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Zhan T, Dai J, Li Y. Noninvasive identification of HER2-zero, -low, or -overexpressing breast cancers: Multiparametric MRI-based quantitative characterization in predicting HER2-low status of breast cancer. Eur J Radiol 2024; 177:111573. [PMID: 38905803 DOI: 10.1016/j.ejrad.2024.111573] [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: 01/20/2024] [Revised: 03/28/2024] [Accepted: 06/12/2024] [Indexed: 06/23/2024]
Abstract
PURPOSE To evaluate the effectiveness of both synthetic magnetic resonance imaging (SyMRI) and conventional diffusion-weighted imaging (DWI) for identifying the human epidermal growth factor receptor 2 (HER2) status in breast cancer (BC) patients. METHOD In this retrospective study, 114 women with DWI and SyMRI were pathologically classified into three groups: HER2-overexpressing (n = 40), HER2-low-expressing (n = 53), and HER2-zero-expressing (n = 21). T1 and T2 relaxation times and proton density (PD) were assessed before and after enhancement, and the resulting quantitative parameters produced by SyMRI were recorded as T1, T2, and PD and T1e, T2e, and PDe. Logistic regression was used to identify the best indicators for classifying patients based on HER2 expression. The discriminative performance of the models was evaluated using receiver operating characteristic (ROC) curves. RESULTS Our preliminary study revealed significant differences in progesterone receptor (PR) status, Ki-67 index, and axillary lymph node (ALN) count among the HER2-zero, -low, and -overexpressing groups (p < 0.001 to p = 0.03). SyMRI quantitative indices showed significant differences among BCs in the three HER2 subgroups, except for ΔT2 (p < 0.05). our results indicate that PDe achieved an area under the curve(AUC)of 0.849 (95 % CI: 0.760-0.915) for distinguishing HER2-low and -overexpressing BCs. Further investigation revealed that both the PDe and ADC were indicators for predicting differences among patients with HER2-zero and HER2-low-expressing BC, with AUCs of 0.765(95 % CI: 0.652-0.855) and 0.684(95 % CI: 0.565-0.787), respectively. The addition of the PDe to the ADC improved the AUC to 0.825(95 % CI: 0.719-0.903). CONCLUSIONS SyMRI could noninvasively and robustly predict the HER2 expression status of patients with BC.
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Affiliation(s)
- Ting Zhan
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, China
| | | | - Yan Li
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, China.
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Qu M, Feng W, Liu X, Li Z, Li Y, Lu X, Lei J. Investigation of synthetic MRI with quantitative parameters for discriminating axillary lymph nodes status in invasive breast cancer. Eur J Radiol 2024; 175:111452. [PMID: 38604092 DOI: 10.1016/j.ejrad.2024.111452] [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: 11/29/2023] [Revised: 03/25/2024] [Accepted: 04/02/2024] [Indexed: 04/13/2024]
Abstract
OBJECTIVE To investigate the potential value of quantitative parameters derived from synthetic magnetic resonance imaging (syMRI) for discriminating axillary lymph nodes metastasis (ALNM) in breast cancer patients. MATERIALS AND METHODS A total of 56 females with histopathologically proven invasive breast cancer who underwent both conventional breast MRI and additional syMRI examinations were enrolled in this study, including 30 patients with ALNM and 26 with non-ALNM. SyMRI has enabled quantification of T1 relaxation time (T1), T2 relaxation time (T2) and proton density (PD). The syMRI quantitative parameters of breast primary tumors before (T1tumor, T2tumor, PDtumor) and after (T1+tumor, T2+tumor, PD+tumor) contrast agent injection were obtained. Similarly, measurements were taken for axillary lymph nodes before (T1LN, T2LN, PDLN) and after (T1+LN, T2+LN, PD+LN) the injection, then theΔT1 (T1-T1+), ΔT2 (T2-T2+), ΔPD (PD-PD+), T1/T2 and T1+/T2+ were calculated. All parameters were compared between ANLM and non-ALNM group. Intraclass correlation coefficient for assessing interobserver agreement. The independent Student's t test or Mann-Whitney U test to determine the relationship between the mean quantitative values and the ALNM. Multivariate logistic regression analyses followed by receiver operating characteristics (ROC) analysis for discriminating ALN status. A P value < 0.05 was considered statistically significant. RESULTS The short-diameter of lymph nodes (DLN) in ALNM group was significantly longer than that in the non-ALNM group (10.22 ± 3.58 mm vs. 5.28 ± 1.39 mm, P < 0.001). The optimal cutoff value was determined to be 5.78 mm, with an AUC of 0.894 (95 % CI: 0.838-0.939), a sensitivity of 86.7 %, and a specificity of 90.2 %. In syMRI quantitative parameters of breast tumors, T2tumor, ΔT2tumor and ΔPDtumor values showed statistically significant differences between the two groups (P < 0.05). T2tumor value had the best performance in discriminating ALN status (AUC = 0.712), and the optimal cutoff was 90.12 ms, the sensitivity and specificity were 65.0 % and 83.6 % respectively. In terms of syMRI quantitative parameters of lymph nodes, T1LN, T2LN, T1LN/T2LN, T2+LN and ΔT1LN values were significantly different between the two groups (P < 0.05), and their AUCs were 0.785, 0.840, 0.886, 0.702 and 0.754, respectively. Multivariate analyses indicated that the T1LN value was the only independent predictor of ALNM (OR=1.426, 95 % CI: 1.130-1.798, P = 0.039). The diagnostic sensitivity and specificity of T1LN was 86.7 % and 69.4 % respectively at the best cutoff point of 1371.00 ms. The combination of T1LN, T2LN, T1LN/T2LN, ΔT1LN and DLN had better performance for differentiating ALNM and non-ALNM, with AUCs of 0.905, 0.957, 0.964 and 0.897, respectively. CONCLUSION The quantitative parameters derived from syMRI have certain value for discriminating ALN status in invasive breast cancer, with T2tumor showing the highest diagnostic efficiency among breast lesions parameters. Moreover, T1LN acted as an independent predictor of ALNM.
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Affiliation(s)
- Mengmeng Qu
- The First Clinical Medical College of Lanzhou University, Lanzhou 730000, China; Department of Radiology, The First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Wen Feng
- The First Clinical Medical College of Lanzhou University, Lanzhou 730000, China; Department of Radiology, The First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Xinran Liu
- The First Clinical Medical College of Lanzhou University, Lanzhou 730000, China; Department of Radiology, The First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Zhifan Li
- The First Clinical Medical College of Lanzhou University, Lanzhou 730000, China; Department of Radiology, The First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Yixiang Li
- The First Clinical Medical College of Lanzhou University, Lanzhou 730000, China; Department of Radiology, The First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Xingru Lu
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou 730000, China; Radiological Clinical Medicine Research Center of Gansu Province, Lanzhou 730000, China
| | - Junqiang Lei
- The First Clinical Medical College of Lanzhou University, Lanzhou 730000, China; Department of Radiology, The First Hospital of Lanzhou University, Lanzhou 730000, China; Radiological Clinical Medicine Research Center of Gansu Province, Lanzhou 730000, China.
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Zhou XX, Zhang L, Cui QX, Li H, Sang XQ, Zhang HX, Zhu YM, Kuai ZX. A Channel-Dimensional Feature-Reconstructed Deep Learning Model for Predicting Breast Cancer Molecular Subtypes on Overall b-Value Diffusion-Weighted MRI. J Magn Reson Imaging 2024; 59:1425-1435. [PMID: 37403945 DOI: 10.1002/jmri.28895] [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: 06/02/2023] [Revised: 06/23/2023] [Accepted: 06/23/2023] [Indexed: 07/06/2023] Open
Abstract
BACKGROUND Dynamic contrast-enhanced (DCE) MRI commonly outperforms diffusion-weighted (DW) MRI in breast cancer discrimination. However, the side effects of contrast agents limit the use of DCE-MRI, particularly in patients with chronic kidney disease. PURPOSE To develop a novel deep learning model to fully exploit the potential of overall b-value DW-MRI without the need for a contrast agent in predicting breast cancer molecular subtypes and to evaluate its performance in comparison with DCE-MRI. STUDY TYPE Prospective. SUBJECTS 486 female breast cancer patients (training/validation/test: 64%/16%/20%). FIELD STRENGTH/SEQUENCE 3.0 T/DW-MRI (13 b-values) and DCE-MRI (one precontrast and five postcontrast phases). ASSESSMENT The breast cancers were divided into four subtypes: luminal A, luminal B, HER2+, and triple negative. A channel-dimensional feature-reconstructed (CDFR) deep neural network (DNN) was proposed to predict these subtypes using pathological diagnosis as the reference standard. Additionally, a non-CDFR DNN (NCDFR-DNN) was built for comparative purposes. A mixture ensemble DNN (ME-DNN) integrating two CDFR-DNNs was constructed to identify subtypes on multiparametric MRI (MP-MRI) combing DW-MRI and DCE-MRI. STATISTICAL TESTS Model performance was evaluated using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Model comparisons were performed using the one-way analysis of variance with least significant difference post hoc test and the DeLong test. P < 0.05 was considered significant. RESULTS The CDFR-DNN (accuracies, 0.79 ~ 0.80; AUCs, 0.93 ~ 0.94) demonstrated significantly improved predictive performance than the NCDFR-DNN (accuracies, 0.76 ~ 0.78; AUCs, 0.92 ~ 0.93) on DW-MRI. Utilizing the CDFR-DNN, DW-MRI attained the predictive performance equal (P = 0.065 ~ 1.000) to DCE-MRI (accuracies, 0.79 ~ 0.80; AUCs, 0.93 ~ 0.95). The predictive performance of the ME-DNN on MP-MRI (accuracies, 0.85 ~ 0.87; AUCs, 0.96 ~ 0.97) was superior to those of both the CDFR-DNN and NCDFR-DNN on either DW-MRI or DCE-MRI. DATA CONCLUSION The CDFR-DNN enabled overall b-value DW-MRI to achieve the predictive performance comparable to DCE-MRI. MP-MRI outperformed DW-MRI and DCE-MRI in subtype prediction. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Xin-Xiang Zhou
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| | - Lan Zhang
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| | - Quan-Xiang Cui
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| | - Hui Li
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xi-Qiao Sang
- Division of Respiratory Disease, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Hong-Xia Zhang
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| | - Yue-Min Zhu
- CREATIS, CNRS UMR 5220-INSERM U1294-University Lyon 1-INSA Lyon-University Jean Monnet Saint-Etienne, Villeurbanne, France
| | - Zi-Xiang Kuai
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
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Gao W, Yang Q, Li X, Zhang Y, He T, Liang W, Wei X, Yang M, Gao B, Zhang G, Zhang S. Quantitative Assessment of Breast Tumor: Comparison of Four Methods of Positioning Region of Interest for Synthetic Relaxometry and Diffusion Measurement. Acad Radiol 2024:S1076-6332(24)00135-1. [PMID: 38508932 DOI: 10.1016/j.acra.2024.02.045] [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: 01/28/2024] [Revised: 02/22/2024] [Accepted: 02/24/2024] [Indexed: 03/22/2024]
Abstract
RATIONALE AND OBJECTIVES To compare the differences in apparent diffusion coefficient (ADC) and synthetic magnetic resonance (MR) measurements of four region of interest (ROI) placement methods for breast tumor and to investigate their diagnostic performance. METHODS 110 (70 malignant, 40 benign) newly diagnosed breast tumors were evaluated. The patients underwent 3.0 T MR examinations including diffusion-weighted imaging and synthetic MR. Two radiologists independently measured ADCs, T1 relaxation time (T1), T2 relaxation time (T2), and proton density (PD) using four ROI methods: round, square, freehand, and whole-tumor volume (WTV). The interclass correlation coefficient (ICC) was used to assess their measurement reliability. Diagnostic performance was evaluated using multivariate logistic regression analysis and the receiver operating characteristic (ROC) curves. RESULTS The mean values of all ROI methods showed good or excellent interobserver reproducibility (0.79-0.99) and showed the best diagnostic performance compared to the minimum and maximum values. The square ROI exhibited superior performance in differentiating between benign from malignant breast lesions, followed by the freehand ROI. T2, PD, and ADC values were significantly lower in malignant breast lesions compared to benign ones for all ROI methods (p < 0.05). Multiparameters of T2 + ADC demonstrated the highest AUC values (0.82-0.95), surpassing the diagnostic efficacy of ADC or T2 alone (p < 0.05). CONCLUSION ROI placement significantly influences ADC and synthetic MR values measured in breast tumors. Square ROI and mean values showed superior performance in differentiating benign and malignant breast lesions. The multiparameters of T2 + ADC surpassed the diagnostic efficacy of a single parameter.
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Affiliation(s)
- Weibo Gao
- Department of Radiology, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Quanxin Yang
- Department of Radiology, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Xiaohui Li
- Department of Radiology, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Yanyan Zhang
- Department of Radiology, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Tuo He
- Department of Radiology, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Wenbin Liang
- Department of Radiology, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | | | - Ming Yang
- Department of Radiology, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Bo Gao
- Department of Radiology, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Guirong Zhang
- Department of Radiology, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Shuqun Zhang
- Department of Oncology, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
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Zeng F, Yang Z, Tang X, Lin L, Lin H, Wu Y, Wang Z, Chen M, Chen L, Chen L, Wu PY, Wang C, Xue Y. Whole-tumor histogram models based on quantitative maps from synthetic MRI for predicting axillary lymph node status in invasive ductal breast cancer. Eur J Radiol 2024; 172:111325. [PMID: 38262156 DOI: 10.1016/j.ejrad.2024.111325] [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: 10/06/2023] [Revised: 01/08/2024] [Accepted: 01/15/2024] [Indexed: 01/25/2024]
Abstract
PURPOSE To investigate the potential of using histogram analysis of synthetic MRI (SyMRI) images before and after contrast enhancement to predict axillary lymph node (ALN) status in patients with invasive ductal carcinoma (IDC). METHODS From January 2022 to October 2022, a total of 212 patients with IDC underwent breast MRI examination including SyMRI. Standard T2 weight images, DCE-MRI and quantitative maps of SyMRI were obtained. 13 features of the entire tumor were extracted from these quantitative maps, standard T2 weight images and DCE-MRI. Statistical analyses, including Student's t-test, Mann-Whiney U test, logistic regression, and receiver operating characteristic (ROC) curves, were used to evaluate the data. The mean values of SyMRI quantitative parameters derived from the conventional 2D region of interest (ROI) were also evaluated. RESULTS The combined model based on T1-Gd quantitative map (energy, minimum, and variance) and clinical features (age and multifocality) achieved the best diagnostic performance in the prediction of ALN between N0 (with non-metastatic ALN) and N+ group (metastatic ALN ≥ 1) with the AUC of 0.879. Among individual quantitative maps and standard sequence-derived models, the synthetic T1-Gd model showed the best performance for the prediction of ALN between N0 and N+ groups (AUC = 0.823). Synthetic T2_entropy and PD-Gd_energy were useful for distinguishing N1 group (metastatic ALN ≥ 1 and ≤ 3) from the N2-3 group (metastatic ALN > 3) with an AUC of 0.722. CONCLUSIONS Whole-tumor histogram features derived from quantitative parameters of SyMRI can serve as a complementary noninvasive method for preoperatively predicting ALN metastases.
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Affiliation(s)
- Fang Zeng
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China
| | - Zheting Yang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China
| | - Xiaoxue Tang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China
| | - Lin Lin
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China
| | - Hailong Lin
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China
| | - Yue Wu
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China
| | - Zongmeng Wang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China
| | - Minyan Chen
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian Province 350001, China
| | - Lili Chen
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian Province 350001, China
| | - Lihong Chen
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China
| | - Pu-Yeh Wu
- GE Healthcare, Beijing 100176, China
| | - Chuang Wang
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian Province 350001, China.
| | - Yunjing Xue
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province 350001, China; School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, Fujian Province 350004, China; Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors (Fujian Medical University), China.
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Zhang Q, Zhao Y, Nie J, Long Q, Wang X, Wang X, Gong G, Liao L, Yi X, Chen BT. Pretreatment synthetic MRI features for triple-negative breast cancer. Clin Radiol 2024; 79:e219-e226. [PMID: 37935611 DOI: 10.1016/j.crad.2023.10.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/08/2023] [Accepted: 10/11/2023] [Indexed: 11/09/2023]
Abstract
AIM To evaluate the quantitative parameters derived from synthetic magnetic resonance imaging (SyMRI) for predicting triple-negative breast cancer (TNBC). MATERIALS AND METHODS This prospective study enrolled participants with invasive ductal breast carcinoma (IDBC) and separated them into a TNBC group and a Non-TNBC group. Preoperative breast MRI included both the SyMRI and conventional MRI sequences. The quantitative parameters derived from the SyMRI included T1 and T2 relaxation times, proton density (PD), and their standard deviations (SD). Clinicopathological characteristics, conventional MRI findings, and quantitative synthetic parameters were assessed for all participants. Multivariable logistic regression analysis was performed to determine the potential independent imaging predictors for TNBC preoperatively. Receiver operating characteristic (ROC) curve analysis was used to evaluate the performance of these parameters. RESULTS A total of 231 participants with histopathological proven IDBC were included in this study (n=46 in the TNBC group and n=185 in the Non-TNBC group). The TNBC group had significantly larger tumour size (p=0.011) and more frequent intratumoural cystic or necrotic lesions (p<0.001) as compared to the Non-TNBC group. The univariate analysis showed that the TNBC tumours had significantly higher T1 (p=0.006) and T2 (p<0.001) values than Non-TNBC tumours. Subsequent multivariable analysis indicated that T2 values and the presence of cystic or necrotic lesions were the independent predictors for TNBC. CONCLUSION The T2 from synthetic imaging and the presence of cystic degeneration or necrosis within the breast cancer may serve as potential imaging biomarkers for preoperative differentiation of TNBC from Non-TNBC.
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Affiliation(s)
- Q Zhang
- Department of Breast Surgery, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China; Clinical Research Center for Breast Cancer, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China; National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha 410008, Hunan, PR China
| | - Y Zhao
- National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha 410008, Hunan, PR China; Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China
| | - J Nie
- National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha 410008, Hunan, PR China; Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China
| | - Q Long
- National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha 410008, Hunan, PR China; Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China
| | - X Wang
- Department of Breast Surgery, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China; Clinical Research Center for Breast Cancer, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China; National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha 410008, Hunan, PR China
| | - X Wang
- Department of Breast Surgery, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China; Clinical Research Center for Breast Cancer, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China; National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha 410008, Hunan, PR China
| | - G Gong
- Department of Pathology, Xiangya School of Medicine, Central South University, Changsha 410008, Hunan, PR China
| | - L Liao
- Department of Breast Surgery, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China; Clinical Research Center for Breast Cancer, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China; National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha 410008, Hunan, PR China.
| | - X Yi
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China; National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Changsha 410008, Hunan, PR China; Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China; Hunan Engineering Research Center of Skin Health and Disease, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China; Department of Dermatology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China.
| | - B T Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, USA
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Zhang H, Zhao J, Dai J, Chang J, Hu S, Wang P. Synthetic MRI quantitative parameters in discriminating stage T1 nasopharyngeal carcinoma and benign hyperplasia: Combination with morphological features. Eur J Radiol 2024; 170:111264. [PMID: 38103492 DOI: 10.1016/j.ejrad.2023.111264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 11/23/2023] [Accepted: 12/11/2023] [Indexed: 12/19/2023]
Abstract
PURPOSE To investigate the feasibility of synthetic MRI (syMRI) quantitative parameters and its combination with morphological features in discriminating stage T1 nasopharyngeal carcinoma (T1-NPC) and benign hyperplasia (BH). MATERIAL AND METHODS Eighty-eight patients with nasopharyngeal lesions (T1-NPC, n = 54; BH, n = 34) were retrospectively enrolled between October 2020 and May 2022. The syMRI quantitative parameters of nasopharyngeal lesions (T1, T2, PD, T1SD, T2SD, PDSD) and longus capitis (T1, T2, PD) were measured, and T1ratio, T2ratio and PDratio were calculated (lesion/longus capitis). The morphological features (lesion pattern, retention cyst, serrated protrusion, middle ear effusion, tumor volume, and retropharyngeal lymph node) were compared. Statistical analyses were performed using the independent sample t test, Chi-square test, logistic regression analysis, receiver operating characteristic curve (ROC), and DeLong test. RESULTS The T1, T2, PD, T1SD, T1ratio, and T2ratio values of T1-NPC were significantly lower than those of BH. The morphological features (lesion pattern, retention cyst, retropharyngeal lymph node) were significant difference between these two entities. T2 value has the highest AUC in all syMRI quantitative parameters, followed by T1, T1ratio, PD, T2ratio and T1SD. Combined syMRI quantitative parameters (T2, PD, T1ratio) can further improve the diagnosis efficiency. Combined syMRI parameters and morphological feature (T2, PD, lesion pattern, retropharyngeal lymph node) has the excellent diagnostic efficiency, with AUC, sensitivity, specificity, and accuracy of 0.979, 96.30%, 97.06%, 96.77%. CONCLUSIONS Synthetic MRI was helpful in distinguishing T1-NPC from BH, and combined syMRI quantitative parameters and morphological features has the optimal diagnostic performance.
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Affiliation(s)
- Heng Zhang
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi 214122, PR China
| | - Jing Zhao
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi 214122, PR China
| | - Jiankun Dai
- GE Healthcare, MR Research China, Beijing 100176, PR China
| | - Jun Chang
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi 214122, PR China
| | - Shudong Hu
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi 214122, PR China.
| | - Peng Wang
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi 214122, PR China.
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Amano M, Fujita S, Takei N, Sano K, Wada A, Sato K, Kikuta J, Kuwatsuru Y, Tachibana R, Sekine T, Horimoto Y, Aoki S. Feasibility of Quantitative MRI Using 3D-QALAS for Discriminating Immunohistochemical Status in Invasive Ductal Carcinoma of the Breast. J Magn Reson Imaging 2023; 58:1752-1759. [PMID: 36951614 DOI: 10.1002/jmri.28683] [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: 01/21/2023] [Revised: 03/05/2023] [Accepted: 03/07/2023] [Indexed: 03/24/2023] Open
Abstract
BACKGROUND Two-dimensional synthetic MRI of the breast has limited spatial coverage. Three-dimensional (3D) synthetic MRI could provide volumetric quantitative parameters that may reflect the immunohistochemical (IHC) status in invasive ductal carcinoma (IDC) of the breast. PURPOSE To evaluate the feasibility of 3D synthetic MRI using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (QALAS) for discriminating the IHC status, including hormone receptor (HR), human epidermal growth factor receptor 2 (HER 2), and Ki-67 expression in IDC. STUDY TYPE Prospective observational study. POPULATION A total of 33 females with IDC of the breast (mean, 52.3 years). FIELD STRENGTH/SEQUENCE A 3-T, 3D-QALAS gradient-echo and fat-suppressed T1-weighted 3D fast spoiled gradient-echo sequences. ASSESSMENT Two radiologists semiautomatically delineated 3D regions of interest (ROIs) of the whole tumors on the dynamic MRI that was registered to the synthetic T1-weighted images acquired from 3D-QALAS. The mean T1 and T2 were measured for each IDC. STATISTICAL TESTS Intraclass correlation coefficient for assessing interobserver agreement. Mann-Whitney U test to determine the relationship between the mean T1 or T2 and the IHC status. Multivariate logistic regression analysis followed by receiver operating characteristics (ROC) analysis for discriminating IHC status. A P value <0.05 was considered statistically significant. RESULTS The interobserver agreement was good to excellent. There was a significant difference in the mean T1 between HR-positive and HR-negative lesions, while the mean T2 value differed between HR-positive and HR-negative lesions, between the triple-negative and HR-positive or HER2-positive lesions, and between the Ki-67 level > 14% and ≤ 14%. Multivariate analysis showed that the mean T2 was higher in HR-negative IDC than in HR-positive IDC. ROC analysis revealed that the mean T2 was predictive for discriminating HR status, triple-negative status, and Ki-67 level. DATA CONCLUSION 3D synthetic MRI using QALAS may be useful for discriminating IHC status in IDC of the breast. EVIDENCE LEVEL 1. TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Maki Amano
- Department of Radiology, Juntendo University Hospital, Tokyo, Japan
- Department of Radiology, Nihon University Hospital, Tokyo, Japan
| | - Shohei Fujita
- Department of Radiology, Juntendo University Hospital, Tokyo, Japan
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | | | - Katsuhiro Sano
- Department of Radiology, Juntendo University Hospital, Tokyo, Japan
| | - Akihiko Wada
- Department of Radiology, Juntendo University Hospital, Tokyo, Japan
| | - Kanako Sato
- Department of Radiology, Juntendo University Hospital, Tokyo, Japan
| | - Junko Kikuta
- Department of Radiology, Juntendo University Hospital, Tokyo, Japan
| | | | - Rina Tachibana
- Department of Radiology, Juntendo University Hospital, Tokyo, Japan
- Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Towa Sekine
- Department of Radiology, Juntendo University Hospital, Tokyo, Japan
- Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Yoshiya Horimoto
- Department of Breast Oncology, Juntendo University Hospital, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University Hospital, Tokyo, Japan
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Liu M, Zhang S, Du Y, Zhang X, Wang D, Ren W, Sun J, Yang S, Zhang G. Identification of Luminal A breast cancer by using deep learning analysis based on multi-modal images. Front Oncol 2023; 13:1243126. [PMID: 38044991 PMCID: PMC10691590 DOI: 10.3389/fonc.2023.1243126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 11/06/2023] [Indexed: 12/05/2023] Open
Abstract
Purpose To evaluate the diagnostic performance of a deep learning model based on multi-modal images in identifying molecular subtype of breast cancer. Materials and methods A total of 158 breast cancer patients (170 lesions, median age, 50.8 ± 11.0 years), including 78 Luminal A subtype and 92 non-Luminal A subtype lesions, were retrospectively analyzed and divided into a training set (n = 100), test set (n = 45), and validation set (n = 25). Mammography (MG) and magnetic resonance imaging (MRI) images were used. Five single-mode models, i.e., MG, T2-weighted imaging (T2WI), diffusion weighting imaging (DWI), axial apparent dispersion coefficient (ADC), and dynamic contrast-enhanced MRI (DCE-MRI), were selected. The deep learning network ResNet50 was used as the basic feature extraction and classification network to construct the molecular subtype identification model. The receiver operating characteristic curve were used to evaluate the prediction efficiency of each model. Results The accuracy, sensitivity and specificity of a multi-modal tool for identifying Luminal A subtype were 0.711, 0.889, and 0.593, respectively, and the area under the curve (AUC) was 0.802 (95% CI, 0.657- 0.906); the accuracy, sensitivity, and AUC were higher than those of any single-modal model, but the specificity was slightly lower than that of DCE-MRI model. The AUC value of MG, T2WI, DWI, ADC, and DCE-MRI model was 0.593 (95%CI, 0.436-0.737), 0.700 (95%CI, 0.545-0.827), 0.564 (95%CI, 0.408-0.711), 0.679 (95%CI, 0.523-0.810), and 0.553 (95%CI, 0.398-0.702), respectively. Conclusion The combination of deep learning and multi-modal imaging is of great significance for diagnosing breast cancer subtypes and selecting personalized treatment plans for doctors.
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Affiliation(s)
- Menghan Liu
- Department of Health Management, The First Affiliated Hospital of Shandong First Medical University & Shandong Engineering Laboratory for Health Management, Shandong Medicine and Health Key Laboratory of Laboratory Medicine, Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Shuai Zhang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
- Postgraduate Department, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, China
| | - Yanan Du
- Department of Health Management, The First Affiliated Hospital of Shandong First Medical University & Shandong Engineering Laboratory for Health Management, Shandong Medicine and Health Key Laboratory of Laboratory Medicine, Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Xiaodong Zhang
- Postgraduate Department, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, China
| | - Dawei Wang
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Wanqing Ren
- Postgraduate Department, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, China
| | - Jingxiang Sun
- Postgraduate Department, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, China
| | - Shiwei Yang
- Department of Anorectal Surgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Guang Zhang
- Department of Health Management, The First Affiliated Hospital of Shandong First Medical University & Shandong Engineering Laboratory for Health Management, Shandong Medicine and Health Key Laboratory of Laboratory Medicine, Shandong Provincial Qianfoshan Hospital, Jinan, China
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Zhang L, Zhou XX, Liu L, Liu AY, Zhao WJ, Zhang HX, Zhu YM, Kuai ZX. Comparison of Dynamic Contrast-Enhanced MRI and Non-Mono-Exponential Model-Based Diffusion-Weighted Imaging for the Prediction of Prognostic Biomarkers and Molecular Subtypes of Breast Cancer Based on Radiomics. J Magn Reson Imaging 2023; 58:1590-1602. [PMID: 36661350 DOI: 10.1002/jmri.28611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/10/2023] [Accepted: 01/10/2023] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Dynamic contrast-enhanced (DCE) MRI and non-mono-exponential model-based diffusion-weighted imaging (NME-DWI) that does not require contrast agent can both characterize breast cancer. However, which technique is superior remains unclear. PURPOSE To compare the performances of DCE-MRI, NME-DWI and their combination as multiparametric MRI (MP-MRI) in the prediction of breast cancer prognostic biomarkers and molecular subtypes based on radiomics. STUDY TYPE Prospective. POPULATION A total of 477 female patients with 483 breast cancers (5-fold cross-validation: training/validation, 80%/20%). FIELD STRENGTH/SEQUENCE A 3.0 T/DCE-MRI (6 dynamic frames) and NME-DWI (13 b values). ASSESSMENT After data preprocessing, high-throughput features were extracted from each tumor volume of interest, and optimal features were selected using recursive feature elimination method. To identify ER+ vs. ER-, PR+ vs. PR-, HER2+ vs. HER2-, Ki-67+ vs. Ki-67-, luminal A/B vs. nonluminal A/B, and triple negative (TN) vs. non-TN, the following models were implemented: random forest, adaptive boosting, support vector machine, linear discriminant analysis, and logistic regression. STATISTICAL TESTS Student's t, chi-square, and Fisher's exact tests were applied on clinical characteristics to confirm whether significant differences exist between different statuses (±) of prognostic biomarkers or molecular subtypes. The model performances were compared between the DCE-MRI, NME-DWI, and MP-MRI datasets using the area under the receiver-operating characteristic curve (AUC) and the DeLong test. P < 0.05 was considered significant. RESULTS With few exceptions, no significant differences (P = 0.062-0.984) were observed in the AUCs of models for six classification tasks between the DCE-MRI (AUC = 0.62-0.87) and NME-DWI (AUC = 0.62-0.91) datasets, while the model performances on the two imaging datasets were significantly poorer than on the MP-MRI dataset (AUC = 0.68-0.93). Additionally, the random forest and adaptive boosting models (AUC = 0.62-0.93) outperformed other three models (AUC = 0.62-0.90). DATA CONCLUSION NME-DWI was comparable with DCE-MRI in predictive performance and could be used as an alternative technique. Besides, MP-MRI demonstrated significantly higher AUCs than either DCE-MRI or NME-DWI. EVIDENCE LEVEL 2. TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Lan Zhang
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xin-Xiang Zhou
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| | - Lu Liu
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| | - Ao-Yu Liu
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| | - Wen-Juan Zhao
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| | - Hong-Xia Zhang
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| | - Yue-Min Zhu
- CREATIS, CNRS UMR 5220-INSERM U1206-University Lyon 1-INSA Lyon-University Jean Monnet Saint-Etienne, Lyon, France
| | - Zi-Xiang Kuai
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
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Zhao R, Du S, Gao S, Shi J, Zhang L. Time Course Changes of Synthetic Relaxation Time During Neoadjuvant Chemotherapy in Breast Cancer: The Optimal Parameter for Treatment Response Evaluation. J Magn Reson Imaging 2023; 58:1290-1302. [PMID: 36621982 DOI: 10.1002/jmri.28597] [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: 10/13/2022] [Revised: 12/24/2022] [Accepted: 12/27/2022] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Synthetic MRI (syMRI) has enabled quantification of multiple relaxation parameters (T1/T2 relaxation time [T1/T2], proton density [PD]), and their longitudinal change during neoadjuvant chemotherapy (NAC) promises to be valuable parameters for treatment response evaluation in breast cancer. PURPOSE To investigate the time course changes of syMRI parameters during NAC and evaluate their value as predictors for pathological complete response (pCR) in breast cancer. STUDY TYPE Retrospective, longitudinal. POPULATION A total of 129 women (median age, 50 years; range, 28-69 years) with locally advanced breast cancer who underwent NAC; all performed multiple conventional breast MRI examinations with added syMRI during NAC. FIELD STRENGTH/SEQUENCE A 3.0 T, T1-weighted dynamic contrast enhanced and syMRI acquired by a multiple-dynamic, multiple-echo sequence. ASSESSMENT Breast MRI was set at four time-points: baseline, after one cycle, after three or four cycles of NAC and preoperation. SyMRI parameters and tumor diameters were measured and their changes from baseline were calculated. All parameters were compared between pCR and non-pCR. Interaction between syMRI parameters and clinicopathological features was analyzed. STATISTICAL TESTS Mann-Whitney U tests, random effects model of repeated measurement, receiver operating characteristic (ROC) analysis, interaction analysis. RESULTS Median synthetic T1/T2/PD and tumor diameter generally decreased throughout NAC. Absolute T1 at early-NAC, T1, and PD at mid-NAC were significantly lower in the pCR group. After early-NAC, the T1 change was significantly higher in the pCR (median ± IQR, 18.17 ± 11.33) than the non-pCR group (median ± IQR, 10.90 ± 10.03), with the highest area under the ROC curves (AUC) of 0.769 (95% CI, 0.684-0.838). Interaction analysis showed that histological grade III patients had higher odds ratio (OR) (OR = 1.206) compared to grade II patients (OR = 1.067). DATA CONCLUSION Synthetic T1 changes after one cycle of NAC maybe useful for early evaluating NAC response in breast cancer during whole treatment cycles. However, its discriminative ability is significantly affected by histological grade. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Ruimeng Zhao
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Siyao Du
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Si Gao
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Jing Shi
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Lina Zhang
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
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Tissue Characteristics of Endometrial Carcinoma Analyzed by Quantitative Synthetic MRI and Diffusion-Weighted Imaging. Diagnostics (Basel) 2022; 12:diagnostics12122956. [PMID: 36552962 PMCID: PMC9776551 DOI: 10.3390/diagnostics12122956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 11/08/2022] [Accepted: 11/24/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND This study investigates the association of T1, T2, proton density (PD) and the apparent diffusion coefficient (ADC) with histopathologic features of endometrial carcinoma (EC). METHODS One hundred and nine EC patients were prospectively enrolled from August 2019 to December 2020. Synthetic magnetic resonance imaging (MRI) was acquired through one acquisition, in addition to diffusion-weighted imaging (DWI) and other conventional sequences using 1.5T MRI. T1, T2, PD derived from synthetic MRI and ADC derived from DWI were compared among different histopathologic features, namely the depth of myometrial invasion (MI), tumor grade, cervical stromal invasion (CSI) and lymphovascular invasion (LVSI) of EC by the Mann-Whitney U test. Classification models based on the significant MRI metrics were constructed with their respective receiver operating characteristic (ROC) curves, and their micro-averaged ROC was used to evaluate the overall performance of these significant MRI metrics in determining aggressive histopathologic features of EC. RESULTS EC with MI had significantly lower T2, PD and ADC than those without MI (p = 0.007, 0.006 and 0.043, respectively). Grade 2-3 EC and EC with LVSI had significantly lower ADC than grade 1 EC and EC without LVSI, respectively (p = 0.005, p = 0.020). There were no differences in the MRI metrics in EC with or without CSI. Micro-averaged ROC of the three models had an area under the curve of 0.83. CONCLUSIONS Synthetic MRI provided quantitative metrics to characterize EC with one single acquisition. Low T2, PD and ADC were associated with aggressive histopathologic features of EC, offering excellent performance in determining aggressive histopathologic features of EC.
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Kazama T, Takahara T, Kwee TC, Nakamura N, Kumaki N, Niikura N, Niwa T, Hashimoto J. Quantitative Values from Synthetic MRI Correlate with Breast Cancer Subtypes. Life (Basel) 2022; 12:life12091307. [PMID: 36143344 PMCID: PMC9501941 DOI: 10.3390/life12091307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 08/21/2022] [Accepted: 08/22/2022] [Indexed: 11/16/2022] Open
Abstract
The purpose of this study is to correlate quantitative T1, T2, and proton density (PD) values with breast cancer subtypes. Twenty-eight breast cancer patients underwent MRI of the breast including synthetic MRI. T1, T2, and PD values were correlated with Ki-67 and were compared between ER-positive and ER-negative cancers, and between Luminal A and Luminal B cancers. The effectiveness of T1, T2, and PD in differentiating the ER-negative from the ER-positive group and Luminal A from Luminal B cancers was evaluated using receiver operating characteristic analysis. Mean T2 relaxation of ER-negative cancers was significantly higher than that of ER-positive cancers (p < 0.05). The T1, T2, and PD values exhibited a strong positive correlation with Ki-67 (Pearson’s r = 0.75, 0.69, and 0.60 respectively; p < 0.001). Among ER-positive cancers, T1, T2, and PD values of Luminal A cancers were significantly lower than those of Luminal B cancers (p < 0.05). The area under the curve (AUC) of T2 for discriminating ER-negative from ER-positive cancers was 0.87 (95% CI: 0.69−0.97). The AUC of T1 for discriminating Luminal A from Luminal B cancers was 0.83 (95% CI: 0.61−0.95). In conclusion, quantitative values derived from synthetic MRI show potential for subtyping of invasive breast cancers.
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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
| | - Thomas C. Kwee
- Department of Radiology, Nuclear Medicine, and Molecular Imaging, University Medical Center Groningen, 9700 RB Groningen, The Netherlands
| | - Noriko Nakamura
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara 259-1193, Japan
| | - Nobue Kumaki
- Department of Pathology, Tokai University School of Medicine, Isehara 259-1193, Japan
| | - Naoki Niikura
- Department of Breast Oncology, Tokai University School of Medicine, Isehara 259-1193, Japan
| | - Tetsu Niwa
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara 259-1193, Japan
| | - Jun Hashimoto
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara 259-1193, Japan
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Yaneva G, Dimitrova T, Ivanov D, Ingilizova G, Slavov S. Immunohistochemical Marker Patterns in Female Breast Cancer. Open Access Maced J Med Sci 2022. [DOI: 10.3889/oamjms.2022.8950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND: Breast cancer (BC) represents the most common cancer in women worldwide and in Bulgaria. Its great medico-social importance determines the intensive complex research devoted to BC prevention, early diagnosis and management.
AIM: The objective of the present investigation is to reveal some essential peculiarities of four main immunohistochemical markers used in the diagnosis of molecular subtypes of female BC.
MATERIALS AND METHODS: During the period between December 1, 2017 and November 30, 2020, we examined a total of 128 randomly selected female BC patients operated on in Marko Markov Specialized Hospital for Active Treatment of Oncological Diseases of Varna, Bulgaria. We analyze BC molecular types and four immunohistochemical markers in BC patients. The expression of estrogen (ER) and progesterone (PR) receptors is assessed in mammary gland biopsies and surgical specimens by using the indirect immunoperoxidase method with EnVision™ FLEX MiniKit, that of HER2 with HercepTest™ and that of Ki-67 proliferation index with Leica Aperio Scan Scope AT2 device. The positivity and negativity of these receptors in single molecular subtypes is evaluated.
RESULTS: The luminal B HER2-positive and the luminal B HER2-negative subtypes are most common - in 36.72% and 35.16% of the cases, respectively. TNBC subtype is established in 11.72%) the luminal A - in 8.59% and the non-luminal HER2-positive subtype - in 7.81% of the cases. The positive expression is statistically significantly more common in ER (t=8.972; p<0.0001) and PR (t=2.828; p<0.01), while the negative expression insignificantly prevails in HER2.
CONCLUSION: Our immunohistochemical results in female BC patients prove the role of single receptor expression for the proper and timely decision making about the necessity and benefit of additional chemotherapy in selected surgically treated cases.
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Matsushima S. [3. Pathological Diagnosis in MRI]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2022; 78:658-663. [PMID: 35718456 DOI: 10.6009/jjrt.2022-2034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Affiliation(s)
- Shigeru Matsushima
- Biomedical Imaging Science, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine
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