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Xie T, Zhao Q, Fu C, Grimm R, Dominik Nickel M, Hu X, Yue L, Peng W, Gu Y. Quantitative analysis from ultrafast dynamic contrast-enhanced breast MRI using population-based versus individual arterial input functions, and comparison with semi-quantitative analysis. Eur J Radiol 2024; 176:111501. [PMID: 38788607 DOI: 10.1016/j.ejrad.2024.111501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 04/27/2024] [Accepted: 05/06/2024] [Indexed: 05/26/2024]
Abstract
PURPOSE To evaluate the value of inline quantitative analysis of ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a population-based arterial input function (P-AIF) compared with offline quantitative analysis with an individual AIF (I-AIF) and semi-quantitative analysis for diagnosing breast cancer. METHODS This prospective study included 99 consecutive patients with 109 lesions (85 malignant and 24 benign). Model-based parameters (Ktrans, kep, and ve) and model-free parameters (washin and washout) were derived from CAIPIRINHA-Dixon-TWIST-VIBE (CDTV) DCE-MRI. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. The AUC and F1 score were assessed for semi-quantitative and two quantitative analyses. RESULTS kep from inline quantitative analysis with P-AIF for diagnosing breast cancer provided an AUC similar to kep from offline quantitative analysis with I-AIF (0.782 vs 0.779, p = 0.954), higher compared to washin from semi-quantitative analysis (0.782 vs 0.630, p = 0.034). Furthermore, the inline quantitative analysis with P-AIF achieved the larger F1 score (0.920) compared with offline quantitative analysis with I-AIF (0.780) and semi-quantitative analysis (0.480). There were no statistically significant differences for kep values between the two quantitative analysis schemes (p = 0.944). CONCLUSION The inline quantitative analysis with P-AIF from CDTV in characterizing breast lesions could offer similar diagnostic accuracy to offline quantitative analysis with I-AIF, and higher diagnostic accuracy to semi-quantitative analysis.
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Affiliation(s)
- Tianwen Xie
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Qiufeng Zhao
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Caixia Fu
- MR Applications Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
| | - Robert Grimm
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | | | - Xiaoxin Hu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Lei Yue
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
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Hu Y, Hu Q, Liu Z, Huang C, Xia L. Histogram analysis comparison of readout-segmented and single-shot echo-planar imaging for differentiating luminal from non-luminal breast cancer. Sci Rep 2024; 14:12135. [PMID: 38802446 PMCID: PMC11130195 DOI: 10.1038/s41598-024-62514-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 05/17/2024] [Indexed: 05/29/2024] Open
Abstract
To compare diffusion-kurtosis imaging (DKI) and diffusion-weighted imaging (DWI) parameters of single-shot echo-planar imaging (ss-EPI) and readout-segmented echo-planar imaging (rs-EPI) in the differentiation of luminal vs. non-luminal breast cancer using histogram analysis. One hundred and sixty women with 111 luminal and 49 non-luminal breast lesions were enrolled in this study. All patients underwent ss-EPI and rs-EPI sequences on a 3.0T scanner. Histogram metrics were derived from mean kurtosis (MK), mean diffusion (MD) and the apparent diffusion coefficient (ADC) maps of two DWI sequences respectively. Student's t test or Mann-Whitney U test was performed for differentiating luminal subtype from non-luminal subtype. The ROC curves were plotted for evaluating the diagnostic performances of significant histogram metrics in differentiating luminal from non-luminal BC. The histogram metrics MKmean, MK50th, MK75th of luminal BC were significantly higher than those of non-luminal BC for both two DWI sequences (all P<0.05). Histogram metrics from rs-EPI sequence had better diagnostic performance in differentiating luminal from non-Luminal breast cancer compared to those from ss-EPI sequence. MK75th derived from rs-EPI sequence was the most valuable single metric (AUC, 0.891; sensitivity, 78.4%; specificity, 87.8%) for differentiating luminal from non-luminal BC among all the histogram metrics. Histogram metrics of MK derived from rs-EPI yielded better diagnostic performance for distinguishing luminal from non-luminal BC than that from ss-EPI. MK75th was the most valuable metric among all the histogram metrics.
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Affiliation(s)
- Yiqi Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Qilan Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Zhiqiang Liu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Cicheng Huang
- Center of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
| | - Liming Xia
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
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Liang S, Xu S, Zhou S, Chang C, Shao Z, Wang Y, Chen S, Huang Y, Guo Y. IMAGGS: a radiogenomic framework for identifying multi-way associations in breast cancer subtypes. J Genet Genomics 2024; 51:443-453. [PMID: 37783335 DOI: 10.1016/j.jgg.2023.09.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 09/18/2023] [Accepted: 09/20/2023] [Indexed: 10/04/2023]
Abstract
Investigating correlations between radiomic and genomic profiling in breast cancer (BC) molecular subtypes is crucial for understanding disease mechanisms and providing personalized treatment. We present a well-designed radiogenomic framework image-gene-gene set (IMAGGS), which detects multi-way associations in BC subtypes by integrating radiomic and genomic features. Our dataset consists of 721 patients, each of whom has 12 ultrasound (US) images captured from different angles and gene mutation data. To better characterize tumor traits, 12 multi-angle US images are fused using two distinct strategies. Then, we analyze complex many-to-many associations between phenotypic and genotypic features using a machine learning algorithm, deviating from the prevalent one-to-one relationship pattern observed in previous studies. Key radiomic and genomic features are screened using these associations. In addition, gene set enrichment analysis is performed to investigate the joint effects of gene sets and delve deeper into the biological functions of BC subtypes. We further validate the feasibility of IMAGGS in a glioblastoma multiforme dataset to demonstrate the scalability of IMAGGS across different modalities and diseases. Taken together, IMAGGS provides a comprehensive characterization for diseases by associating imaging, genes, and gene sets, paving the way for biological interpretation of radiomics and development of targeted therapy.
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Affiliation(s)
- Shuyu Liang
- Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China; The Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Shanghai 200032, China
| | - Sicheng Xu
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai 200433, China
| | - Shichong Zhou
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Cai Chang
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Zhiming Shao
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yuanyuan Wang
- Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China; The Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Shanghai 200032, China
| | - Sheng Chen
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - Yunxia Huang
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - Yi Guo
- Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China; The Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Shanghai 200032, China.
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Tabnak P, HajiEsmailPoor Z, Baradaran B, Pashazadeh F, Aghebati Maleki L. MRI-Based Radiomics Methods for Predicting Ki-67 Expression in Breast Cancer: A Systematic Review and Meta-analysis. Acad Radiol 2024; 31:763-787. [PMID: 37925343 DOI: 10.1016/j.acra.2023.10.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/01/2023] [Accepted: 10/04/2023] [Indexed: 11/06/2023]
Abstract
RATIONALE AND OBJECTIVES The purpose of this systematic review and meta-analysis was to assess the quality and diagnostic accuracy of MRI-based radiomics for predicting Ki-67 expression in breast cancer. MATERIALS AND METHODS A systematic literature search was performed to find relevant studies published in different databases, including PubMed, Web of Science, and Embase up until March 10, 2023. All papers were independently evaluated for eligibility by two reviewers. Studies that matched research questions and provided sufficient data for quantitative synthesis were included in the systematic review and meta-analysis, respectively. The quality of the articles was assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools. The predictive value of MRI-based radiomics for Ki-67 antigen in patients with breast cancer was assessed using pooled sensitivity (SEN), specificity, and area under the curve (AUC). Meta-regression was performed to explore the cause of heterogeneity. Different covariates were used for subgroup analysis. RESULTS 31 studies were included in the systematic review; among them, 21 reported sufficient data for meta-analysis. 20 training cohorts and five validation cohorts were pooled separately. The pooled sensitivity, specificity, and AUC of MRI-based radiomics for predicting Ki-67 expression in training cohorts were 0.80 [95% CI, 0.73-0.86], 0.82 [95% CI, 0.78-0.86], and 0.88 [95%CI, 0.85-0.91], respectively. The corresponding values for validation cohorts were 0.81 [95% CI, 0.72-0.87], 0.73 [95% CI, 0.62-0.82], and 0.84 [95%CI, 0.80-0.87], respectively. Based on QUADAS-2, some risks of bias were detected for reference standard and flow and timing domains. However, the quality of the included article was acceptable. The mean RQS score of the included articles was close to 6, corresponding to 16.6% of the maximum possible score. Significant heterogeneity was observed in pooled sensitivity and specificity of training cohorts (I2 > 75%). We found that using deep learning radiomic methods, magnetic field strength (3 T vs. 1.5 T), scanner manufacturer, region of interest structure (2D vs. 3D), route of tissue sampling, Ki-67 cut-off, logistic regression for model construction, and LASSO for feature reduction as well as PyRadiomics software for feature extraction had a great impact on heterogeneity according to our joint model analysis. Diagnostic performance in studies that used deep learning-based radiomics and multiple MRI sequences (e.g., DWI+DCE) was slightly higher. In addition, radiomic features derived from DWI sequences performed better than contrast-enhanced sequences in terms of specificity and sensitivity. No publication bias was found based on Deeks' funnel plot. Sensitivity analysis showed that eliminating every study one by one does not impact overall results. CONCLUSION This meta-analysis showed that MRI-based radiomics has a good diagnostic accuracy in differentiating breast cancer patients with high Ki-67 expression from low-expressing groups. However, the sensitivity and specificity of these methods still do not surpass 90%, restricting them from being used as a supplement to current pathological assessments (e.g., biopsy or surgery) to predict Ki-67 expression accurately.
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Affiliation(s)
- Peyman Tabnak
- Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H.); Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.); Department of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.)
| | - Zanyar HajiEsmailPoor
- Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H.); Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.); Department of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.)
| | - Behzad Baradaran
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.); Department of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.)
| | - Fariba Pashazadeh
- Research Center for Evidence-Based Medicine, Iranian Evidence-Based Medicine (EBM) Centre: A Joanna Briggs Institute (JBI) Centre of Excellence, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (F.P.)
| | - Leili Aghebati Maleki
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.); Department of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.).
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Xu WJ, Zheng BJ, Lu J, Liu SY, Li HL. Identification of triple-negative breast cancer and androgen receptor expression based on histogram and texture analysis of dynamic contrast-enhanced MRI. BMC Med Imaging 2023; 23:70. [PMID: 37264313 DOI: 10.1186/s12880-023-01022-5] [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: 04/11/2022] [Accepted: 05/23/2023] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND Triple-negative breast cancer (TNBC) is highly malignant and has a poor prognosis due to the lack of effective therapeutic targets. Androgen receptor (AR) has been investigated as a possible therapeutic target. This study quantitatively assessed intratumor heterogeneity by histogram analysis of pharmacokinetic parameters and texture analysis on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to discriminate TNBC from non-triple-negative breast cancer (non-TNBC) and to identify AR expression in TNBC. METHODS This retrospective study included 99 patients with histopathologically proven breast cancer (TNBC: 36, non-TNBC: 63) who underwent breast DCE-MRI before surgery. The pharmacokinetic parameters of DCE-MRI (Ktrans, Kep and Ve) and their corresponding texture parameters were calculated. The independent t-test, or Mann-Whitney U-test was used to compare quantitative parameters between TNBC and non-TNBC groups, and AR-positive (AR+) and AR-negative (AR-) TNBC groups. The parameters with significant difference between two groups were further involved in logistic regression analysis to build a prediction model for TNBC. The ROC analysis was conducted on each independent parameter and the TNBC predicting model for evaluating the discrimination performance. The area under the ROC curve (AUC), sensitivity and specificity were derived. RESULTS The binary logistic regression analysis revealed that Kep_Range (p = 0.032) and Ve_SumVariance (p = 0.005) were significantly higher in TNBC than in non-TNBC. The AUC of the combined model for identifying TNBC was 0.735 (p < 0.001) with a cut-off value of 0.268, and its sensitivity and specificity were 88.89% and 52.38%, respectively. The value of Kep_Compactness2 (p = 0.049), Kep_SphericalDisproportion (p = 0.049), and Ve_GlcmEntropy (p = 0.008) were higher in AR + TNBC group than in AR-TNBC group. CONCLUSION Histogram and texture analysis of breast lesions on DCE-MRI showed potential to identify TNBC, and the specific features can be possible predictors of AR expression, enhancing the ability to individualize the treatment of patients with TNBC.
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Affiliation(s)
- Wen-Juan Xu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Bing-Jie Zheng
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Jun Lu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Si-Yun Liu
- GE healthcare (China), Beijing, 100176, China
| | - Hai-Liang Li
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China.
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Zhu JJ, Shen J, Zhang W, Wang F, Yuan M, Xu H, Yu TF. Quantitative texture analysis based on dynamic contrast enhanced MRI for differential diagnosis between primary thymic lymphoma from thymic carcinoma. Sci Rep 2022; 12:12629. [PMID: 35871647 PMCID: PMC9309158 DOI: 10.1038/s41598-022-16393-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 07/08/2022] [Indexed: 11/09/2022] Open
Abstract
AbstractTo evaluate the value of texture analysis based on dynamic contrast enhanced MRI (DCE-MRI) in the differential diagnosis of thymic carcinoma and thymic lymphoma. Sixty-nine patients with pathologically confirmed (thymic carcinoma, n = 32; thymic lymphoma, n = 37) were enrolled in this retrospective study. Ktrans, Kep and Ve maps were automatically generated, and texture features were extracted, including mean, median, 5th/95th percentile, skewness, kurtosis, diff-variance, diff-entropy, contrast and entropy. The differences in parameters between the two groups were compared and the diagnostic efficacy was calculated. The Ktrans-related significant features yielded an area under the curve (AUC) of 0.769 (sensitivity 90.6%, specificity 51.4%) for the differentiation between thymic carcinoma and thymic lymphoma. The Kep-related significant features yielded an AUC of 0.780 (sensitivity 87.5%, specificity 62.2%). The Ve-related significant features yielded an AUC of 0.807 (sensitivity 75.0%, specificity 78.4%). The combination of DCE-MRI textural features yielded an AUC of 0.962 (sensitivity 93.8%, specificity 89.2%). Five parameters were screened out, including age, Ktrans-entropy, Kep-entropy, Ve-entropy, and Ve-P95. The combination of these five parameters yielded the best discrimination efficiency (AUC of 0.943, 93.7% sensitivity, 81.1% specificity). Texture analysis of DCE-MRI may be helpful to distinguish thymic carcinoma from thymic lymphoma.
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Xie T, Jiang T, Zhao Q, Fu C, Nickel MD, Peng W, Gu Y. Model‐Free and Model‐based Parameters Derived From
CAIPIRINHA‐Dixon‐TWIST‐VIBE DCE‐MRI
: Associations With Prognostic Factors and Molecular Subtypes of Invasive Ductal Breast Cancer. J Magn Reson Imaging 2022. [DOI: 10.1002/jmri.28533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 11/03/2022] [Accepted: 11/05/2022] [Indexed: 11/27/2022] Open
Affiliation(s)
- Tianwen Xie
- Department of Radiology Fudan University Shanghai Cancer Center Shanghai People's Republic of China
- Department of Oncology, Shanghai Medical College Fudan University Shanghai People's Republic of China
| | - Tingting Jiang
- Department of Radiology Fudan University Shanghai Cancer Center Shanghai People's Republic of China
- Department of Oncology, Shanghai Medical College Fudan University Shanghai People's Republic of China
| | - Qiufeng Zhao
- Department of Radiology, Longhua Hospital Shanghai University of Traditional Chinese Medicine Shanghai People's Republic of China
| | - Caixia Fu
- MR Applications Development Siemens Shenzhen Magnetic Resonance Ltd. Shenzhen People's Republic of China
| | | | - Weijun Peng
- Department of Radiology Fudan University Shanghai Cancer Center Shanghai People's Republic of China
- Department of Oncology, Shanghai Medical College Fudan University Shanghai People's Republic of China
| | - Yajia Gu
- Department of Radiology Fudan University Shanghai Cancer Center Shanghai People's Republic of China
- Department of Oncology, Shanghai Medical College Fudan University Shanghai People's Republic of 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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Whole-tumor apparent diffusion coefficient (ADC) analyses of breast lesions based on simultaneous multi-slice readout-segmented echo-planar diffusion-weighted imaging. Magn Reson Imaging 2022; 94:119-126. [PMID: 36191856 DOI: 10.1016/j.mri.2022.09.008] [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: 09/22/2021] [Revised: 01/28/2022] [Accepted: 09/27/2022] [Indexed: 11/24/2022]
Abstract
OBJECTIVE We aimed to evaluate the effectiveness of simultaneous multi-slice readout-segmented echo-planar diffusion-weighted imaging (SMS rs-EPI DWI), compared with readout-segmented echo-planar diffusion-weighted imaging (NOSMS rs-EPI DWI), in discriminating between benign and malignant breast lesions. MATERIALS AND METHODS This retrospective study evaluated breast lesions from 185 consecutive patients who had undergone preoperative breast MRI. The NOSMS rs-EPI DWI and the prototype SMS rs-EPI DWI sequences were performed on a 1.5-T MR scanner. Two independent radiologists evaluated the image quality of the two sequences using a 5-point scale (1 = poor, 5 = excellent) and then assessed the scores through visual grading characteristics analysis (VGC). The values of signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and whole-tumor-based histogram parameter (ADCmedian) were compared between the two sequences using the Wilcoxon test. Then ROC curves were used for statistical analysis. RESULTS The visual assessment showed that the SMS rs-EPI yielded superior overall image quality and lesion delineation than the NOSMS rs-EPI (AUCVGC = 0.980 and 0.984, respectively; all Ps < 0.001). There were no significant differences in relevant artifacts between the two sequences (AUCVGC = 0.531, P = 0.462). The SNR values for SMS rs-EPI DWI were significantly lower than for NOSMS rs-EPI DWI (P = 0.019) while there was no significant difference in CNR values between the two sequences (P = 0.955). In addition, evaluation of the diagnostic performance demonstrated that the difference in ADCmedian values for both DWI sequences between the malignant and benign lesions was statistically significant (P < 0.001). In contrast, the AUC for ADCmedian was higher with SMS rs-EPI than NOSMS rs-EPI (0.879 for SMS vs. 0.839 for NOSMS, P = 0.025). CONCLUSION The SMS technique could further improve the image quality and the diagnostic performance of rs-EPI DWI in a comparable time.
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Sun K, Zhu H, Xia B, Li X, Chai W, Fu C, Thomas B, Liu W, Grimm R, Elisabeth W, Yan F. Image quality and whole-lesion histogram and texture analysis of diffusion-weighted imaging of breast MRI based on advanced ZOOMit and simultaneous multislice readout-segmented echo-planar imaging. Front Oncol 2022; 12:913072. [PMID: 36033543 PMCID: PMC9411810 DOI: 10.3389/fonc.2022.913072] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 07/13/2022] [Indexed: 11/24/2022] Open
Abstract
Objectives To investigate the image quality and diagnostic capability a of whole-lesion histogram and texture analysis of advanced ZOOMit (A-ZOOMit) and simultaneous multislice readout-segmented echo-planar imaging (SMS-RS-EPI) to differentiate benign from malignant breast lesions. Study design From February 2020 to October 2020, diffusion-weighted imaging (DWI) using SMS-RS-EPI and A-ZOOMit were performed on 167 patients. Three breast radiologists independently ranked the image datasets. The inter-/intracorrelation coefficients (ICCs) of mean image quality scores and lesion conspicuity scores were calculated between these three readers. Histogram and texture features were extracted from the apparent diffusion coefficient (ADC) maps, respectively, based on a WL analysis. Student’s t-tests, one-way ANOVAs, Mann–Whitney U tests, and receiver operating characteristic curves were used for statistical analysis. Results The overall image quality scores and lesion conspicuity scores for A-ZOOMit and SMS-RS-EPI showed statistically significant differences (4.92 ± 0.27 vs. 3.92 ± 0.42 and 4.93 ± 0.29 vs. 3.87 ± 0.47, p < 0.0001). The ICCs for the image quality and lesion conspicuity scores had good agreements among the three readers (all ICCs >0.75). To differentiate benign and malignant breast lesions, the entropy of ADCA-Zoomit had the highest area (0.78) under the ROC curve. Conclusions A-ZOOMit achieved higher image quality and lesion conspicuity than SMS-RS-EPI. Entropy based on A-ZOOMit is recommended for differentiating benign from malignant breast lesions.
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Affiliation(s)
- Kun Sun
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- *Correspondence: Kun Sun,
| | - Hong Zhu
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Bingqing Xia
- Department of Radiology, International Peace Maternity and Child Health Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xinyue Li
- Department of Radiology, Ruijin Hospital Luwan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Weimin Chai
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Caixia Fu
- MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
| | - Benkert Thomas
- MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany
| | - Wei Liu
- MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
| | - Robert Grimm
- MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany
| | - Weiland Elisabeth
- MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
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11
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The value of whole-tumor histogram and texture analysis based on apparent diffusion coefficient (ADC) maps for the discrimination of breast fibroepithelial lesions: corresponds to clinical management decisions. Jpn J Radiol 2022; 40:1263-1271. [DOI: 10.1007/s11604-022-01304-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 06/03/2022] [Indexed: 10/17/2022]
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12
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Li X, Zhu H, Sun K, Chai W, Fu C, Yan F. Is simultaneous multi-slice readout-segmented echo-planar imaging valuable for predicting molecular subtypes of breast cancer? Eur J Radiol 2022; 150:110232. [DOI: 10.1016/j.ejrad.2022.110232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 02/15/2022] [Accepted: 02/25/2022] [Indexed: 11/03/2022]
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Kazama T, Takahara T, Hashimoto J. Breast Cancer Subtypes and Quantitative Magnetic Resonance Imaging: A Systemic Review. Life (Basel) 2022; 12:life12040490. [PMID: 35454981 PMCID: PMC9028183 DOI: 10.3390/life12040490] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 02/20/2022] [Accepted: 03/08/2022] [Indexed: 12/12/2022] Open
Abstract
Magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer detection. This systematic review investigated the role of quantitative MRI features in classifying molecular subtypes of breast cancer. We performed a literature search of articles published on the application of quantitative MRI features in invasive breast cancer molecular subtype classification in PubMed from 1 January 2002 to 30 September 2021. Of the 1275 studies identified, 106 studies with a total of 12,989 patients fulfilled the inclusion criteria. Bias was assessed based using the Quality Assessment of Diagnostic Studies. All studies were case-controlled and research-based. Most studies assessed quantitative MRI features using dynamic contrast-enhanced (DCE) kinetic features and apparent diffusion coefficient (ADC) values. We present a summary of the quantitative MRI features and their correlations with breast cancer subtypes. In DCE studies, conflicting results have been reported; therefore, we performed a meta-analysis. Significant differences in the time intensity curve patterns were observed between receptor statuses. In 10 studies, including a total of 1276 lesions, the pooled difference in proportions of type Ⅲ curves (wash-out) between oestrogen receptor-positive and -negative cancers was not significant (95% confidence interval (CI): [−0.10, 0.03]). In nine studies, including a total of 1070 lesions, the pooled difference in proportions of type 3 curves between human epidermal growth factor receptor 2-positive and -negative cancers was significant (95% CI: [0.01, 0.14]). In six studies including a total of 622 lesions, the pooled difference in proportions of type 3 curves between the high and low Ki-67 groups was significant (95% CI: [0.17, 0.44]). However, the type 3 curve itself is a nonspecific finding in breast cancer. Many studies have examined the relationship between mean ADC and breast cancer subtypes; however, the ADC values overlapped significantly between subtypes. The heterogeneity of ADC using kurtosis or difference, diffusion tensor imaging parameters, and relaxation time was reported recently with promising results; however, current evidence is limited, and further studies are required to explore these potential applications.
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Affiliation(s)
- Toshiki Kazama
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara 259-1193, Japan;
- Correspondence: ; Tel.: +81-463-93-1121
| | - Taro Takahara
- Department of Biomedical Engineering, Tokai University School of Engineering, Hiratsuka 259-1207, Japan;
| | - Jun Hashimoto
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara 259-1193, Japan;
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Wang C, Chen X, Luo H, Liu Y, Meng R, Wang M, Liu S, Xu G, Ren J, Zhou P. Development and Internal Validation of a Preoperative Prediction Model for Sentinel Lymph Node Status in Breast Cancer: Combining Radiomics Signature and Clinical Factors. Front Oncol 2021; 11:754843. [PMID: 34820327 PMCID: PMC8606782 DOI: 10.3389/fonc.2021.754843] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 10/14/2021] [Indexed: 12/24/2022] Open
Abstract
Purpose To develop and internally validate a nomogram combining radiomics signature of primary tumor and fibroglandular tissue (FGT) based on pharmacokinetic dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical factors for preoperative prediction of sentinel lymph node (SLN) status in breast cancer patients. Methods This study retrospectively enrolled 186 breast cancer patients who underwent pretreatment pharmacokinetic DCE-MRI with positive (n = 93) and negative (n = 93) SLN. Logistic regression models and radiomics signatures of tumor and FGT were constructed after feature extraction and selection. The radiomics signatures were further combined with independent predictors of clinical factors for constructing a combined model. Prediction performance was assessed by receiver operating characteristic (ROC), calibration, and decision curve analysis. The areas under the ROC curve (AUCs) of models were corrected by 1,000-times bootstrapping method and compared by Delong's test. The added value of each independent model or their combinations was also assessed by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. This report referred to the "Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis" (TRIPOD) statement. Results The AUCs of the tumor radiomic model (eight features) and the FGT radiomic model (three features) were 0.783 (95% confidence interval [CI], 0.717-0.849) and 0.680 (95% CI, 0.604-0.757), respectively. A higher AUC of 0.799 (95% CI, 0.737-0.862) was obtained by combining tumor and FGT radiomics signatures. By further combining tumor and FGT radiomics signatures with progesterone receptor (PR) status, a nomogram was developed and showed better discriminative ability for SLN status [AUC 0.839 (95% CI, 0.783-0.895)]. The IDI and NRI indices also showed significant improvement when combining tumor, FGT, and PR compared with each independent model or a combination of any two of them (all p < 0.05). Conclusion FGT and clinical factors improved the prediction performance of SLN status in breast cancer. A nomogram integrating the DCE-MRI radiomics signature of tumor and FGT and PR expression achieved good performance for the prediction of SLN status, which provides a potential biomarker for clinical treatment decision-making.
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Affiliation(s)
- Chunhua Wang
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoyu Chen
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Hongbing Luo
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuanyuan Liu
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Ruirui Meng
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Wang
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Siyun Liu
- Pharmaceutical Diagnostics, General Electric (GE) Company (Healthcare), Beijing, China
| | - Guohui Xu
- Department of Interventional Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jing Ren
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Peng Zhou
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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15
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Xing P, Chen L, Yang Q, Song T, Ma C, Grimm R, Fu C, Wang T, Peng W, Lu J. Differentiating prostate cancer from benign prostatic hyperplasia using whole-lesion histogram and texture analysis of diffusion- and T2-weighted imaging. Cancer Imaging 2021; 21:54. [PMID: 34579789 PMCID: PMC8477463 DOI: 10.1186/s40644-021-00423-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 09/03/2021] [Indexed: 11/24/2022] Open
Abstract
Background To explore the usefulness of analyzing histograms and textures of apparent diffusion coefficient (ADC) maps and T2-weighted (T2W) images to differentiate prostatic cancer (PCa) from benign prostatic hyperplasia (BPH) using histopathology as the reference. Methods Ninety patients with PCa and 112 patients with BPH were included in this retrospective study. Differences in whole-lesion histograms and texture parameters of ADC maps and T2W images between PCa and BPH patients were evaluated using the independent samples t-test. The diagnostic performance of ADC maps and T2W images in being able to differentiate PCa from BPH was assessed using receiver operating characteristic (ROC) curves. Results The mean, median, 5th, and 95th percentiles of ADC values in images from PCa patients were significantly lower than those from BPH patients (p < 0.05). Significant differences were observed in the means, standard deviations, medians, kurtosis, skewness, and 5th percentile values of T2W image between PCa and BPH patients (p < 0.05). The ADC5th showed the largest AUC (0.906) with a sensitivity of 83.3 % and specificity of 89.3 %. The diagnostic performance of the T2W image histogram and texture analysis was moderate and had the largest AUC of 0.634 for T2WKurtosis with a sensitivity and specificity of 48.9% and 79.5 %, respectively. The diagnostic performance of the combined ADC5th & T2WKurtosis parameters was also similar to that of the ADC5th & ADCDiff−Variance. Conclusions Histogram and texture parameters derived from the ADC maps and T2W images for entire prostatic lesions could be used as imaging biomarkers to differentiate PCa and BPH biologic characteristics, however, histogram parameters outperformed texture parameters in the diagnostic performance.
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Affiliation(s)
- Pengyi Xing
- Department of Radiology, Changhai Hospital of Shanghai, The Second Military Medical University, No.168 Changhai Road, 200433, Shanghai, China
| | - Luguang Chen
- Department of Radiology, Changhai Hospital of Shanghai, The Second Military Medical University, No.168 Changhai Road, 200433, Shanghai, China
| | - Qingsong Yang
- Department of Radiology, Changhai Hospital of Shanghai, The Second Military Medical University, No.168 Changhai Road, 200433, Shanghai, China
| | - Tao Song
- Department of Radiology, Changhai Hospital of Shanghai, The Second Military Medical University, No.168 Changhai Road, 200433, Shanghai, China
| | - Chao Ma
- Department of Radiology, Changhai Hospital of Shanghai, The Second Military Medical University, No.168 Changhai Road, 200433, Shanghai, China
| | - Robert Grimm
- Application Predevelopment, Siemens Healthcare, Erlangen, Germany
| | - Caixia Fu
- MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, China
| | - Tiegong Wang
- Department of Radiology, Changhai Hospital of Shanghai, The Second Military Medical University, No.168 Changhai Road, 200433, Shanghai, China
| | - Wenjia Peng
- Department of Radiology, Changhai Hospital of Shanghai, The Second Military Medical University, No.168 Changhai Road, 200433, Shanghai, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital of Shanghai, The Second Military Medical University, No.168 Changhai Road, 200433, Shanghai, China.
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Xie T, Zhao Q, Fu C, Grimm R, Gu Y, Peng W. Improved value of whole-lesion histogram analysis on DCE parametric maps for diagnosing small breast cancer (≤ 1 cm). Eur Radiol 2021; 32:1634-1643. [PMID: 34505195 DOI: 10.1007/s00330-021-08244-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 07/21/2021] [Accepted: 08/03/2021] [Indexed: 12/27/2022]
Abstract
OBJECTIVES To determine if whole-lesion histogram analysis on dynamic contrast-enhanced (DCE) parametric maps help to improve the diagnostic accuracy of small suspicious breast lesions (≤ 1 cm). METHODS This retrospective study included 99 female patients with 114 lesions (40 malignant and 74 benign lesions) suspicious on magnetic resonance imaging (MRI).Two radiologists reviewed all lesions and descripted the morphologic and kinetic characteristics according to BI-RADS by consensus. Whole lesions were segmented on DCE parametric maps (washin and washout), and quantitative histogram features were extracted. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. Diagnostic performance was assessed and compared with that of qualitative BI-RADS assessment and quantitative histogram analysis by ROC analysis. RESULTS For malignancy defined as a washout or plateau pattern, the qualitative kinetic pattern showed a significant difference between the two groups (p = 0.023), yielding an AUC of 0.603 (95% confidence interval [CI]: 0.507, 0.694). The mean and median of washout were independent quantitative predictors of malignancy (p = 0.002, 0.010), achieving an AUC of 0.796 (95% CI: 0. 709, 0.865). The AUC of the quantitative model was better than that of the qualitative model (p < 0.001). CONCLUSIONS Compared with the qualitative BI-RADS assessment, quantitative whole-lesion histogram analysis on DCE parametric maps was better to discriminate between small benign and malignant breast lesions (≤ 1 cm) initially defined as suspicious on DCE-MRI. KEY POINTS • For malignancy defined as a washout or plateau, the kinetic pattern may provide information to diagnose small breast cancer. • The mean and median of washout map were significantly lower for small malignant breast lesions than for benign lesions. • Quantitative histogram analysis on MRI parametric maps improves diagnostic accuracy for small breast cancer, which may obviate unnecessary biopsy.
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Affiliation(s)
- Tianwen Xie
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Qiufeng Zhao
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, People's Republic of China
| | - Caixia Fu
- MR Applications Development, Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, People's Republic of China
| | - Robert Grimm
- MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China. .,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China. .,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
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Lee EB, Kim SH, Park GE, Lee J, Kang BJ. Risk Stratification of Ductal Carcinoma In Situ and Texture Analysis of Contrast-Enhanced Breast Magnetic Resonance Imaging. J Comput Assist Tomogr 2021; 45:843-848. [PMID: 34347708 DOI: 10.1097/rct.0000000000001205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE The aim of this study was to investigate whether texture analysis of contrast-enhanced T1 weighted images could predict risk of ductal carcinoma in situ (DCIS). METHODS The study included 185 DCIS lesions that were classified as either low risk or non-low risk using surgical pathology records. All magnetic resonance imaging texture analyses were performed using postprocessing software, and texture-derived parameters were extracted. RESULTS The sphericity, compactness, and spherical disproportion were significantly different in the low-risk and non-low risk groups using the Van Nuys Prognostic Index (mean ± SD, 0.479 ± 0.189 vs 0.414 ± 0.176, 0.161 ± 0.159 vs 0.112 ± 0.134, and 2.569 ± 1.434 vs 2.934 ± 1.374, respectively; P < 0.05). In the univariate analyses, sphericity (odds ratio, 7.091; 95% confidence interval, 1.236-40.666; P = 0.028) and compactness (odds ratio, 9.267; 95% confidence interval, 1.125-76.360; P = 0.039) were significantly associated with a high probability of being low risk according to the Van Nuys Prognostic Index. CONCLUSIONS Whole-lesion texture analysis may be helpful in identifying patients classified as having low-risk DCIS before surgery.
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Affiliation(s)
- Eun Byul Lee
- From the Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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Julie L, Ikram D, Mailyn PL, Augustin L, Afef B, Joevin S, Bentoumi I, Cuenod CA, Daniel B. A free time point model for dynamic contrast enhanced exploration. Magn Reson Imaging 2021; 80:39-49. [PMID: 33905829 DOI: 10.1016/j.mri.2021.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 04/08/2021] [Accepted: 04/21/2021] [Indexed: 02/07/2023]
Abstract
Dynamic-Contrast-Enhanced (DCE) Imaging has been widely studied to characterize microcirculatory disorders associated with various diseases. Although numerous studies have demonstrated its diagnostic interest, the physiological interpretation using pharmacokinetic models often remains debatable. Indeed, to be interpretable, a model must provide, at first instance, an accurate description of the DCE data. However, the evaluation and optimization of this accuracy remain rather limited in DCE. Here we established a non-linear Free-Time-Point-Hermite (FTPH) data-description model designed to fit DCE data accurately. Its performance was evaluated on data generated using two contrasting pharmacokinetic microcirculatory hypotheses (MH). The accuracy of data description of the models was evaluated by calculating the mean squared error (QE) from initial and assessed tissue impulse responses. Then, FTPH assessments were provided to blinded observers to evaluate if these assessments allowed observers to identify MH in their data. Regardless of the initial pharmacokinetic model used for data generation, QE was lower than 3% for the noise-free datasets and increased up to 10% for a signal-to-noise-ratio (SNR) of 20. Under SNR = 20, the sensitivity and specificity of the MH identification were over 80%. The performance of the FTPH model was higher than that of the B-Spline model used as a reference. The accuracy of the FTPH model regardless of the initial MH provided an opportunity to have a reference to check the accuracy of other pharmacokinetic models.
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Affiliation(s)
- Levebvre Julie
- Université de Paris, PARCC, INSERM, Paris F-75015, France
| | - Djebali Ikram
- Université de Paris, PARCC, INSERM, Paris F-75015, France
| | | | | | | | - Sourdon Joevin
- Université de Paris, PARCC, INSERM, Paris F-75015, France.
| | - Isma Bentoumi
- Université de Paris, PARCC, INSERM, Paris F-75015, France
| | - Charles-André Cuenod
- Université de Paris, PARCC, INSERM, Paris F-75015, France; Service Radiologie, AP-HP, Hôpital Européen Georges Pompidou, F-75015, France.
| | - Balvay Daniel
- Université de Paris, PARCC, INSERM, Paris F-75015, France; Université de Paris, Plateforme d'Imageries du Vivant, F-75015, France.
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Liu B, Hu L, Wang L, Xing D, Peng L, Chen P, Zeng F, Liu WV, Liu H, Zha Y. Evaluation of microvascular permeability of skeletal muscle and texture analysis based on DCE-MRI in alloxan-induced diabetic rabbits. Eur Radiol 2021; 31:5669-5679. [PMID: 33547478 DOI: 10.1007/s00330-021-07705-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 11/24/2020] [Accepted: 01/21/2021] [Indexed: 02/08/2023]
Abstract
OBJECTIVES To estimate the microvascular permeability and perfusion of skeletal muscle by using quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and explore the feasibility of using texture analysis (TA) to evaluate subtle structural changes of diabetic muscles. METHODS Twenty-four rabbits were randomly divided into diabetic (n = 14) and control (n = 10) groups, and underwent axial DCE-MRI of the multifidus muscle (0, 4, 8, 12, and 16 weeks after alloxan injection). The pharmacokinetic model was used to calculate the permeability parameters; texture parameters were extracted from volume transfer constant (Ktrans) map. The two-sample t test/Mann-Whitney U test, repeated measures analysis of variance/Friedman test, and Pearson correlations were used for data analysis. RESULTS In the diabetic group, Ktrans and rate constant (Kep) increased significantly at week 8 and then showed a decreasing trend. Extravascular extracellular space volume fraction (Ve) increased and plasma volume fraction (Vp) decreased significantly from the 8th week. Skewness began to decrease at the 4th week. Median Ktrans and entropy increased significantly, while inverse difference moment decreased from the 8th week. Energy decreased while contrast increased only at week 8. Muscle fibre cross-sectional area was negatively correlated with Ve. The capillary-to-fibre ratio was positively correlated with Vp (p < 0.05, all). CONCLUSIONS Quantitative DCE-MRI can be used to evaluate microvascular permeability and perfusion in diabetic skeletal muscle at an early stage; TA based on Ktrans map can identify microarchitectural modifications in diabetic muscles. KEY POINTS • Four quantitative parameters of DCE-MRI can be used to evaluate microvascular permeability and perfusion of skeletal muscle in diabetic models at early stages. • Texture analysis based on Ktrans map can identify subtle structural changes in diabetic muscles.
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Affiliation(s)
- Baiyu Liu
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Lei Hu
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Li Wang
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Dong Xing
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Lin Peng
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Pianpian Chen
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Feifei Zeng
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | | | - Huan Liu
- GE Healthcare, Shanghai, 201203, China
| | - Yunfei Zha
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
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Wang R, Su Y, Mao C, Li S, You M, Xiang S. Laser lithotripsy for proximal ureteral calculi in adults: can 3D CT texture analysis help predict treatment success? Eur Radiol 2020; 31:3734-3744. [PMID: 33210203 DOI: 10.1007/s00330-020-07498-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Revised: 09/27/2020] [Accepted: 11/10/2020] [Indexed: 12/20/2022]
Abstract
OBJECTIVE To explore whether multiple 3D computed tomography texture analysis (3D-CTTA) parameters can predict the therapeutic effects of holmium: YAG laser lithotripsy (LL) on ureteral calculi. METHODS The files from 94 patients (102 stones) with proximal ureteral calculi treated only by LL at a single institution were retrospectively retrieved from January 2016 to March 2019. According to intra-operative observations and postoperative reexamination, samples were divided into a completely crushed and a non-crushed group. Preoperative non-contrast-enhanced computed tomography (NCCT) images obtained by multiple CT scanners were imported to MaZda software for 3D texture analysis (TA). The CT-derived value of each target stone was measured, and 15 TA parameters were extracted by delineating volumes of interest (VOIs). Receiver operating characteristic (ROC) curves were drawn to determine the optimal critical value of each parameter based on the Youden index, and univariable and multivariable logistic regression analyses determined the significant factors for LL success. RESULTS In univariable analysis, significant differences (p < 0.05) were observed among 7 parameters. In multivariable analysis, Perc.01 3D > 2062 (p = 0.03) and Z-fraction of image in runs (Z-Fraction) > 0.45570 (p = 0.009) were significant independent predictors, with odds ratios (ORs) of 24.204 and 60.329, respectively. In subgroup analysis based on the cutoff value of the CT-derived value (HU = 960), Perc.01 3D (OR = 44.154, 95% CI (2.379, 819.618), p = 0.011) and Z-Fraction (OR = 14.519, 95% CI (2.088, 100.953), p = 0.007) remained statistically significant. CONCLUSIONS The combination of 3D-CTTA parameters and the CT-derived value can be used as a quantitative reference to predict whether a target stone could be completely crushed by LL. KEY POINTS • Computed tomography texture analysis (CTTA) may be helpful in selecting suitable laser lithotripsy (LL) patients. • 3D-CTTA better predicts stone fragility than commonly used methods (such as the CT-derived value). • The combination of CTTA and the CT-derived value can be used as a preoperative quantitative reference.
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Affiliation(s)
- Rui Wang
- The Clinical School of Medicine, Dali University, 2 Shenghong Road, Gucheng, Dali, 671000, Yunnan Province, China
| | - Yunshan Su
- Department of Radiology, Second People's Hospital of Yunnan Province, 176 Qingnian Road, Wuhua District, Kunming, 650021, Yunnan Province, China.
| | - Chongwen Mao
- Department of Radiology, Second People's Hospital of Yunnan Province, 176 Qingnian Road, Wuhua District, Kunming, 650021, Yunnan Province, China
| | - Song Li
- Department of Urology, Second People's Hospital of Yunnan Province, 176 Qingnian Road, Wuhua District, Kunming, 650021, Yunnan Province, China
| | - Mengjing You
- Department of Radiology, Second People's Hospital of Yunnan Province, 176 Qingnian Road, Wuhua District, Kunming, 650021, Yunnan Province, China
| | - Shutian Xiang
- Department of Radiology, Second People's Hospital of Yunnan Province, 176 Qingnian Road, Wuhua District, Kunming, 650021, Yunnan Province, China
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Multi-parametric MRI lesion heterogeneity biomarkers for breast cancer diagnosis. Phys Med 2020; 80:101-110. [PMID: 33137621 DOI: 10.1016/j.ejmp.2020.10.007] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 10/07/2020] [Accepted: 10/10/2020] [Indexed: 02/07/2023] Open
Abstract
PURPOSE To identify intra-lesion imaging heterogeneity biomarkers in multi-parametric Magnetic Resonance Imaging (mpMRI) for breast lesion diagnosis. METHODS Dynamic Contrast Enhanced (DCE) and Diffusion Weighted Imaging (DWI) of 73 female patients, with 85 histologically verified breast lesions were acquired. Non-rigid multi-resolution registration was utilized to spatially align sequences. Four (4) DCE (2nd post-contrast frame, Initial-Enhancement, Post-Initial-Enhancement and Signal-Enhancement-Ratio) and one (1) DWI (Apparent-Diffusion-Coefficient) representations were analyzed, considering a representative lesion slice. 11 1st-order-statistics and 16 texture features (Gray-Level-Co-occurrence-Matrix (GLCM) and Gray-Level-Run-Length-Matrix (GLRLM) based) were derived from lesion segments, provided by Fuzzy C-Means segmentation, across the 5 representations, resulting in 135 features. Least-Absolute-Shrinkage and Selection-Operator (LASSO) regression was utilized to select optimal feature subsets, subsequently fed into 3 classification schemes: Logistic-Regression (LR), Random-Forest (RF), Support-Vector-Machine-Sequential-Minimal-Optimization (SVM-SMO), assessed with Receiver-Operating-Characteristic (ROC) analysis. RESULTS LASSO regression resulted in 7, 6 and 7 features subsets from DCE, DWI and mpMRI, respectively. Best classification performance was obtained by the RF multi-parametric scheme (Area-Under-ROC-Curve, (AUC) ± Standard-Error (SE), AUC ± SE = 0.984 ± 0.025), as compared to DCE (AUC ± SE = 0.961 ± 0.030) and DWI (AUC ± SE = 0.938 ± 0.032) and statistically significantly higher as compared to DWI. The selected mpMRI feature subset highlights the significance of entropy (1st-order-statistics and 2nd-order-statistics (GLCM)) and percentile features extracted from 2nd post-contrast frame, PIE, SER maps and ADC map. CONCLUSION Capturing breast intra-lesion heterogeneity, across mpMRI lesion segments with 1st-order-statistics and texture features (GLCM and GLRLM based), offers a valuable diagnostic tool for breast cancer.
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Xu X, Zhu H, Li R, Lin H, Grimm R, Fu C, Yan F. Whole-liver histogram and texture analysis on T1 maps improves the risk stratification of advanced fibrosis in NAFLD. Eur Radiol 2020; 31:1748-1759. [PMID: 32897416 PMCID: PMC7880972 DOI: 10.1007/s00330-020-07235-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 06/04/2020] [Accepted: 08/27/2020] [Indexed: 12/16/2022]
Abstract
Objectives To assess whole-liver texture analysis on T1 maps for risk stratification of advanced fibrosis in patients with suspected nonalcoholic fatty liver disease (NAFLD). Methods This retrospective study included 53 patients. Histogram and texture parameters (volume, mean, SD, median, 5th percentile, 95th percentile, skewness, kurtosis, diff-entropy, diff-variance, contrast, and entropy) of T1 maps were calculated based on the semi-automatically segmented whole-liver volume. A two-step approach combining the Nonalcoholic Fatty Liver Disease Fibrosis Score (NFS) and Fibrosis-4 Index (FIB-4) with the liver stiffness measurement (LSM) for the risk stratification was used. Univariate analysis was performed to identify significant parameters. Logistic regression models were then run on the significant features. Diagnostic performance was evaluated with receiver operating characteristic (ROC) analysis. Results In total, 33 (62%) subjects had a low risk and 20 (38%) subjects had an intermediate-to-high risk of advanced fibrosis. The following significantly different parameters with the best performance were diff-entropy, entropy, and diff-variance, with AUROC 0.837 (95% CI 0.73–0.95), 0.821 (95% CI 0.71–0.94), and 0.807 (95% CI 0.69–0.93). The optimal combination of median, 5th percentile, and diff-entropy as a multivariate model improved the diagnostic performance to diagnose an intermediate-to-high risk of advanced fibrosis with AUROC 0.902(95% CI 0.79–0.97). Conclusions Parameters obtained by histogram and texture analysis of T1 maps may be a noninvasive analytical approach for stratifying the risk of advanced fibrosis in NAFLD. Key Points • Variable flip angle (VFA) T1 mapping can be used to acquire 3D T1 maps within a clinically acceptable duration. • Whole-liver histogram and texture parameters on T1 maps in patients with NAFLD can distinguish those with an intermediate-to-high risk of advanced fibrosis. • The multivariate model of combination of texture parameters improved the diagnostic performance for a high risk of advanced fibrosis and clinical parameters offer no added value to the multivariate model. Electronic supplementary material The online version of this article (10.1007/s00330-020-07235-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Xinxin Xu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai, 200025, China
| | - Hong Zhu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai, 200025, China
| | - Ruokun Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai, 200025, China
| | - Huimin Lin
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai, 200025, China
| | - Robert Grimm
- MR Applications Predevelopment, Siemens Healthcare, Erlangen, Germany
| | - Caixia Fu
- MR Applications Development, Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, People's Republic of China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai, 200025, China.
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Hao W, Peng W, Wang C, Zhao B, Wang G. Image quality of the CAIPIRINHA-Dixon-TWIST-VIBE technique for ultra-fast breast DCE-MRI: Comparison with the conventional GRE technique. Eur J Radiol 2020; 129:109108. [PMID: 32563961 DOI: 10.1016/j.ejrad.2020.109108] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 04/20/2020] [Accepted: 05/29/2020] [Indexed: 12/12/2022]
Abstract
PURPOSE The aim of this study was to evaluate image quality of the CAIPIRINHA-Dixon-TWIST-Volume-Interpolated Breath-hold Examination (CDT-VIBE) technique for ultra-fast breast dynamic contrast enhanced (DCE) MRI with respect to conventional Gradient-Recalled Echo (GRE) technique. METHODS A total of 58 patients underwent a DCE-MRI based on CDT-VIBE sequence (temporal resolution: 11.9 s), immediately followed by 1 phase of a conventional T1 weighted GRE sequence (acquisition time: 68 s). The Signal-to-Noise Ratio (SNR) on phantom images, lesion/parenchyma signal ratio (LPSR), image quality, and morphological characterization were compared between the last phase of CDT-VIBE and conventional GRE images. The image quality was assessed by visual grading analysis (VGA). Reader agreement was assessed using Kappa analysis. RESULTS There was no significant difference in SNR (phantom) or LPSR (patient) between CDT-VIBE and conventional GRE images (P > 0.05). Significant parallel acquisition technique (PAT) noise and mild blurriness was observed on CDT-VIBE images. Visual grading analysis (VGA) confirmed significantly worse ratings for CDT-VIBE compared to the conventional GRE sequence in terms of PAT noise, lesion's internal feature clarity, and therefore overall image quality (area under contrast curve [AUC] values: 0.578 ‒ 0.764, P < 0.05), but edge sharpness and lesion conspicuity were equivalent (P > 0.05). Kappa analysis revealed good agreement on image quality scores (к = 0.725 ‒ 0.908) and on morphologic terms (к = 0.745-1.000). CONCLUSION The CDT-VIBE sequence provides excellent spatial resolution and adequate image quality in ultra-fast breast DCE-MRI. Further improvement in PAT noise and internal structure blurriness may be necessary.
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Affiliation(s)
- Wen Hao
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China; Department of MR Imaging, Shandong Medical Imaging Research Institute, Shandong University, Jinan, Shandong, China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Cuiyan Wang
- Department of MR Imaging, Shandong Medical Imaging Research Institute, Shandong University, Jinan, Shandong, China
| | - Bin Zhao
- Department of MR Imaging, Shandong Medical Imaging Research Institute, Shandong University, Jinan, Shandong, China
| | - Guangbin Wang
- Department of MR Imaging, Shandong Medical Imaging Research Institute, Shandong University, Jinan, Shandong, China.
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Clinical Feasibility of Reduced Field-of-View Diffusion-Weighted Magnetic Resonance Imaging with Computed Diffusion-Weighted Imaging Technique in Breast Cancer Patients. Diagnostics (Basel) 2020; 10:diagnostics10080538. [PMID: 32751723 PMCID: PMC7460410 DOI: 10.3390/diagnostics10080538] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 07/20/2020] [Accepted: 07/28/2020] [Indexed: 11/29/2022] Open
Abstract
Background: We evaluated the feasibility of the reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) with computed DWI technique by comparison and analysis of the inter-method agreement among acquired rFOV DWI (rFOVA), rFOV DWI with computed DWI technique (rFOVS), and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in patients with breast cancer. Methods: A total of 130 patients with biopsy-proven breast cancers who underwent breast MRI from April 2017 to December 2017 were included in this study. The rFOVS were reformatted by calculation of the apparent diffusion coefficient curve obtained from rFOVA b = 0 s/mm2 and b = 500 s/mm2. Visual assessment of the image quality of rFOVA b = 1000 s/mm2, rFOVS, and DCE MRI was performed using a four-point grading system. Morphologic analyses of the index cancer was performed on rFOVA, rFOVS, and DCE MRI. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and contrast of tumor-to-parenchyma (TPC) were calculated. Results: Image quality scores with rFOVA, rFOVS, and DCE MRI were not significantly different (p = 0.357). Lesion analysis of shape, margin, and size of the index cancer also did not show significant differences among the three sequences (p = 0.858, p = 0.242, and p = 0.858, respectively). SNR, CNR, and TPC of DCE MRI were significantly higher than those of rFOVA and rFOVS (p < 0.001, p = 0.001, and p = 0.016, respectively). Significant differences were not found between the SNR, CNR, and TPC of rFOVA and those of rFOVS (p > 0.999, p > 0.999, and p > 0.999, respectively). Conclusion: The rFOVA and rFOVS showed nearly equivalent levels of image quality required for morphological analysis of the tumors and for lesion conspicuity compared with DCE MRI.
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Sun K, Qu L, Lian C, Pan Y, Hu D, Xia B, Li X, Chai W, Yan F, Shen D. High-Resolution Breast MRI Reconstruction Using a Deep Convolutional Generative Adversarial Network. J Magn Reson Imaging 2020; 52:1852-1858. [PMID: 32656955 DOI: 10.1002/jmri.27256] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 05/28/2020] [Accepted: 05/28/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND A generative adversarial network could be used for high-resolution (HR) medical image synthesis with reduced scan time. PURPOSE To evaluate the potential of using a deep convolutional generative adversarial network (DCGAN) for generating HRpre and HRpost images based on their corresponding low-resolution (LR) images (LRpre and LRpost ). STUDY TYPE This was a retrospective analysis of a prospectively acquired cohort. POPULATION In all, 224 subjects were randomly divided into 200 training subjects and an independent 24 subjects testing set. FIELD STRENGTH/SEQUENCE Dynamic contrast-enhanced (DCE) MRI with a 1.5T scanner. ASSESSMENT Three breast radiologists independently ranked the image datasets, using the DCE images as the ground truth, and reviewed the image quality of both the original LR images and the generated HR images. The BI-RADS category and conspicuity of lesions were also ranked. The inter/intracorrelation coefficients (ICCs) of mean image quality scores, lesion conspicuity scores, and Breast Imaging Reporting and Data System (BI-RADS) categories were calculated between the three readers. STATISTICAL TEST Wilcoxon signed-rank tests evaluated differences among the multireader ranking scores. RESULTS The mean overall image quality scores of the generated HRpre and HRpost were significantly higher than those of the original LRpre and LRpost (4.77 ± 0.41 vs. 3.27 ± 0.43 and 4.72 ± 0.44 vs. 3.23 ± 0.43, P < 0.0001, respectively, in the multireader study). The mean lesion conspicuity scores of the generated HRpre and HRpost were significantly higher than those of the original LRpre and LRpost (4.18 ± 0.70 vs. 3.49 ± 0.58 and 4.35 ± 0.59 vs. 3.48 ± 0.61, P < 0.001, respectively, in the multireader study). The ICCs of the image quality scores, lesion conspicuity scores, and BI-RADS categories had good agreements among the three readers (all ICCs >0.75). DATA CONCLUSION DCGAN was capable of generating HR of the breast from fast pre- and postcontrast LR and achieved superior quantitative and qualitative performance in a multireader study. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 2 J. MAGN. RESON. IMAGING 2020;52:1852-1858.
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Affiliation(s)
- Kun Sun
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.,Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Liangqiong Qu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Chunfeng Lian
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Yongsheng Pan
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Dan Hu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Bingqing Xia
- Department of Radiology, International Peace Maternity and Child Health Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xinyue Li
- Department of Radiology, Ruijin Hospital Luwan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Weimin Chai
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Dinggang Shen
- Department of Research and Development, Shanghai United Imaging Intelligence Co.,Ltd., Shanghai, China.,Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
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