Dataset Information

Contribution of IVIM to Conventional Dynamic Contrast-Enhanced and Diffusion-Weighted MRI in Differentiating Benign from Malignant Breast Masses.

ABSTRACT: The aim of this study was to determine whether the indicators obtained from intravoxel incoherent motion (IVIM) imaging can improve the characterization of benign and malignant breast masses compared with conventional dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted magnetic resonance imaging (DW-MRI).This study included 23 benign and 31 malignant breast masses of 48 patients. Main indicators were initial enhancement ratio (IER), time-signal intensity curve (TIC), apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudodiffusivity (D*), and perfusion fraction (f). The discriminative abilities of the different models were compared by means of receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) analysis.D had the highest AUC (0.980), sensitivity (93.55%), specificity (100%), and diagnostic accuracy (96.36%). Both D and TIC could provide the independent predicted features for malignant breast masses. The combination of D and TIC had an AUC of up to 0.990.D of IVIM can effectively complement existing conventional DCE-MRI and DW-MRI in differentiating malignant from benign breast masses. IVIM combined with DCE-MRI is a robust means of evaluating breast masses.

SUBMITTER: Wang Q

PROVIDER: S-EPMC5040879 | biostudies-other | 2016 Aug

REPOSITORIES: biostudies-other

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Contribution of IVIM to Conventional Dynamic Contrast-Enhanced and Diffusion-Weighted MRI in Differentiating Benign from Malignant Breast Masses.

Wang Qingjun Q Guo Yong Y Zhang Jing J Wang Zijun Z Huang Minhua M Zhang Yun Y

Breast care (Basel, Switzerland) 20160819 4

<h4>Background</h4>The aim of this study was to determine whether the indicators obtained from intravoxel incoherent motion (IVIM) imaging can improve the characterization of benign and malignant breast masses compared with conventional dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted magnetic resonance imaging (DW-MRI).<h4>Patients and methods</h4>This study included 23 benign and 31 malignant breast masses of 48 patients. Main indicators were initial enhanc ...[more]

PMID: 27721712

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Project description:ObjectiveThe purpose of this study was to validate a kinetic assessment based on visually identified peak enhancement, which is routinely used in clinical practice, for differentiating benign from malignant lesions during fast dynamic contrast-enhanced MRI.Materials and methodsBetween January 2015 and December 2016, 90 consecutively registered patients with 105 breast lesions (40 benign, 65 malignant) underwent dynamic contrast-enhanced 1.5-T MRI that included one unenhanced and eight contrast-enhanced fast temporal resolution (10 seconds) whole-breast acquisitions. Histogram analysis was performed to measure the voxel-based enhancement of the entire lesion to obtain 90th, 75th, and 50th percentile values at each time point and to generate kinetic curves. Two observers selected visually identified peak enhancement within the lesions to generate the kinetic curves. The kinetic curves from histogram and visually identified peak enhancement analyses were fitted by means of an empiric mathematic model (EMM): ΔS(t) = A × (1 - e-αt), where A is the upper limit of signal intensity, e indicates the exponential function, and α (min-1) is the rate of increase in signal intensity. The initial slope of the kinetic curve (A × α) and the initial AUC (AUC30) were calculated. These parameters were compared between benign and malignant lesions, and results from visually identified peak enhancement analysis were compared with those from histogram analysis.ResultsBenign lesions were successfully differentiated from malignant lesions in both visually identified peak enhancement and histogram analyses (90th and 75th percentile values) on the basis of α, A × α, and AUC30 from the EMM. There was no significant difference in ROC AUC in these EMM parameters between visually identified peak enhancement and histogram analyses (p = 0.21).ConclusionKinetic assessment with visually identified peak enhancement was acceptable for differentiating benign from malignant lesions.

Project description:ObjectivesCytogenetic abnormalities are predictors of poor prognosis in multiple myeloma (MM). This paper aims to build and validate a multiparametric conventional and functional whole-body MRI-based prediction model for cytogenetic risk classification in newly diagnosed MM.MethodsPatients with newly diagnosed MM who underwent multiparametric conventional whole-body MRI, spinal dynamic contrast-enhanced (DCE-)MRI, spinal diffusion-weighted MRI (DWI) and had genetic analysis were retrospectively included (2011-2020/Ghent University Hospital/Belgium). Patients were stratified into standard versus intermediate/high cytogenetic risk groups. After segmentation, 303 MRI features were extracted. Univariate and model-based methods were evaluated for feature and model selection. Testing was performed using receiver operating characteristic (ROC) and precision-recall curves. Models comparing the performance for genetic risk classification of the entire MRI protocol and of all MRI sequences separately were evaluated, including all features. Four final models, including only the top three most predictive features, were evaluated.ResultsThirty-one patients were enrolled (mean age 66 ± 7 years, 15 men, 13 intermediate-/high-risk genetics). None of the univariate models and none of the models with all features included achieved good performance. The best performing model with only the three most predictive features and including all MRI sequences reached a ROC-area-under-the-curve of 0.80 and precision-recall-area-under-the-curve of 0.79. The highest statistical performance was reached when all three MRI sequences were combined (conventional whole-body MRI + DCE-MRI + DWI). Conventional MRI always outperformed the other sequences. DCE-MRI always outperformed DWI, except for specificity.ConclusionsA multiparametric MRI-based model has a better performance in the noninvasive prediction of high-risk cytogenetics in newly diagnosed MM than conventional MRI alone.Critical relevance statementAn elaborate multiparametric MRI-based model performs better than conventional MRI alone for the noninvasive prediction of high-risk cytogenetics in newly diagnosed multiple myeloma; this opens opportunities to assess genetic heterogeneity thus overcoming sampling bias.Key points• Standard genetic techniques in multiple myeloma patients suffer from sampling bias due to tumoral heterogeneity. • Multiparametric MRI noninvasively predicts genetic risk in multiple myeloma. • Combined conventional anatomical MRI, DCE-MRI, and DWI had the highest statistical performance to predict genetic risk. • Conventional MRI alone always outperformed DCE-MRI and DWI separately to predict genetic risk. DCE-MRI alone always outperformed DWI separately, except for the parameter specificity to predict genetic risk. • This multiparametric MRI-based genetic risk prediction model opens opportunities to noninvasively assess genetic heterogeneity thereby overcoming sampling bias in predicting genetic risk in multiple myeloma.

Project description:BackgroundMany diseases are accompanied by portal vein thrombosis (PVT), and its nature is closely related to its prognosis and treatment. It is important to evaluate magnetic resonance imaging (MRI) parameters, including susceptibility-weighted imaging (SWI) and qualitative diffusion-weighted imaging (DWI), in the differentiation between benign and malignant PVT.MethodsIn this retrospective study, we collected clinical imaging data from 140 patients with PVTs characterized as benign or malignant based on enhanced MRI between January 2011 and April 2016 and retrospectively analyzed PVTs using SWI and DWI. There were 37 benign and 103 malignant PVTs. Image review was performed by 2 radiologists blinded to clinical information. The signal intensity (SI) of PVTs was recorded on SWI. The apparent diffusion coefficient (ADC) and the ratio of signal intensity (SIR) on SWI (SIRSWI) and ADC (SIRADC) between the PVTs and the spinal cord were calculated. Finally, we generated receiver operating characteristic (ROC) curves to evaluate the efficacy of SIRSWI and SIRADC for distinguishing benign and malignant PVTs.ResultsOn SWI and DWI, 100.0% (36/36) and 80.5% (29/36) of benign PVTs were hypointense, respectively. For malignant PVTs on SWI and DWI, 99.0% (103/104) and 89.4% (93/104) were hyperintense, respectively. The SIRSWI values of benign and malignant PVTs were 0.58±0.13 and 0.88±0.06, respectively, representing a significant difference (P<0.001). The SIRADC values of benign and malignant PVTs were 0.72±0.32 and 0.62±0.17, respectively, representing a significant difference (P=0.034). The area under the ROC curve (AUROC) for SIRSWI [0.990; 95% confidence interval (CI): 0.971-1.000] was significantly higher than that for SIRADC (0.619; 95% CI: 0.500-0.737; P<0.001). The SIRSWI had a sensitivity of 100.0% and a specificity of 97.3% with a cutoff value of 0.749, while the SIRADC had a sensitivity of 45.9% and specificity of 83.3% with a cutoff value of 0.791.ConclusionsThe diagnostic performance of SWI is superior to that of DWI in the differentiation of benign and malignant PVTs.

Project description:PurposeDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly used for breast cancer diagnosis as supplementary to conventional imaging techniques. Combining of diffusion-weighted imaging (DWI) of morphology and kinetic features from DCE-MRI to improve the discrimination power of malignant from benign breast masses is rarely reported.Materials and methodsThe study comprised of 234 female patients with 85 benign and 149 malignant lesions. Four distinct groups of features, coupling with pathological tests, were estimated to comprehensively characterize the pictorial properties of each lesion, which was obtained by a semi-automated segmentation method. Classical machine learning scheme including feature subset selection and various classification schemes were employed to build prognostic model, which served as a foundation for evaluating the combined effects of the multi-sided features for predicting of the types of lesions. Various measurements including cross validation and receiver operating characteristics were used to quantify the diagnostic performances of each feature as well as their combination.ResultsSeven features were all found to be statistically different between the malignant and the benign groups and their combination has achieved the highest classification accuracy. The seven features include one pathological variable of age, one morphological variable of slope, three texture features of entropy, inverse difference and information correlation, one kinetic feature of SER and one DWI feature of apparent diffusion coefficient (ADC). Together with the selected diagnostic features, various classical classification schemes were used to test their discrimination power through cross validation scheme. The averaged measurements of sensitivity, specificity, AUC and accuracy are 0.85, 0.89, 90.9% and 0.93, respectively.ConclusionMulti-sided variables which characterize the morphological, kinetic, pathological properties and DWI measurement of ADC can dramatically improve the discriminatory power of breast lesions.

Dataset Information

Contribution of IVIM to Conventional Dynamic Contrast-Enhanced and Diffusion-Weighted MRI in Differentiating Benign from Malignant Breast Masses.

Publications

Contribution of IVIM to Conventional Dynamic Contrast-Enhanced and Diffusion-Weighted MRI in Differentiating Benign from Malignant Breast Masses.

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