Introduction: Senonetwork, an Italian no profit association, is committed in breast cancer care, to guarantee that all women have access to diagnostic and treatment pathways in compliance with the referring guidelines. The national discipline - related societies dedicated to breast cancer care and the advocacy are represented in Senonetwork scientific committee. Senonetwork has created a datawarehouse (Senonet) to collect Breast Centre data on a voluntary base and in return to issue for each participating Breast Centre a yearly data report towards Senonetwork quality indicators. Objectives: this paper aims at defining evidence-based quality indicators shared among the national discipline related societies dedicated to breast cancer care, to bring them to the attention of the regional and national authorities. Some of the Quality Indicators can be directly taken from the hospital discharge form, but this is not sufficient for a comprehensive evaluation of the different phases of breast cancer patients' diagnosis and treatment within a Breast Centre. Also in this regard, the collaboration among scientific societies and authorities is essential. Methods: to update and increase the set of quality indicators previously published, end 2020 Senonetwork set up a working group of experts and a revision committee including the scientific discipline-related societies represented in Senonetwork (AIOM, AIRO, ANISC, GISMa, SIAPEC-IAP, SICPRE, SIRM). For each quality indicator, based on literature and expert opinion, a minimum standard and a target have been defined. Conclusions: a set of quality indicators, defined by experts and aligned with the indications of the referring scientific societies, going through the different steps of breast cancer diagnosis and treatment, allows Breast Centres to have a complete and integrated vision of the performance reached by the multidisciplinary team. It also represents a help in improving quality of care through benchmarking, educational and research activities within a national network.

In the artificial intelligence era, machine learning (ML) techniques have gained more and more importance in the advanced analysis of medical images in several fields of modern medicine. Radiomics extracts a huge number of medical imaging features revealing key components of tumor phenotype that can be linked to genomic pathways. The multi-dimensional nature of radiomics requires highly accurate and reliable machine-learning methods to create predictive models for classification or therapy response assessment. Multi-parametric breast magnetic resonance imaging (MRI) is routinely used for dense breast imaging as well for screening in high-risk patients and has shown its potential to improve clinical diagnosis of breast cancer. For this reason, the application of ML techniques to breast MRI, in particular to multi-parametric imaging, is rapidly expanding and enhancing both diagnostic and prognostic power. In this review we will focus on the recent literature related to the use of ML in multi-parametric breast MRI for tumor classification and differentiation of molecular subtypes. Indeed, at present, different models and approaches have been employed for this task, requiring a detailed description of the advantages and drawbacks of each technique and a general overview of their performances.

In women at high/intermediate lifetime risk of breast cancer (BC-LTR), contrast-enhanced magnetic resonance imaging (MRI) added to mammography ± ultrasound (MX ± US) increases sensitivity but decreases specificity. Screening with MRI alone is an alternative and potentially more cost-effective strategy. Here, we describe the study protocol and the characteristics of enrolled patients for MRIB feasibility, multicenter, randomized, controlled trial, which aims to compare MRI alone versus MX+US in women at intermediate breast cancer risk (aged 40–59, with a 15–30% BC-LTR and/or extremely dense breasts). Two screening rounds per woman were planned in ten centers experienced in MRI screening, the primary endpoint being the rate of cancers detected in the 2 arms after 5 years of follow-up. From July 2013 to November 2015, 1254 women (mean age 47 years) were enrolled: 624 were assigned to MX+US and 630 to MRI. Most of them were aged below 50 (72%) and premenopausal (45%), and 52% used oral contraceptives. Among postmenopausal women, 15% had used hormone replacement therapy. Breast and/or ovarian cancer in mothers and/or sisters were reported by 37% of enrolled women, 79% had extremely dense breasts, and 41% had a 15–30% BC-LTR. The distribution of the major determinants of breast cancer risk profiles (breast density and family history of breast and ovarian cancer) of enrolled women varied across centers.

Objectives To test whether 3T MRI radiomics of breast malignant lesions improves the performance of predictive models of complete response to neoadjuvant chemotherapy when added to other clinical, histological and radiological information. Methods Women who consecutively had pre-neoadjuvant chemotherapy (NAC) 3T DCE-MRI between January 2016 and October 2019 were retrospectively included in the study. 18F-FDG PET-CT and histological information obtained through lesion biopsy were also available. All patients underwent surgery and specimens were analyzed. Subjects were divided between complete responders (Pinder class 1i or 1ii) and non-complete responders to NAC. Geometric, first order or textural (higher order) radiomic features were extracted from pre-NAC MRI and feature reduction was performed. Five radiomic features were added to other available information to build predictive models of complete response to NAC using three different classifiers (logistic regression, support vector machines regression and random forest) and exploring the whole set of possible feature selections. Results The study population consisted of 20 complete responders and 40 non-complete responders. Models including MRI radiomic features consistently showed better performance compared to combinations of other clinical, histological and radiological information. The AUC (ROC analysis) of predictors that did not include radiomic features reached up to 0.89, while all three classifiers gave AUC higher than 0.90 with the inclusion of radiomic information (range: 0.91-0.98). Conclusions Radiomic features extracted from 3T DCE-MRI consistently improved predictive models of complete response to neo-adjuvant chemotherapy. However, further investigation is necessary before this information can be used for clinical decision making.

The Italian College of Breast Radiologists by the Italian Society of Medical Radiology (SIRM) provides recommendations for breast care provision and procedural prioritization during COVID-19 pandemic, being aware that medical decisions must be currently taken balancing patient’s individual and community safety: (1) patients having a scheduled or to-be-scheduled appointment for in-depth diagnostic breast imaging or needle biopsy should confirm the appointment or obtain a new one; (2) patients who have suspicious symptoms of breast cancer (in particular: new onset palpable nodule; skin or nipple retraction; orange peel skin; unilateral secretion from the nipple) should request non-deferrable tests at radiology services; (3) asymptomatic women performing annual mammographic follow-up after breast cancer treatment should preferably schedule the appointment within 1 year and 3 months from the previous check, compatibly with the local organizational conditions; (4) asymptomatic women who have not responded to the invitation for screening mammography after the onset of the pandemic or have been informed of the suspension of the screening activity should schedule the check preferably within 3 months from the date of the not performed check, compatibly with local organizational conditions. The Italian College of Breast Radiologists by SIRM recommends precautions to protect both patients and healthcare workers (radiologists, radiographers, nurses, and reception staff) from infection or disease spread on the occasion of breast imaging procedures, particularly mammography, breast ultrasound, breast magnetic resonance imaging, and breast intervention procedures.

Objectives This study evaluated the feasibility of DWI for lesion targeting in MRI-guided breast biopsies. Furthermore, it assessed device positioning on DWI during biopsy procedures. Methods A total of 87 biopsy procedures (5/87 bilateral) consecutively performed between March 2019 and June 2020 were retrospectively reviewed: in these procedures, a preliminary DWI sequence ( b = 1300 s/mm 2 ) was acquired to assess lesion detectability. We included 64/87 procedures on lesions detectable at DWI; DWI sequences were added to the standard protocol to localize lesion and biopsy device and to assess the site marker correct positioning. Results Mass lesions ranged from 5 to 48 mm, with a mean size of 10.7 mm and a median size of 8 mm. Non-mass lesions ranged from 7 to 90 mm, with a mean size of 33.9 mm and a median size of 31 mm. Positioning of the coaxial system was confirmed on both T1-weighted and DWI sequences. At DWI, the biopsy needle was detectable in 62/64 (96.9%) cases; it was not visible in 2/64 (3.1%) cases. The site marker was always identified using T1-weighted imaging; a final DWI sequence was acquired in 44/64 cases (68.8%). In 42/44 cases (95.5%), the marker was recognizable at DWI. Conclusions DWI can be used as a cost-effective, highly reliable technique for targeting both mass and non-mass lesions, with a minimum size of 5 mm, detectable at pre-procedural DWI. DWI is also a feasible technique to localize the biopsy device and to confirm the deployment of the site marker. Key Points • MRI-guided breast biopsy is performed in referral centers by an expert dedicated staff, based on prior MR imaging; contrast agent administration is usually needed for lesion targeting. • DWI represents a feasible, highly reliable technique for lesion targeting, avoiding contrast agent administration. • DWI allows a precise localization of both biopsy needle device and site marker.

OBJECTIVES:To test whether 3 T multiparametric magnetic resonance imaging (mMRI) provides information related to molecular subtypes of breast cancer. METHODS:Women with mammographic or US findings of breast lesions (BI-RADS 4-5) underwent 3 T mMRI (DCE, DWI and MR spectroscopy). The histological type of breast cancer was assessed. Estrogen-receptor (ER), progesterone-receptor (PgR), Ki-67 status and HER-2 expression, assessed by immunohistochemistry (IHC), defined four molecular subtypes: Luminal-A, Luminal-B, HER2-enriched and triple-negative. Non-parametric tests (Kruskal-Wallis, k-sample equality of medians, and Mann-Whitney), logistic regression or ANOVA, and a multivariate analysis were performed to investigate correlations between the four molecular subtypes and mMRI (lesion volume, margins or distribution, enhancement pattern, ADC, type of kinetic curve, and total choline (tCho) signal-to-noise-ratio (SNR)). A ROC analysis was finally performed to test the diagnostic power of a multivariate logistic regression model. RESULTS:433 patients (453 lesions) were considered. Volume was smaller in Luminal-B and larger in triple-negative tumours compared to the other subtypes combined. Margins were significantly correlated to Luminal-A and Luminal-B. The type of curve was significantly correlated to Luminal-A. ADC values were higher in Luminal-A. tCho SNR was higher in triple-negative tumours. The ROC analysis showed that the area under the curve (AUC) significantly improved when multiple MRI features were used compared to individual parameters. CONCLUSIONS:A significant correlation was found between some MRI features and molecular subtypes of breast tumours. A multiparametric approach improved the diagnostic power of MRI. However, further research is needed in order to predict the molecular subtype on the sole basis of mMRI.

To test 3T proton magnetic resonance spectroscopy (1H-MRS) for breast mass lesions.

Poster: ECR 2017 / C-2795 / Multi-parametric MRI evaluation of breast lesions: may texture analysis have a role? by: Rossi, C. Cavedon, G. Meliado, L. Camera, I. Baglio, F. Caumo, S. Montemezzi ; Verona/IT

INTRODUCTION:The aim of this work was to assess the role of 3T-MR spectroscopy (MRS) in the multi-parametric MRI evaluation of breast lesions, using a pattern-recognition based classification method. METHODS:291 patients (301 lesions, median 2.3cm3) were enrolled in the study (age 18-85y, mean 54.2y). T1-TSE (TR/TE=400/10ms) and T2-STIR imaging (TR/TE=5000/60ms), dynamic-contrast-enhanced MRI (DCE-MRI), apparent diffusion coefficient (ADC) (b=0-800s/mm2), and single-voxel MRS (10×10×10mm3, PRESS, TR/TE=3000ms/135ms) were performed by means of a 3T scanner. MRS results were accepted if the FWHM of the water peak was ⩽45Hz. Total choline (tCho) was considered detected if the signal-to-noise ratio (SNR) of the 3.2ppmpeak was ⩾2. A classifier-based analysis (support-vector-machines, SVM) was performed with 4-dimensional vectors including type of margin, DCE-MRI kinetic curve type, ADC mean value, and tCho SNR. A comparison with 3-dimensional vectors (without tCho SNR) was used to assess MRS impact on sensitivity, specificity, and positive-negative predictive values (PPV-NPV) for malignancy. RESULTS:228 lesions (180 malignant/48 benign) showed acceptable spectral quality. Comparison of classification results with histopathological examination of surgical specimens showed sensitivity=93.7%, specificity=84.9%, PPV=95.2%, NPV=81.5% without the inclusion of MRS in the SVM analysis. When MRS was included, the figures increased to 95.1%, 90.7%, 97.2%, and 85.0%, respectively. CONCLUSIONS:Inclusion of 3T-MRS in the multi-parametric MRI evaluation of breast lesions improved the performance of the SVM-based classifier, showing a possible role of high-field MR spectroscopy in the differential diagnosis between benign and malignant breast lesions. Further research is however needed to confirm this initial evidence.

Poster: "ECR 2016 / B-1254 / 3T MR spectroscopy in the multi-parametric MRI evaluation of breast lesions: a pattern recognition approach" by: "C. Cavedon, L. Camera, I. Baglio, G. Meliado, F. Caumo, S. Montemezzi; Verona/IT"

Purpose. This study aimed to evaluate whether the Fischer score criteria on contrast-enhanced magnetic resonance (CE-MR) imaging could correlate with histopathological prognostic factors in invasive breast cancer. Materials and methods. Seventy-two women with histologically proven invasive breast cancer underwent preoperative CE-MR imaging. Images were assessed for the following parameters, according to the scoring system described by Fischer in 1999: tumour shape, margins, internal enhancement, signal intensity increase, signal intensity course and overall Fischer score. Evaluated histopathological prognostic factors included histological type, associated extensive intraductal component, diameter, lymph node metastasis, tumour grade, and oestrogen receptor (ER), progesterone receptor (PgR), Ki67 proliferation, oncogene c-erbB-2 (HER2/neu) expression. Fisher’s exact test was used to correlate the CE-MR imaging parameters and histopathological findings (with significance set a p 4 in 58/72 (81%) and 3 in 5/72 (7%; false negative), with a sensitivity of 93%. A significant correlation (p=0.02) was found between stellate‐dendritic shape and the presence of an associated extensive intraductal component (EIC), which was found in 78% of stellate tumours vs. 49% of roundoval tumours. A significant correlation (p=0.039) was found between Ki67 expression and signal intensity course (Ki67 overexpression was present in 81% of tumours with washout course vs. 21% with plateau course). Riassunto