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Contrasts Between Diffusion-Weighted Imaging and Dynamic Contrast-Enhanced MR in Diagnosing Malignancies of Breast
Yan Li1, Jun Chen2, Zhenlu Yang3
1Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Background:
Nonmass enhancement (NME) breast lesions are considered to be the leading cause of unnecessary biopsies. Diffusion-weighted imaging (DWI) or dynamic contrast-enhanced (DCE) sequences are typically used to differentiate between benign and malignant NMEs. It is important to know which one is more effective and reliable.
Purpose:
To compare the diagnostic performance of DCE curves and DWI in discriminating benign and malignant NME lesions on the basis of morphologic characteristics assessment on contrast-enhanced (CE)-MRI images.
Study Type:
Retrospective.
Subjects:
A total of 180 patients with 184 lesions in the training cohort and 75 patients with 77 lesions in the validation cohort with pathological results.
Field Strength/Sequence:
A 3.0 T/multi-b-value DWI (b values = 0, 50, 1000, and 2000 sec/mm2 ) and time-resolved angiography with stochastic trajectories and volume-interpolated breath-hold examination (TWIST-VIBE) sequence.
Assessment:
In the training cohort, a diagnostic model for morphology based on the distribution and internal enhancement characteristics was first constructed. The apparent diffusion coefficient (ADC) model (ADC + morphology) and the time-intensity curves (TIC) model (TIC + morphology) were then established using binary logistic regression with pathological results as the reference standard. Both models were compared for sensitivity, specificity, and area under the curve (AUC) in the training and the validation cohort.
Statistical Tests:
Receiver operating characteristic (ROC) curve analysis and two-sample t-tests/Mann-Whitney U-test/Chi-square test were performed. P < 0.05 was considered statistically significant.
Results:
For the TIC/ADC model in the training cohort, sensitivities were 0.924/0.814, specificities were 0.615/0.615, and AUCs were 0.811 (95%, 0.727, 0.894)/0.769 (95%, 0.681, 0.856). The AUC of the TIC-ADC combined model was significantly higher than ADC model alone, while comparable with the TIC model (P = 0.494). In the validation cohort, the AUCs of TIC/ADC model were 0.799/0.635.
Data Conclusion:
Based on the morphologic analyses, the performance of the TIC model was found to be superior than the ADC model for differentiating between benign and malignant NME lesions.
Evidence Level:
4.
Technical Efficacy:
Stage 2.
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