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Potentially suspicious breast neoplasms could be masked by high tissue density, thus increasing the probability of a false‑negative diagnosis. Furthermore, differentiating breast tissue type enables patient pre‑screening stratification and risk assessment. In this study, we propose and evaluate advanced machine learning methodologies aiming at an objective and reliable method for breast density scoring from routine mammographic images. The proposed image analysis pipeline incorporates texture [Gabor filters and local binary pattern (LBP)] and gradient‑based features [histogram of oriented gradients (HOG) as well as speeded‑up robust features (SURF)]. Additionally, transfer learning approaches with ImageNet trained weights were also used for comparison, as well as a convolutional neural network (CNN). The proposed CNN model was fully trained on two open mammography datasets and was found to be the optimal performing methodology (AUC up to 87.3%). Thus, the findings of this study indicate that automated density scoring in mammograms can aid clinical diagnosis by introducing artificial intelligence‑powered decision‑support systems and contribute to the 'democratization' of healthcare by overcoming limitations, such as the geographic location of patients or the lack of expert radiologists.

Imaging biomarkers (IBs) play a critical role in the clinical management of breast cancer (BRCA) patients throughout the cancer continuum for screening, diagnosis, and therapy assessment, especially in the neoadjuvant setting. However, certain model-based IBs suffer from significant variability due to the complex workflows involved in their computation, whereas model-free IBs have not been properly studied regarding clinical outcome. In this study, IBs from 35 BRCA patients who received neoadjuvant chemotherapy (NAC) were extracted from dynamic contrast-enhanced MR imaging (DCE-MRI) data with two different approaches, a model-free approach based on pattern recognition (PR), and a model-based one using pharmacokinetic compartmental modeling. Our analysis found that both model-free and model-based biomarkers can predict pathological complete response (pCR) after the first cycle of NAC. Overall, eight biomarkers predicted the treatment response after the first cycle of NAC, with statistical significance (p-value < 0.05), and three at the baseline. The best pCR predictors at first follow-up, achieving high AUC and sensitivity and specificity more than 50%, were the hypoxic component with threshold 2 (AUC 90.4%) from the PR method, and the median value of k ep (AUC 73.4%) from the model-based approach. Moreover, the 80 th percentile of v e achieved the highest pCR prediction at baseline with AUC 78.5%. The results suggest that the model-free DCE-MRI IBs could be a more robust alternative to complex, model-based ones such as k ep and favor the hypothesis that the PR image-derived hypoxic image component captures actual tumor hypoxia information able to predict BRCA NAC outcome.

Objective: Insulin-like growth factor (IGF)-axis is involved in human oncogenesis and metastasis development for various solid tumors including breast cancer. Aim of this study was to assess the association between IGF-1, IGF-binding protein-3 (IGFBP-3) serum levels and the presence of circulating tumor cells (CTCs) in the peripheral blood of women diagnosed with early breast cancer (EBC), before and after adjuvant chemotherapy., Design: 171 patients with early-stage breast adenocarcinomas were retrospectively evaluated. Immunoradiometric (IRMA) assays were employed for the in-vitro determination of IGF-1 and IGFBP-3 serum levels in blood samples collected after surgical treatment and before initiation of adjuvant chemotherapy. CTCs' presence was assessed through detection of cytokeratin-19 (CK-19) mRNA transcripts using quantitative real time reverse transcription polymerase chain reaction (RT-PCR). IGF-1, IGFBP-3 serum levels were correlated with CTCs' presence before and after adjuvant chemotherapy as well as with tumor characteristics including tumor size, axillary lymph node status, oestrogen (ER)/progestorene (PR) and human epidermural growth factor receptor 2 (HER2) receptor status. Log-rank test was applied to investigate possible association between IGF-1, IGFBP-3 serum levels and disease-free interval (DFI) and overall survival (OS)., Results: Before initiation of adjuvant therapy IGF-1, IGFBP-3 serum levels were moderately associated (Spearman's rho=0.361, p<0.001) with each other, while presenting significant differences across age groups (all p values<0.05). IGF-1 serum levels did not correlate with the presence of CTCs before initiation (p=0.558) or after completion (p=0.474) of adjuvant chemotherapy. Similarly, IGFBP-3 serum levels did not show significant association with detectable CTCs either before (p=0.487) or after (p=0.134) completion of adjuvant chemotherapy. There was no statistically significant association between the clinical outcome of patients in terms of DFI, OS and IGF-1(DFI: p=0.499; OS: p=0.220) or IGFBP-3 (DFI: p=0.900; OS: p=0.406) serum levels., Conclusions: IGF-1 and IGFBP-3 serum levels before initiation of adjuvant chemotherapy are not indicative of CTCs' presence in the blood and do not correlate with clinical outcome of women with early-stage breast cancer., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Fibroadenoma is the most common benign breast tumor in women of reproductive age, carrying little to no risk of breast cancer development. We report on a case of a woman with history of neuroendocrine tumor who on follow-up imaging tests underwent whole-body PET/CT study using Ga DOTATATE. The scan showed increased focal activity in the right breast, which was biopsied revealing a fibroadenoma. The presented data suggests cell surface overexpression of somatostatin receptors by this benign breast tumor. Moreover, this finding emphasizes the need for cautious interpretation of Ga DOTATATE-avid breast lesions that could mimic malignancy in neuroendocrine tumor patients.