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To investigate whether background parenchymal enhancement (BPE) and breast cancer would correlate searching for any significant difference of BPE pattern distribution in case of benign or malignant lesions.386 patients, including 180 pre-menopausal (group 1) and 206 post-menopausal (group 2), underwent MR examination. Two radiologists evaluated MR images classifying normal BPE as minimal, mild, moderate or marked. The two groups of patients were subdivided into 3 categories based on MRI findings (negative, benign and malignant lesions). The distribution of BPE patterns within the two groups and within the three MR categories was calculated. The χ2 test was used to evaluate BPE type distribution in the three patient categories and any statistically significant correlation of BPE with lesion type was calculated. The Student t test was applied to search for any statistically significant difference between BPE type rates in group 1 and 2.The χ2 test demonstrated a statistically significant difference in the distribution of BPE types in negative patients and benign lesions as compared with malignant ones (p0.05). A significantly higher prevalence of moderate and marked BPE was found among malignant lesions (group 1: 32% and 42%, respectively; group 2: 31% and 46%, respectively) while a predominance of minimal and mild BPE among negative patients (group 1: 60% and 36%, respectively; group 2: 68% and 32%, respectively) and benign lesions (group 1: 54% and 38%, respectively; group 2: 75% and 17%, respectively) was found. The Student t test did not show a statistically significant difference between BPE type rates in group 1 and 2 (p0.05).Normal BPE could correlate with the risk of breast cancer being such BPE patterns as moderate and marked associated with patients with malignant lesions in both pre and post-menopausal women.

Purpose To assess the role of STIR, T2-weighted TSE and DWIBS sequences for detecting and characterizing breast lesions and to compare unenhanced (UE)-MRI results with contrast-enhanced (CE)-MRI and histological findings, having the latter as the reference standard. Materials and Methods Two hundred eighty consecutive patients (age range, 27–73 years; mean age ± standard deviation (SD), 48.8 ± 9.8 years) underwent MR examination with a diagnostic protocol including STIR, T2-weighted TSE, THRIVE and DWIBS sequences. Two radiologists blinded to both dynamic sequences and histological findings evaluated in consensus STIR, T2-weighted TSE and DWIBS sequences and after two weeks CE-MRI images searching for breast lesions. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy for UE-MRI and CE-MRI were calculated. UE-MRI results were also compared with CE- MRI. Results UE-MRI sequences obtained sensitivity, specificity, diagnostic accuracy, PPV and NPV values of 94%, 79%, 86%, 79% and 94%, respectively. CE-MRI sequences obtained sensitivity, specificity, diagnostic accuracy, PPV and NPV values of 98%, 83%, 90%, 84% and 98%, respectively. No statistically significant difference between UE-MRI and CE-MRI was found. Conclusion Breast UE-MRI could represent an accurate diagnostic tool and a valid alternative to CE-MRI for evaluating breast lesions. STIR and DWIBS sequences allow to detect breast lesions while T2-weighted TSE sequences and ADC values could be useful for lesion characterization.

Objectives Diffusion-weighted imaging with background body signal suppression (DWIBS) provides both qualitative and quantitative imaging of breast lesions and are usually performed before contrast material injection (CMI). This study aims to assess whether the administration of gadolinium significantly affects DWIBS imaging. Methods 200 patients were prospectively evaluated by MRI with STIR, TSE-T2, pre-CMI DWIBS, contrast enhanced THRIVE-T1 and post-CMI DWIBS sequences. Pre and post-CMI DWIBS were analyzed searching for the presence of breast lesions and calculating the ADC value. ADC values of ≤ 1.44 × 10- 3 mm2/s were considered suspicious for malignancy. This analysis was then compared with the histological findings. Sensitivity, specificity, diagnostic accuracy (DA), positive predictive value (PPV) and negative (NPV) were calculated for both sequences and represented by ROC analysis. Pre and post-CMI ADC values were compared by using the paired t test. Results In 150/200 (59%) patients, pre and post-CMI DWIBS indicated the presence of breast lesions, 53 (35%) with ADC values of > 1.44 × 10- 3 mm2/s and 97 (65%) with ADC ≤ 1.44 × 10- 3 mm2/s. Pre-CMI and post-DWIBS sequences obtained the same sensitivity, specificity, DA, PPV and NPV values of 97%, 83%, 89%, 79% and 98%. The mean ADC value of benign lesions was 1.831 ± 0.18 × 10- 3 mm2/s before and 1.828 ± 0.18 × 10- 3 mm2/s after CMI. The mean ADC value of the malignant lesions was 1.146 ± 0.16 × 10- 3 mm2/s before and 1.144 ± 0.16 × 10- 3 mm2/s after CMI. No significant difference was found between pre and post CMI ADC values (p > 0.05). Conclusion DWIBS imaging is not influenced by CMI. Breast MR protocol could be modified by placing DWIBS after dynamic contrast enhanced sequences in order to maximize patient cooperation.

Objectives Diffusion imaging represents a new imaging tool for the diagnosis of breast cancer. This study aims to investigate the role of diffusion-weighted MRI with background body signal suppression (DWIBS) for evaluating breast lesions. Methods 90 patients were prospectively evaluated by MRI with STIR, TSE-T2, contrast enhanced THRIVE-T1 and DWIBS sequences. DWIBS were analyzed searching for the presence of breast lesions and calculating the ADC value. ADC values of ≤ 1.44 × 10 - 3 mm 2 /s were considered suspicious for malignancy. This analysis was then compared with the histological findings. Sensitivity, specificity, diagnostic accuracy (DA), positive predictive value (PPV) and negative (NPV) were calculated. Results In 53/90 (59%) patients, DWIBS indicated the presence of breast lesions, 16 (30%) with ADC values of > 1.44 and 37 (70%) with ADC ≤ 1.44. The comparison with histology showed 25 malignant and 28 benign lesions. DWIBS sequences obtained sensitivity, specificity, DA, PPV and NPV values of 100, 82, 87, 68 and 100%, respectively. Conclusion DWIBS can be proposed in the MRI breast protocol representing an accurate diagnostic complement.

Objectives To assess the contribute of intra-prosthetic MRI virtual navigation for evaluating breast implants and detecting implant ruptures. Methods Forty-five breast implants were evaluated by MR examination. Only patients with a clinical indication were assessed. A 1.5-T device equipped with a 4-channel breast coil was used by performing axial TSE-T2, axial silicone-only, axial silicone suppression and sagittal STIR images. The obtained dicom files were also analyzed by using virtual navigation software. Two blinded radiologists evaluated all MR and virtual images. Eight patients for a total of 13 implants underwent surgical replacement. Sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) were calculated for both imaging strategies. Results Intra-capsular rupture was diagnosed in 13 out of 45 (29%) implants by using MRI. Basing on virtual navigation, 9 (20%) cases of intra-capsular rupture were diagnosed. Sensitivity, specificity, accuracy, PPV and NPV values of 100%, 86%, 89%, 62% and 100%, respectively, were found for MRI. Virtual navigation increased the previous values up to 100%, 97%, 98%, 89% and 100%. Conclusion Intra-prosthetic breast MR virtual navigation can represent an additional promising tool for the evaluation of breast implants being able to reduce false positives and to provide a more accurate detection of intra-capsular implant rupture signs.