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Diagnostic performance of quantitative diffusion tensor imaging for the differentiation of breast lesions at 3 T MRI.
Tsougos, Ioannis; Bakosis, Michael; Tsivaka, Dimitra; Athanassiou, Evangelos; Fezoulidis, Ioannis; Arvanitis, Dimitrios; Vassiou, Katerina.
Affiliation
  • Tsougos I; Department of Medical Physics, Medical School, University of Thessaly, Biopolis, Larissa, Greece. Electronic address: tsougos@med.uth.gr.
  • Bakosis M; Department of Medical Physics, Medical School, University of Thessaly, Biopolis, Larissa, Greece.
  • Tsivaka D; Department of Medical Physics, Medical School, University of Thessaly, Biopolis, Larissa, Greece.
  • Athanassiou E; Department of Surgery, Medical School, University of Thessaly, Biopolis, Larissa, Greece.
  • Fezoulidis I; Department of Diagnostic Radiology, Medical School, University of Thessaly, Biopolis, Larissa, Greece.
  • Arvanitis D; Department of Anatomy-Histology-Embryology, University of Thessaly Medical School, Larissa, Greece.
  • Vassiou K; Department of Diagnostic Radiology, Medical School, University of Thessaly, Biopolis, Larissa, Greece; Department of Anatomy-Histology-Embryology, University of Thessaly Medical School, Larissa, Greece.
Clin Imaging ; 53: 25-31, 2019.
Article in En | MEDLINE | ID: mdl-30308430
ABSTRACT

BACKGROUND:

Conventional breast magnetic resonance imaging (MRI), including dynamic contrast-enhanced MR mammography, may lead to ambiguous diagnosis and unnecessary biopsies.

PURPOSE:

To investigate the contribution of quantitative diffusion tensor imaging (DTI) in the discrimination between benign and malignant breast lesions at 3 T MRI. MATERIAL AND

METHODS:

The study included a total of 86 lesions (44 benign and 42 malignant) in 58 women (34 with malignant lesions, 23 with benign lesions and 1 with both types of lesions). All patients were examined on a 3 T MRI scanner. Fractional Anisotropy (FA), Mean Diffusivity (MD), Apparent Diffusion Coefficient (ADC), as well as eigenvalues (λ1, λ2, λ3) were calculated and compared between benign and malignant lesions using two different software packages (GE Functool and ExploreDTI).

RESULTS:

Malignant lesions exhibited significantly lower ADC values compared to benign ones (ADCmal = 1.06 × 10-3 mm2/s, ADCben = 1.54 × 10-3 mm2/s, p-value < 0.0001). FA measurements in carcinomas indicated slightly higher values than those in benign lesions (FAmal = 0.20 ±â€¯0.07, FAben = 0.15 ±â€¯0.05, p-value = 0.0003). Eigenvalues λ1, λ2, λ3, showed significantly lower values in malignant tumors compared to benign lesions and normal breast tissue. ROC curve analysis of ADC and DTI metrics demonstrated that ADC provides high diagnostic performance (AUC = 0.944) while, MD and λ1 showed best discriminative results (AUC = 0.906) for the differentiation of malignant and benign lesions in contrast to other DTI parameters.

CONCLUSION:

The addition of eigenvalue analysis improves DTI's ability to differentiate between benign and malignant breast lesions.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Breast / Breast Neoplasms / Carcinoma / Diffusion Tensor Imaging Type of study: Diagnostic_studies / Evaluation_studies / Prognostic_studies Limits: Adult / Aged / Female / Humans / Middle aged Language: En Journal: Clin Imaging Journal subject: DIAGNOSTICO POR IMAGEM Year: 2019 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Breast / Breast Neoplasms / Carcinoma / Diffusion Tensor Imaging Type of study: Diagnostic_studies / Evaluation_studies / Prognostic_studies Limits: Adult / Aged / Female / Humans / Middle aged Language: En Journal: Clin Imaging Journal subject: DIAGNOSTICO POR IMAGEM Year: 2019 Document type: Article