Multiple b-value diffusion-weighted imaging in differentiating benign from malignant breast lesions: comparison of conventional mono-, bi- and stretched exponential models.
Clin Radiol
; 75(8): 642.e1-642.e8, 2020 08.
Article
in En
| MEDLINE
| ID: mdl-32389372
ABSTRACT
AIM:
To prospectively evaluate multiple b-value diffusion-weighted imaging (DWI) in differentiating malignant from benign breast lesions. MATERIALS ANDMETHODS:
The study cohort included 103 patients who underwent 3 T magnetic resonance imaging (MRI). The conventional sequences included T1-weighted dynamic contrast-enhanced, T1-weighted and T2-weighted fat-suppressed sequences, single b-value (b=0, 1000 s/mm2) DWI, and multiple b-values (12 values, from 0 to 3,000 s/mm2) DWI. Pathological diagnosis of breast lesions was based on the latest World Health Organization (WHO) guide on the pathology and immunohistochemistry of the breast. SPSS Statistics V19.0 was used for the statistics analysis.RESULTS:
The following parameters were calculated apparent diffusion coefficient (ADC), tissue diffusivity (D), perfusion fraction (f), pseudo-diffusion coefficient (D∗), distributed diffusion coefficient (DDC), and alpha (α) by the same radiologist twice (interval time of 3 months). There was good inter/intra-observer agreement for each of the parameters. The D, D∗, f, DDC, and α values were significantly different among malignant tumours, benign lesions, and normal breast tissue (p<0.05).CONCLUSION:
D, f, DDC, α, and ADC values have good sensitivity and specificity, respectively. In addition, the combined use of D and f or DDC and α has good diagnostic performance. Thus, the applications of the new multi-b DWI variables or combined variables are promising.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
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Breast Neoplasms
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Image Enhancement
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Diffusion Magnetic Resonance Imaging
Type of study:
Diagnostic_studies
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Observational_studies
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Prognostic_studies
Limits:
Adult
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Female
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Humans
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Middle aged
Language:
En
Journal:
Clin Radiol
Year:
2020
Document type:
Article