{"title":"时间依赖性扩散MRI和动力学异质性作为3.0 t乳腺MRI诊断可疑乳腺病变的潜在成像生物标志物。","authors":"Xue Li, Chunmei Li, Bin Hua, Lei Jiang, Min Chen","doi":"10.1016/j.mri.2025.110323","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to evaluate the diagnostic efficacy of time-dependent diffusion magnetic resonance imaging (td-dMRI) and dynamic contrast-enhanced MRI (DCE-MRI)-based kinetic heterogeneity in differentiating suspicious breast lesions (categorised as Breast Imaging Reporting and Data System 4 or 5).</p><p><strong>Methods: </strong>This prospective study included 51 females with suspicious breast lesions who underwent preoperative breast MRI, including DCE-MRI and td-dMRI. Six kinetic parameters, namely peak, persistent, plateau, washout component, predominant curve type, and heterogeneity, were extracted from the DCE series using MATLAB and SPM software. The td-dMRI data were analysed using the JOINT model to obtain five microstructural parameters and apparent diffusion coefficient at 50 ms (ADC<sub>50ms</sub>). Chi-square or Fisher's exact test and the Mann-Whitney U test were used to compare these parameters between benign and malignant breast lesions. Univariate and multivariate logistic regression analyses with forward stepwise covariate selection were performed to identify significant clinical and radiologic variables. Differential diagnostic performance was evaluated using receiver operating characteristic curves and logistic regression analyses.</p><p><strong>Results: </strong>For td-dMRI-derived parameters, the values of f<sub>in</sub> and cellularity were significantly higher in malignant breast lesions compared to benign lesions (P = 0.001 and P<0.001, respectively), while ADC<sub>50ms</sub> was significantly lower in malignant lesions (P = 0.001). In the kinetic heterogeneity analysis, the washout component was higher in malignant lesions compared to benign lesions (P = 0.003). When combining significant td-dMRI and kinetic heterogeneity parameters, the area under the curve (AUC) value was 0.875, with an accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 82.69 %, 86.11 %, 75.00 %, 88.57 %, and 70.59 %, respectively. Notably, margin and kinetic pattern emerged as independent predictors of malignant breast lesions (P = 0.019 and 0.006, respectively). Furthermore, incorporating these two clinical-radiologic characteristics further enhanced diagnostic accuracy, yielding an AUC of 0.969, with accuracy, sensitivity, specificity, PPV, and NPV improving to 90.38 %, 86.11 %, 100 %, 100 %, and 76.19 %, respectively.</p><p><strong>Conclusions: </strong>Kinetic heterogeneity- and td-dMRI-derived parameters are potentially non-invasive biomarkers for distinguishing suspicious breast lesions.</p>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"110323"},"PeriodicalIF":2.1000,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Time-dependent diffusion MRI and kinetic heterogeneity as potential imaging biomarkers for diagnosing suspicious breast lesions with 3.0-T breast MRI.\",\"authors\":\"Xue Li, Chunmei Li, Bin Hua, Lei Jiang, Min Chen\",\"doi\":\"10.1016/j.mri.2025.110323\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>This study aimed to evaluate the diagnostic efficacy of time-dependent diffusion magnetic resonance imaging (td-dMRI) and dynamic contrast-enhanced MRI (DCE-MRI)-based kinetic heterogeneity in differentiating suspicious breast lesions (categorised as Breast Imaging Reporting and Data System 4 or 5).</p><p><strong>Methods: </strong>This prospective study included 51 females with suspicious breast lesions who underwent preoperative breast MRI, including DCE-MRI and td-dMRI. Six kinetic parameters, namely peak, persistent, plateau, washout component, predominant curve type, and heterogeneity, were extracted from the DCE series using MATLAB and SPM software. The td-dMRI data were analysed using the JOINT model to obtain five microstructural parameters and apparent diffusion coefficient at 50 ms (ADC<sub>50ms</sub>). Chi-square or Fisher's exact test and the Mann-Whitney U test were used to compare these parameters between benign and malignant breast lesions. Univariate and multivariate logistic regression analyses with forward stepwise covariate selection were performed to identify significant clinical and radiologic variables. Differential diagnostic performance was evaluated using receiver operating characteristic curves and logistic regression analyses.</p><p><strong>Results: </strong>For td-dMRI-derived parameters, the values of f<sub>in</sub> and cellularity were significantly higher in malignant breast lesions compared to benign lesions (P = 0.001 and P<0.001, respectively), while ADC<sub>50ms</sub> was significantly lower in malignant lesions (P = 0.001). In the kinetic heterogeneity analysis, the washout component was higher in malignant lesions compared to benign lesions (P = 0.003). When combining significant td-dMRI and kinetic heterogeneity parameters, the area under the curve (AUC) value was 0.875, with an accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 82.69 %, 86.11 %, 75.00 %, 88.57 %, and 70.59 %, respectively. Notably, margin and kinetic pattern emerged as independent predictors of malignant breast lesions (P = 0.019 and 0.006, respectively). Furthermore, incorporating these two clinical-radiologic characteristics further enhanced diagnostic accuracy, yielding an AUC of 0.969, with accuracy, sensitivity, specificity, PPV, and NPV improving to 90.38 %, 86.11 %, 100 %, 100 %, and 76.19 %, respectively.</p><p><strong>Conclusions: </strong>Kinetic heterogeneity- and td-dMRI-derived parameters are potentially non-invasive biomarkers for distinguishing suspicious breast lesions.</p>\",\"PeriodicalId\":18165,\"journal\":{\"name\":\"Magnetic resonance imaging\",\"volume\":\"117 \",\"pages\":\"110323\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2025-01-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Magnetic resonance imaging\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.mri.2025.110323\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Magnetic resonance imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.mri.2025.110323","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Time-dependent diffusion MRI and kinetic heterogeneity as potential imaging biomarkers for diagnosing suspicious breast lesions with 3.0-T breast MRI.
Purpose: This study aimed to evaluate the diagnostic efficacy of time-dependent diffusion magnetic resonance imaging (td-dMRI) and dynamic contrast-enhanced MRI (DCE-MRI)-based kinetic heterogeneity in differentiating suspicious breast lesions (categorised as Breast Imaging Reporting and Data System 4 or 5).
Methods: This prospective study included 51 females with suspicious breast lesions who underwent preoperative breast MRI, including DCE-MRI and td-dMRI. Six kinetic parameters, namely peak, persistent, plateau, washout component, predominant curve type, and heterogeneity, were extracted from the DCE series using MATLAB and SPM software. The td-dMRI data were analysed using the JOINT model to obtain five microstructural parameters and apparent diffusion coefficient at 50 ms (ADC50ms). Chi-square or Fisher's exact test and the Mann-Whitney U test were used to compare these parameters between benign and malignant breast lesions. Univariate and multivariate logistic regression analyses with forward stepwise covariate selection were performed to identify significant clinical and radiologic variables. Differential diagnostic performance was evaluated using receiver operating characteristic curves and logistic regression analyses.
Results: For td-dMRI-derived parameters, the values of fin and cellularity were significantly higher in malignant breast lesions compared to benign lesions (P = 0.001 and P<0.001, respectively), while ADC50ms was significantly lower in malignant lesions (P = 0.001). In the kinetic heterogeneity analysis, the washout component was higher in malignant lesions compared to benign lesions (P = 0.003). When combining significant td-dMRI and kinetic heterogeneity parameters, the area under the curve (AUC) value was 0.875, with an accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 82.69 %, 86.11 %, 75.00 %, 88.57 %, and 70.59 %, respectively. Notably, margin and kinetic pattern emerged as independent predictors of malignant breast lesions (P = 0.019 and 0.006, respectively). Furthermore, incorporating these two clinical-radiologic characteristics further enhanced diagnostic accuracy, yielding an AUC of 0.969, with accuracy, sensitivity, specificity, PPV, and NPV improving to 90.38 %, 86.11 %, 100 %, 100 %, and 76.19 %, respectively.
Conclusions: Kinetic heterogeneity- and td-dMRI-derived parameters are potentially non-invasive biomarkers for distinguishing suspicious breast lesions.
期刊介绍:
Magnetic Resonance Imaging (MRI) is the first international multidisciplinary journal encompassing physical, life, and clinical science investigations as they relate to the development and use of magnetic resonance imaging. MRI is dedicated to both basic research, technological innovation and applications, providing a single forum for communication among radiologists, physicists, chemists, biochemists, biologists, engineers, internists, pathologists, physiologists, computer scientists, and mathematicians.