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Article ; Online: Bilateral lymphadenopathies on mammograms: a case of mixed connective tissue disease and psoriatic arthropathy.

Giambersio, Emilia / Magni, Veronica / Sardanelli, Francesco

BJR case reports

2023 Volume 9, Issue 2, Page(s) 20220077

Abstract: Axillary lymphadenopathy is defined as abnormality ( ...

Abstract	Axillary lymphadenopathy is defined as abnormality (
Language	English
Publishing date	2023-01-23
Publishing country	England
Document type	Case Reports
ISSN	2055-7159
ISSN (online)	2055-7159
DOI	10.1259/bjrcr.20220077
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Article: The paradox of MRI for breast cancer screening: high-risk and dense breasts-available evidence and current practice.

Sardanelli, Francesco / Magni, Veronica / Rossini, Gabriele / Kilburn-Toppin, Fleur / Healy, Nuala A / Gilbert, Fiona J

Insights into imaging

2024 Volume 15, Issue 1, Page(s) 96

Abstract: In the mid-1990s, the identification of BRCA1/2 genes for breast cancer susceptibility led to testing breast MRI accuracy in screening women at increased risk. From 2000 onwards, ten intraindividual comparative studies showed the marked superiority of ... ...

Abstract	In the mid-1990s, the identification of BRCA1/2 genes for breast cancer susceptibility led to testing breast MRI accuracy in screening women at increased risk. From 2000 onwards, ten intraindividual comparative studies showed the marked superiority of MRI: the sensitivity ranged 25-58% for mammography, 33-52% for ultrasound, 48-67% for mammography plus ultrasound, and 71-100% for MRI; specificity 93-100%, 91-98%, 89-98%, and 81-98%, respectively. Based on the available evidence, in 2006-2007, the UK National Institute for Clinical Excellence and the American Cancer Society recommended MRI screening of high-risk women, followed by other international guidelines. Despite evidence-based medicine ideally requiring randomised controlled trials (RCTs) for policy changes regarding screening procedures, breast MRI for high-risk screening was adopted in many countries worldwide. In 2019, the results of the "DENSE" RCT were published in favour of breast MRI screening of women with extremely dense breasts compared to mammography alone, showing a reduction of more than 80% of the interval cancer rate in women who attended MRI screening. Even though international recommendations in favour of this practice were issued, substantial obstacles still prevent health systems from adopting breast MRI for screening women with extremely dense breasts. A paradox is evident: we adopted a screening procedure without evidence from RCTs, and now that we have this level-1 evidence for the same procedure, we fail to do so. This critical review tries to explain the differences between the two cases, as examples of the complex pathways of translating radiological research into everyday practice.Critical relevance statement The high-level evidence in favour of breast MRI screening of women with extremely dense breasts is failing to persuade policy makers to translate this into clinical practice.Key points• Breast MRI screening of high-risk women was adopted on basis of the evidence provided by test accuracy comparative studies showing an MRI performance greatly superior to that of mammography.• Breast MRI screening of women with extremely dense breasts has not been adopted although the evidence of a large reduction in interval cancer rate from a RCT.• We illustrate the differences between the two cases, as an example of the complex ways of translation of radiological research in clinical practice according to the EBM theory.
Language	English
Publishing date	2024-03-27
Publishing country	Germany
Document type	Journal Article ; Review
ZDB-ID	2543323-4
ISSN	1869-4101
ISSN	1869-4101
DOI	10.1186/s13244-024-01653-4
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Article ; Online: Artificial intelligence for digital breast tomosynthesis: Impact on diagnostic performance, reading times, and workload in the era of personalized screening.

Magni, Veronica / Cozzi, Andrea / Schiaffino, Simone / Colarieti, Anna / Sardanelli, Francesco

European journal of radiology

2022 Volume 158, Page(s) 110631

Abstract: The ultimate goals of the application of artificial intelligence (AI) to digital breast tomosynthesis (DBT) are the reduction of reading times, the increase of diagnostic performance, and the reduction of interval cancer rates. In this review, after ... ...

Abstract	The ultimate goals of the application of artificial intelligence (AI) to digital breast tomosynthesis (DBT) are the reduction of reading times, the increase of diagnostic performance, and the reduction of interval cancer rates. In this review, after outlining the journey from computer-aided detection/diagnosis systems to AI applied to digital mammography (DM), we summarize the results of studies where AI was applied to DBT, noting that long-term advantages of DBT screening and its crucial ability to decrease the interval cancer rate are still under scrutiny. AI has shown the capability to overcome some shortcomings of DBT in the screening setting by improving diagnostic performance and by reducing recall rates (from -2 % to -27 %) and reading times (up to -53 %, with an average 20 % reduction), but the ability of AI to reduce interval cancer rates has not yet been clearly investigated. Prospective validation is needed to assess the cost-effectiveness and real-world impact of AI models assisting DBT interpretation, especially in large-scale studies with low breast cancer prevalence. Finally, we focus on the incoming era of personalized and risk-stratified screening that will first see the application of contrast-enhanced breast imaging to screen women with extremely dense breasts. As the diagnostic advantage of DBT over DM was concentrated in this category, we try to understand if the application of AI to DM in the remaining cohorts of women with heterogeneously dense or non-dense breast could close the gap in diagnostic performance between DM and DBT, thus neutralizing the usefulness of AI application to DBT.
Language	English
Publishing date	2022-12-02
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	138815-0
ISSN	1872-7727 ; 0720-048X
ISSN (online)	1872-7727
ISSN	0720-048X
DOI	10.1016/j.ejrad.2022.110631
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Article ; Online: Contrast-enhanced Mammography: A Systematic Review and Meta-Analysis of Diagnostic Performance.

Cozzi, Andrea / Magni, Veronica / Zanardo, Moreno / Schiaffino, Simone / Sardanelli, Francesco

Radiology

2021 Volume 302, Issue 3, Page(s) 568–581

Abstract: Background Contrast-enhanced mammography (CEM) is a promising technique for breast cancer detection, but conflicting results have been reported in previous meta-analyses. Purpose To perform a systematic review and meta-analysis of CEM diagnostic ... ...

Abstract	Background Contrast-enhanced mammography (CEM) is a promising technique for breast cancer detection, but conflicting results have been reported in previous meta-analyses. Purpose To perform a systematic review and meta-analysis of CEM diagnostic performance considering different interpretation methods and clinical settings. Materials and Methods The MEDLINE, EMBASE, Web of Science, and Cochrane Library databases were systematically searched up to July 15, 2021. Prospective and retrospective studies evaluating CEM diagnostic performance with histopathology and/or follow-up as the reference standard were included. Study quality was assessed with the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Summary diagnostic odds ratio and area under the receiver operating characteristic curve were estimated with the hierarchical summary receiver operating characteristic (HSROC) model. Summary estimates of sensitivity and specificity were obtained with the hierarchical bivariate model, pooling studies with the same image interpretation approach or focused on the same findings. Heterogeneity was investigated through meta-regression and subgroup analysis. Results Sixty studies (67 study parts, 11 049 CEM examinations in 10 605 patients) were included. The overall area under the HSROC curve was 0.94 (95% CI: 0.91, 0.96). Pooled diagnostic odds ratio was 55.7 (95% CI: 42.7, 72.7) with high heterogeneity (τ
MeSH term(s)	Breast Neoplasms/diagnostic imaging ; Contrast Media ; Female ; Humans ; Mammography/methods
Chemical Substances	Contrast Media
Language	English
Publishing date	2021-12-14
Publishing country	United States
Document type	Journal Article ; Meta-Analysis ; Systematic Review
ZDB-ID	80324-8
ISSN	1527-1315 ; 0033-8419
ISSN (online)	1527-1315
ISSN	0033-8419
DOI	10.1148/radiol.211412
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Article ; Online: Mammography biomarkers of cardiovascular and musculoskeletal health: A review.

Magni, Veronica / Capra, Davide / Cozzi, Andrea / Monti, Caterina B / Mobini, Nazanin / Colarieti, Anna / Sardanelli, Francesco

Maturitas

2022 Volume 167, Page(s) 75–81

Abstract: Breast density (BD) and breast arterial calcifications (BAC) can expand the role of mammography. In premenopause, BD is related to body fat composition: breast adipose tissue and total volume are potential indicators of fat storage in visceral depots, ... ...

Abstract	Breast density (BD) and breast arterial calcifications (BAC) can expand the role of mammography. In premenopause, BD is related to body fat composition: breast adipose tissue and total volume are potential indicators of fat storage in visceral depots, associated with higher risk of cardiovascular disease (CVD). Women with fatty breast have an increased likelihood of hypercholesterolemia. Women without cardiometabolic diseases with higher BD have a lower risk of diabetes mellitus, hypertension, chest pain, and peripheral vascular disease, while those with lower BD are at increased risk of cardiometabolic diseases. BAC, the expression of Monckeberg sclerosis, are associated with CVD risk. Their prevalence, 13 % overall, rises after menopause and is reduced in women aged over 65 receiving hormonal replacement therapy. Due to their distinct pathogenesis, BAC are associated with hypertension but not with other cardiovascular risk factors. Women with BAC have an increased risk of acute myocardial infarction, ischemic stroke, and CVD death; furthermore, moderate to severe BAC load is associated with coronary artery disease. The clinical use of BAC assessment is limited by their time-consuming manual/visual quantification, an issue possibly solved by artificial intelligence-based approaches addressing BAC complex topology as well as their large spectrum of extent and x-ray attenuations. A link between BD, BAC, and osteoporosis has been reported, but data are still inconclusive. Systematic, standardised reporting of BD and BAC should be encouraged.
MeSH term(s)	Female ; Humans ; Artificial Intelligence ; Risk Factors ; Mammography ; Breast Diseases/diagnostic imaging ; Breast Diseases/complications ; Breast Diseases/epidemiology ; Myocardial Infarction ; Hypertension/complications ; Biomarkers
Chemical Substances	Biomarkers
Language	English
Publishing date	2022-10-20
Publishing country	Ireland
Document type	Journal Article ; Review
ZDB-ID	80460-5
ISSN	1873-4111 ; 0378-5122
ISSN (online)	1873-4111
ISSN	0378-5122
DOI	10.1016/j.maturitas.2022.10.001
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Article: MRI-Derived Tumour-to-Breast Volume Is Associated with the Extent of Breast Surgery.

Cozzi, Andrea / Schiaffino, Simone / Della Pepa, Gianmarco / Carriero, Serena / Magni, Veronica / Spinelli, Diana / Carbonaro, Luca A / Sardanelli, Francesco

Diagnostics (Basel, Switzerland)

2021 Volume 11, Issue 2

Abstract: The tumour-to-breast volume ratio (TBVR) is a metric that may help surgical decision making. In this retrospective Ethics-Committee-approved study, we assessed the correlation between magnetic resonance imaging (MRI)-derived TBVR and the performed ... ...

Abstract	The tumour-to-breast volume ratio (TBVR) is a metric that may help surgical decision making. In this retrospective Ethics-Committee-approved study, we assessed the correlation between magnetic resonance imaging (MRI)-derived TBVR and the performed surgery. The TBVR was obtained using a fully manual method for the segmentation of the tumour volume (TV) and a growing region semiautomatic method for the segmentation of the whole breast volume (WBV). Two specifically-trained residents (R1 and R2) independently segmented T1-weighted datasets of 51 cancer cases in 51 patients (median age 57 years). The intraobserver and interobserver TBVR reproducibility were calculated. Mann-Whitney
Language	English
Publishing date	2021-01-30
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics11020204
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Article ; Online: Contrast-enhanced mammography for the assessment of screening recalls: a two-centre study.

Cozzi, Andrea / Schiaffino, Simone / Fanizza, Marianna / Magni, Veronica / Menicagli, Laura / Monaco, Cristian Giuseppe / Benedek, Adrienn / Spinelli, Diana / Di Leo, Giovanni / Di Giulio, Giuseppe / Sardanelli, Francesco

European radiology

2022 Volume 32, Issue 11, Page(s) 7388–7399

Abstract: Objectives: To evaluate the potential of contrast-enhanced mammography (CEM) for reducing the biopsy rate of screening recalls.: Methods: Recalled women were prospectively enrolled to undergo CEM alongside standard assessment (SA) through additional ... ...

Abstract	Objectives: To evaluate the potential of contrast-enhanced mammography (CEM) for reducing the biopsy rate of screening recalls. Methods: Recalled women were prospectively enrolled to undergo CEM alongside standard assessment (SA) through additional views, tomosynthesis, and/or ultrasound. Exclusion criteria were symptoms, implants, allergy to contrast agents, renal failure, and pregnancy. SA and CEM were independently evaluated by one of six radiologists, who recommended biopsy or 2-year follow-up. Biopsy rates according to SA or recombined CEM (rCEM) were compared with the McNemar's test. Diagnostic performance was calculated considering lesions with available final histopathology. Results: Between January 2019 and July 2021, 220 women were enrolled, 207 of them (median age 56.6 years) with 225 suspicious findings analysed. Three of 207 patients (1.4%) developed mild self-limiting adverse reactions to iodinated contrast agent. Overall, 135/225 findings were referred for biopsy, 90/225 by both SA and rCEM, 41/225 by SA alone and 4/225 by rCEM alone (2/4 being one DCIS and one invasive carcinoma). The rCEM biopsy rate (94/225, 41.8%, 95% CI 35.5-48.3%) was 16.4% lower (p < 0.001) than the SA biopsy rate (131/225, 58.2%, 95% CI 51.7-64.5%). Considering the 124/135 biopsies with final histopathology (44 benign, 80 malignant), rCEM showed a 93.8% sensitivity (95% CI 86.2-97.3%) and a 65.9% specificity (95% CI 51.1-78.1%), all 5 false negatives being ductal carcinoma in situ detectable as suspicious calcifications on low-energy images. Conclusions: Compared to SA, the rCEM-based work-up would have avoided biopsy for 37/225 (16.4%) suspicious findings. Including low-energy images in interpretation provided optimal overall CEM sensitivity. Key points: • The work-up of suspicious findings detected at mammographic breast cancer screening still leads to a high rate of unnecessary biopsies, involving between 2 and 6% of screened women. • In 207 recalled women with 225 suspicious findings, recombined images of contrast-enhanced mammography (CEM) showed a 93.8% sensitivity and a 65.9% specificity, all 5 false negatives being ductal carcinoma in situ detectable on low-energy images as suspicious calcifications. • CEM could represent an easily available one-stop shop option for the morphofunctional assessment of screening recalls, potentially reducing the biopsy rate by 16.4%.
MeSH term(s)	Humans ; Female ; Middle Aged ; Carcinoma, Intraductal, Noninfiltrating/pathology ; Mammography/methods ; Breast Neoplasms/diagnostic imaging ; Breast Neoplasms/pathology ; Early Detection of Cancer/methods ; Calcinosis/pathology ; Contrast Media/pharmacology
Chemical Substances	Contrast Media
Language	English
Publishing date	2022-06-01
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1085366-2
ISSN	1432-1084 ; 0938-7994 ; 1613-3749
ISSN (online)	1432-1084
ISSN	0938-7994 ; 1613-3749
DOI	10.1007/s00330-022-08868-3
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Article: A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast Masses.

Interlenghi, Matteo / Salvatore, Christian / Magni, Veronica / Caldara, Gabriele / Schiavon, Elia / Cozzi, Andrea / Schiaffino, Simone / Carbonaro, Luca Alessandro / Castiglioni, Isabella / Sardanelli, Francesco

Diagnostics (Basel, Switzerland)

2022 Volume 12, Issue 1

Abstract: We developed a machine learning model based on radiomics to predict the BI-RADS category of ultrasound-detected suspicious breast lesions and support medical decision-making towards short-interval follow-up versus tissue sampling. From a retrospective ... ...

Abstract	We developed a machine learning model based on radiomics to predict the BI-RADS category of ultrasound-detected suspicious breast lesions and support medical decision-making towards short-interval follow-up versus tissue sampling. From a retrospective 2015-2019 series of ultrasound-guided core needle biopsies performed by four board-certified breast radiologists using six ultrasound systems from three vendors, we collected 821 images of 834 suspicious breast masses from 819 patients, 404 malignant and 430 benign according to histopathology. A balanced image set of biopsy-proven benign (n = 299) and malignant (n = 299) lesions was used for training and cross-validation of ensembles of machine learning algorithms supervised during learning by histopathological diagnosis as a reference standard. Based on a majority vote (over 80% of the votes to have a valid prediction of benign lesion), an ensemble of support vector machines showed an ability to reduce the biopsy rate of benign lesions by 15% to 18%, always keeping a sensitivity over 94%, when externally tested on 236 images from two image sets: (1) 123 lesions (51 malignant and 72 benign) obtained from two ultrasound systems used for training and from a different one, resulting in a positive predictive value (PPV) of 45.9% (95% confidence interval 36.3-55.7%) versus a radiologists' PPV of 41.5% (
Language	English
Publishing date	2022-01-13
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics12010187
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Article: Do we still need breast cancer screening in the era of targeted therapies and precision medicine?

Trimboli, Rubina Manuela / Giorgi Rossi, Paolo / Battisti, Nicolò Matteo Luca / Cozzi, Andrea / Magni, Veronica / Zanardo, Moreno / Sardanelli, Francesco

Insights into imaging

2020 Volume 11, Issue 1, Page(s) 105

Abstract: Breast cancer (BC) is the most common female cancer and the second cause of death among women worldwide. The 5-year relative survival rate recently improved up to 90% due to increased population coverage and women's attendance to organised mammography ... ...

Abstract	Breast cancer (BC) is the most common female cancer and the second cause of death among women worldwide. The 5-year relative survival rate recently improved up to 90% due to increased population coverage and women's attendance to organised mammography screening as well as to advances in therapies, especially systemic treatments. Screening attendance is associated with a mortality reduction of at least 30% and a 40% lower risk of advanced disease. The stage at diagnosis remains the strongest predictor of recurrences. Systemic treatments evolved dramatically over the last 20 years: aromatase inhibitors improved the treatment of early-stage luminal BC; targeted monoclonal antibodies changed the natural history of anti-human epidermal growth factor receptor 2-positive (HER2) disease; immunotherapy is currently investigated in patients with triple-negative BC; gene expression profiling is now used with the aim of personalising systemic treatments. In the era of precision medicine, it is a challenging task to define the relative contribution of early diagnosis by screening mammography and systemic treatments in determining BC survival. Estimated contributions before 2000 were 46% for screening and 54% for treatment advances and after 2000, 37% and 63%, respectively. A model showed that the 10-year recurrence rate would be 30% and 25% using respectively chemotherapy or novel treatments in the absence of screening, but would drop to 19% and 15% respectively if associated with mammography screening. Early detection per se has not a curative intent and systemic treatment has limited benefit on advanced stages. Both screening mammography and systemic therapies continue to positively contribute to BC prognosis.
Language	English
Publishing date	2020-09-25
Publishing country	Germany
Document type	Journal Article ; Review
ZDB-ID	2543323-4
ISSN	1869-4101
ISSN	1869-4101
DOI	10.1186/s13244-020-00905-3
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Article ; Online: Development and Validation of an AI-driven Mammographic Breast Density Classification Tool Based on Radiologist Consensus.

Magni, Veronica / Interlenghi, Matteo / Cozzi, Andrea / Alì, Marco / Salvatore, Christian / Azzena, Alcide A / Capra, Davide / Carriero, Serena / Della Pepa, Gianmarco / Fazzini, Deborah / Granata, Giuseppe / Monti, Caterina B / Muscogiuri, Giulia / Pellegrino, Giuseppe / Schiaffino, Simone / Castiglioni, Isabella / Papa, Sergio / Sardanelli, Francesco

Radiology. Artificial intelligence

2022 Volume 4, Issue 2, Page(s) e210199

Abstract: Mammographic breast density (BD) is commonly visually assessed using the Breast Imaging Reporting and Data System (BI-RADS) four-category scale. To overcome inter- and intraobserver variability of visual assessment, the authors retrospectively developed ... ...

Abstract	Mammographic breast density (BD) is commonly visually assessed using the Breast Imaging Reporting and Data System (BI-RADS) four-category scale. To overcome inter- and intraobserver variability of visual assessment, the authors retrospectively developed and externally validated a software for BD classification based on convolutional neural networks from mammograms obtained between 2017 and 2020. The tool was trained using the majority BD category determined by seven board-certified radiologists who independently visually assessed 760 mediolateral oblique (MLO) images in 380 women (mean age, 57 years ± 6 [SD]) from center 1; this process mimicked training from a consensus of several human readers. External validation of the model was performed by the three radiologists whose BD assessment was closest to the majority (consensus) of the initial seven on a dataset of 384 MLO images in 197 women (mean age, 56 years ± 13) obtained from center 2. The model achieved an accuracy of 89.3% in distinguishing BI-RADS a or b (nondense breasts) versus c or d (dense breasts) categories, with an agreement of 90.4% (178 of 197 mammograms) and a reliability of 0.807 (Cohen κ) compared with the mode of the three readers. This study demonstrates accuracy and reliability of a fully automated software for BD classification.
Language	English
Publishing date	2022-03-16
Publishing country	United States
Document type	Journal Article
ISSN	2638-6100
ISSN (online)	2638-6100
DOI	10.1148/ryai.210199
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