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  1. Article ; Online: Editorial comment to artificial intelligence for body composition and sarcopenia evaluation on computed tomography: A systematic review and meta-analysis.

    Schiaffino, Simone

    European journal of radiology

    2022  Volume 151, Page(s) 110292

    MeSH term(s) Artificial Intelligence ; Body Composition ; Humans ; Muscle, Skeletal/pathology ; Sarcopenia/diagnostic imaging ; Sarcopenia/pathology ; Tomography, X-Ray Computed
    Language English
    Publishing date 2022-04-04
    Publishing country Ireland
    Document type Editorial ; Meta-Analysis ; Systematic Review ; Comment
    ZDB-ID 138815-0
    ISSN 1872-7727 ; 0720-048X
    ISSN (online) 1872-7727
    ISSN 0720-048X
    DOI 10.1016/j.ejrad.2022.110292
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    Zs.A 1630: Show issues Location:
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  2. Article ; Online: Preoperative breast MRI in women with dense breasts: can we keep up with a rapidly changing scenario?

    Cozzi, Andrea / Schiaffino, Simone

    European radiology

    2023  Volume 33, Issue 11, Page(s) 8077–8079

    MeSH term(s) Female ; Humans ; Breast Neoplasms/diagnostic imaging ; Breast Neoplasms/surgery ; Breast Density ; Mammography ; Breast/diagnostic imaging ; Breast/pathology ; Magnetic Resonance Imaging
    Language English
    Publishing date 2023-08-30
    Publishing country Germany
    Document type Editorial ; Comment
    ZDB-ID 1085366-2
    ISSN 1432-1084 ; 0938-7994 ; 1613-3749
    ISSN (online) 1432-1084
    ISSN 0938-7994 ; 1613-3749
    DOI 10.1007/s00330-023-10075-7
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    Zs.A 3300: Show issues Location:
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  3. Article ; Online: Editorial for "A Channel-Dimensional Feature-Reconstructed Deep Learning Model for Predicting Breast Cancer Molecular Subtypes on Overall b-Value Diffusion-Weighted MRI".

    Barzegar, Mojtaba / Schiaffino, Simone

    Journal of magnetic resonance imaging : JMRI

    2023  Volume 59, Issue 4, Page(s) 1436–1437

    MeSH term(s) Humans ; Female ; Breast Neoplasms/diagnostic imaging ; Deep Learning ; Diffusion Magnetic Resonance Imaging/methods ; Breast/diagnostic imaging ; Retrospective Studies ; Magnetic Resonance Imaging
    Language English
    Publishing date 2023-07-27
    Publishing country United States
    Document type Editorial
    ZDB-ID 1146614-5
    ISSN 1522-2586 ; 1053-1807
    ISSN (online) 1522-2586
    ISSN 1053-1807
    DOI 10.1002/jmri.28908
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    Zs.A 3648: Show issues Location:
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    Zs.MO 236: Show issues

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  4. Article ; Online: Contrast-enhanced mammography-guided biopsy: why, when, and where we need it.

    Schiaffino, Simone / Cozzi, Andrea

    European radiology

    2022  

    Abstract: Key points: • The use of CEM-guided biopsy is spreading after initial experiences in a few pilot centers.• CEM-guided biopsy has the potential to ensure fast, low-cost, and effective tissue sampling of MRI-detected and CEM-detected lesions that do not ... ...

    Abstract Key points: • The use of CEM-guided biopsy is spreading after initial experiences in a few pilot centers.• CEM-guided biopsy has the potential to ensure fast, low-cost, and effective tissue sampling of MRI-detected and CEM-detected lesions that do not have a corresponding finding at morphological imaging.• The need and utility of CEM-guided biopsy are warranted by the impending expansion of morpho-functional imaging towards breast cancer screening for women with extremely dense breasts.
    Language English
    Publishing date 2022-10-31
    Publishing country Germany
    Document type Editorial ; Comment
    ZDB-ID 1085366-2
    ISSN 1432-1084 ; 0938-7994 ; 1613-3749
    ISSN (online) 1432-1084
    ISSN 0938-7994 ; 1613-3749
    DOI 10.1007/s00330-022-09196-2
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  5. Article ; Online: Artificial intelligence (AI) in biomedical research: discussion on authors' declaration of AI in their articles title.

    Sardanelli, Francesco / Castiglioni, Isabella / Colarieti, Anna / Schiaffino, Simone / Di Leo, Giovanni

    European radiology experimental

    2023  Volume 7, Issue 1, Page(s) 2

    Abstract: Artificial intelligence (AI) and its different approaches, from machine learning to deep learning, are not new. We discuss here about the declaration of AI in the title of those articles dealing with AI. From 1990 to 2021, while AI articles in the PubMed ...

    Abstract Artificial intelligence (AI) and its different approaches, from machine learning to deep learning, are not new. We discuss here about the declaration of AI in the title of those articles dealing with AI. From 1990 to 2021, while AI articles in the PubMed increased from 300 to 59,596, the percentage declaring AI in the title describes a U-like-shaped curve: about 30% in early 1990s, less than 13% in 2005-2014, again 30% in 2020-2021. A similar trend was observed for AI in medical imaging. While the initial decline could be due to the establishment of AI methods, the recent increase could be related to the capacity of AI to outperform humans, especially in image recognition, fuelled by the adoption of graphic processing units for general purpose computing. The recent increase may also be due to the relevance of open issues about AI, including the standardisation of methods, explainability of results, and concerns about AI-induced epoch-making transformations: to say "We are using AI" in the title may also reflect these concerns.
    MeSH term(s) Humans ; Artificial Intelligence ; Machine Learning ; Biomedical Research
    Language English
    Publishing date 2023-01-16
    Publishing country England
    Document type Journal Article
    ISSN 2509-9280
    ISSN (online) 2509-9280
    DOI 10.1186/s41747-022-00316-7
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  6. Article: Radiation Dose of Contrast-Enhanced Mammography: A Two-Center Prospective Comparison.

    Gennaro, Gisella / Cozzi, Andrea / Schiaffino, Simone / Sardanelli, Francesco / Caumo, Francesca

    Cancers

    2022  Volume 14, Issue 7

    Abstract: The radiation dose associated with contrast-enhanced mammography (CEM) has been investigated only by single-center studies. In this retrospective study, we aimed to compare the radiation dose between two centers performing CEM within two prospective ... ...

    Abstract The radiation dose associated with contrast-enhanced mammography (CEM) has been investigated only by single-center studies. In this retrospective study, we aimed to compare the radiation dose between two centers performing CEM within two prospective studies, using the same type of equipment. The CEM mean glandular dose (MGD) was computed for low energy (LE) and high energy (HE) images and their sum was calculated for each view. MGD and related parameters (entrance dose, breast thickness, compression, and density) were compared between the two centers using the Mann−Whitney test. Finally, per-patient MGD was calculated by pooling the two datasets and determining the contribution of LE and HE images. A total of 348 CEM examinations were analyzed (228 from Center 1 and 120 from Center 2). The median total MGD per view was 2.33 mGy (interquartile range 2.19−2.51 mGy) at Center 1 and 2.46 mGy (interquartile range 2.32−2.70 mGy) at Center 2, with a 0.15 mGy median difference (p < 0.001) equal to 6.2%. LE-images contributed between 64% and 77% to the total patient dose in CEM, with the remaining 23−36% being associated with HE images. The mean radiation dose for a two-view bilateral CEM exam was 4.90 mGy, about 30% higher than for digital mammography.
    Language English
    Publishing date 2022-03-31
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2527080-1
    ISSN 2072-6694
    ISSN 2072-6694
    DOI 10.3390/cancers14071774
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  7. 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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    Zs.A 1630: Show issues Location:
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  8. Article ; Online: The What, the Why, and the How of Liver Ablations: A Practical Guide for the Medical Oncologist.

    Gennaro, Nicolò / Schiaffino, Simone / Mauri, Giovanni / Monfardini, Lorenzo

    Oncology

    2021  Volume 99, Issue 11, Page(s) 722–731

    Abstract: Interventional oncology plays a major role within modern oncological patient management. Image-guided thermal ablation has been recognized as a successful local therapeutic option in patients with primary and secondary malignant liver diseases, as also ... ...

    Abstract Interventional oncology plays a major role within modern oncological patient management. Image-guided thermal ablation has been recognized as a successful local therapeutic option in patients with primary and secondary malignant liver diseases, as also recalled by the recent European Society of Medical Oncology (ESMO) guidelines on colorectal metastases. As image-guided treatments may be as effective as surgery in selected patients with liver lesions, the clinical oncologist should be familiar with the indications, risks, and technical aspects of liver ablation in order to provide their patients with the best outcomes. This article provides a broad overview of the most commonly used ablation techniques and highlights the most relevant technical aspects such as the ideal setting in the operating theatre; which image-guided methods are available, including the growing application of fusion imaging; or contrast-enhanced ultrasound for guiding/monitoring the procedure. A further aim is to expand the knowledge among medical oncologists about liver ablation procedures and to provide insights into the future perspectives of percutaneous minimally invasive procedures in the liver.
    MeSH term(s) Combined Modality Therapy/methods ; Cryosurgery/methods ; Electroporation/methods ; High-Intensity Focused Ultrasound Ablation/methods ; Humans ; Hyperthermia, Induced/methods ; Liver Neoplasms/surgery ; Oncologists ; Radiofrequency Ablation/methods ; Surgery, Computer-Assisted/methods ; Surgical Oncology/methods ; Treatment Outcome
    Language English
    Publishing date 2021-08-27
    Publishing country Switzerland
    Document type Journal Article ; Practice Guideline ; Review
    ZDB-ID 250101-6
    ISSN 1423-0232 ; 0030-2414
    ISSN (online) 1423-0232
    ISSN 0030-2414
    DOI 10.1159/000518358
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  9. Article: MRI-Conditional Breast Tissue Expander: First In-Human Multi-Case Assessment of MRI-Related Complications and Image Quality.

    Schiaffino, Simone / Cozzi, Andrea / Pompei, Barbara / Scarano, Angela Lia / Catanese, Carola / Catic, Armin / Rossi, Lorenzo / Del Grande, Filippo / Harder, Yves

    Journal of clinical medicine

    2023  Volume 12, Issue 13

    Abstract: This study aims to assess potential complications and effects on the magnetic resonance imaging (MRI) image quality of a new MRI-conditional breast tissue expander (Motiva ... ...

    Abstract This study aims to assess potential complications and effects on the magnetic resonance imaging (MRI) image quality of a new MRI-conditional breast tissue expander (Motiva Flora
    Language English
    Publishing date 2023-06-30
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2662592-1
    ISSN 2077-0383
    ISSN 2077-0383
    DOI 10.3390/jcm12134410
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  10. Article ; Online: An update on the management of breast atypical ductal hyperplasia.

    Schiaffino, Simone / Cozzi, Andrea / Sardanelli, Francesco

    The British journal of radiology

    2020  Volume 93, Issue 1110, Page(s) 20200117

    Abstract: Among lesions with uncertain malignant potential found at percutaneous breast biopsy, atypical ductal hyperplasia (ADH) carries both the highest risk of underestimation and the closest and most pathologist-dependent differential diagnosis with ductal ... ...

    Abstract Among lesions with uncertain malignant potential found at percutaneous breast biopsy, atypical ductal hyperplasia (ADH) carries both the highest risk of underestimation and the closest and most pathologist-dependent differential diagnosis with ductal carcinoma
    MeSH term(s) Artificial Intelligence ; Biopsy/methods ; Biopsy, Fine-Needle/methods ; Breast/pathology ; Breast/surgery ; Breast Neoplasms/pathology ; Breast Neoplasms/surgery ; Calcinosis/pathology ; Carcinoma, Intraductal, Noninfiltrating/pathology ; Carcinoma, Intraductal, Noninfiltrating/surgery ; Consensus Development Conferences as Topic ; Female ; Humans ; Randomized Controlled Trials as Topic ; Watchful Waiting
    Language English
    Publishing date 2020-04-01
    Publishing country England
    Document type Journal Article
    ZDB-ID 2982-8
    ISSN 1748-880X ; 0007-1285
    ISSN (online) 1748-880X
    ISSN 0007-1285
    DOI 10.1259/bjr.20200117
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