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Yang XL, Ni DH, Yu Y, Zhao JC, Lin R, Xiu C, Chang ZX. Value of magnetic resonance imaging radiomics features in predicting histologic grade of invasive ductal carcinoma of the breast. Technol Health Care 2024; 32:1609-1618. [PMID: 38393931 PMCID: PMC11091567 DOI: 10.3233/thc-230671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 08/10/2023] [Indexed: 02/25/2024]
Abstract
BACKGROUND Breast cancer has the second highest mortality rate of all cancers and occurs mainly in women. OBJECTIVE To investigate the relationship between magnetic resonance imaging (MRI) radiomics features and histological grade of invasive ductal carcinoma (IDC) of the breast and to evaluate its diagnostic efficacy. METHODS The two conventional MRI quantitative indicators, i.e. the apparent diffusion coefficient (ADC) and the initial enhancement rate, were collected from 112 patients with breast cancer. The breast cancer lesions were manually segmented in dynamic contrast-enhanced MRI (DCE-MRI) and ADC images, the differences in radiomics features between Grades I, II and III IDCs were compared and the diagnostic efficacy was evaluated. RESULTS The ADC values (0.77 ± 0.22 vs 0.91 ± 0.22 vs 0.92 ± 0.20, F= 4.204, p< 0.01), as well as the B_sum_variance (188.51 ± 67.803 vs 265.37 ± 77.86 vs 263.74 ± 82.58, F= 6.040, p< 0.01), L_energy (0.03 ± 0.02 vs 0.13 ± 0.11 vs 0.12 ± 0.14, F= 7.118, p< 0.01) and L_sum_average (0.78 ± 0.32 vs 16.34 ± 4.23 vs 015.45 ± 3.74, F= 21.860, p< 0.001) values of patients with Grade III IDC were significantly lower than those of patients with Grades I and II IDC. The B_uniform (0.15 ± 0.12 vs 0.11 ± 0.04 vs 0.12 ± 0.03, F= 3.797, p< 0.01) and L_SRE (0.85 ± 0.07 vs 0.78 ± 0.03 vs 0.79 ± 0.32, F= 3.024, p< 0.01) values of patients with Grade III IDC were significantly higher than those of patients with Grades I and II IDC. All differences were statistically significant (p< 0.05). The ADC radiomics signature model had a higher area-under-the-curve value in identifying different grades of IDC than the ADC value model and the DCE radiomics signature model (0.869 vs 0.711 vs 0.682). The accuracy (0.812 vs 0.647 vs 0.710), specificity (0.731 vs 0.435 vs 0.342), positive predictive value (0.815 vs 0.663 vs 0.669) and negative predictive value (0.753 vs 0.570 vs 0.718) of the ADC radiomics signature model were all significantly better than the ADC value model and the DCE radiomics signature model. CONCLUSION ADC values and breast MRI radiomics signatures are significant in identifying the histological grades of IDC, with the ADC radiomics signatures having greater value.
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Affiliation(s)
- Xin-Lei Yang
- Blood Tumor Treatment Center, Beihua University Affiliated Hospital, Jilin, China
| | - Dong-He Ni
- Blood Tumor Treatment Center, Beihua University Affiliated Hospital, Jilin, China
- Department of Radiology, Jilin Province Integrated Traditional Chinese and Western Medicine Hospital, Jilin, China
| | - Yang Yu
- Department of Radiology, Beihua University Affiliated Hospital, Jilin, China
| | - Jin-Cui Zhao
- Department of Radiology, Beihua University Affiliated Hospital, Jilin, China
| | - Rui Lin
- Blood Tumor Treatment Center, Beihua University Affiliated Hospital, Jilin, China
| | - Chao Xiu
- Department of Radiology, Beihua University Affiliated Hospital, Jilin, China
| | - Zhe-Xing Chang
- Blood Tumor Treatment Center, Beihua University Affiliated Hospital, Jilin, China
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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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Jannusch K, Bittner AK, Bruckmann NM, Morawitz J, Stieglitz C, Dietzel F, Quick HH, Baba HA, Herrmann K, Umutlu L, Antoch G, Kirchner J, Kasimir-Bauer S, Hoffmann O. Correlation between Imaging Markers Derived from PET/MRI and Invasive Acquired Biomarkers in Newly Diagnosed Breast Cancer. Cancers (Basel) 2023; 15:cancers15061651. [PMID: 36980537 PMCID: PMC10046153 DOI: 10.3390/cancers15061651] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 03/06/2023] [Indexed: 03/30/2023] Open
Abstract
PURPOSE Evaluate the diagnostic potential of [18F]FDG-PET/MRI data compared with invasive acquired biomarkers in newly diagnosed early breast cancer (BC). METHODS Altogether 169 women with newly diagnosed BC were included. All underwent a breast- and whole-body [18F]FDG-PET/MRI for initial staging. A tumor-adapted volume of interest was placed in the primaries and defined bone regions on each standard uptake value (SUV)/apparent diffusion coefficient (ADC) dataset. Immunohistochemical markers, molecular subtype, tumor grading, and disseminated tumor cells (DTCs) of each patient were assessed after ultrasound-guided biopsy of the primaries and bone marrow (BM) aspiration. Correlation analysis and group comparisons were assessed. RESULTS A significant inverse correlation of estrogen-receptor (ER) expression and progesterone-receptor (PR) expression towards SUVmax was found (ER: r = 0.27, p < 0.01; PR: r = 0.19, p < 0.05). HER2-receptor expression showed no significant correlation towards SUV and ADC values. A significant positive correlation between Ki67 and SUVmax and SUVmean (r = 0.42 p < 0.01; r = 0.19 p < 0.05) was shown. Tumor grading significantly correlated with SUVmax and SUVmean (ρ = 0.36 and ρ = 0.39, both p's < 0.01). There were no group differences between SUV/ADC values of DTC-positive/-negative patients. CONCLUSIONS [18F]FDG-PET/MRI may give a first impression of BC-receptor status and BC-tumor biology during initial staging by measuring glucose metabolism but cannot distinguish between DTC-positive/-negative patients and replace biopsy.
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Affiliation(s)
- Kai Jannusch
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, 40225 Dusseldorf, Germany
| | - Ann-Kathrin Bittner
- Department Gynecology and Obstetrics, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany
| | - Nils Martin Bruckmann
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, 40225 Dusseldorf, Germany
| | - Janna Morawitz
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, 40225 Dusseldorf, Germany
| | - Cleo Stieglitz
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, 40225 Dusseldorf, Germany
| | - Frederic Dietzel
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, 40225 Dusseldorf, Germany
| | - Harald H Quick
- High-Field and Hybrid MR Imaging, University Hospital Essen, University Duisburg-Essen, 45147 Essen, Germany
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, 45141 Essen, Germany
| | - Hideo A Baba
- Institute of Pathology, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany
| | - Ken Herrmann
- Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany
| | - Lale Umutlu
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany
| | - Gerald Antoch
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, 40225 Dusseldorf, Germany
| | - Julian Kirchner
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, 40225 Dusseldorf, Germany
| | - Sabine Kasimir-Bauer
- Department Gynecology and Obstetrics, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany
| | - Oliver Hoffmann
- Department Gynecology and Obstetrics, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany
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Yang X, Xi X, Yang L, Xu C, Song Z, Nie X, Qiao L, Li C, Shi Q, Yin Y. Multi-modality relation attention network for breast tumor classification. Comput Biol Med 2022; 150:106210. [PMID: 37859295 DOI: 10.1016/j.compbiomed.2022.106210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 09/05/2022] [Accepted: 10/09/2022] [Indexed: 11/03/2022]
Abstract
Automatic breast image classification plays an important role in breast cancer diagnosis, and multi-modality image fusion may improve classification performance. However, existing fusion methods ignore relevant multi-modality information in favor of improving the discriminative ability of single-modality features. To improve classification performance, this paper proposes a multi-modality relation attention network with consistent regularization for breast tumor classification using diffusion-weighted imaging (DWI) and apparent dispersion coefficient (ADC) images. Within the proposed network, a novel multi-modality relation attention module improves the discriminative ability of single-modality features by exploring the correlation information between two modalities. In addition, a module ensures the classification consistency of ADC and DWI modality, thus improving robustness to noise. Experimental results on our database demonstrate that the proposed method is effective for breast tumor classification, and outperforms existing multi-modality fusion methods. The AUC, accuracy, specificity, and sensitivity are 85.1%, 86.7%, 83.3%, and 88.9% respectively.
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Affiliation(s)
- Xiao Yang
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China
| | - Xiaoming Xi
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China.
| | - Lu Yang
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China
| | - Chuanzhen Xu
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China
| | - Zuoyong Song
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China
| | - Xiushan Nie
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China
| | - Lishan Qiao
- School of Mathematical Sciences, Liaocheng University, Liaocheng, 252000, China
| | - Chenglong Li
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China
| | - Qinglei Shi
- Diagnosis Imaging, Siemens Healthcare Ltd, Beijing, 100102, China
| | - Yilong Yin
- School of Software, Shandong University, Jinan, 250101, China
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Lee J, Kim SH, Kim Y, Park J, Park GE, Kang BJ. Radiomics Nomogram: Prediction of 2-Year Disease-Free Survival in Young Age Breast Cancer. Cancers (Basel) 2022; 14:cancers14184461. [PMID: 36139620 PMCID: PMC9497155 DOI: 10.3390/cancers14184461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/04/2022] [Accepted: 09/11/2022] [Indexed: 11/16/2022] Open
Abstract
This study aimed to predict early breast cancer recurrence in women under 40 years of age using radiomics signature and clinicopathological information. We retrospectively investigated 155 patients under 40 years of age with invasive breast cancer who underwent MRI and surgery. Through stratified random sampling, 111 patients were assigned as the training set, and 44 were assigned as the validation set. Recurrence-associated factors were investigated based on recurrence within 5 years during the total follow-up period. A Rad-score was generated through texture analysis (3D slicer, ver. 4.8.0) of breast MRI using the least absolute shrinkage and selection operator Cox regression model. The Rad-score showed a significant association with disease-free survival (DFS) in the training set (p = 0.003) and validation set (p = 0.020) in the Kaplan–Meier analysis. The nomogram was generated through Cox proportional hazards models, and its predictive ability was validated. The nomogram included the Rad-score and estrogen receptor negativity as predictive factors and showed fair DFS predictive ability in both the training and validation sets (C-index 0.63, 95% CI 0.45–0.79). In conclusion, the Rad-score can predict the disease recurrence of invasive breast cancer in women under 40 years of age, and the Rad-score-based nomogram showed reasonably high DFS predictive ability, especially within 2 years of surgery.
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Affiliation(s)
- Jeongmin Lee
- Department of Radiology, College of Medicine, Seoul Saint Mary’s Hospital, The Catholic University of Korea, Seoul 06591, Korea
| | - Sung Hun Kim
- Department of Radiology, College of Medicine, Seoul Saint Mary’s Hospital, The Catholic University of Korea, Seoul 06591, Korea
- Correspondence: ; Tel.: +82-2-2258-6250
| | - Yelin Kim
- Department of Statistics and Data Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 06591, Korea
| | - Jaewoo Park
- Department of Statistics and Data Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 06591, Korea
- Department of Applied Statistics, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 06591, Korea
| | - Ga Eun Park
- Department of Radiology, College of Medicine, Seoul Saint Mary’s Hospital, The Catholic University of Korea, Seoul 06591, Korea
| | - Bong Joo Kang
- Department of Radiology, College of Medicine, Seoul Saint Mary’s Hospital, The Catholic University of Korea, Seoul 06591, Korea
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Hegazy R, Azzam H. Value of apparent diffusion coefficient factor in correlation with the molecular subtypes, tumor grade, and expression of Ki-67 in breast cancer. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022. [DOI: 10.1186/s43055-022-00881-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Breast cancer is known to be the most common cancer in women; in the last decade, contrast-enhanced magnetic resonance imaging has become an important tool in the diagnosis of cancer breast. Numerous studies have analyzed associations between imaging and histopathological features as well as the proliferation potential of breast cancer. The purpose of this study was to evaluate the relationship between the apparent diffusion coefficient (ADC) and expression of Ki-67 as well as tumor molecular subtype in breast cancer.
Results
No significant difference between the mean ADC value of tumors of grade I, II, and III was found. However, there was a significant difference between the mean ADC value of tumors of molecular type A and molecular type B (P = 0.000), HER2 overexpression (P = 0.018), and TN (P = 0.000), respectively. However, there was no significant difference between molecular type B, HER2 overexpression and TN. Also, no significant difference was found between the Ki-67 value of tumors of grade I, II, and III. Yet there was a significant difference between the mean ADC value of tumors of molecular type A and molecular type B (P = 0.000), HER2 overexpression (P = 0.014), and TN (P = 0.000), respectively. However, there was no significant difference between molecular type B, HER2 overexpression, and TN.
Conclusions
There is a significant inverse correlation between ADC values and Ki-67 expression. DWI and Ki-67 could be a good discriminator between tumors of molecular subtype A from other subtypes, yet it did not show a correlation with the tumor grade.
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Wang Z, Ren GY, Yin Q, Wang Q. Correlation of magnetic resonance imaging quantitative parameters and apparent diffusion coefficient value with pathological breast cancer. World J Clin Cases 2022; 10:7333-7340. [PMID: 36158015 PMCID: PMC9353886 DOI: 10.12998/wjcc.v10.i21.7333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/18/2022] [Accepted: 06/26/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND China ranks 120th worldwide for the incidence of breast cancer and 163rd for mortality. Early screening, diagnosis, and timely determination of the optimal treatment plan can help ensure clinical efficacy and prognosis.
AIM To investigate the relationship between quantitative magnetic resonance imaging parameters, apparent diffusion coefficient value, pathological immunohistochemical status, and patient prognosis.
METHODS A total of 108 patients with breast cancer (breast cancer group) and 110 patients with benign breast tumors (benign group) confirmed by pathological examination at our Hospital from September 2013 to August 2016 were selected. All patients had undergone preoperative magnetic resonance imaging (MRI) examinations, and the quantitative parameters of MRI and apparent diffusion coefficient (ADC) values for the two groups were compared. The MRI quantitative parameters and ADC values of patients with different estrogen receptor (ER), progesterone receptor, and human epidermal growth factor receptor-2 expression were statistically analyzed. The relationship between the quantitative parameters of MRI and ADC values and patient recurrence was analyzed using receiver operating curves.
RESULTS The measured values of the quantitative parameters of MRI- Ktrans, Kep, and Ve in the breast cancer group were higher than those in the benign group; the ADC value in the breast cancer group was lower than that in the benign group, and the difference was statistically significant (P < 0.05). The Ktrans, Ve, and ADC values in patients with ER-positive breast cancer were significantly lower than those in patients with negative ER expression (P < 0.05). After 5 years of follow-up, 22 patients with breast cancer experienced postoperative recurrence. The Kep, Ve, and ADC values of the recurrence group were significantly lower than those of the non-recurrence group, and the difference was statistically significant (P < 0.05).
CONCLUSION MRI quantitative parameters and ADC are related to the expression of breast cancer-related immunological receptor factors and have certain clinical value in assessing postoperative recurrence in patients.
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Affiliation(s)
- Zhe Wang
- Department of Medical Imaging, The No. 2 Hospital of Baoding, Baoding 071051, Hebei Province, China
| | - Guan-Ying Ren
- Department of Medical Oncology, Affiliated Hospital of Hebei University, Hebei Key Laboratory of Cancer Radiotherapy and Chemotherapy, Baoding 071000, Hebei Province, China
| | - Qian Yin
- Department of Medical Imaging, The No. 2 Hospital of Baoding, Baoding 071051, Hebei Province, China
| | - Qian Wang
- Department of Medical Imaging, The No. 2 Hospital of Baoding, Baoding 071051, Hebei Province, China
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Prediction for Distant Metastasis of Breast Cancer Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging Images under Deep Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6126061. [PMID: 35720877 PMCID: PMC9200535 DOI: 10.1155/2022/6126061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/05/2022] [Accepted: 05/14/2022] [Indexed: 11/18/2022]
Abstract
This research aimed to explore the effect of using magnetic resonance imaging (MRI) radiomic features to establish a model for predicting distant metastasis under dynamic contrast-enhanced MRI imaging with deep learning algorithms. The deep learning algorithm was used to segment the images. A total of 96 cases with 100 lesions were included in the metastatic group, including 2 cases of bifocal breast cancer and 2 cases of multifocal breast cancer. There were 192 cases in the nonmetastatic group, with 197 lesions, including 5 cases of multifocal breast cancer. After dynamic contrast-enhancement, the morphological features and grayscale statistical features were extracted from the lesions to establish a prediction model through sum-sum check and feature dimension reduction. The accuracy, sensitivity, specificity, and area under receiver operator characteristic curve (AUC) of prediction models based only on imaging features were compared with those created by combining radiomic features with clinical and pathological features. The created predictive model based on radiomic features for distant metastases in breast cancer showed a sensitivity of 66.7%, a specificity of 84.2%, an accuracy of 78.3%, and an AUC of 0.744. The sensitivity of the prediction model for distant metastasis of breast cancer was 67.7%, the specificity was 86.8%, the accuracy was 80.5%, and the AUC was 0.763. Bone, lung, and liver were the most common distant metastatic sites of breast cancer. Under the dynamic contrast-enhanced MRI of deep learning, the prediction model combining radiomic features with clinical and pathological features showed better predictive performance.
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Thakran S, Gupta RK, Singh A. Characterization of breast tumors using machine learning based upon multiparametric magnetic resonance imaging features. NMR IN BIOMEDICINE 2022; 35:e4665. [PMID: 34962326 DOI: 10.1002/nbm.4665] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 11/22/2021] [Accepted: 11/23/2021] [Indexed: 06/14/2023]
Abstract
Magnetic resonance imaging (MRI) is playing an important role in the classification of breast tumors. MRI can be used to obtain multiparametric (mp) information, such as structural, hemodynamic, and physiological information. Quantitative analysis of mp-MRI data has shown potential in improving the accuracy of breast tumor classification. In general, a large set of quantitative and texture features can be generated depending upon the type of methodology used. A suitable combination of selected quantitative and texture features can further improve the accuracy of tumor classification. Machine learning (ML) classifiers based upon features derived from MRI data have shown potential in tumor classification. There is a need for further research studies on selecting an appropriate combination of features and evaluating the performance of different ML classifiers for accurate classification of breast tumors. The objective of the current study was to develop and optimize an ML framework based upon mp-MRI features for the characterization of breast tumors (malignant vs. benign and low- vs. high-grade). This study included the breast mp-MRI data of 60 female patients with histopathology results. A total of 128 features were extracted from the mp-MRI tumor data followed by features selection. Five ML classifiers were evaluated for tumor classification using 10-fold crossvalidation with 10 repetitions. The support vector machine (SVM) classifier based on optimum features selected using a wrapper method with an adaptive boosting (AdaBoost) technique provided the highest sensitivity (0.96 ± 0.03), specificity (0.92 ± 0.09), and accuracy (94% ± 2.91%) in the classification of malignant versus benign tumors. This method also provided the highest sensitivity (0.94 ± 0.07), specificity (0.80 ± 0.05), and accuracy (90% ± 5.48%) in the classification of low- versus high-grade tumors. These findings suggest that the SVM classifier outperformed other ML methods in the binary classification of breast tumors.
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Affiliation(s)
- Snekha Thakran
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Rakesh Kumar Gupta
- Department of Radiology, Fortis Memorial Research Institute, Gurgaon, India
| | - Anup Singh
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
- Department for Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India
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Meyer HJ, Wienke A, Surov A. Diffusion-Weighted Imaging of Different Breast Cancer Molecular Subtypes: A Systematic Review and Meta-Analysis. Breast Care (Basel) 2022; 17:47-54. [PMID: 35355697 PMCID: PMC8914237 DOI: 10.1159/000514407] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 01/08/2021] [Indexed: 02/03/2023] Open
Abstract
Background Magnetic resonance imaging can be used to diagnose breast cancer (BC). Diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) can be used to reflect tumor microstructure. Objectives This analysis aimed to compare ADC values between molecular subtypes of BC based on a large sample of patients. Method The MEDLINE library and Scopus database were screened for the associations between ADC and molecular subtypes of BC up to April 2020. The primary end point of the systematic review was the ADC value in different BC subtypes. Overall, 28 studies were included. Results The included studies comprised a total of 2,990 tumors. Luminal A type was diagnosed in 865 cases (28.9%), luminal B in 899 (30.1%), human epidermal growth factor receptor (Her2)-enriched in 597 (20.0%), and triple-negative in 629 (21.0%). The mean ADC values of the subtypes were as follows: luminal A: 0.99 × 10-3 mm2/s (95% CI 0.94-1.04), luminal B: 0.97 × 10-3 mm2/s (95% CI 0.89-1.05), Her2-enriched: 1.02 × 10-3 mm2/s (95% CI 0.95-1.08), and triple-negative: 0.99 × 10-3 mm2/s (95% CI 0.91-1.07). Conclusions ADC values cannot be used to discriminate between molecular subtypes of BC.
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Affiliation(s)
- Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Andreas Wienke
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Alexey Surov
- Department of Radiology and Nuclear Medicine, University of Magdeburg, Magdeburg, Germany
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Wang C, Chen X, Luo H, Liu Y, Meng R, Wang M, Liu S, Xu G, Ren J, Zhou P. Development and Internal Validation of a Preoperative Prediction Model for Sentinel Lymph Node Status in Breast Cancer: Combining Radiomics Signature and Clinical Factors. Front Oncol 2021; 11:754843. [PMID: 34820327 PMCID: PMC8606782 DOI: 10.3389/fonc.2021.754843] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 10/14/2021] [Indexed: 12/24/2022] Open
Abstract
Purpose To develop and internally validate a nomogram combining radiomics signature of primary tumor and fibroglandular tissue (FGT) based on pharmacokinetic dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical factors for preoperative prediction of sentinel lymph node (SLN) status in breast cancer patients. Methods This study retrospectively enrolled 186 breast cancer patients who underwent pretreatment pharmacokinetic DCE-MRI with positive (n = 93) and negative (n = 93) SLN. Logistic regression models and radiomics signatures of tumor and FGT were constructed after feature extraction and selection. The radiomics signatures were further combined with independent predictors of clinical factors for constructing a combined model. Prediction performance was assessed by receiver operating characteristic (ROC), calibration, and decision curve analysis. The areas under the ROC curve (AUCs) of models were corrected by 1,000-times bootstrapping method and compared by Delong's test. The added value of each independent model or their combinations was also assessed by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. This report referred to the "Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis" (TRIPOD) statement. Results The AUCs of the tumor radiomic model (eight features) and the FGT radiomic model (three features) were 0.783 (95% confidence interval [CI], 0.717-0.849) and 0.680 (95% CI, 0.604-0.757), respectively. A higher AUC of 0.799 (95% CI, 0.737-0.862) was obtained by combining tumor and FGT radiomics signatures. By further combining tumor and FGT radiomics signatures with progesterone receptor (PR) status, a nomogram was developed and showed better discriminative ability for SLN status [AUC 0.839 (95% CI, 0.783-0.895)]. The IDI and NRI indices also showed significant improvement when combining tumor, FGT, and PR compared with each independent model or a combination of any two of them (all p < 0.05). Conclusion FGT and clinical factors improved the prediction performance of SLN status in breast cancer. A nomogram integrating the DCE-MRI radiomics signature of tumor and FGT and PR expression achieved good performance for the prediction of SLN status, which provides a potential biomarker for clinical treatment decision-making.
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Affiliation(s)
- Chunhua Wang
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoyu Chen
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Hongbing Luo
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuanyuan Liu
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Ruirui Meng
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Wang
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Siyun Liu
- Pharmaceutical Diagnostics, General Electric (GE) Company (Healthcare), Beijing, China
| | - Guohui Xu
- Department of Interventional Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jing Ren
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Peng Zhou
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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Feng W, Gao Y, Lu XR, Xu YS, Guo ZZ, Lei JQ. Correlation between molecular prognostic factors and magnetic resonance imaging intravoxel incoherent motion histogram parameters in breast cancer. Magn Reson Imaging 2021; 85:262-270. [PMID: 34740800 DOI: 10.1016/j.mri.2021.10.027] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 07/26/2021] [Accepted: 10/17/2021] [Indexed: 11/16/2022]
Abstract
OBJECTIVE To explore the efficacy of the quantitative parameter histogram analysis of intravoxel incoherent motion (IVIM) for different molecular prognostic factors of breast cancer. MATERIALS AND METHODS A total of 72 patients with breast cancer who were confirmed by surgical pathology and underwent preoperative magnetic resonance imaging (MRI) were analyzed retrospectively. A region of interest (ROI) was drawn in each slice of the IVIM images. Whole-tumor histogram parameters were obtained with Firevoxel's software by accumulating all ROIs. Next, Kolmogorov-Smirnov test, Student's t-test, Mann-Whitney U test, receiver operating characteristic curve analysis and spearman rank correlation analysis were used to assess the relationship between histogram parameters and molecular prognostic factors of breast cancer. RESULTS Among estrogen receptor (ER)-negative ROCs, the apparent diffusion coefficient (ADC) 10th percentile had the highest ROC of 0.792, with a cut-off value of 0.788 × 10-3 mm2/s, and sensitivity and specificity of 0.714 and 0.867, respectively. The negative correlation between lymph node metastasis status and ADC standard deviation was significant (ρ = -0.44, the correlation coefficients was represented by ρ). Positive correlations were observed between hormonal expression of ER and progesterone receptor (PR) with heterogeneity metrics of ADC or perfusion fraction (f), such as ADC inhomogeneity (ρ = 0.37, ρ = 0.29) and f skewness (ρ = 0.32, ρ = 0.28). Negative correlations were observed with numerical metrics, such as the ADC median (ρ = -0.31, ρ = -0.34) and f 45th percentile (ρ = -0.35, ρ = -0.28). The positive correlations between human epidermal receptor factor-2 (HER2) and pseudo-diffusivity (Dp) numerical metrics, Ki-67 expression, and heterogeneity metrics of Dp were high. CONCLUSIONS The ADC 10th percentile had the largest area under the curve in the ER-negative ROC analysis, and the ADC standard deviation was the most valuable in the correlation analysis of lymph node metastasis. Whole-lesion quantitative histogram parameters of IVIM could, therefore, provide a scientific basis for radiomics to further guide clinical practice in the prognosis of breast cancer.
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Affiliation(s)
- Wen Feng
- The First School of Clinical Medicine, Lanzhou University, Lanzhou 730000, Gansu, China; Department of Radiology, the First hospital of Lanzhou University, Lanzhou 730000, Gansu, China
| | - Ya Gao
- Department of Radiology, the First hospital of Lanzhou University, Lanzhou 730000, Gansu, China
| | - Xing-Ru Lu
- Department of Radiology, the First hospital of Lanzhou University, Lanzhou 730000, Gansu, China
| | - Yong-Sheng Xu
- Department of Radiology, the First hospital of Lanzhou University, Lanzhou 730000, Gansu, China
| | - Zhuan-Zhuan Guo
- Department of Radiology, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, Shanxi, China
| | - Jun-Qiang Lei
- Department of Radiology, the First hospital of Lanzhou University, Lanzhou 730000, Gansu, China.
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Santucci D, Faiella E, Calabrese A, Beomonte Zobel B, Ascione A, Cerbelli B, Iannello G, Soda P, de Felice C. On the Additional Information Provided by 3T-MRI ADC in Predicting Tumor Cellularity and Microscopic Behavior. Cancers (Basel) 2021; 13:cancers13205167. [PMID: 34680316 PMCID: PMC8534264 DOI: 10.3390/cancers13205167] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/11/2021] [Accepted: 10/13/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND to evaluate whether Apparent Diffusion Coefficient (ADC) values of invasive breast cancer, provided by 3T Diffusion Weighted-Images (DWI), may represent a non-invasive predictor of pathophysiologic tumor aggressiveness. METHODS 100 Patients with histologically proven invasive breast cancers who underwent a 3T-MRI examination were included in the study. All MRI examinations included dynamic contrast-enhanced and DWI/ADC sequences. ADC value were calculated for each lesion. Tumor grade was determined according to the Nottingham Grading System, and immuno-histochemical analysis was performed to assess molecular receptors, cellularity rate, on both biopsy and surgical specimens, and proliferation rate (Ki-67 index). Spearman's Rho test was used to correlate ADC values with histological (grading, Ki-67 index and cellularity) and MRI features. ADC values were compared among the different grading (G1, G2, G3), Ki-67 (<20% and >20%) and cellularity groups (<50%, 50-70% and >70%), using Mann-Whitney and Kruskal-Wallis tests. ROC curves were performed to demonstrate the accuracy of the ADC values in predicting the grading, Ki-67 index and cellularity groups. RESULTS ADC values correlated significantly with grading, ER receptor status, Ki-67 index and cellularity rates. ADC values were significantly higher for G1 compared with G2 and for G1 compared with G3 and for Ki-67 < 20% than Ki-67 > 20%. The Kruskal-Wallis test showed that ADC values were significantly different among the three grading groups, the three biopsy cellularity groups and the three surgical cellularity groups. The best ROC curves were obtained for the G3 group (AUC of 0.720), for G2 + G3 (AUC of 0.835), for Ki-67 > 20% (AUC of 0.679) and for surgical cellularity rate > 70% (AUC of 0.805). CONCLUSIONS 3T-DWI ADC is a direct predictor of cellular aggressiveness and proliferation in invasive breast carcinoma, and can be used as a supporting non-invasive factor to characterize macroscopic lesion behavior especially before surgery.
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Affiliation(s)
- Domiziana Santucci
- Department of Radiology, University of Rome “Campus Bio-Medico”, Via Alvaro del Portillo 21, 00128 Rome, Italy; (E.F.); (B.B.Z.)
- Correspondence: ; Tel.: +39-333-5376-594
| | - Eliodoro Faiella
- Department of Radiology, University of Rome “Campus Bio-Medico”, Via Alvaro del Portillo 21, 00128 Rome, Italy; (E.F.); (B.B.Z.)
| | - Alessandro Calabrese
- Department of Radiology, University of Rome “Sapienza”, Viale del Policlinico 155, 00161 Rome, Italy; (A.C.); (C.d.F.)
| | - Bruno Beomonte Zobel
- Department of Radiology, University of Rome “Campus Bio-Medico”, Via Alvaro del Portillo 21, 00128 Rome, Italy; (E.F.); (B.B.Z.)
| | - Andrea Ascione
- Department of Radiological, Oncological and Pathological Science, University of Rome “Sapienza”, Viale del Policlinico 155, 00161 Rome, Italy; (A.A.); (B.C.)
| | - Bruna Cerbelli
- Department of Radiological, Oncological and Pathological Science, University of Rome “Sapienza”, Viale del Policlinico 155, 00161 Rome, Italy; (A.A.); (B.C.)
| | - Giulio Iannello
- Unit of Computer Systems and Bioinformatics, Department of Engineering, University of Rome “Campus Bio-Medico”, Via Alvaro del Portillo 21, 00128 Rome, Italy; (G.I.); (P.S.)
| | - Paolo Soda
- Unit of Computer Systems and Bioinformatics, Department of Engineering, University of Rome “Campus Bio-Medico”, Via Alvaro del Portillo 21, 00128 Rome, Italy; (G.I.); (P.S.)
| | - Carlo de Felice
- Department of Radiology, University of Rome “Sapienza”, Viale del Policlinico 155, 00161 Rome, Italy; (A.C.); (C.d.F.)
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The Impact of Tumor Edema on T2-Weighted 3T-MRI Invasive Breast Cancer Histological Characterization: A Pilot Radiomics Study. Cancers (Basel) 2021; 13:cancers13184635. [PMID: 34572862 PMCID: PMC8464682 DOI: 10.3390/cancers13184635] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 09/11/2021] [Accepted: 09/14/2021] [Indexed: 12/24/2022] Open
Abstract
Simple Summary Breast cancer is the most common cancer in women worldwide. Currently the use of MR is mandatory in staging phase. The standard protocol includes T2-weighted sequences for morphology and signal analysis, T1-weighted images for adding information (i.e., ematic or adipous components), diffusion-weighted sequences which provide information on tissue cellularity, and dynamic post-contrast sequences useful for detecting and locating lesions. Although not considered among the main prognostic factors in current guidelines, tumor-associated edema provides useful information on tumor aggressiveness, and has been shown to be associated with the main histological tumor characteristics. With this work, entitled “The Impact of Tumor Edema on T2-weighted 3T-MRI Invasive Breast Cancer Histological Characterization: a Pilot Radiomics Study”, we want to demonstrate that radiomics edema, based on algorithms that allow the extraction of imaging features not visible to the human eye, can further increase the accuracy in the prediction of histological factors compared to the use of traditional information only. Abstract Background: to evaluate the contribution of edema associated with histological features to the prediction of breast cancer (BC) prognosis using T2-weighted MRI radiomics. Methods: 160 patients who underwent staging 3T-MRI from January 2015 to January 2019, with 164 histologically proven invasive BC lesions, were retrospectively reviewed. Patient data (age, menopausal status, family history, hormone therapy), tumor MRI-features (location, margins, enhancement) and histological features (histological type, grading, ER, PgR, HER2, Ki-67 index) were collected. Of the 160 MRI exams, 120 were considered eligible, corresponding to 127 lesions. T2-MRI were used to identify edema, which was classified in four groups: peritumoral, pre-pectoral, subcutaneous, or diffuse. A semi-automatic segmentation of the edema was performed for each lesion, using 3D Slicer open-source software. Main radiomics features were extracted and selected using a wrapper selection method. A Random Forest type classifier was trained to measure the performance of predicting histological factors using semantic features (patient data and MRI features) alone and semantic features associated with edema radiomics features. Results: edema was absent in 37 lesions and present in 127 (62 peritumoral, 26 pre-pectoral, 16 subcutaneous, 23 diffuse). The AUC-classifier obtained by associating edema radiomics with semantic features was always higher compared to the AUC-classifier obtained from semantic features alone, for all five histological classes prediction (0.645 vs. 0.520 for histological type, 0.789 vs. 0.590 for grading, 0.487 vs. 0.466 for ER, 0.659 vs. 0.546 for PgR, and 0.62 vs. 0.573 for Ki67). Conclusions: radiomic features extracted from tumor edema contribute significantly to predicting tumor histology, increasing the accuracy obtained from the combination of patient clinical characteristics and breast imaging data.
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Azzam H, Mansour S, Salem N, El-Assaly H. Correlative study between ADC value and grading of invasive breast cancer. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2020. [DOI: 10.1186/s43055-019-0124-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
AbstractBackgroundStudying breast carcinoma is of great importance as it is the commonest female malignancy. Accurate preoperative assessment of disease characteristics and prognosis would be of great help in the diagnosis and treatment planning of breast cancer. The aim of this study was to evaluate the role of the apparent diffusion coefficient (ADC) value in detecting the grading of invasive breast carcinoma prior to management.ResultsThere was a significant difference between the mean ADC value of tumors of grade I and III (p = 0.001) and between grade I and II (p = 0.002). However, there was no significant difference between grade II and III (p = 0.979). High ADC values were associated with low-grade tumors. The mean ADC value of 0.93 × 10–3 mm2/s showed sensitivity 98%, specificity 100%, PPV 100%, NPV 83.3%, accuracy 98.2%, AUC = 0.994, and 95% confidence interval of 0.978 to 1.000.ConclusionDWI is a contrast-free modality that allows for both morphological and quantitative analysis. ADC value may not be the proper modality to determine the prognosis of breast cancer due to overlap values, yet it could be a good discriminator between low- and high-grade tumors and hence predictor of breast cancer cells that would respond to chemotherapy.
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Tezcan S, Ozturk FU, Uslu N, Akcay EY. The Role of Combined Diffusion-Weighted Imaging and Dynamic Contrast-Enhanced MRI for Differentiating Malignant From Benign Breast Lesions Presenting Washout Curve. Can Assoc Radiol J 2020; 72:460-469. [PMID: 32157892 DOI: 10.1177/0846537120907098] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
PURPOSE The aim of this study is to evaluate the diagnostic performance of combined breast magnetic resonance imaging (MRI) protocol including dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI) in patients with enhancing lesions that demonstrated washout curve and to determine whether applying apparent diffusion coefficient (ADC) cutoff value could improve the diagnostic value of breast MRI. METHODS The retrospective study included 116 patients with 116 suspicious breast lesions, which showed washout curve on DCE-MRI, who underwent subsequent biopsy. Morphologic characteristics on DCE-MRI and ADC values on DWI were evaluated. Apparent diffusion coefficient values and morphologic features of benign and malignant lesions were compared. Diagnostic values of DCE-MRI and combined MRI, including DCE-MRI and DWI (applying an ADC cutoff value) for distinguishing malignancy from benign lesions, were calculated. RESULTS Of the 116 breast lesions, 79 were malignant and 37 were benign. The ADC value of malignant tumors (median ADC, 0.72 × 10-3 mm2/s) was significantly lower than that of benign lesions (median ADC, 1.03 × 10-3 mm2/s; P < .000). The sensitivity and specificity of an ADC cutoff value of 0.89 × 10-3 mm2/s were 92% and 95%, respectively. Dynamic contrast-enhanced MRI alone presented 100% sensitivity and 59.4% specificity. Adding an ADC cutoff value of 0.89 × 10-3 mm2/s provided 100% sensitivity and 81% specificity, which would have prevented biopsy for 21.6% of benign lesions without missing any malignancies. CONCLUSION Applying an ADC cutoff value to DCE-MRI provides an improvement in the diagnostic value of breast MRI for differentiating among lesions presenting washout curve.
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Affiliation(s)
- Sehnaz Tezcan
- Koru Hospital, Kızılırmak Mah, Cukurambar, Ankara, Turkey
| | - Funda Ulu Ozturk
- Radiology Department, Baskent University Hospital, Bahcelievler, Ankara, Turkey
| | - Nihal Uslu
- Radiology Department, Baskent University Hospital, Bahcelievler, Ankara, Turkey
| | - Eda Yilmaz Akcay
- Pathology Department, Baskent University Hospital, Bahcelievler, Ankara, Turkey
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Choi EJ, Youk JH, Choi H, Song JS. Dynamic contrast-enhanced and diffusion-weighted MRI of invasive breast cancer for the prediction of sentinel lymph node status. J Magn Reson Imaging 2020; 51:615-626. [PMID: 31313393 DOI: 10.1002/jmri.26865] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2019] [Revised: 06/26/2019] [Accepted: 06/27/2019] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Although sentinel lymph node biopsy (SLNB) is the current standard for identifying lymph metastasis in breast cancer patients, there are complications of SLNB. PURPOSE To evaluate preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) of invasive breast cancer for predicting sentinel lymph node metastasis. STUDY TYPE Retrospective. POPULATION In all, 309 patients who underwent clinically node-negative invasive breast cancer surgery FIELD STRENGTH/SEQUENCE: 3.0T, DCE-MRI, DWI. ASSESSMENT We collected clinicopathologic variables (age, histologic and nuclear grade, extensive intraductal carcinoma component, lymphovascular invasion, and immunohistochemical profiles) and preoperative MRI features (tumor size, background parenchymal enhancement, internal enhancement, adjacent vessel sign, whole-breast vascularity, initial enhancement pattern, kinetic curve types, quantitative kinetic parameters, tumoral apparent diffusion coefficient [ADC], peritumoral maximal ADC, and peritumoral-tumoral ADC ratio). STATISTICAL TESTS Multivariate logistic regressions were performed to determine independent variables associated with SLN metastasis, and the area under the receiver operating characteristic curve (AUC) was analyzed for those variables. RESULTS 41 (13.3%) of the patients showed SLN metastasis. With MRI, tumor size (odds ratio [OR], 1.11; 95% confidence interval [CI], 1.06-1.17), heterogeneous (OR, 5.33; 95% CI, 1.71-16.58), and rim (OR, 15.54; 95% CI, 2.12-113.72) enhancement and peritumoral-tumoral ADC ratio (OR, 72.79; 95% CI, 7.15-740.82) were independently associated with SLN metastasis. Clinicopathologic variables independently associated with SLN metastasis included age (OR, 0.96; 95% CI, 0.92-0.99) and CD31 (OR, 2.90; 95% CI, 1.04-8.92). The area under the curve (AUC) of MRI features (0.80; 95% CI, 0.73-0.87) was significantly higher than for clinicopathologic variables (0.68; 95% CI, 0.60-0.77; P = 0.048) and was barely below statistical significance for combined MRI features with clinicopathologic variables (0.84; 95% CI 0.78-0.90, P = 0.057). DATA CONCLUSION Preoperative internal enhancement on DCE-MRI and peritumoral-tumoral ADC ratio on DWI might be useful for predicting SLN metastasis in patients with invasive breast cancer. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:615-626.
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Affiliation(s)
- Eun Jung Choi
- Department of Radiology, Research Institute of Clinical Medicine of Chonbuk National University - Biomedical Research Institute of Chonbuk National University Hospital, Chonbuk National University Medical School, Jeonju City, South Korea
| | - Ji Hyun Youk
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Hyemi Choi
- Department of Statistics, Research Institute of Applied Statistics, Chonbuk National University, Jeonbuk, 54896, South Korea
| | - Ji Soo Song
- Department of Radiology, Research Institute of Clinical Medicine of Chonbuk National University - Biomedical Research Institute of Chonbuk National University Hospital, Chonbuk National University Medical School, Jeonju City, South Korea
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Surov A, Meyer HJ, Wienke A. Can apparent diffusion coefficient (ADC) distinguish breast cancer from benign breast findings? A meta-analysis based on 13 847 lesions. BMC Cancer 2019; 19:955. [PMID: 31615463 PMCID: PMC6794799 DOI: 10.1186/s12885-019-6201-4] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Accepted: 09/24/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND The purpose of the present meta-analysis was to provide evident data about use of Apparent Diffusion Coefficient (ADC) values for distinguishing malignant and benign breast lesions. METHODS MEDLINE library and SCOPUS database were screened for associations between ADC and malignancy/benignancy of breast lesions up to December 2018. Overall, 123 items were identified. The following data were extracted from the literature: authors, year of publication, study design, number of patients/lesions, lesion type, mean value and standard deviation of ADC, measure method, b values, and Tesla strength. The methodological quality of the 123 studies was checked according to the QUADAS-2 instrument. The meta-analysis was undertaken by using RevMan 5.3 software. DerSimonian and Laird random-effects models with inverse-variance weights were used without any further correction to account for the heterogeneity between the studies. Mean ADC values including 95% confidence intervals were calculated separately for benign and malign lesions. RESULTS The acquired 123 studies comprised 13,847 breast lesions. Malignant lesions were diagnosed in 10,622 cases (76.7%) and benign lesions in 3225 cases (23.3%). The mean ADC value of the malignant lesions was 1.03 × 10- 3 mm2/s and the mean value of the benign lesions was 1.5 × 10- 3 mm2/s. The calculated ADC values of benign lesions were over the value of 1.00 × 10- 3 mm2/s. This result was independent on Tesla strength, choice of b values, and measure methods (whole lesion measure vs estimation of ADC in a single area). CONCLUSION An ADC threshold of 1.00 × 10- 3 mm2/s can be recommended for distinguishing breast cancers from benign lesions.
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Affiliation(s)
- Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Liebigstr. 20, 04103, Leipzig, Germany. .,Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081, Ulm, Germany.
| | - Hans Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Liebigstr. 20, 04103, Leipzig, Germany
| | - Andreas Wienke
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Magdeburger Str. 8, 06097, Halle, Germany
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Tezcan Ş, Uslu N, Öztürk FU, Akçay EY, Tezcaner T. Diffusion-Weighted Imaging of Breast Cancer: Correlation of the Apparent Diffusion Coefficient Value with Pathologic Prognostic Factors. Eur J Breast Health 2019; 15:262-267. [PMID: 31620686 DOI: 10.5152/ejbh.2019.4860] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 06/24/2019] [Indexed: 01/13/2023]
Abstract
Objective The aim was to evaluate relationship between apparent diffusion coefficient (ADC) values with pathologic prognostic factors in breast carcinoma (BC). Materials and Methods 83 patients were enrolled in this study. Prognostic factors included age, tumor size, expression of estrogen receptor (ER) and progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), nuclear grade (NG), lymph node involvement and histologic type. The relationship between ADC and prognostic factors was determined using Independent sample t-test, ANOVA, Pearson correlation and relative operating characteristics (ROC) analysis. Results There was no significant difference between ADC and prognostic factors, including age, tumor size, ER, HER2 and histologic type. The PR-positive tumors (p=0.03) and axillary lymph node involvement (p=0.000) showed a significant association with lower ADC values. The ADC values were significantly lower in high-grade tumors than low-grade tumors (p=0.000). ROC analysis showed an optimal ADC threshold of 0.66 (×10-3 mm2/s) for differentiating low-grade tumors from high-grade tumors (sensitivity, 85.5%; specificity, 81%; area under curve, 0.90). Conclusion The lower ADC values of BC were significantly associated with positive expression of PR, LN positivity and high-grade tumor. Especially, ADC values were valuable in predicting NG subgroups.
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Affiliation(s)
- Şehnaz Tezcan
- Department of Radiology, Koru Hospital, Ankara, Turkey
| | - Nihal Uslu
- Department of Radiology, Başkent University School of Medicine, Ankara, Turkey
| | - Funda Ulu Öztürk
- Department of Radiology, Başkent University School of Medicine, Ankara, Turkey
| | - Eda Yılmaz Akçay
- Department of Pathology, Başkent University School of Medicine, Ankara, Turkey
| | - Tugan Tezcaner
- Department of General Surgery, Başkent University School of Medicine, Ankara, Turkey
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Song SE, Cho KR, Seo BK, Woo OH, Park KH, Son YH, Grimm R. Intravoxel incoherent motion diffusion-weighted MRI of invasive breast cancer: Correlation with prognostic factors and kinetic features acquired with computer-aided diagnosis. J Magn Reson Imaging 2018; 49:118-130. [PMID: 30238533 DOI: 10.1002/jmri.26221] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Accepted: 05/24/2018] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND As both intravoxel incoherent motion (IVIM) modeling and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provide perfusion parameters, IVIM-derived perfusion parameters might be expected to correlate with the kinetic features from DCE-MRI. PURPOSE To investigate the association between IVIM parameters and prognostic factors and to evaluate the correlation between IVIM parameters and kinetic features in invasive breast cancer patients using computer-aided diagnosis (CAD). STUDY TYPE Retrospective. POPULATION Eighty-five patients (invasive cancers; mean size, 1.8 cm; range, 0.8-4.8 cm) who underwent diffusion-weighted imaging with 12 b-values (0-1000 s/mm2 ). FIELD STRENGTH/SEQUENCE 3.0T MRI axial, IVIM-DWI epi-sequence, and DCE-MRI. ASSESSMENT Two radiologists measured the apparent diffusion coefficient (ADC), diffusion coefficient, pseudodiffusion coefficient, and perfusion fraction (f) using IVIM modeling. Kinetic features such as peak enhancement and early and delayed enhancement profiles were acquired using CAD. STATISTICAL TESTS The correlation between the IVIM parameters and kinetic features and the association between the IVIM parameters and prognostic factors were investigated using Mann-Whitney test and Spearman correlation test. RESULTS There were no significant associations between IVIM parameters and prognostic factors. When IVIM parameters were correlated with kinetic features by CAD, both the ADC and f values showed correlations with delayed enhancement profiles. The ADC values were lower in tumors with lower persistent components (P = 0.013) and higher washout components (P = 0.045) and showed a positive correlation with persistent proportion (Spearman's rho (r) = 0.222, P = 0.041). The f value was higher in tumors with higher persistent components (P = 0.021) and showed a positive correlation with persistent proportion (r = 0.227, P = 0.029). DATA CONCLUSION This analysis revealed that IVIM-derived ADC and f values showed correlations with kinetic features at the delayed phase as assessed by CAD. These results indicate the potential of IVIM imaging biomarkers to provide information on the biological and kinetic properties of breast cancers without a contrast agent. LEVEL OF EVIDENCE 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:118-130.
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Affiliation(s)
- Sung Eun Song
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Kyu Ran Cho
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Bo Kyoung Seo
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, Ansan, Korea
| | - Ok Hee Woo
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
| | - Kyong Hwa Park
- Department of Oncology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
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Can 3.0 Tesla diffusion tensor Imaging parameters be prognostic indicators in breast cancer? Clin Imaging 2018; 51:240-247. [DOI: 10.1016/j.clinimag.2018.03.022] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Revised: 03/08/2018] [Accepted: 03/30/2018] [Indexed: 01/17/2023]
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Zhao S, Guo W, Tan R, Chen P, Li Z, Sun F, Shao G. Correlation between minimum apparent diffusion coefficient values and the histological grade of breast invasive ductal carcinoma. Oncol Lett 2018; 15:8134-8140. [PMID: 29849809 DOI: 10.3892/ol.2018.8343] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Accepted: 12/06/2018] [Indexed: 01/15/2023] Open
Abstract
The present study aimed to investigate the correlation between the minimum apparent diffusion coefficient (ADCmin) value and the histological grade of breast invasive ductal carcinoma (IDC). In total, 129 pathologically verified lesions that were subjected to dynamic breast magnetic resonance imaging and diffusion weighted imaging prior to biopsy were included. The ADCmin value was calculated and its correlation with the tumor histological grade was investigated. Tumors of lower grades demonstrated significantly higher ADCmin values as compared with tumors of higher grades (F=33.49; P<0.01). The mean ADCmin values for IDC of grades I, II and III were (1.14±0.11)×10-3, (0.99±0.12)×10-3 and (0.86±0.13)×10-3 mm2/sec, respectively. Statistically significant differences were detected in the mean ADCmin value between tumors of grades II and III (P<0.01), as well as between tumors of grades I and II (P<0.01). In addition, the mean ADCmin values for the less aggressive (grades I and II) and more aggressive (grade III) groups were (1.01±0.13)×10-3 and (0.86±0.13)×10-3 mm2/sec, respectively (t=5.76, P<0.01). In conclusion, these data indicated that the ADCmin value was correlated with the IDC histological grade, and lower ADCmin values were associated with a higher histological grade and more aggressiveness. Thus, the ADCmin value may be considered as a promising prognostic parameter in identifying tumor aggressiveness.
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Affiliation(s)
- Suhong Zhao
- Department of Radiology, The Second Hospital of Shandong University, Jinan, Shandong 250033, P.R. China
| | - Weihua Guo
- Department of Radiology, The Second Hospital of Shandong University, Jinan, Shandong 250033, P.R. China
| | - Ru Tan
- Department of Radiology, Provincial Hospital of Shandong University, Jinan, Shandong 250021, P.R. China
| | - Peipei Chen
- Department of Radiology, The Second Hospital of Shandong University, Jinan, Shandong 250033, P.R. China
| | - Zhaohua Li
- Department of Radiology, The Second Hospital of Shandong University, Jinan, Shandong 250033, P.R. China
| | - Fengguo Sun
- Department of Radiology, The Second Hospital of Shandong University, Jinan, Shandong 250033, P.R. China
| | - Guangrui Shao
- Department of Radiology, The Second Hospital of Shandong University, Jinan, Shandong 250033, P.R. China
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Abd El-Aleem RA, Abo El-Hamd E, Yousef HA, Radwan ME, Mohammed RAA. The added value of qualitative and quantitative diffusion-weighted magnetic resonance imaging (DW-MRI) in differentiating benign from malignant breast lesions. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2018. [DOI: 10.1016/j.ejrnm.2017.10.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
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Shen L, Zhou G, Tong T, Tang F, Lin Y, Zhou J, Wang Y, Zong G, Zhang L. ADC at 3.0 T as a noninvasive biomarker for preoperative prediction of Ki67 expression in invasive ductal carcinoma of breast. Clin Imaging 2018; 52:16-22. [PMID: 29501957 DOI: 10.1016/j.clinimag.2018.02.010] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2017] [Revised: 01/24/2018] [Accepted: 02/12/2018] [Indexed: 11/17/2022]
Abstract
PURPOSE To investigate the role of apparent diffusion coefficient (ADC) as an imaging biomarker for invasive ductal carcinoma (IDC) in the breast. METHODS Seventy-one patients undergoing 3.0 Tesla DWI were retrospectively enrolled. Correlations between the ADC values and prognostic factors were evaluated. RESULTS Multivariate regression analyses showed that Ki67 expression and molecular subtype were independently associated with the ADC. Discriminant analysis excluded the ADC as a good biomarker for subtype, but the mean ADC significantly distinguished Ki67-positive (low ADC) from Ki67-negative (high ADC) lesions, as observed in the in ROC curves, with a diagnostic sensitivity of 1.00 and a cut-off value of 0.97 × 10-3 mm2/s. CONCLUSION The ADC may be helpful for predicting Ki67 expression in IDC preoperatively.
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Affiliation(s)
- Lu Shen
- Department of Radiology, East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Guoxing Zhou
- Department of Radiology, East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Tong Tong
- Department of Radiology, Shanghai Cancer Center, School of Medicine, Fudan University, Shanghai, 200032, China
| | - Fei Tang
- Department of Radiology, East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Yi Lin
- Department of Radiology, East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Jie Zhou
- Department of Radiology, East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Yibin Wang
- Department of Radiology, East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Genlin Zong
- Department of Radiology, East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Lei Zhang
- Department of Radiology, East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China.
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Aydin H, Guner B, Esen Bostanci I, Bulut ZM, Aribas BK, Dogan L, Gulcelik MA. Is there any relationship between adc values of diffusion-weighted imaging and the histopathological prognostic factors of invasive ductal carcinoma? Br J Radiol 2018; 91:20170705. [PMID: 29299933 DOI: 10.1259/bjr.20170705] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
OBJECTIVE MRI is being used increasingly as a modality that can provide important information about breast cancer. Diffusion-weighted imaging (DWI) is an imaging technique from which apparent diffusion coefficient (ADC) values can be calculated in addition to obtaining important structural information which cannot be obtained from other imaging studies. We did not find any significant relationships between ADC values and prognostic factors, but did provide some explanations for conflicting results in the literature. METHODS The ADC results of 61 females with invasive ductal carcinomas were evaluated. DWI was performed and ADC values were calculated from the area in which restriction of diffusion was the highest in ADC mapping. B value was 500 and region of interest (ROI) was designated between 49 and 100 mm2. Calculations were performed automatically by the device. Tissue samples were obtained for prognostic factor evaluation. The relationships between ADC and prognostic factors were investigated. Comparisons between groups were made with one-way ANOVA and Kruskal Wallis test. Pairwise comparisons were made with Dunn's test. Analyses of categorical variables were made with Chi-square test. RESULTS We found a weak negative correlation between ADC and Ki-67 values (r = -0.279; p = 0.029). When we compared ADC values in regard to tumour type, we found no significant differences for tumour grade, Ki-67 positivity, estrogen receptor positivity, progesterone receptor positivity, C-erb B2, lymphovascular invasion and ductal carcinoma in situ or lobular carcinoma in situ component. On a side note, we found that mean ADC values decreased as tumour grade increased; however, this was not statistically significant. CONCLUSION The literature contains studies that report conflicting results which may be caused by differences in B values, ROI area and magnetic field strength. Multicentre studies and systematic reviews of these findings may produce crucial data for the use of DWI in breast cancer. Advances in knowledge: To determine if any significant relationship exists between DWI findings and prognostic factors of breast cancer.
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Affiliation(s)
- Hale Aydin
- 1 Department of Radiology, Dr AY Ankara Oncology Research and Training Hospital , Ankara , Turkey
| | - Bahar Guner
- 1 Department of Radiology, Dr AY Ankara Oncology Research and Training Hospital , Ankara , Turkey
| | - Isil Esen Bostanci
- 1 Department of Radiology, Dr AY Ankara Oncology Research and Training Hospital , Ankara , Turkey
| | - Zarife Melda Bulut
- 2 Department of Pathology, Dr AY Ankara Oncology Research and Training Hospital , Ankara , Turkey
| | - Bilgin Kadri Aribas
- 1 Department of Radiology, Dr AY Ankara Oncology Research and Training Hospital , Ankara , Turkey
| | - Lutfi Dogan
- 3 Department of General Surgery, Dr AY Ankara Oncology Research and Training Hospital , Ankara , Turkey
| | - Mehmet Ali Gulcelik
- 3 Department of General Surgery, Dr AY Ankara Oncology Research and Training Hospital , Ankara , Turkey.,Department of General Surgery, Gulhane Research and Training Hospital, Ankara , Turkey
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尚 柳, 杨 家, 卢 晶, 王 婷, 周 颖, 邢 新, 王 鑫, 杨 淑, 胡 明. [Correlations between apparent diffusion coefficient in diffusion?weighted magnetic resonance imaging and molecular subtypes of invasive breast cancer masses]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2017; 37:1410-1414. [PMID: 29070476 PMCID: PMC6743964 DOI: 10.3969/j.issn.1673-4254.2017.10.22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 03/18/2017] [Indexed: 06/07/2023]
Abstract
OBJECTIVE To study the correlation of apparent diffusion coefficient (ADC) measured by diffusion-weighted magnetic resonance imaging (MRI) with the molecular subtypes and biological prognostic factors of invasive breast cancer masses. METHODS Breast MRI data (including dynamic enhanced and diffusion-weighted imaging) were collected from 64 patients with pathologically confirmed invasive breast cancer masses (a total of 69 lesions). The mean ADC values of the lesions were calculated and their correlations were analyzed with the 5 molecular subtypes of invasive breast cancer and the biological prognostic factors including estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor 2 (HER2), and Ki-67 index. RESULTS The ADC values did not differ significantly among the 5 molecular subtypes of invasive breast cancer masses (P>0.05) or among lesions with different ER, PR, or HER2 status (P>0.05). The mean ADC values were significantly higher in Ki-67-positive lesions than in the negative lesions (P=0.023 and negatively correlated with the expressions of Ki-67 (r=-0.249). CONCLUSION ADC value can not be used to identify the molecular subtypes of invasive breast cancer masses or to evaluate the biological prognosis of the lesions, but its correlation with Ki-67 expression may help in prognostic evaluation and guiding clinical therapy of the tumors.
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Affiliation(s)
- 柳彤 尚
- 解放军总医院第一附属医院 放射科, 北京 100047Department of Radiology, First Affiliated Hospital of General Hospital of PLA, Beijing 100047, China
| | - 家斐 杨
- 解放军总医院第一附属医院 放射科, 北京 100047Department of Radiology, First Affiliated Hospital of General Hospital of PLA, Beijing 100047, China
| | - 晶 卢
- 解放军总医院第一附属医院 放射科, 北京 100047Department of Radiology, First Affiliated Hospital of General Hospital of PLA, Beijing 100047, China
| | - 婷婷 王
- 新疆医科大学公共卫生学院儿少卫生与妇幼保健学教研室, 新疆 乌鲁木齐 830000Department of Maternal, Child and Adolescent Health, School of Public Health, Xinjiang Medical University, Urumqi 830000, China
| | - 颖 周
- 解放军总医院第一附属医院 病理科, 北京 100047Department of Pathology, First Affiliated Hospital of General Hospital of PLA, Beijing 100047, China
| | - 新博 邢
- 解放军总医院第一附属医院 放射科, 北京 100047Department of Radiology, First Affiliated Hospital of General Hospital of PLA, Beijing 100047, China
| | - 鑫坤 王
- 解放军总医院第一附属医院 放射科, 北京 100047Department of Radiology, First Affiliated Hospital of General Hospital of PLA, Beijing 100047, China
| | - 淑辉 杨
- 解放军总医院第一附属医院 放射科, 北京 100047Department of Radiology, First Affiliated Hospital of General Hospital of PLA, Beijing 100047, China
| | - 明艳 胡
- 解放军总医院第一附属医院 放射科, 北京 100047Department of Radiology, First Affiliated Hospital of General Hospital of PLA, Beijing 100047, China
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Abstract
Diffusion-weighted imaging (DWI) holds promise to address some of the shortcomings of routine clinical breast magnetic resonance imaging (MRI) and to expand the capabilities of imaging in breast cancer management. DWI reflects tissue microstructure, and provides unique information to aid in characterization of breast lesions. Potential benefits under investigation include improving diagnostic accuracy and guiding treatment decisions. As a result, DWI is increasingly being incorporated into breast MRI protocols and multicenter trials are underway to validate single-institution findings and to establish clinical guidelines. Advancements in DWI acquisition and modeling approaches are helping to improve image quality and extract additional biologic information from breast DWI scans, which may extend diagnostic and prognostic value.
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Affiliation(s)
- Savannah C Partridge
- *Department of Radiology, Breast Imaging Section, Seattle Cancer Care Alliance, University of Washington, Seattle, WA †University of Massachusetts Memorial Medical Center, Worcester, MA
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