| Part IDiagnosis, differentiation and Risk Assessment of Breast Cancer according to DTIBackground and PurposeBreast cancer is a common female malignancy which threaten to women’s lives and health around the world. Dynamic contrast-enhanced MRI scan has very important value in breast cancer detection and diagnosis, whose sensitivity is very high, while specificity is relatively low. The combination of DWI with enhanced MRI scan can improve the specificity over enhanced MRI alone in the diagnosis of breast cancer. DTI provide both isotropic and anisotropic parameters provide which benefit the diagnosis and differential of breast disease. In this study, the difference between breast cancer, normal breast tissue and benign lesions in DTI parameters was investigated, the value of DTI parameters in breast cancer diagnosis and differention was analized and the breast cancer risk was assessed.MethodsAccording to the inclusion criteria, a total of 56 cases was collected between June 2012 to July 2014. All of the cases was women, aged 37-68 years with a median age of 47 years old. Among them,40 cases were premenopausal women and 16 caseswere postmenopausal women. All of the case were histopathologically confirmed, including 22 cases of benign lesions and 34 cases of breast cancer.All patients were examined in prone position on the same 1.5T MRI system using a dedicated receiving bilateral breast matrix coil. A routine axial turbo spin echo inversion recovery sequence of fat-suppressed T2WI (TR 5800 ms, TE 56 ms, FOV 275 mm×275 mm, matrix 314×320, slice thickness 6 mm with no intersection gap, NEX 2) was first performed after tomography, then a DTI sequence followed. DTI was performed using a axial 2-dimensional diffusion-weighted echo planer imaging sequence (TR 6900 ms, TE 90 ms, slice thickness 5 mm with zero gap, NEX 4, FOV 380 mm×285 mm, matrix 144×192), and the diffusion gradients were applied in 6 directions with b=0 and 1000 s/mm2. Finally, a dynamic contrast-enhanced sequence containing an axial T1-weighted 3D fast spoiled gradient-recalled echo sequence (TR 4.19ms, TE 1.22 ms, FOV 340 mm×340 mm, matrix 448×340, slice thickness 0.9 mm) was performed. One precontrast acquisition and 5 postcontrast acquisitions of 6min 42s were performed before and after the contrast of Gd-DTPA (Omniscan, GE) with a dose of 0.1 mmol/kg.Diffusion tensor data were post-processed and analyzed by an experienced MRI physician blinded to histopathological findings on the MR Syngo station. By browsing the contrast-enhanced subtraction images, the slice showing the lesion’s maximum diameter was determined, and then the same slice was found in the axial ADC map. By referring to the DCE images, a ROI in ADC maps corresponding to the hyperintensity in DCE images was drawn along the lesion margin, omitting hemorrhagic, cystic, and calcific areas. In the same patient, another ROI was drawn in the contralateral healthy breast containing normal breast tissue only. Then the anisotropy parameters, such as ADC, FA, and eigenvalues (E1, E2, E3) were automatically calculated, where E1, E2, and E3 are the maximum, intermediate, and minimum diffusion tensor eigenvalues, respectively. Every lesion and normal breast tissue was measured 3 times to produce the final averaged measurements.Statistical analysis was performed using SPSS 19.0. Differences of ADC, FA, eigenvalues E1, E2, E3, and the maximum anisotropy index E1-E3 between breast cancers and normal breast tissue in the same patient were compared by 2-tailed paired t-test; DTI-derived metrics between breast cancer and benign lesions were compared by non-parametric test; multi-variant logistic analysis was performed to determine if ADC, FA, eigenvalues E1, E2, E3, and the maximum anisotropy index E1-E3 are independent predictors in characterization of different breast lesions, and their predictive value were calculated. Then the best-fitting regression model combining multiple predictors was created. The full range of ADC, FA, and E1 measurements were divided into equal quartiles and their values in predicting risk of breast cancer in every quartile were calculated. Then, to compare diagnostic performance of ADC, FA, E1, E2, E3,E1-E3, and the multivariate model, receiver operating characteristic curve analysis was performed to calculate the area under the curve (AUC) and 95% confidence interval (CI). Z-tests were used to compare the difference of AUC in detecting and charactering different breast lesions using MedCalc 11.0 software. We calculated the optimal critical value defined as the point with the maximum sum of sensitivity and specificity as well as the corresponding sensitivity and specificity. P-values less than 0.05 were considered to be statistically significant.ResultsCompared to normal tissue, the average decrease of ADC, FA, E1, E2, E3 and E1-E3 in breast cancer were (0.46 ± 0.31) × 10-3mm2/s,0.05 ± 0.04, (0.57 ± 0.38) × 10-3mm2/s,(0.45±0.31)×10-3mm2/s, (0.36±0.26)×10-3mm2/s and (0.23 ± 0.16) × 10-3 mm2/s respectively; The corresponding reduced percentage were (32 ± 17)%, (24 ± 13)%, (33 ± 19)%, (32 ± 17)%, (31 ± 18)%, (37 ± 20)% respectively.ADC, E1, E2, E3 and E1-E3 in the ductal carcinoma in situ and invasive breast cancers were significantly lower than in benign lesions (P<0.01); FA value in invasive breast cancers was slightly higher than in the benign lesions, but the difference did not reach the statistical level (P= 0.09); FA values in the ductal carcinoma in situ and benign lesions were almost the same, and no statistical difference was found (P>0.05).ADC, E1, E2, E3 and E1-E3 were the risk factors for breast cancer, and the breast cancer risk rose with the parameters’reduction; Although the increasing of FA correlated the increasing risk of breast cancer, its risk prediction value was very low.In the differentiation of breast cancer with benign lesions, the AUC for ADC, E1, E2, E3 and E1-E3 was 0.885-0.898, sensitivity was 73.5%-85.3%, and specificity was 90.9%-100%. No significant difference was found in AUC between the ADC, Ei, E2, E3 and E1-E3 (P> 0.05), while the difference between FA and those parameters was statistically significant (P<0.05).ConclusionDTI parameters of ADC, FA, Ei, E2, E3 and E1-E3 are all helpful for the detection of breast cancer; And DTI parameters of both invasive breast cancer and ductal carcinoma in situ were all significantly lower than normal tissue.Compared to the normal breast tissue, DTI eigenvalues of E1, E2, E3 all reduce by a approximately similar proportion, and the reducing degree of ADC, E1, E2, E3 and E1-E3 is much higher than the FA. DTI parameters of ADC, E1, E2, E3 and E1-E3 are helpful in differentiating between breast cancer and benign breast lesions, in which parameters of ADC, E1 and E3 have the highest performance than E2 and E1-E3, but no significantly statistical difference are found between them. FA can not effectively discriminate breast cancer and benign lesions, but the regression model combing FA and E1-E3 can improve the ability to identify breast cancer from benign lesions which have a very similar efficiency to ADC. DTI parameters of ADC, E1, E2, E3 and E1-E3 were all independent predictors of breast cancer, and the reducing in parameter value indicates the increasing risk of breast cancer. Incresing in FA values correlate to the breast cancer risk with a low predictive value.Part IIRelationship between DTI Parameters, Breast Cancer Grade and Cellularity Background and PurposeCellularity was significantly higher and ADC values were significantly reduced in breast cancer than in benign breast lesions and normal glands, and those two parameters showed a significant negative correlation, which is helpful to the differentiation of breast lesions. Developed on the basis of diffusion weighted imaging, diffusion tensor imaging not only provide the averaged diffusion parameters of ADC, also provides a three-dimensional diffusion of water molecules parameters such as fractional anisotropy, which pushed the diffusion magnetic resonance imaging of water molecules extending to three-dimensional diffusion from the two-dimensional plane diffusion, and shows significant value in breast cancer diagnosis.Similar to DWI, DTI also reflects the diffusion activity of water molecules, and can provide more information on microstructure of organization than DWI. But so far, there are few literatures concerning relationship between DTI parameters and microstructure in breast, so the purpose of this study is to explore the relationship between DTI parameters, tumor grade and cellularity of breast cancer, hoping to provide a non-invasive method to characterize breast cancers.Methods62 patients with 62 breast cancer lesions found by MRI and confirmed pathologically underwent DTI and contrast-enhanced MRI scanning by the 1.5T AVANTO system. All patients were examined in prone position on the same 1.5T MRI system using a dedicated receiving bilateral breast matrix coil. DTI was performed using a axial 2-dimensional diffusion-weighted echo planer imaging sequence (TR 6900 ms, TE 90 ms, slice thickness 5 mm with zero gap, NEX 4, FOV 380 mm×285 mm, matrix 144×192), and the diffusion gradients were applied in 6 directions with b=0 and 1000 s/mm2. DTI parameters of ADC, FA, E1, E2, E3, E1-E3 were measured in the syngo workstation for 3 times, and the averaged values were considerd the final measures.All histopathological specimens were reviewed by an experienced pathologist. Biopsy specimens underwent HE staining and analized pathologically; and if necessary, immunohistochemical staining to determine the histological type. All invasive breast cancer (except for medullary carcinoma) were divided into 3 grades according to the microstructure characteristics of breast cancer on the basis of improved SBR grade system. The cellularity calculating was after the HE staining and zooming for 200 times.5 fields of view (FOV) with no necrosis, cystic areas and large blood vessels were photographed randomly. Histopathological photos were imported to computer station and analyzed using Adobe Photoshop 8.0 for calculating cellularity. Color pictures were transformed into black-white ones, and the nucleus showed as black points. As the cellularity was defined as the total area of nucleus divided by that of FOV, histogram of each picture would show these percentages corresponding to cellularity. The averaged cellularity of 5 FOVs was regarded as the final result.DTI parameters between DCIS and invasive breast cancer, as well as the corresponding parameters in different grades of invasive cancer were compared; Relationship between DTI parameters and breast cancer grade were analized by Spearman correlation analysis; And the correlation between DTI parameters and cellularity were analized by Pearson correlation analysis.ResultsDTI parameters of ADC, E1, E2 and E3 in invasive breast cancer were negatively correlated with breast cancer grade, r=-0.416 (P<0.01),-0.408 (P<0.01),-0.406 (P<0.01),-0.476 (P<0.01) respectively, and the difference in ADC, E1, E2, E3 between different grade breast were statistically significant (P<0.05); There was a increase in FA values in higher grade breast cancer, but no statistical correlation was found between FA and cancer grade(r=0.124, P>0.05); And no significant difference was found in E1-E3 between the different grade breast cancers (P> 0.05), and no correlation was found either between E1-E3 and and cancer grade (r=-0.316,P> 0.05).The ADC, E1, E2, E3 and E1-E3 in invasive carcinoma were higher than those in DCIS, while FA values in invasive breast cancer was lower than that in DCIS, but the difference between them did not show statistical significance (P> 0.05 for all).DTI parameters of ADC, E1, E2 and E3 were negatively correlated with cellularity (P<0.01 for all), r=-0.7051,-0.6940,-0.6942 and -0.7034 respectively, and the FA values in invasive breast cancer were positively correlated with cellularity (r=0.3730, P<0.01)ConclusionADC, E1, E2, E3 values can indicate the grade of invasive breast cancer, which were lower in higher grade than in lower grade and were negatively correlated with cancer grade. FA and E1-E3 can not differentiate the different grade of invasive cancer. DTI parameter can not effectively distinguish invasive breast cancers from DCIS. DTI parameters of ADC, E1, E2 and E3 are negatively correlated with cellularity, and the FA values in invasive breast cancer were positively correlated with cellularity. |
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