Differential Diagnosis of Benign and Malignant Breast Tumors Using Apparent Diffusion Coefficient Value Measured Through Diffusion-Weighted Magnetic Resonance Imaging.

OBJECTIVE: Apparent diffusion coefficient (ADC) value measurement of nodes in diffusion-weighted imaging was widely used in differentiating different types of human tumors. The aim of this meta-analysis was to evaluate the clinical value of ADC measurement through diffusion-weighted magnetic resonance imaging (DW-MRI) in the differential diagnosis of benign and malignant breast tumors. METHODS: Relevant studies were identified through computer-based search of databases, which were supplemented through manual search strategies. Case-control studies were selected in adherence with our strict inclusion and exclusion criteria. Statistical analysis was conducted using Stata 12.0 statistical software (StataCorp, College Station, Tex). RESULTS: Our database searches initially retrieved 602 studies (320 studies in Chinese and 282 studies in English), and 31 studies (18 studies in English and 13 studies in Chinese) were eventually selected for meta-analysis. These 31 case-control studies included a total of 926 normal breast tissues and 2323 breast tumors (911 benign tumors and 1412 malignant tumors). Our meta-analysis showed that ADC values measured through DW-MRI were higher in benign breast tumors compared with malignant breast tumors, and this difference was statistically significant. In addition, the ADC values in the normal breast tissues were markedly higher than the benign breast tumors, which were also at a statistically significant level. Consistent with these observations, the ADC values in the normal breast tissues were significantly higher when compared with the values found in the malignant breast tumors. CONCLUSIONS: Our data strongly support the conclusion that the ADC value measured through DW-MRI is an important radiographic index for differential diagnosis of benign and malignant breast tumors and is critical to our assessment of the internal structure of tumors.

Value of magnetic resonance imaging radiomics features in predicting histologic grade of invasive ductal carcinoma of the breast.

BACKGROUND: Breast cancer has the second highest mortality rate of all cancers and occurs mainly in women. OBJECTIVE: To investigate the relationship between magnetic resonance imaging (MRI) radiomics features and histological grade of invasive ductal carcinoma (IDC) of the breast and to evaluate its diagnostic efficacy. METHODS: The two conventional MRI quantitative indicators, i.e. the apparent diffusion coefficient (ADC) and the initial enhancement rate, were collected from 112 patients with breast cancer. The breast cancer lesions were manually segmented in dynamic contrast-enhanced MRI (DCE-MRI) and ADC images, the differences in radiomics features between Grades I, II and III IDCs were compared and the diagnostic efficacy was evaluated. RESULTS: The ADC values (0.77 ± 0.22 vs 0.91 ± 0.22 vs 0.92 ± 0.20, F= 4.204, p< 0.01), as well as the B_sum_variance (188.51 ± 67.803 vs 265.37 ± 77.86 vs 263.74 ± 82.58, F= 6.040, p< 0.01), L_energy (0.03 ± 0.02 vs 0.13 ± 0.11 vs 0.12 ± 0.14, F= 7.118, p< 0.01) and L_sum_average (0.78 ± 0.32 vs 16.34 ± 4.23 vs 015.45 ± 3.74, F= 21.860, p< 0.001) values of patients with Grade III IDC were significantly lower than those of patients with Grades I and II IDC. The B_uniform (0.15 ± 0.12 vs 0.11 ± 0.04 vs 0.12 ± 0.03, F= 3.797, p< 0.01) and L_SRE (0.85 ± 0.07 vs 0.78 ± 0.03 vs 0.79 ± 0.32, F= 3.024, p< 0.01) values of patients with Grade III IDC were significantly higher than those of patients with Grades I and II IDC. All differences were statistically significant (p< 0.05). The ADC radiomics signature model had a higher area-under-the-curve value in identifying different grades of IDC than the ADC value model and the DCE radiomics signature model (0.869 vs 0.711 vs 0.682). The accuracy (0.812 vs 0.647 vs 0.710), specificity (0.731 vs 0.435 vs 0.342), positive predictive value (0.815 vs 0.663 vs 0.669) and negative predictive value (0.753 vs 0.570 vs 0.718) of the ADC radiomics signature model were all significantly better than the ADC value model and the DCE radiomics signature model. CONCLUSION: ADC values and breast MRI radiomics signatures are significant in identifying the histological grades of IDC, with the ADC radiomics signatures having greater value.

microRNA­145 modulates migration and invasion of bladder cancer cells by targeting N­cadherin.

MicroRNA (miRNA)­145 has been demonstrated to serve a role in several types of tumors, however, the potential molecular mechanism of action of miRNA­145 in bladder cancer metastasis remains to be elucidated. This study aimed to investigate the potential modulation of miRNA­145 in bladder carcinoma and elucidate the underlying molecular mechanism. The expression of miRNA­145 in bladder adenocarcinoma tissues and bladder cancer cells was measured by reverse transcription­quantitative polymerase chain reaction. miRNA­145 mimics and inhibitor were transfected into bladder cancer (BC) cells to determine the role of miRNA­145 on cell motility and invasion measured by wound healing and transwell assays. Luciferase assay was performed to confirm whether N­cadherin was the direct target of miRNA­145. Subsequently, expression of N­cadherin and matrix metalloproteinase­9 (MMP9) in BC cells were detected by western blot analysis. miRNA­145 was significantly downregulated cells and tissues from patients with BC, compared with healthy controls. miRNA­145 markedly inhibited the ability of BC cells to migrate and invade. Furthermore, N­cadherin was identified as a target of miRNA­145 in BC cells. MMP9, acting downstream of N­cadherin, was downregulated in BC cells by miRNA­145. In the present study, miRNA­145 suppressed the migration and invasion of BC cells by regulating N­cadherin. The results of the present study indicated that miRNA­145 may function as a tumor suppressor and may have a potential to be a diagnostic and predictive biomarker, and a therapeutic target for treatment of BC.