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Kim JY, Partridge SC. Non-contrast Breast MR Imaging. Radiol Clin North Am 2024; 62:661-678. [PMID: 38777541 PMCID: PMC11116814 DOI: 10.1016/j.rcl.2023.12.009] [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] [Indexed: 05/25/2024]
Abstract
Considering the high cost of dynamic contrast-enhanced MR imaging and various contraindications and health concerns related to administration of intravenous gadolinium-based contrast agents, there is emerging interest in non-contrast-enhanced breast MR imaging. Diffusion-weighted MR imaging (DWI) is a fast, unenhanced technique that has wide clinical applications in breast cancer detection, characterization, prognosis, and predicting treatment response. It also has the potential to serve as a non-contrast MR imaging screening method. Standardized protocols and interpretation strategies can help to enhance the clinical utility of breast DWI. A variety of other promising non-contrast MR imaging techniques are in development, but currently, DWI is closest to clinical integration, while others are still mostly used in the research setting.
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Affiliation(s)
- Jin You Kim
- Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Republic of Korea
| | - Savannah C Partridge
- Department of Radiology, University of Washington, Seattle, WA, USA; Fred Hutchinson Cancer Center, Seattle, WA, USA.
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Zhang X, Qiu Y, Jiang W, Yang Z, Wang M, Li Q, Liu Y, Yan X, Yang G, Shen J. Mean Apparent Propagator MRI: Quantitative Assessment of Tumor-Stroma Ratio in Invasive Ductal Breast Carcinoma. Radiol Imaging Cancer 2024; 6:e230165. [PMID: 38874529 DOI: 10.1148/rycan.230165] [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: 06/15/2024]
Abstract
Purpose To determine whether metrics from mean apparent propagator (MAP) MRI perform better than apparent diffusion coefficient (ADC) value in assessing the tumor-stroma ratio (TSR) status in breast carcinoma. Materials and Methods From August 2021 to October 2022, 271 participants were prospectively enrolled (ClinicalTrials.gov identifier: NCT05159323) and underwent breast diffusion spectral imaging and diffusion-weighted imaging. MAP MRI metrics and ADC were derived from the diffusion MRI data. All participants were divided into high-TSR (stromal component < 50%) and low-TSR (stromal component ≥ 50%) groups based on pathologic examination. Clinicopathologic characteristics were collected, and MRI findings were assessed. Logistic regression was used to determine the independent variables for distinguishing TSR status. The area under the receiver operating characteristic curve (AUC) and sensitivity, specificity, and accuracy were compared between the MAP MRI metrics, either alone or combined with clinicopathologic characteristics, and ADC, using the DeLong and McNemar test. Results A total of 181 female participants (mean age, 49 years ± 10 [SD]) were included. All diffusion MRI metrics differed between the high-TSR and low-TSR groups (P < .001 to P = .01). Radial non-Gaussianity from MAP MRI and lymphovascular invasion were significant independent variables for discriminating the two groups, with a higher AUC (0.81 [95% CI: 0.74, 0.87] vs 0.61 [95% CI: 0.53, 0.68], P < .001) and accuracy (138 of 181 [76%] vs 106 of 181 [59%], P < .001) than that of the ADC. Conclusion MAP MRI may serve as a better approach than conventional diffusion-weighted imaging in evaluating the TSR of breast carcinoma. Keywords: MR Diffusion-weighted Imaging, MR Imaging, Breast, Oncology ClinicalTrials.gov Identifier: NCT05159323 Supplemental material is available for this article. © RSNA, 2024.
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Affiliation(s)
- Xiang Zhang
- From the Department of Radiology (X.Z., Y.Q., W.J., Z.Y., J.S.), Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center (X.Z., Y.Q., W.J., Z.Y., Q.L., Y.L., J.S.), and Department of Pathology (Q.L., Y.L.), Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, People's Republic of China; Department of Radiology, the First People's Hospital of Kashi Prefecture, Kashi, People's Republic of China (Y.Q.); Department of MR Scientific Marketing, Siemens Healthineers, Guangzhou, People's Republic of China (M.W., X.Y.); and Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, People's Republic of China (G.Y.)
| | - Ya Qiu
- From the Department of Radiology (X.Z., Y.Q., W.J., Z.Y., J.S.), Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center (X.Z., Y.Q., W.J., Z.Y., Q.L., Y.L., J.S.), and Department of Pathology (Q.L., Y.L.), Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, People's Republic of China; Department of Radiology, the First People's Hospital of Kashi Prefecture, Kashi, People's Republic of China (Y.Q.); Department of MR Scientific Marketing, Siemens Healthineers, Guangzhou, People's Republic of China (M.W., X.Y.); and Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, People's Republic of China (G.Y.)
| | - Wei Jiang
- From the Department of Radiology (X.Z., Y.Q., W.J., Z.Y., J.S.), Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center (X.Z., Y.Q., W.J., Z.Y., Q.L., Y.L., J.S.), and Department of Pathology (Q.L., Y.L.), Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, People's Republic of China; Department of Radiology, the First People's Hospital of Kashi Prefecture, Kashi, People's Republic of China (Y.Q.); Department of MR Scientific Marketing, Siemens Healthineers, Guangzhou, People's Republic of China (M.W., X.Y.); and Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, People's Republic of China (G.Y.)
| | - Zehong Yang
- From the Department of Radiology (X.Z., Y.Q., W.J., Z.Y., J.S.), Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center (X.Z., Y.Q., W.J., Z.Y., Q.L., Y.L., J.S.), and Department of Pathology (Q.L., Y.L.), Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, People's Republic of China; Department of Radiology, the First People's Hospital of Kashi Prefecture, Kashi, People's Republic of China (Y.Q.); Department of MR Scientific Marketing, Siemens Healthineers, Guangzhou, People's Republic of China (M.W., X.Y.); and Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, People's Republic of China (G.Y.)
| | - Mengzhu Wang
- From the Department of Radiology (X.Z., Y.Q., W.J., Z.Y., J.S.), Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center (X.Z., Y.Q., W.J., Z.Y., Q.L., Y.L., J.S.), and Department of Pathology (Q.L., Y.L.), Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, People's Republic of China; Department of Radiology, the First People's Hospital of Kashi Prefecture, Kashi, People's Republic of China (Y.Q.); Department of MR Scientific Marketing, Siemens Healthineers, Guangzhou, People's Republic of China (M.W., X.Y.); and Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, People's Republic of China (G.Y.)
| | - Qin Li
- From the Department of Radiology (X.Z., Y.Q., W.J., Z.Y., J.S.), Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center (X.Z., Y.Q., W.J., Z.Y., Q.L., Y.L., J.S.), and Department of Pathology (Q.L., Y.L.), Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, People's Republic of China; Department of Radiology, the First People's Hospital of Kashi Prefecture, Kashi, People's Republic of China (Y.Q.); Department of MR Scientific Marketing, Siemens Healthineers, Guangzhou, People's Republic of China (M.W., X.Y.); and Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, People's Republic of China (G.Y.)
| | - Yeqing Liu
- From the Department of Radiology (X.Z., Y.Q., W.J., Z.Y., J.S.), Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center (X.Z., Y.Q., W.J., Z.Y., Q.L., Y.L., J.S.), and Department of Pathology (Q.L., Y.L.), Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, People's Republic of China; Department of Radiology, the First People's Hospital of Kashi Prefecture, Kashi, People's Republic of China (Y.Q.); Department of MR Scientific Marketing, Siemens Healthineers, Guangzhou, People's Republic of China (M.W., X.Y.); and Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, People's Republic of China (G.Y.)
| | - Xu Yan
- From the Department of Radiology (X.Z., Y.Q., W.J., Z.Y., J.S.), Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center (X.Z., Y.Q., W.J., Z.Y., Q.L., Y.L., J.S.), and Department of Pathology (Q.L., Y.L.), Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, People's Republic of China; Department of Radiology, the First People's Hospital of Kashi Prefecture, Kashi, People's Republic of China (Y.Q.); Department of MR Scientific Marketing, Siemens Healthineers, Guangzhou, People's Republic of China (M.W., X.Y.); and Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, People's Republic of China (G.Y.)
| | - Guang Yang
- From the Department of Radiology (X.Z., Y.Q., W.J., Z.Y., J.S.), Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center (X.Z., Y.Q., W.J., Z.Y., Q.L., Y.L., J.S.), and Department of Pathology (Q.L., Y.L.), Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, People's Republic of China; Department of Radiology, the First People's Hospital of Kashi Prefecture, Kashi, People's Republic of China (Y.Q.); Department of MR Scientific Marketing, Siemens Healthineers, Guangzhou, People's Republic of China (M.W., X.Y.); and Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, People's Republic of China (G.Y.)
| | - Jun Shen
- From the Department of Radiology (X.Z., Y.Q., W.J., Z.Y., J.S.), Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center (X.Z., Y.Q., W.J., Z.Y., Q.L., Y.L., J.S.), and Department of Pathology (Q.L., Y.L.), Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, People's Republic of China; Department of Radiology, the First People's Hospital of Kashi Prefecture, Kashi, People's Republic of China (Y.Q.); Department of MR Scientific Marketing, Siemens Healthineers, Guangzhou, People's Republic of China (M.W., X.Y.); and Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, People's Republic of China (G.Y.)
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Fan Z, Guo J, Zhang X, Chen Z, Wang B, Jiang Y, Li Y, Wang Y, Yang G, Wang X. Non-Gaussian diffusion metrics with whole-tumor histogram analysis for bladder cancer diagnosis: muscle invasion and histological grade. Insights Imaging 2024; 15:138. [PMID: 38853200 PMCID: PMC11162990 DOI: 10.1186/s13244-024-01701-z] [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: 02/16/2024] [Accepted: 04/13/2024] [Indexed: 06/11/2024] Open
Abstract
PURPOSE To investigate the performance of histogram features of non-Gaussian diffusion metrics for diagnosing muscle invasion and histological grade in bladder cancer (BCa). METHODS Patients were prospectively allocated to MR scanner1 (training cohort) or MR2 (testing cohort) for conventional diffusion-weighted imaging (DWIconv) and multi-b-value DWI. Metrics of continuous time random walk (CTRW), diffusion kurtosis imaging (DKI), fractional-order calculus (FROC), intravoxel incoherent motion (IVIM), and stretched exponential model (SEM) were simultaneously calculated using multi-b-value DWI. Whole-tumor histogram features were extracted from DWIconv and non-Gaussian diffusion metrics for logistic regression analysis to develop diffusion models diagnosing muscle invasion and histological grade. The models' performances were quantified by area under the receiver operating characteristic curve (AUC). RESULTS MR1 included 267 pathologically-confirmed BCa patients (median age, 67 years [IQR, 46-82], 222 men) and MR2 included 83 (median age, 65 years [IQR, 31-82], 73 men). For discriminating muscle invasion, CTRW achieved the highest testing AUC of 0.915, higher than DWIconv's 0.805 (p = 0.014), and similar to the combined diffusion model's AUC of 0.885 (p = 0.076). For differentiating histological grade of non-muscle-invasion bladder cancer, IVIM outperformed a testing AUC of 0.897, higher than DWIconv's 0.694 (p = 0.020), and similar to the combined diffusion model's AUC of 0.917 (p = 0.650). In both tasks, DKI, FROC, and SEM failed to show diagnostic superiority over DWIconv (p > 0.05). CONCLUSION CTRW and IVIM are two potential non-Gaussian diffusion models to improve the MRI application in assessing muscle invasion and histological grade of BCa, respectively. CRITICAL RELEVANCE STATEMENT Our study validates non-Gaussian diffusion imaging as a reliable, non-invasive technique for early assessment of muscle invasion and histological grade in BCa, enhancing accuracy in diagnosis and improving MRI application in BCa diagnostic procedures. KEY POINTS Muscular invasion largely determines bladder salvageability in bladder cancer patients. Evaluated non-Gaussian diffusion metrics surpassed DWIconv in BCa muscle invasion and histological grade diagnosis. Non-Gaussian diffusion imaging improved MRI application in preoperative diagnosis of BCa.
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Affiliation(s)
- Zhichang Fan
- Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Junting Guo
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Xiaoyue Zhang
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Zeke Chen
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Bin Wang
- Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Yueluan Jiang
- Department of MR Research Collaboration, Siemens Healthineers, Beijing, China
| | - Yan Li
- Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Yongfang Wang
- Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Guoqiang Yang
- Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Xiaochun Wang
- Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
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Gui Y, Chen F, Ren J, Wang L, Chen K, Zhang J. MRI- and DWI-Based Radiomics Features for Preoperatively Predicting Meningioma Sinus Invasion. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1054-1066. [PMID: 38351221 PMCID: PMC11169408 DOI: 10.1007/s10278-024-01024-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 01/05/2024] [Accepted: 01/08/2024] [Indexed: 06/13/2024]
Abstract
The aim of this study was to use multimodal imaging (contrast-enhanced T1-weighted (T1C), T2-weighted (T2), and diffusion-weighted imaging (DWI)) to develop a radiomics model for preoperatively predicting venous sinus invasion in meningiomas. This prediction would assist in selecting the appropriate surgical approach and forecasting the prognosis of meningiomas. A retrospective analysis was conducted on 331 participants who had been pathologically diagnosed with meningiomas. For each participant, 3948 radiomics features were acquired from the T1C, T2, and DWI images. Minimum redundancy maximum correlation, rank sum test, and multi-factor recursive elimination were used to extract the most significant features of different models. Then, multivariate logistic regression was used to build classification models to predict meningioma venous sinus invasion. The diagnostic capabilities were assessed using receiver operating characteristic (ROC) analysis. In addition, a nomogram was constructed by incorporating clinical and radiological characteristics and a radiomics signature. To assess the clinical usefulness of the nomogram, a decision curve analysis (DCA) was performed. Tumor shape, boundary, and enhancement features were independent predictors of meningioma venous sinus invasion (p = 0.013, p = 0.013, p = 0.005, respectively). Eleven (T2:1, T1C:4, DWI:6) of the 3948 radiomics features were screened for strong association with meningioma sinus invasion. The areas under the ROC curves for the training and external test sets were 0.946 and 0.874, respectively. The clinicoradiomic model showed excellent predictive performance for invasive meningioma, which may help to guide surgical approaches and predict prognosis.
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Affiliation(s)
- Yuan Gui
- Department of Radiology, Doumen District, The Fifth affiliated Hospital of Zunyi Medical University, Zhufeng Dadao No. 1439, Zhuhai, China
| | - Fen Chen
- Department of Radiology, Doumen District, The Fifth affiliated Hospital of Zunyi Medical University, Zhufeng Dadao No. 1439, Zhuhai, China
| | - Jialiang Ren
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Beijing, China
| | - Limei Wang
- Department of Radiology, Doumen District, The Fifth affiliated Hospital of Zunyi Medical University, Zhufeng Dadao No. 1439, Zhuhai, China
| | - Kuntao Chen
- Department of Radiology, Doumen District, The Fifth affiliated Hospital of Zunyi Medical University, Zhufeng Dadao No. 1439, Zhuhai, China
| | - Jing Zhang
- Department of Radiology, Doumen District, The Fifth affiliated Hospital of Zunyi Medical University, Zhufeng Dadao No. 1439, Zhuhai, China.
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Sheng Y, Chang H, Xue K, Chen J, Jiao T, Cui D, Wang H, Zhang G, Yang Y, Zeng Q. Characterization of prostatic cancer lesion and gleason grade using a continuous-time random-walk diffusion model at high b-values. Front Oncol 2024; 14:1389250. [PMID: 38854720 PMCID: PMC11157027 DOI: 10.3389/fonc.2024.1389250] [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: 02/21/2024] [Accepted: 05/07/2024] [Indexed: 06/11/2024] Open
Abstract
Background Distinguishing between prostatic cancer (PCa) and chronic prostatitis (CP) is sometimes challenging, and Gleason grading is strongly associated with prognosis in PCa. The continuous-time random-walk diffusion (CTRW) model has shown potential in distinguishing between PCa and CP as well as predicting Gleason grading. Purpose This study aimed to quantify the CTRW parameters (α, β & Dm) and apparent diffusion coefficient (ADC) of PCa and CP tissues; and then assess the diagnostic value of CTRW and ADC parameters in differentiating CP from PCa and low-grade PCa from high-grade PCa lesions. Study type Retrospective (retrospective analysis using prospective designed data). Population Thirty-one PCa patients undergoing prostatectomy (mean age 74 years, range 64-91 years), and thirty CP patients undergoing prostate needle biopsies (mean age 68 years, range 46-79 years). Field strength/Sequence MRI scans on a 3.0T scanner (uMR790, United Imaging Healthcare, Shanghai, China). DWI were acquired with 12 b-values (0, 50, 100, 150, 200, 500, 800, 1200, 1500, 2000, 2500, 3000 s/mm2). Assessment CTRW parameters and ADC were quantified in PCa and CP lesions. Statistical tests The Mann-Whitney U test was used to evaluate the differences in CTRW parameters and ADC between PCa and CP, high-grade PCa, and low-grade PCa. Spearman's correlation of the pathologic grading group (GG) with CTRW parameters and ADC was evaluated. The usefulness of CTRW parameters, ADC, and their combinations (Dm, α and β; Dm, α, β, and ADC) to differentiate PCa from CP and high-grade PCa from low-grade PCa was determined by logistic regression and receiver operating characteristic curve (ROC) analysis. Delong test was used to compare the differences among AUCs. Results Significant differences were found for the CTRW parameters (α, Dm) between CP and PCa (all P<0.001), high-grade PCa, and low-grade PCa (α:P=0.024, Dm:P=0.021). GG is correlated with certain CTRW parameters and ADC(α:P<0.001,r=-0.795; Dm:P<0.001,r=-0.762;ADC:P<0.001,r=-0.790). Moreover, CTRW parameters (α, β, Dm) combined with ADC showed the best diagnostic efficacy for distinguishing between PCa and CP as well as predicting Gleason grading. The differences among AUCs of ADC, CTRW parameters and their combinations were not statistically significant (P=0.051-0.526). Conclusion CTRW parameters α and Dm, as well as their combination were beneficial to distinguish between CA and PCa, low-grade PCa and high-grade PCa lesions, and CTRW parameters and ADC had comparable diagnostic performance.
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Affiliation(s)
- Yurui Sheng
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, China
| | - Huan Chang
- Department of Radiology, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, Shandong, China
| | - Ke Xue
- Magnenic Resonance (MR) Collaboration, United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - Jinming Chen
- Department of Radiology, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, Shandong, China
| | - Tianyu Jiao
- Department of Radiology, Shandong Public Health Clinical Center, Jinan, Shandong, China
| | - Dongqing Cui
- Department of Neurology, The Second Hospital of Shandong University, Jinan, Shandong, China
| | - Hao Wang
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, China
| | - Guanghui Zhang
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, China
| | - Yuxin Yang
- Magnenic Resonance (MR) Collaboration, United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - Qingshi Zeng
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, China
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Zhou M, Bao D, Huang H, Chen M, Jiang W. Utilization of diffusion-weighted derived mathematical models to predict prognostic factors of resectable rectal cancer. Abdom Radiol (NY) 2024:10.1007/s00261-024-04239-2. [PMID: 38744701 DOI: 10.1007/s00261-024-04239-2] [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: 12/21/2023] [Revised: 02/05/2024] [Accepted: 02/05/2024] [Indexed: 05/16/2024]
Abstract
PURPOSE This study explored models of monoexponential diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), stretched exponential (SEM), fractional-order calculus (FROC), and continuous-time random-walk (CTRW) as diagnostic tools for assessing pathological prognostic factors in patients with resectable rectal cancer (RRC). METHODS RRC patients who underwent radical surgery were included. The apparent diffusion coefficient (ADC), the mean kurtosis (MK) and mean diffusion (MD) from the DKI model, the distributed diffusion coefficient (DDC) and α from the SEM model, D, β and u from the FROC model, and D, α and β from the CTRW model were assessed. RESULTS There were a total of 181 patients. The area under the receiver operating characteristic (ROC) curve (AUC) of CTRW-α for predicting histology type was significantly higher than that of FROC-u (0.780 vs. 0.671, p = 0.043). The AUC of CTRW-α for predicting pT stage was significantly higher than that of FROC-u and ADC (0.786 vs.0.683, p = 0.043; 0.786 vs. 0.682, p = 0.030), the difference in predictive efficacy of FROC-u between ADC and MK was not statistically significant [0.683 vs. 0.682, p = 0.981; 0.683 vs. 0.703, p = 0.720]; the difference between the predictive efficacy of MK and ADC was not statistically significant (p = 0.696). The AUC of CTRW (α + β) (0.781) was significantly higher than that of FROC-u (0.781 vs. 0.625, p = 0.003) in predicting pN stage but not significantly different from that of MK (p = 0.108). CONCLUSION The CTRW and DKI models may serve as imaging biomarkers to predict pathological prognostic factors in RRC patients before surgery.
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Affiliation(s)
- Mi Zhou
- Department of Radiology, Sichuan Provincial Orthopedic Hospital, Chengdu, China.
| | - Deying Bao
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - Hongyun Huang
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - Meining Chen
- Department of MR Scientific Marketing, Siemens Healthineers, Shanghai, 200135, China
| | - Wenli Jiang
- Department of Radiology, Second Affiliated Hospital of Chongqing University of Medical Sciences, Chongqing, 400010, China
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Zhang Y, Chen J, Yang C, Dai Y, Zeng M. Preoperative prediction of microvascular invasion in hepatocellular carcinoma using diffusion-weighted imaging-based habitat imaging. Eur Radiol 2024; 34:3215-3225. [PMID: 37853175 DOI: 10.1007/s00330-023-10339-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 07/27/2023] [Accepted: 08/20/2023] [Indexed: 10/20/2023]
Abstract
OBJECTIVES Habitat imaging allows for the quantification and visualization of various subregions within the tumor. We aim to develop an approach using diffusion-weighted imaging (DWI)-based habitat imaging for preoperatively predicting the microvascular invasion (MVI) of hepatocellular carcinoma (HCC). METHODS Sixty-five patients were prospectively included and underwent multi-b DWI examinations. Based on the true diffusion coefficient (Dt), perfusion fraction (f), and mean kurtosis coefficient (MK), which respectively characterize cellular density, perfusion, and heterogeneity, the HCCs were divided into four habitats. The volume fraction of each habitat was quantified. The logistic regression was used to explore the risk factors from habitat fraction and clinical variables. Clinical, habitat, and nomogram models were constructed using the identified risk factors from clinical characteristics, habitat fraction, and their combination, respectively. The diagnostic accuracy was evaluated using the area under the receiver operating characteristic curves (AUCs). RESULTS MVI-positive HCC exhibited a significantly higher fraction of habitat 4 (f4) and a significantly lower fraction of habitat 2 (f2) (p < 0.001), which were selected as risk factors. Additionally, tumor size and elevated alpha-fetoprotein (AFP) were also included as risk factors for MVI. The nomogram model demonstrated the highest diagnostic performance (AUC = 0.807), followed by the habitat model (AUC = 0.777) and the clinical model (AUC = 0.708). Decision curve analysis indicated that the nomogram model offered more net benefit in identifying MVI compared to the clinical model. CONCLUSIONS DWI-based habitat imaging shows clinical potential for noninvasively and preoperatively determining the MVI of HCC with high accuracy. CLINICAL RELEVANCE STATEMENT The proposed strategy, diffusion-weighted imaging-based habitat imaging, can be applied for preoperatively and noninvasively identifying microvascular invasion in hepatocellular carcinoma, which offers potential benefits in terms of prognostic prediction and clinical management. KEY POINTS • This study proposed a strategy of DWI-based habitat imaging for hepatocellular carcinoma. • The habitat imaging-derived metrics can serve as diagnostic markers for identifying the microvascular invasion. • Integrating the habitat-based metric and clinical variable, a predictive nomogram was constructed and displayed high accuracy for predicting microvascular invasion.
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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
| | - Jiejun Chen
- 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
- 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 University, 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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Tang C, Li F, He L, Hu Q, Qin Y, Yan X, Ai T. Comparison of continuous-time random walk and fractional order calculus models in characterizing breast lesions using histogram analysis. Magn Reson Imaging 2024; 108:47-58. [PMID: 38307375 DOI: 10.1016/j.mri.2024.01.012] [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: 08/11/2023] [Revised: 11/11/2023] [Accepted: 01/22/2024] [Indexed: 02/04/2024]
Abstract
OBJECTIVE To compare the diagnostic performance of different mathematical models for DWI and explore whether parameters reflecting spatial and temporal heterogeneity can demonstrate better diagnostic accuracy than the diffusion coefficient parameter in distinguishing benign and malignant breast lesions, using whole-tumor histogram analysis. METHODS This retrospective study was approved by the institutional ethics committee and included 104 malignant and 42 benign cases. All patients underwent breast magnetic resonance imaging (MRI) with a 3.0 T MR scanner using the simultaneous multi-slice (SMS) readout-segment ed echo-planar imaging (rs-EPI). Histogram metrics of Mono- apparent diffusion coefficient (ADC), CTRW, and FROC-derived parameters were compared between benign and malignant breast lesions, and the diagnostic performance of each diffusion parameter was evaluated. Statistical analysis was performed using Mann-Whitney U test and receiver operating characteristic (ROC) curve. RESULTS The DFROC-median exhibited the highest AUC for distinguishing benign and malignant breast lesions (AUC = 0.965). The temporal heterogeneity parameter αCTRW-median generated a statistically higher AUC compared to the spatial heterogeneity parameter βCTRW-median (AUC = 0.850 and 0.741, respectively; p = 0.047). Finally, the combination of median values of CTRW parameters displayed a slightly higher AUC than that of FROC parameters, with no significant difference however (AUC = 0.971 and 0.965, respectively; p = 0.172). CONCLUSIONS The diffusion coefficient parameter exhibited superior diagnostic performance in distinguishing breast lesions when compared to the temporal and spatial heterogeneity parameters.
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Affiliation(s)
- Caili Tang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Feng Li
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei 441021, China
| | - Litong He
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Qilan Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yanjin Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xu Yan
- MR Research Collaboration Team, Siemens Healthineers Ltd, 278, Zhouzhu Road, Nanhui, Shanghai 201318, China
| | - Tao Ai
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, 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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Mao C, Hu L, Jiang W, Qiu Y, Yang Z, Liu Y, Wang M, Wang D, Su Y, Lin J, Yan X, Cai Z, Zhang X, Shen J. Discrimination between human epidermal growth factor receptor 2 (HER2)-low-expressing and HER2-overexpressing breast cancers: a comparative study of four MRI diffusion models. Eur Radiol 2024; 34:2546-2559. [PMID: 37672055 DOI: 10.1007/s00330-023-10198-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: 01/05/2023] [Revised: 06/13/2023] [Accepted: 07/08/2023] [Indexed: 09/07/2023]
Abstract
OBJECTIVES To determine the value of conventional DWI, continuous-time random walk (CTRW), fractional order calculus (FROC), and stretched exponential model (SEM) in discriminating human epidermal growth factor receptor 2 (HER2) status of breast cancer (BC). METHODS This prospective study included 158 women who underwent DWI, CTRW, FROC, and SEM and were pathologically categorized into the HER2-zero-expressing group (n = 10), HER2-low-expressing group (n = 86), and HER2-overexpressing group (n = 62). Nine diffusion parameters, namely ADC, αCTRW, βCTRW, DCTRW, βFROC, DFROC, μFROC, αSEM, and DDCSEM of the primary tumor, were derived from four diffusion models. These diffusion metrics and clinicopathologic features were compared between groups. Logistic regression was used to determine the optimal diffusion metrics and clinicopathologic variables for classifying the HER2-expressing statuses. Receiver operating characteristic (ROC) curves were used to evaluate their discriminative ability. RESULTS The estrogen receptor (ER) status, progesterone receptor (PR) status, and tumor size differed between HER2-low-expressing and HER2-overexpressing groups (p < 0.001 to p = 0.009). The αCTRW, DCTRW, βFROC, DFROC, μFROC, αSEM, and DDCSEM were significantly lower in HER2-low-expressing BCs than those in HER2-overexpressing BCs (p < 0.001 to p = 0.01). Further multivariable logistic regression analysis showed that the αCTRW was the single best discriminative metric, with an area under the curve (AUC) being higher than that of ADC (0.802 vs. 0.610, p < 0.05); the addition of ER status, PR status, and tumor size to the αCTRW improved the AUC to 0.877. CONCLUSIONS The αCTRW could help discriminate the HER2-low-expressing and HER2-overexpressing BCs. CLINICAL RELEVANCE STATEMENT Human epidermal growth factor receptor 2 (HER2)-low-expressing breast cancer (BC) might also benefit from the HER2-targeted therapy. Prediction of HER2-low-expressing BC or HER2-overexpressing BC is crucial for appropriate management. Advanced continuous-time random walk diffusion MRI offers a solution to this clinical issue. KEY POINTS • Human epidermal receptor 2 (HER2)-low-expressing BC had lower αCTRW, DCTRW, βFROC, DFROC, μFROC, αSEM, and DDCSEM values compared with HER2-overexpressing breast cancer. • The αCTRW was the single best diffusion metric (AUC = 0.802) for discrimination between the HER2-low-expressing and HER2-overexpressing breast cancers. • The addition of αCTRW to the clinicopathologic features (estrogen receptor status, progesterone receptor status, and tumor size) further improved the discriminative ability.
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Affiliation(s)
- Chunping Mao
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Lanxin Hu
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Wei Jiang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Ya Qiu
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Zehong Yang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Yeqing Liu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Department of Pathology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Mengzhu Wang
- MR Scientific Marketing, Siemens Healthcare, Guangzhou, Guangdong, China
| | - Dongye Wang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Yun Su
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Jinru Lin
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Xu Yan
- MR Scientific Marketing, Siemens Healthcare, Guangzhou, Guangdong, China
| | - Zhaoxi Cai
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Xiang Zhang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China.
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.
| | - Jun Shen
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China.
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 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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12
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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) 2024:10.1007/s00261-024-04230-x. [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] [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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Gui Y, Zhang J. Research Progress of Artificial Intelligence in the Grading and Classification of Meningiomas. Acad Radiol 2024:S1076-6332(24)00073-4. [PMID: 38413314 DOI: 10.1016/j.acra.2024.02.003] [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: 12/02/2023] [Revised: 02/02/2024] [Accepted: 02/02/2024] [Indexed: 02/29/2024]
Abstract
A meningioma is a common primary central nervous system tumor. The histological features of meningiomas vary significantly depending on the grade and subtype, leading to differences in treatment and prognosis. Therefore, early diagnosis, grading, and typing of meningiomas are crucial for developing comprehensive and individualized diagnosis and treatment plans. The advancement of artificial intelligence (AI) in medical imaging, particularly radiomics and deep learning (DL), has contributed to the increasing research on meningioma grading and classification. These techniques are fast and accurate, involve fully automated learning, are non-invasive and objective, enable the efficient and non-invasive prediction of meningioma grades and classifications, and provide valuable assistance in clinical treatment and prognosis. This article provides a summary and analysis of the research progress in radiomics and DL for meningioma grading and classification. It also highlights the existing research findings, limitations, and suggestions for future improvement, aiming to facilitate the future application of AI in the diagnosis and treatment of meningioma.
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Affiliation(s)
- Yuan Gui
- Department of Radiology, the fifth affiliated hospital of zunyi medical university, zhufengdadao No.1439, Doumen District, Zhuhai, China
| | - Jing Zhang
- Department of Radiology, the fifth affiliated hospital of zunyi medical university, zhufengdadao No.1439, Doumen District, Zhuhai, China.
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Tayebi M, Kwon E, Maller J, McGeown J, Scadeng M, Qiao M, Wang A, Nielsen P, Fernandez J, Holdsworth S, Shim V. Integration of diffusion tensor imaging parameters with mesh morphing for in-depth analysis of brain white matter fibre tracts. Brain Commun 2024; 6:fcae027. [PMID: 38638147 PMCID: PMC11024816 DOI: 10.1093/braincomms/fcae027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 10/06/2023] [Accepted: 02/07/2024] [Indexed: 04/20/2024] Open
Abstract
Averaging is commonly used for data reduction/aggregation to analyse high-dimensional MRI data, but this often leads to information loss. To address this issue, we developed a novel technique that integrates diffusion tensor metrics along the whole volume of the fibre bundle using a 3D mesh-morphing technique coupled with principal component analysis for delineating case and control groups. Brain diffusion tensor MRI scans of high school rugby union players (n = 30, age 16-18) were acquired on a 3 T MRI before and after the sports season. A non-contact sport athlete cohort with matching demographics (n = 12) was also scanned. The utility of the new method in detecting differences in diffusion tensor metrics of the right corticospinal tract between contact and non-contact sport athletes was explored. The first step was to run automated tractography on each subject's native space. A template model of the right corticospinal tract was generated and morphed into each subject's native shape and space, matching individual geometry and diffusion metric distributions with minimal information loss. The common dimension of the 20 480 diffusion metrics allowed further data aggregation using principal component analysis to cluster the case and control groups as well as visualization of diffusion metric statistics (mean, ±2 SD). Our approach of analysing the whole volume of white matter tracts led to a clear delineation between the rugby and control cohort, which was not possible with the traditional averaging method. Moreover, our approach accounts for the individual subject's variations in diffusion tensor metrics to visualize group differences in quantitative MR data. This approach may benefit future prediction models based on other quantitative MRI methods.
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Affiliation(s)
- Maryam Tayebi
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
| | - Eryn Kwon
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
| | | | - Josh McGeown
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
| | - Miriam Scadeng
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
- Faculty of Medical and Health Sciences, The University of Auckland, Auckland, 1023, New Zealand
| | - Miao Qiao
- Department of Computer Science, The University of Auckland, Auckland, 1010, New Zealand
| | - Alan Wang
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand
- Faculty of Medical and Health Sciences, The University of Auckland, Auckland, 1023, New Zealand
| | - Poul Nielsen
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand
- Department of Engineering Science, The University of Auckland, Auckland, 1010, New Zealand
| | - Justin Fernandez
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
- Department of Engineering Science, The University of Auckland, Auckland, 1010, New Zealand
| | - Samantha Holdsworth
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
- Faculty of Medical and Health Sciences, The University of Auckland, Auckland, 1023, New Zealand
| | - Vickie Shim
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
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Zhou L, Qu Y, Quan G, Zuo H, Liu M. Nomogram for Predicting Microvascular Invasion in Hepatocellular Carcinoma Using Gadoxetic Acid-Enhanced MRI and Intravoxel Incoherent Motion Imaging. Acad Radiol 2024; 31:457-466. [PMID: 37491178 DOI: 10.1016/j.acra.2023.06.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/19/2023] [Accepted: 06/26/2023] [Indexed: 07/27/2023]
Abstract
RATIONALE AND OBJECTIVES Microvascular invasion (MVI) is an important risk factor in hepatocellular carcinoma (HCC), but it can only be determined through histopathological results. The aim of this study was to develop and validate a nomogram for preoperative prediction MVI in HCC using gadoxetic acid-enhanced magnetic resonance imaging (MRI) and intravoxel incoherent motion imaging (IVIM). MATERIALS AND METHODS From July 2017 to September 2022, 148 patients with surgically resected HCC who underwent preoperative gadoxetic acid-enhanced MRI and IVIM were included in this retrospective study. Clinical indicators, imaging features, and diffusion parameters were compared between the MVI-positive and MVI-negative groups using the chi-square test, Mann-Whitney U test, and independent sample t test. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance in predicting MVI. Univariate and multivariate analyses were conducted to identify the significant clinical-radiological variables associated with MVI. Subsequently, a predictive nomogram that integrates clinical-radiological risk factors and diffusion parameters was developed and validated. RESULTS Serum alpha-fetoprotein level, tumor size, nonsmooth tumor margin, peritumoral hypo-intensity on hepatobiliary phase (HBP), apparent diffusion coefficient value and D value were statistically significant different between MVI-positive group and MVI-negative group. The results of multivariate analysis identified tumor size (odds ratio [OR], 0.786; 95% confidence interval [CI], 0.675-0.915; P < .01), nonsmooth tumor margin (OR, 2.299; 95% CI, 1.005-5.257; P < .05), peritumoral hypo-intensity on HBP (OR, 2.786; 95% CI, 1.141-6.802; P < .05) and D (OR, 0.293; 95% CI,0.089-0.964; P < .05) was the independent risk factor for the status of MVI. In ROC analysis, the combination of peritumoral hypo-intensity on HBP and D demonstrated the highest area under the curve value (0.902) in prediction MVI status, with sensitivity 92.8% and specificity 87.7%. The nomogram exhibited excellent predictive performance with C-index of 0.936 (95% CI 0.895-0.976) in the patient cohort, and had well-fitted calibration curve. CONCLUSION The nomogram incorporating clinical-radiological risk factors and diffusion parameters achieved satisfactory preoperative prediction of the individualized risk of MVI in patients with HCC.
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Affiliation(s)
- Lisui Zhou
- Department of Radiology, Chengdu Xinhua Hospital Affiliated to North Sichuan Medical College, Chengdu, China (L.Z., H.Z., M.L.)
| | - Yuan Qu
- Department of Radiology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China (Y.Q.)
| | - Guangnan Quan
- MR Research China, GE Healthcare China, Beijing, China (G.Q.)
| | - Houdong Zuo
- Department of Radiology, Chengdu Xinhua Hospital Affiliated to North Sichuan Medical College, Chengdu, China (L.Z., H.Z., M.L.)
| | - Mi Liu
- Department of Radiology, Chengdu Xinhua Hospital Affiliated to North Sichuan Medical College, Chengdu, China (L.Z., H.Z., M.L.).
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Hu R, Zeng GF, Fang Y, Nie L, Liang HL, Wang ZG, Yang H. Intravoxel incoherent motion diffusion-weighted imaging for evaluating the pancreatic perfusion in cirrhotic patients. Abdom Radiol (NY) 2024; 49:492-500. [PMID: 38052890 DOI: 10.1007/s00261-023-04063-0] [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: 04/17/2023] [Revised: 09/11/2023] [Accepted: 09/13/2023] [Indexed: 12/07/2023]
Abstract
PURPOSE To assess the characteristics of pancreatic perfusion in normal pancreas versus cirrhotic patients using intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI). METHODS A total of 67 cirrhotic patients and 33 healthy subjects underwent IVIM on a 3.0 T MRI scanner. Diffusion coefficient (ADCslow), pseudo-diffusion coefficient (ADCfast), and perfusion fraction (f) were calculated based on the bi-exponential model. The pancreatic IVIM-derived parameters were then compared. In the cirrhotic group, the relationship was analyzed between IVIM-derived pancreatic parameters and different classes of hepatic function as determined by the Child-Pugh classification. Also, the pancreatic IVIM-derived parameters were compared among different classes of cirrhosis as determined by the Child-Pugh classification. RESULTS The f value of the pancreas in cirrhotic patients was significantly lower than that in normal subjects (p = 0.01). In the cirrhotic group, the f value of the pancreas decreased with the increase of the Child-Pugh classification (R = - 0.49, p = 0.00). The f value of the pancreas was significantly higher in Child-Pugh class A patients than in class B and C patients (p = 0.02, 0.00, respectively), whereas there was no significant difference between class B and C patients (p = 0.16). CONCLUSION The IVIM-derived perfusion-related parameter (f value) could be helpful for the evaluation of pancreatic perfusion in liver cirrhosis. Our data also suggest that the blood perfusion decrease in the pancreas is present in liver cirrhosis, and the pancreatic perfusion tends to decrease with the increasing severity of hepatic function. TRIAL REGISTRATION Trial registration number is 2021-ky-68 and date of registration for prospectively registered trials is February 23, 2022.
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Affiliation(s)
- Ran Hu
- Department of Radiology, Chongqing Hospital of Traditional Chinese Medicine, No.6, Panxi 7th Road, Jiangbei District, Chongqing, 400021, People's Republic of China
| | - Guo-Fei Zeng
- Department of Radiology, Chongqing Hospital of Traditional Chinese Medicine, No.6, Panxi 7th Road, Jiangbei District, Chongqing, 400021, People's Republic of China
| | - Yu Fang
- Department of Radiology, Chongqing Hospital of Traditional Chinese Medicine, No.6, Panxi 7th Road, Jiangbei District, Chongqing, 400021, People's Republic of China
| | - Lisha Nie
- GE Healthcare, MR Research China, Beijing, People's Republic of China
| | - Hui-Lou Liang
- GE Healthcare, MR Research China, Beijing, People's Republic of China
| | - Zhi-Gang Wang
- Department of Ultrasound, The Second Affiliated Hospital of Chongqing Medical University, 76 Linjiang Road, Yuzhong Distinct, Chongqing, 400010, People's Republic of China.
| | - Hua Yang
- Department of Radiology, Chongqing Hospital of Traditional Chinese Medicine, No.6, Panxi 7th Road, Jiangbei District, Chongqing, 400021, People's Republic of China.
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17
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Wang X, Ye Z, Li S, Yan Z, Cheng J, Ning G, Hou Z. A multicenter study of cervical cancer using quantitative diffusion-weighted imaging. Acta Radiol 2024:2841851231222360. [PMID: 38196316 DOI: 10.1177/02841851231222360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
BACKGROUND Parameters from diffusion-weighted imaging (DWI) have been increasingly used as imaging biomarkers for the diagnosis and monitoring of treatment responses in cancer. The consistency of DWI measurements across different centers remains uncertain, which limits the widespread use of quantitative DWI in clinical settings. PURPOSE To investigate the consistency of quantitative metrics derived from DWI between different scanners in a multicenter clinical setting. MATERIAL AND METHODS A total of 193 patients with cervical cancer from four scanners (MRI1, MRI2, MRI3, and MRI4) at three centers were included in this retrospective study. DWI data were processed using the mono-exponential and intravoxel incoherent motion (IVIM) model, yielding the following parameters: apparent diffusion coefficient (ADC); true diffusion coefficient (D); pseudo-diffusion coefficient (D*); perfusion fraction (f); and the product of f and D* (fD*). Various parameters of cervical cancer obtained from different scanners were compared. RESULTS The parameters D and ADC derived from MRI1 and MRI2 were significantly different from those derived from MRI3 or MRI4 (P <0.01 for all comparisons). However, there was no significant difference in cervical cancer perfusion parameters (D* and fD*) between the different scanners (P >0.05). The P values of comparisons of all DWI parameters (D, D*, fD*, and ADC) between MRI3 and MRI4 (same vendor in different centers) for cervical cancer were all >0.05, except for f (P = 0.05). CONCLUSION Scanners of the same model by the same vendor can yield close measurements of the ADC and IVIM parameters. The perfusion parameters showed higher consistency among the different scanners.
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Affiliation(s)
- Xue Wang
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, PR China
| | - Zhijun Ye
- Department of Radiology, The Second Affiliated Hospital of Sichuan University, Chengdu, PR China
| | - Shujian Li
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, PR China
| | - Zhihan Yan
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, PR China
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, PR China
| | - Gang Ning
- Department of Radiology, The Second Affiliated Hospital of Sichuan University, Chengdu, PR China
| | - Zujun Hou
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, PR China
- Chinese Academy of Sciences, Suzhou Institute of Biomedical Engineering and Technology, Suzhou, PR China
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18
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Bian W, Huang Q, Zhang J, Li J, Song X, Cui S, Zheng Q, Niu J. Intravoxel incoherent motion diffusion-weighted MRI for the evaluation of early spleen involvement in acute leukemia. Quant Imaging Med Surg 2024; 14:98-110. [PMID: 38223126 PMCID: PMC10784019 DOI: 10.21037/qims-23-856] [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: 06/12/2023] [Accepted: 09/30/2023] [Indexed: 01/16/2024]
Abstract
Background The spleen is a frequent organ of leukemia metastasis. This study aimed to investigate the value of intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging (MRI) for assessing pathologic changes in the spleen and identifying early spleen involvement in patients with acute leukemia (AL). Methods Patients with newly diagnosed AL and healthy controls were recruited between June 2020 and November 2022. All participants underwent abdominal IVIM diffusion-weighted imaging (DWI) at our hospital. IVIM parameters [pure diffusion coefficient (D); pseudo-diffusion coefficient (D*); and pseudo-perfusion fraction (f)] of the spleen were calculated by the segmented fitting method, and perfusion-diffusion ratio (PDR) was further calculated from the values of D, D* and f. Spleen volumes (SVs) were obtained by manually segmenting the spleen layer by layer. Clinical biomarkers of AL patients were collected. Patients were divided into splenomegaly group and normal SV group according to the individualized reference intervals for SV. IVIM parameters were compared among the control group, AL with normal SV group, and AL with splenomegaly group using one-way analysis of variance, followed by pairwise post hoc comparisons. The correlations of IVIM parameters with clinical biomarkers were analyzed in AL patients. The diagnostic performances of IVIM parameters and their combinations for differentiating among the three groups were compared. Results Seventy-nine AL patients (AL with splenomegaly: n=54; AL with normal SV: n=25) and 55 healthy controls were evaluated. IVIM parameters were significantly different among the three groups (P<0.001 for D, D* and f; P=0.001 for PDR). D and PDR showed significant differences between the control and AL with normal SV groups in pairwise comparisons (P<0.001, and P=0.031, respectively). D was correlated with white blood cell (WBC) counts (r=-0.424; 95% CI: -0.570, -0.211; P<0.001), lactate dehydrogenase (LDH) (r=-0.285; 95% CI: -0.486, -0.011; P=0.011), and bone marrow blasts (r=-0.283; 95% CI: -0.476, -0.067; P=0.012). D* (r=-0.276; 95% CI: -0.470, -0.025; P=0.014), f (r=0.514; 95% CI: 0.342, 0.664; P<0.001) and PDR (r=0.343; 95% CI: 0.208, 0.549; P=0.002) were correlated with LDH. The combination of IVIM parameters (AUC: 0.830; 95% CI: 0.729, 0.905) demonstrated better diagnostic efficacy than the single D* (AUC: 0.721; 95% CI: 0.608, 0.816; Delong test: Z=2.012, P=0.044) and f (AUC: 0.647; 95% CI: 0.532, 0.752; Delong test: Z=2.829, P=0.005), but was not significantly different from the single D (AUC: 0.756; 95% CI: 0.647, 0.846; Delong test: Z=1.676, P=0.094) in differentiating the splenomegaly group and normal SV group. Conclusions IVIM diffusion-weighted MRI could be a potential alternative for assessing pathologic changes in the spleen from cellularity and angiogenesis, and D and PDR may be viable indicators to identify early spleen involvement in patients with AL.
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Affiliation(s)
- Wenjin Bian
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, China
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, China
| | - Qianqian Huang
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, China
| | - Jianling Zhang
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, China
| | - Jianting Li
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, China
| | - Xiaoli Song
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, China
| | - Sha Cui
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, China
| | - Qian Zheng
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, China
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, China
| | - Jinliang Niu
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, China
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Gao J, Jiang M, Erricolo D, Magin RL, Morfini G, Royston T, Larson AC, Li W. Identifying potential imaging markers for diffusion property changes in a mouse model of amyotrophic lateral sclerosis: Application of the continuous time random walk model to ultrahigh b-value diffusion-weighted MR images of spinal cord tissue. NMR IN BIOMEDICINE 2024; 37:e5037. [PMID: 37721118 DOI: 10.1002/nbm.5037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 08/17/2023] [Accepted: 08/22/2023] [Indexed: 09/19/2023]
Abstract
Diffusion MRI (dMRI) explores tissue microstructures by analyzing diffusion-weighted signal decay measured at different b-values. While relatively low b-values are used for most dMRI models, high b-value diffusion-weighted imaging (DWI) techniques have gained interest given that the non-Gaussian water diffusion behavior observed at high b-values can yield potentially valuable information. In this study, we investigated anomalous diffusion behaviors associated with degeneration of spinal cord tissue using a continuous time random walk (CTRW) model for DWI data acquired across an extensive range of ultrahigh b-values. The diffusion data were acquired in situ from the lumbar level of spinal cords of wild-type and age-matched transgenic SOD1G93A mice, a well-established animal model of amyotrophic lateral sclerosis (ALS) featuring progressive degeneration of axonal tracts in this tissue. Based on the diffusion decay behaviors at low and ultrahigh b-values, we applied the CTRW model using various combinations of b-values and compared diffusion metrics calculated from the CTRW model between the experimental groups. We found that diffusion-weighted signal decay curves measured with ultrahigh b-values (up to 858,022 s/mm2 in this study) were well represented by the CTRW model. The anomalous diffusion coefficient obtained from lumbar spinal cords was significantly higher in SOD1G93A mice compared with control mice (14.7 × 10-5 ± 5.54 × 10-5 vs. 7.87 × 10-5 ± 2.48 × 10-5 mm2 /s, p = 0.01). We believe this is the first study to illustrate the efficacy of the CTRW model for analyzing anomalous diffusion regimes at ultrahigh b-values. The CTRW modeling of ultrahigh b-value dMRI can potentially present a novel approach for noninvasively evaluating alterations in spinal cord tissue associated with ALS pathology.
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Affiliation(s)
- Jin Gao
- Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, Illinois, USA
- Preclinical Imaging Core, University of Illinois Chicago, Chicago, Illinois, USA
| | - Mingchen Jiang
- Department of Physiology, Northwestern University, Chicago, Illinois, USA
| | - Danilo Erricolo
- Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, Illinois, USA
| | - Richard L Magin
- Department of Biomedical Engineering, University of Illinois Chicago, Chicago, Illinois, USA
| | - Gerardo Morfini
- Department of Anatomy and Cell Biology, University of Illinois Chicago, Chicago, Illinois, USA
| | - Thomas Royston
- Department of Biomedical Engineering, University of Illinois Chicago, Chicago, Illinois, USA
| | - Andrew C Larson
- Department of Radiology, Northwestern University, Chicago, Illinois, USA
| | - Weiguo Li
- Preclinical Imaging Core, University of Illinois Chicago, Chicago, Illinois, USA
- Department of Biomedical Engineering, University of Illinois Chicago, Chicago, Illinois, USA
- Department of Radiology, Northwestern University, Chicago, Illinois, USA
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Zhao J, Wang M, Ding X, Fu Y, Peng C, Kang H, Guo H, Bai X, Huang Q, Zhou S, Zhang X, Liu K, Li L, Ye H, Zhang X, Ma X, Wang H. Intravoxel Incoherent Motion Diffusion-Weighted MR Imaging and Venous Tumor Thrombus Consistency in Renal Cell Carcinoma. J Magn Reson Imaging 2024; 59:134-145. [PMID: 37134147 DOI: 10.1002/jmri.28763] [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: 02/26/2023] [Revised: 04/18/2023] [Accepted: 04/19/2023] [Indexed: 05/05/2023] Open
Abstract
BACKGROUND Venous tumor thrombus (VTT) consistency of renal cell carcinoma (RCC) is an important consideration in nephrectomy plus thrombectomy. However, evaluation of VTT consistency through preoperative MR imaging is lacking. PURPOSE To evaluate VTT consistency of RCC through intravoxel incoherent motion-diffusion weighted imaging (IVIM-DWI) derived parameters (Dt , Dp , f, and ADC) and the apparent diffusion coefficient (ADC) value. STUDY TYPE Retrospective. POPULATION One hundred and nineteen patients (aged 55.8 ± 11.5 years, 85 male) with histologically-proven RCC and VTT who underwent radical resection. FIELD STRENGTH/SEQUENCES 3.0-T; two-dimensional single-shot diffusion-weighted echo planar imaging sequence at 9 b-values (0-800 s/mm2 ). ASSESSMENT IVIM parameters and ADC values of the primary tumor and the VTT were calculated. The VTT consistency (friable vs. solid) was determined through intraoperative findings of two urologists. The accuracy of VTT consistency classification based on the individual IVIM parameters of primary tumors and of VTT, and based on models combining parameters, was assessed. Type of operation, intra-operative blood loss, and operation length were recorded. STATISTICAL TESTS Shapiro-Wilk test; Mann-Whitney U test; Student's t-test; Chi-square test; Receiver operating characteristic (ROC) analysis. Statistical significance level was P < 0.05. RESULTS Of the enrolled 119 patients, 33 patients (27.7%) had friable VTT. Patients with friable VTT were significantly more likely to experience open surgery, have significantly more intraoperative blood loss, and significantly longer operative duration. The area under the ROC curve (AUC) values of Dt of the primary tumor and VTT in classifying VTT consistency were 0.758 (95% CI 0.671-0.832) and 0.712 (95% CI 0.622-0.792), respectively. The AUC value of the model combining Dp and Dt of VTT was 0.800 (95% CI 0.717-0.868). Furthermore, the AUC of the model combining Dp and Dt of VTT and Dt of the primary tumor was 0.886 (95% CI 0.814-0.937). CONCLUSION IVIM-derived parameters had the potential to predict VTT consistency of RCC. EVIDENCE LEVEL 3 Technical Efficacy: Stage 2.
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Affiliation(s)
- Jian Zhao
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China
- Department of Radiology, Armed Police Force Hospital of Sichuan, Leshan, Sichuan, China
| | - Meifeng Wang
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China
- Department of Radiology, Sixth Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Xiaohui Ding
- Department of Pathology, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Yonggui Fu
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China
- Department of Radiology, Sixth Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Cheng Peng
- Department of Urology, Chinese PLA General Hospital, Beijing, China
| | - Huanhuan Kang
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Huiping Guo
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Xu Bai
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Qingbo Huang
- Department of Urology, Chinese PLA General Hospital, Beijing, China
| | - Shaopeng Zhou
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Xiaojing Zhang
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Kan Liu
- Department of Urology, Chinese PLA General Hospital, Beijing, China
| | - Lin Li
- Department of Innovative Medical Research, Hospital Management Institute, Chinese PLA General Hospital, Beijing, China
| | - Huiyi Ye
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Xu Zhang
- Department of Urology, Chinese PLA General Hospital, Beijing, China
| | - Xin Ma
- Department of Urology, Chinese PLA General Hospital, Beijing, China
| | - Haiyi Wang
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China
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Karaman M, Zhou XJ. Editorial for "Computed Diffusion-Weighted Images of Rectal Cancer: Image Quality, Restaging, and Treatment Response after Neoadjuvant Therapy". J Magn Reson Imaging 2024; 59:309-310. [PMID: 37194671 DOI: 10.1002/jmri.28769] [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: 04/18/2023] [Accepted: 04/18/2023] [Indexed: 05/18/2023] Open
Abstract
Level of Evidence5Technical Efficacy Stage2
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Affiliation(s)
- Muge Karaman
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Xiaohong Joe Zhou
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, Illinois, USA
- Departments of Radiology and Neurosurgery, University of Illinois at Chicago, Chicago, Illinois, USA
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22
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Ristow I, Kaul MG, Stark M, Zapf A, Riedel C, Lenz A, Mautner VF, Farschtschi S, Apostolova I, Adam G, Bannas P, Salamon J, Well L. Discrimination of benign, atypical, and malignant peripheral nerve sheath tumors in neurofibromatosis type 1 using diffusion-weighted MRI. Neurooncol Adv 2024; 6:vdae021. [PMID: 38468867 PMCID: PMC10926940 DOI: 10.1093/noajnl/vdae021] [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] [Indexed: 03/13/2024] Open
Abstract
Background Neurofibromatosis type 1 (NF1) is associated with the development of benign (BPNST) and malignant (MPNST) peripheral nerve sheath tumors. Recently described atypical neurofibromas (ANF) are considered pre-malignant precursor lesions to MPNSTs. Previous studies indicate that diffusion-weighted magnetic resonance imaging (DW-MRI) can reliably discriminate MPNSTs from BPNSTs. We therefore investigated the diagnostic accuracy of DW-MRI for the discrimination of benign, atypical, and malignant peripheral nerve sheath tumors. Methods In this prospective explorative single-center phase II diagnostic study, 44 NF1 patients (23 male; 30.1 ± 11.8 years) underwent DW-MRI (b-values 0-800 s/mm²) at 3T. Two radiologists independently assessed mean and minimum apparent diffusion coefficients (ADCmean/min) in areas of largest tumor diameters and ADCdark in areas of lowest signal intensity by manual contouring of the tumor margins of 60 BPNSTs, 13 ANFs, and 21 MPNSTs. Follow-up of ≥ 24 months (BPNSTs) or histopathological evaluation (ANFs + MPNSTs) served as diagnostic reference standard. Diagnostic ADC-based cut-off values for discrimination of the three tumor groups were chosen to yield the highest possible specificity while maintaining a clinically acceptable sensitivity. Results ADC values of pre-malignant ANFs clustered between BPNSTs and MPNSTs. Best BPNST vs. ANF + MPNST discrimination was obtained using ADCdark at a cut-off value of 1.6 × 10-3 mm2/s (85.3% sensitivity, 93.3% specificity), corresponding to an AUC of 94.3% (95% confidence interval: 85.2-98.0). Regarding BPNST + ANF vs. MPNST, best discrimination was obtained using an ADCdark cut-off value of 1.4 × 10-3 mm2/s (83.3% sensitivity, 94.5% specificity). Conclusions DW-MRI using ADCdark allows specific and noninvasive discrimination of benign, atypical, and malignant nerve sheath tumors in NF1.
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Affiliation(s)
- Inka Ristow
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Michael G Kaul
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Maria Stark
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Antonia Zapf
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christoph Riedel
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alexander Lenz
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Victor F Mautner
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Said Farschtschi
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ivayla Apostolova
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Gerhard Adam
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Peter Bannas
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Johannes Salamon
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Diagnostic and Interventional Radiology, Medical Care Center Beste Trave, Bad Oldesloe, Germany
| | - Lennart Well
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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Lewis EM, Mao L, Wang L, Swanson KR, Barajas RF, Li J, Tran NL, Hu LS, Plaisier CL. Revealing the biology behind MRI signatures in high grade glioma. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.08.23299733. [PMID: 38168377 PMCID: PMC10760280 DOI: 10.1101/2023.12.08.23299733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Magnetic resonance imaging (MRI) measurements are routinely collected during the treatment of high-grade gliomas (HGGs) to characterize tumor boundaries and guide surgical tumor resection. Using spatially matched MRI and transcriptomics we discovered HGG tumor biology captured by MRI measurements. We strategically overlaid the spatially matched omics characterizations onto a pre-existing transcriptional map of glioblastoma multiforme (GBM) to enhance the robustness of our analyses. We discovered that T1+C measurements, designed to capture vasculature and blood brain barrier (BBB) breakdown and subsequent contrast extravasation, also indirectly reveal immune cell infiltration. The disruption of the vasculature and BBB within the tumor creates a permissive infiltrative environment that enables the transmigration of anti-inflammatory macrophages into tumors. These relationships were validated through histology and enrichment of genes associated with immune cell transmigration and proliferation. Additionally, T2-weighted (T2W) and mean diffusivity (MD) measurements were associated with angiogenesis and validated using histology and enrichment of genes involved in neovascularization. Furthermore, we establish an unbiased approach for identifying additional linkages between MRI measurements and tumor biology in future studies, particularly with the integration of novel MRI techniques. Lastly, we illustrated how noninvasive MRI can be used to map HGG biology spatially across a tumor, and this provides a platform to develop diagnostics, prognostics, or treatment efficacy biomarkers to improve patient outcomes.
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Affiliation(s)
- Erika M Lewis
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, 85287, USA
| | - Lingchao Mao
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Lujia Wang
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Kristin R Swanson
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, 85054, USA
- Department of Neurosurgery, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Ramon F Barajas
- Advanced Imaging Research Center, Oregon Health & Sciences University, USA
- Department of Radiology, Neuroradiology Section, Oregon Health & Sciences University, USA
- Knight Cancer Institute, Oregon Health & Sciences University, USA
| | - Jing Li
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Nhan L Tran
- Department of Neurosurgery, Mayo Clinic, Phoenix, AZ, 85054, USA
- Department of Cancer Biology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Leland S Hu
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, 85054, USA
- Department of Radiology, Mayo Clinic, Phoenix, AZ, 85054, USA
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85281, USA
| | - Christopher L Plaisier
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, 85287, USA
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Fokkinga E, Hernandez-Tamames JA, Ianus A, Nilsson M, Tax CMW, Perez-Lopez R, Grussu F. Advanced Diffusion-Weighted MRI for Cancer Microstructure Assessment in Body Imaging, and Its Relationship With Histology. J Magn Reson Imaging 2023. [PMID: 38032021 DOI: 10.1002/jmri.29144] [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: 08/11/2023] [Revised: 10/30/2023] [Accepted: 10/31/2023] [Indexed: 12/01/2023] Open
Abstract
Diffusion-weighted magnetic resonance imaging (DW-MRI) aims to disentangle multiple biological signal sources in each imaging voxel, enabling the computation of innovative maps of tissue microstructure. DW-MRI model development has been dominated by brain applications. More recently, advanced methods with high fidelity to histology are gaining momentum in other contexts, for example, in oncological applications of body imaging, where new biomarkers are urgently needed. The objective of this article is to review the state-of-the-art of DW-MRI in body imaging (ie, not including the nervous system) in oncology, and to analyze its value as compared to reference colocalized histology measurements, given that demonstrating the histological validity of any new DW-MRI method is essential. In this article, we review the current landscape of DW-MRI techniques that extend standard apparent diffusion coefficient (ADC), describing their acquisition protocols, signal models, fitting settings, microstructural parameters, and relationship with histology. Preclinical, clinical, and in/ex vivo studies were included. The most used techniques were intravoxel incoherent motion (IVIM; 36.3% of used techniques), diffusion kurtosis imaging (DKI; 16.7%), vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT; 13.3%), and imaging microstructural parameters using limited spectrally edited diffusion (IMPULSED; 11.7%). Another notable category of techniques relates to innovative b-tensor diffusion encoding or joint diffusion-relaxometry. The reviewed approaches provide histologically meaningful indices of cancer microstructure (eg, vascularization/cellularity) which, while not necessarily accurate numerically, may still provide useful sensitivity to microscopic pathological processes. Future work of the community should focus on improving the inter-/intra-scanner robustness, and on assessing histological validity in broader contexts. LEVEL OF EVIDENCE: NA TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Ella Fokkinga
- Biomedical Engineering, Track Medical Physics, Delft University of Technology, Delft, The Netherlands
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Juan A Hernandez-Tamames
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Andrada Ianus
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Markus Nilsson
- Department of Diagnostic Radiology, Clinical Sciences Lund, Lund, Sweden
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Center (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Francesco Grussu
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
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Weninger L, Ecke J, Jütten K, Clusmann H, Wiesmann M, Merhof D, Na CH. Diffusion MRI anomaly detection in glioma patients. Sci Rep 2023; 13:20366. [PMID: 37990121 PMCID: PMC10663596 DOI: 10.1038/s41598-023-47563-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/15/2023] [Indexed: 11/23/2023] Open
Abstract
Diffusion-MRI (dMRI) measures molecular diffusion, which allows to characterize microstructural properties of the human brain. Gliomas strongly alter these microstructural properties. Delineation of brain tumors currently mainly relies on conventional MRI-techniques, which are, however, known to underestimate tumor volumes in diffusely infiltrating glioma. We hypothesized that dMRI is well suited for tumor delineation, and developed two different deep-learning approaches. The first diffusion-anomaly detection architecture is a denoising autoencoder, the second consists of a reconstruction and a discrimination network. Each model was exclusively trained on non-annotated dMRI of healthy subjects, and then applied on glioma patients' data. To validate these models, a state-of-the-art supervised tumor segmentation network was modified to generate groundtruth tumor volumes based on structural MRI. Compared to groundtruth segmentations, a dice score of 0.67 ± 0.2 was obtained. Further inspecting mismatches between diffusion-anomalous regions and groundtruth segmentations revealed, that these colocalized with lesions delineated only later on in structural MRI follow-up data, which were not visible at the initial time of recording. Anomaly-detection methods are suitable for tumor delineation in dMRI acquisitions, and may further enhance brain-imaging analysis by detection of occult tumor infiltration in glioma patients, which could improve prognostication of disease evolution and tumor treatment strategies.
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Affiliation(s)
- Leon Weninger
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
- Department of Electrical Engineering, RWTH Aachen University, Aachen, Germany
| | - Jarek Ecke
- Department of Electrical Engineering, RWTH Aachen University, Aachen, Germany
| | - Kerstin Jütten
- Department of Neurosurgery, RWTH Aachen University, Aachen, Germany
| | - Hans Clusmann
- Department of Neurosurgery, RWTH Aachen University, Aachen, Germany
- Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), Aachen, Germany
| | - Martin Wiesmann
- Department of Neuroradiology, RWTH Aachen University, Aachen, Germany
| | - Dorit Merhof
- Faculty of Informatics and Computer Science, University of Regensburg, Regensburg, Germany
- Frauenhofer-Institut für Digitale Medizin, MEVIS, Bremen, Germany
| | - Chuh-Hyoun Na
- Department of Neurosurgery, RWTH Aachen University, Aachen, Germany.
- Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), Aachen, Germany.
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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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Guo J, Fu X, Li Y, Ming H, Lin Y, Yu S, Wei H, Sun C, Zhang K, Yang X. Ultra high b-value diffusion weighted imaging enables better molecular grading stratification over histological grading in adult-type diffuse glioma. Eur J Radiol 2023; 168:111140. [PMID: 37832200 DOI: 10.1016/j.ejrad.2023.111140] [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: 05/31/2023] [Revised: 09/22/2023] [Accepted: 10/05/2023] [Indexed: 10/15/2023]
Abstract
PURPOSE Accurate preoperative radiological staging of adult-type diffuse glioma is crucial for effective prognostic stratification and selection of appropriate therapeutic interventions. The purpose of this study was to compare the effectiveness of apparent diffusion coefficient (ADC) maps generated from ultrahigh b-value diffusion-weighted imaging (DWI) for molecular grading with that for histological grading of adult-type diffuse glioma, and to evaluate the correlation between these ADC maps and molecular and histological biomarkers. METHODS This study retrospectively enrolled forty adult-type diffuse glioma patients, diagnosed using the 2021 WHO classification criteria. Preoperative imaging data, including multiple b-value DWI and conventional magnetic resonance imaging, were collected. Tumors were graded using both histological and molecular criteria. Histogram analysis was conducted to generate 14 parameters for each tumor. Receiver operating characteristic curves and the area under the curve (AUC) were used to evaluate tumor grading and molecular status differentiation. Analysis of histological biomarkers was performed by calculating the Pearson and Spearman correlation coefficients of continuous and hierarchical variables, respectively. RESULTS The intensity-related parameters for molecular grading were found to be superior to those for histological grading for the identification of WHO grade 4 (WHO4) adult-type diffuse glioma. The AUC of both grading systems increased with increasing b-values, with ADC8000-based histogram parameters showing the best results (molecular grading, square root: AUC = 0.897; histological grading, median: AUC = 0.737). The intensity-related parameters could also differentiate molecular WHO4 gliomas from histologically lower-grade gliomas (ADC8000-based square root: AUC = 0.919), and different ADC8000-based kurtosis was observed between molecular and histological WHO4 gliomas (AUC = 0.833). Significant correlations between the Ki-67 index and molecular status prediction for IDH, CDKN2A, and EGFR were also demonstrated. CONCLUSION The histogram parameters derived from high b-value ADC maps were found to be more effective for differentiating molecular grades of WHO4 adult-type diffuse glioma than for differentiating histological grades.
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Affiliation(s)
- Jiahe Guo
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Xiuwei Fu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Yiming Li
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Haolang Ming
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Yu Lin
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Shengping Yu
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Huijie Wei
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Cuiyun Sun
- Department of Neuropathology, Tianjin Medical University General Hospital, Tianjin, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China; Institute for Intelligent Healthcare, Tsinghua University, Beijing, China
| | - Xuejun Yang
- Department of Neurosurgery, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China; Institute for Intelligent Healthcare, Tsinghua University, Beijing, China.
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Zhang Y, Sheng R, Yang C, Dai Y, Zeng M. Higher field reduced FOV diffusion-weighted imaging for abdominal imaging at 5.0 Tesla: image quality evaluation compared with 3.0 Tesla. Insights Imaging 2023; 14:171. [PMID: 37840062 PMCID: PMC10577120 DOI: 10.1186/s13244-023-01513-7] [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: 02/21/2023] [Accepted: 08/27/2023] [Indexed: 10/17/2023] Open
Abstract
OBJECTIVE To evaluate the image quality of reduced field-of-view (rFOV) DWI for abdominal imaging at 5.0 Tesla (T) compared with 3.0 T. METHODS Fifteen volunteers were included into this prospective study. All the subjects underwent the 3.0 T and 5.0 T MR examinations (time interval: 2 ± 1.9 days). Free-breathing (FB), respiratory-triggered (RT), and navigator-triggered (NT) spin-echo echo-planner imaging-based rFOV-DWI examinations were conducted at 3.0 T and 5.0 T (FB3.0 T, NT3.0 T, RT3.0 T, FB5.0 T, NT5.0 T, and RT5.0 T) with two b values (b = 0 and 800 s/mm2), respectively. The signal-to-noise ratio (SNR) of different acquisition approaches were determined and statistically compared. The image quality was assessed and statistically compared with a 5-point scoring system. RESULTS The SNRs of any 5.0 T DWI images were significantly higher than those of any 3.0 T DWI images for same anatomic locations. Moreover, 5.0 T rFOV-DWIs had the significantly higher sharpness scores than 3.0 T rFOV-DWIs. Similar distortion scores were observed at both 3.0 T and 5.0 T. Finally, RT5.0 T displayed the best overall image quality followed by NT5.0 T, FB5.0 T, RT3.0 T, NT3.0 T and FB3.0 T (RT5.0 T = 3.9 ± 0.3, NT5.0 T = 3.8 ± 0.3, FB5.0 T = 3.4 ± 0.3, RT3.0 T = 3.2 ± 0.4, NT3.0 T = 3.1 ± 0.4, and FB3.0 T = 2.7 ± 0.4, p < 0.001). CONCLUSION The 5.0 T rFOV-DWI showed better overall image quality and improved SNR compared to 3.0 T rFOV-DWI, which holds clinical potential for identifying the abdominal abnormalities in routine practice. CRITICAL RELEVANCE STATEMENT This study provided evidence that abdominal 5.0 Tesla reduced field of view diffusion-weighted imaging (5.0 T rFOV-DWI) exhibited enhanced image quality and higher SNR compared to its 3.0 Tesla counterparts, holding clinical promise for accurately visualizing abdominal abnormalities. KEY POINTS • rFOV-DWI was firstly integrated with high-field-MRI for visualizing various abdominal organs. • This study indicated the feasibility of abdominal 5.0 T-rFOV-DWI. • Better image quality was identified for 5.0 T rFOV-DWI.
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Affiliation(s)
- Yunfei Zhang
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, 200032, China
- Central Research Institute, United Imaging Healthcare, Shanghai, 201800, China
| | - Ruofan Sheng
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Chun Yang
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yongming Dai
- School of Biomedical Engineering, ShanghaiTech Univerisity, Shanghai, 200032, China.
| | - Mengsu Zeng
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, 200032, China.
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
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Tsarouchi MI, Hoxhaj A, Mann RM. New Approaches and Recommendations for Risk-Adapted Breast Cancer Screening. J Magn Reson Imaging 2023; 58:987-1010. [PMID: 37040474 DOI: 10.1002/jmri.28731] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 03/23/2023] [Accepted: 03/24/2023] [Indexed: 04/13/2023] Open
Abstract
Population-based breast cancer screening using mammography as the gold standard imaging modality has been in clinical practice for over 40 years. However, the limitations of mammography in terms of sensitivity and high false-positive rates, particularly in high-risk women, challenge the indiscriminate nature of population-based screening. Additionally, in light of expanding research on new breast cancer risk factors, there is a growing consensus that breast cancer screening should move toward a risk-adapted approach. Recent advancements in breast imaging technology, including contrast material-enhanced mammography (CEM), ultrasound (US) (automated-breast US, Doppler, elastography US), and especially magnetic resonance imaging (MRI) (abbreviated, ultrafast, and contrast-agent free), may provide new opportunities for risk-adapted personalized screening strategies. Moreover, the integration of artificial intelligence and radiomics techniques has the potential to enhance the performance of risk-adapted screening. This review article summarizes the current evidence and challenges in breast cancer screening and highlights potential future perspectives for various imaging techniques in a risk-adapted breast cancer screening approach. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 5.
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Affiliation(s)
- Marialena I Tsarouchi
- Department of Radiology, Nuclear Medicine and Anatomy, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Radiology, the Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Alma Hoxhaj
- Department of Radiology, Nuclear Medicine and Anatomy, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Radiology, the Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Ritse M Mann
- Department of Radiology, Nuclear Medicine and Anatomy, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Radiology, the Netherlands Cancer Institute, Amsterdam, the Netherlands
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30
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Bian W, Zhang J, Huang Q, Niu W, Li J, Song X, Cui S, Zheng Q, Niu J, Zhou XJ. Quantitative tumor burden imaging parameters of the spleen at MRI for predicting treatment response in patients with acute leukemia. Heliyon 2023; 9:e20348. [PMID: 37810872 PMCID: PMC10550618 DOI: 10.1016/j.heliyon.2023.e20348] [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: 05/05/2023] [Revised: 09/15/2023] [Accepted: 09/19/2023] [Indexed: 10/10/2023] Open
Abstract
Objectives To study the value of standardized volume and intravoxel incoherent motion (IVIM) parameters of the spleen based on tumor burden for predicting treatment response in newly diagnosed acute leukemia (AL). Methods Patients with newly diagnosed AL were recruited and underwent abdominal IVIM diffusion-weighted imaging within one week before the first induction chemotherapy. Quantitative parameters of magnetic resonance imaging (MRI) included the standardized volume (representing volumetric tumor burden) and IVIM parameters (standard apparent diffusion coefficient [sADC]; pure diffusion coefficient [D]; pseudo-diffusion coefficient [D∗]; and pseudo-perfusion fraction [f], representing functional tumor burden) of the spleen. Clinical biomarkers of tumor burden were collected. Patients were divided into complete remission (CR) and non-CR groups according to the treatment response after the first standardized induction chemotherapy, and the MRI and clinical parameters were compared between the two groups. The correlations of MRI parameters with clinical biomarkers were analyzed. Multivariate logistic regression was performed to determine the independent predictors for treatment response. Receiver operating characteristic curves were used to analyze the predicted performance. Results 76 AL patients (CR: n = 43; non-CR: n = 33) were evaluated. Standardized spleen volume, sADC, D, f, white blood cell counts, and lactate dehydrogenase were significantly different between CR and non-CR groups (all p < 0.05). Standardized spleen volume, sADC, and D were correlated with white blood cell and lactate dehydrogenase, and f was correlated with lactate dehydrogenase (all p < 0.05). Standardized spleen volume (hazard ratio = 4.055, p = 0.042), D (hazard ratio = 0.991, p = 0.027), and f (hazard ratio = 1.142, p = 0.008) were independent predictors for treatment response, and the combination of standardized spleen volume, D, and f showed more favorable discrimination (area under the curve = 0.856) than individual predictors. Conclusion Standardized volume, D, and f of the spleen could be used to predict treatment response in newly diagnosed AL, and the combination of morphological and functional parameters would further improve the predicted performance. IVIM parameters of the spleen may be viable indicators for evaluating functional tumor burden in AL.
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Affiliation(s)
- Wenjin Bian
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Jianling Zhang
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Qianqian Huang
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Weiran Niu
- Department of Mental Health, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Jianting Li
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Xiaoli Song
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Sha Cui
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Qian Zheng
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Jinliang Niu
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Xiaohong Joe Zhou
- Center for MR Research and Departments of Radiology, Neurosurgery, And Biomedical Engineering, University of Illinois at Chicago, Chicago, 60612, Illinois, USA
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Maino C, Vernuccio F, Cannella R, Cortese F, Franco PN, Gaetani C, Giannini V, Inchingolo R, Ippolito D, Defeudis A, Pilato G, Tore D, Faletti R, Gatti M. Liver metastases: The role of magnetic resonance imaging. World J Gastroenterol 2023; 29:5180-5197. [PMID: 37901445 PMCID: PMC10600959 DOI: 10.3748/wjg.v29.i36.5180] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 08/28/2023] [Accepted: 09/11/2023] [Indexed: 09/20/2023] Open
Abstract
The liver is one of the organs most commonly involved in metastatic disease, especially due to its unique vascularization. It's well known that liver metastases represent the most common hepatic malignant tumors. From a practical point of view, it's of utmost importance to evaluate the presence of liver metastases when staging oncologic patients, to select the best treatment possible, and finally to predict the overall prognosis. In the past few years, imaging techniques have gained a central role in identifying liver metastases, thanks to ultrasonography, contrast-enhanced computed tomography (CT), and magnetic resonance imaging (MRI). All these techniques, especially CT and MRI, can be considered the non-invasive reference standard techniques for the assessment of liver involvement by metastases. On the other hand, the liver can be affected by different focal lesions, sometimes benign, and sometimes malignant. On these bases, radiologists should face the differential diagnosis between benign and secondary lesions to correctly allocate patients to the best management. Considering the above-mentioned principles, it's extremely important to underline and refresh the broad spectrum of liver metastases features that can occur in everyday clinical practice. This review aims to summarize the most common imaging features of liver metastases, with a special focus on typical and atypical appearance, by using MRI.
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Affiliation(s)
- Cesare Maino
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Federica Vernuccio
- University Hospital of Padova, Institute of Radiology, Padova 35128, Italy
| | - Roberto Cannella
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Francesco Cortese
- Unit of Interventional Radiology, F Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Paolo Niccolò Franco
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Clara Gaetani
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Riccardo Inchingolo
- Unit of Interventional Radiology, F Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Davide Ippolito
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
- School of Medicine, University of Milano Bicocca, Milano 20100, Italy
| | - Arianna Defeudis
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Giulia Pilato
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Davide Tore
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
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Liao D, Liu YC, Liu JY, Wang D, Liu XF. Differentiating tumour progression from pseudoprogression in glioblastoma patients: a monoexponential, biexponential, and stretched-exponential model-based DWI study. BMC Med Imaging 2023; 23:119. [PMID: 37697237 PMCID: PMC10494379 DOI: 10.1186/s12880-023-01082-7] [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: 12/10/2022] [Accepted: 08/19/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND To investigate the diagnostic performance of parameters derived from monoexponential, biexponential, and stretched-exponential diffusion-weighted imaging models in differentiating tumour progression from pseudoprogression in glioblastoma patients. METHODS Forty patients with pathologically confirmed glioblastoma exhibiting enhancing lesions after completion of chemoradiation therapy were enrolled in the study, which were then classified as tumour progression and pseudoprogression. All patients underwent conventional and multi-b diffusion-weighted MRI. The apparent diffusion coefficient (ADC) from a monoexponential model, the true diffusion coefficient (D), pseudodiffusion coefficient (D*) and perfusion fraction (f) from a biexponential model, and the distributed diffusion coefficient (DDC) and intravoxel heterogeneity index (α) from a stretched-exponential model were compared between tumour progression and pseudoprogression groups. Receiver operating characteristic curves (ROC) analysis was used to investigate the diagnostic performance of different DWI parameters. Interclass correlation coefficient (ICC) was used to evaluate the consistency of measurements. RESULTS The values of ADC, D, DDC, and α values were lower in tumour progression patients than that in pseudoprogression patients (p < 0.05). The values of D* and f were higher in tumour progression patients than that in pseudoprogression patients (p < 0.05). Diagnostic accuracy for differentiating tumour progression from pseudoprogression was highest for α(AUC = 0.94) than that for ADC (AUC = 0.91), D (AUC = 0.92), D* (AUC = 0.81), f (AUC = 0.75), and DDC (AUC = 0.88). CONCLUSIONS Multi-b DWI is a promising method for differentiating tumour progression from pseudoprogression with high diagnostic accuracy. In addition, the α derived from stretched-exponential model is the most promising DWI parameter for the prediction of tumour progression in glioblastoma patients.
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Affiliation(s)
- Dan Liao
- Department of Radiology, Guizhou Provincial People’s Hospital, Guiyang, Guizhou 550002 China
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, 100010 China
| | - Yuan-Cheng Liu
- Department of Radiology, Guizhou Provincial People’s Hospital, Guiyang, Guizhou 550002 China
| | - Jiang-Yong Liu
- Department of Radiology, Guizhou Provincial People’s Hospital, Guiyang, Guizhou 550002 China
| | - Di Wang
- Department of Radiology, Guizhou Provincial People’s Hospital, Guiyang, Guizhou 550002 China
| | - Xin-Feng Liu
- Department of Radiology, Guizhou Provincial People’s Hospital, Guiyang, Guizhou 550002 China
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Saccenti L, Mellon CDM, Scholer M, Jolibois Z, Stemmer A, Weiland E, de Bazelaire C. Combining b2500 diffusion-weighted imaging with BI-RADS improves the specificity of breast MRI. Diagn Interv Imaging 2023; 104:410-418. [PMID: 37208291 DOI: 10.1016/j.diii.2023.05.001] [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: 01/04/2023] [Revised: 04/21/2023] [Accepted: 05/04/2023] [Indexed: 05/21/2023]
Abstract
PURPOSE The purpose of this study was to evaluate the diagnostic performance of visual assessment of diffusion-weighted images (DWI) obtained with a b value of 2500 s/mm2 in addition to a conventional magnetic resonance imaging (MRI) protocol to characterize breast lesions. MATERIALS AND METHODS This single-institution retrospective study included participants who underwent clinically indicated breast MRI and breast biopsy from May 2017 to February 2020. The examination included a conventional MRI protocol including DWI obtained with a b value of 50 s/mm2 (b50DWI) and a b value of 800 s/mm2 (b800DWI) and DWI obtained with a b value of 2500 s/mm2 (b2500DWI). Lesions were classified using Breast Imaging Reporting and Data Systems (BI-RADS) categories. Three independent radiologists assessed qualitatively the signal intensity within the breast lesions relative to breast parenchyma on b2500DW and b800DWI and measured the b50-b800-derived apparent diffusion coefficient (ADC) value. The diagnostic performances of BI-RADS, b2500DWI, b800DWI, ADC and of a model combining b2500DWI and BI-RADS were evaluated using receiver operating characteristic (ROC) curves analysis. RESULTS A total of 260 patients with 212 malignant and 100 benign breast lesions were included. There were 259 women and one man with a median age of 53 years (Q1, Q3: 48, 66 years). b2500DWI was assessable in 97% of the lesions. Interobserver agreement for b2500DWI was substantial (Fleiss kappa = 0.77). b2500DWI yielded larger area under the ROC curve (AUC, 0.81) than ADC with a 1 × 10-3 mm2/s threshold (AUC, 0.58; P = 0.005) and than b800DWI (AUC, 0.57; P = 0.02). The AUC of the model combining b2500DWI and BI-RADS was 0.84 (95% CI: 0.79-0.88). Adding b2500DWI to BI-RADS resulted in a significant increase in specificity from 25% (95% CI: 17-35) to 73% (95% CI: 63-81) (P < 0.001) with a decrease in sensitivity from 100% (95% CI: 97-100) to 94% (95% CI: 90-97), (P < 0.001). CONCLUSION Visual assessment of b2500DWI has substantial interobserver agreement. Visual assessment of b2500DWI offers better diagnostic performance than ADC and b800DWI. Adding visual assessment of b2500DWI to BI-RADS improves the specificity of breast MRI and could avoid unnecessary biopsies.
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Affiliation(s)
- Laetitia Saccenti
- Department of Radiology, Senopole, Hopital Saint-Louis, Assistance Publique Hôpitaux de Paris, 75010 Paris, France.
| | - Constance de Margerie Mellon
- Department of Radiology, Senopole, Hopital Saint-Louis, Assistance Publique Hôpitaux de Paris, 75010 Paris, France; Université Paris Cité, Faculté de Médecine, 75006 Paris, France
| | - Margaux Scholer
- Department of Radiology, Senopole, Hopital Saint-Louis, Assistance Publique Hôpitaux de Paris, 75010 Paris, France
| | - Zoe Jolibois
- Department of Radiology, Senopole, Hopital Saint-Louis, Assistance Publique Hôpitaux de Paris, 75010 Paris, France
| | - Alto Stemmer
- Siemens Healthineers GMBH, 91052 Erlanger, Germany
| | | | - Cedric de Bazelaire
- Department of Radiology, Senopole, Hopital Saint-Louis, Assistance Publique Hôpitaux de Paris, 75010 Paris, France; Université Paris Cité, Faculté de Médecine, 75006 Paris, France
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Zhou M, Gong T, Chen M, Wang Y. High-resolution integrated dynamic shimming diffusion-weighted imaging (DWI) in the assessment of rectal cancer. Eur Radiol 2023; 33:5769-5778. [PMID: 36826497 DOI: 10.1007/s00330-023-09494-3] [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: 09/30/2022] [Revised: 12/19/2022] [Accepted: 01/27/2023] [Indexed: 02/25/2023]
Abstract
OBJECTIVES To investigate the feasibility of high-resolution integrated dynamic shimming echo planar imaging (iEPI) applied to rectal cancer. METHODS A total of seventy-eight patients with non-mucinous rectum adenocarcinoma were enrolled in this study. Using a prototype high-resolution iEPI sequence, high-resolution single-shot EPI (sEPI) sequence, and sEPI sequence, subjective and objective assessment and apparent diffusion coefficient (ADC) value were measured for comparison. The spearman rank correlation analysis test and the receiver operating characteristic curve were performed to evaluate correlation between tumor ADC values, corresponding T stage, and differentiation degree of rectal cancer. RESULTS The subjective assessment of the image quality (IQ) of high-resolution iEPI was rated superior to high-resolution sEPI and sEPI by both readers (p < 0.001). Signal-to-noise ratio, contrast-to-noise, and signal-intensity ratio were significantly higher in high-resolution iEPI than the other two sequences (p < 0.001). There was no significant difference of tumor ADC values among three EPI sequences in the group of low- to well-differentiated rectal cancer. An inverse correlation was noted between ADC values on three DWI sequences and pathological T stage of rectal cancer (r = - 0.693, - 0.689, - 0.640, p < 0.001). The AUC values of high-resolution iEPI, high-resolution sEPI, and sEPI in predicting well-differentiated rectal cancer were 0.910, 0.761, and 0.725 respectively. CONCLUSIONS In conclusion, the high-resolution iEPI provided significantly higher IQ and stable ADC compared to another two sequences. High-resolution iEPI has the highest efficacy among three examined sequences in differentiation of rectal cancer with different degrees of differentiation. KEY POINTS • High-resolution iEPI provided a significantly better IQ than high-resolution sEPI and sEPI when assessing rectal cancer. • The AUC of high-resolution sEPI was the highest among three EPI sequences in predicting well-differentiated rectal cancer.
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Affiliation(s)
- Mi Zhou
- Department of Radiology, West Second Section of First Ring Road, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, No. 32Qingyang District, Chengdu, 610072, People's Republic of China
| | - Tong Gong
- Department of Radiology, West Second Section of First Ring Road, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, No. 32Qingyang District, Chengdu, 610072, People's Republic of China
| | - Meining Chen
- Department of MR Scientific Marketing, Siemens Healthineers, Shanghai, 200135, People's Republic of China
| | - Yuting Wang
- Department of Radiology, West Second Section of First Ring Road, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, No. 32Qingyang District, Chengdu, 610072, People's Republic of China.
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Zhou M, Huang H, Li H, Yan G, Tang B, Chen M, Wang Y. Application value of simultaneous multislice readout-segmented echo-planar imaging for diffusion-weighted MRI in differentiation of rectal cancer grade. MAGMA (NEW YORK, N.Y.) 2023; 36:621-629. [PMID: 36495411 DOI: 10.1007/s10334-022-01054-y] [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: 08/15/2022] [Revised: 12/05/2022] [Accepted: 12/06/2022] [Indexed: 06/17/2023]
Abstract
OBJECTIVE To analyze the association of apparent diffusion coefficient (ADC) values measured by readout-segmented echo-planar imaging (rs-EPI) using different simultaneous multislice (SMS) acceleration factors and the differentiation of rectal cancer grade. MATERIALS AND METHODS Patients with non-mucinous rectal adenocarcinoma diagnosed by biopsy (endoscope-guided biopsy or surgical resection) were retrospectively collected, and each patient underwent an MRI examination. ADC values of rs-EPI, 2 × SMS rs-EPI, and 3 × SMS rs-EPI were recorded as ADC1, ADC2, and ADC3, respectively. RESULTS The scanning time of 2 × SMS rs-EPI was 60 s, 56.2% shorter than 137 s of rs-EPI sequence, while that of 3 × SMS rs-EPI was 51 s, 72.8% less than that of rs-EPI time. The ADC value of the three groups dropped with the decrease in cancer grade (p < 0.05). The AUC values of ADC1, ADC2, and ADC3 in predicting highly differentiated rectal cancer were 0.74, 0.729, and 0.687, respectively. The difference in AUC values between ADC1 and ADC2 was not statistically significant (p = 0.889). DISCUSSION SMS technology with an acceleration factor of 2 could be applied clinically to evaluate the pathological differentiation of rectal cancer grade.
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Affiliation(s)
- Mi Zhou
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, No. 32, West Second Section of First Ring Road, Qingyang District, Chengdu, 610072, People's Republic of China
| | - Hongyun Huang
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, No. 32, West Second Section of First Ring Road, Qingyang District, Chengdu, 610072, People's Republic of China
| | - Hang Li
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, No. 32, West Second Section of First Ring Road, Qingyang District, Chengdu, 610072, People's Republic of China
| | - Guihua Yan
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, No. 32, West Second Section of First Ring Road, Qingyang District, Chengdu, 610072, People's Republic of China
| | - Baijie Tang
- Department of Pathology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, West Second Section of First Ring Road, Qingyang District, Chengdu, 610072, People's Republic of China
| | - Meining Chen
- Department of MR Scientific Marketing, Siemens Healthineers, Shanghai, 200135, People's Republic of China
| | - Yuting Wang
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, No. 32, West Second Section of First Ring Road, Qingyang District, Chengdu, 610072, People's Republic of China.
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Zhong Z, Ryu K, Mao J, Sun K, Dan G, Vasanawala SS, Zhou XJ. Accelerating High b-Value Diffusion-Weighted MRI Using a Convolutional Recurrent Neural Network (CRNN-DWI). Bioengineering (Basel) 2023; 10:864. [PMID: 37508891 PMCID: PMC10376839 DOI: 10.3390/bioengineering10070864] [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: 05/11/2023] [Revised: 07/05/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023] Open
Abstract
PURPOSE To develop a novel convolutional recurrent neural network (CRNN-DWI) and apply it to reconstruct a highly undersampled (up to six-fold) multi-b-value, multi-direction diffusion-weighted imaging (DWI) dataset. METHODS A deep neural network that combines a convolutional neural network (CNN) and recurrent neural network (RNN) was first developed by using a set of diffusion images as input. The network was then used to reconstruct a DWI dataset consisting of 14 b-values, each with three diffusion directions. For comparison, the dataset was also reconstructed with zero-padding and 3D-CNN. The experiments were performed with undersampling rates (R) of 4 and 6. Standard image quality metrics (SSIM and PSNR) were employed to provide quantitative assessments of the reconstructed image quality. Additionally, an advanced non-Gaussian diffusion model was employed to fit the reconstructed images from the different approaches, thereby generating a set of diffusion parameter maps. These diffusion parameter maps from the different approaches were then compared using SSIM as a metric. RESULTS Both the reconstructed diffusion images and diffusion parameter maps from CRNN-DWI were better than those from zero-padding or 3D-CNN. Specifically, the average SSIM and PSNR of CRNN-DWI were 0.750 ± 0.016 and 28.32 ± 0.69 (R = 4), and 0.675 ± 0.023 and 24.16 ± 0.77 (R = 6), respectively, both of which were substantially higher than those of zero-padding or 3D-CNN reconstructions. The diffusion parameter maps from CRNN-DWI also yielded higher SSIM values for R = 4 (>0.8) and for R = 6 (>0.7) than the other two approaches (for R = 4, <0.7, and for R = 6, <0.65). CONCLUSIONS CRNN-DWI is a viable approach for reconstructing highly undersampled DWI data, providing opportunities to reduce the data acquisition burden.
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Affiliation(s)
- Zheng Zhong
- Departments of Radiology, Stanford University, Stanford, CA 94305, USA
- Center for Magnetic Resonance Research, Chicago, IL 60612, USA
| | - Kanghyun Ryu
- Departments of Radiology, Stanford University, Stanford, CA 94305, USA
| | - Jonathan Mao
- Henry M. Gunn High School, Palo Alto, CA 94306, USA
| | - Kaibao Sun
- Center for Magnetic Resonance Research, Chicago, IL 60612, USA
| | - Guangyu Dan
- Center for Magnetic Resonance Research, Chicago, IL 60612, USA
| | | | - Xiaohong Joe Zhou
- Center for Magnetic Resonance Research, Chicago, IL 60612, USA
- Department of Radiology, Neurosurgery and Biomedical Engineering, University of Illinois Chicago, Chicago, IL 60607, USA
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Chen Y, Yang P, Fu C, Bian Y, Shao C, Ma C, Lu J. Variabilities in apparent diffusion coefficient (ADC) measurements of the spleen and the paraspinal muscle: A single center large cohort study. Heliyon 2023; 9:e18166. [PMID: 37519768 PMCID: PMC10372245 DOI: 10.1016/j.heliyon.2023.e18166] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 07/08/2023] [Accepted: 07/10/2023] [Indexed: 08/01/2023] Open
Abstract
Purpose Evaluation of the variabilities in apparent diffusion coefficient (ADC) measurements of the spleen (ADCspleen) and the paraspinal muscles (ADCmuscle) to identify the reference organ for normalizing the ADC from the abdominal diffusion weighted imaging (DWI). Methods Two MRI scanners, with 314 abdominal exams on the GE and 929 on the Siemens system, were used for MRI examinations including DWI (b-values, 50 and 800 s/mm2). For a subset of 73 exams on the Siemens system a second exam was conducted. Four regions of interest (ROIs) in each exam were placed to measure the ADCspleen and the bilateral ADCmuscle. ADC variability between patients (on each scanner separately), ADC variability due to ROI placement between the two ROIs in each organ, and variability in the subset between the first and second exams were assessed. Results The ADCspleen was more scattered and variable than the ADCmuscle in the comparability (n = 929 and 314 for two MRI scanners, respectively) and repeatability (n = 73) datasets. The Bland-Altmann bias and limits of agreement (LoAs) for the ADCspleen (ICC, 0.47; CV, 0.070) and ADCmuscle (ICC, 0.67; CV, 0.023) in the repeatability datasets (n = 73) were -0.1 (-25.7%-25.6%) and -0.3 (-8.8%-8.1%), respectively. For the Siemens system, the Bland-Altmann bias and LoAs for the ADCspleen (ICC, 0.72; CV, 0.061) and ADCmuscle (ICC, 0.53; CV, 0.030) in the comparability datasets (n = 929) were 2.1 (-20.0%-24.2%) and 0.7 (-10.0%-11.4%), respectively. Similar findings have been found in the GE system (n = 314). The CVs for the ADCmuscle measurements were lower than those of the ADCspleen both in the repeatability and the comparability analyses (all p < 0.001). Conclusion Paraspinal muscles demonstrate better reference characteristics than the spleen in estimating ADC variability of abdominal DWI.
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Affiliation(s)
- Yukun Chen
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai, 200433, China
| | - Panpan Yang
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai, 200433, China
| | - Caixia Fu
- Application Developments, Siemens Shenzhen Magnetic Resonance Ltd., Siemens Healthineers, Shenzhen, 518057, China
| | - Yun Bian
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai, 200433, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai, 200433, China
| | - Chao Ma
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai, 200433, China
- College of Electronic and Information Engineering, Tongji University, Shanghai, 201804, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai, 200433, China
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Jiang Y, Qin S, Wang Y, Liu Y, Liu N, Tang L, Fang J, Jia Q, Huang X. Intravoxel incoherent motion diffusion-weighted MRI for predicting the efficacy of high-intensity focused ultrasound ablation for uterine fibroids. Front Oncol 2023; 13:1178649. [PMID: 37427113 PMCID: PMC10324408 DOI: 10.3389/fonc.2023.1178649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 06/08/2023] [Indexed: 07/11/2023] Open
Abstract
Purpose To evaluate the significance of magnetic resonance (MR) intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) quantitative parameters in predicting early efficacy of high-intensity focused ultrasound (HIFU) ablation of uterine fibroids before treatment. Method 64 patients with 89 uterine fibroids undergoing HIFU ablation (51 sufficient ablations and 38 insufficient ablations) were enrolled in the study and completed MR imaging and IVIM-DWI before treatment. The IVIM-DWI parameters, including D (diffusion coefficient), D* (pseudo-diffusion coefficient), f (perfusion fraction) and relative blood flow (rBF) were calculated. The logistic regression (LR) model was constructed to analyze the predictors of efficacy. The receiver operating characteristic (ROC) curve was drawn to assess the model's performance. A nomograph was constructed to visualize the model. Results The D value of the sufficient ablation group (931.0(851.5-987.4) × 10-6 mm2/s) was significantly lower than that of the insufficient ablation group (1052.7(1019.6-1158.7) × 10-6 mm2/s) (p<0.001). However, differences in D*, f, and rBF values between the groups were not significant (p>0.05). The LR model was constructed with D value, fibroid position, ventral skin distance, T2WI signal intensity, and contrast enhanced degree. The area under the ROC curve, specificity, and sensitivity of the model were 0.858 (95% confidence interval: 0.781, 0.935), 0.686, and 0.947. The nomogram and calibration curves confirmed that the model had excellent performance. Conclusion The IVIM-DWI quantitative parameters can be used to predict early effects of HIFU ablation on uterine fibroids. A high D value before treatment may indicate that the treatment will be less effective in the early stages.
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Affiliation(s)
- Yu Jiang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Shize Qin
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Yanlin Wang
- Department of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Yang Liu
- Department of Urology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Nian Liu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Lingling Tang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Jie Fang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Qing Jia
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiaohua Huang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
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Zhang G, Xu Z, Zheng J, Wang M, Ren J, Wei X, Huan Y, Zhang J. Ultra-high b-Value DWI in predicting progression risk of locally advanced rectal cancer: a comparative study with routine DWI. Cancer Imaging 2023; 23:59. [PMID: 37308941 DOI: 10.1186/s40644-023-00582-7] [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: 12/01/2022] [Accepted: 06/02/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND The prognosis prediction of locally advanced rectal cancer (LARC) was important to individualized treatment, we aimed to investigate the performance of ultra-high b-value DWI (UHBV-DWI) in progression risk prediction of LARC and compare with routine DWI. METHODS This retrospective study collected patients with rectal cancer from 2016 to 2019. Routine DWI (b = 0, 1000 s/mm2) and UHBV-DWI (b = 0, 1700 ~ 3500 s/mm2) were processed with mono-exponential model to generate ADC and ADCuh, respectively. The performance of the ADCuh was compared with ADC in 3-year progression free survival (PFS) assessment using time-dependent ROC and Kaplan-Meier curve. Prognosis model was constructed with ADCuh, ADC and clinicopathologic factors using multivariate COX proportional hazard regression analysis. The prognosis model was assessed with time-dependent ROC, decision curve analysis (DCA) and calibration curve. RESULTS A total of 112 patients with LARC (TNM-stage II-III) were evaluated. ADCuh performed better than ADC for 3-year PFS assessment (AUC = 0.754 and 0.586, respectively). Multivariate COX analysis showed that ADCuh and ADC were independent factors for 3-year PFS (P < 0.05). Prognostic model 3 (TNM-stage + extramural venous invasion (EMVI) + ADCuh) was superior than model 2 (TNM-stage + EMVI + ADC) and model 1 (TNM-stage + EMVI) for 3-year PFS prediction (AUC = 0.805, 0.719 and 0.688, respectively). DCA showed that model 3 had higher net benefit than model 2 and model 1. Calibration curve demonstrated better agreement of model 1 than model 2 and model 1. CONCLUSIONS ADCuh from UHBV-DWI performed better than ADC from routine DWI in predicting prognosis of LARC. The model based on combination of ADCuh, TNM-stage and EMVI could help to indicate progression risk before treatment.
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Affiliation(s)
- Guangwen Zhang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, No.127, Chang Le West Road, Xi'an, Shaanxi, 710032, China
| | - Ziliang Xu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, No.127, Chang Le West Road, Xi'an, Shaanxi, 710032, China
| | - Jianyong Zheng
- Department of Gastrointestinal Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, China
| | - Mian Wang
- Department of Gastrointestinal Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, China
| | - Jialiang Ren
- Department of Pharmaceuticals Diagnostics, GE Healthcare China, Beijing, 100176, China
| | - Xiaocheng Wei
- Department of MR Research, GE Healthcare China, Beijing, 100176, China
| | - Yi Huan
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, No.127, Chang Le West Road, Xi'an, Shaanxi, 710032, China
| | - Jinsong Zhang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, No.127, Chang Le West Road, Xi'an, Shaanxi, 710032, China.
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Feng H, Liu H, Wang Q, Song M, Yang T, Zheng L, Wu D, Shao X, Shi G. Breast cancer diagnosis and prognosis using a high b-value non-Gaussian continuous-time random-walk model. Clin Radiol 2023:S0009-9260(23)00227-1. [PMID: 37344324 DOI: 10.1016/j.crad.2023.05.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 05/11/2023] [Accepted: 05/19/2023] [Indexed: 06/23/2023]
Abstract
AIM To compare the diagnostic performance of mono-exponential model-derived apparent diffusion coefficient (ADC), continuous-time random-walk (CTRW) model-derived Dm, α, β and their combinations in discriminating malignancy of breast lesions, and investigate the association between model-derived parameters and prognosis-related immunohistochemical indices. MATERIALS AND METHODS A total of 85 patients with breast lesions (51 malignant, 34 benign) were analysed in this retrospective study. Clinical characteristics include oestrogen receptor (ER), progesterone receptor (PR), human epidermal receptor 2 (HER2), and Ki-67. The ADC was fitted using a mono-exponential model (b-values = 0, 800 s/mm2), while Dm, α, and β were fitted using a CTRW model. Independent Student's t-test and the Mann-Whitney U-test were used for the comparison of parameters. Discrimination performance was accomplished by receiver operating characteristic (ROC) analysis, and Spearman's correlation analysis was used to explore the association between immunohistochemical indices and diffusion parameters, the statistical significance level was p<0.05. RESULTS Dm and ADC demonstrated similar performance in differentiating malignant and benign lesions (AUC = 0.928 versus 0.930), while the combination of Dm, α, and β could improve the AUC to 0.969. The combined parameter generated by ADC, Dm, α, and β was effective in identifying the ER+/ER- and PR+/PR- patients. Temporal heterogeneity parameter α correlated significantly with the expression of PR. CONCLUSION Diffusion parameters derived from the CTRW model could effectively discriminate the malignancy of breast lesions. Meanwhile, the hormone receptor expression could be distinguished by combined diffusion parameters, and have the potential to reflect the prognosis.
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Affiliation(s)
- H Feng
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - H Liu
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Q Wang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - M Song
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - T Yang
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - L Zheng
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - D Wu
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronics Science, East China Normal University, Shanghai, China
| | - X Shao
- Department of Anesthesiology, The Fourth Hospital of Shijiazhuang, Shijiazhuang, China
| | - G Shi
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
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Qi LP, Zhong Z, Sun YS, Li XT, Tang L, Zhou XJ. Optimal selection of b-values for differential diagnosis of mediastinal lymph nodes using diffusion-weighted imaging. Heliyon 2023; 9:e16702. [PMID: 37484276 PMCID: PMC10360569 DOI: 10.1016/j.heliyon.2023.e16702] [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: 09/06/2022] [Revised: 04/16/2023] [Accepted: 05/24/2023] [Indexed: 07/25/2023] Open
Abstract
This study proposed to investigate the optimal selection of b-values in diffusion-weighted imaging for distinguishing malignant from benign mediastinal lymph nodes. Diffusion-weighted imaging with six b-values was performed on 35 patients at 1.5 T. Image quality score, signal-to-noise ratio, and relative contrast ratio of lymph node to chest muscle were compared between the diffusion-weighted images with a b-value up to 800 and 1000 s/mm2. Using a lower and an upper b-value in the range of 0-1000 s/mm2, eight apparent diffusion coefficient maps were obtained from a mono-exponential model. Receiver operating characteristic analysis was employed to evaluate the performance of the apparent diffusion coefficients for distinguishing malignant from benign mediastinal lymph nodes by using the area under the curve as a criterion. The mean image quality score and the relative contrast ratio showed no difference between b-values of 800 and 1000 s/mm2. In the receiver operating characteristic analysis, the areas under the curve of apparent diffusion coefficient with b-value pairs of (0, 800), (0, 1000), and (50, 800) s/mm2 were significantly higher than those from the other b-value pairs. No significant difference was observed among the three b-value pairs. Apparent diffusion coefficient obtained from b-value pairs of (0, 800), (0, 1000), and (50, 800) s/mm2 showed superior diagnostic performance compared to the other b-value combinations. Based on several practical considerations, the b-value pair of (50, 800) s/mm2 is recommended for differential diagnosis of mediastinal lymph nodes.
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Affiliation(s)
- Li-Ping Qi
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital and Institute, Beijing, China
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Zheng Zhong
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, USA
- Department of Biomedcial Engineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Ying-Shi Sun
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital and Institute, Beijing, China
| | - Xiao-Ting Li
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital and Institute, Beijing, China
| | - Lei Tang
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital and Institute, Beijing, China
| | - Xiaohong Joe Zhou
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, USA
- Department of Radiology, University of Illinois at Chicago, Chicago, IL, USA
- Department of Neurosurgery, University of Illinois at Chicago, Chicago, IL, USA
- Department of Biomedcial Engineering, University of Illinois at Chicago, Chicago, IL, USA
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Li C, Wen Y, Xie J, Chen Q, Dang Y, Zhang H, Guo H, Long L. Preoperative prediction of VETC in hepatocellular carcinoma using non-Gaussian diffusion-weighted imaging at high b values: a pilot study. Front Oncol 2023; 13:1167209. [PMID: 37305565 PMCID: PMC10248416 DOI: 10.3389/fonc.2023.1167209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 05/15/2023] [Indexed: 06/13/2023] Open
Abstract
Background Vessels encapsulating tumor clusters (VETC) have been considered an important cause of hepatocellular carcinoma (HCC) metastasis. Purpose To compare the potential of various diffusion parameters derived from the monoexponential model and four non-Gaussian models (DKI, SEM, FROC, and CTRW) in preoperatively predicting the VETC of HCC. Methods 86 HCC patients (40 VETC-positive and 46 VETC-negative) were prospectively enrolled. Diffusion-weighted images were acquired using six b-values (range from 0 to 3000 s/mm2). Various diffusion parameters derived from diffusion kurtosis (DK), stretched-exponential (SE), fractional-order calculus (FROC), and continuous-time random walk (CTRW) models, together with the conventional apparent diffusion coefficient (ADC) derived from the monoexponential model were calculated. All parameters were compared between VETC-positive and VETC-negative groups using an independent sample t-test or Mann-Whitney U test, and then the parameters with significant differences between the two groups were combined to establish a predictive model by binary logistic regression. Receiver operating characteristic (ROC) analyses were used to assess diagnostic performance. Results Among all studied diffusion parameters, only DKI_K and CTRW_α significantly differed between groups (P=0.002 and 0.004, respectively). For predicting the presence of VETC in HCC patients, the combination of DKI_K and CTRW_α had the larger area under the ROC curve (AUC) than the two parameters individually (AUC=0.747 vs. 0.678 and 0.672, respectively). Conclusion DKI_K and CTRW_α outperformed traditional ADC for predicting the VETC of HCC.
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Affiliation(s)
- Chenhui Li
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Yan Wen
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Jinhuan Xie
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Qianjuan Chen
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Yiwu Dang
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Huiting Zhang
- MR Scientific Marketing, Siemens Healthcare Ltd., Wuhan, Hubei, China
| | - Hu Guo
- MR Application, Siemens Healthcare Ltd., Changsha, Hunan, China
| | - Liling Long
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Gaungxi Medical University, Ministry of Education, Nanning, Guangxi, China
- Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
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Chen Z, Zhai X, Chen Z. Computed cancer magnetic susceptibility imaging (canχ): Computational inverse mappings of cancer MRI. Magn Reson Imaging 2023; 102:86-95. [PMID: 37075866 DOI: 10.1016/j.mri.2023.04.003] [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: 01/05/2023] [Revised: 03/31/2023] [Accepted: 04/16/2023] [Indexed: 04/21/2023]
Abstract
PURPOSE We report a new cancer imaging modality in the contrast of tissue intrinsic susceptibility property by computed inverse magnetic resonance imaging (CIMRI). METHODS In MRI physics, an MRI signal is formed from tissue magnetism source (primarily magnetic susceptibility χ) through a cascade of MRI-introduced transformations (e.g. dipole-convolved magnetization) involving MRI setting parameters (e.g. echo time). In two-step computational inverse mappings (from phase image to internal fieldmap to susceptibility source), we could remove the MRI transformations and imaging parameters, thereby obtaining χ-depicted cancer images (canχ) from MRI phase images. Canχ is computationally implemented from clinical cancer MRI phase image by CIMRI. RESULTS As a result of MRI effect removal through computational inverse mappings, the reconstructed χ map (canχ) could provide a new cancerous tissue depiction in contrast of tissue intrinsic magnetism property (i.e. diamagnetism vs paramagnetism) as in an off-scanner state (e.g. in absence of main field B0). CONCLUSION Through retrospective clinical cancer MRI data analysis, we reported on the canχ method in technical details and demonstrated its feasibility of innovating cancer imaging in the contrast of tissue intrinsic paramagnetism/diamagnetism property (in a cancer tissue state free from MRI effect).
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Affiliation(s)
- Zikuan Chen
- Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA 91010, United States of America; Zinv LLC, Albuquerque, NM 87108, United States of America.
| | - Xiulan Zhai
- Zinv LLC, Albuquerque, NM 87108, United States of America
| | - Zeyuan Chen
- Department of Computer Sciences, University of California-Davis, Davis, CA 95616, United States of America; Microsoft Corporation, Seattle, WA 98052, United States of America.
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Mehta R, Bu Y, Zhong Z, Dan G, Zhong PS, Zhou C, Hu W, Zhou XJ, Xu M, Wang S, Karaman MM. Characterization of breast lesions using multi-parametric diffusion MRI and machine learning. Phys Med Biol 2023; 68:085006. [PMID: 36808921 DOI: 10.1088/1361-6560/acbde0] [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: 10/13/2022] [Accepted: 02/21/2023] [Indexed: 02/23/2023]
Abstract
Objective. To investigate quantitative imaging markers based on parameters from two diffusion-weighted imaging (DWI) models, continuous-time random-walk (CTRW) and intravoxel incoherent motion (IVIM) models, for characterizing malignant and benign breast lesions by using a machine learning algorithm.Approach. With IRB approval, 40 women with histologically confirmed breast lesions (16 benign, 24 malignant) underwent DWI with 11b-values (50 to 3000 s/mm2) at 3T. Three CTRW parameters,Dm,α, andβand three IVIM parametersDdiff,Dperf, andfwere estimated from the lesions. A histogram was generated and histogram features of skewness, variance, mean, median, interquartile range; and the value of the 10%, 25% and 75% quantiles were extracted for each parameter from the regions-of-interest. Iterative feature selection was performed using the Boruta algorithm that uses the Benjamin Hochberg False Discover Rate to first determine significant features and then to apply the Bonferroni correction to further control for false positives across multiple comparisons during the iterative procedure. Predictive performance of the significant features was evaluated using Support Vector Machine, Random Forest, Naïve Bayes, Gradient Boosted Classifier (GB), Decision Trees, AdaBoost and Gaussian Process machine learning classifiers.Main Results. The 75% quantile, and median ofDm; 75% quantile off;mean, median, and skewness ofβ;kurtosis ofDperf; and 75% quantile ofDdiffwere the most significant features. The GB differentiated malignant and benign lesions with an accuracy of 0.833, an area-under-the-curve of 0.942, and an F1 score of 0.87 providing the best statistical performance (p-value < 0.05) compared to the other classifiers.Significance. Our study has demonstrated that GB with a set of histogram features from the CTRW and IVIM model parameters can effectively differentiate malignant and benign breast lesions.
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Affiliation(s)
- Rahul Mehta
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States of America
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Yangyang Bu
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
| | - Zheng Zhong
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States of America
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Guangyu Dan
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States of America
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Ping-Shou Zhong
- Department of Mathematics, Statistics, and Computer Science, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Changyu Zhou
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
| | - Weihong Hu
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
| | - Xiaohong Joe Zhou
- Departments of Radiology and Neurosurgery, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Maosheng Xu
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
| | - Shiwei Wang
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
| | - M Muge Karaman
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States of America
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States of America
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Du M, Zou D, Gao P, Yang Z, Hou Y, Zheng L, Zhang N, Liu Y. Evaluation of a continuous-time random-walk diffusion model for the differentiation of malignant and benign breast lesions and its association with Ki-67 expression. NMR IN BIOMEDICINE 2023:e4920. [PMID: 36912198 DOI: 10.1002/nbm.4920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
The purpose of the current study was to evaluate the performance of a continuous-time random-walk (CTRW) diffusion model for differentiating malignant and benign breast lesions and to consider the potential association between CTRW parameters and the Ki-67 expression. Sixty-four patients (46.2 ± 11.4 years) with breast lesions (29 malignant and 35 benign) were evaluated with the CTRW model, intravoxel incoherent motion model, and diffusion-weighted imaging. Echo planar diffusion-weighted imaging was conducted using 13 b-values (0-3000 s/mm2 ). Three CTRW model parameters, including an anomalous diffusion coefficient Dm , and two parameters related to temporal and spatial diffusion heterogeneity, α and β, respectively, were obtained, and had MRI b-values of 0-3000 s/mm2 . Receiver operating characteristic (ROC) analysis was conducted to determine the sensitivity, specificity, and diagnostic accuracy of CTRW parameters for differentiating malignant from benign breast lesions. In malignant breast lesions, the CTRW parameters Dm , α, and β were significantly lower than the corresponding parameters of benign breast lesions. In the malignant breast lesion group, the CTRW parameter Dm was significantly lower in high Ki-67 expression than in low Ki-67 expression. In ROC analysis, the combination of CTRW parameters (Dm , α, β) demonstrated the highest area under the curve value (0.985) and diagnostic accuracy (94.23%) in differentiating malignant and benign breast lesions. The CTRW model effectively differentiated malignant from benign breast lesions. The CTRW diffusion model offers a new way for noninvasive assessment of breast malignancy and better understanding of the proliferation of malignant lesions.
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Affiliation(s)
- Mu Du
- Medical Imaging Center, Shenzhen Hospital, Southern Medical University, Shenzhen, China
- The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Da Zou
- Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Peng Gao
- Medical Imaging Center, Shenzhen Hospital, Southern Medical University, Shenzhen, China
| | - Zhongxian Yang
- Medical Imaging Center, Shenzhen Hospital, Southern Medical University, Shenzhen, China
| | - Yanzhen Hou
- Medical Imaging Center, Shenzhen Hospital, Southern Medical University, Shenzhen, China
| | - Liyun Zheng
- Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Na Zhang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yubao Liu
- Medical Imaging Center, Shenzhen Hospital, Southern Medical University, Shenzhen, China
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Li G, Ma X, Li S, Ye X, Börnert P, Zhou XJ, Guo H. Comparison of uniform-density, variable-density, and dual-density spiral samplings for multi-shot DWI. Magn Reson Med 2023; 90:133-149. [PMID: 36883748 DOI: 10.1002/mrm.29633] [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: 09/03/2022] [Revised: 02/17/2023] [Accepted: 02/21/2023] [Indexed: 03/09/2023]
Abstract
PURPOSE To compare the performances of uniform-density spiral (UDS), variable-density spiral (VDS), and dual-density spiral (DDS) samplings in multi-shot diffusion imaging, and determine a sampling strategy that balances reliability of shot navigator and overall DWI image quality. THEORY AND METHODS UDS, VDS, and DDS trajectories were implemented to achieve four-shot diffusion-weighted spiral imaging. First, the static B0 off-resonance effects in UDS, VDS, and DDS acquisitions were analyzed based on a signal model. Then, in vivo experiments were performed to verify the theoretical analyses, and fractional anisotropy (FA) fitting residuals were used to quantitatively assess the quality of spiral diffusion data for tensor estimation. Finally, the SNR performances and g-factor behavior of the three spiral samplings were evaluated using a Monte Carlo-based pseudo multiple replica method. RESULTS Among the three spiral trajectories with the same readout duration, UDS sampling exhibited the least off-resonance artifacts. This was most evident when the static B0 off-resonance effect was severe. The UDS diffusion images had higher anatomical fidelity and lower FA fitting residuals than the other two counterparts. Furthermore, the four-shot UDS acquisition achieved the best SNR performance in diffusion imaging with 12.11% and 40.85% improvements over the VDS and DDS acquisitions with the same readout duration, respectively. CONCLUSION UDS sampling is an efficient spiral acquisition scheme for high-resolution diffusion imaging with reliable navigator information. It provides superior off-resonance performance and SNR efficiency over the VDS and DDS samplings for the tested scenarios.
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Affiliation(s)
- Guangqi Li
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Xiaodong Ma
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Sisi Li
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Xinyu Ye
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Peter Börnert
- Radiology, C.J. Gorter Center for High-Field MRI, Leiden University Medical Center, Leiden, The Netherlands.,Philips Research, Hamburg, Germany
| | - Xiaohong Joe Zhou
- Center for MR Research and Departments of Radiology, Neurosurgery, and Biomedical Engineering, University of Illinois College of Medicine at Chicago, Chicago, Illinois, USA
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
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Guo D, Jiang B. Noninvasively evaluating the grade and IDH mutation status of gliomas by using mono-exponential, bi-exponential diffusion-weighted imaging and three-dimensional pseudo-continuous arterial spin labeling. Eur J Radiol 2023; 160:110721. [PMID: 36738600 DOI: 10.1016/j.ejrad.2023.110721] [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: 10/13/2022] [Revised: 01/14/2023] [Accepted: 01/30/2023] [Indexed: 02/04/2023]
Abstract
OBJECTIVES To noninvasively assess the diagnostic performance of diffusion-weighted imaging (DWI), bi-exponential intravoxel incoherent motion imaging (IVIM) and three-dimensional pseudo-continuous arterial spin labeling (3D pCASL) in differentiating lower-grade gliomas (LGGs) from high-grade gliomas (HGGs), and predicting the isocitrate dehydrogenase (IDH) mutation status. MATERIALS AND METHODS Ninety-five patients with pathologically confirmed grade 2-4 gliomas with preoperative DWI, IVIM and 3D pCASL were enrolled in this study. The Student's t test and Mann-Whitney U test were used to evaluate differences in parameters of DWI, IVIM and 3D pCASL between LGG and HGG as well as between mutant and wild-type IDH in grade 2 and 3 diffusion astrocytoma; receiver operator characteristic (ROC) analysis was used to assess the diagnostic performance. RESULTS The value of ADCmean, ADCmin, Dmean and Dmin in HGGs were lower than in LGGs, while the value of CBFmean and CBFmax in HGGs were higher than in LGGs. In ROC analysis, the AUC values of Dmean, Dmin and CBFmax were 0.827, 0.878 and 0.839, respectively. The combination of CBFmax and Dmin displayed the highest diagnostic performance to distinguish LGGs from HGGs, with AUC 0.906, sensitivity 82.4 %, and specificity 86.4 %. In grades 2 and 3 diffusion astrocytoma patients, ADCmin, Dmean, Dmin, CBFmean and CBFmax showed significant differences between IDHmut and IDHwt group (p < 0.05, 0.001, 0.001, 0.01 and 0.001, respectively) and the AUC values were 0. 709, 0.849, 0.919, 0.755 and 0.873, respectively. Similarly, the combination of CBFmax and Dmin demonstrated the highest AUC value (0.938) in prediction IDH mutation status, with sensitivity 92.9 %, and specificity 95.5 %. CONCLUSION The combination of IVIM and 3D pCASL can be used in prediction histologic grade and IDH mutation status of glioma noninvasively.
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Affiliation(s)
- Da Guo
- Department of Radiology, The Sixth People's Hospital of Nanchong, Sichuan Province, People's Republic of China
| | - Binghu Jiang
- Department of Radiology, Nanchong Central Hospital, Sichuan Province, People's Republic of China.
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Kallis K, Conlin CC, Zhong AY, Hussain TS, Chatterjee A, Karczmar GS, Rakow-Penner R, Dale A, Seibert T. Comparison of synthesized and acquired high b -value diffusion-weighted MRI for detection of prostate cancer. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.17.23286100. [PMID: 36824958 PMCID: PMC9949172 DOI: 10.1101/2023.02.17.23286100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2023]
Abstract
Background High b -value diffusion-weighted images (DWI) are used for detection of clinically significant prostate cancer (csPCa). To decrease scan time and improve signal-to-noise ratio, high b -value (>1000 s/mm 2 ) images are often synthesized instead of acquired. Purpose Qualitatively and quantitatively compare synthesized DWI (sDWI) to acquired (aDWI) for detection of csPCa. Study Type Retrospective. Subjects 151 consecutive patients who underwent prostate MRI and biopsy. Sequence Axial DWI with b =0, 500, 1000, and 2000 s/mm 2 using a 3T clinical scanner using a 32-channel phased-array body coil. Assessment We synthesized DWI for b =2000 s/mm 2 via extrapolation based on monoexponential decay, using b =0 and b =500 s/mm 2 (sDWI 500 ) and b =0, b =500, and b =1000 s/mm 2 (sDWI 1000 ). Differences between sDWI and aDWI were evaluated within regions of interest (ROIs). The maximum DWI value within each ROI was evaluated for prediction of csPCa. Classification accuracy was also compared to Restriction Spectrum Imaging restriction score (RSIrs), a previously validated biomarker based on multi-exponential DWI. Statistical Tests Discrimination of csPCa was evaluated via area under the receiver operating characteristic curve (AUC). Statistical significance was assessed using bootstrap difference (two-sided α=0.05). Results Within the prostate, mean ± standard deviation of percent mean differences between sDWI and aDWI signal were -46±35% for sDWI 1000 and -67±24% for sDWI 500 . AUC for aDWI, sDWI 500, sDWI 1000 , and RSIrs within the prostate 0.62[95% confidence interval: 0.53, 0.71], 0.63[0.54, 0.72], 0.65[0.56, 0.73] and 0.78[0.71, 0.86], respectively. When considering the whole field of view, classification accuracy and qualitative image quality decreased notably for sDWI compared to aDWI and RSIrs. Data Conclusion sDWI is qualitatively comparable to aDWI within the prostate. However, hyperintense artifacts are introduced with sDWI in the surrounding pelvic tissue that interfere with quantitative cancer detection and might mask metastases. In the prostate, RSIrs yields superior quantitative csPCa detection than sDWI or aDWI.
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Differentiating between normal and fetal growth restriction-complicated placentas: is T2∗ imaging imaging more accurate than conventional diffusion-weighted imaging? Clin Radiol 2023; 78:362-368. [PMID: 36858925 DOI: 10.1016/j.crad.2023.01.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 01/17/2023] [Accepted: 01/26/2023] [Indexed: 02/18/2023]
Abstract
AIM To compare the performance of T2∗ imaging and apparent diffusion coefficient (ADC) in differentiating normal placentas from those complicated by fetal growth restriction (FGR). MATERIALS AND METHODS This prospective study included 28 control and 30 FGR placentas. Gradient-echo magnetic resonance imaging (MRI) at 16 different echo times and diffusion-weighted imaging (b-value of 0 and 800 s/mm2) were performed on all pregnant women using a 3 T MRI system. RESULTS Both T2∗ imaging Z-score and ADC were significantly lower in the FGR placentas (ADC, (1.69 ± 0.19) × 10-3 versus (1.42 ± 0.28) × 10-3 mm2/s, p<0.001; T2∗ imaging Z-score, -0.004 ± 0.95 versus -2.441 ± 1.48, p<0.001). The area under the curve for T2∗ imaging Z-score and ADC was 0.917 (95% confidence interval [CI] = 0.842-0.991) and 0.788 (95% CI = 0.655-0.887), respectively. The performance of T2∗ imaging in differentiating FGR placentas was significantly better than that of ADC (Z = 2.043, p=0.041). CONCLUSION Placental T2∗ imaging was found to be more reliable than ADC in differentiating between normal and FGR placentas.
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Lyu J, Li Y, Yan F, Chen W, Wang C, Li R. Multi-channel GAN-based calibration-free diffusion-weighted liver imaging with simultaneous coil sensitivity estimation and reconstruction. Front Oncol 2023; 13:1095637. [PMID: 36845688 PMCID: PMC9945270 DOI: 10.3389/fonc.2023.1095637] [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: 11/11/2022] [Accepted: 01/09/2023] [Indexed: 02/10/2023] Open
Abstract
Introduction Diffusion-weighted imaging (DWI) with parallel reconstruction may suffer from a mismatch between the coil calibration scan and imaging scan due to motions, especially for abdominal imaging. Methods This study aimed to construct an iterative multichannel generative adversarial network (iMCGAN)-based framework for simultaneous sensitivity map estimation and calibration-free image reconstruction. The study included 106 healthy volunteers and 10 patients with tumors. Results The performance of iMCGAN was evaluated in healthy participants and patients and compared with the SAKE, ALOHA-net, and DeepcomplexMRI reconstructions. The peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), root mean squared error (RMSE), and histograms of apparent diffusion coefficient (ADC) maps were calculated for assessing image qualities. The proposed iMCGAN outperformed the other methods in terms of the PSNR (iMCGAN: 41.82 ± 2.14; SAKE: 17.38 ± 1.78; ALOHA-net: 20.43 ± 2.11 and DeepcomplexMRI: 39.78 ± 2.78) for b = 800 DWI with an acceleration factor of 4. Besides, the ghosting artifacts in the SENSE due to the mismatch between the DW image and the sensitivity maps were avoided using the iMCGAN model. Discussion The current model iteratively refined the sensitivity maps and the reconstructed images without additional acquisitions. Thus, the quality of the reconstructed image was improved, and the aliasing artifact was alleviated when motions occurred during the imaging procedure.
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Affiliation(s)
- Jun Lyu
- School of Computer and Control Engineering, Yantai University, Yantai, Shandong, China
| | - Yan Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weibo Chen
- Philips Healthcare (China), Shanghai, China
| | - Chengyan Wang
- Human Phenome Institute, Fudan University, Shanghai, China,*Correspondence: Chengyan Wang, ; Ruokun Li,
| | - Ruokun Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China,*Correspondence: Chengyan Wang, ; Ruokun Li,
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