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Almutlaq ZM, Bacon SE, Wilson DJ, Sharma N, Dondo T, Buckley DL. The relationship between parameters measured using intravoxel incoherent motion and dynamic contrast-enhanced MRI in patients with breast cancer undergoing neoadjuvant chemotherapy: a longitudinal cohort study. Front Oncol 2024; 14:1356173. [PMID: 38860001 PMCID: PMC11163445 DOI: 10.3389/fonc.2024.1356173] [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: 12/15/2023] [Accepted: 05/07/2024] [Indexed: 06/12/2024] Open
Abstract
Purpose The primary aim of this study was to explore whether intravoxel incoherent motion (IVIM) can offer a contrast-agent-free alternative to dynamic contrast-enhanced (DCE)-MRI for measuring breast tumor perfusion. The secondary aim was to investigate the relationship between tissue diffusion measures from DWI and DCE-MRI measures of the tissue interstitial and extracellular volume fractions. Materials and methods A total of 108 paired DWI and DCE-MRI scans were acquired at 1.5 T from 40 patients with primary breast cancer (median age: 44.5 years) before and during neoadjuvant chemotherapy (NACT). DWI parameters included apparent diffusion coefficient (ADC), tissue diffusion (Dt), pseudo-diffusion coefficient (Dp), perfused fraction (f), and the product f×Dp (microvascular blood flow). DCE-MRI parameters included blood flow (Fb), blood volume fraction (vb), interstitial volume fraction (ve) and extracellular volume fraction (vd). All were extracted from three tumor regions of interest (whole-tumor, ADC cold-spot, and DCE-MRI hot-spot) at three MRI visits: pre-treatment, after one, and three cycles of NACT. Spearman's rank correlation was used for assessing between-subject correlations (r), while repeated measures correlation was employed to assess within-subject correlations (rrm) across visits between DWI and DCE-MRI parameters in each region. Results No statistically significant between-subject or within-subject correlation was found between the perfusion parameters estimated by IVIM and DCE-MRI (f versus vb and f×Dp versus Fb; P=0.07-0.81). Significant moderate positive between-subject and within-subject correlations were observed between ADC and ve (r=0.461, rrm=0.597) and between Dt and ve (r=0.405, rrm=0.514) as well as moderate positive within-subject correlations between ADC and vd and between Dt and vd (rrm=0.619 and 0.564, respectively) in the whole-tumor region. Conclusion No correlations were observed between the perfusion parameters estimated by IVIM and DCE-MRI. This may be attributed to imprecise estimates of fxDp and vb, or an underlying difference in what IVIM and DCE-MRI measure. Care should be taken when interpreting the IVIM parameters (f and f×Dp) as surrogates for those measured using DCE-MRI. However, the moderate positive correlations found between ADC and Dt and the DCE-MRI parameters ve and vd confirms the expectation that as the interstitial and extracellular volume fractions increase, water diffusion increases.
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Affiliation(s)
- Zyad M. Almutlaq
- Biomedical Imaging, Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM), University of Leeds, Leeds, United Kingdom
- Radiological Sciences Department, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Sarah E. Bacon
- Department of Medical Physics & Engineering, Leeds Teaching Hospitals National Health Service (NHS) Trust, Leeds, United Kingdom
| | - Daniel J. Wilson
- Department of Medical Physics & Engineering, Leeds Teaching Hospitals National Health Service (NHS) Trust, Leeds, United Kingdom
| | - Nisha Sharma
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
| | - Tatendashe Dondo
- Clinical and Population Sciences Department, LICAMM, University of Leeds, Leeds, United Kingdom
| | - David L. Buckley
- Biomedical Imaging, Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM), University of Leeds, Leeds, United Kingdom
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Udayakumar D, Madhuranthakam AJ, Doğan BE. Magnetic Resonance Perfusion Imaging for Breast Cancer. Magn Reson Imaging Clin N Am 2024; 32:135-150. [PMID: 38007276 DOI: 10.1016/j.mric.2023.09.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2023]
Abstract
Breast cancer is the most frequently diagnosed cancer among women worldwide, carrying a significant socioeconomic burden. Breast cancer is a heterogeneous disease with 4 major subtypes identified. Each subtype has unique prognostic factors, risks, treatment responses, and survival rates. Advances in targeted therapies have considerably improved the 5-year survival rates for primary breast cancer patients largely due to widespread screening programs that enable early detection and timely treatment. Imaging techniques are indispensable in diagnosing and managing breast cancer. While mammography is the primary screening tool, MRI plays a significant role when mammography results are inconclusive or in patients with dense breast tissue. MRI has become standard in breast cancer imaging, providing detailed anatomic and functional data, including tumor perfusion and cellularity. A key characteristic of breast tumors is angiogenesis, a biological process that promotes tumor development and growth. Increased angiogenesis in tumors generally indicates poor prognosis and increased risk of metastasis. Dynamic contrast-enhanced (DCE) MRI measures tumor perfusion and serves as an in vivo metric for angiogenesis. DCE-MRI has become the cornerstone of breast MRI, boasting a high negative-predictive value of 89% to 99%, although its specificity can vary. This review presents a thorough overview of magnetic resonance (MR) perfusion imaging in breast cancer, focusing on the role of DCE-MRI in clinical applications and exploring emerging MR perfusion imaging techniques.
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Affiliation(s)
- Durga Udayakumar
- Department of Radiology, Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX 75390, USA.
| | - Ananth J Madhuranthakam
- Department of Radiology, Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Başak E Doğan
- Department of Radiology, Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX 75390, USA
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Arian A, Seyed-Kolbadi FZ, Yaghoobpoor S, Ghorani H, Saghazadeh A, Ghadimi DJ. Diagnostic accuracy of intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced (DCE) MRI to differentiate benign from malignant breast lesions: A systematic review and meta-analysis. Eur J Radiol 2023; 167:111051. [PMID: 37632999 DOI: 10.1016/j.ejrad.2023.111051] [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: 03/11/2023] [Revised: 08/01/2023] [Accepted: 08/14/2023] [Indexed: 08/28/2023]
Abstract
PURPOSE Magnetic resonance imaging (MRI) can reduce the need for unnecessary invasive diagnostic tests by nearly half. In this meta-analysis, we investigated the diagnostic accuracy of intravoxel incoherent motion modeling (IVIM) and dynamic contrast-enhanced (DCE) MRI in differentiating benign from malignant breast lesions. METHOD We systematically searched PubMed, EMBASE, and Scopus. We included English articles reporting diagnostic accuracy for both sequences in differentiating benign from malignant breast lesions. Articles were assessed by quality assessment of diagnostic accuracy studies-2 (QUADAS-2) questionnaire. We used a bivariate effects model for standardized mean difference (SMD) analysis and diagnostic test accuracy analysis. RESULTS Ten studies with 537 patients and 707 (435 malignant and 272 benign) lesions were included. The D, f, Ktrans, and Kep mean values significantly differ between benign and malignant lesions. The pooled sensitivity (95 % confidence interval) and specificity were 86.2 % (77.9 %-91.7 %) and 70.3 % (56.5 %-81.1 %) for IVIM, and 93.8 % (85.3 %-97.5 %) and 68.1 % (52.7 %-80.4 %) for DCE, respectively. Combined IVIM and DCE depicted the highest area under the curve of 0.94, with a sensitivity and specificity of 91.8 % (82.8 %-96.3 %) and 87.6 % (73.8 %-94.7 %), respectively. CONCLUSIONS Combined IVIM and DCE had the highest diagnostic accuracy, and multiparametric MRI may help reduce unnecessary benign breast biopsy.
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Affiliation(s)
- Arvin Arian
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran; Cancer Research Institute, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Fatemeh Zahra Seyed-Kolbadi
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran; Evidence-Based Medicine Study Center, Hormozgan University of Medical Sciences, Bandar Abass, Iran
| | - Shirin Yaghoobpoor
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran; Student Research Committee, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamed Ghorani
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran; School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Amene Saghazadeh
- Systematic Review and Meta-Analysis Expert Group (SRMEG), Universal Scientific Education and Research Network (USERN), Tehran, Iran; Research Center for Immunodeficiencies, Children's Medical Center, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Delaram J Ghadimi
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran; School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Quantitative MR Imaging and Spectroscopy Group (QMISG), Tehran University of Medical Sciences, Tehran, Iran.
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Fang LK, Keenan KE, Carl M, Ojeda-Fournier H, Rodríguez-Soto AE, Rakow-Penner RA. Apparent Diffusion Coefficient Reproducibility Across 3 T Scanners in a Breast Diffusion Phantom. J Magn Reson Imaging 2023; 57:812-823. [PMID: 36029225 DOI: 10.1002/jmri.28355] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 07/04/2022] [Accepted: 07/05/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND To date, the accuracy and variability of diffusion-weighted MRI (DW-MRI) metrics have been reported in a limited number of scanner/protocol/coil combinations. PURPOSE To evaluate the reproducibility of DW-MRI estimates across multiple scanners and DW-MRI protocols and to assess the effects of using an 8-channel vs. 16-channel breast coil in a breast phantom. STUDY TYPE Prospective. PHANTOM Breast phantom containing tubes of water and differing polyvinylpyrrolidone (PVP) concentrations with apparent diffusion coefficients (ADCs) matching breast tissue. FIELD STRENGTH/SEQUENCE 3 T (three standard and one wide bore), three DW-MRI single-shot echo planar imaging protocols of varying acquired spatial resolution. ASSESSMENT Accuracy of estimated ADCs was assessed using percent differences (PD) relative to nuclear magnetic resonance spectroscopy-derived reference values. Coefficients of variation (CV) were calculated to determine variation across scanners. CVs based on the median standard deviation (CVM ) were used to evaluate tube-specific dispersion using 8- or 16-channel coils at a single scanner. ADCs of PVP-containing tubes were additionally normalized by the median water tube ADC to account for temperature effects. STATISTICAL TESTS Two-way repeated measures analysis of variance and post hoc tests were used to evaluate differences in ADC, CV, and CVM across scanners and protocols (α = 0.05). RESULTS ADCs were within 11% (interquartile range [IQR] 7%) of reference values and significantly improved to 2% (IQR 7%) after normalization to an internal water reference. Normalization significantly reduced interscanner variability of ADC estimates from 7% to 4%. DW-MRI protocol did not affect ADC accuracy; however, the clinical and higher-resolution clinical protocols resulted in the greatest (9%) and least (6%) interscanner variability, respectively. The 8- and 16-channel receive coils yielded similar accuracy (PD: 12% vs. 16%) and precision (CVM : 2.7% vs. 2.9%). DATA CONCLUSION Normalization by an internal reference improved interscanner ADC reproducibility. High-resolution protocols yielded comparably accurate and significantly less variable ADCs compared to a clinical standard protocol. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Lauren K Fang
- Department of Radiology, University of California-San Diego, La Jolla, California, USA
| | - Kathryn E Keenan
- National Institute of Science and Technology, Boulder, Colorado, USA
| | | | - Haydee Ojeda-Fournier
- Department of Radiology, University of California-San Diego, La Jolla, California, USA
| | - Ana E Rodríguez-Soto
- Department of Radiology, University of California-San Diego, La Jolla, California, USA
| | - Rebecca A Rakow-Penner
- Department of Radiology, University of California-San Diego, La Jolla, California, USA.,Department of Bioengineering, University of California-San Diego, La Jolla, California, USA
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Identification of the Benignity and Malignancy of BI-RADS 4 Breast Lesions Based on a Combined Quantitative Model of Dynamic Contrast-Enhanced MRI and Intravoxel Incoherent Motion. Tomography 2022; 8:2676-2686. [PMID: 36412682 PMCID: PMC9680473 DOI: 10.3390/tomography8060223] [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: 08/18/2022] [Revised: 10/20/2022] [Accepted: 10/29/2022] [Indexed: 11/06/2022] Open
Abstract
The aim of this study was to explore whether intravoxel incoherent motion (IVIM) combined with a dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative model can improve the ability to distinguish between benign and malignant BI-RADS 4 breast lesions. We enrolled 100 patients who underwent breast MRI at our institution and extracted the quantitative parameters of lesions with a post-processing workstation. Statistical differences in these parameters between benign and malignant BI-RADS 4 lesions were assessed using a two independent samples t-test or a Mann-Whitney U test. Binary logistic regression analysis was performed to establish five diagnostic models (model_ADC, model_IVIM, model_DCE, model_DCE+ADC, and model_DCE+IVIM). Receiver operating characteristic (ROC) curves, leave-one-out cross-validation, and the Delong test were used to assess and compare the diagnostic performance of these models. The model_DCE+IVIM showed the highest area under the curve (AUC) of 0.903 (95% confidence interval (CI): 0.828-0.953, sensitivity: 87.50%, specificity: 85.00%), which was significantly higher than that of model_ADC (p = 0.014) and model_IVIM (p = 0.033). The model_ADC had the lowest diagnostic performance (AUC = 0.768, 95%CI: 0.672-0.846) but was not significantly different from model_IVIM (p = 0.168). The united quantitative model with DCE-MRI and IVIM could improve the ability to evaluate the malignancy in BI-RADS 4 lesions, and unnecessary breast biopsies may be obviated.
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Mürtz P, Tsesarskiy M, Sprinkart AM, Block W, Savchenko O, Luetkens JA, Attenberger U, Pieper CC. Simplified intravoxel incoherent motion DWI for differentiating malignant from benign breast lesions. Eur Radiol Exp 2022; 6:48. [PMID: 36171532 PMCID: PMC9519819 DOI: 10.1186/s41747-022-00298-6] [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: 04/06/2022] [Accepted: 07/27/2022] [Indexed: 11/27/2022] Open
Abstract
Background To evaluate simplified intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) for differentiating malignant versus benign breast lesions as (i) stand-alone tool and (ii) add-on to dynamic contrast-enhanced magnetic resonance imaging. Methods 1.5-T DWI data (b = 0, 50, 250, 800 s/mm2) were retrospectively analysed for 126 patients with malignant or benign breast lesions. Apparent diffusion coefficient (ADC) ADC (0, 800) and IVIM-based parameters D1′ = ADC (50, 800), D2′ = ADC (250, 800), f1′ = f (0, 50, 800), f2′ = f (0, 250, 800) and D*′ = D* (0, 50, 250, 800) were voxel-wise calculated without fitting procedures. Regions of interest were analysed in vital tumour and perfusion hot spots. Beside the single parameters, the combined use of D1′ with f1′ and D2′ with f2′ was evaluated. Lesion differentiation was investigated for lesions (i) with hyperintensity on DWI with b = 800 s/mm2 (n = 191) and (ii) with suspicious contrast-enhancement (n = 135). Results All lesions with suspicious contrast-enhancement appeared also hyperintense on DWI with b = 800 s/mm2. For task (i), best discrimination was reached for the combination of D1′ and f1′ using perfusion hot spot regions-of-interest (accuracy 93.7%), which was higher than that of ADC (86.9%, p = 0.003) and single IVIM parameters D1′ (88.0%) and f1′ (87.4%). For task (ii), best discrimination was reached for single parameter D1′ using perfusion hot spot regions-of-interest (92.6%), which were slightly but not significantly better than that of ADC (91.1%) and D2′ (88.1%). Adding f1′ to D1′ did not improve discrimination. Conclusions IVIM analysis yielded a higher accuracy than ADC. If stand-alone DWI is used, perfusion analysis is of special relevance.
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Affiliation(s)
- Petra Mürtz
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.
| | - Mark Tsesarskiy
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Alois M Sprinkart
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Wolfgang Block
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.,Department of Radiotherapy and Radiation Oncology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.,Department of Neuroradiology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Oleksandr Savchenko
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Julian A Luetkens
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Ulrike Attenberger
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Claus C Pieper
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
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Mendez AM, Fang LK, Meriwether CH, Batasin SJ, Loubrie S, Rodríguez-Soto AE, Rakow-Penner RA. Diffusion Breast MRI: Current Standard and Emerging Techniques. Front Oncol 2022; 12:844790. [PMID: 35880168 PMCID: PMC9307963 DOI: 10.3389/fonc.2022.844790] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 05/11/2022] [Indexed: 11/13/2022] Open
Abstract
The role of diffusion weighted imaging (DWI) as a biomarker has been the subject of active investigation in the field of breast radiology. By quantifying the random motion of water within a voxel of tissue, DWI provides indirect metrics that reveal cellularity and architectural features. Studies show that data obtained from DWI may provide information related to the characterization, prognosis, and treatment response of breast cancer. The incorporation of DWI in breast imaging demonstrates its potential to serve as a non-invasive tool to help guide diagnosis and treatment. In this review, current technical literature of diffusion-weighted breast imaging will be discussed, in addition to clinical applications, advanced techniques, and emerging use in the field of radiomics.
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Affiliation(s)
- Ashley M. Mendez
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Lauren K. Fang
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Claire H. Meriwether
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Summer J. Batasin
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Stéphane Loubrie
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Ana E. Rodríguez-Soto
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Rebecca A. Rakow-Penner
- Department of Radiology, University of California San Diego, La Jolla, CA, United States,Department of Bioengineering, University of California San Diego, La Jolla, CA, United States,*Correspondence: Rebecca A. Rakow-Penner,
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Zhong Y, Li M, Zhu J, Zhang B, Liu M, Wang Z, Wang J, Zheng Y, Cheng L, Li X. A simplified scoring protocol to improve diagnostic accuracy with the breast imaging reporting and data system in breast magnetic resonance imaging. Quant Imaging Med Surg 2022; 12:3860-3872. [PMID: 35782247 PMCID: PMC9246725 DOI: 10.21037/qims-21-1036] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 04/19/2022] [Indexed: 12/31/2023]
Abstract
BACKGROUND The breast imaging reporting and data system (BI-RADS) lexicon provides a standardized terminology for describing leision characteristics but does not provide defined rules for converting specific imaging features into diagnostic categories. The inter-reader agreement of the BI-RADS is moderate. In this study, we explored the use of a simplified protocol and scoring system for BI-RADS categorization which integrates the morphologic features (MF), kinetic time-intensity curve (TIC), and apparent diffusion coefficient (ADC) values with equal weights, with a view to providing a convenient and practical method for breast magnetic resonance imaging (MRI) and improving the inter-reader agreement and diagnostic performance of BI-RADS. METHODS This cross-sectional, retrospective, single-center study included 879 patients with 898 histopathologically verified lesions who underwent an MRI scan on a 3.0 Tesla GE Discovery 750 MRI scanner between January 1, 2017, and June 30, 2020. The BI-RADS categorization of the studied lesions was assessed according to the sum of the assigned scores (the presence of malignant MF, lower ADC, and suspicious TIC each warranted a score of +1). Total scores of +2 and +3 were classified as category 5, scores of +1 were classified as category 4, and scores of +0 but with other lesions of interest were classified as category 3. The receiver operating characteristic (ROC) curves were plotted, and the sensitivity, specificity, and accuracy of this categorization were investigated to assess its efficacy and its consistency with pathology. RESULTS There were 472 malignant, 104 risk, and 322 benign lesions. Our simplified scoring protocol had high diagnostic accuracy, with an area under curve (AUC) value of 0.896. In terms of the borderline effect of pathological risk and category 4 lesions, our results showed that when risk lesions were classified together with malignant ones, the AUC value improved (0.876 vs. 0.844 and 0.909 vs. 0.900). When category 4 and 5 lesions were classified as malignant, the specificity, accuracy, and AUC value decreased (82.3% vs. 93.2%, 89.3% vs. 90.2%, and 0.876 vs. 0.909, respectively). Therefore, to improve the diagnostic accuracy of the protocol for BI-RADS categorization, only category 5 lesions should be considered to be malignant. CONCLUSIONS Our simplified scoring protocol that integrates MF, TIC, and ADC values with equal weights for BI-RADS categorization could improve both the diagnostic performance of the protocol for BI-RADS categorization in clinical practice and the understanding of the benign-risk-malignant breast diseases.
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Affiliation(s)
- Yuting Zhong
- Medical School of Chinese People’s Liberation Army, Beijing, China
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Menglu Li
- Department of Radiology, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Jingjin Zhu
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing, China
- School of Medicine, Nankai University, Tianjin, China
| | - Boya Zhang
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing, China
- School of Medicine, Nankai University, Tianjin, China
| | - Mei Liu
- Department of Pathology, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Zhili Wang
- Department of Ultrasound, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Jiandong Wang
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Yiqiong Zheng
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Liuquan Cheng
- Department of Radiology, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Xiru Li
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing, China
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A Dual-Mode Imaging Nanoparticle Probe Targeting PD-L1 for Triple-Negative Breast Cancer. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:2431026. [PMID: 35694705 PMCID: PMC9173980 DOI: 10.1155/2022/2431026] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 04/18/2022] [Accepted: 05/13/2022] [Indexed: 11/19/2022]
Abstract
Chemotherapy has remained the mainstay of treatment of triple-negative breast cancer; however, it is significantly limited by the associated side effects. PD-1/PD-L1 immune checkpoint inhibition therapy (ICI) has been a breakthrough for this patient population in recent years. PD-L1 expression is crucial in immunotherapy since it is a major predictor of PD-1/PD-L1 antibody response, emphasizing the significance of monitoring PD-L1 expression. Nonetheless, it is hard to assess the expression of PD-L1 before surgery, which has highlighted the urgency for a precise and noninvasive approach. Herein, we prepared a dual-mode imaging nanoparticle probe to detect PD-L1. The particle size, zeta potential, biocompatibility, and imaging ability of NPs were characterized. The synthesized NPs showed slight cytotoxicity and good T2 relaxivity. The targeted NPs accumulated more in 4T1 cells than nontargeted NPs in vitro. The in vivo experiment further demonstrated the distribution of targeted NPs in tumor tissues, with changes in NIRF and MR signals observed. Our study indicated that SPIO-aPD-L1-Cy5.5 NPs can be used to monitor PD-L1 expression in breast cancer as NIRF/MR contrast agents.
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Characterizing Errors in Pharmacokinetic Parameters from Analyzing Quantitative Abbreviated DCE-MRI Data in Breast Cancer. ACTA ACUST UNITED AC 2021; 7:253-267. [PMID: 34201654 PMCID: PMC8293327 DOI: 10.3390/tomography7030023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 06/15/2021] [Accepted: 06/21/2021] [Indexed: 12/13/2022]
Abstract
This study characterizes the error that results when performing quantitative analysis of abbreviated dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data of the breast with the Standard Kety-Tofts (SKT) model and its Patlak variant. More specifically, we used simulations and patient data to determine the accuracy with which abbreviated time course data could reproduce the pharmacokinetic parameters, Ktrans (volume transfer constant) and ve (extravascular/extracellular volume fraction), when compared to the full time course data. SKT analysis of simulated abbreviated time courses (ATCs) based on the imaging parameters from two available datasets (collected with a 3T MRI scanner) at a temporal resolution of 15 s (N = 15) and 7.23 s (N = 15) found a concordance correlation coefficient (CCC) greater than 0.80 for ATCs of length 3.0 and 2.5 min, respectively, for the Ktrans parameter. Analysis of the experimental data found that at least 90% of patients met this CCC cut-off of 0.80 for the ATCs of the aforementioned lengths. Patlak analysis of experimental data found that 80% of patients from the 15 s resolution dataset and 90% of patients from the 7.27 s resolution dataset met the 0.80 CCC cut-off for ATC lengths of 1.25 and 1.09 min, respectively. This study provides evidence for both the feasibility and potential utility of performing a quantitative analysis of abbreviated breast DCE-MRI in conjunction with acquisition of current standard-of-care high resolution scans without significant loss of information in the community setting.
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Abstract
The National Cancer Institute's Quantitative Imaging Network (QIN) has thrived over the past 12 years with an emphasis on the development of image-based decision support software tools for improving measurements of imaging metrics. An overarching goal has been to develop advanced tools that could be translated into clinical trials to provide for improved prediction of response to therapeutic interventions. This article provides an overview of the successes in development and translation of new algorithms into the clinical workflow by the many research teams of the Quantitative Imaging Network.
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