Diffusion tensor magnetic resonance imaging in differentiation of breast lesions

Abstract

Background

Diffusion tensor imaging (DTI) is a novel approach which uses extra gradients to quantify diffusion in several directions (at least six). The purpose of this research was to determine the role of diffusion tensor magnetic resonance imaging in breast lesion differentiation.

Results

Apparent diffusion coefficient (ADC) values were significantly lower in malignant than benign lesions, with a cut-off value of 1.21 × 10⁻³ mm²/s, this gives a sensitivity of 88.46%, specificity 87.50% and accuracy 86.7%. Values of fractional anisotropy (FA) were higher significantly in malignant compared to benign lesions with a 0.15 cut-off value, has a 95.83% sensitivity, 96.15% specificity, and 95.6%, accuracy. Values of RA were significantly higher in malignant (0.180 ± 0.068) compared to benign lesions, with 0.13 cut-off value. Sensitivity, specificity, and accuracy were, respectively, 91.69%, 92.31%, and 90.2%. Values of λ1 were significantly lower in malignant (1.4 ± 0.453 × 10⁻³ mm²/s) than in benign (2.19 ± 0.659 × 10⁻³ mm²/s) lesions with a cut-off value of 1.71 × 10⁻³ mm²/s. Sensitivity and specificity were, respectively, 95.83 and 96.15%. The combined evaluation by (dynamic contrast enhancement) Sensitivity improved to 100% with DCE and DTI readings, while specificity remained at 95.6%.

Conclusions

DTI breast imaging is a noninvasive procedure which demonstrated a high potential utility for cancer detection and serving as a standalone technique or in conjunction with DCE-MRI, the discriminating values of FA, λ1 and λ1–λ3 were high. Their measurements were strongly associated with identification breast malignancy and combined evaluation by DTI parameters and DCE-MRI DTI enhanced the sensitivity, lowered the rate of false-negatives, and completely improved the accuracy of breast lesions differential diagnosis.

Background

Breast cancer is the most a frequent kind of cancer in females globally and is expected to be the second leading reason of death due to cancer [1, 2]. While the prevalence of breast cancer has grown across the previous two decades, the death rate has decreased significantly. Hence, early identification and precise differentiation of benign and malignant breast tumors perform a critical function in determining treatment selections and the efficacy of the chosen treatment technique [3].

Mammography and ultrasound are both well-established imaging techniques for diagnosing breast cancer [4]; however, in certain instances, these imaging techniques might produce equivocal results in which breast cancer cannot be ruled out [1]. Due to its exceptional sensitivity and contrast between soft and hard tissues, magnetic resonance imaging (MRI) can be utilized as a procedure for resolving problems to prevent or steer biopsies [5].

When dense breast tissue is seen on a mammogram, MRI of breast is a very sensitive imaging technique for cancer breast diagnosis. Additionally, it has been approved for use in various therapeutic conditions, for instance, screening females at risk and evaluation of breast cancer pre-to-surgical intervention [6].

Diffusion tensor imaging (DTI) is a new approach that makes use of extra gradients to quantify diffusion in several directions (at least six). This allows for the three-dimensional (3D) space quantification of diffusion and to determine the degree to which water diffuses in an anisotropic manner inside a tissue, quantified with the parameters relative anisotropy (RA) and fractional anisotropy (FA) [7].

In DTI sequence, mean diffusivity (MD) is described as an average of the apparent diffusion coefficient (ADC) values in three orthogonal directions, while another DTI indicator, FA, is a scalar number between 0 and 1 that quantifies the anisotropy degree in the process of diffusion. A value of zero indicates isotropic diffusion, which is unfettered (or equally limited) in every way. A value of one indicates that diffusion happens along a single axis and is completely prohibited in all other directions. Furthermore, DTI measures eigenvectors of diffusion in the three main x-, y-, and z-axes and their maximum, middle, and minimal eigenvalues (λ1, λ2, and λ3), from which the MD, maximal anisotropy index (λ1–λ3), can be calculated [8].

This work aimed to identify the role of DTI MRI in breast lesions differentiation.

Methods

Setting of study

The MRI unit of the radio-diagnosis and imaging department at Tanta University Hospital during the period from October 2019 to October 2021.

This prospective observational cohort research was performed on 50 females aged 25–75 years old featured with breast complaints and/or abnormal sono-mammographic findings.

After a thorough discussion of the procedure's advantages and dangers, all patients involved in this research provided written informed permission. Any unexpected risks appeared during the study were timely disclosed to participants and the ethics committee.

The criteria for exclusion were as follows: previous breast surgery, chemotherapy or radiation and presence of cardiac pacemakers, ferromagnetic intracranial aneurysm clips.

Two groups of patients were formed: in accordance with histopathological diagnosis benign (n = 26) and malignant (n = 24).

Each patient had a thorough history taking, clinical examination, laboratory testing and breast MRI.

MRI

All MR imaging was carried out on a 1.5 Tesla (GE Health Care, Sigma HDX., W) using a bilateral breast coil. Each patient had the usual breast protocol, including:

Conventional MRI

• Axial T1-weighted imaging (TE 10 ms, TR 413 ms thickness of section 3 mm, 340_512 matrix, field of view [FOV] 457 mm).

• T2WI axial (TE 120 ms, TR 4,374 ms).

• T2WI axial with suppression of fat (TE 70 ms, TR 3,997 ms, 3-mm section thickness).

• STIR (TE = 30 ms, TR = 3000 ms, TI = 150 ms) in the transverse and sagittal plane; slice thickness: 4 mm; spacing: 1 mm; image matrix: 320 × 314. [9]

Diffusion weighted imaging (DWI)

In all patients, DWI was done using the following settings with b-values of 0, 800, and 1000 s/mm²: TE 77 ms, TR 2,267 ms, section thickness of 5 mm, 256–256 matrix, 450 mm FOV, size of voxel 4.7–4.6 mm, sections number 30, gap section 0, anterior to posterior phase encoding) [1].

All patients had ADC maps produced, and the findings were compiled.

DTI

DTI was done in the axial plane using the procedure of medium three-dimensional (3D) DTI with the following parameters: 48 sections without a gap, TR 3802 ms, TE 106 ms, four excitations, 34 mm FOV, matrix 136_134, collection length 9 min 30 s, 3-mm section thickness, suppression of fat: recovery of spectral adiabatic inversion in 40 directions applied [9].

Dynamic contrast enhanced (DCE)

It was performed to 45 patients, a rapid spoiled gradient-recalled echo sequence in three dimensions with axial TWI was acquired (TE 1.22 ms, TR 4.19 ms 340_340 mm FOV, matrix of 448 340, thickness of section 0.9 mm, time of acquisition 6 min 42 s, Q-fat sat.) with gadolinium diethylenetriaminepentaacetic acid of a dosage of 0.1 mmol/kg administered across an indwelling catheter intravenously [1].

Clinical MRI interpretation

Lesion characteristics were assessed including size and enhancement pattern (focus, mass, non-mass enhancement [NME]) for masses, the form (round, irregular or oval), the edge (circumscribed, spiculated, or irregular), and the internal enhancement features (heterogeneous, homogeneous, presence of non-enhanced septa and rim enhancement), and non-masses were characterized in terms of their distribution and internal enhancement pattern.

Kinetic pattern of the worst curve type in DCE (characterized as the most suspect, with washout > plateau > persistence) was reported.

Final BI-RADS breast MRI lexicon evaluation.

DTI post-processing

All tensor-diffusion data were transferred and post-processed on the workstation.

Circular/free hand region of interest (ROI) was drawn manually along the lesion margin on the areas that show the greatest contrast enhancement and diffusion restriction, omitting regions that are hemorrhagic, cystic, or calcific.

• Each lesion's ADC was established by creating a ROI around it. If the lesion was larger than three centimeters in diameter, ADC values were obtained twice, and the average of the two values was utilized.

• The eigenvalues of diffusion tensor λ1, λ2, and λ3 were then computed and evaluated using DTI parametric maps where λ1, λ2, and λ3 are the eigenvalues of the diffusion tensor's maximum, intermediate, and minimum diffusion tensors, respectively,

• Additionally, the empirical parameter λ1–λ3 (maximum anisotropy index).

• MD was determined as the average of the three eigenvalues. MD represents the degree of mobility or hindrance of water molecules.

• FA and RA are diffusion anisotropy indices constructed from the eigenvalues of the diffusion tensor's terms. FA is a unitless index representing diffusion's degree of directionality, ranging from 0 (totally isotropic) to 1 (entirely directional) (completely anisotropic),

• Results were presented parametric maps of the eigenvalues in color-coded maps, and fiber tractography was drawn in the direction of mammary ducts.

Statistical analysis

SPSS v26 was used to conduct the statistical analysis (IBM Inc., Chicago, IL, USA). To compare the means and standard deviations of quantitative variables between the two groups, the unpaired Student's t test was utilized. Chi-square and Fisher's exact tests were used to analyze qualitative variables, which were provided as frequency and percentage (percentage). To be statistically significant, two-tailed P values less than 0.05 were required. This is an assessment of the test's sensitivity and specificity as well as the test's positive and negative predictive values (NPV). For this study, two-tailed P values of 0.05 or below were deemed statistically significant.

Results

Table 1 shows age, clinical presentation, parity, family history, biopsy method, histopathologic diagnosis, and examination of the studied patients

Table 1 Age, clinical presentation, parity, family history, biopsy method, histopathologic diagnosis, and examination of the studied patients

Malignant breast lesions were more common in patients with positive family history of breast cancer. Most of the mass with heterogeneous enhancing and non-mass enhancement patterns were in malignant (18 cases, 82%) that was statistically significant than benign lesions (eight cases, 35%) (p < 0.01). Correlation with histopathological results demonstrated that dynamic contrast enhancement MRI produced sensitivity, specificity and accuracy of 95.4%, 86.95% and 91.1%, respectively. Type I curve was significantly correlated with higher values of ADC, while type II and III show lower values of ADC (Table 2).

Table 2 Relation of histopathological diagnosis to clinical data and correlation between MRI enhancement pattern and histopathological diagnosis correlation between type of kinetic curve and ADC values

ADC values were significantly lower in malignant (1.054 ± 0.284 × 10⁻³ mm²/s) than in benign lesions (1.388 ± 0.228 × 10⁻³ mm²/s), with a cut-off value of 1.21 × 10⁻³ mm²/s; this gives a sensitivity of 88.46%, specificity 87.50% and accuracy 86.7%. FA values were significantly higher in malignant (0.202 ± 0.065) than in benign lesions (0.129 ± 0.033) with a cut-off value of 0.15, gives sensitivity, specificity, and accuracy of 95.83%, 96.15%, and 95.6%, respectively. RA values were significantly higher in malignant (0.180 ± 0.068) than in benign lesions (0.117 ± 0.034), with a cut-off value of 0.13. Sensitivity, specificity, and accuracy were 91.69%, 92.31%, and 90.2%, respectively. Values of λ1 were significantly lower in malignant (1.4 ± 0.453 × 10⁻³ mm²/s) than in benign (2.19 ± 0.659 × 10⁻³ mm²/s) lesions with a cut-off value of 1.71 × 10⁻³ mm²/s. Sensitivity and specificity were 95.83% and 96.15%, respectively (Fig. 1).

Fig. 1

a ROC curve for evaluation of ADC values, b FA values, c RA values and d λ1 values in detection of malignant lesions. ADC: Apparent diffusion coefficient, FA: fractional anisotropy, RA: relative anisotropy, λ1; maximum eigenvalue

Values of λ1–λ3 were significantly lower in malignant (0.545 ± 0.133 × 10⁻³ mm²/s) than in benign (0.745 ± 0.102 × 10⁻³ mm²/s) lesions, with a cut-off value of 0.65 × 10⁻³ mm²/s. Sensitivity and specificity were 95.83% and 96.15%, respectively (Table 3).

Table 3 Correlation of eigenvalues in-between benign and malignant lesions and specificity and sensitivity of eigenvalues in detection of malignant lesions

MD values were significantly lower in malignant (1.232 ± 0.442 × 10⁻³ mm²/s) than in benign lesions (1.671 ± 0.459 × 10⁻³ mm²/s), with a cut-off value of 1.41 × 10⁻³ mm²/s. Sensitivity, specificity, and accuracy were 91.6%, 92.3%, and 91.4%, respectively Fig. 2.

Fig. 2

ROC curve for evaluation of MD values in detection of malignant lesions

Collectively, analyzing data of the present study, among DTI parameters, λ1, λ1–λ3, and FA showed the highest diagnostic accuracy with sensitivity (95.83%) and specificity (96.15%). Following, DCE sensitivity and specificity were 86.95% and 95.4%, respectively, and MD with specificity and sensitivity of 91.67% and 92.31%, respectively (Table 4).

Table 4 Diagnostic performance of DTI parameters

The sensitivity of DCE-MRI measurement and DTI measurements in 45 patients (who evaluated by DCE-MRI) is the same, each was 95.4%, while the specificity of DTI was higher (95.6%) than that of DCE-MRI (86.9%) (Table 5).

Table 5 Evaluation of diagnostic performance of DCE-MRI and DTI in comparison with histopathology (n = 45)

The combined evaluation by DCE and DTI values increased the sensitivity to 100%, but specificity still 95.6% (Table 6) (Figs. 3, 4, and 5).

Table 6 Evaluation of diagnostic performance of combined DCE-MRI images and DTI parameters in comparison with histopathology (n = 45)

Fig. 3

a Axial T1WI FAT SAT pre-contrast: The mass displays isointense signal, b Axial T1WI post-contrast, c Post-subtraction images: Homogenous non-mass enhancement, d Axial T2WI: Low signal intensity, e Axial T2WI FS: The mass displays high signal intensity (no suppression), f Axial STIR: The mass displays high signal intensity, g kinetic curve: Type I, rising curve, h Axial DWI: The mass displays high signal, i ADC value = 1.37 × 10⁻³ mm²/s (cut-off value of < 1.21 × 10⁻³ mm²/s), j FA = 0.09 (cut-off value of > 0.15), k RA = 0.11 (cut-off value of > 0.13), l Maximum eigenvalue (λ1) = 2.4 × 10⁻³ mm²/s (cut-off value of < 1.71 × 10⁻³ mm²/s), m: Middle eigenvalue (λ2) = 2 × 10⁻³ mm²/s (cut- off value of < 1.31 × 10⁻³ mm²/s), n Minimum eigenvalue (λ3) = 1.34 × 10⁻³ mm²/s (cut- off value of < 1.19 × 10⁻³ mm²/s) and o Fiber Tractography: Intact fibers suggesting benign nature of the mass. Provisional diagnosis by DCE-MRI and DTI parameters: Benign lesion. Histopathological diagnosis: Chronic granulomatous mastitis

Fig. 4

a Axial T1WI FS pre-contrast: Lesion displays intermediate signal intensity, b Axial T1WI post-contrast, c Post-subtraction images: Homogenous enhancement lesion. d Axial T2WI: The lesion displays low signal intensity, e Axial STIR: The lesion displays high signal Intensity, f kinetic curve: Type III, washout curve, g DWI: The lesion displays high signal intensity, h ADC value = 1.09 × 10⁻³ mm²/s (cut-off value of < 1.21 × 10⁻³ mm²/s,. i FA = 0.22 (cut-off value of > 0.15), j RA = 0.25 (cut-off value of > 0.13), k Maximum eigenvalue (λ1) = 1.31 × 10⁻³ mm²/s (cut-off value of < 1.71 × 10⁻³ mm²/s), l Middle eigenvalue (λ2) = 1.16 × 10⁻³ mm²/s (cut-off value of < 1.31 × 10⁻³ mm²/s), m Minimum eigenvalue (λ3) = 1.06 × 10⁻³ mm²/s (cut-off value of < 1.19 × 10⁻³ mm²/s) and n Fiber Tractography: Distortion and discontinuity of fiber tracts, suggesting malignant lesion. Provisional diagnosis by DCE-MRI and DTI parameters: Malignant lesion. Histopathological diagnosis: Invasive ductal carcinoma

Fig. 5

a Axial T1WI post-contrast, b Post-subtraction imaging: An oval-shaped lesion with homogenous enhancement, c Axial T2WI: The lesion displays low signal intensity, d Axial T2WI FS: The lesion displays high signal, no fat suppression, e Axial STIR: The lesion displays high signal intensity, f kinetic curve: Type I, rising curve, g Axial DWI: The lesion displays high signal intensity, h ADC value = 1.5 × 10⁻³ mm²/s (cut-off value of < 1.21 × 10⁻³ mm²/s), i FA = 0.05 (cut-off value of > 0.15), j RA = 0.10 (cut-off value of > 0.13), k Maximum eigenvalue (λ1) = 2.21 × 10⁻³ mm²/s (cut-off value of < 1.71 × 10⁻³ mm²/s), l Middle eigenvalue (λ2) = 1.39 × 10⁻³ mm²/s (cut-off value of < 1.31 × 10⁻³ mm²/s), m Minimum eigenvalue (λ3) = 1.2 × 10⁻³ mm²/s (cut-off value of < 1.19 × 10⁻³ mm²/s) and n Fiber Tractography: Almost intact fiber tracts, suggesting benign lesion. Provisional diagnosis by DCE-MRI and DTI parameters: Benign lesion. Histopathological diagnosis: Fibroadenoma

Discussion

DTI is an innovative approach that makes use of extra gradients to quantify diffusion in many directions (at least six).This permits the characterization of diffusion in a space that is three-dimensional (3D) and the quantification water diffusion in tissues that is anisotropic, quantified with the parameters FA and RA [10].

Diffusion tensor imaging (DTI) parameters λ1, λ2, λ3, λ1–λ3, and MD in malignant tumors, in the present study, were significantly lower compared to benign lesions. This coincides with that reported in the work of Partridge et al. [7], Cakir et al. [11], Jiang et al. [12] and Onaygil et al. [13].

Jiang et al. [12] stated that MD and FA were shown to be substantially correlated with tissue cellularity in breast lesion, and the breast cancer had a higher cellularity than benign lesions.

Beppu et al. [14] suggested that the decrease in diffusion coefficients might be a result of malignant tissues' increased cellularity, which would limit the diffusion activity of water molecules in the extracellular compartment. Additionally, cancer cells obstructing ducts and lobules may lead to a drop in diffusion coefficients in all directions.

The assessment of eigenvalues (λ1, λ2, λ3) may permit the three-dimensional description of diffusion, estimating the anisotropic water diffusion coefficient in a tissue [9].

Concerning the results of λ1 measurements, the current work showed lower values in malignant tumors (1.4 ± 0.453 × 10⁻³ mm²/s) than benign lesions (2.192 ± 0.659 × 10⁻³ mm²/s) with a cut-off value of 1.71 × 10⁻³ mm²/s, 95.83% sensitivity, and 96.15% specificity.

This is relatively comparable to the results of Onaygil et al. [13] who demonstrated that the mean value for benign and malignant lesions was 1.916 ± 0.30 × 10⁻³ mm²/s and 1.276 ± 0.19 × 10⁻³ mm²/s, respectively, with a value of cut-off point 1.59, sensitivity (97.8%), and specificity (87.2%). Additionally, 1 is capable of differentiating infiltrating breast cancer from ductal carcinoma in situ. Wang et al. [15] showed that invasive carcinomas exhibited a larger cellular density and a denser extracellular matrix than ductal carcinomas in situ, which hindered water transport.

Additionally, interstitial fibrosis was seen in the stroma of infiltrating breast cancer as a result of a desmoplastic response. This resulted in a decline of λ1 in infiltrating breast cancer [15].

That is quite similar to the findings in the present study, where λ1 value was obviously lower in invasive (1.2653 × 10⁻³ mm²/s) than noninvasive lesions (1.623 × 10⁻³ mm²/s).

Regarding λ2 and λ3 values, our study showed significantly higher value in benign (1.477 ± 0.377 and 1.461 ± 0.410 × 10⁻³ mm²/s, respectively) than in malignant (1.157 ± 0.249 and 1.034 ± 0.274 × 10⁻³ mm²/s) lesions, respectively, with cut-off values of 1.31 and 1.19 × 10⁻³ mm²/s, respectively. Sensitivity and specificity of λ2 were 87.50% and 84.62% and those for λ3 were 90.6% and 89%, respectively.

There is a great similarity with the finding of Onaygil et al. [13] who concluded that the mean value of λ2 and λ3 in benign lesions was 1.686 ± 0.28 and 1.466 ± 0.27 × 10⁻³ mm²/s, respectively, while in malignant lesions, the mean value of λ2 and λ3 were 1.016 ± 0.20 and 0.816 ± 0.24 × 10⁻³ mm²/s, respectively. The cut-off value of λ2 was 1.20 × 10⁻³ mm²/s with a specificity of 95.7% and a sensitivity of 95.6%. The cut-off value of λ3 was 1.06 × 10⁻³ mm²/s giving specificity 93.6% and sensitivity 95.6%.

The diagnostic performance of maximum anisotropic index (λ1–λ3) in distinguishing malignant and benign lesions was evaluated, and we obtained lower values in malignant breast lesions (0.545 ± 0.133 × 10⁻³ mm²/s) compared to benign lesions (0.745 ± 0.102 × 10⁻³ mm²/s) with a cut-off value of 0.65 × 10⁻³ mm²/s. The maps of λ1–λ3 provided a high sensitivity (95.83%) and specificity (96.15%).

These results agree with that previously reported by others (Eyal et al. [16] and Onaygil et al. [13]). However, Luo et al. [17] reported that maximum anisotropic index (λ1–λ3) did not demonstrate a significant difference between malignant and benign lesions.

In the present work, the mean MD values were significantly lower in malignant (1.232 ± 0.442 × 10⁻³ mm²/s) than in benign (1.671 ± 0.459 × 10⁻³ mm²/s) breast lesions with a cut-off value of 1.41 × 10⁻³ mm²/s giving a sensitivity of 91.6% and a specificity of 92.3%.

In agreement, Onaygil et al. [13] showed that the mean value of MD in benign lesions was 1.686 ± 0.27 and in malignant lesions was 1.036 ± 0.19 × 10⁻³ mm²/s, with a cut-off value of 1.24 × 10⁻³ mm²/s giving a sensitivity of 95.6% and a specificity of 93.6%.

Analyzing the results of the present study and evaluating ADC and DTI parameters as discriminators of benign and malignant lesions, ADC sensitivity and specificity were 88.4% and 87.5%, respectively. DTI parameters λ1, λ1–λ3, and FA obviously shows higher sensitivity (95.83%) and specificity (96.15%).

However, this is different form that reported by Tsougos et al. [3] who evaluated parameters of the ADC and DTI as discriminators between benign and malignant tissues. The ADC has a sensitivity of 85% and a specificity of 84.4%, respectively, while those of FA were 65.8% and 67.4%, respectively.

Among the variable studied parameters and reviewing ROC curve analysis, we concluded that the λ1, λ1–λ3 and FA demonstrated the highest diagnostic efficiency (sensitivity, 95.83% and specificity, 96.15%), followed by DCE (sensitivity, 95.4% and specificity, 86.95%) then MD values (sensitivity, 92.31% and specificity, 91.67%).

Concerning the research that examined the role of DTI in differentiating breast lesions, Tsougos et al. [3] reported that MD and λ1 are the most discriminative DTI parameters for breast lesion detection (sensitivity of 82.5% and specificity of 81.4%).

Wang et al. [15] showed that ADC and λ1 have the greatest discriminative values, followed by λ1– λ3, with λ1 and λ2 performing extremely similarly to ADC in terms of sensitivity and specificity.

The discrepancy between our results and those of the previous studies may be partly attributed to the heterogeneity related to b-values.

Different selected b-values, and the number of gradient directions varies between studies, which may have an effect on the estimation of parameters of DTI [18]. In our study, we used b-values of 0, 800 and 1000 s/mm² and 40 directions.

In comparison with previous studies, Luo et al. [17] used b-value of 0, 100, 800, and six directions, Onaygil et al. [13] used b-value of 0, 700, and 30 directions, while Cakir et al. [11] used b-value of 0, 1000, and 16 directions.

On comparing the diagnostic performance of DTI and DCE in 45 patients who are examined by both techniques, DTI achieved a higher specificity than DCE-MRI, and it showed about 95.6% compared to 86.9% of DCE; both showed the same sensitivity 95.4%. The combined evaluation increased the sensitivity to 100% and specificity of DCE to 95.6%.

This agrees with results of a study done by Onaygil et al. [13] who showed that addition of DTI parameters to DCE-MRI increases the specificity of DCE-MRI from 83.0 to 93.6% and sensitivity to 100%.

Wang et al. [15] also revealed that adding DTI to DCE-MRI might improve performance of diagnosis, as the specificity of DCE-MRI increased from 84 to 94% when DTI was included. Therefore, using DTI in conjunction with traditional breast cancer in routine clinical practice, DCE-MRI may help minimize the frequency of needless benign biopsies [15].

Limitations: The sample size was relatively small. The study was in a single center.

Conclusions

Values of FA and RA were significantly higher in malignant than benign lesions, values of ADC, λ1, λ2, λ3, λ1–λ3, and MD were significantly lower in malignant than benign breast lesions, breast DTI is a noninvasive method that demonstrated a high potential utility for cancer detection and serving as a standalone technique or in conjunction with DCE-MRI, the discriminating values of FA, λ1 and λ1–λ3 were high. Their measurements were strongly associated with identification breast malignancy and combined evaluation by DTI parameters and DCE-MRI DTI enhanced the sensitivity, lowered the rate of false-negatives, and completely improved the accuracy of breast lesions differential diagnosis.