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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
Diagnostic value of multiple b-value diffusion-weighted imaging in discriminating the malignant from benign breast
Chu-Xin Lin1, Ye Tian1, Jia-Min Li1
1Department of Radiology, Zhuhai Hospital Affiliated With Jinan University (Zhuhai People's Hospital), 79 Kangning Road, Zhuhai, 519000, People's Republic of China.
Objective:
The conventional breast Diffusion-weighted imaging (DWI) was subtly influenced by microcirculation owing to the insufficient selection of the b values. However, the multiparameter derived from multiple b-value exhibits more reliable image quality and maximize the diagnostic accuracy. We aim to evaluate the diagnostic performance of stand-alone parameter or in combination with multiparameter derived from multiple b-value DWI in differentiating malignant from benign breast lesions.
Methods:
A total of forty-one patients diagnosed with benign breast tumor and thirty-eight patients with malignant breast tumor underwent DWI using thirteen b values and other MRI functional sequence at 3.0 T magnetic resonance. Data were accepted mono-exponential, bi-exponential, stretched-exponential, aquaporins (AQP) model analysis. A receiver operating characteristic curve (ROC) was used to evaluate the diagnostic performance of quantitative parameter or multiparametric combination. The Youden index, sensitivity and specificity were used to assess the optimal diagnostic model. T-test, logistic regression analysis, and Z-test were used. P value < 0.05 was considered statistically significant.
Result:
The ADCavg, ADCmax, f, and α value of the malignant group were lower than the benign group, while the ADCfast value was higher instead. The ADCmin, ADCslow, DDC and ADCAQP showed no statistical significance. The combination (ADCavg-ADCfast) yielded the largest area under curve (AUC = 0.807) with sensitivity (68.42%), specificity (87.8%) and highest Youden index, indicating that multiparametric combination (ADCavg-ADCfast) was validated to be a useful model in differentiating the benign from breast malignant lesion.
Conclusion:
The current study based on the multiple b-value diffusion model demonstrated quantitatively multiparametric combination (ADCavg-ADCfast) exhibited the optimal diagnostic efficacy to differentiate malignant from benign breast lesions, suggesting that multiparameter would be a promising non-invasiveness to diagnose breast lesions.
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