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Colarieti A, Sardanelli F. The radiologist as an independent "third party" to the patient and clinicians in the era of generative AI. LA RADIOLOGIA MEDICA 2025; 130:281-283. [PMID: 40009151 DOI: 10.1007/s11547-025-01967-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2024] [Accepted: 02/05/2025] [Indexed: 02/27/2025]
Abstract
Radiologists are crucial in the diagnostic workflow. They must maintain an independent perspective, being a "third party" to the patients and referral clinicians. This is important when documenting the absence of relevant abnormalities or providing information that contradicts the self-reported symptoms and/or the impression of the colleagues, as well as in the case of incidental findings that impact patient management. Appropriate communication is the outcome of this professional independence, becoming more and more important in the current era of application of generative artificial intelligence to the radiological world. In fact, while radiological reports could be improved by our smart use of large language models, patients are already using such tools to understand their meaning and practical implications.
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Affiliation(s)
- Anna Colarieti
- Dipartimento Di Medicina Traslazionale, Università del Piemonte Orientale, UPO, Via Solaroli 17, 28100, Novara, Italy.
| | - Francesco Sardanelli
- Lega Italiana Per La Lotta Contro I Tumori (LILT) Milano Monza Brianza, Piazzale Gorini 22, 20133, Milan, Italy
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2
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Cè M, Cellina M, Ueanukul T, Carrafiello G, Manatrakul R, Tangkittithaworn P, Jaovisidha S, Fuangfa P, Resnick D. Multimodal Imaging of Osteosarcoma: From First Diagnosis to Radiomics. Cancers (Basel) 2025; 17:599. [PMID: 40002194 PMCID: PMC11852380 DOI: 10.3390/cancers17040599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2024] [Revised: 02/06/2025] [Accepted: 02/07/2025] [Indexed: 02/27/2025] Open
Abstract
Osteosarcoma is a primary malignant bone tumor characterized by the production of an osteoid matrix. Although histology remains the definitive diagnostic standard, imaging plays a crucial role in diagnosis, therapeutic planning, and follow-up. Conventional radiography serves as the initial checkpoint for detecting this pathology, which often presents diagnostic challenges due to vague and nonspecific symptoms, especially in its early stages. Today, the integration of different imaging techniques enables an increasingly personalized diagnosis and management, with each contributing unique and complementary information. Conventional radiography typically initiates the imaging assessment, and the Bone Reporting and Data System (Bone-RADS) of the Society of Skeletal Radiology (SSR) is a valuable tool for stratifying the risk of suspicious bone lesions. CT is the preferred modality for evaluating the bone matrix, while bone scans and PET/CT are effective for detecting distant metastases. MRI reveals the extent of the lesion in adjacent soft tissues, the medullary canal, and joints, as well as its relationship to neurovascular structures and the presence of skip lesions. Advanced techniques such as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), diffusion-weighted imaging (DWI), and perfusion MRI help characterize the tumor environment and assess treatment response. Osteosarcoma comprises a range of subtypes with differing clinical and imaging characteristics, some of which are particularly distinctive, such as in the case of telangiectatic osteosarcoma. Knowledge of these variants can guide radiologists in the differential diagnosis, which includes both central and surface forms, ranging from highly aggressive to more indolent types. In this review, we present a wide range of representative cases from our hospital case series to illustrate both typical and atypical imaging presentations. Finally, we discuss recent advancements and challenges in applying artificial intelligence approaches to the imaging of osteosarcoma.
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Affiliation(s)
- Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy; (M.C.); (G.C.)
| | - Michaela Cellina
- Radiology Department, ASST Fatebenefratelli Sacco, Piazza Principessa Clotilde 3, 20121 Milan, Italy;
| | - Thirapapha Ueanukul
- Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand; (T.U.); (R.M.); (P.T.); (S.J.)
| | - Gianpaolo Carrafiello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy; (M.C.); (G.C.)
- Radiology Department, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy
| | - Rawee Manatrakul
- Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand; (T.U.); (R.M.); (P.T.); (S.J.)
| | - Phatthawit Tangkittithaworn
- Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand; (T.U.); (R.M.); (P.T.); (S.J.)
| | - Suphaneewan Jaovisidha
- Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand; (T.U.); (R.M.); (P.T.); (S.J.)
| | - Praman Fuangfa
- Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand; (T.U.); (R.M.); (P.T.); (S.J.)
| | - Donald Resnick
- Department of Radiology, University of California, San Diego, CA 92093, USA
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Cè M, Chiriac MD, Cozzi A, Macrì L, Rabaiotti FL, Irmici G, Fazzini D, Carrafiello G, Cellina M. Decoding Radiomics: A Step-by-Step Guide to Machine Learning Workflow in Hand-Crafted and Deep Learning Radiomics Studies. Diagnostics (Basel) 2024; 14:2473. [PMID: 39594139 PMCID: PMC11593328 DOI: 10.3390/diagnostics14222473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2024] [Revised: 10/25/2024] [Accepted: 10/28/2024] [Indexed: 11/28/2024] Open
Abstract
Although radiomics research has experienced rapid growth in recent years, with numerous studies dedicated to the automated extraction of diagnostic and prognostic information from various imaging modalities, such as CT, PET, and MRI, only a small fraction of these findings has successfully transitioned into clinical practice. This gap is primarily due to the significant methodological challenges involved in radiomics research, which emphasize the need for a rigorous evaluation of study quality. While many technical aspects may lie outside the expertise of most radiologists, having a foundational knowledge is essential for evaluating the quality of radiomics workflows and contributing, together with data scientists, to the development of models with a real-world clinical impact. This review is designed for the new generation of radiologists, who may not have specialized training in machine learning or radiomics, but will inevitably play a role in this evolving field. The paper has two primary objectives: first, to provide a clear, systematic guide to radiomics study pipeline, including study design, image preprocessing, feature selection, model training and validation, and performance evaluation. Furthermore, given the critical importance of evaluating the robustness of radiomics studies, this review offers a step-by-step guide to the application of the METhodological RadiomICs Score (METRICS, 2024)-a newly proposed tool for assessing the quality of radiomics studies. This roadmap aims to support researchers and reviewers alike, regardless of their machine learning expertise, in utilizing this tool for effective study evaluation.
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Affiliation(s)
- Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy
| | | | - Andrea Cozzi
- Imaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900 Lugano, Switzerland;
| | - Laura Macrì
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy
| | - Francesca Lucrezia Rabaiotti
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy
| | - Giovanni Irmici
- Breast Imaging Department, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Giacomo Venezian 1, 20133 Milan, Italy
| | - Deborah Fazzini
- Radiology Department, Centro Diagnostico Italiano, Via Saint Bon 20, 20147 Milan, Italy
| | - Gianpaolo Carrafiello
- Radiology Department, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy
- Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy
| | - Michaela Cellina
- Radiology Department, ASST Fatebenefratelli Sacco, Piazza Principessa Clotilde 3, 20121 Milan, Italy
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Zanardo M, Visser JJ, Colarieti A, Cuocolo R, Klontzas ME, Pinto Dos Santos D, Sardanelli F. Impact of AI on radiology: a EuroAIM/EuSoMII 2024 survey among members of the European Society of Radiology. Insights Imaging 2024; 15:240. [PMID: 39373853 PMCID: PMC11458846 DOI: 10.1186/s13244-024-01801-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2024] [Accepted: 08/09/2024] [Indexed: 10/08/2024] Open
Abstract
In order to assess the perceptions and expectations of the radiology staff about artificial intelligence (AI), we conducted an online survey among ESR members (January-March 2024). It was designed considering that conducted in 2018, updated according to recent advancements and emerging topics, consisting of seven questions regarding demographics and professional background and 28 AI questions. Of 28,000 members contacted, 572 (2%) completed the survey. AI impact was predominantly expected on breast and oncologic imaging, primarily involving CT, mammography, and MRI, and in the detection of abnormalities in asymptomatic subjects. About half of responders did not foresee an impact of AI on job opportunities. For 273/572 respondents (48%), AI-only reports would not be accepted by patients; and 242/572 respondents (42%) think that the use of AI systems will not change the relationship between the radiological team and the patient. According to 255/572 respondents (45%), radiologists will take responsibility for any AI output that may influence clinical decision-making. Of 572 respondents, 274 (48%) are currently using AI, 153 (27%) are not, and 145 (25%) are planning to do so. In conclusion, ESR members declare familiarity with AI technologies, as well as recognition of their potential benefits and challenges. Compared to the 2018 survey, the perception of AI's impact on job opportunities is in general slightly less optimistic (more positive from AI users/researchers), while the radiologist's responsibility for AI outputs is confirmed. The use of large language models is declared not only limited to research, highlighting the need for education in AI and its regulations. CRITICAL RELEVANCE STATEMENT: This study critically evaluates the current impact of AI on radiology, revealing significant usage patterns and clinical implications, thereby guiding future integration strategies to enhance efficiency and patient care in clinical radiology. KEY POINTS: The survey examines ESR member's views about the impact of AI on radiology practice. AI use is relevant in CT and MRI, with varying impacts on job roles. AI tools enhance clinical efficiency but require radiologist oversight for patient acceptance.
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Affiliation(s)
- Moreno Zanardo
- Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy
| | - Jacob J Visser
- Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Anna Colarieti
- Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery, and Dentistry, University of Salerno, Baronissi, Italy
| | - Michail E Klontzas
- Department of Radiology, School of Medicine, University of Crete, Heraklion, Greece
- Division of Radiology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden
- Computational Biomedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (ICS-FORTH), Crete, Greece
| | - Daniel Pinto Dos Santos
- Department of Radiology, University Hospital of Frankfurt, Frankfurt, Germany
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
| | - Francesco Sardanelli
- Lega Italiana per la Lotta contro i Tumori (LILT) Milano Monza Brianza, Milan, Italy.
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Cè M, Ibba S, Cellina M, Tancredi C, Fantesini A, Fazzini D, Fortunati A, Perazzo C, Presta R, Montanari R, Forzenigo L, Carrafiello G, Papa S, Alì M. Radiologists' perceptions on AI integration: An in-depth survey study. Eur J Radiol 2024; 177:111590. [PMID: 38959557 DOI: 10.1016/j.ejrad.2024.111590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 06/18/2024] [Accepted: 06/24/2024] [Indexed: 07/05/2024]
Abstract
PURPOSE To assess the perceptions and attitudes of radiologists toward the adoption of artificial intelligence (AI) in clinical practice. METHODS A survey was conducted among members of the SIRM Lombardy. Radiologists' attitudes were assessed comprehensively, covering satisfaction with AI-based tools, propensity for innovation, and optimism for the future. The questionnaire consisted of two sections: the first gathered demographic and professional information using categorical responses, while the second evaluated radiologists' attitudes toward AI through Likert-type responses ranging from 1 to 5 (with 1 representing extremely negative attitudes, 3 indicating a neutral stance, and 5 reflecting extremely positive attitudes). Questionnaire refinement involved an iterative process with expert panels and a pilot phase to enhance consistency and eliminate redundancy. Exploratory data analysis employed descriptive statistics and visual assessment of Likert plots, supported by non-parametric tests for subgroup comparisons for a thorough analysis of specific emerging patterns. RESULTS The survey yielded 232 valid responses. The findings reveal a generally optimistic outlook on AI adoption, especially among young radiologist (<30) and seasoned professionals (>60, p<0.01). However, while 36.2 % (84 out 232) of subjects reported daily use of AI-based tools, only a third considered their contribution decisive (30 %, 25 out of 84). AI literacy varied, with a notable proportion feeling inadequately informed (36 %, 84 out of 232), particularly among younger radiologists (46 %, p < 0.01). Positive attitudes towards the potential of AI to improve detection, characterization of anomalies and reduce workload (positive answers > 80 %) and were consistent across subgroups. Radiologists' opinions were more skeptical about the role of AI in enhancing decision-making processes, including the choice of further investigation, and in personalized medicine in general. Overall, respondents recognized AI's significant impact on the radiology profession, viewing it as an opportunity (61 %, 141 out of 232) rather than a threat (18 %, 42 out of 232), with a majority expressing belief in AI's relevance to future radiologists' career choices (60 %, 139 out of 232). However, there were some concerns, particularly among breast radiologists (20 of 232 responders), regarding the potential impact of AI on the profession. Eighty-four percent of the respondents consider the final assessment by the radiologist still to be essential. CONCLUSION Our results indicate an overall positive attitude towards the adoption of AI in radiology, though this is moderated by concerns regarding training and practical efficacy. Addressing AI literacy gaps, especially among younger radiologists, is essential. Furthermore, proactively adapting to technological advancements is crucial to fully leverage AI's potential benefits. Despite the generally positive outlook among radiologists, there remains significant work to be done to enhance the integration and widespread use of AI tools in clinical practice.
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Affiliation(s)
- Maurizio Cè
- Postgraduation School of Radiodiagnostic, University of Milan, via Festa del Perdono 7, 20122 Milan, Italy
| | - Simona Ibba
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy.
| | - Michaela Cellina
- Radiology Department, ASST Fatebenefratelli Sacco, Piazza Principessa Clotilde 3, 20121 Milan, Italy.
| | - Chiara Tancredi
- University Suor Orsola Benincasa, corso Vittorio Emanuele 292, 80135 Naples, Italy.
| | | | - Deborah Fazzini
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy.
| | - Alice Fortunati
- Postgraduation School of Radiodiagnostic, University of Milan, via Festa del Perdono 7, 20122 Milan, Italy.
| | - Chiara Perazzo
- Postgraduation School of Radiodiagnostic, University of Milan, via Festa del Perdono 7, 20122 Milan, Italy.
| | - Roberta Presta
- University Suor Orsola Benincasa, corso Vittorio Emanuele 292, 80135 Naples, Italy.
| | - Roberto Montanari
- University Suor Orsola Benincasa, corso Vittorio Emanuele 292, 80135 Naples, Italy; RE:LAB s.r.l., Via Tamburini, 5, 42122 Reggio Emilia, Italy.
| | - Laura Forzenigo
- Radiology Department, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Via Francesco Sforza, 35, 20122, Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School of Radiodiagnostic, University of Milan, via Festa del Perdono 7, 20122 Milan, Italy; Radiology Department, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Via Francesco Sforza, 35, 20122, Milan, Italy; Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133 Milan, Italy
| | - Sergio Papa
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy.
| | - Marco Alì
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy; Bracco Imaging SpA, Via Caduti di Marcinelle, 20134 Milan, Italy.
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Smits M, Rockall A, Constantinescu SN, Sardanelli F, Martí-Bonmatí L. Translating radiological research into practice-from discovery to clinical impact. Insights Imaging 2024; 15:13. [PMID: 38228934 DOI: 10.1186/s13244-023-01596-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 11/17/2023] [Indexed: 01/18/2024] Open
Abstract
At the European Society of Radiology (ESR), we strive to provide evidence for radiological practices that improve patient outcomes and have a societal impact. Successful translation of radiological research into clinical practice requires multiple factors including tailored methodology, a multidisciplinary approach aiming beyond technical validation, and a focus on unmet clinical needs. Low levels of evidence are a threat to radiology, resulting in low visibility and credibility. Here, we provide the background and rationale for the thematic series Translating radiological research into practice-from discovery to clinical impact, inviting authors to describe their processes of achieving clinically impactful radiological research. We describe the challenges unique to radiological research. Additionally, a survey was sent to non-radiological clinical societies. The majority of respondents (6/11) were in the field of gastrointestinal/abdominal medicine. The implementation of CT/MRI techniques for disease characterisation, detection and staging of cancer, and treatment planning and radiological interventions were mentioned as the most important radiological developments in the past years. The perception was that patients are substantially unaware of the impact of these developments. Unmet clinical needs were mostly early diagnosis and staging of cancer, microstructural/functional assessment of tissues and organs, and implant assessment. All but one respondent considered radiology important for research in their discipline, but five indicated that radiology is currently not involved in their research. Radiology research holds the potential for being transformative to medical practice. It is our responsibility to take the lead in studies including radiology and strive towards the highest levels of evidence.Critical relevance statement For radiological research to make a clinical and societal impact, radiologists should take the lead in radiological studies, go beyond the assessment of technical feasibility and diagnostic accuracy, and-in a multidisciplinary approach-address clinical unmet needs.Key points• Multiple factors are essential for radiological research to make a clinical and societal impact.• Radiological research needs to go beyond diagnostic accuracy and address unmet clinical needs.• Radiologists should take the lead in radiological studies with a multidisciplinary approach.
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Affiliation(s)
- Marion Smits
- Department of Radiology & Nuclear Medicine, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands.
- Medical Delta, Delft, The Netherlands.
| | - Andrea Rockall
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
- Department of Radiology, Imperial College Healthcare NHS Trust, London, UK
| | - Stefan N Constantinescu
- Ludwig Institute for Cancer Research, Brussels, Belgium
- de Duve Institute, Université Catholique de Louvain, Brussels, Belgium
- WEL Research Institute, WELBIO Department, Wavre, Belgium
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, Oxford University, Oxford, UK
| | - Francesco Sardanelli
- Department of Biomedical Sciences for Health, Università Degli Studi Di Milano, Milan, Italy
- Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
| | - Luis Martí-Bonmatí
- Department of Radiology and GIBI230 Research Group On Biomedical Imaging, Hospital Universitario y Politécnico La Fe and Instituto de Investigación Sanitaria La Fe, Valencia, Spain
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Cellina M, Cacioppa LM, Cè M, Chiarpenello V, Costa M, Vincenzo Z, Pais D, Bausano MV, Rossini N, Bruno A, Floridi C. Artificial Intelligence in Lung Cancer Screening: The Future Is Now. Cancers (Basel) 2023; 15:4344. [PMID: 37686619 PMCID: PMC10486721 DOI: 10.3390/cancers15174344] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/27/2023] [Accepted: 08/28/2023] [Indexed: 09/10/2023] Open
Abstract
Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients' survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients' outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in this context, artificial intelligence (AI) plays a key role. AI may have a role in each process of the lung cancer screening workflow. First, in the acquisition of low-dose computed tomography for screening programs, AI-based reconstruction allows a further dose reduction, while still maintaining an optimal image quality. AI can help the personalization of screening programs through risk stratification based on the collection and analysis of a huge amount of imaging and clinical data. A computer-aided detection (CAD) system provides automatic detection of potential lung nodules with high sensitivity, working as a concurrent or second reader and reducing the time needed for image interpretation. Once a nodule has been detected, it should be characterized as benign or malignant. Two AI-based approaches are available to perform this task: the first one is represented by automatic segmentation with a consequent assessment of the lesion size, volume, and densitometric features; the second consists of segmentation first, followed by radiomic features extraction to characterize the whole abnormalities providing the so-called "virtual biopsy". This narrative review aims to provide an overview of all possible AI applications in lung cancer screening.
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Affiliation(s)
- Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, 20121 Milano, Italy;
| | - Laura Maria Cacioppa
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy; (L.M.C.); (N.R.); (A.B.)
- Division of Interventional Radiology, Department of Radiological Sciences, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (M.C.); (V.C.); (M.C.); (Z.V.); (D.P.); (M.V.B.)
| | - Vittoria Chiarpenello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (M.C.); (V.C.); (M.C.); (Z.V.); (D.P.); (M.V.B.)
| | - Marco Costa
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (M.C.); (V.C.); (M.C.); (Z.V.); (D.P.); (M.V.B.)
| | - Zakaria Vincenzo
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (M.C.); (V.C.); (M.C.); (Z.V.); (D.P.); (M.V.B.)
| | - Daniele Pais
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (M.C.); (V.C.); (M.C.); (Z.V.); (D.P.); (M.V.B.)
| | - Maria Vittoria Bausano
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (M.C.); (V.C.); (M.C.); (Z.V.); (D.P.); (M.V.B.)
| | - Nicolò Rossini
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy; (L.M.C.); (N.R.); (A.B.)
| | - Alessandra Bruno
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy; (L.M.C.); (N.R.); (A.B.)
| | - Chiara Floridi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy; (L.M.C.); (N.R.); (A.B.)
- Division of Interventional Radiology, Department of Radiological Sciences, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
- Division of Radiology, Department of Radiological Sciences, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
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Sardanelli F, Colarieti A. Open peer review: pros and cons. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01660-3. [PMID: 37306935 DOI: 10.1007/s11547-023-01660-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 05/26/2023] [Indexed: 06/13/2023]
Affiliation(s)
- Francesco Sardanelli
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy
- Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
| | - Anna Colarieti
- Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy.
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Teixeira da Silva JA. For a paradigm shift in peer review, bold steps need to be taken. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01656-z. [PMID: 37285066 DOI: 10.1007/s11547-023-01656-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 05/26/2023] [Indexed: 06/08/2023]
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10
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Granata V, Fusco R, De Muzio F, Cutolo C, Grassi F, Brunese MC, Simonetti I, Catalano O, Gabelloni M, Pradella S, Danti G, Flammia F, Borgheresi A, Agostini A, Bruno F, Palumbo P, Ottaiano A, Izzo F, Giovagnoni A, Barile A, Gandolfo N, Miele V. Risk Assessment and Cholangiocarcinoma: Diagnostic Management and Artificial Intelligence. BIOLOGY 2023; 12:213. [PMID: 36829492 PMCID: PMC9952965 DOI: 10.3390/biology12020213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/21/2023] [Accepted: 01/25/2023] [Indexed: 01/31/2023]
Abstract
Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver tumor, with a median survival of only 13 months. Surgical resection remains the only curative therapy; however, at first detection, only one-third of patients are at an early enough stage for this approach to be effective, thus rendering early diagnosis as an efficient approach to improving survival. Therefore, the identification of higher-risk patients, whose risk is correlated with genetic and pre-cancerous conditions, and the employment of non-invasive-screening modalities would be appropriate. For several at-risk patients, such as those suffering from primary sclerosing cholangitis or fibropolycystic liver disease, the use of periodic (6-12 months) imaging of the liver by ultrasound (US), magnetic Resonance Imaging (MRI)/cholangiopancreatography (MRCP), or computed tomography (CT) in association with serum CA19-9 measurement has been proposed. For liver cirrhosis patients, it has been proposed that at-risk iCCA patients are monitored in a similar fashion to at-risk HCC patients. The possibility of using Artificial Intelligence models to evaluate higher-risk patients could favor the diagnosis of these entities, although more data are needed to support the practical utility of these applications in the field of screening. For these reasons, it would be appropriate to develop screening programs in the research protocols setting. In fact, the success of these programs reauires patient compliance and multidisciplinary cooperation.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
| | - Federica De Muzio
- Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, University of Molise, 86100 Campobasso, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Salerno, Italy
| | - Francesca Grassi
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80138 Naples, Italy
| | - Maria Chiara Brunese
- Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, University of Molise, 86100 Campobasso, Italy
| | - Igino Simonetti
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Orlando Catalano
- Radiology Unit, Istituto Diagnostico Varelli, Via Cornelia dei Gracchi 65, 80126 Naples, Italy
| | - Michela Gabelloni
- Nuclear Medicine Unit, Department of Translational Research, University of Pisa, 56216 Pisa, Italy
| | - Silvia Pradella
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Ginevra Danti
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Federica Flammia
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Conca 71, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Andrea Agostini
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Conca 71, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Federico Bruno
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
| | - Pierpaolo Palumbo
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
| | - Alessandro Ottaiano
- SSD Innovative Therapies for Abdominal Metastases, Istituto Nazionale Tumori IRCCS-Fondazione G. Pascale, 80130 Naples, Italy
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Conca 71, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Antonio Barile
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, Corso Scassi 1, 16149 Genoa, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
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