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Shima T, Fujima N, Yamano S, Kameda H, Suzuka M, Takeuchi A, Kinoshita Y, Iwai N, Kudo K, Minowa K. Non-Gaussian model-based diffusion-weighted imaging of oral squamous cell carcinoma: associations with Ki-67 proliferation status. Oral Radiol 2023; 39:661-667. [PMID: 36971988 DOI: 10.1007/s11282-023-00682-x] [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: 08/31/2022] [Accepted: 03/15/2023] [Indexed: 03/29/2023]
Abstract
OBJECTIVES To investigate possible associations between diffusion-weighted imaging (DWI) parameters derived from a non-Gaussian model fitting and Ki-67 status in patients with oral squamous cell carcinoma (OSCC). METHODS Twenty-four patients with newly diagnosed OSCC were prospectively recruited. DWI was performed using six b-values (0-2500). The diffusion-related parameters of kurtosis value (K), kurtosis-corrected diffusion coefficient (DK), diffusion heterogeneity (α), distributed diffusion coefficient (DDC), slow diffusion coefficient (Dslow), and apparent diffusion coefficient (ADC) were calculated from four diffusion fitting models. Ki-67 status was categorized as low (Ki-67 percentage score < 20%), middle (20-50%), or high (> 50%). Kruskal-Wallis tests were performed between each non-Gaussian diffusion model parameters and Ki-67 grade. RESULTS The Kruskal-Wallis tests revealed that multiple parameters (K, ADC, Dk, DDC and Dslow) showed statistically significant differences between the three levels of Ki-67 status (K: p = 0.020, ADC: p = 0.012, Dk: p = 0.027, DDC: p = 0.007 and Dslow: p = 0.026). CONCLUSIONS Several non-Gaussian diffusion model parameters and ADC values were significantly associated with Ki-67 status and have potential as promising prognostic biomarkers in patients with OSCC.
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Affiliation(s)
- Tomoka Shima
- Radiology, Department of Oral Pathobiological Science, Division of Oral Medical Science, Graduate School of Dental Medicine, Hokkaido University, Kita 13, Nishi 7, Kita-Ku, Sapporo, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
| | - Shigeru Yamano
- Radiology, Department of Oral Pathobiological Science, Division of Oral Medical Science, Graduate School of Dental Medicine, Hokkaido University, Kita 13, Nishi 7, Kita-Ku, Sapporo, Japan
| | - Hiroyuki Kameda
- Radiology, Department of Oral Pathobiological Science, Division of Oral Medical Science, Graduate School of Dental Medicine, Hokkaido University, Kita 13, Nishi 7, Kita-Ku, Sapporo, Japan
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
| | - Masaaki Suzuka
- Radiology, Department of Oral Pathobiological Science, Division of Oral Medical Science, Graduate School of Dental Medicine, Hokkaido University, Kita 13, Nishi 7, Kita-Ku, Sapporo, Japan
- Department of Radiation Oncology, Nikko Memorial Hospital, Muroran, Japan
| | - Akiko Takeuchi
- Radiology, Department of Oral Pathobiological Science, Division of Oral Medical Science, Graduate School of Dental Medicine, Hokkaido University, Kita 13, Nishi 7, Kita-Ku, Sapporo, Japan
- Center for Cause of Death Investigation, Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Yurika Kinoshita
- Radiology, Department of Oral Pathobiological Science, Division of Oral Medical Science, Graduate School of Dental Medicine, Hokkaido University, Kita 13, Nishi 7, Kita-Ku, Sapporo, Japan
- Department of Radiation Oncology, Asahikawa City Hospital, Asahikawa, Japan
| | - Nanami Iwai
- Radiology, Department of Oral Pathobiological Science, Division of Oral Medical Science, Graduate School of Dental Medicine, Hokkaido University, Kita 13, Nishi 7, Kita-Ku, Sapporo, Japan
| | - Kohsuke Kudo
- Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Kazuyuki Minowa
- Radiology, Department of Oral Pathobiological Science, Division of Oral Medical Science, Graduate School of Dental Medicine, Hokkaido University, Kita 13, Nishi 7, Kita-Ku, Sapporo, Japan.
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Li Q, Zhang T, Che F, Yao S, Gao F, Nie L, Tang H, Wei Y, Song B. Intravoxel incoherent motion diffusion weighted imaging for preoperative evaluation of liver regeneration after hepatectomy in hepatocellular carcinoma. Eur Radiol 2023; 33:5222-5235. [PMID: 36892648 DOI: 10.1007/s00330-023-09496-1] [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: 04/28/2022] [Revised: 12/07/2022] [Accepted: 01/30/2023] [Indexed: 03/10/2023]
Abstract
OBJECTIVES To explore whether intravoxel incoherent motion (IVIM) parameters could evaluate liver regeneration preoperatively. METHODS A total of 175 HCC patients were initially recruited. The apparent diffusion coefficient, true diffusion coefficient (D), pseudodiffusion coefficient (D*), pseudodiffusion fraction (f), diffusion distribution coefficient, and diffusion heterogeneity index (Alpha) were measured by two independent radiologists. Spearman's correlation test was used to assess correlations between IVIM parameters and the regeneration index (RI), calculated as 100% × (the volume of the postoperative remnant liver - the volume of the preoperative remnant liver) / the volume of the preoperative remnant liver. Multivariate linear regression analyses were used to identify the factors for RI. RESULTS Finally, 54 HCC patients (45 men and 9 women, mean age 51.26 ± 10.41 years) were retrospectively analyzed. The intraclass correlation coefficient ranged from 0.842 to 0.918. In all patients, fibrosis stage was reclassified as F0-1 (n = 10), F2-3 (n = 26), and F4 (n = 18) using the METAVIR system. Spearman correlation test showed D* (r = 0.303, p = 0.026) was associated with RI; however, multivariate analysis showed that only D value was a significant predictor (p < 0.05) of RI. D and D*showed moderate correlations with fibrosis stage (r = -0.361, p = 0.007; r = -0.457, p = 0.001). Fibrosis stage showed a negative correlation with RI (r = -0.263, p = 0.015). In the 29 patients who underwent minor hepatectomy, only the D value showed a positive association (p < 0.05) with RI, and a negative correlation with fibrosis stage (r = -0.360, p = 0.018). However, in the 25 patients who underwent major hepatectomy, no IVIM parameters were associated with RI (p > 0.05). CONCLUSIONS The D and D* values, especially the D value, may be reliable preoperative predictors of liver regeneration. KEY POINTS • The D and D* values, especially the D value, derived from IVIM diffusion-weighted imaging may be useful markers for the preoperative prediction of liver regeneration in patients with HCC. • The D and D* values derived from IVIM diffusion-weighted imaging show significant negative correlations with fibrosis, an important predictor of liver regeneration. • No IVIM parameters were associated with liver regeneration in patients who underwent major hepatectomy, but the D value was a significant predictor of liver regeneration in patients who underwent minor hepatectomy.
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Affiliation(s)
- Qian Li
- Department of Radiology, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, China
| | - Tong Zhang
- Department of Radiology, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, China
| | - Feng Che
- Department of Radiology, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, China
| | - Shan Yao
- Department of Radiology, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, China
| | - Feifei Gao
- Department of Radiology, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, China
| | - Lisha Nie
- GE Healthcare, MR Research China, Beijing, China
| | - Hehan Tang
- Department of Radiology, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, China
| | - Yi Wei
- Department of Radiology, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, China.
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, China.
- Department of Radiology, Sanya People's Hospital, Sanya, 572000, China.
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Lin CX, Tian Y, Li JM, Liao ST, Liu YT, Zhan RG, Du ZL, Yu XR. Diagnostic value of multiple b-value diffusion-weighted imaging in discriminating the malignant from benign breast lesions. BMC Med Imaging 2023; 23:10. [PMID: 36631781 PMCID: PMC9832757 DOI: 10.1186/s12880-022-00950-y] [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: 05/04/2022] [Accepted: 12/14/2022] [Indexed: 01/13/2023] Open
Abstract
OBJECTIVE The conventional breast Diffusion-weighted imaging (DWI) was subtly influenced by microcirculation owing to the insufficient selection of the b values. However, the multiparameter derived from multiple b-value exhibits more reliable image quality and maximize the diagnostic accuracy. We aim to evaluate the diagnostic performance of stand-alone parameter or in combination with multiparameter derived from multiple b-value DWI in differentiating malignant from benign breast lesions. METHODS A total of forty-one patients diagnosed with benign breast tumor and thirty-eight patients with malignant breast tumor underwent DWI using thirteen b values and other MRI functional sequence at 3.0 T magnetic resonance. Data were accepted mono-exponential, bi-exponential, stretched-exponential, aquaporins (AQP) model analysis. A receiver operating characteristic curve (ROC) was used to evaluate the diagnostic performance of quantitative parameter or multiparametric combination. The Youden index, sensitivity and specificity were used to assess the optimal diagnostic model. T-test, logistic regression analysis, and Z-test were used. P value < 0.05 was considered statistically significant. RESULT The ADCavg, ADCmax, f, and α value of the malignant group were lower than the benign group, while the ADCfast value was higher instead. The ADCmin, ADCslow, DDC and ADCAQP showed no statistical significance. The combination (ADCavg-ADCfast) yielded the largest area under curve (AUC = 0.807) with sensitivity (68.42%), specificity (87.8%) and highest Youden index, indicating that multiparametric combination (ADCavg-ADCfast) was validated to be a useful model in differentiating the benign from breast malignant lesion. CONCLUSION The current study based on the multiple b-value diffusion model demonstrated quantitatively multiparametric combination (ADCavg-ADCfast) exhibited the optimal diagnostic efficacy to differentiate malignant from benign breast lesions, suggesting that multiparameter would be a promising non-invasiveness to diagnose breast lesions.
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Affiliation(s)
- Chu-Xin Lin
- grid.452930.90000 0004 1757 8087Department of Radiology, Zhuhai Hospital Affiliated With Jinan University (Zhuhai People’s Hospital), 79 Kangning Road, Zhuhai, 519000 People’s Republic of China
| | - Ye Tian
- grid.452930.90000 0004 1757 8087Department of Radiology, Zhuhai Hospital Affiliated With Jinan University (Zhuhai People’s Hospital), 79 Kangning Road, Zhuhai, 519000 People’s Republic of China
| | - Jia-Min Li
- grid.452930.90000 0004 1757 8087Department of Radiology, Zhuhai Hospital Affiliated With Jinan University (Zhuhai People’s Hospital), 79 Kangning Road, Zhuhai, 519000 People’s Republic of China
| | - Shu-Ting Liao
- grid.452930.90000 0004 1757 8087Department of Radiology, Zhuhai Hospital Affiliated With Jinan University (Zhuhai People’s Hospital), 79 Kangning Road, Zhuhai, 519000 People’s Republic of China
| | - Yu-Tao Liu
- grid.452930.90000 0004 1757 8087Department of Radiology, Zhuhai Hospital Affiliated With Jinan University (Zhuhai People’s Hospital), 79 Kangning Road, Zhuhai, 519000 People’s Republic of China
| | - Run-Gen Zhan
- grid.452930.90000 0004 1757 8087Department of Radiology, Zhuhai Hospital Affiliated With Jinan University (Zhuhai People’s Hospital), 79 Kangning Road, Zhuhai, 519000 People’s Republic of China
| | - Zhong-Li Du
- grid.452930.90000 0004 1757 8087Department of Radiology, Zhuhai Hospital Affiliated With Jinan University (Zhuhai People’s Hospital), 79 Kangning Road, Zhuhai, 519000 People’s Republic of China
| | - Xiang-Rong Yu
- grid.452930.90000 0004 1757 8087Department of Radiology, Zhuhai Hospital Affiliated With Jinan University (Zhuhai People’s Hospital), 79 Kangning Road, Zhuhai, 519000 People’s Republic of China
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Wang J, Zhang H, Dang X, Rui W, Cheng H, Wang J, Zhang Y, Qiu T, Yao Z, Liu H, Pang H, Ren Y. Multi-b-value diffusion stretched-exponential model parameters correlate with MIB-1 and CD34 expression in Glioma patients, an intraoperative MR-navigated, biopsy-based histopathologic study. Front Oncol 2023; 13:1104610. [PMID: 37182187 PMCID: PMC10171458 DOI: 10.3389/fonc.2023.1104610] [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: 11/23/2022] [Accepted: 04/13/2023] [Indexed: 05/16/2023] Open
Abstract
Background To understand the pathological correlations of multi-b-value diffusion-weighted imaging (MDWI) stretched-exponential model (SEM) parameters of α and diffusion distribution index (DDC) in patients with glioma. SEM parameters, as promising biomarkers, played an important role in histologically grading gliomas. Methods Biopsy specimens were grouped as high-grade glioma (HGG) or low-grade glioma (LGG). MDWI-SEM parametric mapping of DDC1500, α1500 fitted by 15 b-values (0-1,500 sec/mm2)and DDC5000 and α5000 fitted by 22 b-values (0-5,000 sec/mm2) were matched with pathological samples (stained by MIB-1 and CD34) by coregistered localized biopsies, and all SEM parameters were correlated with these pathological indices pMIB-1(percentage of MIB-1 expression positive rate) and CD34-MVD (CD34 expression positive microvascular density for each specimen). The two-tailed Spearman's correlation was calculated for pathological indexes and SEM parameters, as well as WHO grades and SEM parameters. Results MDWI-derived α1500 negatively correlated with CD34-MVD in both LGG (6 specimens) and HGG (26 specimens) (r=-0.437, P =0.012). MDWI-derived DDC1500 and DDC5000 negatively correlated with MIB-1 expression in all glioma patients (P<0.05). WHO grades negatively correlated with α1500(r=-0.485; P=0.005) and α5000(r=-0.395; P=0.025). Conclusions SEM-derived DDC and α are significant in histologically grading gliomas, DDC may indicate the proliferative ability, and CD34 stained microvascular perfusion may be an important determinant of water diffusion inhomogeneity α in glioma.
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Affiliation(s)
- Junlong Wang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Hua Zhang
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, China
| | - Xuefei Dang
- Department of Oncology, Minhang Branch of Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Wenting Rui
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Haixia Cheng
- Department of Neuropathology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jing Wang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yong Zhang
- Department of Magnetic Resonance Research, General Electric Healthcare, Shanghai, China
| | - Tianming Qiu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhenwei Yao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Hanqiu Liu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- *Correspondence: Hanqiu Liu, ; Haopeng Pang, ; Yan Ren,
| | - Haopeng Pang
- Minimally Invasive Therapy Center, Shanghai Cancer Center, Fudan University, Shanghai, China
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Hanqiu Liu, ; Haopeng Pang, ; Yan Ren,
| | - Yan Ren
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- *Correspondence: Hanqiu Liu, ; Haopeng Pang, ; Yan Ren,
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Shi B, Xue K, Yin Y, Xu Q, Shi B, Wu D, Ye J. Grading of clear cell renal cell carcinoma using diffusion MRI with a fractional order calculus model. Acta Radiol 2022; 64:421-430. [PMID: 35040361 DOI: 10.1177/02841851211072482] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
BACKGROUND The fractional order calculus (FROC) model has been developed to describe restrained motion of water molecules as well as microstructural heterogeneity, providing a novel tool for non-invasive tumor grading. PURPOSE To evaluate the role of the FROC model in characterizing clear cell renal cell carcinoma (ccRCC) grades. MATERIAL AND METHODS A total of 59 patients diagnosed with ccRCC were included in this prospective study. The diffusion metrics derived from the mono-exponential model (apparent diffusion coefficient [ADC]), intra-voxel incoherent motion [IVIM] model [D, D*, f], and FROC model [Dfroc, β, μ]) were calculated and compared between low- and high-grade ccRCCs. Binary logistic regression analysis was performed to establish the diagnostic models. Receiver operating characteristic (ROC) analysis and DeLong test were performed to evaluate and compare the diagnostic performance of metrics in grading ccRCC. RESULTS All the metrics except D* and f exhibited statistical differences between low- and high-grade ccRCCs. ROC analysis showed individual FROC parameters, μ, Dfroc, and β, outperformed ADC and IVIM parameters in grading ccRCC. For single parameter, μ demonstrated the highest AUC value, sensitivity, and diagnostic accuracy in discriminating the two ccRCC groups while β exhibited the optimal specificity. Importantly, the combination of Dfroc, μ, and β could further improve the diagnostic performance. CONCLUSION The FROC parameters were superior to ADC and IVIM parameters in grading ccRCC, indicating the great potential of the FROC model in distinguishing low- and high-grade ccRCCs.
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Affiliation(s)
- Bowen Shi
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, PR China
| | - Ke Xue
- Central Research Institute, United Imaging Healthcare, Shanghai, PR China
| | - Yili Yin
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, PR China
| | - Qing Xu
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, PR China
| | - Binbin Shi
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, PR China
| | - Dongmei Wu
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronics Science, East China Normal University, Shanghai, PR China
| | - Jing Ye
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, PR China
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Papoutsaki MV, Sidhu HS, Dikaios N, Singh S, Atkinson D, Kanber B, Beale T, Morley S, Forster M, Carnell D, Mendes R, Punwani S. Utility of diffusion MRI characteristics of cervical lymph nodes as disease classifier between patients with head and neck squamous cell carcinoma and healthy volunteers. NMR IN BIOMEDICINE 2021; 34:e4587. [PMID: 34240782 DOI: 10.1002/nbm.4587] [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: 02/04/2021] [Revised: 06/10/2021] [Accepted: 06/11/2021] [Indexed: 06/13/2023]
Abstract
Diffusion MRI characteristics assessed by apparent diffusion coefficient (ADC) histogram analysis in head and neck squamous cell carcinoma (HNSCC) have been reported as helpful in classifying tumours based on diffusion characteristics. There is little reported on HNSCC lymph nodes classification by diffusion characteristics. The aim of this study was to determine whether pretreatment nodal microstructural diffusion MRI characteristics can classify diseased nodes of patients with HNSCC from normal nodes of healthy volunteers. Seventy-nine patients with histologically confirmed HNSCC prior to chemoradiotherapy, and eight healthy volunteers, underwent diffusion-weighted (DW) MRI at a 1.5-T MR scanner. Two radiologists contoured lymph nodes on DW (b = 300 s/m2 ) images. ADC, distributed diffusion coefficient (DDC) and alpha (α) values were calculated by monoexponential and stretched exponential models. Histogram analysis metrics of drawn volume were compared between patients and volunteers using a Mann-Whitney test. The classification performance of each metric between the normal and diseased nodes was determined by receiver operating characteristic (ROC) analysis. Intraclass correlation coefficients determined interobserver reproducibility of each metric based on differently drawn ROIs by two radiologists. Sixty cancerous and 40 normal nodes were analysed. ADC histogram analysis revealed significant differences between patients and volunteers (p ≤0.0001 to 0.0046), presenting ADC distributions that were more skewed (1.49 for patients, 1.03 for volunteers; p = 0.0114) and 'peaked' (6.82 for patients, 4.20 for volunteers; p = 0.0021) in patients. Maximum ADC values exhibited the highest area under the curve ([AUC] 0.892). Significant differences were revealed between patients and volunteers for DDC and α value histogram metrics (p ≤0.0001 to 0.0044); the highest AUC were exhibited by maximum DDC (0.772) and the 25th percentile α value (0.761). Interobserver repeatability was excellent for mean ADC (ICC = 0.88) and the 25th percentile α value (ICC = 0.78), but poor for all other metrics. These results suggest that pretreatment microstructural diffusion MRI characteristics in lymph nodes, assessed by ADC and α value histogram analysis, can identify nodal disease.
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Affiliation(s)
| | | | - Nikolaos Dikaios
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK
| | - Saurabh Singh
- Centre for Medical Imaging, University College London, London, UK
| | - David Atkinson
- Centre for Medical Imaging, University College London, London, UK
| | - Baris Kanber
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Timothy Beale
- Department of Radiology, University College London Hospital, London, UK
| | - Simon Morley
- Department of Radiology, University College London Hospital, London, UK
| | - Martin Forster
- Department of Oncology, University College London, Cancer Institute, London, UK
- Department of Oncology, University College London Hospital, London, UK
| | - Dawn Carnell
- Department of Oncology, University College London Hospital, London, UK
| | - Ruheena Mendes
- Department of Oncology, University College London Hospital, London, UK
| | - Shonit Punwani
- Centre for Medical Imaging, University College London, London, UK
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Sun K, Jiao Z, Zhu H, Chai W, Yan X, Fu C, Cheng JZ, Yan F, Shen D. Radiomics-based machine learning analysis and characterization of breast lesions with multiparametric diffusion-weighted MR. J Transl Med 2021; 19:443. [PMID: 34689804 PMCID: PMC8543912 DOI: 10.1186/s12967-021-03117-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 10/13/2021] [Indexed: 12/29/2022] Open
Abstract
Background This study aimed to evaluate the utility of radiomics-based machine learning analysis with multiparametric DWI and to compare the diagnostic performance of radiomics features and mean diffusion metrics in the characterization of breast lesions. Methods This retrospective study included 542 lesions from February 2018 to November 2018. One hundred radiomics features were computed from mono-exponential (ME), biexponential (BE), stretched exponential (SE), and diffusion-kurtosis imaging (DKI). Radiomics-based analysis was performed by comparing four classifiers, including random forest (RF), principal component analysis (PCA), L1 regularization (L1R), and support vector machine (SVM). These four classifiers were trained on a training set with 271 patients via ten-fold cross-validation and tested on an independent testing set with 271 patients. The diagnostic performance of the mean diffusion metrics of ME (mADCall b, mADC0–1000), BE (mD, mD*, mf), SE (mDDC, mα), and DKI (mK, mD) were also calculated for comparison. The area under the receiver operating characteristic curve (AUC) was used to compare the diagnostic performance. Results RF attained higher AUCs than L1R, PCA and SVM. The AUCs of radiomics features for the differential diagnosis of breast lesions ranged from 0.80 (BE_D*) to 0.85 (BE_D). The AUCs of the mean diffusion metrics ranged from 0.54 (BE_mf) to 0.79 (ME_mADC0–1000). There were significant differences in the AUCs between the mean values of all diffusion metrics and radiomics features of AUCs (all P < 0.001) for the differentiation of benign and malignant breast lesions. Of the radiomics features computed, the most important sequence was BE_D (AUC: 0.85), and the most important feature was FO-10 percentile (Feature Importance: 0.04). Conclusions The radiomics-based analysis of multiparametric DWI by RF enables better differentiation of benign and malignant breast lesions than the mean diffusion metrics. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-021-03117-5.
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Affiliation(s)
- Kun Sun
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Zhicheng Jiao
- Department of Diagnostic Imaging, Alpert Medical School of Brown University, Providence, USA
| | - Hong Zhu
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Weimin Chai
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xu Yan
- Scientific Marketing, Siemens Shanghai Magnetic Resonance Ltd., Shanghai, China
| | - Caixia Fu
- MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
| | - Jie-Zhi Cheng
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
| | - Dinggang Shen
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China. .,School of BME, Shanghai Tech University, Shanghai, China.
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Zhao L, Liang M, Yang Y, Zhang H, Zhao X. Prediction of false-negative extramural venous invasion in patients with rectal cancer using multiple mathematical models of diffusion-weighted imaging. Eur J Radiol 2021; 139:109731. [PMID: 33905979 DOI: 10.1016/j.ejrad.2021.109731] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 04/10/2021] [Accepted: 04/16/2021] [Indexed: 01/12/2023]
Abstract
PURPOSE To investigate the parameters from mono-exponential, stretched-exponential, and intravoxel incoherent motion diffusion-weighted imaging (DWI) models for evaluating false-negative extramural venous invasion (EMVI) on conventional magnetic resonance imaging (MRI) in rectal cancer patients. MATERIAL AND METHODS Seventy-two rectal cancer patients with negative EMVI on conventional MRI who underwent direct surgical resection were enrolled in this prospective study. The apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), distributed diffusion coefficient (DDC), and water molecular diffusion heterogeneity index (α) values within the whole tumor were obtained to identify the patients with false-negative EMVI. Receiver operating characteristic (ROC) curves were applied to evaluate the diagnostic performance. Multivariate binary logistic regression analysis was conducted to determine the independent risk factors. RESULTS The DDC, D*, f, and α values were significantly different in the EMVI-positive and EMVI-negative groups (P = 0.018, and P < 0.001, respectively). The D*, f, and α values demonstrated good diagnostic performance with area under the ROC curve (AUC) of 0.861, 0.824, and 0.854, respectively. The combined model, including D*, α, and tumor location, proved superior diagnostic performance with the AUC, sensitivity, specificity, and accuracy of 0.971, 0.917, 0.967, and 0.931, respectively. The AUC of the combined model was significantly higher than that of the D*, f, and DDC (P = 0.004, 0.045, and 0.002, respectively). CONCLUSION Multi-b-value DWI may be a potential tool for identifying micro-EMVI in rectal cancer. The combination of DWI parameters and tumor location leads to superior diagnostic performance.
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Affiliation(s)
- Li Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College. No.17, Panjiayuan Nanli, Chaoyang District, Beijing 100021, China.
| | - Meng Liang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College. No.17, Panjiayuan Nanli, Chaoyang District, Beijing 100021, China.
| | - Yang Yang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College. No.17, Panjiayuan Nanli, Chaoyang District, Beijing 100021, China.
| | - Hongmei Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College. No.17, Panjiayuan Nanli, Chaoyang District, Beijing 100021, China.
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College. No.17, Panjiayuan Nanli, Chaoyang District, Beijing 100021, China.
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9
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Li Q, Wang T, Huang Y, Li Q, Liu P, Grimm R, Fu C, Zhang Y, Gu Y. Whole-Tumor Histogram and Texture Imaging Features on Magnetic Resonance Imaging Combined With Epstein-Barr Virus Status to Predict Disease Progression in Patients With Nasopharyngeal Carcinoma. Front Oncol 2021; 11:610804. [PMID: 33767984 PMCID: PMC7986723 DOI: 10.3389/fonc.2021.610804] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Accepted: 02/05/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose: We aimed to investigate whether Epstein–Barr virus (EBV) could produce differences on MRI by examining the histogram and texture imaging features. We also sought to determine the predictive value of pretreatment MRI texture analyses incorporating with EBV status for disease progression (PD) in patients with primary nasopharyngeal carcinoma (NPC). Materials and Methods: Eighty-one patients with primary T2-T4 NPC and known EBV status who underwent contrast-enhanced MRI were included in this retrospective study. Whole-tumor-based histogram and texture features were extracted from pretreatment T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced (CE)-T1WI images. Mann–Whitney U-tests were performed to identify the differences in histogram and texture parameters between EBV DNA-positive and EBV DNA-negative NPC images. The effects of clinical variables as well as histogram and texture features were estimated by using univariate and multivariate logistic regression analyses. Receiver operating characteristic (ROC) curve analysis was used to predict the EBV status and PD. Finally, an integrated model with the best performance was built. Results: Of the 81 patients included, 54 had EBV DNA-positive NPC, and 27 had EBV DNA-negative NPC. Patients who were tested EBV DNA-positive had higher overall stage (P = 0.016), more lymphatic metastases (p < 0.0001), and easier distant metastases (P = 0.026) than the patients who were tested EBV DNA-negative. Tumor volume, T1WISkewness and T2WIKurtosis showed significant differences between the two groups. The combination of the three features achieved an AUC of 0.783 [95% confidence interval (CI) 0.678–0.888] with a sensitivity and specificity of 70.4 and 74.1%, respectively, in differentiating EBV DNA-positive tumors from EBV DNA-negative tumors. The combination of overall stage and tumor volume of T2WIKurtosis and EBV status was the most effective model for predicting PD in patients with primary NPC. The overall accuracy was 84.6%, with a sensitivity and specificity of 93.8 and 66.2%, respectively (AUC, 0.800; 95% CI 0.700–0.900). Conclusion: This study demonstrates that MRI-based radiological features and EBV status can be used as an aid tool for the evaluation of PD, in order to develop tailored treatment targeting specific characteristics of individual patients.
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Affiliation(s)
- Qiao Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - TingTing Wang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yan Huang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qin Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - PeiYao Liu
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Robert Grimm
- Magnetic Resonance Application Predevelopment, Siemens Healthcare, Erlangen, Germany
| | - CaiXia Fu
- Magnetic Resonance Applications Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
| | - YunYan Zhang
- Department of Radiology, Shanghai Proton and Heavy Ion Center, Shanghai, China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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10
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Liu B, Ma WL, Zhang GW, Sun Z, Wei MQ, Hou WH, Hou BX, Wei LC, Huan Y. Potentialities of multi-b-values diffusion-weighted imaging for predicting efficacy of concurrent chemoradiotherapy in cervical cancer patients. BMC Med Imaging 2020; 20:97. [PMID: 32799809 PMCID: PMC7429470 DOI: 10.1186/s12880-020-00496-x] [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: 12/24/2019] [Accepted: 08/06/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND To testify whether multi-b-values diffusion-weighted imaging (DWI) can be used to ultra-early predict treatment response of concurrent chemoradiotherapy (CCRT) in cervical cancer patients and to assess the predictive ability of concerning parameters. METHODS Fifty-three patients with biopsy proved cervical cancer were retrospectively recruited in this study. All patients underwent pelvic multi-b-values DWI before and at the 3rd day during treatment. The apparent diffusion coefficient (ADC), true diffusion coefficient (Dslow), perfusion-related pseudo-diffusion coefficient (Dfast), perfusion fraction (f), distributed diffusion coefficient (DDC) and intravoxel diffusion heterogeneity index(α) were generated by mono-exponential, bi-exponential and stretched exponential models. Treatment response was assessed based on Response Evaluation Criteria in Solid Tumors (RECIST v1.1) at 1 month after the completion of whole CCRT. Parameters were compared using independent t test or Mann-Whitney U test as appropriate. Receiver operating characteristic (ROC) curves was used for statistical evaluations. RESULTS ADC-T0 (p = 0.02), Dslow-T0 (p < 0.01), DDC-T0 (p = 0.03), ADC-T1 (p < 0.01), Dslow-T1 (p < 0.01), ΔADC (p = 0.04) and Δα (p < 0.01) were significant lower in non-CR group patients. ROC analyses showed that ADC-T1 and Δα exhibited high prediction value, with area under the curves of 0.880 and 0.869, respectively. CONCLUSIONS Multi-b-values DWI can be used as a noninvasive technique to assess and predict treatment response in cervical cancer patients at the 3rd day of CCRT. ADC-T1 and Δα can be used to differentiate good responders from poor responders.
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Affiliation(s)
- Bing Liu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, 127 Changle Western Road, Xi'an, P. R. China, 710032
| | - Wan-Ling Ma
- Department of radiology, Longgang District People's Hospital, Shenzhen, Guangdong, P. R. China, 518172
| | - Guang-Wen Zhang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, 127 Changle Western Road, Xi'an, P. R. China, 710032
| | - Zhen Sun
- Department of Orthopaedics, Xijing Hospital, Fourth Military Medical University, 127 Changle Western Road, Xi'an, P. R. China, 710032
| | - Meng-Qi Wei
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, 127 Changle Western Road, Xi'an, P. R. China, 710032
| | - Wei-Huan Hou
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, 127 Changle Western Road, Xi'an, P. R. China, 710032
| | - Bing-Xin Hou
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, 127 Changle Western Road, Xi'an, P. R. China, 710032
| | - Li-Chun Wei
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, 127 Changle Western Road, Xi'an, P. R. China, 710032
| | - Yi Huan
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, 127 Changle Western Road, Xi'an, P. R. China, 710032.
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11
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Shima T, Fujima N, Yamano S, Kudo K, Hirata K, Minowa K. Evaluation of non-Gaussian model-based diffusion-weighted imaging in oral squamous cell carcinoma: comparison with tumour functional information derived from positron-emission tomography. Clin Radiol 2020; 75:397.e15-397.e21. [PMID: 31987487 DOI: 10.1016/j.crad.2019.12.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 12/23/2019] [Indexed: 10/25/2022]
Abstract
AIM To evaluate and compare diffusion-weighted imaging (DWI) parameters derived from a non-Gaussian fitting model and positron-emission tomography (PET) parameters derived from 18F-fluoromisonidazole-PET (FMISO-PET) in patients with oral squamous cell carcinoma (OSCC). MATERIALS AND METHODS Primary sites were evaluated prospectively in 18 patients. DWI was performed using six b-values (0-2,500). Diffusion-related parameters of kurtosis value (K), the kurtosis-corrected diffusion coefficient (DK), diffusion heterogeneity (α), distributed diffusion coefficient (DDC), the slow diffusion coefficient (Dslow), and the apparent diffusion coefficient (ADC) were calculated from four diffusion-fitting models. Maximal standardised uptake values (SUVmax), mean standardised uptake values (SUVmean), and the tumour-to-muscle ration (TMR) of the SUV value were calculated for FMISO-PET. Spearman's correlation coefficient was used to evaluate the correlation between each non-Gaussian diffusion model parameters and PET parameter. RESULTS There was moderate correlation between FMISO-PET SUVmax and Dslow (ρ=-0.45, p=0.06). In addition, there was good correlation between TMRmax and five non-Gaussian diffusion model parameters (K: ρ=0.65, p=0.004, DK: ρ=-0.72, p=0.0008, DDC: ρ=-0.75, p=0.0003, ADC: ρ=-0.74, p=0.0005, and Dslow: ρ= -0.65, p=0.003), and between TMRmean and five non-Gaussian model parameters (K: ρ=0.64, p=0.005, DK: ρ=-0.61, p=0.007, DDC: ρ=-0.63, p=0.005, ADC: ρ=-0.61, p=0.007, and Dslow: ρ=-0.56, p=0.015). CONCLUSION Non-Gaussian diffusion model parameters can be related to tumour hypoxia.
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Affiliation(s)
- T Shima
- Radiology, Department of Oral Pathobiological Science, Division of Oral Medical Science, Graduate School of Dental Medicine, Hokkaido University, Kita 13, Nishi 7, Kita-ku, Sapporo, 060-8586, Japan
| | - N Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Kita 15, Nishi 7, Kita-ku, Sapporo, 060-8638, Japan
| | - S Yamano
- Radiology, Department of Oral Pathobiological Science, Division of Oral Medical Science, Graduate School of Dental Medicine, Hokkaido University, Kita 13, Nishi 7, Kita-ku, Sapporo, 060-8586, Japan
| | - K Kudo
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Kita 15, Nishi 7, Kita-ku, Sapporo, 060-8638, Japan
| | - K Hirata
- Department of Nuclear Medicine, Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, 060-8638, Japan
| | - K Minowa
- Radiology, Department of Oral Pathobiological Science, Division of Oral Medical Science, Graduate School of Dental Medicine, Hokkaido University, Kita 13, Nishi 7, Kita-ku, Sapporo, 060-8586, Japan.
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12
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Zhang H, Zhou Y, Li J, Zhang P, Li Z, Guo J. The value of DWI in predicting the response to synchronous radiochemotherapy for advanced cervical carcinoma: comparison among three mathematical models. Cancer Imaging 2020; 20:8. [PMID: 31937371 PMCID: PMC6961298 DOI: 10.1186/s40644-019-0285-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Accepted: 12/30/2019] [Indexed: 12/14/2022] Open
Abstract
Background Diffusion weighted imaging(DWI) mode mainly includes intravoxel incoherent motion (IVIM), stretched exponential model (SEM) and Gaussian diffusion model, but it is still unclear which mode is the most valuable in predicting the response to radiochemotherapy for cervical cancer. This study aims to compare the values of three mathematical models in predicting the response to synchronous radiochemotherapy for cervical cancer. Methods Eighty-four patients with cervical cancer were enrolled into this study. They underwent DWI examination by using 12 b-values prior to treatment. The imaging parameters were calculated on the basis of IVIM, SEM and Gaussian diffusion models respectively. The imaging parameters derived from three mathematical modes were compared between responders and non-responders groups. The repeatability of each imaging parameter was assessed. Results The ADC, D or DDC value was lower in responders than in non-responders groups (P = 0.03, 0.02, 0.01). The α value was higher in responders group than in non-responders group (P = 0.03). DDC had the largest area under curves (AUC) (=0.948) in predicting the response to treatment. The imaging parameters derived from SEM had better repeatability (CCC for DDC and α were 0.969 and 0.924 respectively) than that derived from other exponential models. Conclusion Three exponential modes of DWI are useful for predicting the response to radiochemotherapy for cervical cancer, and SEM may be used as a potential optimal model for predicting treatment effect.
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Affiliation(s)
- Hui Zhang
- Department of Radiology, The Second Affiliated Hospital of Zhengzhou University, No. 2 Jingba Avenue, Zhengzhou, 450014, Henan Province, China
| | - Yuyang Zhou
- Department of Cardiac Surgery, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, 450014, Henan Province, China
| | - Jie Li
- Department of Radiology, The Second Affiliated Hospital of Zhengzhou University, No. 2 Jingba Avenue, Zhengzhou, 450014, Henan Province, China
| | - Pengjuan Zhang
- Department of Radiology, The Second Affiliated Hospital of Zhengzhou University, No. 2 Jingba Avenue, Zhengzhou, 450014, Henan Province, China
| | - Zhenzhen Li
- Department of Radiology, The Second Affiliated Hospital of Zhengzhou University, No. 2 Jingba Avenue, Zhengzhou, 450014, Henan Province, China
| | - Junwu Guo
- Department of Radiology, The Second Affiliated Hospital of Zhengzhou University, No. 2 Jingba Avenue, Zhengzhou, 450014, Henan Province, China.
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13
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Lin L, Xue Y, Duan Q, Chen X, Chen H, Jiang R, Zhong T, Xu G, Geng D, Zhang J. Grading meningiomas using mono-exponential, bi-exponential and stretched exponential model-based diffusion-weighted MR imaging. Clin Radiol 2019; 74:651.e15-651.e23. [DOI: 10.1016/j.crad.2019.04.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Accepted: 04/03/2019] [Indexed: 02/07/2023]
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14
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Liu W, Liu XH, Tang W, Gao HB, Zhou BN, Zhou LP. Histogram analysis of stretched-exponential and monoexponential diffusion-weighted imaging models for distinguishing low and intermediate/high gleason scores in prostate carcinoma. J Magn Reson Imaging 2018; 48:491-498. [PMID: 29412492 DOI: 10.1002/jmri.25958] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Accepted: 01/12/2018] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Noninvasive measures to evaluate the aggressiveness of prostate carcinoma (PCa) may benefit patients. PURPOSE To assess the value of stretched-exponential and monoexponential diffusion-weighted imaging (DWI) for predicting the aggressiveness of PCa. STUDY TYPE Retrospective study. SUBJECTS Seventy-five patients with PCa. FIELD STRENGTH 3T DWI examinations were performed using b-values of 0, 500, 1000, and 2000 s/mm2 . ASSESSMENT The research were based on entire-tumor histogram analysis and the reference standard was radical prostectomy. STATISTICAL TESTS The correlation analysis was programmed with Spearman's rank-order analysis between the histogram variables and Gleason grade group (GG). Receiver operating characteristic (ROC) regression was used to analyze the ability of these histogram variables to differentiate low-grade (LG) from intermediate/high-grade (HG) PCa. RESULTS The percentiles and mean of apparent diffusion coefficient (ADC) and distributed diffusion coefficient (DDC) were correlated with GG (ρ: 0.414-0.593), while there was no significant relation among α value, skewnesses, and kurtosises with GG (ρ:0.034-0.323). HG tumors (ADC:484 ± 136, 592 ± 139, 670 ± 144, 788 ± 146, 895 ± 141 mm2 /s; DDC: 410 ± 142, 532 ± 172, 666 ± 193, 786 ± 196, 914 ± 181 mm2 /s) had lower values in the 10th , 25th , 50th , 75th percentiles and means than LG tumors (ADC: 644 ± 779, 737 ± 84, 836 ± 83, 919 ± 82, 997 ± 107 mm2 /s; DDC: 552 ± 82, 680 ± 94, 829 ± 112, 931 ± 106, 1045 ± 100 mm2 /s). However, there was no difference between LG and HG tumors in α value (0.671 ± 0.041 vs. 0.633 ± 0.114), kurtosises (ADC 0.09 vs. 0.086; DDC -0.033 vs. -0.317), or skewnesses (ADC -0.036 vs. 0.073; DDC -0.063 vs. 0.136). The above statistics were P < 0.01. ADC10 with AUC = 0.840 and DDC10 with AUC = 0.799 were similar in discriminating between LG and HG PCa at P < 0.05. DATA CONCLUSION Histogram variables of DDC and ADC may predict the aggressiveness of PCa, while α value does not. The abilities of ADC10 and DDC10 to discriminate LG from HG tumors were similar, and both better than their respective means. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2018;48:491-498.
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Affiliation(s)
- Wei Liu
- Shanghai Institute of Medical Imaging, Shanghai, China.,Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Xiao H Liu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wei Tang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Hong B Gao
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Bing N Zhou
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Liang P Zhou
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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15
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Xiao Z, Zhong Y, Tang Z, Qiang J, Qian W, Wang R, Wang J, Wu L, Tang W, Zhang Z. Standard diffusion-weighted, diffusion kurtosis and intravoxel incoherent motion MR imaging of sinonasal malignancies: correlations with Ki-67 proliferation status. Eur Radiol 2018; 28:2923-2933. [PMID: 29383521 DOI: 10.1007/s00330-017-5286-x] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2017] [Revised: 12/11/2017] [Accepted: 12/22/2017] [Indexed: 01/12/2023]
Abstract
OBJECTIVES To explore the correlations of parameters derived from standard diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) with the Ki-67 proliferation status. METHODS Seventy-five patients with histologically proven sinonasal malignancies who underwent standard DWI, DKI and IVIM were retrospectively reviewed. The mean, minimum, maximum and whole standard DWI [apparent diffusion coefficient (ADC)], DKI [diffusion kurtosis (K) and diffusion coefficient (Dk)] and IVIM [pure diffusion coefficient (D), pseudo-diffusion coefficient (D*) and perfusion fraction (f)] parameters were measured and correlated with the Ki-67 labelling index (LI). The Ki-67 LI was categorised as high (> 50%) or low (≤ 50%). RESULTS The K and f values were positively correlated with the Ki-67 LI (rho = 0.295~0.532), whereas the ADC, Dk and D values were negatively correlated with the Ki-67 LI (rho = -0.443~-0.277). The ADC, Dk and D values were lower, whereas the K value was higher in sinonasal malignancies with a high Ki-67 LI than in those in a low Ki-67 LI (all p < 0.05). A higher maximum K value (Kmax > 0.977) independently predicted a high Ki-67 status [odds ratio (OR) = 7.614; 95% confidence interval (CI) = 2.197-38.674; p = 0.017]. CONCLUSION ADC, Dk, K, D and f are correlated with Ki-67 LI. Kmax is the strongest independent factor for predicting Ki-67 status. KEY POINTS • DWI-derived parameters from different models are capable of providing different pathophysiological information. • DWI, DKI and IVIM parameters are associated with Ki-67 proliferation status. • K max derived from DKI is the strongest independent factor for the prediction of Ki-67 proliferation status.
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Affiliation(s)
- Zebin Xiao
- Department of Radiology, Eye & ENT Hospital of Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China
| | - Yufeng Zhong
- Department of Radiology, Eye & ENT Hospital of Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China.,Department of Radiology, Jinshan Hospital of Shanghai Medical School, Fudan University, 1508 Longhang Road, Shanghai, 201508, People's Republic of China
| | - Zuohua Tang
- Department of Radiology, Eye & ENT Hospital of Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China.
| | - Jinwei Qiang
- Department of Radiology, Jinshan Hospital of Shanghai Medical School, Fudan University, 1508 Longhang Road, Shanghai, 201508, People's Republic of China.
| | - Wen Qian
- Department of Radiology, Eye & ENT Hospital of Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China
| | - Rong Wang
- Department of Radiology, Eye & ENT Hospital of Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China
| | - Jie Wang
- Department of Radiotherapy, Eye & ENT Hospital of Shanghai Medical School, Fudan University, Shanghai, 200031, China
| | - Lingjie Wu
- Department of Otolaryngology, Eye & ENT Hospital of Shanghai Medical School, Fudan University, Shanghai, 200031, China
| | - Wenlin Tang
- Siemens Healthcare Ltd., Shanghai, 201318, People's Republic of China
| | - Zhongshuai Zhang
- Siemens Healthcare Ltd., Shanghai, 201318, People's Republic of China
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16
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Jelescu IO, Budde MD. Design and validation of diffusion MRI models of white matter. FRONTIERS IN PHYSICS 2017; 28:61. [PMID: 29755979 PMCID: PMC5947881 DOI: 10.3389/fphy.2017.00061] [Citation(s) in RCA: 122] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Diffusion MRI is arguably the method of choice for characterizing white matter microstructure in vivo. Over the typical duration of diffusion encoding, the displacement of water molecules is conveniently on a length scale similar to that of the underlying cellular structures. Moreover, water molecules in white matter are largely compartmentalized which enables biologically-inspired compartmental diffusion models to characterize and quantify the true biological microstructure. A plethora of white matter models have been proposed. However, overparameterization and mathematical fitting complications encourage the introduction of simplifying assumptions that vary between different approaches. These choices impact the quantitative estimation of model parameters with potential detriments to their biological accuracy and promised specificity. First, we review biophysical white matter models in use and recapitulate their underlying assumptions and realms of applicability. Second, we present up-to-date efforts to validate parameters estimated from biophysical models. Simulations and dedicated phantoms are useful in assessing the performance of models when the ground truth is known. However, the biggest challenge remains the validation of the "biological accuracy" of estimated parameters. Complementary techniques such as microscopy of fixed tissue specimens have facilitated direct comparisons of estimates of white matter fiber orientation and densities. However, validation of compartmental diffusivities remains challenging, and complementary MRI-based techniques such as alternative diffusion encodings, compartment-specific contrast agents and metabolites have been used to validate diffusion models. Finally, white matter injury and disease pose additional challenges to modeling, which are also discussed. This review aims to provide an overview of the current state of models and their validation and to stimulate further research in the field to solve the remaining open questions and converge towards consensus.
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Affiliation(s)
- Ileana O Jelescu
- Centre d'Imagerie Biomédicale, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Matthew D Budde
- Zablocki VA Medical Center, Dept. of Neurosurgery, Medical College Wisconsin, Milwaukee, WI, USA
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17
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Jiang J, Xiao Z, Tang Z, Zhong Y, Qiang J. Differentiating between benign and malignant sinonasal lesions using dynamic contrast-enhanced MRI and intravoxel incoherent motion. Eur J Radiol 2017; 98:7-13. [PMID: 29279173 DOI: 10.1016/j.ejrad.2017.10.028] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 10/04/2017] [Accepted: 10/29/2017] [Indexed: 11/30/2022]
Abstract
PURPOSE To explore the value of dynamic contrast-enhanced MRI (DCE-MRI) and intravoxel incoherent motion (IVIM) for distinguishing between benign and malignant sinonasal lesions and investigate the correlations between the two methods. METHODS AND MATERIALS Patients with sinonasal lesions (42 benign and 31 malignant) who underwent DCE-MRI and IVIM before confirmation by histopathology were enrolled in this prospective study. Parameters derived from DCE-MRI and IVIM were measured, the optimal cut-off values for differential diagnosis were determined, and the correlations between the two methods were evaluated. Statistical analyses were performed using the Wilcoxon rank sum test, receiver operating characteristic (ROC) curve analysis, and Spearman's rank correlation. RESULTS Significantly higher Ktrans and Kep values but lower D and f values were found in malignant lesions than in benign lesions (all p<0.001). There were no significant differences in the Ve and D* values between the two groups. The area under the curve (AUC) of Ktrans was significantly higher than those of other parameters. There was no significant difference between the AUCs of DCE-MRI and IVIM with parameters combined (p=0.86). Significant inverse but weak correlations were found between D and Ktrans (r=-0.46, p<0.001), f and Ktrans (r=-0.41, p<0.001), D and Kep (r=-0.37, p=0.008), and f and Kep (r=-0.33, p=0.004). CONCLUSIONS DCE-MRI and IVIM can effectively differentiate between benign and malignant sinonasal lesions. IVIM findings correlate with DCE-MRI results and may represent an alternative to DCE-MRI.
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Affiliation(s)
- Jingxuan Jiang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai 201508, China; Department of Radiology, Eye and ENT Hospital, Fudan University, Shanghai 200031, China; Department of Radiology, Affiliated Hospital of Nantong University, Nantong 226001, China
| | - Zebin Xiao
- Department of Radiology, Eye and ENT Hospital, Fudan University, Shanghai 200031, China
| | - Zuohua Tang
- Department of Radiology, Eye and ENT Hospital, Fudan University, Shanghai 200031, China.
| | - Yufeng Zhong
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai 201508, China; Department of Radiology, Eye and ENT Hospital, Fudan University, Shanghai 200031, China
| | - Jinwei Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai 201508, China.
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18
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Bedair R, Priest AN, Patterson AJ, McLean MA, Graves MJ, Manavaki R, Gill AB, Abeyakoon O, Griffiths JR, Gilbert FJ. Assessment of early treatment response to neoadjuvant chemotherapy in breast cancer using non-mono-exponential diffusion models: a feasibility study comparing the baseline and mid-treatment MRI examinations. Eur Radiol 2017; 27:2726-2736. [PMID: 27798751 PMCID: PMC5486805 DOI: 10.1007/s00330-016-4630-x] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2016] [Revised: 09/29/2016] [Accepted: 10/03/2016] [Indexed: 12/22/2022]
Abstract
OBJECTIVES To assess the feasibility of the mono-exponential, bi-exponential and stretched-exponential models in evaluating response of breast tumours to neoadjuvant chemotherapy (NACT) at 3 T. METHODS Thirty-six female patients (median age 53, range 32-75 years) with invasive breast cancer undergoing NACT were enrolled for diffusion-weighted MRI (DW-MRI) prior to the start of treatment. For assessment of early response, changes in parameters were evaluated on mid-treatment MRI in 22 patients. DW-MRI was performed using eight b values (0, 30, 60, 90, 120, 300, 600, 900 s/mm2). Apparent diffusion coefficient (ADC), tissue diffusion coefficient (D t), vascular fraction (ƒ), distributed diffusion coefficient (DDC) and alpha (α) parameters were derived. Then t tests compared the baseline and changes in parameters between response groups. Repeatability was assessed at inter- and intraobserver levels. RESULTS All patients underwent baseline MRI whereas 22 lesions were available at mid-treatment. At pretreatment, mean diffusion coefficients demonstrated significant differences between groups (p < 0.05). At mid-treatment, percentage increase in ADC and DDC showed significant differences between responders (49 % and 43 %) and non-responders (21 % and 32 %) (p = 0.03, p = 0.04). Overall, stretched-exponential parameters showed excellent repeatability. CONCLUSION DW-MRI is sensitive to baseline and early treatment changes in breast cancer using non-mono-exponential models, and the stretched-exponential model can potentially monitor such changes. KEY POINTS • Baseline diffusion coefficients demonstrated significant differences between complete pathological responders and non-responders. • Increase in ADC and DDC at mid-treatment can discriminate responders and non-responders. • The ƒ fraction at mid-treatment decreased in responders whereas increased in non-responders. • The mono- and stretched-exponential models showed excellent inter- and intrarater repeatability. • Treatment effects can potentially be assessed by non-mono-exponential diffusion models.
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Affiliation(s)
- Reem Bedair
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK
| | - Andrew N Priest
- Department of Radiology, Addenbrookes Hospital, Cambridge University Hospitals NHS Foundation Trust, Hills Road, Cambridge, CB2 0QQ, UK
| | - Andrew J Patterson
- Department of Radiology, Addenbrookes Hospital, Cambridge University Hospitals NHS Foundation Trust, Hills Road, Cambridge, CB2 0QQ, UK
| | - Mary A McLean
- Department of Radiology, Addenbrookes Hospital, Cambridge University Hospitals NHS Foundation Trust, Hills Road, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, CB2 0RE, UK
| | - Martin J Graves
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK
- Department of Radiology, Addenbrookes Hospital, Cambridge University Hospitals NHS Foundation Trust, Hills Road, Cambridge, CB2 0QQ, UK
| | - Roido Manavaki
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK
| | - Andrew B Gill
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK
| | - Oshaani Abeyakoon
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK
| | - John R Griffiths
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, CB2 0RE, UK
| | - Fiona J Gilbert
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK.
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Differentiating metastatic from nonmetastatic lymph nodes in cervical cancer patients using monoexponential, biexponential, and stretched exponential diffusion-weighted MR imaging. Eur Radiol 2017; 27:5272-5279. [DOI: 10.1007/s00330-017-4873-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Revised: 04/18/2017] [Accepted: 05/02/2017] [Indexed: 12/31/2022]
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Wang F, Wang Y, Zhou Y, Liu C, Xie L, Zhou Z, Liang D, Shen Y, Yao Z, Liu J. Comparison between types I and II epithelial ovarian cancer using histogram analysis of monoexponential, biexponential, and stretched-exponential diffusion models. J Magn Reson Imaging 2017; 46:1797-1809. [PMID: 28379611 DOI: 10.1002/jmri.25722] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2016] [Accepted: 03/14/2017] [Indexed: 12/23/2022] Open
Abstract
PURPOSE To evaluate the utility of histogram analysis of monoexponential, biexponential, and stretched-exponential models to a dualistic model of epithelial ovarian cancer (EOC). MATERIALS AND METHODS Fifty-two patients with histopathologically proven EOC underwent preoperative magnetic resonance imaging (MRI) (including diffusion-weighted imaging [DWI] with 11 b-values) using a 3.0T system and were divided into two groups: types I and II. Apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), distributed diffusion coefficient (DDC), and intravoxel water diffusion heterogeneity (α) histograms were obtained based on solid components of the entire tumor. The following metrics of each histogram were compared between two types: 1) mean; 2) median; 3) 10th percentile and 90th percentile. Conventional MRI morphological features were also recorded. RESULTS Significant morphological features for predicting EOC type were maximum diameter (P = 0.007), texture of lesion (P = 0.001), and peritoneal implants (P = 0.001). For ADC, D, f, DDC, and α, all metrics were significantly lower in type II than type I (P < 0.05). Mean, median, 10th, and 90th percentile of D* were not significantly different (P = 0.336, 0.154, 0.779, and 0.203, respectively). Most histogram metrics of ADC, D, and DDC had significantly higher area under the receiver operating characteristic curve values than those of f and α (P < 0.05) CONCLUSION: It is feasible to grade EOC by morphological features and three models with histogram analysis. ADC, D, and DDC have better performance than f and α; f and α may provide additional information. LEVEL OF EVIDENCE 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1797-1809.
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Affiliation(s)
- Feng Wang
- Department of Radiology, Peking University Third Hospital, Haidian District, Beijing, P.R. China
| | - Yuxiang Wang
- Department of Pathology, School of Basic Medical Science, Peking University Third Hospital, Peking University Health Science Center, Beijing, P.R. China
| | - Yan Zhou
- Department of Radiology, Peking University Third Hospital, Haidian District, Beijing, P.R. China
| | - Congrong Liu
- Department of Pathology, School of Basic Medical Science, Peking University Third Hospital, Peking University Health Science Center, Beijing, P.R. China
| | - Lizhi Xie
- GE Healthcare, MR Research China, Beijing, P.R. China
| | - Zhenyu Zhou
- GE Healthcare, MR Research China, Beijing, P.R. China
| | - Dong Liang
- Siemens Ltd., China, Chaoyang District, Beijing, P.R. China
| | - Yang Shen
- Department of Radiology, Peking University Third Hospital, Haidian District, Beijing, P.R. China
| | - Zhihang Yao
- Department of Radiology, Peking University Third Hospital, Haidian District, Beijing, P.R. China
| | - Jianyu Liu
- Department of Radiology, Peking University Third Hospital, Haidian District, Beijing, P.R. China
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Yiping L, Kawai S, Jianbo W, Li L, Daoying G, Bo Y. Evaluation parameters between intra-voxel incoherent motion and diffusion-weighted imaging in grading and differentiating histological subtypes of meningioma: A prospective pilot study. J Neurol Sci 2017; 372:60-69. [DOI: 10.1016/j.jns.2016.11.037] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Revised: 10/24/2016] [Accepted: 11/16/2016] [Indexed: 01/18/2023]
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22
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Zhu HB, Zhang XY, Zhou XH, Li XT, Liu YL, Wang S, Sun YS. Assessment of pathological complete response to preoperative chemoradiotherapy by means of multiple mathematical models of diffusion-weighted MRI in locally advanced rectal cancer: A prospective single-center study. J Magn Reson Imaging 2016; 46:175-183. [PMID: 27981667 DOI: 10.1002/jmri.25567] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2016] [Accepted: 11/10/2016] [Indexed: 12/13/2022] Open
Abstract
PURPOSE To assess stretched-exponential, mono-exponential and intravoxel incoherent motion (IVIM) models of diffusion-weighted MRI(DWI) in predicting pathological complete response (pCR) to neoadjuvant chemoradiotherapy (CRT) in rectal cancer patients. MATERIALS AND METHODS This prospective study recruited 98 consecutive patients with locally advanced rectal cancer who underwent 3 Tesla MR examination before, during and after CRT. The apparent diffusion coefficient (ADC), IVIM-derived parameters (D, f, and D*), and stretched-exponential model-derived parameters (DDC and α) were measured. The parameters and their corresponding changes during and after CRT were compared between pCR and non-pCR. Receiver-operating characteristic curve analysis was performed to evaluate the diagnostic performance. Coefficient of variations and intraclass correlation coefficient were calculated to assess reliability and agreement. RESULTS Nineteen patients achieved pCR while 79 did not. The pCR group had higher ADC and α (ADC2 and α2 ), and their changes (ΔADC2 , and Δα2 ) at the endpoint than non-pCR group. α2 and ADC2 yielded similar AUCs (P = 0.339), Δα2 and ΔADC2 yielded similar AUCs (P = 0.263) ADC and α presented substantial agreement, and α presented the minimum CV (5.0-7.0%). CONCLUSION ADC and α were useful for assessing pCR after CRT. α might be more useful because it demonstrated better diagnostic performance than IVIM-derived parameters and better reliability than ADC. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:175-183.
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Affiliation(s)
- Hai-Bin Zhu
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No. 52, Beijing, China
| | - Xiao-Yan Zhang
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No. 52, Beijing, China
| | - Xiao-Hong Zhou
- Center for Magnetic Research, Medical Hospital, University of Illinois Hospital, Chicago, Illinois, USA
| | - Xiao-Ting Li
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No. 52, Beijing, China
| | - Yu-Liang Liu
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No. 52, Beijing, China
| | - Shuai Wang
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No. 52, Beijing, China
| | - Ying-Shi Sun
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No. 52, Beijing, China
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Li H, Liang L, Li A, Hu Y, Hu D, Li Z, Kamel IR. Monoexponential, biexponential, and stretched exponential diffusion-weighted imaging models: Quantitative biomarkers for differentiating renal clear cell carcinoma and minimal fat angiomyolipoma. J Magn Reson Imaging 2016; 46:240-247. [PMID: 27859853 DOI: 10.1002/jmri.25524] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Accepted: 10/07/2016] [Indexed: 12/24/2022] Open
Abstract
PURPOSE To determine the utility of various diffusion parameters obtained from monoexponential, biexponential, and stretched exponential diffusion-weighted imaging (DWI) models in differentiating between minimal fat angiomyolipoma (MFAML) and clear cell renal cell carcinoma (ccRCC). MATERIALS AND METHODS One hundred thirty-one patients with pathologically confirmed MFAML (n = 27) or ccRCC (n = 104) underwent multi-b value DWI (0∼1700 s/mm2 ) imaging at 3.0 Tesla MRI. An isotropic apparent diffusion coefficient (ADC) was calculated from diffusion-weighted images by using a monoexponential model. A pseudo-ADC (Dp ), true ADC (Dt ), and perfusion fraction (fp ) were calculated from diffusion-weighted images by using a biexponential model. A water molecular diffusion heterogeneity index (α) and distributed diffusion coefficient (DDC) were calculated from diffusion-weighted images by using a stretched exponential model. All parameters were compared between MFAML and ccRCC by using the Student's t test. Receiver operating characteristic and intraclass correlation coefficient analysis were used for statistical evaluations. RESULTS ADC, Dt , and α values were significantly lower in the MFAML group than in the ccRCC group (P < 0.001). Dp , fp , and DDC values were slightly higher in the MFAML group than in the ccRCC group; however, the difference was not significant (P = 0.136, 0.090, and 0.424, respectively). The AUC values for both α (0.953) and Dt (0.964) were significantly higher than those for ADC (0860), Dp (0.605), fp (0.596), and DDC (0.477) in the differentiation of MFAML from ccRCC (P < 0.001). CONCLUSION Water molecular diffusion heterogeneity index (α) and Dt may provide additional information and could lead to improved differentiation with better sensitivity and specificity between MFAML and ccRCC compared with conventional diffusion parameters. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:240-247.
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Affiliation(s)
- Haojie Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lili Liang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Anqin Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yao Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Daoyu Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ihab R Kamel
- Russell H. Morgan Department of Radiology and Radiological Science, the Johns Hopkins Medical Institutions, Baltimore, Maryland, USA
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Contribution of mono-exponential, bi-exponential and stretched exponential model-based diffusion-weighted MR imaging in the diagnosis and differentiation of uterine cervical carcinoma. Eur Radiol 2016; 27:2400-2410. [DOI: 10.1007/s00330-016-4596-8] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2016] [Revised: 08/24/2016] [Accepted: 09/01/2016] [Indexed: 10/20/2022]
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Yuan J, Lo G, King AD. Functional magnetic resonance imaging techniques and their development for radiation therapy planning and monitoring in the head and neck cancers. Quant Imaging Med Surg 2016; 6:430-448. [PMID: 27709079 PMCID: PMC5009093 DOI: 10.21037/qims.2016.06.11] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Accepted: 05/27/2016] [Indexed: 01/05/2023]
Abstract
Radiation therapy (RT), in particular intensity-modulated radiation therapy (IMRT), is becoming a more important nonsurgical treatment strategy in head and neck cancer (HNC). The further development of IMRT imposes more critical requirements on clinical imaging, and these requirements cannot be fully fulfilled by the existing radiotherapeutic imaging workhorse of X-ray based imaging methods. Magnetic resonance imaging (MRI) has increasingly gained more interests from radiation oncology community and holds great potential for RT applications, mainly due to its non-ionizing radiation nature and superior soft tissue image contrast. Beyond anatomical imaging, MRI provides a variety of functional imaging techniques to investigate the functionality and metabolism of living tissue. The major purpose of this paper is to give a concise and timely review of some advanced functional MRI techniques that may potentially benefit conformal, tailored and adaptive RT in the HNC. The basic principle of each functional MRI technique is briefly introduced and their use in RT of HNC is described. Limitation and future development of these functional MRI techniques for HNC radiotherapeutic applications are discussed. More rigorous studies are warranted to translate the hypotheses into credible evidences in order to establish the role of functional MRI in the clinical practice of head and neck radiation oncology.
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Affiliation(s)
- Jing Yuan
- Department of Medical Physics and Research, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
| | - Gladys Lo
- Department of Diagnostic & Interventional Radiology, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
| | - Ann D. King
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
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Fujima N, Yoshida D, Sakashita T, Homma A, Tsukahara A, Shimizu Y, Tha KK, Kudo K, Shirato H. Prediction of the treatment outcome using intravoxel incoherent motion and diffusional kurtosis imaging in nasal or sinonasal squamous cell carcinoma patients. Eur Radiol 2016; 27:956-965. [DOI: 10.1007/s00330-016-4440-1] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2015] [Revised: 03/10/2016] [Accepted: 05/23/2016] [Indexed: 12/11/2022]
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