1
|
Kondylakis H, Catalan R, Alabart SM, Barelle C, Bizopoulos P, Bobowicz M, Bona J, Fotiadis DI, Garcia T, Gomez I, Jimenez-Pastor A, Karatzanis G, Lekadir K, Kogut-Czarkowska M, Lalas A, Marias K, Marti-Bonmati L, Munuera J, Nikiforaki K, Pelissier M, Prior F, Rutherford M, Saint-Aubert L, Sakellariou Z, Seymour K, Trouillard T, Votis K, Tsiknakis M. Documenting the de-identification process of clinical and imaging data for AI for health imaging projects. Insights Imaging 2024; 15:130. [PMID: 38816658 PMCID: PMC11139818 DOI: 10.1186/s13244-024-01711-x] [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: 02/16/2024] [Accepted: 04/26/2024] [Indexed: 06/01/2024] Open
Abstract
Artificial intelligence (AI) is revolutionizing the field of medical imaging, holding the potential to shift medicine from a reactive "sick-care" approach to a proactive focus on healthcare and prevention. The successful development of AI in this domain relies on access to large, comprehensive, and standardized real-world datasets that accurately represent diverse populations and diseases. However, images and data are sensitive, and as such, before using them in any way the data needs to be modified to protect the privacy of the patients. This paper explores the approaches in the domain of five EU projects working on the creation of ethically compliant and GDPR-regulated European medical imaging platforms, focused on cancer-related data. It presents the individual approaches to the de-identification of imaging data, and describes the problems and the solutions adopted in each case. Further, lessons learned are provided, enabling future projects to optimally handle the problem of data de-identification. CRITICAL RELEVANCE STATEMENT: This paper presents key approaches from five flagship EU projects for the de-identification of imaging and clinical data offering valuable insights and guidelines in the domain. KEY POINTS: ΑΙ models for health imaging require access to large amounts of data. Access to large imaging datasets requires an appropriate de-identification process. This paper provides de-identification guidelines from the AI for health imaging (AI4HI) projects.
Collapse
Affiliation(s)
| | - Rocio Catalan
- La Fe University and Polytechnic Hospital, La Fe Health Research Institute, Valencia, Spain
| | | | | | - Paschalis Bizopoulos
- Centre for Research & Technology Hellas, Information Technologies Institute (CERTH-ITI), Central Directorate, Thermi, Thessaloniki, Greece
| | | | - Jonathan Bona
- University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Dimitrios I Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
| | - Teresa Garcia
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Ignacio Gomez
- La Fe University and Polytechnic Hospital, La Fe Health Research Institute, Valencia, Spain
| | | | | | - Karim Lekadir
- Artificial Intelligence in Medicine Labm Universitat de Barcelona, Barcelona, Spain
| | | | - Antonios Lalas
- Centre for Research & Technology Hellas, Information Technologies Institute (CERTH-ITI), Central Directorate, Thermi, Thessaloniki, Greece
| | | | - Luis Marti-Bonmati
- Hospital Universitario y Politécnico La Fe, Grupo de Investigación Biomédica en Imagen IIS La Fe, Valencia, España
| | - Jose Munuera
- Quantitative Imaging Biomarkers in Medicine, Quibim, Valencia, Spain
| | | | | | - Fred Prior
- University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | | | | | - Zisis Sakellariou
- Centre for Research & Technology Hellas, Information Technologies Institute (CERTH-ITI), Central Directorate, Thermi, Thessaloniki, Greece
| | | | | | - Konstantinos Votis
- Centre for Research & Technology Hellas, Information Technologies Institute (CERTH-ITI), Central Directorate, Thermi, Thessaloniki, Greece
| | | |
Collapse
|
2
|
Barat M, Pellat A, Hoeffel C, Dohan A, Coriat R, Fishman EK, Nougaret S, Chu L, Soyer P. CT and MRI of abdominal cancers: current trends and perspectives in the era of radiomics and artificial intelligence. Jpn J Radiol 2024; 42:246-260. [PMID: 37926780 DOI: 10.1007/s11604-023-01504-0] [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: 09/13/2023] [Accepted: 10/12/2023] [Indexed: 11/07/2023]
Abstract
Abdominal cancers continue to pose daily challenges to clinicians, radiologists and researchers. These challenges are faced at each stage of abdominal cancer management, including early detection, accurate characterization, precise assessment of tumor spread, preoperative planning when surgery is anticipated, prediction of tumor aggressiveness, response to therapy, and detection of recurrence. Technical advances in medical imaging, often in combination with imaging biomarkers, show great promise in addressing such challenges. Information extracted from imaging datasets owing to the application of radiomics can be used to further improve the diagnostic capabilities of imaging. However, the analysis of the huge amount of data provided by these advances is a difficult task in daily practice. Artificial intelligence has the potential to help radiologists in all these challenges. Notably, the applications of AI in the field of abdominal cancers are expanding and now include diverse approaches for cancer detection, diagnosis and classification, genomics and detection of genetic alterations, analysis of tumor microenvironment, identification of predictive biomarkers and follow-up. However, AI currently has some limitations that need further refinement for implementation in the clinical setting. This review article sums up recent advances in imaging of abdominal cancers in the field of image/data acquisition, tumor detection, tumor characterization, prognosis, and treatment response evaluation.
Collapse
Affiliation(s)
- Maxime Barat
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France
| | - Anna Pellat
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France
| | - Christine Hoeffel
- Department of Radiology, Hopital Robert Debré, CHU Reims, Université Champagne-Ardennes, 51092, Reims, France
| | - Anthony Dohan
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France
| | - Romain Coriat
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Stéphanie Nougaret
- Department of Radiology, Montpellier Cancer Institute, 34000, Montpellier, France
- PINKCC Lab, IRCM, U1194, 34000, Montpellier, France
| | - Linda Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Philippe Soyer
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France.
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France.
| |
Collapse
|
3
|
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.
Collapse
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
| |
Collapse
|
4
|
Bobowicz M, Rygusik M, Buler J, Buler R, Ferlin M, Kwasigroch A, Szurowska E, Grochowski M. Attention-Based Deep Learning System for Classification of Breast Lesions-Multimodal, Weakly Supervised Approach. Cancers (Basel) 2023; 15:2704. [PMID: 37345041 DOI: 10.3390/cancers15102704] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 05/02/2023] [Accepted: 05/05/2023] [Indexed: 06/23/2023] Open
Abstract
Breast cancer is the most frequent female cancer, with a considerable disease burden and high mortality. Early diagnosis with screening mammography might be facilitated by automated systems supported by deep learning artificial intelligence. We propose a model based on a weakly supervised Clustering-constrained Attention Multiple Instance Learning (CLAM) classifier able to train under data scarcity effectively. We used a private dataset with 1174 non-cancer and 794 cancer images labelled at the image level with pathological ground truth confirmation. We used feature extractors (ResNet-18, ResNet-34, ResNet-50 and EfficientNet-B0) pre-trained on ImageNet. The best results were achieved with multimodal-view classification using both CC and MLO images simultaneously, resized by half, with a patch size of 224 px and an overlap of 0.25. It resulted in AUC-ROC = 0.896 ± 0.017, F1-score 81.8 ± 3.2, accuracy 81.6 ± 3.2, precision 82.4 ± 3.3, and recall 81.6 ± 3.2. Evaluation with the Chinese Mammography Database, with 5-fold cross-validation, patient-wise breakdowns, and transfer learning, resulted in AUC-ROC 0.848 ± 0.015, F1-score 78.6 ± 2.0, accuracy 78.4 ± 1.9, precision 78.8 ± 2.0, and recall 78.4 ± 1.9. The CLAM algorithm's attentional maps indicate the features most relevant to the algorithm in the images. Our approach was more effective than in many other studies, allowing for some explainability and identifying erroneous predictions based on the wrong premises.
Collapse
Affiliation(s)
- Maciej Bobowicz
- 2nd Department of Radiology, Medical University of Gdansk, 80-214 Gdansk, Poland
| | - Marlena Rygusik
- 2nd Department of Radiology, Medical University of Gdansk, 80-214 Gdansk, Poland
| | - Jakub Buler
- Department of Intelligent Control Systems and Decision Support, Faculty of Electrical and Control Engineering, Gdansk University of Technology, 80-233 Gdansk, Poland
| | - Rafał Buler
- Department of Intelligent Control Systems and Decision Support, Faculty of Electrical and Control Engineering, Gdansk University of Technology, 80-233 Gdansk, Poland
| | - Maria Ferlin
- Department of Intelligent Control Systems and Decision Support, Faculty of Electrical and Control Engineering, Gdansk University of Technology, 80-233 Gdansk, Poland
| | - Arkadiusz Kwasigroch
- Department of Intelligent Control Systems and Decision Support, Faculty of Electrical and Control Engineering, Gdansk University of Technology, 80-233 Gdansk, Poland
| | - Edyta Szurowska
- 2nd Department of Radiology, Medical University of Gdansk, 80-214 Gdansk, Poland
| | - Michał Grochowski
- Department of Intelligent Control Systems and Decision Support, Faculty of Electrical and Control Engineering, Gdansk University of Technology, 80-233 Gdansk, Poland
| |
Collapse
|
5
|
Mumuni AN, Hasford F, Udeme NI, Dada MO, Awojoyogbe BO. A SWOT analysis of artificial intelligence in diagnostic imaging in the developing world: making a case for a paradigm shift. PHYSICAL SCIENCES REVIEWS 2022. [DOI: 10.1515/psr-2022-0121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Abstract
Diagnostic imaging (DI) refers to techniques and methods of creating images of the body’s internal parts and organs with or without the use of ionizing radiation, for purposes of diagnosing, monitoring and characterizing diseases. By default, DI equipment are technology based and in recent times, there has been widespread automation of DI operations in high-income countries while low and middle-income countries (LMICs) are yet to gain traction in automated DI. Advanced DI techniques employ artificial intelligence (AI) protocols to enable imaging equipment perceive data more accurately than humans do, and yet automatically or under expert evaluation, make clinical decisions such as diagnosis and characterization of diseases. In this narrative review, SWOT analysis is used to examine the strengths, weaknesses, opportunities and threats associated with the deployment of AI-based DI protocols in LMICs. Drawing from this analysis, a case is then made to justify the need for widespread AI applications in DI in resource-poor settings. Among other strengths discussed, AI-based DI systems could enhance accuracies in diagnosis, monitoring, characterization of diseases and offer efficient image acquisition, processing, segmentation and analysis procedures, but may have weaknesses regarding the need for big data, huge initial and maintenance costs, and inadequate technical expertise of professionals. They present opportunities for synthetic modality transfer, increased access to imaging services, and protocol optimization; and threats of input training data biases, lack of regulatory frameworks and perceived fear of job losses among DI professionals. The analysis showed that successful integration of AI in DI procedures could position LMICs towards achievement of universal health coverage by 2030/2035. LMICs will however have to learn from the experiences of advanced settings, train critical staff in relevant areas of AI and proceed to develop in-house AI systems with all relevant stakeholders onboard.
Collapse
Affiliation(s)
| | - Francis Hasford
- Department of Medical Physics , University of Ghana, Ghana Atomic Energy Commission , Accra , Ghana
| | | | | | | |
Collapse
|
6
|
Zhong J, Hu Y, Zhang G, Xing Y, Ding D, Ge X, Pan Z, Yang Q, Yin Q, Zhang H, Zhang H, Yao W. An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics. Insights Imaging 2022; 13:138. [PMID: 35986808 PMCID: PMC9392674 DOI: 10.1186/s13244-022-01277-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 07/24/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Objective
To update the systematic review of radiomics in osteosarcoma.
Methods
PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and Wanfang Data were searched to identify articles on osteosarcoma radiomics until May 15, 2022. The studies were assessed by Radiomics Quality Score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, Checklist for Artificial Intelligence in Medical Imaging (CLAIM), and modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. The evidence supporting radiomics application for osteosarcoma was rated according to meta-analysis results.
Results
Twenty-nine articles were included. The average of the ideal percentage of RQS, the TRIPOD adherence rate and the CLAIM adherence rate were 29.2%, 59.2%, and 63.7%, respectively. RQS identified a radiomics-specific issue of phantom study. TRIPOD addressed deficiency in blindness of assessment. CLAIM and TRIPOD both pointed out shortness in missing data handling and sample size or power calculation. CLAIM identified extra disadvantages in data de-identification and failure analysis. External validation and open science were emphasized by all the above three tools. The risk of bias and applicability concerns were mainly related to the index test. The meta-analysis of radiomics predicting neoadjuvant chemotherapy response by MRI presented a diagnostic odds ratio (95% confidence interval) of 28.83 (10.27–80.95) on testing datasets and was rated as weak evidence.
Conclusions
The quality of osteosarcoma radiomics studies is insufficient. More investigation is needed before using radiomics to optimize osteosarcoma treatment. CLAIM is recommended to guide the design and reporting of radiomics research.
Collapse
|