Quantitative diffusion weighted imaging parameters in tumor and peritumoral stroma for prediction of molecular subtypes in breast cancer

2018 ◽  
Author(s):  
Juan Zhang ◽  
Guoliang Shao ◽  
Lihua Li ◽  
Ting He ◽  
Ming Fan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Diffusion Weighted Imaging ◽  
Molecular Subtypes ◽  
Imaging Parameters ◽  
Diffusion Weighted ◽  
Peritumoral Stroma

scholarly journals Diffusion-Weighted Imaging of Different Breast Cancer Molecular Subtypes: A Systematic Review and Meta-Analysis

Breast Care ◽  
2021 ◽  
pp. 1-8
Author(s):  
Hans-Jonas Meyer ◽  
Andreas Wienke ◽  
Alexey Surov
Keyword(s):  
Breast Cancer ◽  
Systematic Review ◽  
Diffusion Weighted Imaging ◽  
Triple Negative ◽  
Molecular Subtypes ◽  
Luminal A ◽  
Diagnose Breast Cancer ◽  
Luminal B ◽  
Diffusion Weighted ◽  
Adc Values

Background: Magnetic resonance imaging can be used to diagnose breast cancer (BC).Diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) can be used to reflect tumor microstructure. Objectives: This analysis aimed to compare ADC values between molecular subtypes of BC based on a large sample of patients. Method: The MEDLINE library and Scopus database were screened for the associations between ADC and molecular subtypes of BC up to April 2020. The primary end point of the systematic review was the ADC value in different BC subtypes. Overall, 28 studies were included. Results: The included studies comprised a total of 2,990 tumors. Luminal A type was diagnosed in 865 cases (28.9%), luminal B in 899 (30.1%), human epidermal growth factor receptor (Her2)-enriched in 597 (20.0%), and triple-negative in 629 (21.0%). The mean ADC values of the subtypes were as follows: luminal A: 0.99 × 10–3 mm2/s (95% CI 0.94–1.04), luminal B: 0.97 × 10–3 mm2/s (95% CI 0.89–1.05), Her2-enriched: 1.02 × 10–3 mm2/s (95% CI 0.95–1.08), and triple-negative: 0.99 × 10–3 mm2/s (95% CI 0.91–1.07). Conclusions: ADC values cannot be used to discriminate between molecular subtypes of BC.

scholarly journals Radiomic Signatures Derived from Diffusion-Weighted Imaging for the Assessment of Breast Cancer Receptor Status and Molecular Subtypes

2019 ◽  
Vol 22 (2) ◽  
pp. 453-461 ◽  
Author(s):  
Doris Leithner ◽  
Blanca Bernard-Davila ◽  
Danny F. Martinez ◽  
Joao V. Horvat ◽  
Maxine S. Jochelson ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Diffusion Weighted Imaging ◽  
Molecular Subtypes ◽  
Receptor Status ◽  
Diffusion Weighted

scholarly journals Diffusion tensor imaging on 3-T MRI breast: diagnostic performance in comparison to diffusion-weighted imaging

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Dalia Abdelhady ◽  
Amany Abdelbary ◽  
Ahmed H. Afifi ◽  
Alaa-eldin Abdelhamid ◽  
Hebatallah H. M. Hassan
Keyword(s):  
Breast Cancer ◽  
Diffusion Tensor Imaging ◽  
Sensitivity And Specificity ◽  
Diffusion Weighted Imaging ◽  
Diffusion Tensor ◽  
Breast Mri ◽  
Breast Lesions ◽  
Dce Mri ◽  
Additional Tool ◽  
Diffusion Weighted

Abstract Background Breast cancer is the most prevalent cancer among females. Dynamic contrast-enhanced MRI (DCE-MRI) breast is highly sensitive (90%) in the detection of breast cancer. Despite its high sensitivity in detecting breast cancer, its specificity (72%) is moderate. Owing to 3-T breast MRI which has the advantage of a higher signal to noise ratio and shorter scanning time rather than the 1.5-T MRI, the adding of new techniques as diffusion tensor imaging (DTI) to breast MRI became more feasible. Diffusion-weighted imaging (DWI) which tracks the diffusion of the tissue water molecule as well as providing data about the integrity of the cell membrane has been used as a valuable additional tool of DCE-MRI to increase its specificity. Based on DWI, more details about the microstructure could be detected using diffusion tensor imaging. The DTI applies diffusion in many directions so apparent diffusion coefficient (ADC) will vary according to the measured direction raising its sensitivity to microstructure elements and cellular density. This study aimed to investigate the diagnostic accuracy of DTI in the assessment of breast lesions in comparison to DWI. Results By analyzing the data of the 50 cases (31 malignant cases and 19 benign cases), the sensitivity and specificity of DWI in differentiation between benign and malignant lesions were about 90% and 63% respectively with PPV 90% and NPV 62%, while the DTI showed lower sensitivity and specificity about 81% and 51.7%, respectively, with PPV 78.9% and NPV 54.8% (P-value ≤ 0.05). Conclusion While the DWI is still the most established diffusion parameter, DTI may be helpful in the further characterization of tumor microstructure and differentiation between benign and malignant breast lesions.

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