The contribution of diffusion tensor imaging and magnetic resonance spectroscopy for the differentiation of breast lesions at 3T

Tsougos, Ioannis; Svolos, Patricia; Kousi, Evanthia; Athanassiou, Evangelos; Theodorou, K.; Arvanitis, Dimitrios L.; Fezoulidis, Ioannis; Vassiou, Katerina

doi:10.1177/0284185113492152

Cited by 42 publications

(40 citation statements)

References 34 publications

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“…Although several studies 9,10,15 have shown that FA is very helpful in the detection and diagnosis of breast cancer, and the combination of ADC and FA increases the diagnostic accuracy of breast cancer, the role of FA in the prediction of malignancy still remains controversial. In our study, the mean FA value of breast cancers was significantly higher than that of benign lesions, which is in accordance with the result of Baltzer et al 10 but contrary to that of Cakir et al 8 As our study showed, there seemed to be a rising tendency in FA values with the increase of malignancy and grade of breast tumours (Table 3); but, the FA values between DCIS and IDC had no statistical difference.…”

Section: Discussionmentioning

confidence: 99%

Diffusion tensor imaging of breast lesions: evaluation of apparent diffusion coefficient and fractional anisotropy and tissue cellularity

Jiang

Hua

et al. 2016

BJR

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Section: Discussionmentioning

confidence: 99%

Diffusion tensor imaging of breast lesions: evaluation of apparent diffusion coefficient and fractional anisotropy and tissue cellularity

Jiang

Hua

et al. 2016

BJR

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“…More recently, diffusion weighted MRI and the resulting maps of apparent diffusion coefficient (ADC) have been evaluated as a complement method to DCE-MRI and it was shown that ADC values can help distinguish between cancers, benign lesions and normal breast tissue 9,10 . In addition, studies of breast diffusion tensor imaging (DTI) were initiated in healthy volunteers and patients with breast lesions at field strength of 1.5 T [11][12][13][14][15] and of 3 T [16][17][18][19][20][21][22][23][24] . Most of these studies reported ADC and fractional anisotropy (FA) values 11,12,14,15,[20][21][22][23] and found these two parameters to be reproducible with ADC values more reproducible than FA 13,20 .…”

Section: Introductionmentioning

confidence: 99%

“…In addition, studies of breast diffusion tensor imaging (DTI) were initiated in healthy volunteers and patients with breast lesions at field strength of 1.5 T [11][12][13][14][15] and of 3 T [16][17][18][19][20][21][22][23][24] . Most of these studies reported ADC and fractional anisotropy (FA) values 11,12,14,15,[20][21][22][23] and found these two parameters to be reproducible with ADC values more reproducible than FA 13,20 . The results of these studies indicated that malignant lesions exhibit low ADC values as compared to normal tissue and benign lesions, however, conflicting results were reported on the values and diagnostic capability of FA 11,12,14,[20][21][22][23] .…”

Section: Introductionmentioning

confidence: 99%

“…Most of these studies reported ADC and fractional anisotropy (FA) values 11,12,14,15,[20][21][22][23] and found these two parameters to be reproducible with ADC values more reproducible than FA 13,20 . The results of these studies indicated that malignant lesions exhibit low ADC values as compared to normal tissue and benign lesions, however, conflicting results were reported on the values and diagnostic capability of FA 11,12,14,[20][21][22][23] . In a set of 3 T-DTI studies the values of the three tensor eigenvalues and eigenvectors in the breast tissue frame were reported as well, and the results were presented in vector maps of the main eigenvector and parametric maps of the eigenvalues, ADC, FA and a maximal anisotropy index [16][17][18][19]24 .…”

Section: Introductionmentioning

confidence: 99%

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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Nissan

Furman‐Haran

Feinberg-Shapiro

et al. 2014

JoVE

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Breast cancer is the most common cause of cancer among women worldwide. Early detection of breast cancer has a critical role in improving the quality of life and survival of breast cancer patients. In this paper a new approach for the detection of breast cancer is described, based on tracking the mammary architectural elements using diffusion tensor imaging (DTI).The paper focuses on the scanning protocols and image processing algorithms and software that were designed to fit the diffusion properties of the mammary fibroglandular tissue and its changes during malignant transformation. The final output yields pixel by pixel vector maps that track the architecture of the entire mammary ductal glandular trees and parametric maps of the diffusion tensor coefficients and anisotropy indices.The efficiency of the method to detect breast cancer was tested by scanning women volunteers including 68 patients with breast cancer confirmed by histopathology findings. Regions with cancer cells exhibited a marked reduction in the diffusion coefficients and in the maximal anisotropy index as compared to the normal breast tissue, providing an intrinsic contrast for delineating the boundaries of malignant growth. Overall, the sensitivity of the DTI parameters to detect breast cancer was found to be high, particularly in dense breasts, and comparable to the current standard breast MRI method that requires injection of a contrast agent. Thus, this method offers a completely non-invasive, safe and sensitive tool for breast cancer detection.

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“…16,17 The FA or ADC value has a strong correlation with cell density, 18 and malignant tumours usually have a reduced ADC value and an elevated FA value. 19,20 While DTI is widely applied in multiple organs, 18,[21][22][23][24] its application, particularly the FA value in parotid tumours, is rarely reported.…”

Section: Introductionmentioning

confidence: 99%

Application of DTI and ARFI imaging in differential diagnosis of parotid tumours

Yu¹,

Du²,

Lu³

et al. 2016

Dentomaxillofacial Radiology

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Objectives: To explore the utility of diffusion tensor imaging (DTI) and acoustic radiation force impulse (ARFI) imaging in the diagnosis of parotid tumours. Methods: 51 patients with parotid tumours were examined with DTI on 3.0-T MRI and ARFI imaging on an ultrasound scanner before surgery. Values of apparent diffusion coefficient (ADC), fractional anisotropy (FA) and shear-wave velocity (SWV) were calculated and analyzed with independent samples Wilcoxon-Mann-Whitney test. Cut-off values, sensitivity and specificity were calculated with receiver-operating characteristic (ROC) curve analysis. The value of combination was calculated through parallel test for the cut-off value of ADC, FA and SWV (combination 5 0 or 1); then, ROC analysis was performed with pathological results as the gold standard to calculate the sensitivity and specificity for the combination of the three parameters distinguishing benign and malignant parotid tumours. Pathological diagnosis for every patient was made post-operatively from the tumour tissue taken during operation. Results: There was a significant difference between benign and malignant tumours in the values of ADC, FA and SWV (p 5 0.032, p 5 0.011 and p , 0.0001); a significant difference in the values was also found between pleomorphic adenoma and malignant tumour (p 5 0.0012, p , 0.0001 and p 5 0.0002). The diagnosis cut-off points between benign and malignant tumours for ADC, FA and SWV were 1.02 3 10 23 mm 2 s 21 , 0.24 and 2.76 m s 21 , respectively; the sensitivity for ADC, FA and SWV was 87.50, 62.50 and 68.75%; the specificity was 45.71, 82.86 and 97.14%. Analysis of the combination of the three parameters increased the sensitivity, specificity, Youden index and area under the ROC curve compared with analysis of each parameter alone for distinguishing benign and malignant tumours.

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The contribution of diffusion tensor imaging and magnetic resonance spectroscopy for the differentiation of breast lesions at 3T

Abstract: In conclusion, applying DTI and 1H-MRS together, adds incremental diagnostic value in the characterization of breast lesions and may sufficiently improve the low specificity of conventional breast MRI.

Cited by 42 publications

References 34 publications

Diffusion tensor imaging of breast lesions: evaluation of apparent diffusion coefficient and fractional anisotropy and tissue cellularity

Diffusion tensor imaging of breast lesions: evaluation of apparent diffusion coefficient and fractional anisotropy and tissue cellularity

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Application of DTI and ARFI imaging in differential diagnosis of parotid tumours

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