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Objective: To evaluate the performance of radiomic analysis on contrast-enhanced mammography images to identify different histotypes of breast cancer mainly in order to predict grading, to identify hormone receptors, to discriminate human epidermal growth factor receptor 2 (HER2) and to identify luminal histotype of the breast cancer., Methods: From four Italian centers were recruited 180 malignant lesions and 68 benign lesions. However, only the malignant lesions were considered for the analysis. All patients underwent contrast-enhanced mammography in cranium caudal (CC) and medium lateral oblique (MLO) view. Considering histological findings as the ground truth, four outcomes were considered: (1) G1 + G2 vs. G3; (2) HER2 + vs. HER2 - ; (3) HR + vs. HR - ; and (4) non-luminal vs. luminal A or HR + /HER2-  and luminal B or HR + /HER2 + . For multivariate analysis feature selection, balancing techniques and patter recognition approaches were considered., Results: The univariate findings showed that the diagnostic performance is low for each outcome, while the results of the multivariate analysis showed that better performances can be obtained. In the HER2 + detection, the best performance (73% of accuracy and AUC = 0.77) was obtained using a linear regression model (LRM) with 12 features extracted by MLO view. In the HR + detection, the best performance (77% of accuracy and AUC = 0.80) was obtained using a LRM with 14 features extracted by MLO view. In grading classification, the best performance was obtained by a decision tree trained with three predictors extracted by MLO view reaching an accuracy of 82% on validation set. In the luminal versus non-luminal histotype classification, the best performance was obtained by a bagged tree trained with 15 predictors extracted by CC view reaching an accuracy of 94% on validation set., Conclusions: The results suggest that radiomics analysis can be effectively applied to design a tool to support physician decision making in breast cancer classification. In particular, the classification of luminal versus non-luminal histotypes can be performed with high accuracy., (© 2024. Italian Society of Medical Radiology.)

Background: So far, baseline Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has played a key role for the application of sophisticated artificial intelligence-based models using Convolutional Neural Networks (CNNs) to extract quantitative imaging information as earlier indicators of pathological Complete Response (pCR) achievement in breast cancer patients treated with neoadjuvant chemotherapy (NAC). However, these models did not exploit the DCE-MRI exams in their full geometry as 3D volume but analysed only few individual slices independently, thus neglecting the depth information., Method: This study aimed to develop an explainable 3D CNN, which fulfilled the task of pCR prediction before the beginning of NAC, by leveraging the 3D information of post-contrast baseline breast DCE-MRI exams. Specifically, for each patient, the network took in input a 3D sequence containing the tumor region, which was previously automatically identified along the DCE-MRI exam. A visual explanation of the decision-making process of the network was also provided., Results: To the best of our knowledge, our proposal is competitive than other models in the field, which made use of imaging data alone, reaching a median AUC value of 81.8%, 95%CI [75.3%; 88.3%], a median accuracy value of 78.7%, 95%CI [74.8%; 82.5%], a median sensitivity value of 69.8%, 95%CI [59.6%; 79.9%] and a median specificity value of 83.3%, 95%CI [82.6%; 84.0%], respectively. The median and CIs were computed according to a 10-fold cross-validation scheme for 5 rounds., Conclusion: Finally, this proposal holds high potential to support clinicians on non-invasively early pursuing or changing patient-centric NAC pathways., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objective: The objective of the study was to evaluate the accuracy of radiomics features obtained by MR images to predict Breast Cancer Histological Outcome., Methods: A total of 217 patients with malignant lesions were analysed underwent MRI examinations. Considering histological findings as the ground truth, four different types of findings were used in both univariate and multivariate analyses: (1) G1 + G2 vs G3 classification; (2) presence of human epidermal growth factor receptor 2 (HER2 + vs HER2 -); (3) presence of the hormone receptor (HR + vs HR -); and (4) presence of luminal subtypes of breast cancer., Results: The best accuracy for discriminating HER2 + versus HER2 - breast cancers was obtained considering nine predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 88% on validation set). The best accuracy for discriminating HR + versus HR - breast cancers was obtained considering nine predictors by T2-weighted subtraction images and a decision tree (accuracy of 90% on validation set). The best accuracy for discriminating G1 + G2 versus G3 breast cancers was obtained considering 16 predictors by early phase T1-weighted subtraction images in a linear regression model with an accuracy of 75%. The best accuracy for discriminating luminal versus non-luminal breast cancers was obtained considering 27 predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 94% on validation set)., Conclusions: The combination of radiomics analysis and artificial intelligence techniques could be used to support physician decision-making in prediction of Breast Cancer Histological Outcome., (© 2023. Italian Society of Medical Radiology.)

Background: About 15%-20% of breast cancer (BC) cases is classified as Human Epidermal growth factor Receptor type 2 (HER2) positive. The Neoadjuvant chemotherapy (NAC) was initially introduced for locally advanced and inflammatory BC patients to allow a less extensive surgical resection, whereas now it represents the current standard for early-stage and operable BC. However, only 20%-40% of patients achieve pathologic complete response (pCR). According to the results of practice-changing clinical trials, the addition of trastuzumab to NAC brings improvements to pCR, and recently, the use of pertuzumab plus trastuzumab has registered further statistically significant and clinically meaningful improvements in terms of pCR. The goal of our work is to propose a machine learning model to predict the pCR to NAC in HER2-positive patients based on a subset of clinical features., Method: First, we evaluated the significant association of clinical features with pCR on the retrospectively collected data referred to 67 patients afferent to Istituto Tumori "Giovanni Paolo II." Then, we performed a feature selection procedure to identify a subset of features to be used for training a machine learning-based classification algorithm. As a result, pCR to NAC was associated with ER status, Pgr status, and HER2 score., Results: The machine learning model trained on a subgroup of essential features reached an AUC of 73.27% (72.44%-73.66%) and an accuracy of 71.67% (71.64%-73.13%). According to our results, the clinical features alone are not enough to define a support system useful for clinical pathway., Conclusion: Our results seem worthy of further investigation in large validation studies and this work could be the basis of future study that will also involve radiomics analysis of biomedical images., (© 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Digital Breast Tomosynthesis (DBT) is a cutting-edge technology introduced in recent years as an in-depth analysis of breast cancer diagnostics. Compared with 2D Full-Field Digital Mammography, DBT has demonstrated greater sensitivity and specificity in detecting breast tumors. This work aims to quantitatively evaluate the impact of the systematic introduction of DBT in terms of Biopsy Rate and Positive Predictive Values for the number of biopsies performed (PPV-3). For this purpose, we collected 69,384 mammograms and 7894 biopsies, of which 6484 were Core Biopsies and 1410 were stereotactic Vacuum-assisted Breast Biopsies (VABBs), performed on female patients afferent to the Breast Unit of the Istituto Tumori "Giovanni Paolo II" of Bari from 2012 to 2021, thus, in the period before, during and after the systematic introduction of DBT. Linear regression analysis was then implemented to investigate how the Biopsy Rate had changed over the 10 year screening. The next step was to focus on VABBs, which were generally performed during in-depth examinations of mammogram detected lesions. Finally, three radiologists from the institute's Breast Unit underwent a comparative study to ascertain their performances in terms of breast cancer detection rates before and after the introduction of DBT. As a result, it was demonstrated that both the overall Biopsy Rate and the VABBs Biopsy Rate significantly decreased following the introduction of DBT, with the diagnosis of an equal number of tumors. Besides, no statistically significant differences were observed among the three operators evaluated. In conclusion, this work highlights how the systematic introduction of DBT has significantly impacted the breast cancer diagnostic procedure, by improving the diagnostic quality and thereby reducing needless biopsies, resulting in a consequent reduction in costs., (© 2023. The Author(s).)

For endocrine-positive Her2 negative breast cancer patients at an early stage, the benefit of adding chemotherapy to adjuvant endocrine therapy is not still confirmed. Several genomic tests are available on the market but are very expensive. Therefore, there is the urgent need to explore novel reliable and less expensive prognostic tools in this setting. In this paper, we shown a machine learning survival model to estimate Invasive Disease-Free Events trained on clinical and histological data commonly collected in clinical practice. We collected clinical and cytohistological outcomes of 145 patients referred to Istituto Tumori "Giovanni Paolo II". Three machine learning survival models are compared with the Cox proportional hazards regression according to time-dependent performance metrics evaluated in cross-validation. The c-index at 10 years obtained by random survival forest, gradient boosting, and component-wise gradient boosting is stabled with or without feature selection at approximately 0.68 in average respect to 0.57 obtained to Cox model. Moreover, machine learning survival models have accurately discriminated low- and high-risk patients, and so a large group which can be spared additional chemotherapy to hormone therapy. The preliminary results obtained by including only clinical determinants are encouraging. The integrated use of data already collected in clinical practice for routine diagnostic investigations, if properly analyzed, can reduce time and costs of the genomic tests., (© 2023. The Author(s).)

Designing targeted treatments for breast cancer patients after primary tumor removal is necessary to prevent the occurrence of invasive disease events (IDEs), such as recurrence, metastasis, contralateral and second tumors, over time. However, due to the molecular heterogeneity of this disease, predicting the outcome and efficacy of the adjuvant therapy is challenging. A novel ensemble machine learning classification approach was developed to address the task of producing prognostic predictions of the occurrence of breast cancer IDEs at both 5- and 10-years. The method is based on the concept of voting among multiple models to give a final prediction for each individual patient. Promising results were achieved on a cohort of 529 patients, whose data, related to primary breast cancer, were provided by Istituto Tumori "Giovanni Paolo II" in Bari, Italy. Our proposal greatly improves the performances returned by the baseline original model, i.e., without voting, finally reaching a median AUC value of 77.1% and 76.3% for the IDE prediction at 5-and 10-years, respectively. Finally, the proposed approach allows to promote more intelligible decisions and then a greater acceptability in clinical practice since it returns an explanation of the IDE prediction for each individual patient through the voting procedure., Competing Interests: The authors have declared that no competing interests exist.

In breast cancer patients, an accurate detection of the axillary lymph node metastasis status is essential for reducing distant metastasis occurrence probabilities. In case of patients resulted negative at both clinical and instrumental examination, the nodal status is commonly evaluated performing the sentinel lymph-node biopsy, that is a time-consuming and expensive intraoperative procedure for the sentinel lymph-node (SLN) status assessment. The aim of this study was to predict the nodal status of 142 clinically negative breast cancer patients by means of both clinical and radiomic features extracted from primary breast tumor ultrasound images acquired at diagnosis. First, different regions of interest (ROIs) were segmented and a radiomic analysis was performed on each ROI. Then, clinical and radiomic features were evaluated separately developing two different machine learning models based on an SVM classifier. Finally, their predictive power was estimated jointly implementing a soft voting technique. The experimental results showed that the model obtained by combining clinical and radiomic features provided the best performances, achieving an AUC value of 88.6%, an accuracy of 82.1%, a sensitivity of 100% and a specificity of 78.2%. The proposed model represents a promising non-invasive procedure for the SLN status prediction in clinically negative patients., (© 2022. The Author(s).)

The dynamic contrast-enhanced MR imaging plays a crucial role in evaluating the effectiveness of neoadjuvant chemotherapy (NAC) even since its early stage through the prediction of the final pathological complete response (pCR). In this study, we proposed a transfer learning approach to predict if a patient achieved pCR (pCR) or did not (non-pCR) by exploiting, separately or in combination, pre-treatment and early-treatment exams from I-SPY1 TRIAL public database. First, low-level features, i.e., related to local structure of the image, were automatically extracted by a pre-trained convolutional neural network (CNN) overcoming manual feature extraction. Next, an optimal set of most stable features was detected and then used to design an SVM classifier. A first subset of patients, called fine-tuning dataset (30 pCR; 78 non-pCR), was used to perform the optimal choice of features. A second subset not involved in the feature selection process was employed as an independent test (7 pCR; 19 non-pCR) to validate the model. By combining the optimal features extracted from both pre-treatment and early-treatment exams with some clinical features, i.e., ER, PgR, HER2 and molecular subtype, an accuracy of 91.4% and 92.3%, and an AUC value of 0.93 and 0.90, were returned on the fine-tuning dataset and the independent test, respectively. Overall, the low-level CNN features have an important role in the early evaluation of the NAC efficacy by predicting pCR. The proposed model represents a first effort towards the development of a clinical support tool for an early prediction of pCR to NAC.

Objectives: This study sought to determine whether the breast gland adipose tissue is associated with different rates of major adverse cardiac events (MACEs) in pre-menopausal women., Background: To our knowledge, no study investigated the impact of breast adipose tissue infiltration on MACEs in pre-menopausal women., Methods: Prospective multicenter cohort study conducted on pre-menopausal women >40 years of age without cardiovascular disease and breast cancer at enrollment. The study started in January 2000 and ended in January 2009, and the end of the follow-up for the evaluation of MACEs was in January 2019. Participants underwent mammography to evaluate breast density and were divided into 4 groups according to their breast density. The primary endpoint was the probability of a MACE at 10 years of follow-up in patients staged for different breast deposition/adipose tissue deposition., Results: The propensity score matching divided the baseline population of 16,763 pre-menopausal women, leaving 3,272 women according to the category of breast density from A to D. These women were assigned to 4 groups of the study according to baseline breast density. At 10 years of follow-up, we had 160 MACEs in group 1, 62 MACEs in group 2, 27 MACEs in group 3, and 16 MACEs in group 4. MACEs were predicted by the initial diagnosis of lowest breast density (hazard ratio: 3.483; 95% confidence interval: 1.476 to 8.257). Further randomized clinical trials are needed to translate the results of the present study into clinical practice. The loss of ex vivo breast density models to study the cellular/molecular pathways implied in MACE is another study limitation., Conclusions: Among pre-menopausal women, a higher evidence of adipose tissue at the level of breast gland (lowest breast density, category A) versus higher breast density shows higher rates of MACEs. Therefore, the screening mammography could be proposed in overweight women to stage breast density and to predict MACEs. (Breast Density in Pre-menopausal Women Is Predictive of Cardiovascular Outcomes at 10 Years of Follow-Up [BRECARD]; NCT03779217)., Competing Interests: Funding Support and Author Disclosures This work was supported by Ricerca Ateneo fund for Outcomes Research in Cardiovascular Diseases. The authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2021 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

The prediction of lymph node involvement represents an important task which could reduce unnecessary surgery and improve the definition of oncological therapies. An artificial intelligence model able to predict it in pre-operative phase requires the interface among multiple data structures. The trade-off among time consuming, expensive and invasive methodologies is emerging in experimental setups exploited for the analysis of sentinel lymph nodes, where machine learning algorithms represent a key ingredient in recorded data elaboration. The accuracy required for clinical applications is obtainable matching different kind of data. Statistical associations of prognostic factors with symptoms and predictive models implemented also through on-line softwares represent useful diagnostic support tools which translate into patients quality of life improvement and costs reduction.

Purpose: The onset characteristics of the anaplastic large cell lymphoma (BI-ALCL) are non-specific and the diagnosis is often difficult and based on clinical suspicion and cytological sampling. The presence of non-pathognomonic radiological signs may delay the diagnosis of BI-ALCL, influencing patient prognosis. This could have an important social impact, considering that the incidence of BI-ALCL correlates with the number of prosthetic implants, which is in constant increase worldwide. The aim of this study was to verify if fibrin can represent a potential early radiological sign of the disease., Methods: In this study, we present two cases of our series and review the previous studies already described in literature, searching for any early radiological sign of the disease and reporting a diagnostic work-up process for an early diagnosis., Results: Signs clearly recognizable only of magnetic resonance were the following: thickening and hyperemia of the fibrous capsule with seroma and amorphous material (fibrin) present in 8 out of 10 cases (80%) detected on magnetic resonance images (certain or doubtful)., Conclusion: The presence of fibrin in the periprosthetic effusion, well detectable by magnetic resonance imaging, could represent an early pathognomonic sign of the disease.

Purpose: Extramammary breast tumors are quite unusual but they might represent the first semiotic sign of non negative mammography. Thus, the need for an early and accurate diagnosis is crucial, with the purpose of planning and optimize the therapeuthical strategy and consequently to improve the clinical outcome of patients., Methods: Due to the intrinsic characteristics of this technique, CESM lends itself as a useful and reliable tool for a complex diagnosis, since it may simultaneously provide both the data of the mammographic semiotic and the dynamic one of an examination with a contrast medium., Results: In this article, the most common radiological signs of this type of lesions are summarized through an analysis of the published literature. The article focuses on the different mammographic semeiotics in primary and secondary malignant lesions in the breast, on the different aspects of metastases deriving from blood and lymphatic spread, as well as on the common analogies between metastatic lesions and fibroadenomas. Moreover, the characteristics of a unique case of breast metastasis from pleural mesothelioma, analyzed by Contrast-Enhanced Spectral Mammography, are described., Conclusions: On the basis of our experience, CESM could represent an extremely valid method to address a correct diagnosis in complex cases of potentially metastatic lesions.

Objective: The aim of this study is to present a 6-year prospective evaluation of second-look ultrasound (US) using volume navigation (V Nav) for MRI-detected additional breast lesions., Methods: After IRB approvals in both institutions, 1930 consecutive prone MRI breast examinations in 1437 patients were prospectively evaluated. All patients with an MRI-detected additional lesion underwent second-look US, and if occult, contrast-enhanced MRI in supine position was performed for US and MRI co-registration. For patients with breast hypertrophy, MRI-guided biopsy was performed directly. Pathologic examination was the standard of reference. One-way ANOVA and chi-square tests were used., Results: In 490 MRI examinations (25.4%, 490/1930), at least one additional breast lesion was detected for a total of 722 only MRI-detected lesions. Second-look US identified 549 additional lesions (23 ± 8 mm); 362 (65.9%, 362/549) proved benign at pathology and 187 (34.1%, 187/549) malignant. Second-look US with V Nav identified 151 additional lesions (17 ± 9 mm, p = n.s.); 67 (44.4%, 67/151) proved benign at pathology and 84 (55.6%, 84/151) malignant. MRI-guided biopsy was performed on 22 additional breast lesions (22 ± 8 mm, p = n.s.); pathology revealed 20 (90.9%, 20/22) benign lesions and 2 (9.1%, 2/22) malignant ones. Mass lesions were significantly higher in the second-look US group (p < 0.001). No significant difference in lesion dimension was found between the three groups (p = 0.729)., Conclusions: Second-look US with V Nav can be effective in detecting a large number of additional breast lesions occult at second-look US and to biopsy a significant number of malignant lesions safely and irrespective of distance from skin or lesion position., Key Points: • Second-look US with volume navigation is effective in detecting occult additional lesions. • Permits safe biopsies irrespective of position and depth • Reduces the need for MRI-guided biopsy.

Purpose: To evaluate the distribution of MRI breast parenchymal enhancement (BPE) among different breast cancer subtypes searching for any significant difference in terms of immunohistochemical and receptorial patterns (Estrogen Receptor -ER, Progesterone Receptor - PR, Human Epidermal Growth Factor Receptor 2 - HER2)., Methods: 82 consecutive patients affected by breast cancer underwent breast DCE-MRI. Two radiologists retrospectively evaluated all subtracted MR enhanced images for classifying BPE. ER, PR and HER2 expression was assessed by immunohistochemical analysis. ER and PR status was evaluated using Allred score (positive values: score ≥3). The intensity of the cerbB-2 staining was scored as 0, 1+, 2+, or 3+ (positive values: ≥ 3+; negative:0 and 1+; 2+ value assessed with silver in-situ hybridization). Patients were classified into five categories based on cancer subtypes: Luminal A, Luminal B HER2 negative, Luminal B HER2 positive, HER2 positive non luminal, triple negative. The χ2 test was used for evaluating the significance of BPE type distribution into the five groups of tumor subtypes and the distribution of the five breast cancer subtypes among every single BPE type. The correlation of BPE with factors such as age, menopausal status and lesion diameter was investigated using multivariate regression analysis and logistic regression. Cohen's kappa statistics was used in order to assess inter-observer agreement for classifying BPE., Results: 6/82 cases were Luminal A-like (7.3%), 42/82 Luminal B-like (HER2-) (51.2%), 12/82 Luminal B-like (HER2+) (14.6%), 4/82 Non Luminal (HER+) (4.9%), 18/82 Triple Negative (ductal) (22%). 16/82 cases showed minimal BPE, 28/82 mild BPE, 22/82 moderate BPE, 16/82 marked BPE. Mild BPE pattern was significantly more prevalent (p = 0.0001) than other BPE types only in the luminal B (HER-) tumors. Moderate and marked BPE prevailed over minimal and mild, in triple negatives. Among all patients with mild BPE, luminal B (HER2-) tumors were significantly higher (p = 0.0001). Among all patients with marked BPE, triple negative subtypes were significantly higher (p = 0.0074). No significant confounder to BPE qualitative evaluation was found (p = 0.39). The inter-rater agreement in evaluating BPE patterns on MRI was almost perfect with Cohen's k = 0.83., Conclusions: BPE could play a crucial role as an imaging bridge to molecular breast cancer subtype allowing an additional risk stratification in the field of breast MRI and targeted screening tests. Luminal B (HER2-) tumors could prevail in case of mild BPE on CE-MRI examinations and TN tumors in patients with marked BPE. Further studies on larger series are needed to confirm this hypothesis., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Background: Increased risk of breast cancer (BC) and increased risk of an interval BC at mammography screening are associated with high mammographic density. Adjunct imaging detects additional BCs not detected at mammography screening in women with dense breasts., Aim: The aim is to estimate the incremental cancer detection rate (CDR) and false-positive recall for each of tomosynthesis and ultrasound, as adjunct screening modalities in women with mammography-negative dense breasts., Methods: A multicentre prospective comparative trial of adjunct screening with tomosynthesis or ultrasound in women with mammography-negative dense breasts (ASTOUND-2) recruited asymptomatic women attending Italian breast screening services. All participants had independently interpreted tomosynthesis and ultrasound. Outcomes were ascertained from excision histopathology or completed assessment. Paired binary data were compared using McNemar's test., Results: We recruited 5300 screening participants with median age of 50 (interquartile range 43-79) years who had negative mammography and dense breasts (April 2015-September 2017). Adjunct screening detected 29 additional BCs (27 invasive, 2 in situ): 12 detected on both tomosynthesis and ultrasound, 3 detected only on tomosynthesis, 14 detected only on ultrasound. Incremental CDR for tomosynthesis (+15 cancers) was 2.83/1000 screens (95% confidence interval [CI]: 1.58-4.67) versus ultrasound (+26 cancers) with an incremental CDR of 4.90/1000 screens (95% CI: 3.21-7.19), P = 0.015. Mean size of these cancers was 14.2 mm (standard deviation: 7.8 mm), and six had nodal metastases. Incremental false-positive recall was 1.22% (95% CI: 0.91%-1.49%) and differed significantly between tomosynthesis (0.30%) and ultrasound (1.0%), P < 0.001., Conclusions: Ultrasound detected more BCs but caused more false positives than tomosynthesis, underscoring trade-offs in screening outcomes when adjunct imaging is used for screening dense breasts., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Purpose: To compare Digital Breast Tomosynthesis (DBT) for cancers and normal screens in women with dense breasts and negative mammography using a Radiomics approach., Materials and Methods: A substudy (N = 40) of the 'Adjunct Screening With Tomosynthesis or Ultrasound in Women With Mammography-Negative Dense Breasts (ASTOUND)' trial was done based on 20 women who had DBT-detected, histology-proven, breast cancer and 20 controls matched for age and density. Using a Radiomics approach normal and pathological breast parenchyma were evaluated, and correlations among Radiomics features and clinical and prognostic parameters were investigated., Results: The median age of the patients was 50 years (range 39-70 years). After Radiomics feature number reduction, 3 of 6 (50%) selected features differed between controls and cancers (Skewness (0.002); Entropy (p.004); 90percentile (p.006)). Three Radiomics features (Energy, Entropy and Dissimilarity) significantly correlated to tumor size (r = -0.15,r = 0.49,r = 0.51), but not with prognostic factors. Entropy correlated with Estrogen Receptor status (r = -0,46; p.004)., Conclusion: Radiomics features in patients with dense breasts and negative mammography appear to differ between cancerous and normal breast tissue, with evidence of correlation with tumor size and estrogen receptors. This new information warrants further evaluation in larger studies and could contribute to improved understanding of breast cancer through imaging, and may support tailored screening and treatments., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Breast cancer is the main cause of female malignancy worldwide. Effective early detection by imaging studies remains critical to decrease mortality rates, particularly in women at high risk for developing breast cancer. Breast Magnetic Resonance Imaging (MRI) is a common diagnostic tool in the management of breast diseases, especially for high-risk women. However, during this examination, both normal and abnormal breast tissues enhance after contrast material administration. Specifically, the normal breast tissue enhancement is known as background parenchymal enhancement: it may represent breast activity and depends on several factors, varying in degree and distribution in different patients as well as in the same patient over time. While a light degree of normal breast tissue enhancement generally causes no interpretative difficulties, a higher degree may cause difficulty to detect and classify breast lesions at Magnetic Resonance Imaging even for experienced radiologists. In this work, we intend to investigate the exploitation of some statistical measurements to automatically characterize the enhancement trend of the whole breast area in both normal and abnormal tissues independently from the presence of a background parenchymal enhancement thus to provide a diagnostic support tool for radiologists in the MRI analysis.