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Loubrie S, Batasin S, Rakow-Penner R. Editorial for "Intraobserver and Interobserver Reproducibility of Breast Diffusion-Weighted Imaging Quantitative Parameters: Readout-Segmented vs. Single-Shot Echo-Planar Imaging". J Magn Reson Imaging 2023; 58:1737-1738. [PMID: 37000421 DOI: 10.1002/jmri.28710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 03/17/2023] [Indexed: 04/01/2023] Open
Affiliation(s)
- Stephane Loubrie
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Summer Batasin
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Rebecca Rakow-Penner
- Department of Radiology, University of California San Diego, La Jolla, California, USA
- Department of Bioengineering, University of California San Diego, La Jolla, California, USA
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Chen X, Yang Z, Huang R, Li Y, Liao Y, Li G, Wang M, Chen X, Dai Z, Fan W. Development and validation of a point-based scoring system for predicting axillary lymph node metastasis and disease outcome in breast cancer using clinicopathological and multiparametric MRI features. Cancer Imaging 2023; 23:54. [PMID: 37264446 DOI: 10.1186/s40644-023-00564-9] [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: 10/20/2022] [Accepted: 05/01/2023] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND Axillary lymph node (ALN) metastasis is used to select treatment strategies and define the prognosis in breast cancer (BC) patients and is typically assessed using an invasive procedure. Noninvasive, simple, and reliable tools to accurately predict ALN status are desirable. We aimed to develop and validate a point-based scoring system (PSS) for stratifying the ALN metastasis risk of BC based on clinicopathological and quantitative MRI features and to explore its prognostic significance. METHODS A total of 219 BC patients were evaluated. The clinicopathological and quantitative MRI features of the tumors were collected. A multivariate logistic regression analysis was used to create the PSS. The performance of the models was evaluated using receiver operating characteristic curves, and the area under the curve (AUC) of the models was calculated. Kaplan-Meier curves were used to analyze the survival outcomes. RESULTS Clinical features, including the American Joint Committee on Cancer (AJCC) stage, T stage, human epidermal growth factor receptor-2, estrogen receptor, and quantitative MRI features, including maximum tumor diameter, Kep, Ve, and TTP, were identified as risk factors for ALN metastasis and were assigned scores for the PSS. The PSS achieved an AUC of 0.799 in the primary cohort and 0.713 in the validation cohort. The recurrence-free survival (RFS) and overall survival (OS) of the high-risk (> 19.5 points) groups were significantly shorter than those of the low-risk (≤ 19.5 points) groups in the PSS. CONCLUSION PSS could predict the ALN metastasis risk of BC. A PSS greater than 19.5 was demonstrated to be a predictor of short RFS and OS.
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Affiliation(s)
- Xiaofeng Chen
- Department of Radiology, Meizhou People's Hospital, Meizhou, 514031, China
- Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, Meizhou, 514031, People's Republic of China
| | - Zhiqi Yang
- Department of Radiology, Meizhou People's Hospital, Meizhou, 514031, China
- Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, Meizhou, 514031, People's Republic of China
| | - Ruibin Huang
- Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515000, People's Republic of China
| | - Yue Li
- Department of Radiology, Meizhou People's Hospital, Meizhou, 514031, China
| | | | - Guijin Li
- MR Application, Siemens Healthineers, Shanghai, 201318, China
| | - Mengzhu Wang
- MR Scientific Marketing, Siemens Healthineers, Guangzhou, 510620, China
| | - Xiangguang Chen
- Department of Radiology, Meizhou People's Hospital, Meizhou, 514031, China
- Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, Meizhou, 514031, People's Republic of China
| | - Zhuozhi Dai
- Department of Radiology, Shantou Central Hospital, Shantou, Guangdong, 515041, People's Republic of China.
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, Guangdong, China.
| | - Weixiong Fan
- Department of Radiology, Meizhou People's Hospital, Meizhou, 514031, China.
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Dai X, Shen Y, Gao Y, Huang G, Lin B, Liu Y. Correlation study between apparent diffusion coefficients and the prognostic factors in breast cancer. Clin Radiol 2023; 78:347-355. [PMID: 36746720 DOI: 10.1016/j.crad.2022.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 11/10/2022] [Accepted: 11/17/2022] [Indexed: 01/05/2023]
Abstract
AIM To analyse the correlation between apparent diffusion coefficients (ADC) derived from intratumoural and peritumoural regions with prognostic factors and immune-inflammatory markers in breast cancer (BC). MATERIALS AND METHODS In this retrospective study, 89 patients (age range, 28-66 years; median, 45 years) with a diagnosis of invasive BC who underwent routine blood tests and multiparametric magnetic resonance imaging (MRI) were enrolled. The study cohort was stratified according to tumour maximum cross-section ≥20 mm, lymph node metastasis (LNM), time-signal intensity curve (TIC) type, and receptor status. Minimum, maximum, mean, and heterogeneity values of tumour ADC (ADCtmin, ADCtmax, ADCtmean, and ADCheter), maximum values of peritumoural ADC (ADCpmax), and the ratio of peritumoural-tumour ADC (ADCratio) were obtained on the ADC maps. Linear regression analyses were performed to investigate the correlation between immune-inflammatory markers, prognostic factors and ADC values. RESULTS HER-2 was positively associated with ADCtmax, ADCtmean, and ADCpmax values (β = 0.306, p=0.004; β = 0.283, p=0.007; β = 0.262, p=0.007, respectively), while platelet-to-lymphocyte ratio (PLR) was positively associated with ADCpmax and ADCratio values (β = 0.227, p=0.020; β = 0.231, p=0.020, respectively). Among ADC parameters, ADCpmax showed the highest predictive values for evaluating the presence of LNM (AUC, 0.751; sensitivity, 70.4%; specificity, 77.1%). CONCLUSION The ADCpmax value could provide additional assistance in predicting prognostic factors of BC.
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Affiliation(s)
- X Dai
- Shenzhen Clinical Medical College, Guangzhou University of Chinese Medicine, Shenzhen, China; Department of Radiology, Longgang Central Hospital of Shenzhen, Shenzhen, China
| | - Y Shen
- Shenzhen Clinical Medical College, Guangzhou University of Chinese Medicine, Shenzhen, China; Department of Radiology, Longgang Central Hospital of Shenzhen, Shenzhen, China.
| | - Y Gao
- The First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - G Huang
- Department of Pathology, Longgang Central Hospital of Shenzhen, Shenzhen, China
| | - B Lin
- Department of Breast Surgery, Longgang Central Hospital of Shenzhen, Shenzhen, China
| | - Y Liu
- Shenzhen Clinical Medical College, Guangzhou University of Chinese Medicine, Shenzhen, China; Department of Radiology, Longgang Central Hospital of Shenzhen, Shenzhen, China
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Liang X, Chen X, Yang Z, Liao Y, Wang M, Li Y, Fan W, Dai Z, Zhang Y. Early prediction of pathological complete response to neoadjuvant chemotherapy combining DCE-MRI and apparent diffusion coefficient values in breast Cancer. BMC Cancer 2022; 22:1250. [PMID: 36460972 PMCID: PMC9716688 DOI: 10.1186/s12885-022-10315-x] [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: 05/16/2022] [Accepted: 11/14/2022] [Indexed: 12/04/2022] Open
Abstract
INTRODUCTION Improving the early prediction of neoadjuvant chemotherapy (NAC) efficacy in breast cancer can lead to an improved prediction of the final prognosis of patients, which would be useful for promoting individualized treatment. This study aimed to explore the value of the combination of dynamic contrast-enhanced (DCE)-MRI parameters and apparent diffusion coefficient (ADC) values in the early prediction of pathological complete response (pCR) to NAC for breast cancer. METHODS A total of 119 (range, 28-69 years) patients with biopsy-proven breast cancer who received two cycles of NAC before breast surgery were retrospectively enrolled from our hospital database. Patients were divided into pCR and non pCR groups according to their pathological responses; a total of 24 patients achieved pCR, while 95 did not. The quantitative (Ktrans; Kep; Ve; IAUC) and semiquantitative parameters (W-in; W-out; TTP) of DCE-MRI that were significantly different between groups were combined with ADC values to explore their value in the early prediction of pCR to NAC for breast cancer. The independent T test was performed to compare the differences in DCE-MRI parameters and ADC values between the two groups. Receiver operating characteristic (ROC) curves were plotted, and the area under the ROC curve (AUC), sensitivity and specificity were calculated to evaluate the performance of the prediction. RESULTS The Ktrans, Kep, IAUC, ADC, W-in and TTP values were significantly different between the pCR and non pCR groups after NAC. The AUC (0.845) and specificity (95.79%) of the combined Ktrans, Kep, IAUC and ADC values were both higher than those of the individual parameters. The combination of W-in, TTP and ADC values had the highest AUC value (0.886) in predicting pCR, with a sensitivity and specificity of 87.5% and 82.11%, respectively. CONCLUSIONS The results suggested that the combination of ADC values and quantitative and semiquantitative DCE-MRI parameters, especially the combination of W-in, TTP, and ADC values, may improve the early prediction of pCR in breast cancer.
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Affiliation(s)
- Xinhong Liang
- grid.459766.fDepartment of Radiology, Meizhou People’s Hospital, Meizhou, 514031 China
| | - Xiaofeng Chen
- grid.459766.fDepartment of Radiology, Meizhou People’s Hospital, Meizhou, 514031 China
| | - Zhiqi Yang
- grid.459766.fDepartment of Radiology, Meizhou People’s Hospital, Meizhou, 514031 China
| | | | - Mengzhu Wang
- MR Scientific Marketing, Siemens Healthineers, Guangzhou, 510620 China
| | - Yulin Li
- grid.459766.fDepartment of Radiology, Meizhou People’s Hospital, Meizhou, 514031 China
| | - Weixiong Fan
- grid.459766.fDepartment of Radiology, Meizhou People’s Hospital, Meizhou, 514031 China
| | - Zhuozhi Dai
- grid.452734.3Department of Radiology, Shantou Central Hospital, Guangdong, 515041 China
| | - Yunuo Zhang
- grid.459766.fDepartment of Oncology, Meizhou People’s Hospital, Meizhou, 514031 China
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Lai T, Chen X, Yang Z, Huang R, Liao Y, Chen X, Dai Z. Quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging to predict lymphovascular invasion and survival outcome in breast cancer. Cancer Imaging 2022; 22:61. [PMID: 36273200 PMCID: PMC9587620 DOI: 10.1186/s40644-022-00499-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 08/21/2022] [Accepted: 10/10/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Lymphovascular invasion (LVI) predicts a poor outcome of breast cancer (BC), but LVI can only be postoperatively diagnosed by histopathology. We aimed to determine whether quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can preoperatively predict LVI and clinical outcome of BC patients. METHODS A total of 189 consecutive BC patients who underwent multiparametric MRI scans were retrospectively evaluated. Quantitative (Ktrans, Ve, Kep) and semiquantitative DCE-MRI parameters (W- in, W- out, TTP), and clinicopathological features were compared between LVI-positive and LVI-negative groups. All variables were calculated by using univariate logistic regression analysis to determine the predictors for LVI. Multivariate logistic regression was used to build a combined-predicted model for LVI-positive status. Receiver operating characteristic (ROC) curves evaluated the diagnostic efficiency of the model and Kaplan-Meier curves showed the relationships with the clinical outcomes. Multivariate analyses with a Cox proportional hazard model were used to analyze the hazard ratio (HR) for recurrence-free survival (RFS) and overall survival (OS). RESULTS LVI-positive patients had a higher Kep value than LVI-negative patients (0.92 ± 0.30 vs. 0.81 ± 0.23, P = 0.012). N2 stage [odds ratio (OR) = 3.75, P = 0.018], N3 stage (OR = 4.28, P = 0.044), and Kep value (OR = 5.52, P = 0.016) were associated with LVI positivity. The combined-predicted LVI model that incorporated the N stage and Kep yielded an accuracy of 0.735 and a specificity of 0.801. The median RFS was significantly different between the LVI-positive and LVI-negative groups (31.5 vs. 34.0 months, P = 0.010) and between the combined-predicted LVI-positive and LVI-negative groups (31.8 vs. 32.0 months, P = 0.007). The median OS was not significantly different between the LVI-positive and LVI-negative groups (41.5 vs. 44.0 months, P = 0.270) and between the combined-predicted LVI-positive and LVI-negative groups (42.8 vs. 43.5 months, P = 0.970). LVI status (HR = 2.40), N2 (HR = 3.35), and the combined-predicted LVI model (HR = 1.61) were independently associated with disease recurrence. CONCLUSION The quantitative parameter of Kep could predict LVI. LVI status, N stage, and the combined-predicted LVI model were predictors of a poor RFS but not OS.
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Affiliation(s)
- Tianfu Lai
- Department of Radiology, Meizhou People's Hospital, 514031, Meizhou, China
| | - Xiaofeng Chen
- Department of Radiology, Meizhou People's Hospital, 514031, Meizhou, China.
- Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational, Research of Hakka Population, 514031, Meizhou, China.
| | - Zhiqi Yang
- Department of Radiology, Meizhou People's Hospital, 514031, Meizhou, China
- Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational, Research of Hakka Population, 514031, Meizhou, China
| | - Ruibin Huang
- Department of Radiology, First Affiliated Hospital of Shantou University Medical College, 515000, Shantou, China
| | | | - Xiangguang Chen
- Department of Radiology, Meizhou People's Hospital, 514031, Meizhou, China.
- Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational, Research of Hakka Population, 514031, Meizhou, China.
| | - Zhuozhi Dai
- Department of Radiology, Shantou Central Hospital, 515031, Shantou, Guangdong, China.
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