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Iima M, Kataoka M, Honda M, Le Bihan D. Diffusion-Weighted MRI for the Assessment of Molecular Prognostic Biomarkers in Breast Cancer. Korean J Radiol 2024; 25:623-633. [PMID: 38942456 PMCID: PMC11214919 DOI: 10.3348/kjr.2023.1188] [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: 03/02/2023] [Revised: 02/28/2024] [Accepted: 04/11/2024] [Indexed: 06/30/2024] Open
Abstract
This study systematically reviewed the role of diffusion-weighted imaging (DWI) in the assessment of molecular prognostic biomarkers in breast cancer, focusing on the correlation of apparent diffusion coefficient (ADC) with hormone receptor status and prognostic biomarkers. Our meta-analysis includes data from 52 studies examining ADC values in relation to estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2), and Ki-67 status. The results indicated significant differences in ADC values among different receptor statuses, with ER-positive, PgR-positive, HER2-negative, and Ki-67-positive tumors having lower ADC values compared to their negative counterparts. This study also highlights the potential of advanced DWI techniques such as intravoxel incoherent motion and non-Gaussian DWI to provide additional insights beyond ADC. Despite these promising findings, the high heterogeneity among the studies underscores the need for standardized DWI protocols to improve their clinical utility in breast cancer management.
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Affiliation(s)
- Mami Iima
- Department of Fundamental Development for Advanced Low Invasive Diagnostic Imaging, Nagoya University Graduate School of Medicine, Nagoya, Japan
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.
| | - Masako Kataoka
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Maya Honda
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Diagnostic Radiology, Kansai Electric Power Hospital, Osaka, Japan
| | - Denis Le Bihan
- NeuroSpin, Joliot Institute, Department of Fundamental Research, Commissariat à l'Energie Atomique (CEA)-Saclay, Gif-sur-Yvette, France
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2
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Kim JY, Partridge SC. Non-contrast Breast MR Imaging. Radiol Clin North Am 2024; 62:661-678. [PMID: 38777541 PMCID: PMC11116814 DOI: 10.1016/j.rcl.2023.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Considering the high cost of dynamic contrast-enhanced MR imaging and various contraindications and health concerns related to administration of intravenous gadolinium-based contrast agents, there is emerging interest in non-contrast-enhanced breast MR imaging. Diffusion-weighted MR imaging (DWI) is a fast, unenhanced technique that has wide clinical applications in breast cancer detection, characterization, prognosis, and predicting treatment response. It also has the potential to serve as a non-contrast MR imaging screening method. Standardized protocols and interpretation strategies can help to enhance the clinical utility of breast DWI. A variety of other promising non-contrast MR imaging techniques are in development, but currently, DWI is closest to clinical integration, while others are still mostly used in the research setting.
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Affiliation(s)
- Jin You Kim
- Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Republic of Korea
| | - Savannah C Partridge
- Department of Radiology, University of Washington, Seattle, WA, USA; Fred Hutchinson Cancer Center, Seattle, WA, USA.
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3
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Joy A, Lin M, Joines M, Saucedo A, Lee-Felker S, Baker J, Chien A, Emir U, Macey PM, Thomas MA. Ensemble Learning for Breast Cancer Lesion Classification: A Pilot Validation Using Correlated Spectroscopic Imaging and Diffusion-Weighted Imaging. Metabolites 2023; 13:835. [PMID: 37512542 PMCID: PMC10385820 DOI: 10.3390/metabo13070835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/04/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023] Open
Abstract
The main objective of this work was to evaluate the application of individual and ensemble machine learning models to classify malignant and benign breast masses using features from two-dimensional (2D) correlated spectroscopy spectra extracted from five-dimensional echo-planar correlated spectroscopic imaging (5D EP-COSI) and diffusion-weighted imaging (DWI). Twenty-four different metabolite and lipid ratios with respect to diagonal fat peaks (1.4 ppm, 5.4 ppm) from 2D spectra, and water and fat peaks (4.7 ppm, 1.4 ppm) from one-dimensional non-water-suppressed (NWS) spectra were used as the features. Additionally, water fraction, fat fraction and water-to-fat ratios from NWS spectra and apparent diffusion coefficients (ADC) from DWI were included. The nine most important features were identified using recursive feature elimination, sequential forward selection and correlation analysis. XGBoost (AUC: 93.0%, Accuracy: 85.7%, F1-score: 88.9%, Precision: 88.2%, Sensitivity: 90.4%, Specificity: 84.6%) and GradientBoost (AUC: 94.3%, Accuracy: 89.3%, F1-score: 90.7%, Precision: 87.9%, Sensitivity: 94.2%, Specificity: 83.4%) were the best-performing models. Conventional biomarkers like choline, myo-Inositol, and glycine were statistically significant predictors. Key features contributing to the classification were ADC, 2D diagonal peaks at 0.9 ppm, 2.1 ppm, 3.5 ppm, and 5.4 ppm, cross peaks between 1.4 and 0.9 ppm, 4.3 and 4.1 ppm, 2.3 and 1.6 ppm, and the triglyceryl-fat cross peak. The results highlight the contribution of the 2D spectral peaks to the model, and they demonstrate the potential of 5D EP-COSI for early breast cancer detection.
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Affiliation(s)
- Ajin Joy
- Radiological Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.J.); (M.L.); (M.J.); (A.S.); (S.L.-F.); (A.C.)
| | - Marlene Lin
- Radiological Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.J.); (M.L.); (M.J.); (A.S.); (S.L.-F.); (A.C.)
| | - Melissa Joines
- Radiological Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.J.); (M.L.); (M.J.); (A.S.); (S.L.-F.); (A.C.)
| | - Andres Saucedo
- Radiological Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.J.); (M.L.); (M.J.); (A.S.); (S.L.-F.); (A.C.)
- Physics and Biology in Medicine-Inter-Departmental Graduate Program, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Stephanie Lee-Felker
- Radiological Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.J.); (M.L.); (M.J.); (A.S.); (S.L.-F.); (A.C.)
| | - Jennifer Baker
- Surgery, University of California Los Angeles, Los Angeles, CA 90095, USA;
| | - Aichi Chien
- Radiological Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.J.); (M.L.); (M.J.); (A.S.); (S.L.-F.); (A.C.)
| | - Uzay Emir
- School of Health Sciences, College of Health and Human Sciences, Purdue University, West Lafayette, IN 47907, USA;
| | - Paul M. Macey
- School of Nursing, University of California Los Angeles, Los Angeles, CA 90095, USA;
| | - M. Albert Thomas
- Radiological Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.J.); (M.L.); (M.J.); (A.S.); (S.L.-F.); (A.C.)
- Physics and Biology in Medicine-Inter-Departmental Graduate Program, University of California Los Angeles, Los Angeles, CA 90095, USA
- BioEngineering, University of California Los Angeles, Los Angeles, CA 90095, USA
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Yao FF, Zhang Y. A review of quantitative diffusion-weighted MR imaging for breast cancer: Towards noninvasive biomarker. Clin Imaging 2023; 98:36-58. [PMID: 36996598 DOI: 10.1016/j.clinimag.2023.03.016] [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: 01/10/2023] [Revised: 03/03/2023] [Accepted: 03/21/2023] [Indexed: 04/01/2023]
Abstract
Quantitative diffusion-weighted imaging (DWI) is an important adjunct to conventional breast MRI and shows promise as a noninvasive biomarker of breast cancer in multiple clinical scenarios, from the discrimination of benign and malignant lesions, prediction, and evaluation of treatment response to a prognostic assessment of breast cancer. Various quantitative parameters are derived from different DWI models based on special prior knowledge and assumptions, have different meanings, and are easy to confuse. In this review, we describe the quantitative parameters derived from conventional and advanced DWI models commonly used in breast cancer and summarize the promising clinical applications of these quantitative parameters. Although promising, it is still challenging for these quantitative parameters to become clinically useful noninvasive biomarkers in breast cancer, as multiple factors may result in variations in quantitative parameter measurements. Finally, we briefly describe some considerations regarding the factors that cause variations.
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Affiliation(s)
- Fei-Fei Yao
- Department of MRI in the First Affiliated Hospital, Zhengzhou University, Zhengzhou, China.
| | - Yan Zhang
- Department of MRI in the First Affiliated Hospital, Zhengzhou University, Zhengzhou, China
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Zhang L, Hao J, Guo J, Zhao X, Yin X. Predicting of Ki-67 Expression Level Using Diffusion-Weighted and Synthetic Magnetic Resonance Imaging in Invasive Ductal Breast Cancer. Breast J 2023; 2023:6746326. [PMID: 37063453 PMCID: PMC10098409 DOI: 10.1155/2023/6746326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 02/26/2023] [Accepted: 03/27/2023] [Indexed: 04/18/2023]
Abstract
Objectives To investigate the association between quantitative parameters generated using synthetic magnetic resonance imaging (SyMRI) and diffusion-weighted imaging (DWI) and Ki-67 expression level in patients with invasive ductal breast cancer (IDC). Method We retrospectively reviewed the records of patients with IDC who underwent SyMRI and DWI before treatment. Precontrast and postcontrast relaxation times (T1, longitudinal; T2, transverse), proton density (PD) parameters, and apparent diffusion coefficient (ADC) values were measured in breast lesions. Univariate and multivariate regression analyses were performed to screen for statistically significant variables to differentiate the high (≥30%) and low (<30%) Ki-67 expression groups. Their performance was evaluated by receiver operating characteristic (ROC) curve analysis. Results We analyzed 97 patients. Multivariate regression analysis revealed that the high Ki-67 expression group (n = 57) had significantly higher parameters generated using SyMRI (pre-T1, p=0.001) and lower ADC values (p=0.036) compared with the low Ki-67 expression group (n = 40). Pre-T1 showed the best diagnostic performance for predicting the Ki-67 expression level in patients with invasive ductal breast cancer (areas under the ROC curve (AUC), 0.711; 95% confidence interval (CI), 0.609-0.813). Conclusions Pre-T1 could be used to predict the pretreatment Ki-67 expression level in invasive ductal breast cancer.
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Affiliation(s)
- Liying Zhang
- Third Affiliated Hospital of Zhengzhou University, Department of Radiology, Zhengzhou, China
| | - Jisen Hao
- Third Affiliated Hospital of Zhengzhou University, Department of Radiology, Zhengzhou, China
| | - Jia Guo
- Third Affiliated Hospital of Zhengzhou University, Department of Radiology, Zhengzhou, China
| | - Xin Zhao
- Third Affiliated Hospital of Zhengzhou University, Department of Radiology, Zhengzhou, China
| | - Xing Yin
- Third Affiliated Hospital of Zhengzhou University, Department of Radiology, Zhengzhou, China
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Shear-wave elastography-based nomograms predicting 21-gene recurrence score for adjuvant chemotherapy decisions in patients with breast cancer. Eur J Radiol 2023; 158:110638. [PMID: 36476677 DOI: 10.1016/j.ejrad.2022.110638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 11/07/2022] [Accepted: 11/25/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE To develop and validate nomograms based on shear-wave elastography (SWE) combined with clinicopathologic features for predicting Oncotype DX recurrence score (RS) for use with adjuvant systemic therapy guidelines. METHODS In a retrospective study, patients with breast cancer who underwent definitive surgery of the breast between August 2011 and December 2019 were eligible for this study. Those with surgery between August 2011 and March 2019 were assigned to a development set and the rest were assigned to an independent validation set. Clinicopathologic features and SWE elasticity indices were assessed with logistic regression to develop nomograms for predicting RS ≥ 16 and ≥ 26. Analysis of the area under the receiver operating characteristic curve (AUROC) was used to assess the performance of the nomograms. RESULTS Of a total 381 women (mean age, 51 ± 9 years), 286 (mean age, 51 ± 9 years) were in the development set and 95 (mean age, 51 ± 9 years) in the validation set. All SWE elasticity indices were independently associated with each RS cutoff (odds ratio, 1.006-1.039 for RS ≥ 16; odds ratio, 1.008-1.076 for RS ≥ 26). Nomograms based on SWE combined with clinicopathologic features were developed and validated for RS ≥ 16 (mean elasticity [AUROC, 0.74; 95% CI: 0.68, 0.80] and maximum elasticity [AUROC, 0.74; 95% CI: 0.69, 0.80]) and for RS ≥ 26 (mean elasticity [AUROC, 0.81; 95% CI: 0.73, 0.89], maximum elasticity [AUROC, 0.82; 95% CI: 0.74, 0.89], and elasticity ratio [AUROC, 0.86; 95% CI: 0.80, 0.93]). CONCLUSION Nomograms based on SWE can predict Oncotype DX RS for use in adjuvant systemic therapy decisions.
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Chen Y, Tang W, Liu W, Li R, Wang Q, Shen X, Gong J, Gu Y, Peng W. Multiparametric
MR
Imaging Radiomics Signatures for Assessing the Recurrence Risk of
ER
+/
HER2
− Breast Cancer Quantified With 21‐Gene Recurrence Score. J Magn Reson Imaging 2022. [DOI: 10.1002/jmri.28547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 11/08/2022] [Accepted: 11/10/2022] [Indexed: 11/29/2022] Open
Affiliation(s)
- Yang Chen
- 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
| | - Wei Liu
- Department of Radiology Fudan University Shanghai Cancer Center Shanghai China
- Department of Oncology, Shanghai Medical College Fudan University Shanghai China
| | - Ruimin Li
- Department of Radiology Fudan University Shanghai Cancer Center Shanghai China
- Department of Oncology, Shanghai Medical College Fudan University Shanghai China
| | - Qifeng Wang
- Department of Oncology, Shanghai Medical College Fudan University Shanghai China
- Department of Pathology Fudan University Shanghai Cancer Center Shanghai China
| | - Xigang Shen
- Department of Radiology Fudan University Shanghai Cancer Center Shanghai China
- Department of Oncology, Shanghai Medical College Fudan University Shanghai China
| | - Jing Gong
- Department of Radiology Fudan University Shanghai Cancer Center Shanghai China
- Department of Oncology, Shanghai Medical College Fudan University Shanghai China
| | - Yajia Gu
- Department of Radiology Fudan University Shanghai Cancer Center Shanghai China
- Department of Oncology, Shanghai Medical College Fudan University Shanghai China
| | - Weijun Peng
- Department of Radiology Fudan University Shanghai Cancer Center Shanghai China
- Department of Oncology, Shanghai Medical College Fudan University Shanghai China
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Precision Medicine in Breast Cancer: Do MRI Biomarkers Identify Patients Who Truly Benefit from the Oncotype DX Recurrence Score ® Test? Diagnostics (Basel) 2022; 12:diagnostics12112730. [PMID: 36359573 PMCID: PMC9689656 DOI: 10.3390/diagnostics12112730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/27/2022] [Accepted: 11/02/2022] [Indexed: 11/10/2022] Open
Abstract
The aim of this study was to combine breast MRI-derived biomarkers with clinical-pathological parameters to identify patients who truly need an Oncotype DX Breast Recurrence Score® (ODXRS) genomic assay, currently used to predict the benefit of adjuvant chemotherapy in ER-positive/HER2-negative early breast cancer, with the ultimate goal of customizing therapeutic decisions while reducing healthcare costs. Patients who underwent a preoperative multiparametric MRI of the breast and ODXRS tumor profiling were retrospectively included in this study. Imaging sets were evaluated independently by two breast radiologists and classified according to the 2013 American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) lexicon. In a second step of the study, a combined oncologic and radiologic assessment based on clinical-pathological and radiological data was performed, in order to identify patients who may need adjuvant chemotherapy. Results were correlated with risk levels expressed by ODXRS, using the decision made on the basis of the ODXRS test as a gold standard. The χ2 test was used to evaluate associations between categorical variables, and significant ones were further investigated using logistic regression analyses. A total of 58 luminal-like, early-stage breast cancers were included. A positive correlation was found between ODXRS and tumor size (p = 0.003), staging (p = 0.001) and grading (p = 0.005), and between BI-RADS categories and ODXRS (p < 0.05 for both readers), the latter being confirmed at multivariate regression analysis. Moreover, BI-RADS categories proved to be positive predictors of the therapeutic decision taken after performing an ODXRS assay. A statistically significant association was also found between the therapeutic decision based on the ODXRS and the results of combined onco-radiologic assessment (p < 0.001). Our study suggests that there is a correlation between BI-RADS categories at MRI and ODXRS and that a combined onco-radiological assessment may predict the decision made on the basis of the results of ODXRS genomic test.
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Joy A, Saucedo A, Joines M, Lee-Felker S, Kumar S, Sarma MK, Sayre J, DiNome M, Thomas MA. Correlated MR spectroscopic imaging of breast cancer to investigate metabolites and lipids: acceleration and compressed sensing reconstruction. BJR Open 2022; 4:20220009. [PMID: 36860693 PMCID: PMC9969076 DOI: 10.1259/bjro.20220009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 08/23/2022] [Accepted: 08/25/2022] [Indexed: 11/05/2022] Open
Abstract
Objectives The main objective of this work was to detect novel biomarkers in breast cancer by spreading the MR spectra over two dimensions in multiple spatial locations using an accelerated 5D EP-COSI technology. Methods The 5D EP-COSI data were non-uniformly undersampled with an acceleration factor of 8 and reconstructed using group sparsity-based compressed sensing reconstruction. Different metabolite and lipid ratios were then quantified and statistically analyzed for significance. Linear discriminant models based on the quantified metabolite and lipid ratios were generated. Spectroscopic images of the quantified metabolite and lipid ratios were also reconstructed. Results The 2D COSY spectra generated using the 5D EP-COSI technique showed differences among healthy, benign, and malignant tissues in terms of their mean values of metabolite and lipid ratios, especially the ratios of potential novel biomarkers based on unsaturated fatty acids, myo-inositol, and glycine. It is further shown the potential of choline and unsaturated lipid ratio maps, generated from the quantified COSY signals across multiple locations in the breast, to serve as complementary markers of malignancy that can be added to the multiparametric MR protocol. Discriminant models using metabolite and lipid ratios were found to be statistically significant for classifying benign and malignant tumor from healthy tissues. Conclusions Accelerated 5D EP-COSI technique demonstrates the potential to detect novel biomarkers such as glycine, myo-inositol, and unsaturated fatty acids in addition to commonly reported choline in breast cancer, and facilitates metabolite and lipid ratio maps which have the potential to play a significant role in breast cancer detection. Advances in knowledge This study presents the first evaluation of a multidimensional MR spectroscopic imaging technique for the detection of potentially novel biomarkers based on glycine, myo-inositol, and unsaturated fatty acids, in addition to commonly reported choline. Spatial mapping of choline and unsaturated fatty acid ratios with respect to water in malignant and benign breast masses are also shown. These metabolic characteristics may serve as additional biomarkers for improving the diagnostic and therapeutic evaluation of breast cancer.
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Affiliation(s)
- Ajin Joy
- Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | | | - Melissa Joines
- Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - Stephanie Lee-Felker
- Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - Sumit Kumar
- Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - Manoj K Sarma
- Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - James Sayre
- Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - Maggie DiNome
- Surgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
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Fang S, Zhu J, Wang Y, Zhou J, Wang G, Xu W, Zhang W. The value of whole-lesion histogram analysis based on field‑of‑view optimized and constrained undistorted single shot (FOCUS) DWI for predicting axillary lymph node status in early-stage breast cancer. BMC Med Imaging 2022; 22:163. [PMID: 36088299 PMCID: PMC9464403 DOI: 10.1186/s12880-022-00891-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 08/31/2022] [Indexed: 12/28/2022] Open
Abstract
Abstract
Background
This study aims to estimate the amount of axillary lymph node (ALN) involvement in early-stage breast cancer utilizing a field of view (FOV) optimized and constrained undistorted single-shot (FOCUS) diffusion-weighted imaging (DWI) approach, as well as a whole-lesion histogram analysis.
Methods
This retrospective analysis involved 81 individuals with invasive breast cancer. The patients were divided into three groups: N0 (negative ALN metastasis), N1–2 (low metastatic burden with 1–2 ALNs), and N≥3 (heavy metastatic burden with ≥ 3 ALNs) based on their sentinel lymph node biopsy (SLNB) or axillary lymph node dissection (ALND). Histogram parameters of apparent diffusion coefficient (ADC) depending basically on FOCUS DWI were performed using 3D-Slicer software for whole lesions. The typical histogram characteristics for N0, N1–2, and N≥ 3 were compared to identify the significantly different parameters. To determine the diagnostic efficacy of significantly different factors, the area under their receiver operating characteristic (ROC) curves was examined.
Results
There were significant differences in the energy, maximum, 90 percentile, range, and lesion size among N0, N1–2, and N≥ 3 groups (P < 0.05). The energy differed significantly between N0 and N1–2 groups (P < 0.05), and some certain ADC histogram parameters and lesion sizes differed significantly between N0 and N≥3, or N1–2 and N≥3 groups. For ROC analysis, the energy yielded the best diagnostic performance in distinguishing N0 and N1–2 groups from N≥3 group with an AUC value of0.853. All parameters revealed excellent inter-observer agreement with inter-reader consistencies data ranging from0.919 to 0.982.
Conclusion
By employing FOCUS DWI method, the analysis of whole-lesion ADC histogram quantitatively provides a non-invasive way to evaluate the degree of ALN metastatic spread in early-stage breast cancer.
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Wang W, Lv S, Xun J, Wang L, Zhao F, Wang J, Zhou Z, Chen Y, Sun Z, Zhu L. Comparison of diffusion kurtosis imaging and dynamic contrast enhanced MRI in prediction of prognostic factors and molecular subtypes in patients with breast cancer. Eur J Radiol 2022; 154:110392. [DOI: 10.1016/j.ejrad.2022.110392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 05/18/2022] [Accepted: 05/31/2022] [Indexed: 11/16/2022]
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Zhong M, Yang Z, Chen X, Huang R, Wang M, Fan W, Dai Z, Chen X. Readout-Segmented Echo-Planar Diffusion-Weighted MR Imaging Improves the Differentiation of Breast Cancer Receptor Statuses Compared With Conventional Diffusion-Weighted Imaging. J Magn Reson Imaging 2022; 56:691-699. [PMID: 35038210 PMCID: PMC9542110 DOI: 10.1002/jmri.28065] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 01/04/2022] [Accepted: 01/05/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Readout-segmented echo-planar diffusion-weighted imaging (RS-EPI) can improve image quality and signal-to-noise ratio, the resulting apparent diffusion coefficient (ADC) value acts as a more sensitive biomarker to characterize tumors. However, data regarding the differentiation of breast cancer (BC) receptor statuses using RS-EPI are limited. PURPOSE To determine whether RS-EPI improves the differentiation of receptor statuses compared with conventional single-shot (SS) EPI in breast MRI. STUDY TYPE Retrospective. POPULATION A total of 151 BC women with the mean age of 50.6 years. FIELD STRENGTH/SEQUENCE A 3 T/ RS-EPI and SS-EPI. ASSESSMENT The ADCs of the lesion and normal background tissue from the two sequences were collected by two radiologists with 15 years of experience working of breast MRI (M.H.Z. and X.F.C.), and a normalized ADC was calculated by dividing the mean ADC value of the lesion by the mean ADC value of the normal background tissue. STATISTICAL TESTS Agreement between the ADC measurements from the two sequences was assessed using the Pearson correlation coefficient and Bland-Altman plots. One-way analysis of variance, Kruskal-Wallis test, and median difference were used to compare the ADC measurements for all lesions and different receptor statuses. A P value less than 0.05 indicated a significant result. RESULTS The ADC measurements of all lesions and normal background tissues were significantly higher on RS-EPI than on SS-EPI (1.82 ± 0.33 vs. 1.55 ± 0.30 and 0.83 ± 0.11 vs. 0.79 ± 0.10). The normalized ADC was lower on RS-EPI than on SS-EPI (0.47 ± 0.11 vs. 0.53 ± 0.12, a median difference of -0.04 [95% CI: -0.256 to 0.111]). For both diffusion methods, only the ADC measurement of RS-EPI was higher for human epidermal growth factor receptor-2 (HER-2)-positive tumors than for HER-2-negative tumors (0.87 ± 0.10 vs. 0.81 ± 0.11), and this measurement was associated with HER-2 positive status (adjusted odds ratio [OR] = 654.4); however, similar results were not observed for the ADC measurement of SS-EPI (0.80 ± 0.10 vs. 0.78 ± 0.11 with P = 0.199 and adjusted OR = 0.21 with P = 0.464, respectively). DATA CONCLUSION RS-EPI can improve the distinction between HER-2-positive and HER-2-negative breast cancer, complementing the clinical application of diffusion imaging. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Minghao Zhong
- Department of Radiology, Meizhou People's HospitalMeizhou514031China
| | - Zhiqi Yang
- Department of Radiology, Meizhou People's Hospital, Meizhou, 514031 China
- Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, Meizhou, 514031 China
| | - Xiaofeng Chen
- Department of Radiology, Meizhou People's HospitalMeizhou514031China
- Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka PopulationMeizhou514031China
| | - Ruibin Huang
- Department of RadiologyFirst Affiliated Hospital of Shantou University Medical CollegeShantou515000China
| | - Mengzhu Wang
- MR Scientific Marketing, Siemens HealthineersGuangzhou510620China
| | - Weixiong Fan
- Department of Radiology, Meizhou People's HospitalMeizhou514031China
| | - Zhuozhi Dai
- Department of Radiology, Shantou Central Hospital, Shantou, Guangdong, 515041 China
| | - Xiangguang Chen
- Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, Meizhou, 514031 China
- Department of Radiology, Meizhou People's Hospital, Meizhou, 514031 China
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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: 9] [Impact Index Per Article: 4.5] [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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Zhang XY, Cai SM, Zhang L, Zhu QL, Sun Q, Jiang YX, Wang HY, Li JC. Association Between Vascular Index Measured via Superb Microvascular Imaging and Molecular Subtype of Breast Cancer. Front Oncol 2022; 12:861151. [PMID: 35387128 PMCID: PMC8979674 DOI: 10.3389/fonc.2022.861151] [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: 01/24/2022] [Accepted: 02/21/2022] [Indexed: 11/15/2022] Open
Abstract
Background To determine whether vascular index (VI; defined as the ratio of Doppler signal pixels to pixels in the total lesion) measured via superb microvascular imaging in breast cancer correlates with immunohistochemically defined subtype and is able to predict molecular subtypes. Methods This prospective study involved 225 patients with 225 mass-type invasive breast cancers (mean size 2.6 ± 1.4 cm, range 0.4~5.9 cm) who underwent ultrasound and superb microvascular imaging (SMI) at Peking Union Medical College Hospital before breast surgery from December 2016 to June 2018. The correlations between primary tumor VI measured via SMI, clinicopathological findings, and molecular subtype were analyzed. The performance of VI for prediction of molecular subtypes in invasive breast cancer was investigated. Results The median VI of the 225 tumors was 7.3% (4.2%~11.8%) (range 0%~54.4%). Among the subtypes of the 225 tumors, 41 (18.2%) were luminal A, 91 (40.4%) were luminal B human epidermal growth factor receptor-2 (HER-2)-negative, 26 (11.6%) were luminal B HER-2-positive, 17 (7.6%) were HER-2-positive, and 50 (22.2%) were triple-negative, and the corresponding median VI values were 5.9% (2.6%~11.6%) (range 0%~47.1%), 7.3 (4.4%~10.5%) (range 0%~29.5%), 6.3% (3.9%~11.3%) (range 0.6%~22.2%), 8.2% (4.9%~15.6%) (range 0.9%~54.4%), and 9.2% (5.1%~15.3%) (range 0.7%~32.9%), respectively. Estrogen receptor (ER) negativity, higher tumor grade, and higher Ki-67 index (≥20%) were significantly associated with a higher VI value. Tumor size, ER status, and Ki-67 index were shown to independently influence VI. A cutoff value of 4.1% yielded 79.9% sensitivity and 41.5% specificity with an area under the receiver operating characteristic curve (AUC) of 0.58 for predicting that a tumor was of the luminal A subtype. A cutoff value of 16.4% yielded 30.0% sensitivity and 90.3% specificity with an AUC of 0.60 for predicting a triple-negative subtype. Conclusions VI, as a quantitative index obtained by SMI examination, could reflect histologic vascular changes in invasive breast cancer and was found to be higher in more biologically aggressive breast tumors. VI shows a certain degree of correlation with the molecular subtype of invasive breast cancer and plays a limited role in predicting the luminal A with high sensitivity and triple-negative subtype with high specificity.
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Affiliation(s)
- Xiao-Yan Zhang
- Department of Diagnostic Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Si-Man Cai
- Department of Diagnostic Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Li Zhang
- Department of Diagnostic Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Qing-Li Zhu
- Department of Diagnostic Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Qiang Sun
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Yu-Xin Jiang
- Department of Diagnostic Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Hong-Yan Wang
- Department of Diagnostic Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Jian-Chu Li
- Department of Diagnostic Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
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15
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Kazama T, Takahara T, Hashimoto J. Breast Cancer Subtypes and Quantitative Magnetic Resonance Imaging: A Systemic Review. Life (Basel) 2022; 12:life12040490. [PMID: 35454981 PMCID: PMC9028183 DOI: 10.3390/life12040490] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 02/20/2022] [Accepted: 03/08/2022] [Indexed: 12/12/2022] Open
Abstract
Magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer detection. This systematic review investigated the role of quantitative MRI features in classifying molecular subtypes of breast cancer. We performed a literature search of articles published on the application of quantitative MRI features in invasive breast cancer molecular subtype classification in PubMed from 1 January 2002 to 30 September 2021. Of the 1275 studies identified, 106 studies with a total of 12,989 patients fulfilled the inclusion criteria. Bias was assessed based using the Quality Assessment of Diagnostic Studies. All studies were case-controlled and research-based. Most studies assessed quantitative MRI features using dynamic contrast-enhanced (DCE) kinetic features and apparent diffusion coefficient (ADC) values. We present a summary of the quantitative MRI features and their correlations with breast cancer subtypes. In DCE studies, conflicting results have been reported; therefore, we performed a meta-analysis. Significant differences in the time intensity curve patterns were observed between receptor statuses. In 10 studies, including a total of 1276 lesions, the pooled difference in proportions of type Ⅲ curves (wash-out) between oestrogen receptor-positive and -negative cancers was not significant (95% confidence interval (CI): [−0.10, 0.03]). In nine studies, including a total of 1070 lesions, the pooled difference in proportions of type 3 curves between human epidermal growth factor receptor 2-positive and -negative cancers was significant (95% CI: [0.01, 0.14]). In six studies including a total of 622 lesions, the pooled difference in proportions of type 3 curves between the high and low Ki-67 groups was significant (95% CI: [0.17, 0.44]). However, the type 3 curve itself is a nonspecific finding in breast cancer. Many studies have examined the relationship between mean ADC and breast cancer subtypes; however, the ADC values overlapped significantly between subtypes. The heterogeneity of ADC using kurtosis or difference, diffusion tensor imaging parameters, and relaxation time was reported recently with promising results; however, current evidence is limited, and further studies are required to explore these potential applications.
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Affiliation(s)
- Toshiki Kazama
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara 259-1193, Japan;
- Correspondence: ; Tel.: +81-463-93-1121
| | - Taro Takahara
- Department of Biomedical Engineering, Tokai University School of Engineering, Hiratsuka 259-1207, Japan;
| | - Jun Hashimoto
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara 259-1193, Japan;
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16
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Wang W, Zhang X, Zhu L, Chen Y, Dou W, Zhao F, Zhou Z, Sun Z. Prediction of Prognostic Factors and Genotypes in Patients With Breast Cancer Using Multiple Mathematical Models of MR Diffusion Imaging. Front Oncol 2022; 12:825264. [PMID: 35174093 PMCID: PMC8841854 DOI: 10.3389/fonc.2022.825264] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/07/2022] [Indexed: 01/31/2023] Open
Abstract
Purpose To explore the clinical value of apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) based on diffusion-weighted MRI (DW-MRI) for predicting genotypes and prognostic factors of breast cancer. Materials and Methods A total of 227 patients with breast cancer confirmed by pathology were reviewed retrospectively. Diffusion-weighted imaging (DWI), IVIM, and DKI were performed in all patients. The corresponding ADC, true diffusion coefficient (D), perfusion-related diffusion coefficient (D*), perfusion fraction (f), mean diffusion rate (MD), and mean kurtosis value (MK) were measured. Multivariate logistic regression analysis and receiver operating characteristic (ROC) curve were used to analyze the diagnostic efficacy in predicting the Nottingham prognostic index (NPI), the expression of antigen Ki-67, and the molecular subtypes of breast cancer. The nomogram of the combined genotype-prediction model was established based on the multivariate logistic regression model results. Results D* and MK values were significantly higher in the high-grade Nottingham group (NPI ≥ 3.4) than the low-grade Nottingham group (NPI < 3.4) (p < 0.01). When D* ≥ 30.95 × 10−3 mm2/s and MK ≥ 0.69, the NPI tended to be high grade (with areas under the curve (AUCs) of 0.712 and 0.647, respectively). The combination of D* and MK demonstrated the highest AUC of 0.734 in grading NPI with sensitivity and accuracy of 71.7% and 77.1%, respectively. Additionally, higher D*, f, and MK and lower ADC and D values were observed in the high Ki-67 than low Ki-67 expression groups (p < 0.05). The AUC of the combined model (D + D* + f + MK) was 0.755, being significantly higher than that of single parameters (Z = 2.770~3.244, p = 0.001~0.006) in distinguishing high from low Ki-67 expression. D* and f values in the Luminal A subtype were significantly lower than in other subtypes (p < 0.05). Luminal B showed decreased D value compared with other subtypes (p < 0.05). The HER-2-positive subtype demonstrated increased ADC values compared with the Luminal B subtype (p < 0.05). Luminal A/B showed significantly lower D, D*, MD, and MK than the non-Luminal subtypes (p < 0.05). The combined model (D + D* + MD + MK) showed an AUC of 0.830 in diagnosing the Luminal and non-Luminal subtypes, which is significantly higher than that of a single parameter (Z = 3.273~4.440, p < 0.01). f ≥ 54.30% [odds ratio (OR) = 1.038, p < 0.001] and MK ≥ 0.68 (OR = 24.745, p = 0.012) were found to be significant predictors of triple-negative subtypes. The combination of f and MK values demonstrated superior diagnostic performance with AUC, sensitivity, specificity, and accuracy of 0.756, 67.5%, 77.5%, and 82.4%, respectively. Moreover, as shown in the calibration curve, strong agreements were observed between nomogram prediction probability and actual findings in the prediction of genotypes (p = 0.22, 0.74). Conclusion DWI, IVIM, and DKI, as MR diffusion imaging techniques with different mathematical models showed potential to identify the prognosis and genotype of breast cancer. In addition, the combination of these three models can improve the diagnostic efficiency and thus may contribute to opting for an appropriate therapeutic approach in clinic treatment.
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Affiliation(s)
- Weiwei Wang
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
| | - Xindong Zhang
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
| | - Laimin Zhu
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
| | - Yueqin Chen
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
| | | | - Fan Zhao
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
| | - Zhe Zhou
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
| | - Zhanguo Sun
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
- *Correspondence: Zhanguo Sun,
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Cheng C, Zhao H, Tian W, Hu C, Zhao H. Predicting the expression level of Ki-67 in breast cancer using multi-modal ultrasound parameters. BMC Med Imaging 2021; 21:150. [PMID: 34656085 PMCID: PMC8520259 DOI: 10.1186/s12880-021-00684-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 09/28/2021] [Indexed: 11/17/2022] Open
Abstract
Objective This study investigated the feasibility of predicting the expression levels of Ki-67 in breast cancer using ultrasonographic findings and clinical features. Methods Fifty-eight breast cancer patients, with 82 lesions confirmed by surgical pathology, were selected retrospectively for this study. Conventional ultrasound examination and elastography examination were performed before surgery. Clinical features (age, estrogen receptor (ER), progesterone receptor, and human epidermal growth factor receptor-2 expression levels), ultrasonographic findings, and elastography scores, including the maximum size, location, number, margin, borderline, blood flow, and elastography score of the mass, were collected. The expression of Ki-67 was recorded using immunohistochemical staining, and the patients were divided into a high (≥ 20%) expression group and a low (< 20%) expression group. SPSS 23.0 software was used for statistical analysis. An independent sample t-test was used for measurement data, and a χ2 test was used for enumeration data. Logistic regression analysis was performed for meaningful indicators, and the receiver operating characteristic curve was used to calculate the best diagnostic cut-off point. Results Monofactorial analysis showed that there was a statistically significant difference (p < 0.05) between the high expression of Ki-67 and the maximum diameter of the mass, the margin of the mass, the color Doppler flow imaging of the blood flow, and the resistance index of the blood flow. There were no significant differences in age, mass location, number, morphology, borderline, microcalcification, and elastography score (p > 0.05). Multiple factor regression analysis showed that a large mass and a mass with a rich blood flow had an independent predictive value for Ki-67. When the diameter was > 21.5 mm, the diagnostic sensitivity and specificity were 91.9% and 71.3%, respectively. The expression level of Ki-67 was negatively correlated with that of ER. Conclusion The tumor size and blood flow of breast cancer is correlated with the expression level of Ki-67 and, thus, it could be used to predict the expression level of Ki-67 in ultrasound diagnosis. The margin condition and the expression level of ER of an ultrasonic mass could also indirectly reflect the Ki-67 expression level of the mass.
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Affiliation(s)
- Chen Cheng
- Department of Imaging, First Affiliated Hospital of Soochow University, No. 188, Shizi Street, Canglang District, Suzhou, 215006, China
| | - Hongyan Zhao
- Department of Ultrasound, Lianyungang Traditional Chinese Medicine Hospital, Lianyungang, 222004, China
| | - Wei Tian
- Department of Endocrinology, Lianyungang Traditional Chinese Medicine Hospital, Lianyungang, 222004, China
| | - Chunhong Hu
- Department of Imaging, First Affiliated Hospital of Soochow University, No. 188, Shizi Street, Canglang District, Suzhou, 215006, China.
| | - Haitao Zhao
- Department of General Surgery, Lianyungang Traditional Chinese Medicine Hospital, Lianyungang, 222004, China
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Uslu H, Önal T, Tosun M, Arslan AS, Ciftci E, Utkan NZ. Intravoxel incoherent motion magnetic resonance imaging for breast cancer: A comparison with molecular subtypes and histological grades. Magn Reson Imaging 2021; 78:35-41. [PMID: 33556485 DOI: 10.1016/j.mri.2021.02.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 01/09/2021] [Accepted: 02/03/2021] [Indexed: 12/17/2022]
Abstract
PURPOSE The purpose of this paper is to investigate whether the IVIM parameters (D, D *, f) helps to determine the molecular subtypes and histological grades of breast cancer. METHODS Fifty-one patients with breast cancer were included in the study. All subjects were examined by 3 T Magnetic Resonance Imaging (MRI). Diffusion-weighted imaging (DWI) was undertaken with 16 b-values. IVIM parameters [D (true diffusion coefficient), D* (pseudo-diffusion coefficient), f (perfusion fraction)] were calculated. Histopathological reports were reviewed to histological grade, histological type, and immunohistochemistry. IVIM parameters of tumors with different histological grades and molecular subtypes were compared. RESULTS D* and f were significantly different between molecular subtypes (p = 0.019, p = 0.03 respectively). D* and f were higher in the HER-2 group and lower in Triple negative (-) group (D*:36.8 × 10-3 ± 5.3 × 10-3 mm2/s, f:29.5%, D*:29.8 × 10-3 ± 5.6 × 10-3 mm2/s, f:21.5% respectively). There was a significant difference in D* and f between HER-2 and Triple (-) subgroups (p = 0,028, p = 0.024, respectively). D* was also significantly different between the HER-2 group and the Luminal group (p = 0,041). While histological grades increase, D and f values tend to decrease, and D* tends to increase. While the Ki-67 index increases, D* and f values tend to increase, and D tend to decrease. CONCLUSION D* and f values measured with IVIM imaging were useful for assessing breast cancer molecular subtyping. IVIM imaging may be an alternative to breast biopsy for sub-typing of breast cancer with further research.
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Affiliation(s)
- Hande Uslu
- Department of Radiology, School of Medicine, Kocaeli University, Kocaeli, Turkey.
| | | | - Mesude Tosun
- Department of Radiology, School of Medicine, Kocaeli University, Kocaeli, Turkey
| | - Arzu S Arslan
- Department of Radiology, School of Medicine, Kocaeli University, Kocaeli, Turkey
| | - Ercument Ciftci
- Department of Radiology, School of Medicine, Kocaeli University, Kocaeli, Turkey
| | - Nihat Zafer Utkan
- Department of General Surgery, School of Medicine, Kocaeli University, Kocaeli, Turkey
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Mori M, Fujioka T, Katsuta L, Yashima Y, Nomura K, Yamaga E, Hosoya T, Oda G, Nakagawa T, Kubota K, Tateishi U. Clinical usefulness of the fast protocol of breast diffusion-weighted imaging using 3T magnetic resonance imaging with a 16-channel breast coil. Clin Imaging 2021; 78:217-222. [PMID: 34051405 DOI: 10.1016/j.clinimag.2021.04.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 03/22/2021] [Accepted: 04/25/2021] [Indexed: 11/27/2022]
Abstract
We aimed to evaluate the usefulness of a fast protocol of diffusion-weighted imaging (DWI) with one excitation using 3T magnetic resonance imaging (MRI) and a 16-channel breast coil. We analyzed 30 lesions from 27 women between February 2020 and June 2020. The visibility score (from 1 = extremely poor to 5 = excellent) and apparent diffusion coefficient (ADC) value between one and four excitations were evaluated by two readers. The image acquisition time was 40 s for one excitation and 1 min 52 s for four excitations. The visibility scores were 4.630 ± 0.718 and 4.267 ± 1.015 for one excitation and 4.730 ± 0.691 and 4.200 ± 1.000 for four excitations by the two readers. There was no significant difference in the visibility (P = 0.184 and P = 0.423), mean ADC value (P = 0.918 and P = 0.417), and minimum ADC value (P = 0.936 and P = 0.443) between one and four excitations by the two readers. Despite the short acquisition time, the visibility score and ADC values of one-excitation DWI were comparable to that with four excitations. Our fast DWI protocol could provide reproducible visibility and ADC value, potentially helping radiologists to efficiently diagnose patients.
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Affiliation(s)
- Mio Mori
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113 - 8510, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113 - 8510, Japan.
| | - Leona Katsuta
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113 - 8510, Japan
| | - Yuka Yashima
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113 - 8510, Japan
| | - Kyoko Nomura
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113 - 8510, Japan
| | - Emi Yamaga
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113 - 8510, Japan
| | - Tokuko Hosoya
- Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113 - 8510, Japan
| | - Goshi Oda
- Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113 - 8510, Japan
| | - Tsuyoshi Nakagawa
- Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113 - 8510, Japan
| | - Kazunori Kubota
- Department of Radiology, Dokkyo Medical University Hospital, 880 Kitakobayashi, Mibu, Shimotsugagun, Tochigi 321-0293, Japan
| | - Ukihide Tateishi
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113 - 8510, Japan
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Liu W, Cheng Y, Liu Z, Liu C, Cattell R, Xie X, Wang Y, Yang X, Ye W, Liang C, Li J, Gao Y, Huang C, Liang C. Preoperative Prediction of Ki-67 Status in Breast Cancer with Multiparametric MRI Using Transfer Learning. Acad Radiol 2021; 28:e44-e53. [PMID: 32278690 DOI: 10.1016/j.acra.2020.02.006] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 01/31/2020] [Accepted: 02/01/2020] [Indexed: 02/05/2023]
Abstract
RATIONALE AND OBJECTIVES Ki-67 is one of the most important biomarkers of breast cancer traditionally measured invasively via immunohistochemistry. In this study, deep learning based radiomics models were established for preoperative prediction of Ki-67 status using multiparametric magnetic resonance imaging (mp-MRI). MATERIALS AND METHODS Total of 328 eligible patients were retrospectively reviewed [training dataset (n = 230) and a temporal validation dataset (n = 98)]. Deep learning imaging features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast enhanced T1-weighted imaging (T1+C). Transfer learning techniques constructed four feature sets based on the individual three MR sequences and their combination (i.e., mp-MRI). Multilayer perceptron classifiers were trained for final prediction of Ki-67 status. Mann-Whitney U test compared the predictive performance of individual models. RESULTS The area under curve (AUC) of models based on T2WI,T1+C,DWI and mp-MRI were 0.727, 0.873, 0.674, and 0.888 in the training dataset, respectively, and 0.706, 0.829, 0.643, and 0.875 in the validation dataset, respectively. The predictive performance of mp-MRI classification model in the AUC value was significantly better than that of the individual sequence model (all p< 0.01). CONCLUSION In clinical practice, a noninvasive approach to improve the performance of radiomics in preoperative prediction of Ki-67 status can be provided by extracting breast cancer specific structural and functional features from mp-MRI images obtained from conventional scanning sequences using the advanced deep learning methods. This could further personalize medicine and computer aided diagnosis.
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Affiliation(s)
- Weixiao Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No.106, Zhongshan 2nd road, Guangzhou 510080 Guangdong, PR China; Graduate College, Shantou University Medical College, Shantou, Guangdong, PR China
| | - Yulin Cheng
- The School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, PR China
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No.106, Zhongshan 2nd road, Guangzhou 510080 Guangdong, PR China
| | - Chunling Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No.106, Zhongshan 2nd road, Guangzhou 510080 Guangdong, PR China
| | - Renee Cattell
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York
| | - Xinyan Xie
- The School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, PR China
| | - Yingyi Wang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No.106, Zhongshan 2nd road, Guangzhou 510080 Guangdong, PR China
| | - Xiaojun Yang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No.106, Zhongshan 2nd road, Guangzhou 510080 Guangdong, PR China
| | - Weitao Ye
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No.106, Zhongshan 2nd road, Guangzhou 510080 Guangdong, PR China
| | - Cuishan Liang
- Department of Radiology, Foshan Fetal Medicine Institute, Foshan Maternity and Children's Healthcare Hospital Affiliated to Southern Medical University, Foshan Guangdong, PR China
| | - Jiao Li
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No.106, Zhongshan 2nd road, Guangzhou 510080 Guangdong, PR China
| | - Ying Gao
- The School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, PR China
| | - Chuan Huang
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York; Department of Radiology, Stony Brook Medicine, Stony Brook, New York; Department of Psychiatry, Stony Brook Medicine, Stony Brook, New York
| | - Changhong Liang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No.106, Zhongshan 2nd road, Guangzhou 510080 Guangdong, PR China.
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Shin HJ, Lee SH, Moon WK. Diffusion-Weighted Imaging as a Stand-Alone Breast Imaging Modality. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2021; 82:29-48. [PMID: 36237448 PMCID: PMC9432391 DOI: 10.3348/jksr.2020.0215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 01/11/2021] [Accepted: 01/11/2021] [Indexed: 12/03/2022]
Abstract
확산강조영상은 유방암의 진단과 스크리닝에 있어 독립적 검사 방법으로서의 기대되는 결과를 보여주는 빠른 비조영증강 검사 방법이다. 현재까지의 연구 결과 유방암 진단에 있어 독립적 검사 방법으로서 확산강조영상의 민감도는 역동적 조영증강 검사보다는 낮으나 유방촬영술보다는 높으며, 이로써 유방암 스크리닝에 대한 유용한 대안이 될 수 있을 것으로 보인다. 확산강조영상의 표준화된 영상 획득과 판독을 통해 영상 화질이 개선될 수 있고, 판독 결과의 다양성도 감소할 것으로 기대된다. 또한, 최신 기법과 후처리 기법을 사용한 고해상도 확산강조영상을 시행함으로써 1 cm 미만의 작은 암의 발견율을 증가시킬 수 있고, 가음성 및 가양성 결과를 감소시킬 것으로 보인다. 현재 한국에서 진행 중인 고위험군 여성에서의 확산강조영상 스크리닝에 대한 다기관 연구 결과가 나온다면 독립적 검사로서의 확산강조영상의 사용을 촉진시킬 수 있을 것으로 기대된다.
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Affiliation(s)
- Hee Jung Shin
- Department of Radiology, Asan Medical Center, University of Ulsan, Seoul, Korea
| | - Su Hyun Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Woo Kyung Moon
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
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Gelezhe PB, Blokhin IA, Marapov DI, Morozov SP. Quantitative parameters of MRI and 18F-FDG PET/CT in the prediction of breast cancer prognosis and molecular type: an original study. AMERICAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING 2020; 10:279-292. [PMID: 33329930 PMCID: PMC7724282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 10/07/2020] [Indexed: 06/12/2023]
Abstract
The purpose of this work is to evaluate the quantitative parameters of magnetic resonance imaging (MRI), particularly diffusion-weighted imaging (DWI) and dynamic contrast enhancement (DCE) as well as positron-emission tomography, combined with computer tomography (PET/CT), with 18F-fluorodesoxyglucose, in the prediction of breast cancer molecular type. We studied the correlation between a set of parameters in the invasive ductal carcinoma of the breast, not otherwise specified (IDC-NOS) as it is the most common invasive breast tumor. The parameters were as follows: apparent diffusion coefficient (ADC) in DWI, positive enhancement integral (PEI) in DCE, maximum standardized uptake value (SUVmax) in 18F-FDG PET/CT, tumor size, grade, and Ki-67 index, level of lymph node metastatic lesions. We also evaluated the probability of a statistically significant difference in mean ADC, PEI, and SUVmax values for patient groups with different Nottingham prognostic index (NPI) and molecular tumor type. Statistically significant correlations between SUVmax, tumor size, and NPI, mean and minimal ADC values with Ki-67 and molecular tumor type were found. The PEI showed a correlation with the NPI risk level and was characterized by a relationship with the magnitude of the predicted NPI risk and regional lymph node involvement. The prognostic model created in our work allows for NPI risk group prediction. The SUVmax, ADC and PEI are non-invasive prognostic markers in the invasive breast cancer of no specific type. The correlation between ADC values and the expression of some tumor receptors can be used for in vivo molecular tumor type monitoring and treatment adjustment.
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Affiliation(s)
- Pavel Borisovich Gelezhe
- Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies, Department of Health Care of MoscowMoscow 109029, Russia
- Joint-Stock Company “European Medical Center”Russia
| | - Ivan Andreevich Blokhin
- Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies, Department of Health Care of MoscowMoscow 109029, Russia
| | - Damir Ildarovich Marapov
- Educational and Methodical Center “Lean Technologies in Healthcare”, Kazan State Medical UniversityMoscow 109029, Russia
| | - Sergey Pavlovich Morozov
- Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies, Department of Health Care of MoscowMoscow 109029, Russia
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23
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Iima M. Perfusion-driven Intravoxel Incoherent Motion (IVIM) MRI in Oncology: Applications, Challenges, and Future Trends. Magn Reson Med Sci 2020; 20:125-138. [PMID: 32536681 PMCID: PMC8203481 DOI: 10.2463/mrms.rev.2019-0124] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Recent developments in MR hardware and software have allowed a surge of interest in intravoxel incoherent motion (IVIM) MRI in oncology. Beyond diffusion-weighted imaging (and the standard apparent diffusion coefficient mapping most commonly used clinically), IVIM provides information on tissue microcirculation without the need for contrast agents. In oncology, perfusion-driven IVIM MRI has already shown its potential for the differential diagnosis of malignant and benign tumors, as well as for detecting prognostic biomarkers and treatment monitoring. Current developments in IVIM data processing, and its use as a method of scanning patients who cannot receive contrast agents, are expected to increase further utilization. This paper reviews the current applications, challenges, and future trends of perfusion-driven IVIM in oncology.
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Affiliation(s)
- Mami Iima
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine.,Department of Clinical Innovative Medicine, Institute for Advancement of Clinical and Translational Science (iACT), Kyoto University Hospital
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Jung Y, Jeong S, Kim JY, Kang DK, Kim TH. Correlations of female hormone levels with background parenchymal enhancement and apparent diffusion coefficient values in premenopausal breast cancer patients: Effects on cancer visibility. Eur J Radiol 2020; 124:108818. [PMID: 31935597 DOI: 10.1016/j.ejrad.2020.108818] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 12/31/2019] [Accepted: 01/02/2020] [Indexed: 10/25/2022]
Abstract
PURPOSE To evaluate the relationships between female hormone levels and background parenchymal enhancement (BPE) or apparent diffusion coefficient (ADC) values of breast parenchyma, as well as the effects of BPE and ADC values on cancer visibility. METHODS This prospective study was performed in 164 consecutive premenopausal patients who were diagnosed with invasive breast cancer from November 2016 to December 2018. Two radiologists analyzed the qualitative, quantitative BPE and ADC values of normal contralateral breast parenchyma. We also analyzed the cancer visibility using a three-point scale (0: no visibility, 1: slight visibility, 2: excellent visibility). RESULTS The progesterone level was significantly correlated with qualitative BPE grade and quantitative values of the BPE, as well as with the mean ADC. On contrast enhanced image (CEI), the visibility score was significantly associated with tumor size, qualitative and quantitative BPE. On diffusion weighted image (DWI), tumor size was significantly associated with the visibility score, whereas the ADC value was not. Of four lesions with a score of 0 on CEI, three had a score of 2 and one a score of 1 on DWI. Regarding the visibility score on DWI, tumor size and histologic type were significantly different among the three groups. CONCLUSIONS Qualitative or quantitative BPE of breast parenchyma was positively correlated with the progesterone level and the mean ADC was negatively correlated. The cancer visibility was affected by BPE on CEI, but not by ADC on DWI. Small-sized cancer and invasive lobular cancer could be the causes of false-negative diagnoses on DWI.
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Affiliation(s)
- Yongsik Jung
- Department of Surgery, Ajou University School of Medicine and Graduate School of Medicine, South Korea
| | - Seongkyun Jeong
- Department of Aerospace Engineering, Korea Advanced Institute of Science and Technology, South Korea
| | - Ji Young Kim
- Department of Surgery, Ajou University School of Medicine and Graduate School of Medicine, South Korea
| | - Doo Kyoung Kang
- Department of Radiology, Ajou University School of Medicine and Graduate School of Medicine, South Korea
| | - Tae Hee Kim
- Department of Radiology, Ajou University School of Medicine and Graduate School of Medicine, South Korea.
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25
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Grimm LJ, Mazurowski MA. Breast Cancer Radiogenomics: Current Status and Future Directions. Acad Radiol 2020; 27:39-46. [PMID: 31818385 DOI: 10.1016/j.acra.2019.09.012] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Revised: 07/17/2019] [Accepted: 09/08/2019] [Indexed: 12/13/2022]
Abstract
Radiogenomics is an area of research that aims to identify associations between imaging phenotypes ("radio-") and tumor genome ("-genomics"). Breast cancer radiogenomics research in particular has been an especially prolific area of investigation in recent years as evidenced by the wide number and variety of publications and conferences presentations. To date, research has primarily been focused on dynamic contrast enhanced pre-operative breast MRI and breast cancer molecular subtypes, but investigations have extended to all breast imaging modalities as well as multiple additional genetic markers including those that are commercially available. Furthermore, both human and computer-extracted features as well as deep learning techniques have been explored. This review will summarize the specific imaging modalities used in radiogenomics analysis, describe the methods of extracting imaging features, and present the types of genomics, molecular, and related information used for analysis. Finally, the limitations and future directions of breast cancer radiogenomics research will be discussed.
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26
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Surov A, Chang YW, Li L, Martincich L, Partridge SC, Kim JY, Wienke A. Apparent diffusion coefficient cannot predict molecular subtype and lymph node metastases in invasive breast cancer: a multicenter analysis. BMC Cancer 2019; 19:1043. [PMID: 31690273 PMCID: PMC6833245 DOI: 10.1186/s12885-019-6298-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Accepted: 10/27/2019] [Indexed: 12/14/2022] Open
Abstract
Background Radiological imaging plays a central role in the diagnosis of breast cancer (BC). Some studies suggest MRI techniques like diffusion weighted imaging (DWI) may provide further prognostic value by discriminating between tumors with different biologic characteristics including receptor status and molecular subtype. However, there is much contradictory reported data regarding such associations in the literature. The purpose of the present study was to provide evident data regarding relationships between quantitative apparent diffusion coefficient (ADC) values on DWI and pathologic prognostic factors in BC. Methods Data from 5 centers (661 female patients, mean age, 51.4 ± 10.5 years) were acquired. Invasive ductal carcinoma (IDC) was diagnosed in 625 patients (94.6%) and invasive lobular carcinoma in 36 cases (5.4%). Luminal A carcinomas were diagnosed in 177 patients (28.0%), luminal B carcinomas in 279 patients (44.1%), HER 2+ carcinomas in 66 cases (10.4%), and triple negative carcinomas in 111 patients (17.5%). The identified lesions were staged as T1 in 51.3%, T2 in 43.0%, T3 in 4.2%, and as T4 in 1.5% of the cases. N0 was found in 61.3%, N1 in 33.1%, N2 in 2.9%, and N3 in 2.7%. ADC values between different groups were compared using the Mann–Whitney U test and by the Kruskal-Wallis H test. The association between ADC and Ki 67 values was calculated by Spearman’s rank correlation coefficient. Results ADC values of different tumor subtypes overlapped significantly. Luminal B carcinomas had statistically significant lower ADC values compared with luminal A (p = 0.003) and HER 2+ (p = 0.007) lesions. No significant differences of ADC values were observed between luminal A, HER 2+ and triple negative tumors. There were no statistically significant differences of ADC values between different T or N stages of the tumors. Weak statistically significant correlation between ADC and Ki 67 was observed in luminal B carcinoma (r = − 0.130, p = 0.03). In luminal A, HER 2+ and triple negative tumors there were no significant correlations between ADC and Ki 67. Conclusion ADC was not able to discriminate molecular subtypes of BC, and cannot be used as a surrogate marker for disease stage or proliferation activity.
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Affiliation(s)
- Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Ulm, Albert-Einstein-Allee 23, 89081, Ulm, Germany.
| | - Yun-Woo Chang
- Department of Radiology, Soonchunhyang University Hospital, 59 Daesakwan-ro, Yongsan-gu, Seoul, 140-743, Republic of Korea
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Laura Martincich
- Unit of Radiology, Institute for Cancer Research and Treatment (IRCC), Strada Provinciale 142, 10060 Candiolo, Turin, Italy
| | - Savannah C Partridge
- Department of Radiology, University of Washington, Seattle, Washington 825 Eastlake Ave. E, G2-600, Seattle, WA, 98109, USA
| | - Jin You Kim
- Department of Radiology, Pusan National University Hospital, Pusan National University School of Medicine and Medical Research Institute 1-10, Ami-Dong, Seo-gu, Busan, 602-739, South Korea
| | - Andreas Wienke
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Magdeburger Str, 06097, Halle, Germany
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27
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Iima M, Honda M, Sigmund EE, Ohno Kishimoto A, Kataoka M, Togashi K. Diffusion MRI of the breast: Current status and future directions. J Magn Reson Imaging 2019; 52:70-90. [PMID: 31520518 DOI: 10.1002/jmri.26908] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 08/12/2019] [Indexed: 12/30/2022] Open
Abstract
Diffusion-weighted imaging (DWI) is increasingly being incorporated into routine breast MRI protocols in many institutions worldwide, and there are abundant breast DWI indications ranging from lesion detection and distinguishing malignant from benign tumors to assessing prognostic biomarkers of breast cancer and predicting treatment response. DWI has the potential to serve as a noncontrast MR screening method. Beyond apparent diffusion coefficient (ADC) mapping, which is a commonly used quantitative DWI measure, advanced DWI models such as intravoxel incoherent motion (IVIM), non-Gaussian diffusion MRI, and diffusion tensor imaging (DTI) are extensively exploited in this field, allowing the characterization of tissue perfusion and architecture and improving diagnostic accuracy without the use of contrast agents. This review will give a summary of the clinical literature along with future directions. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:70-90.
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Affiliation(s)
- Mami Iima
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.,Department of Clinical Innovative Medicine, Institute for Advancement of Clinical and Translational Science, Kyoto University Hospital, Kyoto, Japan
| | - Maya Honda
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Eric E Sigmund
- Department of Radiology, NYU Langone Health, New York, New York, USA.,Center for Advanced Imaging and Innovation (CAI2R), New York, New York, USA
| | - Ayami Ohno Kishimoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Masako Kataoka
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Kaori Togashi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
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Diffusion-weighted MRI of estrogen receptor-positive, HER2-negative, node-negative breast cancer: association between intratumoral heterogeneity and recurrence risk. Eur Radiol 2019; 30:66-76. [PMID: 31385051 DOI: 10.1007/s00330-019-06383-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 07/03/2019] [Accepted: 07/19/2019] [Indexed: 01/01/2023]
Abstract
OBJECTIVES To investigate possible associations between quantitative apparent diffusion coefficient (ADC) metrics derived from whole-lesion histogram analysis and breast cancer recurrence risk in women with estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, node-negative breast cancer who underwent the Oncotype DX assay. METHODS This retrospective study was conducted on 105 women (median age, 48 years) with ER-positive, HER2-negative, node-negative breast cancer who underwent the Oncotype DX test and preoperative diffusion-weighted imaging (DWI). Histogram analysis of pixel-based ADC data of whole tumors was performed, and various ADC histogram parameters (mean, 5th, 25th, 50th, 75th, and 95th percentiles of ADCs) were extracted. The ADC difference value (defined as the difference between the 5th and 95th percentiles of ADCs) was calculated to assess intratumoral heterogeneity. Associations between quantitative ADC metrics and the recurrence risk, stratified using the Oncotype DX recurrence score (RS), were evaluated. RESULTS Whole-lesion histogram analysis showed that the ADC difference value was different between the low-risk recurrence (RS < 18) and the non-low-risk recurrence (RS ≥ 18; intermediate to high risk of recurrence) groups (0.600 × 10-3 mm2/s vs. 0.746 × 10-3 mm2/s, p < 0.001). Multivariate regression analysis demonstrated that a lower ADC difference value (< 0.559 × 10-3 mm2/s; odds ratio [OR] = 5.998; p = 0.007) and a small tumor size (≤ 2 cm; OR = 3.866; p = 0.012) were associated with a low risk of recurrence after adjusting for clinicopathological factors. CONCLUSIONS The ADC difference value derived from whole-lesion histogram analysis might serve as a quantitative DWI biomarker of the recurrence risk in women with ER-positive, HER2-negative, node-negative invasive breast cancer. KEY POINTS • A lower ADC difference value and a small tumor size were associated with a low risk of recurrence of breast cancer. • The ADC difference value could be a quantitative marker for intratumoral heterogeneity. • Whole-lesion histogram analysis of the ADC could be helpful for discriminating the low-risk from non-low-risk recurrence groups.
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Camps-Herrero J. Diffusion-weighted imaging of the breast: current status as an imaging biomarker and future role. BJR Open 2019; 1:20180049. [PMID: 33178933 PMCID: PMC7592470 DOI: 10.1259/bjro.20180049] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 02/07/2019] [Accepted: 02/12/2019] [Indexed: 12/13/2022] Open
Abstract
Diffusion-weighted imaging (DWI) of the breast is a MRI sequence that shows several advantages when compared to the dynamic contrast-enhanced sequence: it does not need intravenous contrast, it is relatively quick and easy to implement (artifacts notwithstanding). In this review, the current applications of DWI for lesion characterization and prognosis as well as for response evaluation are analyzed from the point of view of the necessary steps to become a useful surrogate of underlying biological processes (tissue architecture and cellularity): from the proof of concept, to the proof of mechanism, the proof of principle and finally the proof of effectiveness. Future applications of DWI in screening, DWI modeling and radiomics are also discussed.
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Affiliation(s)
- Julia Camps-Herrero
- Head of Radiology Department, Breast Unit. Hospital Universitario de la Ribera, Alzira, Spain
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30
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Radiomic analysis of imaging heterogeneity in tumours and the surrounding parenchyma based on unsupervised decomposition of DCE-MRI for predicting molecular subtypes of breast cancer. Eur Radiol 2019; 29:4456-4467. [PMID: 30617495 DOI: 10.1007/s00330-018-5891-3] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Revised: 10/02/2018] [Accepted: 11/13/2018] [Indexed: 10/27/2022]
Abstract
OBJECTIVES This study aimed to predict the molecular subtypes of breast cancer via intratumoural and peritumoural radiomic analysis with subregion identification based on the decomposition of contrast-enhanced magnetic resonance imaging (DCE-MRI). METHODS The study included 211 women with histopathologically confirmed breast cancer. We utilised a completely unsupervised convex analysis of mixtures (CAM) method by unmixing dynamic imaging series from heterogeneous tissues. Each tumour and the surrounding parenchyma were thus decomposed into multiple subregions, representing different vascular characterisations, from which radiomic features were extracted. A random forest model was trained and tested using a leave-one-out cross-validation (LOOCV) method to predict breast cancer subtypes. The predictive models from tumoural and peritumoural subregions were fused for classification. RESULTS Tumour and peritumour DCE-MR images were decomposed into three compartments, representing plasma input, fast-flow kinetics, and slow-flow kinetics. The tumour subregion related to fast-flow kinetics showed the best performance among the subregions for differentiating between patients with four molecular subtypes (area under the receiver operating characteristic curve (AUC) = 0.832), exhibiting an AUC value significantly (p < 0.0001) higher than that obtained with the entire tumour (AUC = 0.719). When the tumour- and parenchyma-based predictive models were fused, the performance, measured as the AUC, increased to 0.897; this value was significantly higher than that obtained with other tumour partition methods. CONCLUSIONS Radiomic analysis of intratumoural and peritumoural heterogeneity based on the decomposition of image time-series signals has the potential to more accurately identify tumour kinetic features and serve as a valuable clinical marker to enhance the prediction of breast cancer subtypes. KEY POINTS • Decomposition of image time-series signals has the potential to more accurately identify tumour kinetic features. • Fusion of intratumoural- and peritumoural-based predictive models improves the prediction of breast cancer subtypes.
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31
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Application of Radiomics and Decision Support Systems for Breast MR Differential Diagnosis. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2018; 2018:7417126. [PMID: 30344618 PMCID: PMC6174735 DOI: 10.1155/2018/7417126] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 07/24/2018] [Accepted: 09/04/2018] [Indexed: 01/17/2023]
Abstract
Over the years, MR systems have evolved from imaging modalities to advanced computational systems producing a variety of numerical parameters that can be used for the noninvasive preoperative assessment of breast pathology. Furthermore, the combination with state-of-the-art image analysis methods provides a plethora of quantifiable imaging features, termed radiomics that increases diagnostic accuracy towards individualized therapy planning. More importantly, radiomics can now be complemented by the emerging deep learning techniques for further process automation and correlation with other clinical data which facilitate the monitoring of treatment response, as well as the prediction of patient's outcome, by means of unravelling of the complex underlying pathophysiological mechanisms which are reflected in tissue phenotype. The scope of this review is to provide applications and limitations of radiomics towards the development of clinical decision support systems for breast cancer diagnosis and prognosis.
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Surov A, Clauser P, Chang YW, Li L, Martincich L, Partridge SC, Kim JY, Meyer HJ, Wienke A. Can diffusion-weighted imaging predict tumor grade and expression of Ki-67 in breast cancer? A multicenter analysis. Breast Cancer Res 2018; 20:58. [PMID: 29921323 PMCID: PMC6011203 DOI: 10.1186/s13058-018-0991-1] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Accepted: 05/18/2018] [Indexed: 01/24/2023] Open
Abstract
Background Numerous studies have analyzed associations between apparent diffusion coefficient (ADC) and histopathological features such as Ki-67 proliferation index in breast cancer (BC), with mixed results. The purpose of this study was to perform a multicenter analysis to determine relationships between ADC and expression of Ki-67 and tumor grade in BC. Methods For this study, data from six centers were acquired. The sample comprises 870 patients (all female; mean age, 52.6 ± 10.8 years). In every case, breast magnetic resonance imaging with diffusion-weighted imaging was performed. The comparison of ADC values in groups was performed by Mann-Whitney U test where the p values are adjusted for multiple testing (Bonferroni correction). The association between ADC and Ki-67 values was calculated by Spearman’s rank correlation coefficient. Sensitivity, specificity, negative and positive predictive values, accuracy, and AUC were calculated for the diagnostic procedures. ADC thresholds were chosen to maximize the Youden index. Results Overall, data of 870 patients were acquired for this study. The mean ADC value of the tumors was 0.98 ± 0.22 × 10− 3 mm2 s− 1. ROC analysis showed that it is impossible to differentiate high/moderate grade tumors from grade 1 lesions using ADC values. Youden index identified a threshold ADC value of 1.03 with a sensitivity of 56.2% and specificity of 67.9%. The positive predictive value was 18.2%, and the negative predictive value was 92.4%. The level of the Ki-67 proliferation index was available for 845 patients. The mean value was 12.33 ± 21.77%. ADC correlated with weak statistical significant with expression of Ki-67 (p = − 0.202, p < 0.001). ROC analysis was performed to distinguish tumors with high proliferative potential from tumors with low expression of Ki-67 using ADC values. Youden index identified a threshold ADC value of 0.91 (sensitivity 64%, specificity 50%, positive predictive value 67.7%, negative predictive value 45.0%). Conclusions ADC cannot be used as a surrogate marker for proliferation activity and/or for tumor grade in breast cancer.
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Affiliation(s)
- Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Liebigstrasse 20, 04103, Leipzig, Germany.
| | - Paola Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Währinger Gürtel, 18-20 1090, Vienna, Austria
| | - Yun-Woo Chang
- Department of Radiology, Soonchunhyang University Hospital, 59 Daesakwan-ro, Yongsan-gu, Seoul, 140-743, Republic of Korea
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Laura Martincich
- Unit of Radiology, Institute for Cancer Research and Treatment of Candiolo (IRCC), Strada Provinciale 142, 10060 Candiolo, Turin, Italy
| | - Savannah C Partridge
- Department of Radiology, University of Washington, 825 Eastlake Avenue E, G2-600, Seattle, WA, 98109, USA
| | - Jin You Kim
- Department of Radiology, Pusan National University Hospital, Pusan National University School of Medicine and Medical Research Institute, 1-10, Ami-Dong, Seo-gu, Busan, 602-739, Korea
| | - Hans Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Liebigstrasse 20, 04103, Leipzig, Germany
| | - Andreas Wienke
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Magdeburger Strasse, 06097, Halle, Germany
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Macchini M, Ponziani M, Iamurri AP, Pistelli M, De Lisa M, Berardi R, Giuseppetti GM. Role of DCE-MR in predicting breast cancer subtypes. Radiol Med 2018; 123:753-764. [PMID: 29869226 DOI: 10.1007/s11547-018-0908-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Accepted: 05/24/2018] [Indexed: 12/31/2022]
Abstract
OBJECTIVE The purpose of this retrospective study is to find a correlation between dynamic contrast-enhanced MR features with histological, immunohistochemical and loco-regional characteristics of breast cancer. MATERIALS AND METHODS A total of 149 patients with histopathologically confirmed invasive breast carcinoma underwent MR imaging. Histological analysis included: histological features (histological type, necrosis, vascular invasion and Mib-1), immunohistochemical characterization (immunophenotype, receptor status, HER2-neu and grading) and loco-regional characteristics (T and N). The kinetic MR features analyzed were: curve type, maximum enhancement, time to peak, wash-in and wash-out rate, brevity of enhancement and area under curve. RESULTS MRI kinetic parameters and immunohistological features were compared using chi square test, two-tailed student t test and Anova test, with p = 0.05 level of significance. Vascular invasion was shown to be significantly related to time to peak (p = 0.02). The immunohistotype was shown to be significantly related with maximum enhancement (p = 0.05), time to peak (p = 0.04) and wash-in rate (p = 0.01). ER status correlates with maximum and relative enhancement (p = 0.004 and p = 0.028), wash-in rate (p = 0.0018) and area under curve (p = 0.006). PR status was significantly related to time to peak (p = 0.048) and wash-in rate (p = 0.05). CONCLUSION Maximum enhancement absolute and relative, time to peak, wash-in rate and area under the curve significantly correlate with several prognostic factors, like ER status, immune profile and tumoral vascular invasion, and may predict the aggressiveness of the tumor.
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Affiliation(s)
- Marco Macchini
- Sc. Spec. Radiologia, Università Politecnica delle Marche, Ancona, Italy.
| | - Martina Ponziani
- Sc. Spec. Radiologia, Università Politecnica delle Marche, Ancona, Italy
| | | | - Mirco Pistelli
- Azienda Ospedaliero Universitaria Ospedali Riuniti Clinica di Oncologia, Università Politecnica delle Marche, Ancona, Italy
| | - Mariagrazia De Lisa
- Azienda Ospedaliero Universitaria Ospedali Riuniti Clinica di Oncologia, Università Politecnica delle Marche, Ancona, Italy
| | - Rossana Berardi
- Azienda Ospedaliero Universitaria Ospedali Riuniti Clinica di Oncologia, Università Politecnica delle Marche, Ancona, Italy
| | - Gian Marco Giuseppetti
- Azienda Ospedaliero Universitaria Ospedali Riuniti Clinica di Radiologia, Università Politecnica delle Marche, Ancona, Italy.,Dipartimento Radiologia Clinica, Ospedali Riuniti Azienda Ospedaliero Universitaria Ospedali Riuniti, Via Tronto 10, 60126, Ancona, AN, Italy
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