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Takatsu Y, Ueyama T, Iwasaki T, Tateyama T, Miyati T. Simulation of time-intensity curve based on k-space filling in breast dynamic contrast-enhanced three-dimensional magnetic resonance imaging. Radiol Phys Technol 2024; 17:536-552. [PMID: 38613653 DOI: 10.1007/s12194-024-00793-y] [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: 09/21/2023] [Revised: 02/19/2024] [Accepted: 03/04/2024] [Indexed: 04/15/2024]
Abstract
This study elucidated the effects of a three-dimensional k-space trajectory incorporating the partial Fourier (PF) technique on a time-intensity curve (TIC) in a dynamic contrast-enhanced magnetic resonance imaging of a typical malignant breast tumor using a digital phantom. Images were obtained from the Cancer Imaging Archive Open Data for Breast Cancer, and 1-min scans with high temporal resolution were analyzed. The order of the k-space trajectory was set as Linear (sequential), Low-High (centric), PF (62.5%; Z-, Y-, and both directions), and Low-High Radial. k0 (center of the k-space) timing and TIC shape were affected by the chosen k-space trajectory and implementation of the PF technique. A small TIC gradient was obtained using a Low-High Radial order. If the k-space filling method (particularly the radial method) produces a gentle TIC gradient, misinterpretation could arise during the assessment of tumor malignancy status.
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Affiliation(s)
- Yasuo Takatsu
- Molecular Imaging, School of Medical Sciences, Fujita Health University, 1-98 Dengakugakubo, Kutsukake-Cho, Toyoake, Aichi, 470-1192, Japan.
- Division of Health Sciences, Graduate School of Medical Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, 920-0942, Japan.
| | - Tsuyoshi Ueyama
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
| | - Takahiro Iwasaki
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan
| | - Tomoko Tateyama
- Department of Intelligent Information Engineering, School of Medical Sciences, Fujita Health University, 1‑98, Dengakugakubo, Kutsukake‑Cho, Toyoake, Aichi, 470‑1192, Japan
| | - Tosiaki Miyati
- Division of Health Sciences, Graduate School of Medical Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, 920-0942, Japan
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Fathi Kazerooni A, Nabil M, Alviri M, Koopaei S, Salahshour F, Assili S, Saligheh Rad H, Aghaghazvini L. Radiomic Analysis of Multi-parametric MR Images (MRI) for Classification of Parotid Tumors. J Biomed Phys Eng 2022; 12:599-610. [PMID: 36569565 PMCID: PMC9759641 DOI: 10.31661/jbpe.v0i0.2007-1140] [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: 07/09/2020] [Accepted: 11/13/2020] [Indexed: 12/05/2022]
Abstract
Background Characterization of parotid tumors before surgery using multi-parametric magnetic resonance imaging (MRI) scans can support clinical decision making about the best-suited therapeutic strategy for each patient. Objective This study aims to differentiate benign from malignant parotid tumors through radiomics analysis of multi-parametric MR images, incorporating T2-w images with ADC-map and parametric maps generated from Dynamic Contrast Enhanced MRI (DCE-MRI). Material and Methods MRI scans of 31 patients with histopathologically-confirmed parotid gland tumors (23 benign, 8 malignant) were included in this retrospective study. For DCE-MRI, semi-quantitative analysis, Tofts pharmacokinetic (PK) modeling, and five-parameter sigmoid modeling were performed and parametric maps were generated. For each patient, borders of the tumors were delineated on whole tumor slices of T2-w image, ADC-map, and the late-enhancement dynamic series of DCE-MRI, creating regions-of-interest (ROIs). Radiomic analysis was performed for the specified ROIs. Results Among the DCE-MRI-derived parametric maps, wash-in rate (WIR) and PK-derived Ktrans parameters surpassed the accuracy of other parameters based on support vector machine (SVM) classifier. Radiomics analysis of ADC-map outperformed the T2-w and DCE-MRI techniques using the simpler classifier, suggestive of its inherently high sensitivity and specificity. Radiomics analysis of the combination of T2-w image, ADC-map, and DCE-MRI parametric maps resulted in accuracy of 100% with both classifiers with fewer numbers of selected texture features than individual images. Conclusion In conclusion, radiomics analysis is a reliable quantitative approach for discrimination of parotid tumors and can be employed as a computer-aided approach for pre-operative diagnosis and treatment planning of the patients.
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Affiliation(s)
- Anahita Fathi Kazerooni
- PhD, Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Iran
| | - Mahnaz Nabil
- PhD, Department of Mathematics, Islamic Azad University, Qazvin Branch, Qazvin, Iran
| | - Mohammadreza Alviri
- MSc, Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Iran
| | - Soheila Koopaei
- MSc, Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Iran
| | - Faeze Salahshour
- MD, Department of Radiology, Advanced Diagnostic and Invasive Radiology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Sanam Assili
- MSc, Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Iran
| | - Hamidreza Saligheh Rad
- PhD, Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Iran
- PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Iran
| | - Leila Aghaghazvini
- MD, Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
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Koori N, Miyati T, Ohno N, Kawashima H, Nishikawa H. Sigmoid model analysis of breast dynamic contrast-enhanced MRI: Distinguishing between benign and malignant breast masses and breast cancer subtype prediction. J Appl Clin Med Phys 2022; 23:e13651. [PMID: 35594028 PMCID: PMC9195041 DOI: 10.1002/acm2.13651] [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: 10/30/2021] [Revised: 04/25/2022] [Accepted: 04/29/2022] [Indexed: 11/23/2022] Open
Abstract
Dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) is performed to distinguish between benign and malignant lesions by evaluating the changes in signal intensity of the acquired image (kinetic curve). This study aimed to verify whether the existing breast DCE‐MRI analyzed by the sigmoid model can accurately distinguish between benign and invasive ductal carcinoma (IDC) and predict the subtype. A total of 154 patients who underwent breast MRI for detailed breast mass examinations were included in this study (38 with benign masses and 116 with IDC. The sigmoid model involved the acquisition of images at seven timepoints in 1‐min intervals to determine the change in signal intensity before and after contrast injection. From this curve, the magnitude of the increase in signal intensity in the early phase, the time to reach the maximum increase, and the slopes in the early and late phases were calculated. The Mann–Whitney U‐test was used for the statistical analysis. The IDC group exhibited a significantly larger and faster signal increase in the early phase and a significantly smaller rate of increase in the late phase than the benign group (P < 0.001). The luminal A‐like group demonstrated a significantly longer time to reach the maximum signal increase rate than other IDC subtypes (P < 0.05). The sigmoid model analysis of breast DCE‐MRI can distinguish between benign lesions and IDC and may also help in predicting luminal A‐like breast cancer.
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Affiliation(s)
- Norikazu Koori
- Department of Radiology, Komaki City Hospital, Komaki, Aichi, Japan.,Division of Health Sciences, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Ishikawa, Japan
| | - Tosiaki Miyati
- Division of Health Sciences, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Ishikawa, Japan
| | - Naoki Ohno
- Division of Health Sciences, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Ishikawa, Japan
| | - Hiroko Kawashima
- Division of Health Sciences, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Ishikawa, Japan
| | - Hiroko Nishikawa
- Department of Radiology, Komaki City Hospital, Komaki, Aichi, Japan
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Xu H, Ye L, Chen Y. Editorial for "Added-Value of Quantitative Analysis of Diffusion-Weighted Imaging in Ovarian-Adnexal Reporting and Data System Magnetic Resonance Imaging (O-RADS MRI): A Prospective Cohort Study". J Magn Reson Imaging 2021; 56:171-172. [PMID: 34866277 DOI: 10.1002/jmri.28011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 11/18/2021] [Indexed: 02/05/2023] Open
Affiliation(s)
- Hui Xu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Lei Ye
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yuntian Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
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Wei M, Bo F, Cao H, Zhou W, Shan W, Bai G. Diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging for malignant ovarian tumors: a systematic review and meta-analysis. Acta Radiol 2021; 62:966-978. [PMID: 32741199 DOI: 10.1177/0284185120944916] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
BACKGROUND Accurate preoperative diagnosis of malignant ovarian tumors (MOTs) is particularly important for selecting the optimal treatment strategy and avoiding overtreatment. PURPOSE To evaluate the diagnostic efficacy of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for MOTs. MATERIAL AND METHODS A systematic search was performed in PubMed, Embase, the Cochrane Library, and Web of Science databases to find relevant original articles up to October 2019. The included studies were assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Studies on the diagnosis of MOTs with quantitative or semi-quantitative DCE-MRI were analyzed separately. The bivariate random-effects model was used to assess the diagnostic authenticity. Meta-regression analyses were performed to analyze the potential heterogeneity. RESULTS For semi-quantitative DCE-MRI, the pooled sensitivity, specificity, positive likelihood ratio (LR), negative LR, diagnostic odds ratio (DOR), and the area under the summary receiver operating characteristic curves (AUC) were 85% (95% confidence interval [CI] 0.75-0.92), 85% (95% CI 0.77-0.91), 5.8 (95% CI 3.8-8.8), 0.17 (95% CI 0.10-0.30), 33 (95% CI 18-61), and 0.92 (95% CI 0.89-0.94), respectively. For quantitative DCE-MRI, the pooled sensitivity, specificity, positive LR, negative LR, DOR, and AUC were 88% (95% CI 0.65-0.96), 93% (95% CI 0.78-0.98), 12.3 (95% CI 3.4-43.9), 0.13 (95% CI 0.04-0.45), 91 (95% CI 10-857), and 0.96 (95% CI 0.94-0.98), respectively. CONCLUSION DCE-MRI has great diagnostic value for MOTs. Semi-quantitative DCE-MRI may be a relatively mature approach; however, quantitative DCE-MRI appears to be more promising than semi-quantitative DCE-MRI.
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Affiliation(s)
- Mingxiang Wei
- Department of Radiology, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huaian, Jiangsu, PR China
| | - Fan Bo
- Department of Radiology, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huaian, Jiangsu, PR China
| | - Hui Cao
- Department of Radiology, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huaian, Jiangsu, PR China
| | - Wei Zhou
- Department of Radiology, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huaian, Jiangsu, PR China
| | - Wenli Shan
- Department of Radiology, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huaian, Jiangsu, PR China
| | - Genji Bai
- Department of Radiology, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huaian, Jiangsu, PR China
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Fathi Kazerooni A, Nabil M, Haghighat Khah H, Alviri M, Heidari-Sooreshjaani M, Gity M, Malek M, Saligheh Rad H. ADC-derived spatial features can accurately classify adnexal lesions. J Magn Reson Imaging 2017; 47:1061-1071. [PMID: 28901638 DOI: 10.1002/jmri.25854] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Accepted: 08/29/2017] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The role of quantitative apparent diffusion coefficient (ADC) maps in differentiating adnexal masses is unresolved. PURPOSE/HYPOTHESIS To propose an objective diagnostic method devised based on spatial features for predicting benignity/malignancy of adnexal masses in ADC maps. STUDY TYPE Prospective. POPULATION In all, 70 women with sonographically indeterminate and histopathologically confirmed adnexal masses (38 benign, 3 borderline, and 29 malignant) were considered for this study. FIELD STRENGTH/SEQUENCE Conventional and diffusion-weighted magnetic resonance (MR) images (b-values = 50, 400, 1000 s/mm2 ) were acquired on a 3T scanner. ASSESSMENT For each patient, two radiologists in consensus manually delineated lesion borders in whole ADC map volumes, which were consequently analyzed using spatial models (first-order histogram [FOH], gray-level co-occurrence matrix [GLCM], run-length matrix [RLM], and Gabor filters). Two independent radiologists were asked to identify the attributed (benign/malignant) classes of adnexal masses based on morphological features on conventional MRI. STATISTICAL TESTS Leave-one-out cross-validated feature selection followed by cross-validated classification were applied to the feature space to choose the spatial models that best discriminate benign from malignant adnexal lesions. Two schemes of feature selection/classification were evaluated: 1) including all benign and malignant masses, and 2) scheme 1 after excluding endometrioma, hemorrhagic cysts, and teratoma (14 benign, 29 malignant masses). The constructed feature subspaces for benign/malignant lesion differentiation were tested for classification of benign/borderline/malignant and also borderline/malignant adnexal lesions. RESULTS The selected feature subspace consisting of RLM features differentiated benign from malignant adnexal masses with a classification accuracy of ∼92%. The same model discriminated benign, borderline, and malignant lesions with 87% and borderline from malignant with 100% accuracy. Qualitative assessment of the radiologists based on conventional MRI features reached an accuracy of 80%. DATA CONCLUSION The spatial quantification methodology proposed in this study, which works based on cellular distributions within ADC maps of adnexal masses, may provide a helpful computer-aided strategy for objective characterization of adnexal masses. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1061-1071.
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Affiliation(s)
- Anahita Fathi Kazerooni
- Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Iran.,Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Iran
| | - Mahnaz Nabil
- Department of Mathematics, Islamic Azad University, Qazvin Branch, Qazvin, Iran
| | - Hamidreza Haghighat Khah
- Department of Diagnostic Imaging, Shohada-e-Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammadreza Alviri
- Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Iran
| | | | - Masoumeh Gity
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran.,Department of Radiology, Medical Imaging Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahrooz Malek
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran.,Department of Radiology, Medical Imaging Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamidreza Saligheh Rad
- Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Iran.,Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Iran
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