1
|
Pillar N, Li Y, Zhang Y, Ozcan A. Virtual Staining of Nonfixed Tissue Histology. Mod Pathol 2024; 37:100444. [PMID: 38325706 DOI: 10.1016/j.modpat.2024.100444] [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: 11/02/2023] [Revised: 01/19/2024] [Accepted: 01/29/2024] [Indexed: 02/09/2024]
Abstract
Surgical pathology workflow involves multiple labor-intensive steps, such as tissue removal, fixation, embedding, sectioning, staining, and microscopic examination. This process is time-consuming and costly and requires skilled technicians. In certain clinical scenarios, such as intraoperative consultations, there is a need for faster histologic evaluation to provide real-time surgical guidance. Currently, frozen section techniques involving hematoxylin and eosin (H&E) staining are used for intraoperative pathology consultations. However, these techniques have limitations, including a turnaround time of 20 to 30 minutes, staining artifacts, and potential tissue loss, negatively impacting accurate diagnosis. To address these challenges, researchers are exploring alternative optical imaging modalities for rapid microscopic tissue imaging. These modalities differ in optical characteristics, tissue preparation requirements, imaging equipment, and output image quality and format. Some of these imaging methods have been combined with computational algorithms to generate H&E-like images, which could greatly facilitate their adoption by pathologists. Here, we provide a comprehensive, organ-specific review of the latest advancements in emerging imaging modalities applied to nonfixed human tissue. We focused on studies that generated H&E-like images evaluated by pathologists. By presenting up-to-date research progress and clinical utility, this review serves as a valuable resource for scholars and clinicians, covering some of the major technical developments in this rapidly evolving field. It also offers insights into the potential benefits and drawbacks of alternative imaging modalities and their implications for improving patient care.
Collapse
Affiliation(s)
- Nir Pillar
- Electrical and Computer Engineering Department, University of California, Los Angeles, California; Bioengineering Department, University of California, Los Angeles, California; California NanoSystems Institute (CNSI), University of California, Los Angeles, California
| | - Yuzhu Li
- Electrical and Computer Engineering Department, University of California, Los Angeles, California; Bioengineering Department, University of California, Los Angeles, California; California NanoSystems Institute (CNSI), University of California, Los Angeles, California
| | - Yijie Zhang
- Electrical and Computer Engineering Department, University of California, Los Angeles, California; Bioengineering Department, University of California, Los Angeles, California; California NanoSystems Institute (CNSI), University of California, Los Angeles, California
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, California; Bioengineering Department, University of California, Los Angeles, California; California NanoSystems Institute (CNSI), University of California, Los Angeles, California.
| |
Collapse
|
2
|
Zheng T, Xie X, Ni Z, Tang L, Wu PY, Song B. Quantitative evaluation of diffusion-weighted MRI for differentiating benign and malignant thyroid nodules larger than 4 cm. BMC Med Imaging 2023; 23:212. [PMID: 38093189 PMCID: PMC10720093 DOI: 10.1186/s12880-023-01141-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 10/26/2023] [Indexed: 12/17/2023] Open
Abstract
PURPOSE Our study aimed to diagnose benign or malignant thyroid nodules larger than 4 cm using quantitative diffusion-weighted imaging (DWI) analysis. METHODS Eighty-two thyroid nodules were investigated retrospectively and divided them into benign (n = 62) and malignant groups (n = 20). We calculated quantitative features DWI and apparent diffusion coefficient (ADC) signal intensity standard deviation (DWISD and ADCSD), DWI and ADC signal intensity ratio (DWISIR and ADCSIR), mean ADC and minimum ADC value (ADCmean and ADCmin) and ADC value standard deviation (ADCVSD). Univariate and multivariate logistic regression were conducted to identify independent predictors, and develop a prediction model. We performed receiver operating characteristic (ROC) analysis to determine the optimal threshold of risk factors, and constructed combined threshold models. Our study calculated diagnostic performance including area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and unnecessary biopsy rate of all models were calculated and compared them with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) result. RESULTS Two independent predictors of malignant nodules were identified by multivariate analysis: DWISIR (P = 0.007) and ADCmin (P < 0.001). The AUCs for multivariate prediction model, combined DWISIR and ADCmin thresholds model, combined DWISIR and ADCSIR thresholds model and ACR-TIRADS were 0.946 (0.896-0.996), 0.875 (0.759-0.991), 0.777 (0.648-0.907) and 0.722 (0.588-0.857). The combined DWISIR and ADCmin threshold model had the lowest unnecessary biopsy rate of 0%, compared with 56.3% for ACR-TIRADS. CONCLUSION Quantitative DWI demonstrated favorable malignant thyroid nodule diagnostic efficacy. The combined DWISIR and ADCmin thresholds model significantly reduced the unnecessary biopsy rate.
Collapse
Affiliation(s)
- Tingting Zheng
- Department of Radiology, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, Shanghai, 201199, China
| | - Xiaoli Xie
- Department of Pathology, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, Shanghai, 201199, China
| | - Zhaoxian Ni
- Department of General Surgery, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, Shanghai, 201199, China
| | - Lang Tang
- Department of Ultrasound, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, Shanghai, 201199, China
| | - Pu-Yeh Wu
- GE Healthcare, MR Research China, Beijing, China
| | - Bin Song
- Department of Radiology, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, Shanghai, 201199, China.
| |
Collapse
|
3
|
Brabec J, Friedjungová M, Vašata D, Englund E, Bengzon J, Knutsson L, Szczepankiewicz F, van Westen D, Sundgren PC, Nilsson M. Meningioma microstructure assessed by diffusion MRI: An investigation of the source of mean diffusivity and fractional anisotropy by quantitative histology. Neuroimage Clin 2023; 37:103365. [PMID: 36898293 PMCID: PMC10020119 DOI: 10.1016/j.nicl.2023.103365] [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: 11/25/2022] [Revised: 02/08/2023] [Accepted: 02/25/2023] [Indexed: 03/06/2023]
Abstract
BACKGROUND Mean diffusivity (MD) and fractional anisotropy (FA) from diffusion MRI (dMRI) have been associated with cell density and tissue anisotropy across tumors, but it is unknown whether these associations persist at the microscopic level. PURPOSE To quantify the degree to which cell density and anisotropy, as determined from histology, account for the intra-tumor variability of MD and FA in meningioma tumors. Furthermore, to clarify whether other histological features account for additional intra-tumor variability of dMRI parameters. MATERIALS AND METHODS We performed ex-vivo dMRI at 200 μm isotropic resolution and histological imaging of 16 excised meningioma tumor samples. Diffusion tensor imaging (DTI) was used to map MD and FA, as well as the in-plane FA (FAIP). Histology images were analyzed in terms of cell nuclei density (CD) and structure anisotropy (SA; obtained from structure tensor analysis) and were used separately in a regression analysis to predict MD and FAIP, respectively. A convolutional neural network (CNN) was also trained to predict the dMRI parameters from histology patches. The association between MRI and histology was analyzed in terms of out-of-sample (R2OS) on the intra-tumor level and within-sample R2 across tumors. Regions where the dMRI parameters were poorly predicted from histology were analyzed to identify features apart from CD and SA that could influence MD and FAIP, respectively. RESULTS Cell density assessed by histology poorly explained intra-tumor variability of MD at the mesoscopic level (200 μm), as median R2OS = 0.04 (interquartile range 0.01-0.26). Structure anisotropy explained more of the variation in FAIP (median R2OS = 0.31, 0.20-0.42). Samples with low R2OS for FAIP exhibited low variations throughout the samples and thus low explainable variability, however, this was not the case for MD. Across tumors, CD and SA were clearly associated with MD (R2 = 0.60) and FAIP (R2 = 0.81), respectively. In 37% of the samples (6 out of 16), cell density did not explain intra-tumor variability of MD when compared to the degree explained by the CNN. Tumor vascularization, psammoma bodies, microcysts, and tissue cohesivity were associated with bias in MD prediction based solely on CD. Our results support that FAIP is high in the presence of elongated and aligned cell structures, but low otherwise. CONCLUSION Cell density and structure anisotropy account for variability in MD and FAIP across tumors but cell density does not explain MD variations within the tumor, which means that low or high values of MD locally may not always reflect high or low tumor cell density. Features beyond cell density need to be considered when interpreting MD.
Collapse
Affiliation(s)
- Jan Brabec
- Medical Radiation Physics, Clinical Sciences, Lund University, Lund, Sweden; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Magda Friedjungová
- Faculty of Information Technology, Czech Technical University in Prague, Prague, Czech Republic
| | - Daniel Vašata
- Faculty of Information Technology, Czech Technical University in Prague, Prague, Czech Republic
| | | | - Johan Bengzon
- Neurosurgery, Clinical Sciences, Lund University, Lund, Sweden
| | - Linda Knutsson
- Medical Radiation Physics, Clinical Sciences, Lund University, Lund, Sweden; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | | | - Pia C Sundgren
- Diagnostic Radiology, Clinical Sciences, Lund University, Lund, Sweden; Lund University Bioimaging Centre, Lund University, Lund, Sweden; Department of Medical Imaging and Physiology, Skåne University Hospital, Lund University, Lund, Sweden
| | - Markus Nilsson
- Medical Radiation Physics, Clinical Sciences, Lund University, Lund, Sweden; Diagnostic Radiology, Clinical Sciences, Lund University, Lund, Sweden
| |
Collapse
|
4
|
Shahbazi-Gahrouei D, Aminolroayaei F, Nematollahi H, Ghaderian M, Gahrouei SS. Advanced Magnetic Resonance Imaging Modalities for Breast Cancer Diagnosis: An Overview of Recent Findings and Perspectives. Diagnostics (Basel) 2022; 12:2741. [PMID: 36359584 PMCID: PMC9689118 DOI: 10.3390/diagnostics12112741] [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: 10/08/2022] [Revised: 10/26/2022] [Accepted: 11/07/2022] [Indexed: 08/28/2023] Open
Abstract
Breast cancer is the most prevalent cancer among women and the leading cause of death. Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are advanced magnetic resonance imaging (MRI) procedures that are widely used in the diagnostic and treatment evaluation of breast cancer. This review article describes the characteristics of new MRI methods and reviews recent findings on breast cancer diagnosis. This review study was performed on the literature sourced from scientific citation websites such as Google Scholar, PubMed, and Web of Science until July 2021. All relevant works published on the mentioned scientific citation websites were investigated. Because of the propensity of malignancies to limit diffusion, DWI can improve MRI diagnostic specificity. Diffusion tensor imaging gives additional information about diffusion directionality and anisotropy over traditional DWI. Recent findings showed that DWI and DTI and their characteristics may facilitate earlier and more accurate diagnosis, followed by better treatment. Overall, with the development of instruments and novel MRI modalities, it may be possible to diagnose breast cancer more effectively in the early stages.
Collapse
Affiliation(s)
- Daryoush Shahbazi-Gahrouei
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
| | - Fahimeh Aminolroayaei
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
| | - Hamide Nematollahi
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
| | - Mohammad Ghaderian
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
| | - Sogand Shahbazi Gahrouei
- Department of Management, School of Humanities, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran
| |
Collapse
|
5
|
Syversen IF, Elschot M, Sandsmark E, Bertilsson H, Bathen TF, Goa PE. Exploring the diagnostic potential of adding T2 dependence in diffusion-weighted MR imaging of the prostate. PLoS One 2021; 16:e0252387. [PMID: 34043735 PMCID: PMC8158951 DOI: 10.1371/journal.pone.0252387] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 05/14/2021] [Indexed: 12/02/2022] Open
Abstract
Background Magnetic resonance imaging (MRI) is essential in the detection and staging of prostate cancer. However, improved tools to distinguish between low-risk and high-risk cancer are needed in order to select the appropriate treatment. Purpose To investigate the diagnostic potential of signal fractions estimated from a two-component model using combined T2- and diffusion-weighted imaging (T2-DWI). Material and methods 62 patients with prostate cancer and 14 patients with benign prostatic hyperplasia (BPH) underwent combined T2-DWI (TE = 55 and 73 ms, b-values = 50 and 700 s/mm2) following clinical suspicion of cancer, providing a set of 4 measurements per voxel. Cancer was confirmed in post-MRI biopsy, and regions of interest (ROIs) were delineated based on radiology reporting. Signal fractions of the slow component (SFslow) of the proposed two-component model were calculated from a model fit with 2 free parameters, and compared to conventional bi- and mono-exponential apparent diffusion coefficient (ADC) models. Results All three models showed a significant difference (p<0.0001) between peripheral zone (PZ) tumor and normal tissue ROIs, but not between non-PZ tumor and BPH ROIs. The area under the receiver operating characteristics curve distinguishing tumor from prostate voxels was 0.956, 0.949 and 0.949 for the two-component, bi-exponential and mono-exponential models, respectively. The corresponding Spearman correlation coefficients between tumor values and Gleason Grade Group were fair (0.370, 0.499 and -0.490), but not significant. Conclusion Signal fraction estimates from a two-component model based on combined T2-DWI can differentiate between tumor and normal prostate tissue and show potential for prostate cancer diagnosis. The model performed similarly to conventional diffusion models.
Collapse
Affiliation(s)
- Ingrid Framås Syversen
- Kavli Institute for Systems Neuroscience, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- * E-mail:
| | - Mattijs Elschot
- Department of Circulation and Medical Imaging, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Elise Sandsmark
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Helena Bertilsson
- Department of Urology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- Department of Clinical and Molecular Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Tone Frost Bathen
- Department of Circulation and Medical Imaging, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Pål Erik Goa
- Department of Physics, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| |
Collapse
|
6
|
Andreassen MMS, Rodríguez-Soto AE, Conlin CC, Vidić I, Seibert TM, Wallace AM, Zare S, Kuperman J, Abudu B, Ahn GS, Hahn M, Jerome NP, Østlie A, Bathen TF, Ojeda-Fournier H, Goa PE, Rakow-Penner R, Dale AM. Discrimination of Breast Cancer from Healthy Breast Tissue Using a Three-component Diffusion-weighted MRI Model. Clin Cancer Res 2021; 27:1094-1104. [PMID: 33148675 PMCID: PMC8174004 DOI: 10.1158/1078-0432.ccr-20-2017] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 08/29/2020] [Accepted: 10/29/2020] [Indexed: 11/16/2022]
Abstract
PURPOSE Diffusion-weighted MRI (DW-MRI) is a contrast-free modality that has demonstrated ability to discriminate between predefined benign and malignant breast lesions. However, how well DW-MRI discriminates cancer from all other breast tissue voxels in a clinical setting is unknown. Here we explore the voxelwise ability to distinguish cancer from healthy breast tissue using signal contributions from the newly developed three-component multi-b-value DW-MRI model. EXPERIMENTAL DESIGN Patients with pathology-proven breast cancer from two datasets (n = 81 and n = 25) underwent multi-b-value DW-MRI. The three-component signal contributions C 1 and C 2 and their product, C 1 C 2, and signal fractions F 1, F 2, and F 1 F 2 were compared with the image defined on maximum b-value (DWI max), conventional apparent diffusion coefficient (ADC), and apparent diffusion kurtosis (K app). The ability to discriminate between cancer and healthy breast tissue was assessed by the false-positive rate given a sensitivity of 80% (FPR80) and ROC AUC. RESULTS Mean FPR80 for both datasets was 0.016 [95% confidence interval (CI), 0.008-0.024] for C 1 C 2, 0.136 (95% CI, 0.092-0.180) for C 1, 0.068 (95% CI, 0.049-0.087) for C 2, 0.462 (95% CI, 0.425-0.499) for F 1 F 2, 0.832 (95% CI, 0.797-0.868) for F 1, 0.176 (95% CI, 0.150-0.203) for F 2, 0.159 (95% CI, 0.114-0.204) for DWI max, 0.731 (95% CI, 0.692-0.770) for ADC, and 0.684 (95% CI, 0.660-0.709) for K app. Mean ROC AUC for C 1 C 2 was 0.984 (95% CI, 0.977-0.991). CONCLUSIONS The C 1 C 2 parameter of the three-component model yields a clinically useful discrimination between cancer and healthy breast tissue, superior to other DW-MRI methods and obliviating predefining lesions. This novel DW-MRI method may serve as noncontrast alternative to standard-of-care dynamic contrast-enhanced MRI.
Collapse
Affiliation(s)
- Maren M Sjaastad Andreassen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ana E Rodríguez-Soto
- Department of Radiology, University of California San Diego, La Jolla, California
| | - Christopher C Conlin
- Department of Radiology, University of California San Diego, La Jolla, California
| | - Igor Vidić
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
| | - Tyler M Seibert
- Department of Radiology, University of California San Diego, La Jolla, California
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California
- Department of Bioengineering, University of California San Diego, La Jolla, California
| | - Anne M Wallace
- Department of Surgery, University of California San Diego, La Jolla, California
| | - Somaye Zare
- Department of Pathology, University of California San Diego, La Jolla, California
| | - Joshua Kuperman
- Department of Radiology, University of California San Diego, La Jolla, California
| | - Boya Abudu
- School of Medicine, University of California San Diego, La Jolla, California
| | - Grace S Ahn
- School of Medicine, University of California San Diego, La Jolla, California
| | - Michael Hahn
- Department of Radiology, University of California San Diego, La Jolla, California
| | - Neil P Jerome
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
| | - Agnes Østlie
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Tone F Bathen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway
| | | | - Pål Erik Goa
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway
| | - Rebecca Rakow-Penner
- Department of Radiology, University of California San Diego, La Jolla, California.
| | - Anders M Dale
- Department of Radiology, University of California San Diego, La Jolla, California
- Department of Neuroscience, University of California San Diego, La Jolla, California
| |
Collapse
|
7
|
Bourgot I, Primac I, Louis T, Noël A, Maquoi E. Reciprocal Interplay Between Fibrillar Collagens and Collagen-Binding Integrins: Implications in Cancer Progression and Metastasis. Front Oncol 2020; 10:1488. [PMID: 33014790 PMCID: PMC7461916 DOI: 10.3389/fonc.2020.01488] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 07/13/2020] [Indexed: 12/14/2022] Open
Abstract
Cancers are complex ecosystems composed of malignant cells embedded in an intricate microenvironment made of different non-transformed cell types and extracellular matrix (ECM) components. The tumor microenvironment is governed by constantly evolving cell-cell and cell-ECM interactions, which are now recognized as key actors in the genesis, progression and treatment of cancer lesions. The ECM is composed of a multitude of fibrous proteins, matricellular-associated proteins, and proteoglycans. This complex structure plays critical roles in cancer progression: it functions as the scaffold for tissues organization and provides biochemical and biomechanical signals that regulate key cancer hallmarks including cell growth, survival, migration, differentiation, angiogenesis, and immune response. Cells sense the biochemical and mechanical properties of the ECM through specialized transmembrane receptors that include integrins, discoidin domain receptors, and syndecans. Advanced stages of several carcinomas are characterized by a desmoplastic reaction characterized by an extensive deposition of fibrillar collagens in the microenvironment. This compact network of fibrillar collagens promotes cancer progression and metastasis, and is associated with low survival rates for cancer patients. In this review, we highlight how fibrillar collagens and their corresponding integrin receptors are modulated during cancer progression. We describe how the deposition and alignment of collagen fibers influence the tumor microenvironment and how fibrillar collagen-binding integrins expressed by cancer and stromal cells critically contribute in cancer hallmarks.
Collapse
Affiliation(s)
| | | | | | | | - Erik Maquoi
- Laboratory of Tumor and Development Biology, GIGA-Cancer, University of Liège, Liège, Belgium
| |
Collapse
|
8
|
Rahbar H, Partridge SC, Ha R. Editorial for “Stromal Collagen Content in Breast Tumors Correlates With in vivo Diffusion‐Weighted Imaging: A Comparison of Multi b‐Value DWI With Histologic Specimen From Benign and Malignant Breast Lesions”. J Magn Reson Imaging 2020; 51:1879-1880. [DOI: 10.1002/jmri.27085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 01/27/2020] [Indexed: 11/08/2022] Open
Affiliation(s)
- Habib Rahbar
- Department of Radiology, Clinical Director of Breast Imaging, Seattle Cancer Care AllianceUniversity of Washington School of Medicine Seattle Washington USA
| | - Savannah C. Partridge
- Department of Radiology, Seattle Cancer Care AllianceUniversity of Washington School of Medicine Seattle Washington USA
| | - Richard Ha
- Department of RadiologyNew York Presbyterian Hospital, Columbia University Medical Center New York New York USA
| |
Collapse
|