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Bagheri M, Ghorbani F, Akbari-Lalimi H, Akbari-Zadeh H, Asadinezhad M, Shafaghi A, Montazerabadi A. Histopathological graded liver lesions: what role does the IVIM analysis method have? MAGMA (NEW YORK, N.Y.) 2023; 36:565-575. [PMID: 36943581 DOI: 10.1007/s10334-022-01060-0] [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: 10/27/2022] [Revised: 12/23/2022] [Accepted: 12/29/2022] [Indexed: 03/23/2023]
Abstract
PURPOSE This study aims to investigate three different image processing methods on quantitative parameters of IVIM sequence, as well as apparent diffusion coefficients and simple perfusion fractions, for benign and malignant liver tumors. MATERIALS AND METHODS IVIM images with 8 b-values (0-1000 s/mm2) and 1.5 T MRI scanner in 16 patients and 3 healthy people were obtained. Next, the regions of interest were selected for malignant, benign, and healthy liver regions (50, 56, and 12, respectively). Then, the bi-exponential equation of the IVIM technique was fitted with two segmented fitting methods as well as one full fitting method (three methods in total). Using the segmented fitting method, diffusion coefficient (D) is fixed with a mono-exponential equation with b-values that are greater than 200 s/mm2. The perfusion fraction (f) can then be calculated by extrapolating, as the first method, or fitting simultaneously with the pseudo-diffusion coefficient (D*) as the second method. In the full fitting method, as the third method, all IVIM parameters were obtained simultaneously. The mean values of parameters from different methods were compared in different grades of lesions. RESULTS Our results indicate that the image processing method can change statistical comparisons between different groups for each parameter. The D value is the only quantity in this technique that does not depend on the fitting process and can be used as an indicator of comparison between studies (P < 0.05). The most effective method to distinguish liver lesions is the extrapolated f method (first method). This method created a significant difference (P < 0.05) between the perfusion parameters between benign and malignant lesions. CONCLUSION Using extrapolated f is the most effective method of distinguishing liver lesions using IVIM parameters. The comparison between groups does not depend on the fitting method only for parameter D.
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Affiliation(s)
- Mona Bagheri
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Farzaneh Ghorbani
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hossein Akbari-Lalimi
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hadi Akbari-Zadeh
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohsen Asadinezhad
- Department of Radiology Technology, School of Paramedical Sciences, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Afshin Shafaghi
- Caspian Digestive Disease Research Center, Guilan University of Medical Sciences, Rasht, Iran
| | - Alireza Montazerabadi
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
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Jiang L, Chen J, Huang H, Wu J, Zhang J, Lan X, Liu D, Zhang J. Comparison of the Differential Diagnostic Performance of Intravoxel Incoherent Motion Imaging and Diffusion Kurtosis Imaging in Malignant and Benign Thyroid Nodules. Front Oncol 2022; 12:895972. [PMID: 35936691 PMCID: PMC9354485 DOI: 10.3389/fonc.2022.895972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 06/22/2022] [Indexed: 11/29/2022] Open
Abstract
Objective This study aimed to compare the diagnostic capacity between IVIM and DKI in differentiating malignant from benign thyroid nodules. Material and Methods This study is based on magnetic resonance imaging data of the thyroid with histopathology as the reference standard. Spearman analysis was used to assess the relationship of IVIM-derived parameters D, f, D* and the DKI-derived parameters Dapp and Kapp. The parameters of IVIM and DKI were compared between the malignant and benign groups. Binary logistic regression analysis was performed to establish the diagnostic model, and receiver operating characteristic (ROC) curve analysis was subsequently performed. The DeLong test was used to compare the diagnostic effectiveness of different prediction models. Spearman analysis was used to assess the relationship of Ki-67 expression and parameters of IVIM and DKI. Results Among the 93 nodules, 46 nodules were malignant, and 47 nodules were benign. The Dapp of DKI-derived parameter was related to the D (P < 0.001, r = 0.863) of IVIM-derived parameter. The Kapp of DKI-derived parameter was related to the D (P < 0.001, r = -0.831) of IVIM-derived parameters. The malignant group had a significantly lower D value (P < 0.001) and f value (P = 0.013) than the benign group. The malignant group had significantly higher Kapp and lower Dapp values (all P < 0.001). The D+f had an area under the curve (AUC) of 0.951. The Dapp+Kapp had an AUC of 0.943. The D+f+Dapp+Kapp had an AUC of 0.954. The DeLong test showed no statistical significance among there prediction models. The D (P = 0.007) of IVIM-derived parameters and Dapp (P = 0.045) of DKI-derived parameter were correlated to the Ki-67 expression. Conclusions IVIM and DKI were alternative for each other in in differentiating malignant from benign thyroid nodules.
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Affiliation(s)
- Liling Jiang
- Department of Radiology, Cancer Hospital, Chongqing University, Chongqing, China
| | - Jiao Chen
- Department of Radiology, Cancer Hospital, Chongqing University, Chongqing, China
| | - Haiping Huang
- Department of Pathology, Cancer Hospital, Chongqing University, Chongqing, China
| | - Jian Wu
- Head and Neck Cancer Center, Cancer Hospital, Chongqing University, Chongqing, China
| | - Junbin Zhang
- Head and Neck Cancer Center, Cancer Hospital, Chongqing University, Chongqing, China
| | - Xiaosong Lan
- Department of Radiology, Cancer Hospital, Chongqing University, Chongqing, China
| | - Daihong Liu
- Department of Radiology, Cancer Hospital, Chongqing University, Chongqing, China
| | - Jiuquan Zhang
- Department of Radiology, Cancer Hospital, Chongqing University, Chongqing, China
- *Correspondence: Jiuquan Zhang,
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Artificial Intelligence Algorithm-Based MRI for Differentiation Diagnosis of Prostate Cancer. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8123643. [PMID: 35799629 PMCID: PMC9256308 DOI: 10.1155/2022/8123643] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 05/10/2022] [Accepted: 05/14/2022] [Indexed: 12/16/2022]
Abstract
The rapid increase in prostate cancer (PCa) patients is similar to that of benign prostatic hyperplasia (BPH) patients, but the treatments are quite different. In this research, magnetic resonance imaging (MRI) images under the weighted low-rank matrix restoration algorithm (RLRE) were utilized to differentiate PCa from BPH. The diagnostic effects of different sequences of MRI images were evaluated to provide a more effective examination method for the clinical differential diagnosis of PCa and BPH. 150 patients with suspected PCa were taken as the research objects. Pathological examination revealed that 137 patients had PCa and 13 patients had BPH. The pathological results were the gold standard and were compared with the MRI results of different sequences. Therefore, the accuracy of the MRI results was evaluated. The results showed that with the rise of Gaussian noise, the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of all three algorithms gradually decreased, but the PSNR and SSIM of the RLRE algorithm were always higher than those of the RL and BM3D algorithms (P < 0.05). The sensitivity (97.08%), specificity (92.31%), accuracy (96.67%), and consistency (0.678) of the dynamic contrast enhancement (DCE) sequence were higher than those of the plain scan (86.13%, 69.23%, 84.67%, and 0.469, respectively). In conclusion, the RLRE algorithm could promote the resolution of MRI images and improve the display effect. DCE could better differentiate PCa from BPH, had great clinical application value, and was worthy of clinical promotion.
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Li J, Lin L, Gao X, Li S, Cheng J. Amide Proton Transfer Weighted and Intravoxel Incoherent Motion Imaging in Evaluation of Prognostic Factors for Rectal Adenocarcinoma. Front Oncol 2022; 11:783544. [PMID: 35047400 PMCID: PMC8761907 DOI: 10.3389/fonc.2021.783544] [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: 09/26/2021] [Accepted: 12/06/2021] [Indexed: 11/13/2022] Open
Abstract
Objectives To analyze the value of amide proton transfer (APT) weighted and intravoxel incoherent motion (IVIM) imaging in evaluation of prognostic factors for rectal adenocarcinoma, compared with diffusion weighted imaging (DWI). Materials and Methods Preoperative pelvic MRI data of 110 patients with surgical pathologically confirmed diagnosis of rectal adenocarcinoma were retrospectively evaluated. All patients underwent high-resolution T2-weighted imaging (T2WI), APT, IVIM, and DWI. Parameters including APT signal intensity (APT SI), pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), and apparent diffusion coefficient (ADC) were measured in different histopathologic types, grades, stages, and structure invasion statuses. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy, and the corresponding area under the curves (AUCs) were calculated. Results APT SI, D and ADC values of rectal mucinous adenocarcinoma (MC) were significantly higher than those of rectal common adenocarcinoma (AC) ([3.192 ± 0.661%] vs. [2.333 ± 0.471%], [1.153 ± 0.238×10-3 mm2/s] vs. [0.792 ± 0.173×10-3 mm2/s], and [1.535 ± 0.203×10-3 mm2/s] vs. [0.986 ± 0.124×10-3 mm2/s], respectively; all P<0.001). In AC group, the APT SI and D values showed significant differences between low- and high-grade tumors ([2.226 ± 0.347%] vs. [2.668 ± 0.638%], and [0.842 ± 0.148×10-3 mm2/s] vs. [0.777 ± 0.178×10-3 mm2/s], respectively, both P<0.05). The D value had significant difference between positive and negative extramural vascular invasion (EMVI) tumors ([0.771 ± 0.175×10-3 mm2/s] vs. [0.858 ± 0.151×10-3 mm2/s], P<0.05). No significant difference of APT SI, D, D*, f or ADC was observed in different T stages, N stages, perineural and lymphovascular invasions (all P>0.05). The ROC curves showed that the AUCs of APT SI, D and ADC values for distinguishing MC from AC were 0.921, 0.893 and 0.995, respectively. The AUCs of APT SI and D values in distinguishing low- from high-grade AC were 0.737 and 0.663, respectively. The AUC of the D value for evaluating EMVI involvement was 0.646. Conclusion APT and IVIM were helpful to assess the prognostic factors related to rectal adenocarcinoma, including histopathological type, tumor grade and the EMVI status.
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Affiliation(s)
- Juan Li
- Department of Magnetic Resonance Imaging (MRI), The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Liangjie Lin
- Advanced Technical Support, Philips Healthcare, Beijing, China
| | - Xuemei Gao
- Department of Magnetic Resonance Imaging (MRI), The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shenglei Li
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging (MRI), The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Li C, Yu L, Jiang Y, Cui Y, Liu Y, Shi K, Hou H, Liu M, Zhang W, Zhang J, Zhang C, Chen M. The Histogram Analysis of Intravoxel Incoherent Motion-Kurtosis Model in the Diagnosis and Grading of Prostate Cancer-A Preliminary Study. Front Oncol 2021; 11:604428. [PMID: 34778020 PMCID: PMC8579734 DOI: 10.3389/fonc.2021.604428] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 10/06/2021] [Indexed: 12/09/2022] Open
Abstract
Objectives This study was conducted in order to explore the value of histogram analysis of the intravoxel incoherent motion-kurtosis (IVIM-kurtosis) model in the diagnosis and grading of prostate cancer (PCa), compared with monoexponential model (MEM). Materials and Methods Thirty patients were included in this study. Single-shot echo-planar imaging (SS-EPI) diffusion-weighted images (b-values of 0, 20, 50, 100, 200, 500, 1,000, 1,500, 2,000 s/mm2) were acquired. The pathologies were confirmed by in-bore MR-guided biopsy. The postprocessing and measurements were processed using the software tool Matlab R2015b for the IVIM-kurtosis model and MEM. Regions of interest (ROIs) were drawn manually. Mean values of D, D*, f, K, ADC, and their histogram parameters were acquired. The values of these parameters in PCa and benign prostatic hyperplasia (BPH)/prostatitis were compared. Receiver operating characteristic (ROC) curves were used to investigate the diagnostic efficiency. The Spearman test was used to evaluate the correlation of these parameters and Gleason scores (GS) of PCa. Results For the IVIM-kurtosis model, D (mean, 10th, 25th, 50th, 75th, 90th), D* (90th), and f (10th) were significantly lower in PCa than in BPH/prostatitis, while D (skewness), D* (kurtosis), and K (mean, 75th, 90th) were significantly higher in PCa than in BPH/prostatitis. For MEM, ADC (mean, 10th, 25th, 50th, 75th, 90th) was significantly lower in PCa than in BPH/prostatitis. The area under the ROC curve (AUC) of the IVIM-kurtosis model was higher than MEM, without significant differences (z = 1.761, P = 0.0783). D (mean, 50th, 75th, 90th), D* (mean, 10th, 25th, 50th, 75th), and f (skewness, kurtosis) correlated negatively with GS, while D (kurtosis), D* (skewness, kurtosis), f (mean, 75th, 90th), and K (mean, 75th, 90th) correlated positively with GS. The histogram parameters of ADC did not show correlations with GS. Conclusion The IVIM-kurtosis model has potential value in the differential diagnosis of PCa and BPH/prostatitis. IVIM-kurtosis histogram analysis may provide more information in the grading of PCa than MEM.
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Affiliation(s)
- Chunmei Li
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Lu Yu
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Yuwei Jiang
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Yadong Cui
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Ying Liu
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | | | - Huimin Hou
- Department of Urology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Ming Liu
- Department of Urology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Wei Zhang
- Department of Pathology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Jintao Zhang
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Chen Zhang
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Min Chen
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
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Xing P, Chen L, Yang Q, Song T, Ma C, Grimm R, Fu C, Wang T, Peng W, Lu J. Differentiating prostate cancer from benign prostatic hyperplasia using whole-lesion histogram and texture analysis of diffusion- and T2-weighted imaging. Cancer Imaging 2021; 21:54. [PMID: 34579789 PMCID: PMC8477463 DOI: 10.1186/s40644-021-00423-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 09/03/2021] [Indexed: 11/24/2022] Open
Abstract
Background To explore the usefulness of analyzing histograms and textures of apparent diffusion coefficient (ADC) maps and T2-weighted (T2W) images to differentiate prostatic cancer (PCa) from benign prostatic hyperplasia (BPH) using histopathology as the reference. Methods Ninety patients with PCa and 112 patients with BPH were included in this retrospective study. Differences in whole-lesion histograms and texture parameters of ADC maps and T2W images between PCa and BPH patients were evaluated using the independent samples t-test. The diagnostic performance of ADC maps and T2W images in being able to differentiate PCa from BPH was assessed using receiver operating characteristic (ROC) curves. Results The mean, median, 5th, and 95th percentiles of ADC values in images from PCa patients were significantly lower than those from BPH patients (p < 0.05). Significant differences were observed in the means, standard deviations, medians, kurtosis, skewness, and 5th percentile values of T2W image between PCa and BPH patients (p < 0.05). The ADC5th showed the largest AUC (0.906) with a sensitivity of 83.3 % and specificity of 89.3 %. The diagnostic performance of the T2W image histogram and texture analysis was moderate and had the largest AUC of 0.634 for T2WKurtosis with a sensitivity and specificity of 48.9% and 79.5 %, respectively. The diagnostic performance of the combined ADC5th & T2WKurtosis parameters was also similar to that of the ADC5th & ADCDiff−Variance. Conclusions Histogram and texture parameters derived from the ADC maps and T2W images for entire prostatic lesions could be used as imaging biomarkers to differentiate PCa and BPH biologic characteristics, however, histogram parameters outperformed texture parameters in the diagnostic performance.
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Affiliation(s)
- Pengyi Xing
- Department of Radiology, Changhai Hospital of Shanghai, The Second Military Medical University, No.168 Changhai Road, 200433, Shanghai, China
| | - Luguang Chen
- Department of Radiology, Changhai Hospital of Shanghai, The Second Military Medical University, No.168 Changhai Road, 200433, Shanghai, China
| | - Qingsong Yang
- Department of Radiology, Changhai Hospital of Shanghai, The Second Military Medical University, No.168 Changhai Road, 200433, Shanghai, China
| | - Tao Song
- Department of Radiology, Changhai Hospital of Shanghai, The Second Military Medical University, No.168 Changhai Road, 200433, Shanghai, China
| | - Chao Ma
- Department of Radiology, Changhai Hospital of Shanghai, The Second Military Medical University, No.168 Changhai Road, 200433, Shanghai, China
| | - Robert Grimm
- Application Predevelopment, Siemens Healthcare, Erlangen, Germany
| | - Caixia Fu
- MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, China
| | - Tiegong Wang
- Department of Radiology, Changhai Hospital of Shanghai, The Second Military Medical University, No.168 Changhai Road, 200433, Shanghai, China
| | - Wenjia Peng
- Department of Radiology, Changhai Hospital of Shanghai, The Second Military Medical University, No.168 Changhai Road, 200433, Shanghai, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital of Shanghai, The Second Military Medical University, No.168 Changhai Road, 200433, Shanghai, China.
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Liang J, Zeng S, Li Z, Kong Y, Meng T, Zhou C, Chen J, Wu Y, He N. Intravoxel Incoherent Motion Diffusion-Weighted Imaging for Quantitative Differentiation of Breast Tumors: A Meta-Analysis. Front Oncol 2020; 10:585486. [PMID: 33194733 PMCID: PMC7606934 DOI: 10.3389/fonc.2020.585486] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 09/14/2020] [Indexed: 01/12/2023] Open
Abstract
Objectives: The diagnostic performance of intravoxel incoherent motion diffusion–weighted imaging (IVIM-DWI) in the differential diagnosis of breast tumors remains debatable among published studies. Therefore, this meta-analysis aimed to pool relevant evidence regarding the diagnostic performance of IVIM-DWI in the differential diagnosis of breast tumors. Methods: Studies on the differential diagnosis of breast lesions using IVIM-DWI were systemically searched in the PubMed, Embase and Web of Science databases in recent 10 years. The standardized mean difference (SMD) and 95% confidence intervals of the apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudodiffusivity (D*), and perfusion fraction (f) were calculated using Review Manager 5.3, and Stata 12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC), as well as assess publication bias and heterogeneity. Fagan's nomogram was used to predict the posttest probabilities. Results: Sixteen studies comprising 1,355 malignant and 362 benign breast lesions were included. Most of these studies showed a low to unclear risk of bias and low concerns regarding applicability. Breast cancer had significant lower ADC (SMD = −1.38, P < 0.001) and D values (SMD = −1.50, P < 0.001), and higher f value (SMD = 0.89, P = 0.001) than benign lesions, except D* value (SMD = −0.30, P = 0.20). Invasive ductal carcinoma showed lower ADC (SMD = 1.34, P = 0.01) and D values (SMD = 1.04, P = 0.001) than ductal carcinoma in situ. D value demonstrated the best diagnostic performance (sensitivity = 86%, specificity = 86%, AUC = 0.91) and highest post-test probability (61, 48, 46, and 34% for D, ADC, f, and D* values) in the differential diagnosis of breast tumors, followed by ADC (sensitivity = 76%, specificity = 79%, AUC = 0.85), f (sensitivity = 80%, specificity = 76%, AUC = 0.85) and D* values (sensitivity = 84%, specificity = 59%, AUC = 0.71). Conclusion: IVIM-DWI parameters are adequate and superior to the ADC in the differentiation of breast tumors. ADC and D values can further differentiate invasive ductal carcinoma from ductal carcinoma in situ. IVIM-DWI is also superior in identifying lymph node metastasis, histologic grade, and hormone receptors, and HER2 and Ki-67 status.
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Affiliation(s)
- Jianye Liang
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Sihui Zeng
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Zhipeng Li
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yanan Kong
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Tiebao Meng
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Chunyan Zhou
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Jieting Chen
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - YaoPan Wu
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Ni He
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
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He N, Li Z, Li X, Dai W, Peng C, Wu Y, Huang H, Liang J. Intravoxel Incoherent Motion Diffusion-Weighted Imaging Used to Detect Prostate Cancer and Stratify Tumor Grade: A Meta-Analysis. Front Oncol 2020; 10:1623. [PMID: 33042805 PMCID: PMC7518084 DOI: 10.3389/fonc.2020.01623] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 07/27/2020] [Indexed: 12/17/2022] Open
Abstract
Objectives: Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) is a promising non-invasive imaging technique to detect and grade prostate cancer (PCa). However, the results regarding the diagnostic performance of IVIM-DWI in the characterization and classification of PCa have been inconsistent among published studies. This meta-analysis was performed to summarize the diagnostic performance of IVIM-DWI in the differential diagnosis of PCa from non-cancerous tissues and to stratify the tumor Gleason grades in PCa. Materials and Methods: Studies concerning the differential diagnosis of prostate lesions using IVIM-DWI were systemically searched in PubMed, Embase, and Web of Science without time limitation. Review Manager 5.3 was used to calculate the standardized mean difference (SMD) and 95% confidence intervals of the apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudodiffusivity (D*), and perfusion fraction (f). Stata 12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC), as well as publication bias and heterogeneity. Fagan's nomogram was used to predict the post-test probabilities. Results: Twenty studies with 854 patients confirmed with PCa were included. Most of the included studies showed a low to unclear risk of bias and low concerns regarding applicability. PCa showed a significantly lower ADC (SMD = −2.34; P < 0.001) and D values (SMD = −1.86; P < 0.001) and a higher D* value (SMD = 0.29; P = 0.01) than non-cancerous tissues, but no difference was noted with the f value (SMD = −0.16; P = 0.50). Low-grade PCa showed higher ADC (SMD = 0.63; P < 0.001) and D values (SMD = 0.80; P < 0.001) than the high-grade lesions. ADC showed comparable diagnostic performance (sensitivity = 86%; specificity = 86%; AUC = 0.87) but higher post-test probabilities (60, 53, 36, and 36% for ADC, D, D*, and f values, respectively) compared with the D (sensitivity = 82%; specificity = 82%; AUC = 0.85), D* (sensitivity = 70%; specificity = 70%; AUC = 0.75), and f values (sensitivity = 73%; specificity = 68%; AUC = 0.76). Conclusion: IVIM parameters are adequate to differentiate PCa from non-cancerous tissues with good diagnostic performance but are not superior to the ADC value. Diffusion coefficients can further stratify the tumor Gleason grades in PCa.
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Affiliation(s)
- Ni He
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Zhipeng Li
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Xie Li
- Department of Radiology, Maoming People's Hospital, Maoming, China
| | - Wei Dai
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Chuan Peng
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yaopan Wu
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Haitao Huang
- Department of Radiology, Maoming People's Hospital, Maoming, China
| | - Jianye Liang
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
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