prevention and Treatment of Non small cell lung cancer (NSCLC), Department of Oncology, The Second Clinical College of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou University of Chinese Medicine, Guangzhou, China
- *Correspondence: Wanyin Wu, ; Ling Han, ; Sumei Wang,
| | - Ling Han
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
- Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- *Correspondence: Wanyin Wu, ; Ling Han, ; Sumei Wang,
| | - Sumei Wang
- Guangdong-Hong Kong-Macau Joint Lab on Chinese Medicine and Immune Disease Research, Clinical and Basic Research Team of Traditional Chinese Medicine (TCM) Prevention and Treatment of Non small cell lung cancer (NSCLC), Department of Oncology, The Second Clinical College of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou University of Chinese Medicine, Guangzhou, China
- *Correspondence: Wanyin Wu, ; Ling Han, ; Sumei Wang,
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19
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Prediction of Histological Grades and Ki-67 Expression of Hepatocellular Carcinoma Based on Sonazoid Contrast Enhanced Ultrasound Radiomics Signatures. Diagnostics (Basel) 2022; 12:diagnostics12092175. [PMID: 36140576 PMCID: PMC9497787 DOI: 10.3390/diagnostics12092175] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/04/2022] [Accepted: 09/05/2022] [Indexed: 01/27/2023] Open
Abstract
Objectives: Histopathological tumor grade and Ki-67 expression level are key aspects concerning the prognosis of patients with hepatocellular carcinoma (HCC) lesions. The aim of this study was to investigate whether the radiomics model derived from Sonazoid contrast-enhanced (S-CEUS) images could predict histological grades and Ki-67 expression of HCC lesions. Methods: This prospective study included 101 (training cohort: n = 71; validation cohort: n = 30) patients with surgical resection and histopathologically confirmed HCC lesions. Radiomics features were extracted from the B mode and Kupffer phase of S-CEUS images. Maximum relevance minimum redundancy (MRMR) and least absolute shrinkage and selection operator (LASSO) were used for feature selection, and a stepwise multivariate logit regression model was trained for prediction. Model accuracy, sensitivity, and specificity in both training and testing datasets were used to evaluate performance. Results: The prediction model derived from Kupffer phase images (CE-model) displayed a significantly better performance in the prediction of stage III HCC patients, with an area under the receiver operating characteristic curve (AUROC) of 0.908 in the training dataset and 0.792 in the testing set. The CE-model demonstrated generalizability in identifying HCC patients with elevated Ki-67 expression (>10%) with a training AUROC of 0.873 and testing AUROC of 0.768, with noticeably higher specificity of 92.3% and 80.0% in training and testing datasets, respectively. Conclusions: The radiomics model constructed from the Kupffer phase of S-CEUS images has the potential for predicting Ki-67 expression and histological stages in patients with HCC.
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Magnetic Fields and Cancer: Epidemiology, Cellular Biology, and Theranostics. Int J Mol Sci 2022; 23:ijms23031339. [PMID: 35163262 PMCID: PMC8835851 DOI: 10.3390/ijms23031339] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/22/2022] [Accepted: 01/22/2022] [Indexed: 02/08/2023] Open
Abstract
Humans are exposed to a complex mix of man-made electric and magnetic fields (MFs) at many different frequencies, at home and at work. Epidemiological studies indicate that there is a positive relationship between residential/domestic and occupational exposure to extremely low frequency electromagnetic fields and some types of cancer, although some other studies indicate no relationship. In this review, after an introduction on the MF definition and a description of natural/anthropogenic sources, the epidemiology of residential/domestic and occupational exposure to MFs and cancer is reviewed, with reference to leukemia, brain, and breast cancer. The in vivo and in vitro effects of MFs on cancer are reviewed considering both human and animal cells, with particular reference to the involvement of reactive oxygen species (ROS). MF application on cancer diagnostic and therapy (theranostic) are also reviewed by describing the use of different magnetic resonance imaging (MRI) applications for the detection of several cancers. Finally, the use of magnetic nanoparticles is described in terms of treatment of cancer by nanomedical applications for the precise delivery of anticancer drugs, nanosurgery by magnetomechanic methods, and selective killing of cancer cells by magnetic hyperthermia. The supplementary tables provide quantitative data and methodologies in epidemiological and cell biology studies. Although scientists do not generally agree that there is a cause-effect relationship between exposure to MF and cancer, MFs might not be the direct cause of cancer but may contribute to produce ROS and generate oxidative stress, which could trigger or enhance the expression of oncogenes.
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Yang Y, Zou X, Zhou W, Yuan G, Hu D, Kuang D, Shen Y, Xie Q, Zhang Q, Hu X, Li Z. Multiparametric MRI-Based Radiomic Signature for Preoperative Evaluation of Overall Survival in Intrahepatic Cholangiocarcinoma After Partial Hepatectomy. J Magn Reson Imaging 2022; 56:739-751. [PMID: 35049076 DOI: 10.1002/jmri.28071] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 01/05/2022] [Accepted: 01/08/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The clinical outcomes of patients with intrahepatic cholangiocarcinoma (ICC) after partial hepatectomy remain suboptimal. Identifying patients with poor outcomes before surgery is urgently required. PURPOSE To develop a multiparametric magnetic resonance imaging (MRI)-based radiomic signature to evaluate overall survival (OS) preoperatively and to investigate its incremental value for disease stratification. STUDY TYPE Retrospective. SUBJECTS One hundred and sixty-three patients with pathologically defined ICC, divided into training (N = 115) and validation sets (N = 48). SEQUENCE Three-dimensional T1-weighted gradient-echo sequence with and without contrast agent, T2-weighted fast spin-echo sequence, and diffusion-weighted imaging with single-shot echo-planar sequence at 1.5 T or 3.0 T. ASSESSMENT OS was defined as the time from the date of surgery to death or last contact. The radiomic signature was built based on the least absolute shrinkage and selection operator regression model. A clinicopathologic-radiographic (CPR) model and a combined model integrating radiomic signature with CPR factors were developed with multivariable Cox regression models. STATISTICAL TESTS Harrell's concordance index (C-index) was used to compare the discrimination of different models. Net reclassification index (NRI) and integrated discrimination improvement (IDI) were used to quantify the improvement of prognostic accuracy after adding radiomic signature. RESULTS The high-risk patients of death defined by the radiomic signature showed significantly lower OS compared with low-risk patients in validation set (3-year OS 17.1% vs. 56.4%, P < 0.001). Integrating radiomic signature into tumor, node, and metastasis (TNM) staging system significantly improved the prognostic accuracy compared with TNM stage alone (validation set C-index 0.745 vs. 0.649, P = 0.039, NRI improvement 39.9%-43.8%, IDI improvement 16.1%-19.4%). The radiomic signature showed no significant difference of C-index with postoperative CPR model (validation set, 0.698 vs. 0.674, P = 0.752). Incorporating the radiomic signature into CPR model significantly improved prognostic accuracy (NRI improvement 32.5%-34.3%, IDI improvement 8.1%-12.9%). DATA CONCLUSION Multiparametric MRI-based radiomic signature is a potential biomarker for preoperative prognostic evaluation of ICC patients. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY: Stage 4.
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Affiliation(s)
- Yang Yang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xianlun Zou
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wei Zhou
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Guanjie Yuan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Daoyu Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Dong Kuang
- Department of Pathology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yaqi Shen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qingguo Xie
- Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Qingpeng Zhang
- School of Data Science, City University of Hong Kong, Hong Kong, China
| | - Xuemei Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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