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Iima M, Kataoka M, Honda M, Le Bihan D. Diffusion-Weighted MRI for the Assessment of Molecular Prognostic Biomarkers in Breast Cancer. Korean J Radiol 2024; 25:623-633. [PMID: 38942456 PMCID: PMC11214919 DOI: 10.3348/kjr.2023.1188] [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: 03/02/2023] [Revised: 02/28/2024] [Accepted: 04/11/2024] [Indexed: 06/30/2024] Open
Abstract
This study systematically reviewed the role of diffusion-weighted imaging (DWI) in the assessment of molecular prognostic biomarkers in breast cancer, focusing on the correlation of apparent diffusion coefficient (ADC) with hormone receptor status and prognostic biomarkers. Our meta-analysis includes data from 52 studies examining ADC values in relation to estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2), and Ki-67 status. The results indicated significant differences in ADC values among different receptor statuses, with ER-positive, PgR-positive, HER2-negative, and Ki-67-positive tumors having lower ADC values compared to their negative counterparts. This study also highlights the potential of advanced DWI techniques such as intravoxel incoherent motion and non-Gaussian DWI to provide additional insights beyond ADC. Despite these promising findings, the high heterogeneity among the studies underscores the need for standardized DWI protocols to improve their clinical utility in breast cancer management.
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Affiliation(s)
- Mami Iima
- Department of Fundamental Development for Advanced Low Invasive Diagnostic Imaging, Nagoya University Graduate School of Medicine, Nagoya, Japan
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.
| | - Masako Kataoka
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Maya Honda
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Diagnostic Radiology, Kansai Electric Power Hospital, Osaka, Japan
| | - Denis Le Bihan
- NeuroSpin, Joliot Institute, Department of Fundamental Research, Commissariat à l'Energie Atomique (CEA)-Saclay, Gif-sur-Yvette, France
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Liu X, Meng N, Zhou Y, Fu F, Yuan J, Wang Z, Yang Y, Xiong Z, Zou C, Wang M. Tri-Compartmental Restriction Spectrum Imaging Based on 18F-FDG PET/MR for Identification of Primary Benign and Malignant Lung Lesions. J Magn Reson Imaging 2024. [PMID: 38886922 DOI: 10.1002/jmri.29438] [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: 01/30/2024] [Revised: 04/25/2024] [Accepted: 04/25/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Restriction spectrum imaging (RSI), as an advanced quantitative diffusion-weighted magnetic resonance imaging technique, has the potential to distinguish primary benign and malignant lung lesions. OBJECTIVE To explore how well the tri-compartmental RSI performs in distinguishing primary benign from malignant lung lesions compared with diffusion-weighted imaging (DWI), and to further explore whether positron emission tomography/magnetic resonance imaging (PET/MRI) can improve diagnostic efficacy. STUDY TYPE Prospective. POPULATION 137 patients, including 108 malignant and 29 benign lesions (85 males, 52 females; average age = 60.0 ± 10.0 years). FIELD STRENGTH/SEQUENCE T2WI, T1WI, multi-b value DWI, MR-based attenuation correction, and PET imaging on a 3.0 T whole-body PET/MR system. ASSESSMENT The apparent diffusion coefficient (ADC), RSI-derived parameters (restricted diffusionf 1 $$ {f}_1 $$ , hindered diffusionf 2 $$ {f}_2 $$ , and free diffusionf 3 $$ {f}_3 $$ ) and the maximum standardized uptake value (SUVmax) were calculated and analyzed for diagnostic efficacy individually or in combination. STATISTICAL TESTS Student's t-test, Mann-Whitney U test, receiver operating characteristic (ROC) curves, Delong test, Spearman's correlation analysis. P < 0.05 was considered statistically significant. RESULTS Thef 1 $$ {f}_1 $$ , SUVmax were significantly higher, andf 3 $$ {f}_3 $$ , ADC were significantly lower in the malignant group [0.717 ± 0.131, 9.125 (5.753, 13.058), 0.194 ± 0.099, 1.240 (0.972, 1.407)] compared to the benign group [0.504 ± 0.236, 3.390 (1.673, 6.030), 0.398 ± 0.195, 1.485 ± 0.382]. The area under the ROC curve (AUC) values ranked from highest to lowest as follows: AUC (SUVmax) > AUC (f 3 $$ {f}_3 $$ ) > AUC (f 1 $$ {f}_1 $$ ) > AUC (ADC) > AUC (f 2 $$ {f}_2 $$ ) (AUC = 0.819, 0.811, 0.770, 0.745, 0549). The AUC (AUC = 0.900) of the combined model of RSI with PET was significantly higher than that of either single-modality imaging. CONCLUSION RSI-derived parameters (f 1 $$ {f}_1 $$ ,f 3 $$ {f}_3 $$ ) might help to distinguish primary benign and malignant lung lesions and the discriminatory utility off 2 $$ {f}_2 $$ was not observed. The RSI exhibits comparable or potentially enhanced performance compared with DWI, and the combined RSI and PET model might improve diagnostic efficacy. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Xue Liu
- Department of Medical Imaging, Zhengzhou University People's Hospital, Zhengzhou, China
- Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, China
| | - Nan Meng
- Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, China
| | - Yihang Zhou
- Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, China
- Department of Medical Imaging, Xinxiang Medical University Henan Provincial People's Hospital, Zhengzhou, China
| | - Fangfang Fu
- Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, China
| | - Jianmin Yuan
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Zhe Wang
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Yang Yang
- Beijing United Imaging Research Institute of Intelligent Imaging, United Imaging Healthcare Group, Beijing, China
| | - Zhongyan Xiong
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Chao Zou
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Meiyun Wang
- Department of Medical Imaging, Zhengzhou University People's Hospital, Zhengzhou, China
- Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Biomedical Research Institute, Henan Academy of Sciences, Zhengzhou, China
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Zhang Y, Sheng R, Yang C, Dai Y, Zeng M. Detecting microvascular invasion in hepatocellular carcinoma using the impeded diffusion fraction technique to sense macromolecular coordinated water. Abdom Radiol (NY) 2024; 49:1892-1904. [PMID: 38526597 DOI: 10.1007/s00261-024-04230-x] [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: 12/31/2023] [Revised: 01/27/2024] [Accepted: 01/29/2024] [Indexed: 03/26/2024]
Abstract
OBJECTIVES Impeded diffusion fraction (IDF) is a novel and promising diffusion-weighted imaging (DWI) technique that allows for the detection of various diffusion compartments, including macromolecular coordinated water, free diffusion, perfusion, and cellular free water. This study aims to investigate the clinical potential of IDF-DWI in detecting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHODS 66 patients were prospectively included. Metrics derived from IDF-DWI and the apparent diffusion coefficient (ADC) were calculated. Multivariate logistic regression was employed to identify clinical risk factors. Diagnostic performance was evaluated using the area under the receiver operating characteristics curve (AUC-ROC), the area under the precision-recall curve (AUC-PR), and the calibration error (cal-error). Additionally, a power analysis was conducted to determine the required sample size. RESULTS The results suggested a significantly higher fraction of impeded diffusion (FID) originating from IDF-DWI in MVI-positive HCCs (p < 0.001). Moreover, the ADC was found to be significantly lower in MVI-positive HCCs (p = 0.019). Independent risk factors of MVI included larger tumor size and elevated alpha-fetoprotein (AFP) levels. The nomogram model incorporating ADC, FID, tumor size, and AFP level yielded the highest diagnostic accuracy for MVI (AUC-PR = 0.804, AUC-ROC = 0.783, cal-error = 0.044), followed by FID (AUC-PR = 0.693, AUC-ROC = 0.760, cal-error = 0.060) and ADC (AUC-PR = 0.570, AUC-ROC = 0.651, cal-error = 0.164). CONCLUSION IDF-DWI shows great potential in noninvasively, accurately, and preoperatively detecting MVI in HCC and may offer clinical benefits for prognostic prediction and determination of treatment strategy.
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Affiliation(s)
- Yunfei Zhang
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Ruofan Sheng
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Chun Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Yongming Dai
- School of Biomedical Engineering, State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech Univerisity, Shanghai, 200032, China.
| | - Mengsu Zeng
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
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He L, Qin Y, Hu Q, Liu Z, Zhang Y, Ai T. Quantitative characterization of breast lesions and normal fibroglandular tissue using compartmentalized diffusion-weighted model: comparison of intravoxel incoherent motion and restriction spectrum imaging. Breast Cancer Res 2024; 26:71. [PMID: 38658999 PMCID: PMC11044413 DOI: 10.1186/s13058-024-01828-3] [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: 12/18/2023] [Accepted: 04/15/2024] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND To compare the compartmentalized diffusion-weighted models, intravoxel incoherent motion (IVIM) and restriction spectrum imaging (RSI), in characterizing breast lesions and normal fibroglandular tissue. METHODS This prospective study enrolled 152 patients with 157 histopathologically verified breast lesions (41 benign and 116 malignant). All patients underwent a full-protocol preoperative breast MRI, including a multi-b-value DWI sequence. The diffusion parameters derived from the mono-exponential model (ADC), IVIM model (Dt, Dp, f), and RSI model (C1, C2, C3, C1C2, F1, F2, F3, F1F2) were quantitatively measured and then compared among malignant lesions, benign lesions and normal fibroglandular tissues using Kruskal-Wallis test. The Mann-Whitney U-test was used for the pairwise comparisons. Diagnostic models were built by logistic regression analysis. The ROC analysis was performed using five-fold cross-validation and the mean AUC values were calculated and compared to evaluate the discriminative ability of each parameter or model. RESULTS Almost all quantitative diffusion parameters showed significant differences in distinguishing malignant breast lesions from both benign lesions (other than C2) and normal fibroglandular tissue (all parameters) (all P < 0.0167). In terms of the comparisons of benign lesions and normal fibroglandular tissues, the parameters derived from IVIM (Dp, f) and RSI (C1, C2, C1C2, F1, F2, F3) showed significant differences (all P < 0.005). When using individual parameters, RSI-derived parameters-F1, C1C2, and C2 values yielded the highest AUCs for the comparisons of malignant vs. benign, malignant vs. normal tissue and benign vs. normal tissue (AUCs = 0.871, 0.982, and 0.863, respectively). Furthermore, the combined diagnostic model (IVIM + RSI) exhibited the highest diagnostic efficacy for the pairwise discriminations (AUCs = 0.893, 0.991, and 0.928, respectively). CONCLUSIONS Quantitative parameters derived from the three-compartment RSI model have great promise as imaging indicators for the differential diagnosis of breast lesions compared with the bi-exponential IVIM model. Additionally, the combined model of IVIM and RSI achieves superior diagnostic performance in characterizing breast lesions.
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Affiliation(s)
- Litong He
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, NO. 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030, China
| | - Yanjin Qin
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, 58th the Second Zhongshan Road, Guangzhou, 510080, China
| | - Qilan Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, NO. 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030, China
| | - Zhiqiang Liu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, NO. 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030, China
| | - Yunfei Zhang
- MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Tao Ai
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, NO. 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030, China.
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Zhang Y, Sheng R, Dai Y, Yang C, Zeng M. The value of varying diffusion curvature MRI for assessing the microvascular invasion of hepatocellular carcinoma. Abdom Radiol (NY) 2024; 49:1154-1164. [PMID: 38311671 DOI: 10.1007/s00261-023-04168-6] [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: 10/18/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 02/06/2024]
Abstract
PURPOSE Varying diffusion curvature (VDC) MRI is an emerging diffusion-weighted imaging (DWI) technique that can capture non-Gaussian diffusion behavior and reflect tissue heterogeneity. However, its clinical utility has hardly been evaluated. We aimed to investigate the value of the VDC technique in noninvasively assessing microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHODS 74 patients with HCCs, including 39 MVI-positive and 35 MVI-negative HCCs were included into this prospective study. Quantitative metrics between subgroups, clinical risk factors, as well as diagnostic performance were evaluated. The power analysis was also carried out to determine the statistical power. RESULTS MVI-positive HCCs exhibited significantly higher VDC-derived structural heterogeneity measure, D1 (0.680 ± 0.100 × 10-3 vs 0.572 ± 0.148 × 10-3 mm2/s, p = 0.001) and lower apparent diffusion coefficient (ADC) (1.350 ± 0.166 × 10-3 vs 1.471 ± 0.322 × 10-3 mm2/s, p = 0.0495) compared to MVI-negative HCCs. No statistical significance was observed for VDC-derived diffusion coefficient, D0 between the subgroups (p = 0.562). Tumor size (odds ratio (OR) = 1.242) and alpha-fetoprotein (AFP) (OR = 2.527) were identified as risk factors for MVI. A predictive nomogram was constructed based on D1, ADC, tumor size, and AFP, which exhibited the highest diagnostic accuracy (AUC = 0.817), followed by D1 (AUC = 0.753) and ADC (AUC = 0.647). The diagnostic performance of the nomogram-based model was also validated by the calibration curve and decision curve. CONCLUSION VDC can aid in the noninvasive and preoperative diagnosis of HCC with MVI, which may result in the clinical benefit in terms of prognostic prediction and clinical decision-making.
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Affiliation(s)
- Yunfei Zhang
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Ruofan Sheng
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Yongming Dai
- School of Biomedical Engineering, ShanghaiTech Univerisity, Shanghai, 200032, China
| | - Chun Yang
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
| | - Mengsu Zeng
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
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Song M, Wang Q, Feng H, Wang L, Zhang Y, Liu H. Preoperative Grading of Rectal Cancer with Multiple DWI Models, DWI-Derived Biological Markers, and Machine Learning Classifiers. Bioengineering (Basel) 2023; 10:1298. [PMID: 38002422 PMCID: PMC10669695 DOI: 10.3390/bioengineering10111298] [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: 08/14/2023] [Revised: 10/05/2023] [Accepted: 10/25/2023] [Indexed: 11/26/2023] Open
Abstract
Background: this study aimed to utilize various diffusion-weighted imaging (DWI) techniques, including mono-exponential DWI, intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI), for the preoperative grading of rectal cancer. Methods: 85 patients with rectal cancer were enrolled in this study. Mann-Whitney U tests or independent Student's t-tests were conducted to identify DWI-derived parameters that exhibited significant differences. Spearman or Pearson correlation tests were performed to assess the relationships among different DWI-derived biological markers. Subsequently, four machine learning classifier-based models were trained using various DWI-derived parameters as input features. Finally, diagnostic performance was evaluated using ROC analysis with 5-fold cross-validation. Results: With the exception of the pseudo-diffusion coefficient (Dp), IVIM-derived and DKI-derived parameters all demonstrated significant differences between low-grade and high-grade rectal cancer. The logistic regression-based machine learning classifier yielded the most favorable diagnostic efficacy (AUC: 0.902, 95% Confidence Interval: 0.754-1.000; Specificity: 0.856; Sensitivity: 0.925; Youden Index: 0.781). Conclusions: utilizing multiple DWI-derived biological markers in conjunction with a strategy employing multiple machine learning classifiers proves valuable for the noninvasive grading of rectal cancer.
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Affiliation(s)
- Mengyu Song
- Department of Radiology, Fourth Hospital of Hebei Medical University, No.12 Jiankang Road, Shijiazhuang 050000, China
| | - Qi Wang
- Department of Radiology, Fourth Hospital of Hebei Medical University, No.12 Jiankang Road, Shijiazhuang 050000, China
| | - Hui Feng
- Department of Radiology, Fourth Hospital of Hebei Medical University, No.12 Jiankang Road, Shijiazhuang 050000, China
| | - Lijia Wang
- Department of Radiology, Fourth Hospital of Hebei Medical University, No.12 Jiankang Road, Shijiazhuang 050000, China
| | - Yunfei Zhang
- Central Research Institute, United Imaging Healthcare, Shanghai 201800, China
| | - Hui Liu
- Department of Radiology, Fourth Hospital of Hebei Medical University, No.12 Jiankang Road, Shijiazhuang 050000, China
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Zhang Y, Sheng R, Yang C, Dai Y, Zeng M. The Feasibility of Using Tri-Exponential Intra-Voxel Incoherent Motion DWI for Identifying the Microvascular Invasion in Hepatocellular Carcinoma. J Hepatocell Carcinoma 2023; 10:1659-1671. [PMID: 37799828 PMCID: PMC10547827 DOI: 10.2147/jhc.s433948] [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: 08/29/2023] [Accepted: 09/21/2023] [Indexed: 10/07/2023] Open
Abstract
Purpose To assess the effectiveness of tri-exponential Intra-Voxel Incoherent Motion (tri-IVIM) MRI in preoperatively identifying microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Patients and Methods In this prospective study, 67 patients with HCC were included. Metrics from bi-exponential IVIM (bi-IVIM) and tri-IVIM were calculated. Subgroup comparisons were analyzed using the independent Student's t-test or Mann-Whitney U-test. Logistic regression was performed to explore clinical risk factors. Diagnostic performance was assessed using receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis. Results MVI-positive HCCs exhibited significantly lower true diffusion coefficient (Dt) from bi-IVIM, as well as fast-diffusion coefficients (Df) and slow-diffusion coefficients (Ds) from tri-IVIM, compared to MVI-negative HCCs (p < 0.05). Tumor size and alpha-fetoprotein (AFP) were identified as risk factors. The combination of tri-IVIM-derived metrics (Ds and Df) yielded higher diagnostic accuracy (AUC = 0.808) compared to bi-IVIM (AUC = 0.741). A predictive model based on a nomogram was constructed using Ds, Df, tumor size, and AFP, resulting in the highest diagnostic accuracy (AUC = 0.859). Decision curve analysis indicated that the constructed model, provided the highest net benefit by accurately stratifying the risk of MVI, followed by tri-IVIM and bi-IVIM. Conclusion Tri-IVIM can provide information on perfusion and diffusion for evaluating MVI in HCC. Additionally, tri-IVIM outperformed bi-IVIM in identifying MVI-positive HCC. By integrating clinical risk factors and metrics from tri-IVIM, a predictive nomogram exhibited the highest diagnostic accuracy, potentially aiding in the noninvasive and preoperative assessment of MVI.
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Affiliation(s)
- Yunfei Zhang
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, 200032, People’s Republic of China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, People’s Republic of China
| | - Ruofan Sheng
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, 200032, People’s Republic of China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, People’s Republic of China
| | - Chun Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, People’s Republic of China
| | - Yongming Dai
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, 200032, People’s Republic of China
| | - Mengsu Zeng
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, 200032, People’s Republic of China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, People’s Republic of China
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Zhang Y, Yang C, Sheng R, Dai Y, Zeng M. Preoperatively Identify the Microvascular Invasion of Hepatocellular Carcinoma with the Restricted Spectrum Imaging. Acad Radiol 2023; 30 Suppl 1:S30-S39. [PMID: 37442719 DOI: 10.1016/j.acra.2023.06.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/07/2023] [Accepted: 06/14/2023] [Indexed: 07/15/2023]
Abstract
RATIONALE AND OBJECTIVES To noninvasively and preoperatively identify the microvascular invasion (MVI) of hepatocellular carcinoma (HCC) with the restricted spectrum imaging (RSI). MATERIALS AND METHODS 62 patients were included into this prospective study and underwent the RSI examination with a 3.0-T scanner. Mono-exponential diffusion-weighted imaging-derived apparent diffusion coefficient (ADC) and RSI-derived metrics including f1 (fraction of restricted diffusion), f2 (fraction of hindered diffusion), f3 (fraction of free diffusion), and f1f2 (the multiply of f1 and f2) were calculated. Univariate and multivariate logistic regression were used to select the independent risk factors. Nomogram-based model was constructed with the selected indexes. Receiver operative characteristics analysis and calibration curve were used to evaluate the diagnostic accuracy. RESULTS MVI-positive HCC showed significantly higher f1 and lower ADC values (ADC: 1.549 ± 0.228 ×10-3 vs 1.365 ± 0.239 ×10-3 mm2/s, P = .003; f1: 0.1633 ± 0.0341 vs 0.2221 ± 0.0491, P < .001). Tumor size and f1 were selected as independent risk factors for MVI. The nomogram-based model was then constructed with tumor size and f1. Nomogram-based model (area under ROC curve [AUC]= 0.856) yielded the best diagnostic accuracy followed by f1 (AUC=0.842) and ADC (AUC=0.708). The AUC of both the f1 and nomogram model were significantly higher than that of ADC. CONCLUSION RSI-derived metrics can be utilized to noninvasively and efficiently identify the MVI of HCC. Considering the importance of MVI as a significant prognostic factor for HCC, the utilization of RSI has the potential to assist in prognostic prediction and clinical management.
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Affiliation(s)
- Yunfei Zhang
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China (Y.Z., R.S., M.Z.); Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai 200032, China (Y.Z., C.Y., R.S., M.Z.)
| | - Chun Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai 200032, China (Y.Z., C.Y., R.S., M.Z.)
| | - Ruofan Sheng
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China (Y.Z., R.S., M.Z.); Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai 200032, China (Y.Z., C.Y., R.S., M.Z.)
| | - Yongming Dai
- School of Biomedical Engineering, ShanghaiTech Univerisity, Shanghai, China (Y.D.)
| | - Mengsu Zeng
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China (Y.Z., R.S., M.Z.); Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai 200032, China (Y.Z., C.Y., R.S., M.Z.).
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