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Yang J, Yang C, Feng J, Zhu F, Zhao Z. Predicting Microwave Ablation Early Efficacy in Pulmonary Malignancies via Δ Radiomics Models. J Comput Assist Tomogr 2024:00004728-990000000-00314. [PMID: 38657155 DOI: 10.1097/rct.0000000000001611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
OBJECTIVE This study aimed to explore the value of preoperative and postoperative computed tomography (CT)-based radiomic signatures and Δ radiomic signatures for evaluating the early efficacy of microwave ablation (MWA) for pulmonary malignancies. METHODS In total, 115 patients with pulmonary malignancies who underwent MWA treatment were categorized into response and nonresponse groups according to relevant guidelines and consensus. Quantitative image features of the largest pulmonary malignancies were extracted from CT noncontrast scan images preoperatively (time point 0, TP0) and immediately postoperatively (time point 1, TP1). Critical features were selected from TP0 and TP1 and as Δ radiomics signatures for building radiomics models. In addition, a combined radiomics model (C-RO) was developed by integrating radiomics parameters with clinical risk factors. Prediction performance was assessed using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). RESULTS The radiomics model using Δ features outperformed the radiomics model using TP0 and TP1 features, with training and validation AUCs of 0.892, 0.808, and 0.787, and 0.705, 0.825, and 0.778, respectively. By combining the TP0, TP1, and Δ features, the logistic regression model exhibited the best performance, with training and validation AUCs of 0.945 and 0.744, respectively. The DCA confirmed the clinical utility of the Δ radiomics model. CONCLUSIONS A combined prediction model, including TP0, TP1, and Δ radiometric features, can be used to evaluate the early efficacy of MWA in pulmonary malignancies.
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Affiliation(s)
- Jing Yang
- From the School of Medicine, Shaoxing University
| | - Chen Yang
- Department of Radiology, Shaoxing People's Hospital (Zhejiang University Shaoxing Hospital), Shaoxing
| | - Jianju Feng
- Department of Radiology, Zhuji People's Hospital, Zhuji, Zhejiang, China
| | - Fandong Zhu
- Department of Radiology, Shaoxing People's Hospital (Zhejiang University Shaoxing Hospital), Shaoxing
| | - Zhenhua Zhao
- Department of Radiology, Shaoxing People's Hospital (Zhejiang University Shaoxing Hospital), Shaoxing
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Zhang R, Wang Y, Li Z, Shi Y, Yu D, Huang Q, Chen F, Xiao W, Hong Y, Feng Z. Dynamic radiomics based on contrast-enhanced MRI for predicting microvascular invasion in hepatocellular carcinoma. BMC Med Imaging 2024; 24:80. [PMID: 38584254 PMCID: PMC11000376 DOI: 10.1186/s12880-024-01258-9] [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: 11/20/2023] [Accepted: 03/26/2024] [Indexed: 04/09/2024] Open
Abstract
OBJECTIVE To exploit the improved prediction performance based on dynamic contrast-enhanced (DCE) MRI by using dynamic radiomics for microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHODS We retrospectively included 175 and 75 HCC patients who underwent preoperative DCE-MRI from September 2019 to August 2022 in institution 1 (development cohort) and institution 2 (validation cohort), respectively. Static radiomics features were extracted from the mask, arterial, portal venous, and equilibrium phase images and used to construct dynamic features. The static, dynamic, and dynamic-static radiomics (SR, DR, and DSR) signatures were separately constructed based on the feature selection method of LASSO and classification algorithm of logistic regression. The receiver operating characteristic (ROC) curves and the area under the curve (AUC) were plotted to evaluate and compare the predictive performance of each signature. RESULTS In the three radiomics signatures, the DSR signature performed the best. The AUCs of the SR, DR, and DSR signatures in the training set were 0.750, 0.751 and 0.805, respectively, while in the external validation set, the corresponding AUCs were 0.706, 0756 and 0.777. The DSR signature showed significant improvement over the SR signature in predicting MVI status (training cohort: P = 0.019; validation cohort: P = 0.044). After external validation, the AUC value of the SR signature decreased from 0.750 to 0.706, while the AUC value of the DR signature did not show a decline (AUCs: 0.756 vs. 0.751). CONCLUSIONS The dynamic radiomics had an improved effect on the MVI prediction in HCC, compared with the static DCE MRI-based radiomics models.
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Affiliation(s)
- Rui Zhang
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yao Wang
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhi Li
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yushu Shi
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Danping Yu
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qiang Huang
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Feng Chen
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenbo Xiao
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuan Hong
- College of Mathematical Medicine, Zhejiang Normal University School, Jinhua, China
| | - Zhan Feng
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
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Corrias G, Lai E, Ziranu P, Mariani S, Donisi C, Liscia N, Saba G, Pretta A, Persano M, Fanni D, Spanu D, Balconi F, Loi F, Deidda S, Restivo A, Pusceddu V, Puzzoni M, Solinas C, Massa E, Madeddu C, Gerosa C, Zorcolo L, Faa G, Saba L, Scartozzi M. Prediction of Response to Anti-Angiogenic Treatment for Advanced Colorectal Cancer Patients: From Biological Factors to Functional Imaging. Cancers (Basel) 2024; 16:1364. [PMID: 38611042 PMCID: PMC11011199 DOI: 10.3390/cancers16071364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 03/24/2024] [Accepted: 03/27/2024] [Indexed: 04/14/2024] Open
Abstract
Colorectal cancer (CRC) is a leading tumor worldwide. In CRC, the angiogenic pathway plays a crucial role in cancer development and the process of metastasis. Thus, anti-angiogenic drugs represent a milestone for metastatic CRC (mCRC) treatment and lead to significant improvement of clinical outcomes. Nevertheless, not all patients respond to treatment and some develop resistance. Therefore, the identification of predictive factors able to predict response to angiogenesis pathway blockade is required in order to identify the best candidates to receive these agents. Unfortunately, no predictive biomarkers have been prospectively validated to date. Over the years, research has focused on biologic factors such as genetic polymorphisms, circulating biomarkers, circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and microRNA. Moreover, research efforts have evaluated the potential correlation of molecular biomarkers with imaging techniques used for tumor assessment as well as the application of imaging tools in clinical practice. In addition to functional imaging, radiomics, a relatively newer technique, shows real promise in the setting of correlating molecular medicine to radiological phenotypes.
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Affiliation(s)
- Giuseppe Corrias
- Department of Radiology, University of Cagliari, 09042 Cagliari, Italy;
| | - Eleonora Lai
- Medical Oncology Unit, University Hospital and University of Cagliari, 09042 Cagliari, Italy; (E.L.); (P.Z.); (S.M.); (C.D.); (G.S.); (A.P.); (M.P.); (D.S.); (F.B.); (F.L.); (V.P.); (M.P.); (C.S.); (E.M.); (C.M.); (M.S.)
| | - Pina Ziranu
- Medical Oncology Unit, University Hospital and University of Cagliari, 09042 Cagliari, Italy; (E.L.); (P.Z.); (S.M.); (C.D.); (G.S.); (A.P.); (M.P.); (D.S.); (F.B.); (F.L.); (V.P.); (M.P.); (C.S.); (E.M.); (C.M.); (M.S.)
| | - Stefano Mariani
- Medical Oncology Unit, University Hospital and University of Cagliari, 09042 Cagliari, Italy; (E.L.); (P.Z.); (S.M.); (C.D.); (G.S.); (A.P.); (M.P.); (D.S.); (F.B.); (F.L.); (V.P.); (M.P.); (C.S.); (E.M.); (C.M.); (M.S.)
| | - Clelia Donisi
- Medical Oncology Unit, University Hospital and University of Cagliari, 09042 Cagliari, Italy; (E.L.); (P.Z.); (S.M.); (C.D.); (G.S.); (A.P.); (M.P.); (D.S.); (F.B.); (F.L.); (V.P.); (M.P.); (C.S.); (E.M.); (C.M.); (M.S.)
| | - Nicole Liscia
- Department of Medical Oncology, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, 20132 Milan, Italy;
| | - Giorgio Saba
- Medical Oncology Unit, University Hospital and University of Cagliari, 09042 Cagliari, Italy; (E.L.); (P.Z.); (S.M.); (C.D.); (G.S.); (A.P.); (M.P.); (D.S.); (F.B.); (F.L.); (V.P.); (M.P.); (C.S.); (E.M.); (C.M.); (M.S.)
| | - Andrea Pretta
- Medical Oncology Unit, University Hospital and University of Cagliari, 09042 Cagliari, Italy; (E.L.); (P.Z.); (S.M.); (C.D.); (G.S.); (A.P.); (M.P.); (D.S.); (F.B.); (F.L.); (V.P.); (M.P.); (C.S.); (E.M.); (C.M.); (M.S.)
| | - Mara Persano
- Medical Oncology Unit, University Hospital and University of Cagliari, 09042 Cagliari, Italy; (E.L.); (P.Z.); (S.M.); (C.D.); (G.S.); (A.P.); (M.P.); (D.S.); (F.B.); (F.L.); (V.P.); (M.P.); (C.S.); (E.M.); (C.M.); (M.S.)
| | - Daniela Fanni
- Division of Pathology, Department of Medical Sciences and Public Health, AOU Cagliari, University of Cagliari, 09124 Cagliari, Italy; (D.F.); (C.G.); (G.F.)
| | - Dario Spanu
- Medical Oncology Unit, University Hospital and University of Cagliari, 09042 Cagliari, Italy; (E.L.); (P.Z.); (S.M.); (C.D.); (G.S.); (A.P.); (M.P.); (D.S.); (F.B.); (F.L.); (V.P.); (M.P.); (C.S.); (E.M.); (C.M.); (M.S.)
| | - Francesca Balconi
- Medical Oncology Unit, University Hospital and University of Cagliari, 09042 Cagliari, Italy; (E.L.); (P.Z.); (S.M.); (C.D.); (G.S.); (A.P.); (M.P.); (D.S.); (F.B.); (F.L.); (V.P.); (M.P.); (C.S.); (E.M.); (C.M.); (M.S.)
| | - Francesco Loi
- Medical Oncology Unit, University Hospital and University of Cagliari, 09042 Cagliari, Italy; (E.L.); (P.Z.); (S.M.); (C.D.); (G.S.); (A.P.); (M.P.); (D.S.); (F.B.); (F.L.); (V.P.); (M.P.); (C.S.); (E.M.); (C.M.); (M.S.)
| | - Simona Deidda
- Colorectal Surgery Unit, A.O.U. Cagliari, Department of Surgical Science, University of Cagliari, 09042 Cagliari, Italy; (S.D.); (A.R.); (L.Z.)
| | - Angelo Restivo
- Colorectal Surgery Unit, A.O.U. Cagliari, Department of Surgical Science, University of Cagliari, 09042 Cagliari, Italy; (S.D.); (A.R.); (L.Z.)
| | - Valeria Pusceddu
- Medical Oncology Unit, University Hospital and University of Cagliari, 09042 Cagliari, Italy; (E.L.); (P.Z.); (S.M.); (C.D.); (G.S.); (A.P.); (M.P.); (D.S.); (F.B.); (F.L.); (V.P.); (M.P.); (C.S.); (E.M.); (C.M.); (M.S.)
| | - Marco Puzzoni
- Medical Oncology Unit, University Hospital and University of Cagliari, 09042 Cagliari, Italy; (E.L.); (P.Z.); (S.M.); (C.D.); (G.S.); (A.P.); (M.P.); (D.S.); (F.B.); (F.L.); (V.P.); (M.P.); (C.S.); (E.M.); (C.M.); (M.S.)
| | - Cinzia Solinas
- Medical Oncology Unit, University Hospital and University of Cagliari, 09042 Cagliari, Italy; (E.L.); (P.Z.); (S.M.); (C.D.); (G.S.); (A.P.); (M.P.); (D.S.); (F.B.); (F.L.); (V.P.); (M.P.); (C.S.); (E.M.); (C.M.); (M.S.)
| | - Elena Massa
- Medical Oncology Unit, University Hospital and University of Cagliari, 09042 Cagliari, Italy; (E.L.); (P.Z.); (S.M.); (C.D.); (G.S.); (A.P.); (M.P.); (D.S.); (F.B.); (F.L.); (V.P.); (M.P.); (C.S.); (E.M.); (C.M.); (M.S.)
| | - Clelia Madeddu
- Medical Oncology Unit, University Hospital and University of Cagliari, 09042 Cagliari, Italy; (E.L.); (P.Z.); (S.M.); (C.D.); (G.S.); (A.P.); (M.P.); (D.S.); (F.B.); (F.L.); (V.P.); (M.P.); (C.S.); (E.M.); (C.M.); (M.S.)
| | - Clara Gerosa
- Division of Pathology, Department of Medical Sciences and Public Health, AOU Cagliari, University of Cagliari, 09124 Cagliari, Italy; (D.F.); (C.G.); (G.F.)
| | - Luigi Zorcolo
- Colorectal Surgery Unit, A.O.U. Cagliari, Department of Surgical Science, University of Cagliari, 09042 Cagliari, Italy; (S.D.); (A.R.); (L.Z.)
| | - Gavino Faa
- Division of Pathology, Department of Medical Sciences and Public Health, AOU Cagliari, University of Cagliari, 09124 Cagliari, Italy; (D.F.); (C.G.); (G.F.)
| | - Luca Saba
- Department of Radiology, University of Cagliari, 09042 Cagliari, Italy;
| | - Mario Scartozzi
- Medical Oncology Unit, University Hospital and University of Cagliari, 09042 Cagliari, Italy; (E.L.); (P.Z.); (S.M.); (C.D.); (G.S.); (A.P.); (M.P.); (D.S.); (F.B.); (F.L.); (V.P.); (M.P.); (C.S.); (E.M.); (C.M.); (M.S.)
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Sheng L, Yang C, Chen Y, Song B. Machine Learning Combined with Radiomics Facilitating the Personal Treatment of Malignant Liver Tumors. Biomedicines 2023; 12:58. [PMID: 38255165 PMCID: PMC10813632 DOI: 10.3390/biomedicines12010058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/13/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024] Open
Abstract
In the realm of managing malignant liver tumors, the convergence of radiomics and machine learning has redefined the landscape of medical practice. The field of radiomics employs advanced algorithms to extract thousands of quantitative features (including intensity, texture, and structure) from medical images. Machine learning, including its subset deep learning, aids in the comprehensive analysis and integration of these features from diverse image sources. This potent synergy enables the prediction of responses of malignant liver tumors to various treatments and outcomes. In this comprehensive review, we examine the evolution of the field of radiomics and its procedural framework. Furthermore, the applications of radiomics combined with machine learning in the context of personalized treatment for malignant liver tumors are outlined in aspects of surgical therapy and non-surgical treatments such as ablation, transarterial chemoembolization, radiotherapy, and systemic therapies. Finally, we discuss the current challenges in the amalgamation of radiomics and machine learning in the study of malignant liver tumors and explore future opportunities.
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Affiliation(s)
- Liuji Sheng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.S.); (C.Y.)
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Chongtu Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.S.); (C.Y.)
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yidi Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.S.); (C.Y.)
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.S.); (C.Y.)
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Department of Radiology, Sanya People’s Hospital, Sanya 572000, China
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Abbas E, Fanni SC, Bandini C, Francischello R, Febi M, Aghakhanyan G, Ambrosini I, Faggioni L, Cioni D, Lencioni RA, Neri E. Delta-radiomics in cancer immunotherapy response prediction: A systematic review. Eur J Radiol Open 2023; 11:100511. [PMID: 37520768 PMCID: PMC10371799 DOI: 10.1016/j.ejro.2023.100511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/11/2023] [Accepted: 07/12/2023] [Indexed: 08/01/2023] Open
Abstract
Background The new immunotherapies have not only changed the oncological therapeutic approach but have also made it necessary to develop new imaging methods for assessing the response to treatment. Delta radiomics consists of the analysis of radiomic features variation between different medical images, usually before and after therapy. Purpose This review aims to evaluate the role of delta radiomics in the immunotherapy response assessment. Methods A systematic search was performed in PubMed, Scopus, and Web Of Science using "delta radiomics AND immunotherapy" as search terms. The included articles' methodological quality was measured using the Radiomics Quality Score (RQS) tool. Results Thirteen articles were finally included in the systematic review. Overall, the RQS of the included studies ranged from 4 to 17, with a mean RQS total of 11,15 ± 4,18 with a corresponding percentage of 30.98 ± 11.61 %. Eleven articles out of 13 performed imaging at multiple time points. All the included articles performed feature reduction. No study carried out prospective validation, decision curve analysis, or cost-effectiveness analysis. Conclusions Delta radiomics has been demonstrated useful in evaluating the response in oncologic patients undergoing immunotherapy. The overall quality was found law, due to the lack of prospective design and external validation. Thus, further efforts are needed to bring delta radiomics a step closer to clinical implementation.
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Affiliation(s)
- Engy Abbas
- The Joint Department of Medical Imaging, University of Toronto, University Health Network, Sinai Health System, Women’s College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9
| | | | - Claudio Bandini
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Roberto Francischello
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Maria Febi
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Gayane Aghakhanyan
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Ilaria Ambrosini
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Lorenzo Faggioni
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Dania Cioni
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | | | - Emanuele Neri
- The Joint Department of Medical Imaging, University of Toronto, University Health Network, Sinai Health System, Women’s College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
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Wang F, Wang CL, Yi YQ, Zhang T, Zhong Y, Zhu JJ, Li H, Yang G, Yu TF, Xu H, Yuan M. Comparison and fusion prediction model for lung adenocarcinoma with micropapillary and solid pattern using clinicoradiographic, radiomics and deep learning features. Sci Rep 2023; 13:9302. [PMID: 37291251 PMCID: PMC10250309 DOI: 10.1038/s41598-023-36409-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 06/02/2023] [Indexed: 06/10/2023] Open
Abstract
To investigate whether the combination scheme of deep learning score (DL-score) and radiomics can improve preoperative diagnosis in the presence of micropapillary/solid (MPP/SOL) patterns in lung adenocarcinoma (ADC). A retrospective cohort of 514 confirmed pathologically lung ADC in 512 patients after surgery was enrolled. The clinicoradiographic model (model 1) and radiomics model (model 2) were developed with logistic regression. The deep learning model (model 3) was constructed based on the deep learning score (DL-score). The combine model (model 4) was based on DL-score and R-score and clinicoradiographic variables. The performance of these models was evaluated with area under the receiver operating characteristic curve (AUC) and compared using DeLong's test internally and externally. The prediction nomogram was plotted, and clinical utility depicted with decision curve. The performance of model 1, model 2, model 3 and model 4 was supported by AUCs of 0.848, 0.896, 0.906, 0.921 in the Internal validation set, that of 0.700, 0.801, 0.730, 0.827 in external validation set, respectively. These models existed statistical significance in internal validation (model 4 vs model 3, P = 0.016; model 4 vs model 1, P = 0.009, respectively) and external validation (model 4 vs model 2, P = 0.036; model 4 vs model 3, P = 0.047; model 4 vs model 1, P = 0.016, respectively). The decision curve analysis (DCA) demonstrated that model 4 predicting the lung ADC with MPP/SOL structure would be more beneficial than the model 1and model 3 but comparable with the model 2. The combined model can improve preoperative diagnosis in the presence of MPP/SOL pattern in lung ADC in clinical practice.
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Affiliation(s)
- Fen Wang
- Department of Medical Imaging Center, The Affiliated Huaian NO.1 People's Hospital of Nanjing Medical University, No. 1 West Huanghe Road, Huaian, 223300, China
| | - Cheng-Long Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Yin-Qiao Yi
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Teng Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 GuangZhou Road, Nanjing, 210029, China
| | - Yan Zhong
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 GuangZhou Road, Nanjing, 210029, China
| | - Jia-Jia Zhu
- Department of Radiology, Jiangsu Province Official Hospital, Nanjing, 210024, China
| | - Hai Li
- Department of Pathology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Tong-Fu Yu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 GuangZhou Road, Nanjing, 210029, China
| | - Hai Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 GuangZhou Road, Nanjing, 210029, China.
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province, 300, Guangzhou Road, Nanjing, 210029, China.
| | - Mei Yuan
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 GuangZhou Road, Nanjing, 210029, China.
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province, 300, Guangzhou Road, Nanjing, 210029, China.
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