1
|
Xu R, Yu D, Luo P, Li X, Jiang L, Chang S, Li G. Do Habitat MRI and Fractal Analysis Help Distinguish Triple-Negative Breast Cancer From Non-Triple-Negative Breast Carcinoma. Can Assoc Radiol J 2024; 75:584-592. [PMID: 38389194 DOI: 10.1177/08465371241231573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2024] Open
Abstract
Purpose: To determine whether multiparametric MRI-based spatial habitats and fractal analysis can help distinguish triple-negative breast cancer (TNBC) from non-TNBC. Method: Multiparametric DWI and DCE-MRI at 3T were obtained from 142 biopsy- and surgery-proven breast cancer with 148 breast lesions (TNBC = 26 and non-TNBC = 122). The contrast-enhancing lesions were divided into 3 spatial habitats based on perfusion and diffusion patterns using K-means clustering. The fractal dimension (FD) of the tumour subregions was calculated. The accuracy of the habitat segmentation was measured using the Dice index. Inter- and intra-reader reliability were evaluated with the intraclass correlation coefficient (ICC). The ability to predict TNBC status was assessed using the receiver operating characteristic curve. Results: The Dice index for the whole tumour was 0.81 for inter-reader and 0.88 for intra-reader reliability. The inter- and intra-reader reliability were excellent for all 3 tumour habitats and fractal features (ICC > 0.9). TNBC had a lower hypervascular cellular habitat and higher FD 1 compared to non-TNBC (all P < .001). Multivariate analysis confirmed that hypervascular cellular habitat (OR = 0.88) and FD 1 (OR = 1.35) were independently associated with TNBC (all P < .001) after adjusting for rim enhancement, axillary lymph nodes status, and histological grade. The diagnostic model combining hypervascular cellular habitat and FD 1 showed excellent discriminatory ability for TNBC, with an AUC of 0.951 and an accuracy of 91.9%. Conclusions: The fraction of hypervascular cellular habitat and its FD may serve as useful imaging biomarkers for predicting TNBC status.
Collapse
Affiliation(s)
- Run Xu
- Department of Radiology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Dan Yu
- United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - Peng Luo
- Department of Radiology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xuefeng Li
- Department of Radiology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Lei Jiang
- Department of Radiology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Shixin Chang
- Department of Radiology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Guanwu Li
- Department of Radiology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| |
Collapse
|
2
|
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] [MESH Headings] [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.
Collapse
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.
| |
Collapse
|
3
|
Xia L, Yang L, Hu M. Diagnostic Accuracy of Combined 3.0T Magnetic Resonance Imaging and Molybdenum Target X-Ray in Triple-Negative Breast Cancer: Correlation with Prognosis in Patients Undergoing Sentinel Lymph Node Biopsy. WOMEN'S HEALTH REPORTS (NEW ROCHELLE, N.Y.) 2024; 5:546-553. [PMID: 39035134 PMCID: PMC11257123 DOI: 10.1089/whr.2023.0080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/17/2024] [Indexed: 07/23/2024]
Abstract
Objective This study assessed the diagnostic efficacy of combining 3.0T MRI and molybdenum target X-ray in triple-negative breast carcinoma (TNBC) and its association with the prognosis of sentinel lymph node biopsy (SLNB). Methods The retrospective analysis included 128 patients suspected of having TNBC, who underwent 3.0T MRI and molybdenum target X-ray. Sensitivity and specificity were calculated for each imaging technique, and their combined diagnosis was evaluated using the four-table method. Consistency between the imaging techniques and pathological examination was assessed using the consistency checking method. Additionally, changes in imaging indicators were compared among patients with different prognostic indicators. Results Among the 128 patients, 86 were diagnosed with TNBC through pathological examination. The sensitivity and specificity of 3.0T MRI for TNBC were 82.56% and 76.19%, respectively. Molybdenum target X-ray exhibited a sensitivity of 77.91% and specificity of 78.57%. The combined diagnosis of the two techniques showed a sensitivity of 90.70% and specificity of 86.36%. There was good agreement between both imaging techniques and pathological examination results. Significant differences were observed in imaging indicators based on tumor diameter, histological grade, and lymph node metastasis. Conclusion Both 3.0T MRI and molybdenum target X-ray are valuable in diagnosing TNBC. Additionally, these imaging techniques provide prognostic information and can aid in treatment decision-making. The findings highlight the importance of 3.0T MRI and molybdenum target X-ray in improving the outcomes of patients with TNBC.
Collapse
Affiliation(s)
- Li Xia
- Shanghai Ninth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Huangpu, China
| | - Ling Yang
- Shanghai Ninth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Huangpu, China
| | - Meng Hu
- Shanghai Ninth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Huangpu, China
| |
Collapse
|
4
|
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.
Collapse
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.
| |
Collapse
|
5
|
Mao C, Hu L, Jiang W, Qiu Y, Yang Z, Liu Y, Wang M, Wang D, Su Y, Lin J, Yan X, Cai Z, Zhang X, Shen J. Discrimination between human epidermal growth factor receptor 2 (HER2)-low-expressing and HER2-overexpressing breast cancers: a comparative study of four MRI diffusion models. Eur Radiol 2024; 34:2546-2559. [PMID: 37672055 DOI: 10.1007/s00330-023-10198-x] [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: 01/05/2023] [Revised: 06/13/2023] [Accepted: 07/08/2023] [Indexed: 09/07/2023]
Abstract
OBJECTIVES To determine the value of conventional DWI, continuous-time random walk (CTRW), fractional order calculus (FROC), and stretched exponential model (SEM) in discriminating human epidermal growth factor receptor 2 (HER2) status of breast cancer (BC). METHODS This prospective study included 158 women who underwent DWI, CTRW, FROC, and SEM and were pathologically categorized into the HER2-zero-expressing group (n = 10), HER2-low-expressing group (n = 86), and HER2-overexpressing group (n = 62). Nine diffusion parameters, namely ADC, αCTRW, βCTRW, DCTRW, βFROC, DFROC, μFROC, αSEM, and DDCSEM of the primary tumor, were derived from four diffusion models. These diffusion metrics and clinicopathologic features were compared between groups. Logistic regression was used to determine the optimal diffusion metrics and clinicopathologic variables for classifying the HER2-expressing statuses. Receiver operating characteristic (ROC) curves were used to evaluate their discriminative ability. RESULTS The estrogen receptor (ER) status, progesterone receptor (PR) status, and tumor size differed between HER2-low-expressing and HER2-overexpressing groups (p < 0.001 to p = 0.009). The αCTRW, DCTRW, βFROC, DFROC, μFROC, αSEM, and DDCSEM were significantly lower in HER2-low-expressing BCs than those in HER2-overexpressing BCs (p < 0.001 to p = 0.01). Further multivariable logistic regression analysis showed that the αCTRW was the single best discriminative metric, with an area under the curve (AUC) being higher than that of ADC (0.802 vs. 0.610, p < 0.05); the addition of ER status, PR status, and tumor size to the αCTRW improved the AUC to 0.877. CONCLUSIONS The αCTRW could help discriminate the HER2-low-expressing and HER2-overexpressing BCs. CLINICAL RELEVANCE STATEMENT Human epidermal growth factor receptor 2 (HER2)-low-expressing breast cancer (BC) might also benefit from the HER2-targeted therapy. Prediction of HER2-low-expressing BC or HER2-overexpressing BC is crucial for appropriate management. Advanced continuous-time random walk diffusion MRI offers a solution to this clinical issue. KEY POINTS • Human epidermal receptor 2 (HER2)-low-expressing BC had lower αCTRW, DCTRW, βFROC, DFROC, μFROC, αSEM, and DDCSEM values compared with HER2-overexpressing breast cancer. • The αCTRW was the single best diffusion metric (AUC = 0.802) for discrimination between the HER2-low-expressing and HER2-overexpressing breast cancers. • The addition of αCTRW to the clinicopathologic features (estrogen receptor status, progesterone receptor status, and tumor size) further improved the discriminative ability.
Collapse
Affiliation(s)
- Chunping Mao
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Lanxin Hu
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Wei Jiang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Ya Qiu
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Zehong Yang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Yeqing Liu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Department of Pathology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Mengzhu Wang
- MR Scientific Marketing, Siemens Healthcare, Guangzhou, Guangdong, China
| | - Dongye Wang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Yun Su
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Jinru Lin
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Xu Yan
- MR Scientific Marketing, Siemens Healthcare, Guangzhou, Guangdong, China
| | - Zhaoxi Cai
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Xiang Zhang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China.
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.
| | - Jun Shen
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China.
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
Qi X, Wang W, Pan S, Liu G, Xia L, Duan S, He Y. Predictive value of triple negative breast cancer based on DCE-MRI multi-phase full-volume ROI clinical radiomics model. Acta Radiol 2024; 65:173-184. [PMID: 38017694 DOI: 10.1177/02841851231215145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
BACKGROUND Since no studies compared the value of radiomics features of distinct phases of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting triple-negative breast cancer (TNBC). PURPOSE To identify the optimal phase of DCE-MRI for diagnosing TNBC and, in combination with clinical factors, to develop a clinical-radiomics model to well predict TNBC. MATERIAL AND METHODS This retrospective study included 158 patients with pathology-confirmed breast cancer, including 38 cases of TNBC. The patients were randomly divided into the training and validation set (7:3). Eight radiomics models were built based on eight DCE-MR phases, and their performances were evaluated using receiver operating characteristic curve (ROC) and DeLong's test. The Radscore derived from the best radiomics model was integrated with independent clinical risk factors to construct a clinical-radiomics predictive model, and evaluate its performance using ROC analysis, calibration, and decision curve analyses. RESULTS WHO classification, margin, and T2-weighted (T2W) imaging signals were significantly correlated with TNBC and independent risk factors for TNBC (P<0.05). The clinical model yielded areas under the curve (AUCs) of 0.867 and 0.843 in the training and validation sets, respectively. The radiomics model based on DCEphase7 achieved the highest efficacy, with an AUC of 0.818 and 0.777. The AUC of the clinical-radiomics model was 0.936 and 0.886 in the training and validation sets, respectively. The decision curve showed the clinical utility of the clinical-radiomics model. CONCLUSION The radiomics features of DCE-MRI had the potential to predict TNBC and could improve the performance of clinical risk factors for preoperative personalized prediction of TNBC.
Collapse
Affiliation(s)
- Xuan Qi
- Department of Radiology, Ma'anshan People's Hospital, Maanshan, PR China
| | - Wuling Wang
- Department of Radiology, Ma'anshan People's Hospital, Maanshan, PR China
| | - Shuya Pan
- Department of Radiology, Ma'anshan People's Hospital, Maanshan, PR China
| | - Guangzhu Liu
- Ma'anshan Clinical College, Anhui Medical University, Hefei, PR China
| | - Liang Xia
- Department of Radiology, Sir Run Run Hospital affiliated to Nanjing Medical University, Nanjing, PR China
| | - Shaofeng Duan
- Precision Health Institution, GE Healthcare China, Shanghai, China
| | - Yongsheng He
- Department of Radiology, Ma'anshan People's Hospital, Maanshan, PR China
| |
Collapse
|
8
|
Kataoka M, Iima M, Miyake KK, Honda M. Multiparametric Approach to Breast Cancer With Emphasis on Magnetic Resonance Imaging in the Era of Personalized Breast Cancer Treatment. Invest Radiol 2024; 59:26-37. [PMID: 37994113 DOI: 10.1097/rli.0000000000001044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2023]
Abstract
ABSTRACT A multiparametric approach to breast cancer imaging offers the advantage of integrating the diverse contributions of various parameters. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is the most important MRI sequence for breast imaging. The vascularity and permeability of lesions can be estimated through the use of semiquantitative and quantitative parameters. The increased use of ultrafast DCE-MRI has facilitated the introduction of novel kinetic parameters. In addition to DCE-MRI, diffusion-weighted imaging provides information associated with tumor cell density, with advanced diffusion-weighted imaging techniques such as intravoxel incoherent motion, diffusion kurtosis imaging, and time-dependent diffusion MRI opening up new horizons in microscale tissue evaluation. Furthermore, T2-weighted imaging plays a key role in measuring the degree of tumor aggressiveness, which may be related to the tumor microenvironment. Magnetic resonance imaging is, however, not the only imaging modality providing semiquantitative and quantitative parameters from breast tumors. Breast positron emission tomography demonstrates superior spatial resolution to whole-body positron emission tomography and allows comparable delineation of breast cancer to MRI, as well as providing metabolic information, which often precedes vascular and morphological changes occurring in response to treatment. The integration of these imaging-derived factors is accomplished through multiparametric imaging. In this article, we explore the relationship among the key imaging parameters, breast cancer diagnosis, and histological characteristics, providing a technical and theoretical background for these parameters. Furthermore, we review the recent studies on the application of multiparametric imaging to breast cancer and the significance of the key imaging parameters.
Collapse
Affiliation(s)
- Masako Kataoka
- From the Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine Kyoto University, Kyoto, Japan (M.K., M.I., M.H.); Institute for Advancement of Clinical and Translational Science, Kyoto University Hospital, Kyoto, Japan (M.I.); Department of Advanced Imaging in Medical Magnetic Resonance, Graduate School of Medicine Kyoto University, Kyoto, Japan (K.K.M); and Department of Diagnostic Radiology, Kansai Electric Power Hospital, Osaka, Japan (M.H.)
| | | | | | | |
Collapse
|
9
|
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.
Collapse
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
| |
Collapse
|
10
|
Zhou J, Jin Y, Miao H, Lu S, Liu X, He Y, Liu H, Zhao Y, Zhang Y, Liu YL, Pan Z, Chen JH, Wang M, Su MY. Magnetic Resonance Imaging Features Associated with a High and Low Expression of Tumor-Infiltrating Lymphocytes: A Stratified Analysis According to Molecular Subtypes. Cancers (Basel) 2023; 15:5672. [PMID: 38067374 PMCID: PMC10705181 DOI: 10.3390/cancers15235672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 11/23/2023] [Accepted: 11/27/2023] [Indexed: 01/19/2024] Open
Abstract
A total of 457 patients, including 241 HR+/HER2- patients, 134 HER2+ patients, and 82 TN patients, were studied. The percentage of TILs in the stroma adjacent to the tumor cells was assessed using a 10% cutoff. The low TIL percentages were 82% in the HR+ patients, 63% in the HER2+ patients, and 56% in the TN patients (p < 0.001). MRI features such as morphology as mass or non-mass enhancement (NME), shape, margin, internal enhancement, presence of peritumoral edema, and the DCE kinetic pattern were assessed. Tumor sizes were smaller in the HR+/HER2- group (p < 0.001); HER2+ was more likely to present as NME (p = 0.031); homogeneous enhancement was mostly seen in HR+ (p < 0.001); and the peritumoral edema was present in 45% HR+, 71% HER2+, and 80% TN (p < 0.001). In each subtype, the MR features between the high- vs. low-TIL groups were compared. In HR+/HER2-, peritumoral edema was more likely to be present in those with high TILs (70%) than in those with low TILs (40%, p < 0.001). In TN, those with high TILs were more likely to present a regular shape (33%) than those with low TILs (13%, p = 0.029) and more likely to present the circumscribed margin (19%) than those with low TILs (2%, p = 0.009).
Collapse
Affiliation(s)
- Jiejie Zhou
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; (J.Z.); (H.M.); (X.L.); (Y.H.); (H.L.); (Y.Z.)
- Department of Radiological Sciences, University of California, Irvine, CA 92697, USA; (Y.Z.); (Y.-L.L.); (J.-H.C.)
| | - Yi Jin
- Department of Pathology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; (Y.J.); (S.L.)
| | - Haiwei Miao
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; (J.Z.); (H.M.); (X.L.); (Y.H.); (H.L.); (Y.Z.)
| | - Shanshan Lu
- Department of Pathology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; (Y.J.); (S.L.)
| | - Xinmiao Liu
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; (J.Z.); (H.M.); (X.L.); (Y.H.); (H.L.); (Y.Z.)
| | - Yun He
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; (J.Z.); (H.M.); (X.L.); (Y.H.); (H.L.); (Y.Z.)
| | - Huiru Liu
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; (J.Z.); (H.M.); (X.L.); (Y.H.); (H.L.); (Y.Z.)
| | - Youfan Zhao
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; (J.Z.); (H.M.); (X.L.); (Y.H.); (H.L.); (Y.Z.)
| | - Yang Zhang
- Department of Radiological Sciences, University of California, Irvine, CA 92697, USA; (Y.Z.); (Y.-L.L.); (J.-H.C.)
| | - Yan-Lin Liu
- Department of Radiological Sciences, University of California, Irvine, CA 92697, USA; (Y.Z.); (Y.-L.L.); (J.-H.C.)
| | - Zhifang Pan
- Zhejiang Engineering Research Center of Intelligent Medicine, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China;
| | - Jeon-Hor Chen
- Department of Radiological Sciences, University of California, Irvine, CA 92697, USA; (Y.Z.); (Y.-L.L.); (J.-H.C.)
| | - Meihao Wang
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; (J.Z.); (H.M.); (X.L.); (Y.H.); (H.L.); (Y.Z.)
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, CA 92697, USA; (Y.Z.); (Y.-L.L.); (J.-H.C.)
- Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung 840203, Taiwan
| |
Collapse
|
11
|
Jiang W, Du S, Gao S, Xie L, Xie Z, Wang M, Peng C, Shi J, Zhang L. Correlation between synthetic MRI relaxometry and apparent diffusion coefficient in breast cancer subtypes with different neoadjuvant therapy response. Insights Imaging 2023; 14:162. [PMID: 37775610 PMCID: PMC10541382 DOI: 10.1186/s13244-023-01492-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 07/25/2023] [Indexed: 10/01/2023] Open
Abstract
BACKGROUND To evaluate the correlation between synthetic MRI (syMRI) relaxometry and apparent diffusion coefficient (ADC) maps in different breast cancer subtypes and treatment response subgroups. METHODS Two hundred sixty-three neoadjuvant therapy (NAT)-treated breast cancer patients with baseline MRI were enrolled. Tumor annotations were obtained by drawing regions of interest (ROIs) along the lesion on T1/T2/PD and ADC maps respectively. Histogram features from T1/T2/PD and ADC maps were respectively calculated, and the correlation between each pair of identical features was analyzed. Meanwhile, features between different NAT treatment response groups were compared, and their discriminatory power was evaluated. RESULTS Among all patients, 20 out of 27 pairs of features weakly correlated (r = - 0.13-0.30). For triple-negative breast cancer (TNBC), features from PD map in the pathological complete response (pCR) group (r = 0.60-0.86) showed higher correlation with ADC than that of the non-pCR group (r = 0.30-0.43), and the mean from the ADC and PD maps in the pCR group strongly correlated (r = 0.86). For HER2-positive, few correlations were found both in the pCR and non-pCR groups. For luminal HER2-negative, T2 map correlated more with ADC than T1 and PD maps. Significant differences were seen in T2 low percentiles and median in the luminal-HER2 negative subtype, yielding moderate AUCs (0.68/0.72/0.71). CONCLUSIONS The relationship between ADC and PD maps in TNBC may indicate different NAT responses. The no-to-weak correlation between the ADC and syMRI suggests their complementary roles in tumor microenvironment evaluation. CRITICAL RELEVANCE STATEMENT The relationship between ADC and PD maps in TNBC may indicate different NAT responses, and the no-to-weak correlation between the ADC and syMRI suggests their complementary roles in tumor microenvironment evaluation. KEY POINTS • The relationship between ADC and PD in TNBC indicates different NAT responses. • The no-to-weak correlations between ADC and syMRI complementarily evaluate tumor microenvironment. • T2 low percentiles and median predict NAT response in luminal-HER2-negative subtype.
Collapse
Affiliation(s)
- Wenhong Jiang
- 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
| | - Lizhi Xie
- GE Healthcare, MR Research China, Beijing, China
| | - Zichuan Xie
- Guangzhou institute of technology, Xidian University, Guangzhou, China
| | - Mengfan Wang
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Can Peng
- 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.
| |
Collapse
|
12
|
Houser M, Rapelyea JA. Using Synthetic MRI and Radiomics to Predict Treatment Response in Triple-Negative Breast Cancer. Radiol Imaging Cancer 2023; 5:e230095. [PMID: 37505105 PMCID: PMC10413287 DOI: 10.1148/rycan.230095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/05/2023] [Accepted: 07/10/2023] [Indexed: 07/29/2023]
Affiliation(s)
- Margaret Houser
- From the Department of Radiology, The George Washington University
School of Medicine and Health Sciences, 2150 Pennsylvania Ave NW, Washington, DC
20037
| | - Jocelyn A. Rapelyea
- From the Department of Radiology, The George Washington University
School of Medicine and Health Sciences, 2150 Pennsylvania Ave NW, Washington, DC
20037
| |
Collapse
|
13
|
Hwang KP, Elshafeey NA, Kotrotsou A, Chen H, Son JB, Boge M, Mohamed RM, Abdelhafez AH, Adrada BE, Panthi B, Sun J, Musall BC, Zhang S, Candelaria RP, White JB, Ravenberg EE, Tripathy D, Yam C, Litton JK, Huo L, Thompson AM, Wei P, Yang WT, Pagel MD, Ma J, Rauch GM. A Radiomics Model Based on Synthetic MRI Acquisition for Predicting Neoadjuvant Systemic Treatment Response in Triple-Negative Breast Cancer. Radiol Imaging Cancer 2023; 5:e230009. [PMID: 37505106 PMCID: PMC10413296 DOI: 10.1148/rycan.230009] [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: 02/08/2023] [Revised: 04/18/2023] [Accepted: 06/03/2023] [Indexed: 07/29/2023]
Abstract
Purpose To determine if a radiomics model based on quantitative maps acquired with synthetic MRI (SyMRI) is useful for predicting neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC). Materials and Methods In this prospective study, 181 women diagnosed with stage I-III TNBC were scanned with a SyMRI sequence at baseline and at midtreatment (after four cycles of NAST), producing T1, T2, and proton density (PD) maps. Histopathologic analysis at surgery was used to determine pathologic complete response (pCR) or non-pCR status. From three-dimensional tumor contours drawn on the three maps, 310 histogram and textural features were extracted, resulting in 930 features per scan. Radiomic features were compared between pCR and non-pCR groups by using Wilcoxon rank sum test. To build a multivariable predictive model, logistic regression with elastic net regularization and cross-validation was performed for texture feature selection using 119 participants (median age, 52 years [range, 26-77 years]). An independent testing cohort of 62 participants (median age, 48 years [range, 23-74 years]) was used to evaluate and compare the models by area under the receiver operating characteristic curve (AUC). Results Univariable analysis identified 15 T1, 10 T2, and 12 PD radiomic features at midtreatment that predicted pCR with an AUC greater than 0.70 in both the training and testing cohorts. Multivariable radiomics models of maps acquired at midtreatment demonstrated superior performance over those acquired at baseline, achieving AUCs as high as 0.78 and 0.72 in the training and testing cohorts, respectively. Conclusion SyMRI-based radiomic features acquired at midtreatment are potentially useful for identifying early NAST responders in TNBC. Keywords: MR Imaging, Breast, Outcomes Analysis ClinicalTrials.gov registration no. NCT02276443 Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Houser and Rapelyea in this issue.
Collapse
Affiliation(s)
- Ken-Pin Hwang
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Nabil A. Elshafeey
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Aikaterini Kotrotsou
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Huiqin Chen
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Jong Bum Son
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Medine Boge
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Rania M. Mohamed
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Abeer H. Abdelhafez
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Beatriz E. Adrada
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Bikash Panthi
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Jia Sun
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Benjamin C. Musall
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Shu Zhang
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Rosalind P. Candelaria
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Jason B. White
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Elizabeth E. Ravenberg
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Debu Tripathy
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Clinton Yam
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Jennifer K. Litton
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Lei Huo
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Alastair M. Thompson
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Peng Wei
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Wei T. Yang
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Mark D. Pagel
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Jingfei Ma
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Gaiane M. Rauch
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| |
Collapse
|
14
|
Zhang L, Hao J, Guo J, Zhao X, Yin X. Predicting of Ki-67 Expression Level Using Diffusion-Weighted and Synthetic Magnetic Resonance Imaging in Invasive Ductal Breast Cancer. Breast J 2023; 2023:6746326. [PMID: 37063453 PMCID: PMC10098409 DOI: 10.1155/2023/6746326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 02/26/2023] [Accepted: 03/27/2023] [Indexed: 04/18/2023]
Abstract
Objectives To investigate the association between quantitative parameters generated using synthetic magnetic resonance imaging (SyMRI) and diffusion-weighted imaging (DWI) and Ki-67 expression level in patients with invasive ductal breast cancer (IDC). Method We retrospectively reviewed the records of patients with IDC who underwent SyMRI and DWI before treatment. Precontrast and postcontrast relaxation times (T1, longitudinal; T2, transverse), proton density (PD) parameters, and apparent diffusion coefficient (ADC) values were measured in breast lesions. Univariate and multivariate regression analyses were performed to screen for statistically significant variables to differentiate the high (≥30%) and low (<30%) Ki-67 expression groups. Their performance was evaluated by receiver operating characteristic (ROC) curve analysis. Results We analyzed 97 patients. Multivariate regression analysis revealed that the high Ki-67 expression group (n = 57) had significantly higher parameters generated using SyMRI (pre-T1, p=0.001) and lower ADC values (p=0.036) compared with the low Ki-67 expression group (n = 40). Pre-T1 showed the best diagnostic performance for predicting the Ki-67 expression level in patients with invasive ductal breast cancer (areas under the ROC curve (AUC), 0.711; 95% confidence interval (CI), 0.609-0.813). Conclusions Pre-T1 could be used to predict the pretreatment Ki-67 expression level in invasive ductal breast cancer.
Collapse
Affiliation(s)
- Liying Zhang
- Third Affiliated Hospital of Zhengzhou University, Department of Radiology, Zhengzhou, China
| | - Jisen Hao
- Third Affiliated Hospital of Zhengzhou University, Department of Radiology, Zhengzhou, China
| | - Jia Guo
- Third Affiliated Hospital of Zhengzhou University, Department of Radiology, Zhengzhou, China
| | - Xin Zhao
- Third Affiliated Hospital of Zhengzhou University, Department of Radiology, Zhengzhou, China
| | - Xing Yin
- Third Affiliated Hospital of Zhengzhou University, Department of Radiology, Zhengzhou, China
| |
Collapse
|
15
|
Fujiwara Y. [19. Basic Principle and Clinical Application of Synthetic MRI]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2023; 79:851-856. [PMID: 37599070 DOI: 10.6009/jjrt.2023-2243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/22/2023]
Affiliation(s)
- Yasuhiro Fujiwara
- Department of Medical Image Sciences, Faculty of Life Sciences, Kumamoto University
| |
Collapse
|
16
|
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.
Collapse
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
| |
Collapse
|
17
|
Matsuda M, Fukuyama N, Matsuda T, Kikuchi S, Shiraishi Y, Takimoto Y, Kamei Y, Kurata M, Kitazawa R, Kido T. Utility of synthetic MRI in predicting pathological complete response of various breast cancer subtypes prior to neoadjuvant chemotherapy. Clin Radiol 2022; 77:855-863. [DOI: 10.1016/j.crad.2022.06.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 06/24/2022] [Accepted: 06/27/2022] [Indexed: 11/25/2022]
|
18
|
Kazama T, Takahara T, Hashimoto J. Breast Cancer Subtypes and Quantitative Magnetic Resonance Imaging: A Systemic Review. Life (Basel) 2022; 12:life12040490. [PMID: 35454981 PMCID: PMC9028183 DOI: 10.3390/life12040490] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 02/20/2022] [Accepted: 03/08/2022] [Indexed: 12/12/2022] Open
Abstract
Magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer detection. This systematic review investigated the role of quantitative MRI features in classifying molecular subtypes of breast cancer. We performed a literature search of articles published on the application of quantitative MRI features in invasive breast cancer molecular subtype classification in PubMed from 1 January 2002 to 30 September 2021. Of the 1275 studies identified, 106 studies with a total of 12,989 patients fulfilled the inclusion criteria. Bias was assessed based using the Quality Assessment of Diagnostic Studies. All studies were case-controlled and research-based. Most studies assessed quantitative MRI features using dynamic contrast-enhanced (DCE) kinetic features and apparent diffusion coefficient (ADC) values. We present a summary of the quantitative MRI features and their correlations with breast cancer subtypes. In DCE studies, conflicting results have been reported; therefore, we performed a meta-analysis. Significant differences in the time intensity curve patterns were observed between receptor statuses. In 10 studies, including a total of 1276 lesions, the pooled difference in proportions of type Ⅲ curves (wash-out) between oestrogen receptor-positive and -negative cancers was not significant (95% confidence interval (CI): [−0.10, 0.03]). In nine studies, including a total of 1070 lesions, the pooled difference in proportions of type 3 curves between human epidermal growth factor receptor 2-positive and -negative cancers was significant (95% CI: [0.01, 0.14]). In six studies including a total of 622 lesions, the pooled difference in proportions of type 3 curves between the high and low Ki-67 groups was significant (95% CI: [0.17, 0.44]). However, the type 3 curve itself is a nonspecific finding in breast cancer. Many studies have examined the relationship between mean ADC and breast cancer subtypes; however, the ADC values overlapped significantly between subtypes. The heterogeneity of ADC using kurtosis or difference, diffusion tensor imaging parameters, and relaxation time was reported recently with promising results; however, current evidence is limited, and further studies are required to explore these potential applications.
Collapse
Affiliation(s)
- Toshiki Kazama
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara 259-1193, Japan;
- Correspondence: ; Tel.: +81-463-93-1121
| | - Taro Takahara
- Department of Biomedical Engineering, Tokai University School of Engineering, Hiratsuka 259-1207, Japan;
| | - Jun Hashimoto
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara 259-1193, Japan;
| |
Collapse
|
19
|
Gao W, Yang Q, Li X, Chen X, Wei X, Diao Y, Zhang Y, Chen C, Guo B, Wang Y, Lei Z, Zhang S. Synthetic MRI with quantitative mappings for identifying receptor status, proliferation rate, and molecular subtypes of breast cancer. Eur J Radiol 2022; 148:110168. [DOI: 10.1016/j.ejrad.2022.110168] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 12/06/2021] [Accepted: 01/15/2022] [Indexed: 12/21/2022]
|
20
|
Mao C, Jiang W, Huang J, Wang M, Yan X, Yang Z, Wang D, Zhang X, Shen J. Quantitative Parameters of Diffusion Spectrum Imaging: HER2 Status Prediction in Patients With Breast Cancer. Front Oncol 2022; 12:817070. [PMID: 35186753 PMCID: PMC8850631 DOI: 10.3389/fonc.2022.817070] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 01/13/2022] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVE To explore the value of quantitative parameters derived from diffusion spectrum imaging (DSI) in preoperatively predicting human epidermal growth factor receptor 2 (HER2) status in patients with breast cancer. METHODS In this prospective study, 114 and 56 female patients with invasive ductal carcinoma were consecutively included in a derivation cohort and an independent validation cohort, respectively. Each patient was categorized into HER2-positive or HER2-negative groups based on the pathologic result. All patients underwent DSI and conventional MRI including dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI). The tumor size, type of the time-signal intensity curve (TIC) from DCE-MRI, apparent diffusion coefficient (ADC) from DWI, and quantitative parameters derived from DSI, including diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) of primary tumors, were measured and compared between the HER2-positive and HER2-negative groups in the derivation cohort. Univariable and multivariable logistic regression analyses were used to determine the potential independent predictors of HER2 status. The discriminative ability of quantitative parameters was assessed by receiver operating characteristic (ROC) curve analyses and validated in the independent cohort. RESULTS In the derivation cohort, the tumor size, TIC type, and ADC values did not differ between the HER2-positive and HER2-negative groups (p = 0.126-0.961). DSI quantitative parameters including axial kurtosis of DKI (DKI_AK), non-Gaussianity (MAP_NG), axial non-Gaussianity (MAP_NGAx), radial non-Gaussianity (MAP_NGRad), return-to-origin probability (MAP_RTOP), return-to-axis probability of MAP (MAP_RTAP), and intracellular volume fraction of NODDI (NODDI_ICVF) were lower in the HER2-positive group than in the HER2-negative group (p ≤ 0.001-0.035). DSI quantitative parameters including radial diffusivity (DTI_RD), mean diffusivity of DTI (DTI_MD), mean squared diffusion (MAP_MSD), and q-space inverse variance of MAP (MAP_QIV) were higher in the HER2-positive group than in the HER2-negative group (p = 0.016-0.049). The ROC analysis showed that the area under the curve (AUC) of ADC was 0.632 and 0.568, respectively, in the derivation and validation cohorts. The AUC values of DSI quantitative parameters ranged from 0.628 to 0.700 and from 0.673 to 0.721, respectively, in the derivation and validation cohorts. Logistic regression analysis showed that only NODDI_ICVF was an independent predictor of HER2 status (p = 0.001), with an AUC of 0.700 and 0.721, respectively, in the derivation and validation cohorts. CONCLUSIONS DSI could be helpful for preoperative prediction of HER2, but DSI alone may not be sufficient in predicting HER2 status preoperatively in patients with breast cancer.
Collapse
Affiliation(s)
- Chunping Mao
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Wei Jiang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jiayi Huang
- Department of Radiology, Shenshan Central Hospital, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Shanwei, China
| | - Mengzhu Wang
- MR Scientific Marketing, Siemens Healthcare, Guangzhou, China
| | - Xu Yan
- MR Scientific Marketing, Siemens Healthcare, Guangzhou, China
| | - Zehong Yang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Dongye Wang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Xiang Zhang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jun Shen
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| |
Collapse
|
21
|
Matsuda M, Tsuda T, Ebihara R, Toshimori W, Okada K, Takeda S, Okumura A, Shiraishi Y, Suekuni H, Kamei Y, Kurata M, Kitazawa R, Mochizuki T, Kido T. Triple-negative breast cancer on contrast-enhanced MRI and synthetic MRI: A comparison with non-triple-negative breast carcinoma. Eur J Radiol 2021; 142:109838. [PMID: 34217136 DOI: 10.1016/j.ejrad.2021.109838] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 06/22/2021] [Accepted: 06/24/2021] [Indexed: 12/20/2022]
Abstract
PURPOSE This study aimed to compare the characteristics of triple-negative breast cancer (TNBC) with non-TNBC on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and synthetic MRI. METHOD This retrospective study included 79 patients with histopathologically proven breast cancer (TNBC: 16, non-TNBC: 63) who underwent synthetic MRI. Using synthetic MR images, we obtained T1 and T2 relaxation times in breast lesions before (Pre-T1, Pre-T2, Pre-PD) and after (Gd-T1, Gd-T2, Gd-PD) contrast agent injection. Subsequently, we calculated the ΔT1 (Pre-T1 - Gd-T1), ΔT2 (Pre-T2 - Gd-T2), Pre-T1/T2, and Gd-T1/T2. We compared the aforementioned quantitative values, as well as several morphologic features between TNBCs and non-TNBCs that were identified on DCE-MRI. RESULTS The multivariate analyses revealed that the Pre-T2 (P = 0.037) and the presence of rim enhancement (P-RIM) (P = 0.034) were significant and independent predictors of TNBC. The area under the receiver operating characteristics curve for all breast cancers was greater when a combination of Pre-T2 and P-RIM (Pre-T2+P-RIM; Method 3, AUC (area under the curve) = 0.858) was used to distinguish between TNBCs and non-TNBCs versus the use of either Pre-T2 alone (Method 1, AUC = 0.786) or P-RIM alone (Method 2, AUC = 0.747). CONCLUSIONS Pre-T2 obtained using synthetic MRI and P-RIM identified on DCE-MRI allowed the differentiation between TNBCs and non-TNBCs. However, these results are preliminary and need to be verified by further studies.
Collapse
Affiliation(s)
- Megumi Matsuda
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime 791-0295, Japan.
| | - Takaharu Tsuda
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime 791-0295, Japan.
| | - Rui Ebihara
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime 791-0295, Japan
| | - Wataru Toshimori
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime 791-0295, Japan
| | - Kanako Okada
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime 791-0295, Japan
| | - Shiori Takeda
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime 791-0295, Japan
| | - Aya Okumura
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime 791-0295, Japan
| | - Yasuhiro Shiraishi
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime 791-0295, Japan.
| | - Hiroshi Suekuni
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime 791-0295, Japan
| | - Yoshiaki Kamei
- Breast Center, Ehime University Hospital, Shitsukawa, Toon, Ehime 791-0295, Japan
| | - Mie Kurata
- Department of Pathology, Ehime University Proteo-Science Center, Shitsukawa, Toon, Ehime 791-0295, Japan; Department of Analytical Pathology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime 791-0295, Japan.
| | - Riko Kitazawa
- Division of Diagnostic Pathology, Ehime University Hospital, Shitsukawa, Toon, Ehime 791-0295, Japan.
| | - Teruhito Mochizuki
- Department of Radiology, I.M. Sechenov First Moscow State Medical University, 8-2 Trubetskaya str., Moscow 119991, Russian Federation
| | - Teruhito Kido
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime 791-0295, Japan
| |
Collapse
|