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Dietzel M, Clauser P, Kapetas P, Schulz-Wendtland R, Baltzer PAT. Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm. ROFO-FORTSCHR RONTG 2021; 193:898-908. [PMID: 33535260 DOI: 10.1055/a-1346-0095] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
BACKGROUND Considering radiological examinations not as mere images, but as a source of data, has become the key paradigm in the diagnostic imaging field. This change of perspective is particularly popular in breast imaging. It allows breast radiologists to apply algorithms derived from computer science, to realize innovative clinical applications, and to refine already established methods. In this context, the terminology "imaging biomarker", "radiomics", and "artificial intelligence" are of pivotal importance. These methods promise noninvasive, low-cost (e. g., in comparison to multigene arrays), and workflow-friendly (automated, only one examination, instantaneous results, etc.) delivery of clinically relevant information. METHODS AND RESULTS This paper is designed as a narrative review on the previously mentioned paradigm. The focus is on key concepts in breast imaging and important buzzwords are explained. For all areas of breast imaging, exemplary studies and potential clinical use cases are discussed. CONCLUSION Considering radiological examination as a source of data may optimize patient management by guiding individualized breast cancer diagnosis and oncologic treatment in the age of precision medicine. KEY POINTS · In conventional breast imaging, examinations are interpreted based on patterns perceivable by visual inspection.. · The radiomics paradigm treats breast images as a source of data, containing information beyond what is visible to our eyes.. · This results in radiomic signatures that may be considered as imaging biomarkers, as they provide diagnostic, predictive, and prognostic information.. · Radiomics derived imaging biomarkers may be used to individualize breast cancer treatment in the era of precision medicine.. · The concept and key research of radiomics in the field of breast imaging will be discussed in this narrative review.. CITATION FORMAT · Dietzel M, Clauser P, Kapetas P et al. Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm. Fortschr Röntgenstr 2021; 193: 898 - 908.
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Affiliation(s)
| | - Paola Clauser
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
| | - Panagiotis Kapetas
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
| | | | - Pascal Andreas Thomas Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
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Alongi P, Stefano A, Comelli A, Laudicella R, Scalisi S, Arnone G, Barone S, Spada M, Purpura P, Bartolotta TV, Midiri M, Lagalla R, Russo G. Radiomics analysis of 18F-Choline PET/CT in the prediction of disease outcome in high-risk prostate cancer: an explorative study on machine learning feature classification in 94 patients. Eur Radiol 2021; 31:4595-4605. [PMID: 33443602 DOI: 10.1007/s00330-020-07617-8] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 11/10/2020] [Accepted: 12/07/2020] [Indexed: 02/06/2023]
Abstract
OBJECTIVE The aim of this study was (1) to investigate the application of texture analysis of choline PET/CT images in prostate cancer (PCa) patients and (2) to propose a machine-learning radiomics model able to select PET features predictive of disease progression in PCa patients with a same high-risk class at restaging. MATERIAL AND METHODS Ninety-four high-risk PCa patients who underwent restaging Cho-PET/CT were analyzed. Follow-up data were recorded for a minimum of 13 months after the PET/CT scan. PET images were imported in LIFEx toolbox to extract 51 features from each lesion. A statistical system based on correlation matrix and point-biserial-correlation coefficient has been implemented for features reduction and selection, while Discriminant analysis (DA) was used as a method for features classification in a whole sample and sub-groups for primary tumor or local relapse (T), nodal disease (N), and metastatic disease (M). RESULTS In the whole group, 2 feature (HISTO_Entropy_log10; HISTO_Energy_Uniformity) results were able to discriminate the occurrence of disease progression at follow-up, obtaining the best performance in DA classification (sensitivity 47.1%, specificity 76.5%, positive predictive value (PPV) 46.7%, and accuracy 67.6%). In the sub-group analysis, the best performance in DA classification for T was obtained by selecting 3 features (SUVmin; SHAPE_Sphericity; GLCM_Correlation) with a sensitivity of 91.6%, specificity 84.1%, PPV 79.1%, and accuracy 87%; for N by selecting 2 features (HISTO = _Energy Uniformity; GLZLM_SZLGE) with a sensitivity of 68.1%, specificity 91.4%, PPV 83%, and accuracy 82.6%; and for M by selecting 2 features (HISTO_Entropy_log10 - HISTO_Entropy_log2) with a sensitivity 64.4%, specificity 74.6%, PPV 40.6%, and accuracy 72.5%. CONCLUSION This machine learning model demonstrated to be feasible and useful to select Cho-PET features for T, N, and M with valuable association with high-risk PCa patients' outcomes. KEY POINTS • Artificial intelligence applications are feasible and useful to select Cho-PET features. • Our model demonstrated the presence of specific features for T, N, and M with valuable association with high-risk PCa patients' outcomes. • Further prospective studies are necessary to confirm our results and to develop the application of artificial intelligence in PET imaging of PCa.
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Affiliation(s)
- Pierpaolo Alongi
- Nuclear Medicine Unit, Fondazione Istituto G. Giglio, Contrada Pietrapollastra Pisciotto, 90015, Cefalù, PA, Italy.
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (CNR), Cefalù, PA, Italy
| | | | - Riccardo Laudicella
- Department of Biomedical and Dental Sciences and of Morpho-functional Imaging, Nuclear Medicine Unit, University of Messina, Messina, Italy
| | - Salvatore Scalisi
- Nuclear Medicine Unit, Fondazione Istituto G. Giglio, Contrada Pietrapollastra Pisciotto, 90015, Cefalù, PA, Italy
| | - Giuseppe Arnone
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy
| | - Stefano Barone
- Dipartimento di Scienze Agronomiche, Alimentari e Forestali (SAAF), University of Palermo, Palermo, Italy
| | | | - Pierpaolo Purpura
- Department of Radiology, Fondazione Istituto Giuseppe Giglio Ct.da Pietrapollastra, Via Pisciotto, 90015, Cefalù (Palermo), Italy
| | - Tommaso Vincenzo Bartolotta
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy
- Department of Radiology, Fondazione Istituto Giuseppe Giglio Ct.da Pietrapollastra, Via Pisciotto, 90015, Cefalù (Palermo), Italy
| | - Massimo Midiri
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy
| | - Roberto Lagalla
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy
| | - Giorgio Russo
- Institute of Molecular Bioimaging and Physiology, National Research Council (CNR), Cefalù, PA, Italy
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Li W, Liu H, Cheng F, Li Y, Li S, Yan J. Artificial intelligence applications for oncological positron emission tomography imaging. Eur J Radiol 2020; 134:109448. [PMID: 33307463 DOI: 10.1016/j.ejrad.2020.109448] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 10/07/2020] [Accepted: 11/26/2020] [Indexed: 12/16/2022]
Abstract
Positron emission tomography (PET), a functional and dynamic molecular imaging technique, is generally used to reveal tumors' biological behavior. Radiomics allows a high-throughput extraction of multiple features from images with artificial intelligence (AI) approaches and develops rapidly worldwide. Quantitative and objective features of medical images have been explored to recognize reliable biomarkers, with the development of PET radiomics. This paper will review the current clinical exploration of PET-based classical machine learning and deep learning methods, including disease diagnosis, the prediction of histological subtype, gene mutation status, tumor metastasis, tumor relapse, therapeutic side effects, therapeutic intervention and evaluation of prognosis. The applications of AI in oncology will be mainly discussed. The image-guided biopsy or surgery assisted by PET-based AI will be introduced as well. This paper aims to present the applications and methods of AI for PET imaging, which may offer important details for further clinical studies. Relevant precautions are put forward and future research directions are suggested.
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Affiliation(s)
- Wanting Li
- Shanxi Medical University, Taiyuan 030009, PR China; Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Taiyuan 030001, PR China; Collaborative Innovation Center for Molecular Imaging, Taiyuan 030001, PR China
| | - Haiyan Liu
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Taiyuan 030001, PR China; Collaborative Innovation Center for Molecular Imaging, Taiyuan 030001, PR China; Cellular Physiology Key Laboratory of Ministry of Education, Translational Medicine Research Center, Shanxi Medical University, Taiyuan 030001, PR China
| | - Feng Cheng
- Shanxi Medical University, Taiyuan 030009, PR China
| | - Yanhua Li
- Shanxi Medical University, Taiyuan 030009, PR China
| | - Sijin Li
- Shanxi Medical University, Taiyuan 030009, PR China; Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Taiyuan 030001, PR China; Collaborative Innovation Center for Molecular Imaging, Taiyuan 030001, PR China.
| | - Jiangwei Yan
- Shanxi Medical University, Taiyuan 030009, PR China.
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Dana J, Agnus V, Ouhmich F, Gallix B. Multimodality Imaging and Artificial Intelligence for Tumor Characterization: Current Status and Future Perspective. Semin Nucl Med 2020; 50:541-548. [PMID: 33059823 DOI: 10.1053/j.semnuclmed.2020.07.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Research in medical imaging has yet to do to achieve precision oncology. Over the past 30 years, only the simplest imaging biomarkers (RECIST, SUV,…) have become widespread clinical tools. This may be due to our inability to accurately characterize tumors and monitor intratumoral changes in imaging. Artificial intelligence, through machine learning and deep learning, opens a new path in medical research because it can bring together a large amount of heterogeneous data into the same analysis to reach a single outcome. Supervised or unsupervised learning may lead to new paradigms by identifying unrevealed structural patterns across data. Deep learning will provide human-free, undefined upstream, reproducible, and automated quantitative imaging biomarkers. Since tumor phenotype is driven by its genotype and thus indirectly defines tumoral progression, tumor characterization using machine learning and deep learning algorithms will allow us to monitor molecular expression noninvasively, anticipate therapeutic failure, and lead therapeutic management. To follow this path, quality standards have to be set: standardization of imaging acquisition as it has been done in the field of biology, transparency of the model development as it should be reproducible by different institutions, validation, and testing through a high-quality process using large and complex open databases and better interpretability of these algorithms.
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Affiliation(s)
- Jérémy Dana
- IHU of Strasbourg, Strasbourg, France; Inserm & University of Strasbourg UMR-S1110, Strasbourg, France; Faculty of Medicine, University of Paris, Paris, France
| | - Vincent Agnus
- IHU of Strasbourg, Strasbourg, France; Icube Laboratory, University of Strasbourg, Strasbourg, France
| | - Farid Ouhmich
- IHU of Strasbourg, Strasbourg, France; Icube Laboratory, University of Strasbourg, Strasbourg, France
| | - Benoit Gallix
- IHU of Strasbourg, Strasbourg, France; Icube Laboratory, University of Strasbourg, Strasbourg, France; Faculty of Medicine, University of Strasbourg, Strasbourg, France; Faculty of Medicine, McGill University, Montreal, Quebec, Canada.
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