Do Calcifications Seen on Mammography After Neoadjuvant Chemotherapy for Breast Cancer Always Need to Be Excised?

This study aimed to determine the relationship between mammographic calcifications and magnetic resonance imaging (MRI) tumoral enhancement before and after neoadjuvant chemotherapy (NAC) and to assess the impact of these findings on surgical management. This Institutional Review Board-approved, Health Insurance Portability and Accountability Act (HIPAA)-compliant retrospective study involved breast cancer patients who underwent NAC between 2009 and 2015. The study cohort comprised 90 patients with pre- and posttreatment MRI and mammograms demonstrating calcifications within the tumor bed either at presentation or after treatment. The data gathered included pre- and post-NAC imaging findings and post-NAC histopathology, particularly findings associated with calcifications. Comparisons were made using Fisher's exact test, with p values lower than 0.05 considered significant. Complete resolution of MRI enhancement occurred for 44% of the patients, and a pathologic complete response (pCR) was achieved for 32% of the patients. No statistically significant correlation between changes in mammographic calcifications and MRI enhancement was found (p = 0.12). Resolution of enhancement was strongly correlated with pCR (p < 0.0001). The majority of the patients with pCR demonstrated complete resolution of enhancement (79%, 23/29). No statistically significant relationship was found between changes in calcifications and rates of pCR (p = 0.06). A pCR was achieved most frequently for patients with resolution of enhancement and new, increasing, or unchanged calcifications (p < 0.0001). Although calcifications seen on post-NAC mammography may be associated with benign disease, loss of MRI enhancement does not predict the absence of residual tumor with sufficient accuracy to leave calcifications in place. Complete excision of tumor bed calcifications remains standard practice and a substantial limitation to NAC use for downstaging patients to be eligible for breast conservation treatment

Radiomics for Tumor Characterization in Breast Cancer Patients: A Feasibility Study Comparing Contrast-Enhanced Mammography and Magnetic Resonance Imaging

The aim of our intra-individual comparison study was to investigate and compare the potential of radiomics analysis of contrast-enhanced mammography (CEM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast for the non-invasive assessment of tumor invasiveness, hormone receptor status, and tumor grade in patients with primary breast cancer. This retrospective study included 48 female patients with 49 biopsy-proven breast cancers who underwent pretreatment breast CEM and MRI. Radiomics analysis was performed by using MaZda software. Radiomics parameters were correlated with tumor histology (invasive vs. non-invasive), hormonal status (HR+ vs. HR&minus;), and grading (low grade G1 + G2 vs. high grade G3). CEM radiomics analysis yielded classification accuracies of up to 92% for invasive vs. non-invasive breast cancers, 95.6% for HR+ vs. HR&minus; breast cancers, and 77.8% for G1 + G2 vs. G3 invasive cancers. MRI radiomics analysis yielded classification accuracies of up to 90% for invasive vs. non-invasive breast cancers, 82.6% for HR+ vs. HR&minus; breast cancers, and 77.8% for G1+G2 vs. G3 cancers. Preliminary results indicate a potential of both radiomics analysis of DCE-MRI and CEM for non-invasive assessment of tumor-invasiveness, hormone receptor status, and tumor grade. CEM may serve as an alternative to MRI if MRI is not available or contraindicated

Does every woman presenting with malignant calcifications require a post lumpectomy mammogram?

PurposeSuccessful breast-conserving surgery (BCS) followed by radiation therapy (XRT) is dependent on complete removal of the cancer with clear surgical margins, providing survival rates equivalent to those observed following mastectomy. In patients who have cancers presenting with microcalcifications, post lumpectomy mammograms (PLM) prior to radiation (XRT) can be performed to ensure that no cancer has been left behind. The purpose of this study was to assess the benefit of PLM in patients with malignant breast tumors presenting with microcalcifications.MethodsIn this IRB-approved retrospective study, we reviewed medical records for patients with breast cancers presenting with microcalcifications who underwent BCS between February 2008 and June 2013. 198 patients who had a PLM prior to XRT for cancers presenting with microcalcifications were included.ResultsHistopathology of the initial lumpectomy revealed invasive carcinoma in 78/198 (39.4%) and DCIS alone in 120/198 (60.6%). 114/198 (58%) patients had negative surgical margins. 7/114 (6%) patients with negative margins had positive PLM and re-excisions that were positive for malignancy: sensitivity 88%, specificity 95%, PPV 58%, NPV 99%. 84/198 patients had positive surgical margins. The diagnostic performance of PLM in this group was: sensitivity 55%, specificity 71%, PPV 66%, NPV 61%.ConclusionPLM plays an important role in the evaluation of patients undergoing breast conservation for breast cancer presenting with microcalcifications. Residual malignancy was detected on positive PLM in 6% of patients with negative margins

Comparison of Background Parenchymal Enhancement at Contrast-enhanced Spectral Mammography and Breast MR Imaging

PURPOSE: To assess the extent of background parenchymal enhancement (BPE) at contrast material–enhanced (CE) spectral mammography and breast magnetic resonance (MR) imaging, to evaluate interreader agreement in BPE assessment, and to examine the relationships between clinical factors and BPE. MATERIALS AND METHODS: This was a retrospective, institutional review board–approved, HIPAA-compliant study. Two hundred seventy-eight women from 25 to 76 years of age with increased breast cancer risk who underwent CE spectral mammography and MR imaging for screening or staging from 2010 through 2014 were included. Three readers independently rated BPE on CE spectral mammographic and MR images with the ordinal scale: minimal, mild, moderate, or marked. To assess pairwise agreement between BPE levels on CE spectral mammographic and MR images and among readers, weighted κ coefficients with quadratic weights were calculated. For overall agreement, mean κ values and bootstrapped 95% confidence intervals were calculated. The univariate and multivariate associations between BPE and clinical factors were examined by using generalized estimating equations separately for CE spectral mammography and MR imaging. RESULTS: Most women had minimal or mild BPE at both CE spectral mammography (68%–76%) and MR imaging (69%–76%). Between CE spectral mammography and MR imaging, the intrareader agreement ranged from moderate to substantial (κ = 0.55–0.67). Overall agreement on BPE levels between CE spectral mammography and MR imaging and among readers was substantial (κ = 0.66; 95% confidence interval: 0.61, 0.70). With both modalities, BPE demonstrated significant association with menopausal status, prior breast radiation therapy, hormonal treatment, breast density on CE spectral mammographic images, and amount of fibroglandular tissue on MR images (P < .001 for all). CONCLUSION: There was substantial agreement between readers for BPE detected on CE spectral mammographic and MR images. (©) RSNA, 201

Non-Invasive Assessment of Breast Cancer Molecular Subtypes with Multiparametric Magnetic Resonance Imaging Radiomics

We evaluated the performance of radiomics and artificial intelligence (AI) from multiparametric magnetic resonance imaging (MRI) for the assessment of breast cancer molecular subtypes. Ninety-one breast cancer patients who underwent 3T dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping were included retrospectively. Radiomic features were extracted from manually drawn regions of interest (n = 704 features per lesion) on initial DCE-MRI and ADC maps. The ten best features for subtype separation were selected using probability of error and average correlation coefficients. For pairwise comparisons with &gt;20 patients in each group, a multi-layer perceptron feed-forward artificial neural network (MLP-ANN) was used (70% of cases for training, 30%, for validation, five times each). For all other separations, linear discriminant analysis (LDA) and leave-one-out cross-validation were applied. Histopathology served as the reference standard. MLP-ANN yielded an overall median area under the receiver-operating-characteristic curve (AUC) of 0.86 (0.77&ndash;0.92) for the separation of triple negative (TN) from other cancers. The separation of luminal A and TN cancers yielded an overall median AUC of 0.8 (0.75&ndash;0.83). Radiomics and AI from multiparametric MRI may aid in the non-invasive differentiation of TN and luminal A breast cancers from other subtypes