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Russo L, Charles-Davies D, Bottazzi S, Sala E, Boldrini L. Radiomics for clinical decision support in radiation oncology. Clin Oncol (R Coll Radiol) 2024; 36:e269-e281. [PMID: 38548581 DOI: 10.1016/j.clon.2024.03.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 02/14/2024] [Accepted: 03/08/2024] [Indexed: 07/09/2024]
Abstract
Radiomics is a promising tool for the development of quantitative biomarkers to support clinical decision-making. It has been shown to improve the prediction of response to treatment and outcome in different settings, particularly in the field of radiation oncology by optimising the dose delivery solutions and reducing the rate of radiation-induced side effects, leading to a fully personalised approach. Despite the promising results offered by radiomics at each of these stages, standardised methodologies, reproducibility and interpretability of results are still lacking, limiting the potential clinical impact of these tools. In this review, we briefly describe the principles of radiomics and the most relevant applications of radiomics at each stage of cancer management in the framework of radiation oncology. Furthermore, the integration of radiomics into clinical decision support systems is analysed, defining the challenges and offering possible solutions for translating radiomics into a clinically applicable tool.
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Affiliation(s)
- L Russo
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Dipartimento di Scienze Radiologiche ed Ematologiche. Università Cattolica Del Sacro Cuore, Rome, Italy.
| | - D Charles-Davies
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - S Bottazzi
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - E Sala
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Dipartimento di Scienze Radiologiche ed Ematologiche. Università Cattolica Del Sacro Cuore, Rome, Italy
| | - L Boldrini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
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2
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Veiga-Canuto D, Cerdá Alberich L, Fernández-Patón M, Jiménez Pastor A, Lozano-Montoya J, Miguel Blanco A, Martínez de Las Heras B, Sangüesa Nebot C, Martí-Bonmatí L. Imaging biomarkers and radiomics in pediatric oncology: a view from the PRIMAGE (PRedictive In silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers) project. Pediatr Radiol 2024; 54:562-570. [PMID: 37747582 DOI: 10.1007/s00247-023-05770-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 09/01/2023] [Accepted: 09/03/2023] [Indexed: 09/26/2023]
Abstract
This review paper presents the practical development of imaging biomarkers in the scope of the PRIMAGE (PRedictive In silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers) project, as a noninvasive and reliable way to improve the diagnosis and prognosis in pediatric oncology. The PRIMAGE project is a European multi-center research initiative that focuses on developing medical imaging-derived artificial intelligence (AI) solutions designed to enhance overall management and decision-making for two types of pediatric cancer: neuroblastoma and diffuse intrinsic pontine glioma. To allow this, the PRIMAGE project has created an open-cloud platform that combines imaging, clinical, and molecular data together with AI models developed from this data, creating a comprehensive decision support environment for clinicians managing patients with these two cancers. In order to achieve this, a standardized data processing and analysis workflow was implemented to generate robust and reliable predictions for different clinical endpoints. Magnetic resonance (MR) image harmonization and registration was performed as part of the workflow. Subsequently, an automated tool for the detection and segmentation of tumors was trained and internally validated. The Dice similarity coefficient obtained for the independent validation dataset was 0.997, indicating compatibility with the manual segmentation variability. Following this, radiomics and deep features were extracted and correlated with clinical endpoints. Finally, reproducible and relevant imaging quantitative features were integrated with clinical and molecular data to enrich both the predictive models and a set of visual analytics tools, making the PRIMAGE platform a complete clinical decision aid system. In order to ensure the advancement of research in this field and to foster engagement with the wider research community, the PRIMAGE data repository and platform are currently being integrated into the European Federation for Cancer Images (EUCAIM), which is the largest European cancer imaging research infrastructure created to date.
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Affiliation(s)
- Diana Veiga-Canuto
- Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026, Valencia, Spain.
- Área Clínica de Imagen Médica, Área Clínica de Imagen Médica, Hospital Universitari i Politècnic La Fe, Avinguda Fernando Abril Martorell, 106 Torre E planta 0, 46026, València, Spain.
| | - Leonor Cerdá Alberich
- Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026, Valencia, Spain
| | - Matías Fernández-Patón
- Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026, Valencia, Spain
| | | | | | - Ana Miguel Blanco
- Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026, Valencia, Spain
| | - Blanca Martínez de Las Heras
- Pediatric Oncology Department, Hospital Universitario y Politécnico La Fe, Avenida Fernando Abril Martorell, 106 Torre G planta 2, 46026, Valencia, Spain
| | - Cinta Sangüesa Nebot
- Área Clínica de Imagen Médica, Área Clínica de Imagen Médica, Hospital Universitari i Politècnic La Fe, Avinguda Fernando Abril Martorell, 106 Torre E planta 0, 46026, València, Spain
| | - Luis Martí-Bonmatí
- Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026, Valencia, Spain
- Área Clínica de Imagen Médica, Área Clínica de Imagen Médica, Hospital Universitari i Politècnic La Fe, Avinguda Fernando Abril Martorell, 106 Torre E planta 0, 46026, València, Spain
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Bäuerle T, Dietzel M, Pinker K, Bonekamp D, Zhang KS, Schlemmer HP, Bannas P, Cyran CC, Eisenblätter M, Hilger I, Jung C, Schick F, Wegner F, Kiessling F. Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma. ROFO-FORTSCHR RONTG 2024; 196:354-362. [PMID: 37944934 DOI: 10.1055/a-2175-4446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
BACKGROUND Imaging biomarkers are quantitative parameters from imaging modalities, which are collected noninvasively, allow conclusions about physiological and pathophysiological processes, and may consist of single (monoparametric) or multiple parameters (bi- or multiparametric). METHOD This review aims to present the state of the art for the quantification of multimodal and multiparametric imaging biomarkers. Here, the use of biomarkers using artificial intelligence will be addressed and the clinical application of imaging biomarkers in breast and prostate cancers will be explained. For the preparation of the review article, an extensive literature search was performed based on Pubmed, Web of Science and Google Scholar. The results were evaluated and discussed for consistency and generality. RESULTS AND CONCLUSION Different imaging biomarkers (multiparametric) are quantified based on the use of complementary imaging modalities (multimodal) from radiology, nuclear medicine, or hybrid imaging. From these techniques, parameters are determined at the morphological (e. g., size), functional (e. g., vascularization or diffusion), metabolic (e. g., glucose metabolism), or molecular (e. g., expression of prostate specific membrane antigen, PSMA) level. The integration and weighting of imaging biomarkers are increasingly being performed with artificial intelligence, using machine learning algorithms. In this way, the clinical application of imaging biomarkers is increasing, as illustrated by the diagnosis of breast and prostate cancers. KEY POINTS · Imaging biomarkers are quantitative parameters to detect physiological and pathophysiological processes.. · Imaging biomarkers from multimodality and multiparametric imaging are integrated using artificial intelligence algorithms.. · Quantitative imaging parameters are a fundamental component of diagnostics for all tumor entities, such as for mammary and prostate carcinomas.. CITATION FORMAT · Bäuerle T, Dietzel M, Pinker K et al. Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma. Fortschr Röntgenstr 2024; 196: 354 - 362.
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Affiliation(s)
- Tobias Bäuerle
- Institute of Radiology, University Medical Center Erlangen, Germany
| | - Matthias Dietzel
- Institute of Radiology, University Medical Center Erlangen, Germany
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, United States
| | - David Bonekamp
- Department of Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Kevin S Zhang
- Department of Radiology, German Cancer Research Center, Heidelberg, Germany
| | | | - Peter Bannas
- Institute of Diagnostic and Interventional Radiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Clemens C Cyran
- Institute of Radiology, University Medical Center München (LMU), München, Germany
| | - Michel Eisenblätter
- Diagnostische und Interventionelle Radiologie, Universitätsklinikum OWL, Universität Bielefeld Campus Klinikum Lippe, 32756 Detmold, Germany
| | - Ingrid Hilger
- Experimental Radiology, University Medical Center Jena, Germany
| | - Caroline Jung
- Institute of Diagnostic and Interventional Radiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Fritz Schick
- Experimental Radiology, University Medical Center Tübingen, Germany
| | - Franz Wegner
- Department of Radiology, University Hospital Schleswig-Holstein Campus Lübeck, Germany
| | - Fabian Kiessling
- Experimental Molecular Imaging, University Medical Center Aachen, Germany
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Molière S, Hamzaoui D, Granger B, Montagne S, Allera A, Ezziane M, Luzurier A, Quint R, Kalai M, Ayache N, Delingette H, Renard-Penna R. Reference standard for the evaluation of automatic segmentation algorithms: Quantification of inter observer variability of manual delineation of prostate contour on MRI. Diagn Interv Imaging 2024; 105:65-73. [PMID: 37822196 DOI: 10.1016/j.diii.2023.08.001] [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: 05/02/2023] [Revised: 07/28/2023] [Accepted: 08/01/2023] [Indexed: 10/13/2023]
Abstract
PURPOSE The purpose of this study was to investigate the relationship between inter-reader variability in manual prostate contour segmentation on magnetic resonance imaging (MRI) examinations and determine the optimal number of readers required to establish a reliable reference standard. MATERIALS AND METHODS Seven radiologists with various experiences independently performed manual segmentation of the prostate contour (whole-gland [WG] and transition zone [TZ]) on 40 prostate MRI examinations obtained in 40 patients. Inter-reader variability in prostate contour delineations was estimated using standard metrics (Dice similarity coefficient [DSC], Hausdorff distance and volume-based metrics). The impact of the number of readers (from two to seven) on segmentation variability was assessed using pairwise metrics (consistency) and metrics with respect to a reference segmentation (conformity), obtained either with majority voting or simultaneous truth and performance level estimation (STAPLE) algorithm. RESULTS The average segmentation DSC for two readers in pairwise comparison was 0.919 for WG and 0.876 for TZ. Variability decreased with the number of readers: the interquartile ranges of the DSC were 0.076 (WG) / 0.021 (TZ) for configurations with two readers, 0.005 (WG) / 0.012 (TZ) for configurations with three readers, and 0.002 (WG) / 0.0037 (TZ) for configurations with six readers. The interquartile range decreased slightly faster between two and three readers than between three and six readers. When using consensus methods, variability often reached its minimum with three readers (with STAPLE, DSC = 0.96 [range: 0.945-0.971] for WG and DSC = 0.94 [range: 0.912-0.957] for TZ, and interquartile range was minimal for configurations with three readers. CONCLUSION The number of readers affects the inter-reader variability, in terms of inter-reader consistency and conformity to a reference. Variability is minimal for three readers, or three readers represent a tipping point in the variability evolution, with both pairwise-based metrics or metrics with respect to a reference. Accordingly, three readers may represent an optimal number to determine references for artificial intelligence applications.
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Affiliation(s)
- Sébastien Molière
- Department of Radiology, Hôpitaux Universitaire de Strasbourg, Hôpital de Hautepierre, 67200, Strasbourg, France; Breast and Thyroid Imaging Unit, Institut de Cancérologie Strasbourg Europe, 67200, Strasbourg, France; IGBMC, Institut de Génétique et de Biologie Moléculaire et Cellulaire, 67400, Illkirch, France.
| | - Dimitri Hamzaoui
- Inria, Epione Team, Sophia Antipolis, Université Côte d'Azur, 06902, Nice, France
| | - Benjamin Granger
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, IPLESP, AP-HP, Hôpital Pitié Salpêtrière, Département de Santé Publique, 75013, Paris, France
| | - Sarah Montagne
- Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France; Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique-Hôpitaux de Paris, 75013, Paris, France; GRC N° 5, Oncotype-Uro, Sorbonne Université, 75020, Paris, France
| | - Alexandre Allera
- Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique-Hôpitaux de Paris, 75013, Paris, France
| | - Malek Ezziane
- Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique-Hôpitaux de Paris, 75013, Paris, France
| | - Anna Luzurier
- Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique-Hôpitaux de Paris, 75013, Paris, France
| | - Raphaelle Quint
- Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique-Hôpitaux de Paris, 75013, Paris, France
| | - Mehdi Kalai
- Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique-Hôpitaux de Paris, 75013, Paris, France
| | - Nicholas Ayache
- Department of Radiology, Hôpitaux Universitaire de Strasbourg, Hôpital de Hautepierre, 67200, Strasbourg, France
| | - Hervé Delingette
- Department of Radiology, Hôpitaux Universitaire de Strasbourg, Hôpital de Hautepierre, 67200, Strasbourg, France
| | - Raphaële Renard-Penna
- Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France; Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique-Hôpitaux de Paris, 75013, Paris, France; GRC N° 5, Oncotype-Uro, Sorbonne Université, 75020, Paris, France
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5
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Rouvière O. Evaluation of automated prostate segmentation: The complex issue of the optimal number of expert segmentations. Diagn Interv Imaging 2024; 105:45-46. [PMID: 37863708 DOI: 10.1016/j.diii.2023.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 10/09/2023] [Indexed: 10/22/2023]
Affiliation(s)
- Olivier Rouvière
- Hospices Civils de Lyon, Department of Radiology, Hôpital Edouard Herriot, Lyon 69437, France; Université de Lyon, Lyon, France; Université Lyon 1, Lyon, France; Faculté de Médecine Lyon Est, Lyon 69003, France; LabTau, INSERM U1032, Lyon 69003, France.
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6
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Kocak B, Akinci D'Antonoli T, Mercaldo N, Alberich-Bayarri A, Baessler B, Ambrosini I, Andreychenko AE, Bakas S, Beets-Tan RGH, Bressem K, Buvat I, Cannella R, Cappellini LA, Cavallo AU, Chepelev LL, Chu LCH, Demircioglu A, deSouza NM, Dietzel M, Fanni SC, Fedorov A, Fournier LS, Giannini V, Girometti R, Groot Lipman KBW, Kalarakis G, Kelly BS, Klontzas ME, Koh DM, Kotter E, Lee HY, Maas M, Marti-Bonmati L, Müller H, Obuchowski N, Orlhac F, Papanikolaou N, Petrash E, Pfaehler E, Pinto Dos Santos D, Ponsiglione A, Sabater S, Sardanelli F, Seeböck P, Sijtsema NM, Stanzione A, Traverso A, Ugga L, Vallières M, van Dijk LV, van Griethuysen JJM, van Hamersvelt RW, van Ooijen P, Vernuccio F, Wang A, Williams S, Witowski J, Zhang Z, Zwanenburg A, Cuocolo R. METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII. Insights Imaging 2024; 15:8. [PMID: 38228979 PMCID: PMC10792137 DOI: 10.1186/s13244-023-01572-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 11/20/2023] [Indexed: 01/18/2024] Open
Abstract
PURPOSE To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies. METHODS We conducted an online modified Delphi study with a group of international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members to identify the items to be voted; and Stage#3, four rounds of the modified Delphi exercise by panelists to determine the items eligible for the METRICS and their weights. The consensus threshold was 75%. Based on the median ranks derived from expert panel opinion and their rank-sum based conversion to importance scores, the category and item weights were calculated. RESULT In total, 59 panelists from 19 countries participated in selection and ranking of the items and categories. Final METRICS tool included 30 items within 9 categories. According to their weights, the categories were in descending order of importance: study design, imaging data, image processing and feature extraction, metrics and comparison, testing, feature processing, preparation for modeling, segmentation, and open science. A web application and a repository were developed to streamline the calculation of the METRICS score and to collect feedback from the radiomics community. CONCLUSION In this work, we developed a scoring tool for assessing the methodological quality of the radiomics research, with a large international panel and a modified Delphi protocol. With its conditional format to cover methodological variations, it provides a well-constructed framework for the key methodological concepts to assess the quality of radiomic research papers. CRITICAL RELEVANCE STATEMENT A quality assessment tool, METhodological RadiomICs Score (METRICS), is made available by a large group of international domain experts, with transparent methodology, aiming at evaluating and improving research quality in radiomics and machine learning. KEY POINTS • A methodological scoring tool, METRICS, was developed for assessing the quality of radiomics research, with a large international expert panel and a modified Delphi protocol. • The proposed scoring tool presents expert opinion-based importance weights of categories and items with a transparent methodology for the first time. • METRICS accounts for varying use cases, from handcrafted radiomics to entirely deep learning-based pipelines. • A web application has been developed to help with the calculation of the METRICS score ( https://metricsscore.github.io/metrics/METRICS.html ) and a repository created to collect feedback from the radiomics community ( https://github.com/metricsscore/metrics ).
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Affiliation(s)
- Burak Kocak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, Turkey
| | - Tugba Akinci D'Antonoli
- Institute of Radiology and Nuclear Medicine, Cantonal Hospital Baselland, Liestal, Switzerland.
| | - Nathaniel Mercaldo
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Bettina Baessler
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Ilaria Ambrosini
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Anna E Andreychenko
- Laboratory for Digital Public Health Technologies, ITMO University, St. Petersburg, Russian Federation
| | - Spyridon Bakas
- Division of Computational Pathology, Department of Pathology and Laboratory Medicine, School of Medicine, Indiana University, Indianapolis, IN, USA
- Center for Federated Learning in Precision Medicine, Indiana University, Indianapolis, IN, USA
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands
- Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Keno Bressem
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Irene Buvat
- Institut Curie, Inserm, PSL University, Laboratory of Translational Imaging in Oncology, Orsay, France
| | - Roberto Cannella
- Section of Radiology - Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy
| | | | - Armando Ugo Cavallo
- Division of Radiology, Istituto Dermopatico dell'Immacolata (IDI) IRCCS, Rome, Italy
| | - Leonid L Chepelev
- Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Canada
| | - Linda Chi Hang Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, USA
| | - Aydin Demircioglu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital , Essen, Germany
| | - Nandita M deSouza
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, UK
- Department of Imaging, The Royal Marsden National Health Service (NHS) Foundation Trust, London, UK
| | - Matthias Dietzel
- Department of Radiology, University Hospital Erlangen, Erlangen, Germany
| | | | - Andrey Fedorov
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Laure S Fournier
- Department of Radiology, Université Paris Cité, AP-HP, Hôpital Européen Georges Pompidou, PARCC UMRS 970, INSERM, Paris, France
| | | | - Rossano Girometti
- Institute of Radiology, Department of Medicine, University of Udine, University Hospital S. Maria della Misericordia, Udine, Italy
| | - Kevin B W Groot Lipman
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands
- Department of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Georgios Kalarakis
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
- Department of Clinical Science, Division of Radiology, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden
- Department of Radiology, Medical School, University of Crete, Heraklion, Greece
| | - Brendan S Kelly
- Department of Radiology, St Vincent's University Hospital, Dublin, Ireland
- Insight Centre for Data Analytics, UCD, Dublin, Ireland
- School of Medicine, University College Dublin, Dublin, Ireland
| | - Michail E Klontzas
- Department of Medical Imaging, University Hospital of Heraklion, Crete, Greece
- Department of Radiology, School of Medicine, University of Crete, Heraklion, Crete, Greece
- Computational Biomedicine Laboratory, Institute of Computer Science, FORTH, Heraklion, Crete, Greece
| | - Dow-Mu Koh
- Department of Radiology, Royal Marsden Hospital, Sutton, UK
| | - Elmar Kotter
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | - Ho Yun Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea
| | - Mario Maas
- Department of Radiology & Nuclear Medicine, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - Luis Marti-Bonmati
- Medical Imaging Department and Biomedical Imaging Research Group, Hospital Universitario y Politécnico La Fe and Health Research Institute, Valencia, Spain
| | - Henning Müller
- University of Applied Sciences of Western Switzerland (HES-SO Valais), Sierra, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UniGe), Geneva, Switzerland
| | - Nancy Obuchowski
- Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Fanny Orlhac
- Institut Curie, Inserm, PSL University, Laboratory of Translational Imaging in Oncology, Orsay, France
| | - Nikolaos Papanikolaou
- Computational Clinical Imaging Group, Centre for the Unknown, Champalimaud Foundation, Lisbon, Portugal
- Department of Radiology, Royal Marsden Hospital and The Institute of Cancer Research, London, UK
| | - Ekaterina Petrash
- Radiology department, Research Institute of Pediatric Oncology and Hematology n. a. L.A. Durnov, National Medical Research Center of Oncology n. a. N.N. Blokhin Ministry of Health of Russian Federation, Moscow, Russia
- Medical Department IRA-Labs, Moscow, Russia
| | - Elisabeth Pfaehler
- Institute for advanced simulation (IAS-8): Machine learning and data analytics, Forschungszentrum Jülich, Jülich, Germany
| | - Daniel Pinto Dos Santos
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
- Institute for Diagnostic and Interventional Radiology, Goethe-University Frankfurt Am Main, Frankfurt, Germany
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Sebastià Sabater
- Department of Radiation Oncology, Complejo Hospitalario Universitario de Albacete, Albacete, Spain
| | - 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
| | - Philipp Seeböck
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Nanna M Sijtsema
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Alberto Traverso
- Department of Radiotherapy, Maastro Clinic, Maastricht, the Netherlands
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Canada
- Centre de recherche du Centre hospitalier universitaire de Sherbrooke, Sherbrooke, Canada
| | - Lisanne V van Dijk
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | | | - Robbert W van Hamersvelt
- Department of Radiology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Peter van Ooijen
- Department of Radiotherapy, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Federica Vernuccio
- Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnosis (Bi.N.D), University of Palermo, Palermo, 90127, Italy
| | - Alan Wang
- Centre for Medical Imaging & Centre for Brain Research, Faculty of Medical and Health Sciences, Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Stuart Williams
- Department of Radiology, Norfolk & Norwich University Hospital, Colney Lane, Norwich, Norfolk, UK
| | - Jan Witowski
- Department of Radiology, New York University Grossman School of Medicine, New York, USA
| | - Zhongyi Zhang
- School of Information and Communication Technology, Griffith University, Nathan, Brisbane, Australia
| | - Alex Zwanenburg
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
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Liu H, Wei Z, Xv Y, Tan H, Liao F, Lv F, Jiang Q, Chen T, Xiao M. Validity of a multiphase CT-based radiomics model in predicting the Leibovich risk groups for localized clear cell renal cell carcinoma: an exploratory study. Insights Imaging 2023; 14:167. [PMID: 37816901 PMCID: PMC10564697 DOI: 10.1186/s13244-023-01526-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 09/10/2023] [Indexed: 10/12/2023] Open
Abstract
OBJECTIVE To develop and validate a multiphase CT-based radiomics model for preoperative risk stratification of patients with localized clear cell renal cell carcinoma (ccRCC). METHODS A total of 425 patients with localized ccRCC were enrolled and divided into training, validation, and external testing cohorts. Radiomics features were extracted from three-phase CT images (unenhanced, arterial, and venous), and radiomics signatures were constructed by the least absolute shrinkage and selection operator (LASSO) regression algorithm. The radiomics score (Rad-score) for each patient was calculated. The radiomics model was established and visualized as a nomogram by incorporating significant clinical factors and Rad-score. The predictive performance of the radiomics model was evaluated by the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). RESULTS The AUC of the triphasic radiomics signature reached 0.862 (95% CI: 0.809-0.914), 0.853 (95% CI: 0.785-0.921), and 0.837 (95% CI: 0.714-0.959) in three cohorts, respectively, which were higher than arterial, venous, and unenhanced radiomics signatures. Multivariate logistic regression analysis showed that Rad-score (OR: 4.066, 95% CI: 3.495-8.790) and renal vein invasion (OR: 12.914, 95% CI: 1.118-149.112) were independent predictors and used to develop the radiomics model. The radiomics model showed good calibration and discrimination and yielded an AUC of 0.872 (95% CI: 0.821-0.923), 0.865 (95% CI: 0.800-0.930), and 0.848 (95% CI: 0.728-0.967) in three cohorts, respectively. DCA showed the clinical usefulness of the radiomics model in predicting the Leibovich risk groups. CONCLUSIONS The radiomics model can be used as a non-invasive and useful tool to predict the Leibovich risk groups for localized ccRCC patients. CRITICAL RELEVANCE STATEMENT The triphasic CT-based radiomics model achieved favorable performance in preoperatively predicting the Leibovich risk groups in patients with localized ccRCC. Therefore, it can be used as a non-invasive and effective tool for preoperative risk stratification of patients with localized ccRCC. KEY POINTS • The triphasic CT-based radiomics signature achieves better performance than the single-phase radiomics signature. • Radiomics holds prospects in preoperatively predicting the Leibovich risk groups for ccRCC. • This study provides a non-invasive method to stratify patients with localized ccRCC.
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Affiliation(s)
- Huayun Liu
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Zongjie Wei
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Yingjie Xv
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Hao Tan
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Fangtong Liao
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qing Jiang
- Department of Urology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Tao Chen
- Department of Pathology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mingzhao Xiao
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China.
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Kocak B, Yardimci AH, Nazli MA, Yuzkan S, Mutlu S, Guzelbey T, Sam Ozdemir M, Akin M, Yucel S, Bulut E, Bayrak ON, Okumus AA. REliability of consensus-based segMentatIoN in raDiomic feature reproducibility (REMIND): A word of caution. Eur J Radiol 2023; 165:110893. [PMID: 37285646 DOI: 10.1016/j.ejrad.2023.110893] [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: 03/31/2023] [Revised: 05/01/2023] [Accepted: 05/23/2023] [Indexed: 06/09/2023]
Abstract
OBJECTIVE To evaluate the reliability of consensus-based segmentation in terms of reproducibility of radiomic features. METHODS In this retrospective study, three tumor data sets were investigated: breast cancer (n = 30), renal cell carcinoma (n = 30), and pituitary macroadenoma (n = 30). MRI was utilized for breast and pituitary data sets, while CT was used for renal data set. 12 readers participated in the segmentation process. Consensus segmentation was created by making corrections on a previous region or volume of interest. Four experiments were designed to evaluate the reproducibility of radiomic features. Reliability was assessed with intraclass correlation coefficient (ICC) with two cut-off values: 0.75 and 0.9. RESULTS Considering the lower bound of the 95% confidence interval and the ICC threshold of 0.90, at least 61% of the radiomic features were not reproducible in the inter-consensus analysis. In the susceptibility experiment, at least half (54%) became non-reproducible when the first reader is replaced with a different reader. In the intra-consensus analysis, at least about one-third (32%) were non-reproducible when the same second reader segmented the image over the same first reader two weeks later. Compared to inter-reader analysis based on independent single readers, the inter-consensus analysis did not statistically significantly improve the rates of reproducible features in all data sets and analyses. CONCLUSIONS Despite the positive connotation of the word "consensus", it is essential to REMIND that consensus-based segmentation has significant reproducibility issues. Therefore, the usage of consensus-based segmentation alone should be avoided unless a reliability analysis is performed, even if it is not practical in clinical settings.
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Affiliation(s)
- Burak Kocak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey.
| | - Aytul Hande Yardimci
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Mehmet Ali Nazli
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Sabahattin Yuzkan
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Samet Mutlu
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Tevfik Guzelbey
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Merve Sam Ozdemir
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Meliha Akin
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Serap Yucel
- Department of Radiology, Baskent University, Istanbul Hospital, Istanbul, Turkey
| | - Elif Bulut
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Osman Nuri Bayrak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Ahmet Arda Okumus
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
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Hirschler L, Sollmann N, Schmitz‐Abecassis B, Pinto J, Arzanforoosh F, Barkhof F, Booth T, Calvo‐Imirizaldu M, Cassia G, Chmelik M, Clement P, Ercan E, Fernández‐Seara MA, Furtner J, Fuster‐Garcia E, Grech‐Sollars M, Guven NT, Hatay GH, Karami G, Keil VC, Kim M, Koekkoek JAF, Kukran S, Mancini L, Nechifor RE, Özcan A, Ozturk‐Isik E, Piskin S, Schmainda K, Svensson SF, Tseng C, Unnikrishnan S, Vos F, Warnert E, Zhao MY, Jancalek R, Nunes T, Emblem KE, Smits M, Petr J, Hangel G. Advanced MR Techniques for Preoperative Glioma Characterization: Part 1. J Magn Reson Imaging 2023; 57:1655-1675. [PMID: 36866773 PMCID: PMC10946498 DOI: 10.1002/jmri.28662] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 03/04/2023] Open
Abstract
Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast-enhanced MRI, arterial spin labeling, diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility-weighted imaging, MRI-PET, MR elastography, and MR-based radiomics applications. Evidence Level: 3 Technical Efficacy: Stage 2.
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Affiliation(s)
- Lydiane Hirschler
- C.J. Gorter MRI Center, Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Nico Sollmann
- Department of Diagnostic and Interventional RadiologyUniversity Hospital UlmUlmGermany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der IsarTechnical University of MunichMunichGermany
- TUM‐Neuroimaging Center, Klinikum rechts der IsarTechnical University of MunichMunichGermany
| | - Bárbara Schmitz‐Abecassis
- Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
- Medical Delta FoundationDelftThe Netherlands
| | - Joana Pinto
- Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordOxfordUK
| | | | - Frederik Barkhof
- Department of Radiology & Nuclear MedicineAmsterdam UMC, Vrije UniversiteitAmsterdamThe Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image ComputingUniversity College LondonLondonUK
| | - Thomas Booth
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Department of NeuroradiologyKing's College Hospital NHS Foundation TrustLondonUK
| | | | | | - Marek Chmelik
- Department of Technical Disciplines in Medicine, Faculty of Health CareUniversity of PrešovPrešovSlovakia
| | - Patricia Clement
- Department of Diagnostic SciencesGhent UniversityGhentBelgium
- Department of Medical ImagingGhent University HospitalGhentBelgium
| | - Ece Ercan
- Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Maria A. Fernández‐Seara
- Department of RadiologyClínica Universidad de NavarraPamplonaSpain
- IdiSNA, Instituto de Investigación Sanitaria de NavarraPamplonaSpain
| | - Julia Furtner
- Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
- Research Center of Medical Image Analysis and Artificial IntelligenceDanube Private UniversityKrems an der DonauAustria
| | - Elies Fuster‐Garcia
- Biomedical Data Science Laboratory, Instituto Universitario de Tecnologías de la Información y ComunicacionesUniversitat Politècnica de ValènciaValenciaSpain
| | - Matthew Grech‐Sollars
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUK
- Lysholm Department of Neuroradiology, National Hospital for Neurology and NeurosurgeryUniversity College London Hospitals NHS Foundation TrustLondonUK
| | - Nazmiye Tugay Guven
- Institute of Biomedical EngineeringBogazici University IstanbulIstanbulTurkey
| | - Gokce Hale Hatay
- Institute of Biomedical EngineeringBogazici University IstanbulIstanbulTurkey
| | - Golestan Karami
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Vera C. Keil
- Department of Radiology & Nuclear MedicineAmsterdam UMC, Vrije UniversiteitAmsterdamThe Netherlands
- Cancer Center AmsterdamAmsterdamThe Netherlands
| | - Mina Kim
- Centre for Medical Image Computing, Department of Medical Physics & Biomedical Engineering and Department of NeuroinflammationUniversity College LondonLondonUK
| | - Johan A. F. Koekkoek
- Department of NeurologyLeiden University Medical CenterLeidenThe Netherlands
- Department of NeurologyHaaglanden Medical CenterThe HagueThe Netherlands
| | - Simran Kukran
- Department of BioengineeringImperial College LondonLondonUK
- Department of Radiotherapy and ImagingInstitute of Cancer ResearchLondonUK
| | - Laura Mancini
- Lysholm Department of Neuroradiology, National Hospital for Neurology and NeurosurgeryUniversity College London Hospitals NHS Foundation TrustLondonUK
- Department of Brain Repair and Rehabilitation, Institute of NeurologyUniversity College LondonLondonUK
| | - Ruben Emanuel Nechifor
- Department of Clinical Psychology and PsychotherapyInternational Institute for the Advanced Studies of Psychotherapy and Applied Mental Health, Babes‐Bolyai UniversityCluj‐NapocaRomania
| | - Alpay Özcan
- Electrical and Electronics Engineering DepartmentBogazici University IstanbulIstanbulTurkey
| | - Esin Ozturk‐Isik
- Institute of Biomedical EngineeringBogazici University IstanbulIstanbulTurkey
| | - Senol Piskin
- Department of Mechanical Engineering, Faculty of Natural Sciences and EngineeringIstinye University IstanbulIstanbulTurkey
| | - Kathleen Schmainda
- Department of BiophysicsMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Siri F. Svensson
- Department of Physics and Computational RadiologyOslo University HospitalOsloNorway
- Department of PhysicsUniversity of OsloOsloNorway
| | - Chih‐Hsien Tseng
- Medical Delta FoundationDelftThe Netherlands
- Department of Imaging PhysicsDelft University of TechnologyDelftThe Netherlands
| | - Saritha Unnikrishnan
- Faculty of Engineering and DesignAtlantic Technological University (ATU) SligoSligoIreland
- Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), ATU SligoSligoIreland
| | - Frans Vos
- Medical Delta FoundationDelftThe Netherlands
- Department of Radiology & Nuclear MedicineErasmus MCRotterdamThe Netherlands
- Department of Imaging PhysicsDelft University of TechnologyDelftThe Netherlands
| | - Esther Warnert
- Department of Radiology & Nuclear MedicineErasmus MCRotterdamThe Netherlands
| | - Moss Y. Zhao
- Department of RadiologyStanford UniversityStanfordCaliforniaUSA
- Stanford Cardiovascular InstituteStanford UniversityStanfordCaliforniaUSA
| | - Radim Jancalek
- Department of NeurosurgerySt. Anne's University Hospital, BrnoBrnoCzech Republic
- Faculty of Medicine, Masaryk UniversityBrnoCzech Republic
| | - Teresa Nunes
- Department of NeuroradiologyHospital Garcia de OrtaAlmadaPortugal
| | - Kyrre E. Emblem
- Department of Physics and Computational RadiologyOslo University HospitalOsloNorway
| | - Marion Smits
- Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordOxfordUK
- Department of Radiology & Nuclear MedicineErasmus MCRotterdamThe Netherlands
- Brain Tumour CentreErasmus MC Cancer InstituteRotterdamThe Netherlands
| | - Jan Petr
- Helmholtz‐Zentrum Dresden‐RossendorfInstitute of Radiopharmaceutical Cancer ResearchDresdenGermany
| | - Gilbert Hangel
- Department of NeurosurgeryMedical University of ViennaViennaAustria
- High Field MR Centre, Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
- Christian Doppler Laboratory for MR Imaging BiomarkersViennaAustria
- Medical Imaging ClusterMedical University of ViennaViennaAustria
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Caroli A, Perico N. An Artificial Intelligence Algorithm for ADPKD: Are We Close to Successful Clinical Implementation? Mayo Clin Proc 2023; 98:650-652. [PMID: 37137638 DOI: 10.1016/j.mayocp.2023.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 03/20/2023] [Indexed: 05/05/2023]
Affiliation(s)
- Anna Caroli
- Department of Biomedical Engineering, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy
| | - Norberto Perico
- Department of Renal Medicine, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy.
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Veiga-Canuto D, Cerdà-Alberich L, Jiménez-Pastor A, Carot Sierra JM, Gomis-Maya A, Sangüesa-Nebot C, Fernández-Patón M, Martínez de las Heras B, Taschner-Mandl S, Düster V, Pötschger U, Simon T, Neri E, Alberich-Bayarri Á, Cañete A, Hero B, Ladenstein R, Martí-Bonmatí L. Independent Validation of a Deep Learning nnU-Net Tool for Neuroblastoma Detection and Segmentation in MR Images. Cancers (Basel) 2023; 15:cancers15051622. [PMID: 36900410 PMCID: PMC10000775 DOI: 10.3390/cancers15051622] [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: 01/20/2023] [Revised: 02/22/2023] [Accepted: 03/05/2023] [Indexed: 03/08/2023] Open
Abstract
OBJECTIVES To externally validate and assess the accuracy of a previously trained fully automatic nnU-Net CNN algorithm to identify and segment primary neuroblastoma tumors in MR images in a large children cohort. METHODS An international multicenter, multivendor imaging repository of patients with neuroblastic tumors was used to validate the performance of a trained Machine Learning (ML) tool to identify and delineate primary neuroblastoma tumors. The dataset was heterogeneous and completely independent from the one used to train and tune the model, consisting of 300 children with neuroblastic tumors having 535 MR T2-weighted sequences (486 sequences at diagnosis and 49 after finalization of the first phase of chemotherapy). The automatic segmentation algorithm was based on a nnU-Net architecture developed within the PRIMAGE project. For comparison, the segmentation masks were manually edited by an expert radiologist, and the time for the manual editing was recorded. Different overlaps and spatial metrics were calculated to compare both masks. RESULTS The median Dice Similarity Coefficient (DSC) was high 0.997; 0.944-1.000 (median; Q1-Q3). In 18 MR sequences (6%), the net was not able neither to identify nor segment the tumor. No differences were found regarding the MR magnetic field, type of T2 sequence, or tumor location. No significant differences in the performance of the net were found in patients with an MR performed after chemotherapy. The time for visual inspection of the generated masks was 7.9 ± 7.5 (mean ± Standard Deviation (SD)) seconds. Those cases where manual editing was needed (136 masks) required 124 ± 120 s. CONCLUSIONS The automatic CNN was able to locate and segment the primary tumor on the T2-weighted images in 94% of cases. There was an extremely high agreement between the automatic tool and the manually edited masks. This is the first study to validate an automatic segmentation model for neuroblastic tumor identification and segmentation with body MR images. The semi-automatic approach with minor manual editing of the deep learning segmentation increases the radiologist's confidence in the solution with a minor workload for the radiologist.
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Affiliation(s)
- Diana Veiga-Canuto
- Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026 Valencia, Spain
- Área Clínica de Imagen Médica, Hospital Universitario y Politécnico La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026 Valencia, Spain
- Correspondence: (D.V.-C.); (L.M.-B.)
| | - Leonor Cerdà-Alberich
- Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026 Valencia, Spain
| | - Ana Jiménez-Pastor
- Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, 46026 Valencia, Spain
| | - José Miguel Carot Sierra
- Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain
| | - Armando Gomis-Maya
- Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026 Valencia, Spain
| | - Cinta Sangüesa-Nebot
- Área Clínica de Imagen Médica, Hospital Universitario y Politécnico La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026 Valencia, Spain
| | - Matías Fernández-Patón
- Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026 Valencia, Spain
| | - Blanca Martínez de las Heras
- Unidad de Oncohematología Pediátrica, Hospital Universitario y Politécnico La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026 Valencia, Spain
| | - Sabine Taschner-Mandl
- St. Anna Children’s Cancer Research Institute, Zimmermannplatz 10, 1090 Vienna, Austria
| | - Vanessa Düster
- St. Anna Children’s Cancer Research Institute, Zimmermannplatz 10, 1090 Vienna, Austria
| | - Ulrike Pötschger
- St. Anna Children’s Cancer Research Institute, Zimmermannplatz 10, 1090 Vienna, Austria
| | - Thorsten Simon
- Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical Faculty, University of Cologne, 50937 Cologne, Germany
| | - Emanuele Neri
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma, 67, 56126 Pisa, Italy
| | | | - Adela Cañete
- Unidad de Oncohematología Pediátrica, Hospital Universitario y Politécnico La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026 Valencia, Spain
| | - Barbara Hero
- Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical Faculty, University of Cologne, 50937 Cologne, Germany
| | - Ruth Ladenstein
- St. Anna Children’s Cancer Research Institute, Zimmermannplatz 10, 1090 Vienna, Austria
| | - Luis Martí-Bonmatí
- Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026 Valencia, Spain
- Área Clínica de Imagen Médica, Hospital Universitario y Politécnico La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026 Valencia, Spain
- Correspondence: (D.V.-C.); (L.M.-B.)
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12
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Index lesion contouring on prostate MRI for targeted MRI/US fusion biopsy - Evaluation of mismatch between radiologists and urologists. Eur J Radiol 2023; 162:110763. [PMID: 36898172 DOI: 10.1016/j.ejrad.2023.110763] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 02/04/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023]
Abstract
PURPOSE Mistargeting of focal lesions due to inaccurate segmentations can lead to false-negative findings on MRI-guided targeted biopsies. The purpose of this retrospective study was to examine inter-reader agreement of prostate index lesion segmentations from actual biopsy data between urologists and radiologists. METHOD Consecutive patients undergoing transperineal MRI-targeted prostate biopsy for PI-RADS 3-5 lesions between January 2020 and December 2021 were included. Agreement between segmentations on T2w-images between urologists and radiologists was assessed with Dice similarity coefficient (DSC) and 95 % Hausdorff distance (95 % HD). Differences in similarity scores were compared using Wilcoxon test. Differences depending on lesion features (size, zonal location, PI-RADS scores, lesion distinctness) were tested with Mann-Whitney U test. Correlation with prostate signal-intensity homogeneity score (PSHS) and lesion size was tested with Spearman's rank correlation. RESULTS Ninety-three patients (mean age 64.9 ± 7.1y, median serum PSA 6.5 [4.33-10.00]) were included. Mean similarity scores were statistically significantly lower between urologists and radiologists compared to radiologists only (DSC 0.41 ± 0.24 vs. 0.59 ± 0.23, p < 0.01; 95 %HD 6.38 ± 5.45 mm vs. 4.47 ± 4.12 mm, p < 0.01). There was a moderate and strong positive correlation between DSC scores and lesion size for segmentations from urologists and radiologists (ρ = 0.331, p = 0.002) and radiologists only (ρ = 0.501, p < 0.001). Similarity scores were worse in lesions ≤ 10 mm while other lesion features did not significantly influence similarity scores. CONCLUSION There is significant mismatch of prostate index lesion segmentations between urologists and radiologists. Segmentation agreement positively correlates with lesion size. PI-RADS scores, zonal location, lesion distinctness, and PSHS show no significant impact on segmentation agreement. These findings could underpin benefits of perilesional biopsies.
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