Search

Purpose: This study aimed to investigate the use of contrast-free magnetic resonance imaging (MRI) as an innovative screening method for detecting breast cancer in high-risk asymptomatic women. Specifically, the researchers evaluated the diagnostic performance of diffusion-weighted imaging (DWI) in this population. Methods: MR images from asymptomatic women, carriers of a germline mutation in either the BRCA1 or BRCA2 gene, collected in a single center from January 2019 to December 2021 were retrospectively evaluated. A radiologist with experience in breast imaging (R1) and a radiology resident (R2) independently evaluated DWI/ADC maps and, in case of doubts, T2-WI. The standard of reference was the pathological diagnosis through biopsy or surgery, or ≥1 year of clinical and radiological follow-up. Diagnostic performances were calculated for both readers with a 95% confidence interval (CI). The agreement was assessed using Cohen’s kappa (κ) statistics. Results: Out of 313 women, 145 women were included (49.5 ± 12 years), totaling 344 breast MRIs with DWI/ADC maps. The per-exam cancer prevalence was 11/344 (3.2%). The sensitivity was 8/11 (73%; 95% CI: 46–99%) for R1 and 7/11 (64%; 95% CI: 35–92%) for R2. The specificity was 301/333 (90%; 95% CI: 87–94%) for both readers. The diagnostic accuracy was 90% for both readers. R1 recalled 40/344 exams (11.6%) and R2 recalled 39/344 exams (11.3%). Inter-reader reproducibility between readers was in moderate agreement (κ = 0.43). Conclusions: In female carriers of a BRCA1/2 mutation, breast DWI supplemented with T2-WI allowed breast cancer detection with high sensitivity and specificity by a radiologist with extensive experience in breast imaging, which is comparable to other screening tests. The findings suggest that DWI and T2-WI have the potential to serve as a stand-alone method for unenhanced breast MRI screening in a selected population, opening up new perspectives for prospective trials.

Simple Summary Artificial Intelligence (AI) has been increasingly used in radiology to improve diagnostic procedures over the past decades. The application of AI at the time of cancer diagnosis also creates challenges in the way doctors should communicate the use of AI to patients. The present systematic review deals with the patient's psycho-cognitive perspective on AI and the interpersonal skills between patients and physicians when AI is implemented in cancer diagnosis communication. Evidence from the retrieved studies pointed out that the use of AI in radiology is negatively associated with patient trust in AI and patient-centered communication in cancer disease. Background: In the past decade, interest in applying Artificial Intelligence (AI) in radiology to improve diagnostic procedures increased. AI has potential benefits spanning all steps of the imaging chain, from the prescription of diagnostic tests to the communication of test reports. The use of AI in the field of radiology also poses challenges in doctor-patient communication at the time of the diagnosis. This systematic review focuses on the patient role and the interpersonal skills between patients and physicians when AI is implemented in cancer diagnosis communication. Methods: A systematic search was conducted on PubMed, Embase, Medline, Scopus, and PsycNet from 1990 to 2021. The search terms were: ("artificial intelligence" or "intelligence machine") and "communication" "radiology" and "oncology diagnosis". The PRISMA guidelines were followed. Results: 517 records were identified, and 5 papers met the inclusion criteria and were analyzed. Most of the articles emphasized the success of the technological support of AI in radiology at the expense of patient trust in AI and patient-centered communication in cancer disease. Practical implications and future guidelines were discussed according to the results. Conclusions: AI has proven to be beneficial in helping clinicians with diagnosis. Future research may improve patients' trust through adequate information about the advantageous use of AI and an increase in medical compliance with adequate training on doctor-patient diagnosis communication.

Objective: Although breast cancer screening can benefit from Artificial Intelligence (AI), it is still unknown whether, to which extent or under which conditions, the use of AI is going to be accepted by the general population. The aim of our study is to evaluate what the females who are eligible for breast cancer screening know about AI and how they perceive such innovation. Methods: We used a prospective survey consisting of a 11-multiple-choice questionnaire evaluating statistical associations with Chi-Square-test or Fisher-exact-test. Multinomial-logistic-regression was performed on items with more than two response categories. Odds ratio (OR) with 95% CI were computed to estimate the probability of a specific response according to patient’s characteristics. Results: In the 800 analysed questionnaires, 51% of respondents confirmed to have knowledge of AI. Of these, 88% expressed a positive opinion about its use in medicine. Non-Italian respondents were associated with the belief of having a deep awareness about AI more often than Italian respondents (OR = 1.91;95% CI[1.10–3.33]). Higher education level was associated with better opinions on the use of AI in medicine (OR = 4.69;95% CI[1.36–16.12]). According to 94% of respondents, the radiologists should always produce their own report on mammograms, whilst 77% agreed that AI should be used as a second reader. Most respondents (52%) considered that both the software developer and the radiologist should be held accountable for AI errors. Conclusions: Most of the females undergoing screening in our Institute approve the introduction of AI, although only as a support to radiologist, and not in substitution thereof. Yet, accountability in case of AI errors is still unsolved. advances in knowledge: This survey may be considered as a pilot-study for the development of large-scale studies to understand females’s demands and concerns about AI applications in breast cancer screening.

Radiomics is an emerging translational field of medicine based on the extraction of high-dimensional data from radiological images, with the purpose to reach reliable models to be applied into clinical practice for the purposes of diagnosis, prognosis and evaluation of disease response to treatment. We aim to provide the basic information on radiomics to radiologists and clinicians who are focused on breast cancer care, encouraging cooperation with scientists to mine data for a better application in clinical practice. We investigate the workflow and clinical application of radiomics in breast cancer care, as well as the outlook and challenges based on recent studies. Currently, radiomics has the potential ability to distinguish between benign and malignant breast lesions, to predict breast cancer’s molecular subtypes, the response to neoadjuvant chemotherapy and the lymph node metastases. Even though radiomics has been used in tumor diagnosis and prognosis, it is still in the research phase and some challenges need to be faced to obtain a clinical translation. In this review, we discuss the current limitations and promises of radiomics for improvement in further research.

This study aims to evaluate the Average Glandular Dose (AGD) and diagnostic performance of CEM versus Digital Mammography (DM) as well as versus DM plus one-view Digital Breast Tomosynthesis (DBT), which were performed in the same patients at short intervals of time. A preventive screening examination in high-risk asymptomatic patients between 2020 and 2022 was performed with two-view Digital Mammography (DM) projections (Cranio Caudal and Medio Lateral) plus one Digital Breast Tomosynthesis (DBT) projection (mediolateral oblique, MLO) in a single session examination. For all patients in whom we found a suspicious lesion by using DM + DBT, we performed (within two weeks) a CEM examination. AGD and compression force were compared between the diagnostic methods. All lesions identified by DM + DBT were biopsied; then, we assessed whether lesions found by DBT were also highlighted by DM alone and/or by CEM. We enrolled 49 patients with 49 lesions in the study. The median AGD was lower for DM alone than for CEM (3.41 mGy vs. 4.24 mGy, p = 0.015). The AGD for CEM was significantly lower than for the DM plus one single projection DBT protocol (4.24 mGy vs. 5.55 mGy, p < 0.001). We did not find a statistically significant difference in the median compression force between the CEM and DM + DBT. DM + DBT allows the identification of one more invasive neoplasm one in situ lesion and two high-risk lesions, compared to DM alone. The CEM, compared to DM + DBT, failed to identify only one of the high-risk lesions. According to these results, CEM could be used in the screening of asymptomatic high-risk patients.

Recent technological advances in the field of artificial intelligence hold promise in addressing medical challenges in breast cancer care, such as early diagnosis, cancer subtype determination and molecular profiling, prediction of lymph node metastases, and prognostication of treatment response and probability of recurrence. Radiomics is a quantitative approach to medical imaging, which aims to enhance the existing data available to clinicians by means of advanced mathematical analysis using artificial intelligence. Various published studies from different fields in imaging have highlighted the potential of radiomics to enhance clinical decision making. In this review, we describe the evolution of AI in breast imaging and its frontiers, focusing on handcrafted and deep learning radiomics. We present a typical workflow of a radiomics analysis and a practical “how-to” guide. Finally, we summarize the methodology and implementation of radiomics in breast cancer, based on the most recent scientific literature to help researchers and clinicians gain fundamental knowledge of this emerging technology. Alongside this, we discuss the current limitations of radiomics and challenges of integration into clinical practice with conceptual consistency, data curation, technical reproducibility, adequate accuracy, and clinical translation. The incorporation of radiomics with clinical, histopathological, and genomic information will enable physicians to move forward to a higher level of personalized management of patients with breast cancer.

The study aimed to evaluate the performance of radiomics features and one ultrasound CAD (computer-aided diagnosis) in the prediction of the malignancy of a breast lesion detected with ultrasound and to develop a nomogram incorporating radiomic score and available information on CAD performance, conventional Breast Imaging Reporting and Data System evaluation (BI-RADS), and clinical information. Data on 365 breast lesions referred for breast US with subsequent histologic analysis between January 2020 and March 2022 were retrospectively collected. Patients were randomly divided into a training group (n = 255) and a validation test group (n = 110). A radiomics score was generated from the US image. The CAD was performed in a subgroup of 209 cases. The radiomics score included seven radiomics features selected with the LASSO logistic regression model. The multivariable logistic model incorporating CAD performance, BI-RADS evaluation, clinical information, and radiomic score as covariates showed promising results in the prediction of the malignancy of breast lesions: Area under the receiver operating characteristic curve, [AUC]: 0.914; 95% Confidence Interval, [CI]: 0.876–0.951. A nomogram was developed based on these results for possible future applications in clinical practice.

A digital twin is a virtual model developed to accurately reflect a physical thing or a system. In radiology, a digital twin of a radiological device enables developers to test its characteristics, make alterations to the design or materials, and test the success or failure of the modifications in a virtual environment. Innovative technologies, such as AI and -omics sciences, may build virtual models for patients that are continuously adjustable based on live-tracked health/lifestyle parameters. Accordingly, healthcare could use digital twins to improve personalized medicine. Furthermore, the accumulation of digital twin models from real-world deployments will enable large cohorts of digital patients that may be used for virtual clinical trials and population studies. Through their further refinement, development, and application into clinical practice, digital twins could be crucial in the era of personalized medicine, revolutionizing how diseases are detected and managed. Although significant challenges remain in the development of digital twins, a structural modification to the current operating models is occurring, and radiologists can guide the introduction of such technology into healthcare.

Background: To create a predictive score of malignancy of a breast lesion based on the main contrast enhancement features ascertained by contrast-enhanced spectral mammography (CESM). Methods: In this single-centre prospective study, patients with suspicious breast lesions (BIRADS > 3) were enrolled between January 2013 and February 2022. All participants underwent CESM prior to breast biopsy, and eventually surgery. A radiologist with 20 years’ experience in breast imaging evaluated the presence or absence of enhancement and the following enhancement descriptors: intensity, pattern, margin, and ground glass. A score of 0 or 1 was given for each descriptor, depending on whether the enhancement characteristic was predictive of benignity or malignancy (both in situ and invasive). Then, an overall enhancement score ranging from 0 to 4 was obtained. The histological results were considered the gold standard in the evaluation of the relationship between enhancement patterns and malignancy. Results: A total of 321 women (median age: 51 years; range: 22–83) with 377 suspicious breast lesions were evaluated. Two hundred forty-nine lesions (66%) have malignant histological results (217 invasive and 32 in situ). Considering an overall enhancement score ≥ 2 as predictive of malignancy, we obtain an overall sensitivity of 92.4%; specificity of 89.8%; positive predictive value of 94.7%; and negative predictive value of 85.8%. Conclusions: Our proposed predictive score on the enhancement descriptors of CESM to predict the malignancy of a breast lesion shows excellent results and can help in early breast cancer diagnosis and in avoiding unnecessary biopsies.

Since ancient times, breast cancer treatment has crucially relied on surgeons and clinicians making great efforts to find increasingly conservative approaches to cure the tumor. In the Halstedian era (mid-late 19th century), the predominant practice consisted of the radical and disfiguring removal of the breast, much to the detriment of women’s psycho-physical well-being. Thanks to enlightened scientists such as Professor Umberto Veronesi, breast cancer surgery has since impressively progressed and adopted a much more conservative approach. Over the last three decades, a better understanding of tumor biology and of its significant biomarkers has made the assessment of genetic and molecular profiles increasingly important. At the same time, neo-adjuvant treatments have been introduced, and great improvements in genetics, imaging technologies and in both oncological and reconstructive surgical techniques have been made. The future of breast cancer management must now rest on an ever more precise and targeted type of surgery that, through an increasingly multidisciplinary and personalized approach, can ensure oncological radicality while offering the best possible quality of life.

Objectives: We aimed to determine whether radiomic features extracted from a highly homogeneous database of breast MRI could non-invasively predict pathological complete responses (pCR) to neoadjuvant chemotherapy (NACT) in patients with breast cancer. Methods: One hundred patients with breast cancer receiving NACT in a single center (01/2017–06/2019) and undergoing breast MRI were retrospectively evaluated. For each patient, radiomic features were extracted within the biopsy-proven tumor on T1-weighted (T1-w) contrast-enhanced MRI performed before NACT. The pCR to NACT was determined based on the final surgical specimen. The association of clinical/biological and radiomic features with response to NACT was evaluated by univariate and multivariable analysis by using random forest and logistic regression. The performances of all models were assessed using the areas under the receiver operating characteristic curves (AUC) with 95% confidence intervals (CI). Results: Eighty-three patients (mean (SD) age, 47.26 (8.6) years) were included. Patients with HER2+, basal-like molecular subtypes and Ki67 ≥ 20% presented a pCR to NACT more frequently, the clinical/biological model’s AUC (95% CI) was 0.81 (0.71–0.90). Using 136 representative radiomics features selected through cluster analysis from the 1037 extracted features, a radiomic score was calculated to predict the response to NACT, with AUC (95% CI): 0.64 (0.51–0.75). After combining the clinical/biological and radiomics models, the AUC (95% CI) was 0.83 (0.73–0.92). Conclusions: MRI-based radiomic features slightly improved the pre-treatment prediction of pCR to NACT, in addiction to biological characteristics. If confirmed on larger cohorts, it could be helpful to identify such patients, to avoid unnecessary treatment.

Simple Summary The role of magnetic resonance imaging (MRI) in breast cancer has expanded in the last decade, and studies have demonstrated good sensitivity and specificity of diffusion-weighted imaging (DWI), a functional imaging technique reflecting water diffusion properties in tissues. However, clear results about the reproducibility of DWI are still missing. To utilize DWI as a reliable stand-alone technique for breast cancer detection, the inter-reader agreement of the measurement must be assessed. Accordingly, in this study, we assess the inter-reader reproducibility to retrospectively evaluate the agreement of breast cancer detection using DWI as a stand-alone technique. As our results show a good agreement only in expert readers, the assumption that a breast MRI based only on qualitative analysis of DWI, with fewer variables, may be easier for a non-expert reader to learn seems disproved, and future prospective studies should assess the right time for appropriate training for radiologists to investigate the potential role of DWI as a stand-alone method for un-enhanced breast MRI. Abstract Purpose: In order to evaluate the use of un-enhanced magnetic resonance imaging (MRI) for detecting breast cancer, we evaluated the accuracy and the agreement of diffusion-weighted imaging (DWI) through the inter-reader reproducibility between expert and non-expert readers. Material and Methods: Consecutive breast MRI performed in a single centre were retrospectively evaluated by four radiologists with different levels of experience. The per-breast standard of reference was the histological diagnosis from needle biopsy or surgical excision, or at least one-year negative follow-up on imaging. The agreement across readers (by inter-reader reproducibility) was examined for each breast examined using Cohen’s and Fleiss’ kappa (κ) statistics. The Wald test was used to test the difference in inter-reader agreement between expert and non-expert readers. Results: Of 1131 examinations, according to our inclusion and exclusion criteria, 382 women were included (49.5 ± 12 years old), 40 of them with unilateral mastectomy, totaling 724 breasts. Overall inter-reader reproducibility was substantial (κ = 0.74) for expert readers and poor (κ = 0.37) for non- expert readers. Pairwise agreement between expert readers and non-expert readers was moderate (κ = 0.60) and showed a statistically superior agreement of the expert readers over the non-expert readers (p = 0.003). Conclusions: DWI showed substantial inter-reader reproducibility among expert-level readers. Pairwise comparison showed superior agreement of the expert readers over the non-expert readers, with the expert readers having higher inter-reader reproducibility than the non-expert readers. These findings open new perspectives for prospective studies investigating the actual role of DWI as a stand-alone method for un-enhanced breast MRI.

Simple Summary A diagnosis of ductal carcinoma in situ, made on biopsy, is often followed by surgery or radiotherapy because of the risk of an upgrading disease upon subsequent surgical specimens, finding invasive carcinoma. In order to select which patients can be spared overtreatments and alternatively followed with active surveillance, we retrospectively reviewed 2173 vacuum assisted breast biopsies. Our goal was to demonstrate if complete removal of the lesion by biopsy, documented by mammograms, can be a valid criterion to select the patients that can be spared further treatments. The results of our study demonstrate a significant lower upgrading rate of disease when the lesion is completely removed. Thus, performing a mammogram to document the absence of residual lesion following vacuum-assisted breast biopsy (VABB) allows us to reduce overtreatments and to select which patients can be followed with an active surveillance, sparing unjustified public health costs. Abstract Background: Considering highly selected patients with ductal carcinoma in situ (DCIS), active surveillance is a valid alternative to surgery. Our study aimed to show the reliability of post-biopsy complete lesion removal, documented by mammogram, as additional criterion to select these patients. Methods: A total of 2173 vacuum-assisted breast biopsies (VABBs) documented as DCIS were reviewed. Surgery was performed in all cases. We retrospectively collected the reports of post-VABB complete lesion removal and the histological results of the biopsy and surgery. We calculated the rate of upgrade of DCIS identified on VABB upon excision for patients with post-biopsy complete lesion removal and for those showing residual lesion. Results: We observed 2173 cases of DCIS: 408 classified as low-grade, 1262 as intermediate-grade, and 503 as high-grade. The overall upgrading rate to invasive carcinoma was 15.2% (330/2173). The upgrade rate was 8.2% in patients showing mammographically documented complete removal of the lesion and 19% in patients without complete removal. Conclusion: The absence of mammographically documented residual lesion following VABB was found to be associated with a lower upgrading rate of DCIS to invasive carcinoma on surgical excision and should be considered when deciding the proper management DCIS diagnosis.

(1) Background: Considering highly selected patients with ductal carcinoma in situ (DCIS), active surveillance is a valid alternative to surgery. Our study is aimed at showing the reliability of post-biopsy complete lesion removal, documented by mammogram, as additional criterion to select these patients. (2) Methods: 2173 Vacuum Assisted Breast Biopsies (VABB) documented as DCIS have been reviewed. Surgery has been performed in all cases. We retrospectively collected the reports of post-VABB complete lesion removal and the histological results of the biopsy and surgery. We calculated the rate of upgrade of DCIS identified on VABB upon excision for patients with post-biopsy complete lesion removal and for those showing residual lesion. (3) Results: We observed 2173 cases of DCIS: 408 classified as low grade; 1262 as intermediate grade; 503 as high grade. The overall upgrading rate to invasive carcinoma was 15.2% (330/2173). The upgrade rate was significantly lower (8.2%) when considering patients showing mammographically documented complete removal of the lesion. (4) Conclusion: The absence of mammographically documented residual lesion following VABB is associated to a lower upgrading rate of DCIS to invasive carcinoma on surgical excision and should be considered when deciding the proper management DCIS diagnosis.

Italy has one of the highest COVID-19 clinical burdens in the world and Lombardy region accounts for more than half of the deaths of the country. Since COVID-19 is a novel disease, early impactful decisions are often based on experience of referral centres. We report the re-organisation which our institute (IEO, European Institute of Oncology), a cancer referral centre in Lombardy, went through to make our breast-imaging division pandemic-proof. Using personal-protective-equipment and innovative protocols, we provided essential breast-imaging procedures during COVID-19 pandemic without compromising cancer outcomes. The emergency management and infection-control-measures implemented in our division protected both the patients and the staff, making this experience useful for other radiology departments dealing with the pandemic.

Numerous imaging modalities may be used for the staging of women with advanced breast cancer. Although bone scintigraphy and multiplanar-CT are the most frequently used tests, others including PET, MRI and hybrid scans are also utilised, with no specific recommendations of which test should be preferentially used. We review the evidence behind the imaging modalities that characterise metastases in breast cancer and to update the evidence on comparative imaging accuracy.

E-cadherin (CDH1 gene) germline mutations are associated with the development of diffuse gastric cancer in the context of the so-called hereditary diffuse gastric syndrome, and with an inherited predisposition of lobular breast carcinoma. In 2019, the international gastric cancer linkage consortium revised the clinical criteria and established guidelines for the genetic screening of CDH1 germline syndromes. Nevertheless, the introduction of multigene panel testing in clinical practice has led to an increased identification of E-cadherin mutations in individuals without a positive family history of gastric or breast cancers. This observation motivated us to review and present a novel multidisciplinary clinical approach (nutritional, surgical, and image screening) for single subjects who present germline CDH1 mutations but do not fulfil the classic clinical criteria, namely those identified as—(1) incidental finding and (2) individuals with lobular breast cancer without family history of gastric cancer (GC). This manuscript was supported by the Italian Ministry of Health (Project Code GR-2016-02361655) and was partially supported by the Ricerca Corrente and 5 × 1000 funds.

MRI-guided vacuum-assisted breast biopsy (VABB) is used for suspicious breast cancer (BC) lesions which are detectable only with MRI: because the high sensitivity but limited specificity of breast MRI it is a fundamental tool in breast imaging divisions. We analyse our experience of MRI-guided VABB and critically discuss the potentialities of diffusion-weighted imaging (DWI) and artificial intelligence (AI) in this matter. We retrospectively analysed a population of consecutive women underwent VABB at our tertiary referral BC centre from 01/2011 to 01/2019. Reference standard was histological diagnosis or at least 1-year negative follow-up. McNemar, Mann–Whitney and χ2 tests at 95% level of significance were used as statistical exams. 217 women (mean age = 52, 18–72 years) underwent MRI-guided VABB; 11 were excluded and 208 MRI-guided VABB lesions were performed: 34/208 invasive carcinomas, 32/208 DCIS, 8/208 LCIS, 3/208 high-risk lesions and 131/208 benign lesions were reported. Accuracy of MRI-guided VABB was 97%. The predictive features for malignancy were mass with irregular shape (OR 8.4; 95% CI 0.59–31.6), size of the lesion (OR 4.4; 95% CI 1.69–9.7) and mass with irregular/spiculated margins (OR 5.4; 95% CI 6.8–31.1). Six-month follow-up showed 4 false-negative cases (1.9%). Invasive BC showed a statistically significant higher hyperintense signal at DWI compared to benign lesions (p = 0.03). No major complications occurred. MR-guided VABB showed high accuracy. Benign-concordant lesions should be followed up with breast MRI in 6–12 months due to the risk of false-negative results. DWI and AI applications showed potential benefit as support tools for radiologists.

The diagnosis of breast cancer currently relies on radiological and clinical evaluation, confirmed by histopathological examination. However, such approach has some limitations as the suboptimal sensitivity, the long turnaround time for recall tests, the invasiveness of the procedure and the risk that some features of target lesions may remain undetected, making re-biopsy a necessity. Recent technological advances in the field of artificial intelligence hold promise in addressing such medical challenges not only in cancer diagnosis, but also in treatment assessment, and monitoring of disease progression. In the perspective of a truly personalised medicine, based on the early diagnosis and individually tailored treatments, two new technologies, namely radiomics and liquid biopsy, are rising as means to obtain information from diagnosis to molecular profiling and response assessment, without the need of a biopsied tissue sample. Radiomics works through the extraction of quantitative peculiar features of cancer from radiological data, while liquid biopsy gets the whole of the malignancy's biology from something as easy as a blood sample. Both techniques hopefully will identify diagnostic and prognostic information of breast cancer potentially reducing the need for invasive (and often difficult to perform) biopsies and favouring an approach that is as personalised as possible for each patient. Nevertheless, such techniques will not substitute tissue biopsy in the near future, and even in further times they will require the aid of other parameters to be correctly interpreted and acted upon.

Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) is a rare and newly recognized subtype of T cell Non-Hodgkin Lymphoma (NHLs) associated with breast implants. The mechanism involved in the development of this kind of lymphoma is still uncertain. BIA-ALCL is generally an indolent disease localized to the breast implant and its capsule and effectively treated with capsulectomy alone without chemotherapy. Clinically, BIA-ALCL may typically present a sudden-onset breast-swelling secondary to periimplant effusion. The minority of BIA-ALCL patients present a more aggressive mass-forming subtype, for which systemic therapy is mandatory. Despite the number of cases has recently increased, BIA-ALCL remains a rare disease described mainly in several case reports and small case series. Breast imaging, including mammography, ultrasound and breast MRI are routinely used in the screening of breast cancer; however, guidelines for the imaging and pathological diagnosis of this disease have only recently been proposed and included in the 2019 National Comprehensive Cancer Network (NCCN) consensus guidelines for BIA-ALCL. The main purpose of this pictorial is to illustrate the MRI signs of BIA-ALCL and correlate them with the corresponding pathology features in order to improve the knowledge of the principals MRI features of this type of lymphoma.

To estimate the performance of diffusion-weighted imaging (DWI) for breast cancer detection. Consecutive breast magnetic resonance imaging examinations performed from January to September 2016 were retrospectively evaluated. Examinations performed before/after neoadjuvant therapy, lacking DWI sequences or reference standard were excluded; breasts after mastectomy were also excluded. Two experienced breast radiologists (R1, R2) independently evaluated only DWI. Final pathology or > 1-year follow-up served as reference standard. Mc Nemar, χ2, and κ statistics were applied. Of 1,131 examinations, 672 (59.4%) lacked DWI sequence, 41 (3.6%) had no reference standard, 30 (2.7%) were performed before/after neoadjuvant therapy, and 10 (0.9%) had undergone bilateral mastectomy. Thus, 378 women aged 49 ± 11 years (mean ± standard deviation) were included, 51 (13%) with unilateral mastectomy, totaling 705 breasts. Per-breast cancer prevalence was 96/705 (13.6%). Per-breast sensitivity was 83/96 (87%, 95% confidence interval 78–93%) for both R1 and R2, 89/96 (93%, 86–97%) for double reading (DR) (p = 0.031); per-lesion DR sensitivity for cancers ≤ 10 mm was 22/31 (71%, 52–86%). Per-breast specificity was 562/609 (93%, 90–94%) for R1, 538/609 (88%, 86–91%) for R2, and 526/609 (86%¸ 83–89%) for DR (p

Breast implant-associated anaplastic large cell lymphoma (BI-ALCL) is a variant of anaplastic large cell lymphoma arising within seroma effusion associated with breast implants. BI-ALCL is a rare disease, recently recognized as a new provisional entity by the 2017 revised World Health Organization classification. All BI-ALCLs tested so far showed a "triple-negative" genetic profile-negative for ALK, DUSP22, and TP63 rearrangements-and were characterized by mutational and gene expression profiles consistent with aberrant activation of the JAK/STAT pathway. The active form of STAT3 (pSTAT3) is constantly expressed in BI-ALCLs and may favor tumor immune escape by triggering the transcription of PDL1 (CD274), a gene encoding the immune-checkpoint molecule programmed cell death ligand 1 (PDL1); immunohistochemical positivity for PDL1 has been recently described in 3 BI-ALCL cases, and one of them also harbored PDL1 gene amplification. We evaluated PDL1 and pSTAT expression by immunohistochemistry and PDL1 copy number alterations (CNAs) at chromosome 9p24.1 by fluorescent in situ hybridization in a cohort of 9 BI-ALCL cases; we also investigated the presence of tumor-infiltrating programmed cell death 1 (PD1)+ T cells (tumor-infiltrating lymphocytes, or TILs) and PDL1+ tumor-associated macrophages (TAMs) in BI-ALCL microenvironment. Tumor cells expressed PDL1 in 5 (56%) of 9 cases and harbored PDL1 CNAs in 3 (33%) of 9 cases; immunohistochemistry for pSTAT3 was positive in all 6 cases tested (100%), indicative of active JAK/STAT signaling. We observed PDL1 CNAs only among PDL1-positive cases, whereas PD1+ TILs and PDL1+ TAMs were present at variable levels in both PDL1-positive and PDL1-negative BI-ALCLs. We report frequent PDL1 expression and recurrent PDL1 CNAs in BI-ALCLs: our data suggest that 9p24.1 alterations represent a common mechanism of PDL1 overexpression in this disease, likely acting in synergy with constitutive pSTAT3 signaling. In PDL1-positive cases without chromosomal aberration, PDL1 expression may be induced by JAK/STAT signaling alone and/or others alternative pathways. BI-ALCL microenvironment hosts variable amounts of PD1+ TILs and PDL1+ TAMs, suggesting the presence of an active PD1/PDL1 axis. These findings may be of therapeutic value in advanced-stage patients who may benefit from a PD1/PDL1 blocking treatment.

Neurofibromas are benign peripheral nerve sheath tumours, which are usually solitary and sporadic. Solitary neurofibromas of the breast are rare. The most common location of a breast neurofibroma is the nipple–areola complex. We report a rare case of a 56-year-old woman with a solitary neurofibroma of the right breast sulcus.

INTRODUCTION AND BACKGROUND: Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) is a rare form of T-cell non-Hodgkin lymphoma, recently recognized by the World Health Organization classification of lymphoid neoplasms. Clinical and histologic findings suggest a distinct entity from systemic ALCL. Except in advanced cases, BIA-ALCL usually has an indolent course. The prognosis is often favorable and complete surgical excision for localized disease is generally the only treatment. The disease often presents with a delayed seroma around the breast implant - almost exclusively with a textured surface - breast pain, swelling and asymmetry, capsular contracture, but it can also present with a breast mass and lymph node involvement. Tumor nodules, axillary node involvement, bilateral breast involvement and infiltrative pattern on capsule histology are associated with more aggressive behavior. Systemic involvement has also been less commonly reported. In accordance with the FDA recommendation, all cases of histologically confirmed BIA-ALCL should be reported to the BIA-ALCL PROFILE Registry. In Italy, the Ministry of Health sensitizes to notify each BIA-ALCL diagnosis through the compilation of a specific online form. Since 2015 all the notified cases were collected in the Italian Ministry of Health's database, named Dispovigilance. So far, 47 cases have been reported, as of the last updated survey. METHODS: We report our single center experience at European Institute of Oncology (IEO) in Milan, Italy. A total of 27.588 breast implants has been placed in our Institute between 01/2001 and 12/2018.The first case of BI-ALCL was diagnosed in july 2015 and a total of 7 cases have been diagnosed to date. In 4 cases implants were placed at EIO. Median age at diagnosis was 52 (32-71). All patients presented with monolateral and localized disease, with stage IA (TNM classification). In 6 patients periprosthetic seroma preceded diagnosis and fine needle aspirate of the periprosthetic effusion documented BIA-ALCL, while breast pain and tumor nodule were present in one patient. In 5 out of 7 patients breast implantation was performed for reconstruction after breast cancer surgery, in 2 patients for cosmetic purposes. Textured devices had been implanted in all of them. All but one patients with previous hIstory of breast cancer had 2 surgery for reconstruction. Overall, interval between first implant and ALCL diagnosis ranged between 5 and 22 years (median: 11 ys). Interval between second implant and ALCL diagnosis was of 4-11 years (median: 6 ys). Presurgery PET/CT scan and MRI were performed in most patients. Treatment included total capsulectomy in all patient, bilateral surgery in case of bilateral implants. One patient received RT after surgery for higly suggestive residual disease. Immediate reconstruction was performed in 1 patient. With a median follow up of 16 months (range 1-49), all patients are alive with no relapse observed to date. CONCLUSIONS: This report represents the largest single centre experience in Italy. Given the peculiarity of this new recently recognized malignancy, a multidisciplinary approach is essential for the diagnosis and management of these patients. Management requires experts in diagnostic imaging, pathology, hematology/oncology, plastic surgery and surgical oncology and radiation oncology. It is critical that physicians are educated about the clinical presentation, diagnostic criteria, and treatment modalities. This comprehensive approach allows a quite favorable outcome in this rare and challenging complication of breast reconstruction. Disclosures Derenzini: TG-THERAPEUTICS: Research Funding.

Purpose. The purpose of this study was to investigate the intraobserver and interobserver agreements in the diagnosis of malignant versus benign adnexal masses using two-dimensional ultrasonography (2D US) and three-dimensional ultrasonography (3D US). Methods. Two experienced sonographers performed a retrospective review of digitally stored 2D images and 3D data from a sample of 41 consecutive patients with a diagnosis of adnexal mass. Each observer independently, and blinded to each other, evaluated the 2D static images of each adnexal mass and then the 3D volumes 1 week later. The observers were required to classify the adnexal lesion as benign or malignant according to pattern recognition analysis. Intraobserver and interobserver agreement were assessed by calculating the kappa index (κ). Results. Intraobserver agreement between 2D US and 3D US for the observer A was 1.00 and for the observer B was 0.69. Interobserver agreement was 0.69 for 2D US and 1.00 for 3D US (p > 0.05) Conclusions. Although 3D US in the diagnosis of adnexal masses appeared more reproducible than 2D US, the difference was not statistically significant. © 2011 Wiley Periodicals, Inc. J Clin Ultrasound, 2011

The objective of this study was to compare two different scanning protocols in patients suspected to have multiple trauma using multidetector 16-row computed tomography (CT) to better define scanning time, imaging quality and radiation exposure. Forty-six patients, between March 2004 and March 2005, with suspected multiple trauma (cerebral, spine, chest, abdominal and pelvis) were evaluated with two different protocols: Protocol "A" 26 patients; Protocol "B" 20 patients. Protocol A consists of a single-pass continuous whole-body acquisition (from vertex to pubic symphysis), whereas Protocol B of conventional segmented acquisition with scanning of body segments individually. Both protocols were performed using a multidetector 16-rows CT (Light-Speed 16, General Electric Medical System, Milwaukee, WI, USA) with the same technical factors. Radiation dose was evaluated in two ways: computer tomography dose index (CTDI) = dose measured in central and peripheral region of the subjects as a direct result of a CT section acquisition of T millimeters thick (independent from the two protocols) and dose length product (DLP) = total dose deposited over the length of the acquisition (dependent from the two protocols). Image quality was rated according to the following scores: 1, excellent; 2, good; 3, satisfactory; 4, moderate and 5, poor. The results were compared using Wilcoxon's test to identify significant difference in terms of image quality, scanning time, radiation exposure and presence of artifacts, assuming significance at a p value of

Purpose To assess how frequently foci are identified on MRI in high-risk patients, and their association with malignancy, breast density, and background parenchymal enhancement (BPE). Materials and methods In this multicentric study, two readers, in consensus, retrospectively reviewed screening breast MRI of 245 high-risk women, performed between 2009 and 2014. Eligible patients had at least two consecutive screening MRI, and a follow-up of at least 1 year after a lesion was first detected; histology was available for all suspicious findings. Breast density, BPE (both using BI-RADS lexicon), presence, and changes at follow-up for foci were evaluated. Clinical history of the patients was reviewed. Chi-square test was used to define significant correlations. Results 166 women (mean age 43 years), who underwent a median of 4 MRI (range 2–6) during the study period, were included. 68 foci were found in 58 women [34.9 %, 95 % confidence interval (CI) 28.1–42.5 %]. Foci were more frequent in dense breasts (P = 0.079) and with moderate or marked BPE (P