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Hayn D, Kreiner K, Sandner E, Baumgartner M, Jammerbund B, Falgenhauer M, Düster V, Devi-Marulkar P, Schleiermacher G, Ladenstein R, Schreier G. Use Cases Requiring Privacy-Preserving Record Linkage in Paediatric Oncology. Cancers (Basel) 2024; 16:2696. [PMID: 39123424 PMCID: PMC11311357 DOI: 10.3390/cancers16152696] [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: 06/29/2024] [Revised: 07/24/2024] [Accepted: 07/24/2024] [Indexed: 08/12/2024] Open
Abstract
Large datasets in paediatric oncology are inherently rare. Therefore, it is paramount to fully exploit all available data, which are distributed over several resources, including biomaterials, images, clinical trials, and registries. With privacy-preserving record linkage (PPRL), personalised or pseudonymised datasets can be merged, without disclosing the patients' identities. Although PPRL is implemented in various settings, use case descriptions are currently fragmented and incomplete. The present paper provides a comprehensive overview of current and future use cases for PPRL in paediatric oncology. We analysed the literature, projects, and trial protocols, identified use cases along a hypothetical patient journey, and discussed use cases with paediatric oncology experts. To structure PPRL use cases, we defined six key dimensions: distributed personalised records, pseudonymisation, distributed pseudonymised records, record linkage, linked data, and data analysis. Selected use cases were described (a) per dimension and (b) on a multi-dimensional level. While focusing on paediatric oncology, most aspects are also applicable to other (particularly rare) diseases. We conclude that PPRL is a key concept in paediatric oncology. Therefore, PPRL strategies should already be considered when starting research projects, to avoid distributed data silos, to maximise the knowledge derived from collected data, and, ultimately, to improve outcomes for children with cancer.
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Affiliation(s)
- Dieter Hayn
- Center for Health and Bioresources, AIT Austrian Institute of Technology, 8020 Graz, Austria (M.B.)
| | - Karl Kreiner
- Center for Health and Bioresources, AIT Austrian Institute of Technology, 8020 Graz, Austria (M.B.)
| | - Emanuel Sandner
- Center for Health and Bioresources, AIT Austrian Institute of Technology, 8020 Graz, Austria (M.B.)
| | - Martin Baumgartner
- Center for Health and Bioresources, AIT Austrian Institute of Technology, 8020 Graz, Austria (M.B.)
- Institute of Neural Engineering, Graz University of Technology, 8010 Graz, Austria
| | - Bernhard Jammerbund
- Center for Health and Bioresources, AIT Austrian Institute of Technology, 8020 Graz, Austria (M.B.)
| | - Markus Falgenhauer
- Center for Health and Bioresources, AIT Austrian Institute of Technology, 8020 Graz, Austria (M.B.)
| | - Vanessa Düster
- St. Anna Kinderkrebsforschungs GmbH, 1090 Wien, Austria (R.L.)
| | | | | | - Ruth Ladenstein
- St. Anna Kinderkrebsforschungs GmbH, 1090 Wien, Austria (R.L.)
| | - Guenter Schreier
- Center for Health and Bioresources, AIT Austrian Institute of Technology, 8020 Graz, Austria (M.B.)
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Valenti A, Falcone I, Valenti F, Ricciardi E, Di Martino S, Maccallini MT, Cerro M, Desiderio F, Miseo L, Russillo M, Guerrisi A. Biobanks as an Indispensable Tool in the "Era" of Precision Medicine: Key Role in the Management of Complex Diseases, Such as Melanoma. J Pers Med 2024; 14:731. [PMID: 39063985 PMCID: PMC11278009 DOI: 10.3390/jpm14070731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2024] [Revised: 07/02/2024] [Accepted: 07/03/2024] [Indexed: 07/28/2024] Open
Abstract
In recent years, medicine has undergone profound changes, strongly entering a new phase defined as the "era of precision medicine". In this context, patient clinical management involves various scientific approaches that allow for a comprehensive pathology evaluation: from preventive processes (where applicable) to genetic and diagnostic studies. In this scenario, biobanks play an important role and, over the years, have gained increasing prestige, moving from small deposits to large collections of samples of various natures. Disease-oriented biobanks are rapidly developing as they provide useful information for the management of complex diseases, such as melanoma. Indeed, melanoma, given its highly heterogeneous characteristics, is one of the oncologic diseases with the greatest clinical and therapeutic management complexity. So, the possibility of extrapolating tissue, genetic and imaging data from dedicated biobanks could result in more selective study approaches. In this review, we specifically analyze the several biobank types to evaluate their role in technology development, patient monitoring and research of new biomarkers, especially in the melanoma context.
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Affiliation(s)
- Alessandro Valenti
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy; (F.D.); (L.M.); (A.G.)
| | - Italia Falcone
- SAFU, Department of Research, Advanced Diagnostics, and Technological Innovation, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy;
| | - Fabio Valenti
- UOC Oncological Translational Research, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy; (F.V.); (E.R.)
| | - Elena Ricciardi
- UOC Oncological Translational Research, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy; (F.V.); (E.R.)
| | - Simona Di Martino
- UOC Pathology Unit, Biobank IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy;
| | - Maria Teresa Maccallini
- Department of Clinical and Molecular Medicine, Università La Sapienza di Roma, 00185 Rome, Italy; (M.T.M.); (M.C.)
| | - Marianna Cerro
- Department of Clinical and Molecular Medicine, Università La Sapienza di Roma, 00185 Rome, Italy; (M.T.M.); (M.C.)
| | - Flora Desiderio
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy; (F.D.); (L.M.); (A.G.)
| | - Ludovica Miseo
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy; (F.D.); (L.M.); (A.G.)
| | - Michelangelo Russillo
- Division of Medical Oncology, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy;
| | - Antonino Guerrisi
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy; (F.D.); (L.M.); (A.G.)
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Veiga-Canuto D, Fernández-Patón M, Cerdà Alberich L, Jiménez Pastor A, Gomis Maya A, Carot Sierra JM, Sangüesa Nebot C, Martínez de las Heras B, Pötschger U, Taschner-Mandl S, Neri E, Cañete A, Ladenstein R, Hero B, Alberich-Bayarri Á, Martí-Bonmatí L. Reproducibility Analysis of Radiomic Features on T2-weighted MR Images after Processing and Segmentation Alterations in Neuroblastoma Tumors. Radiol Artif Intell 2024; 6:e230208. [PMID: 38864742 PMCID: PMC11294951 DOI: 10.1148/ryai.230208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 04/22/2024] [Accepted: 05/30/2024] [Indexed: 06/13/2024]
Abstract
Purpose To evaluate the reproducibility of radiomics features extracted from T2-weighted MR images in patients with neuroblastoma. Materials and Methods A retrospective study included 419 patients (mean age, 29 months ± 34 [SD]; 220 male, 199 female) with neuroblastic tumors diagnosed between 2002 and 2023, within the scope of the PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers (ie, PRIMAGE) project, involving 746 T2/T2*-weighted MRI sequences at diagnosis and/or after initial chemotherapy. Images underwent processing steps (denoising, inhomogeneity bias field correction, normalization, and resampling). Tumors were automatically segmented, and 107 shape, first-order, and second-order radiomics features were extracted, considered as the reference standard. Subsequently, the previous image processing settings were modified, and volumetric masks were applied. New radiomics features were extracted and compared with the reference standard. Reproducibility was assessed using the concordance correlation coefficient (CCC); intrasubject repeatability was measured using the coefficient of variation (CoV). Results When normalization was omitted, only 5% of the radiomics features demonstrated high reproducibility. Statistical analysis revealed significant changes in the normalization and resampling processes (P < .001). Inhomogeneities removal had the least impact on radiomics (83% of parameters remained stable). Shape features remained stable after mask modifications, with a CCC greater than 0.90. Mask modifications were the most favorable changes for achieving high CCC values, with a radiomics features stability of 70%. Only 7% of second-order radiomics features showed an excellent CoV of less than 0.10. Conclusion Modifications in the T2-weighted MRI preparation process in patients with neuroblastoma resulted in changes in radiomics features, with normalization identified as the most influential factor for reproducibility. Inhomogeneities removal had the least impact on radiomics features. Keywords: Pediatrics, MR Imaging, Oncology, Radiomics, Reproducibility, Repeatability, Neuroblastic Tumors Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Safdar and Galaria in this issue.
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Affiliation(s)
- Diana Veiga-Canuto
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Matías Fernández-Patón
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Leonor Cerdà Alberich
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Ana Jiménez Pastor
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Armando Gomis Maya
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Jose Miguel Carot Sierra
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Cinta Sangüesa Nebot
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Blanca Martínez de las Heras
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Ulrike Pötschger
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Sabine Taschner-Mandl
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Emanuele Neri
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Adela Cañete
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Ruth Ladenstein
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Barbara Hero
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Ángel Alberich-Bayarri
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
| | - Luis Martí-Bonmatí
- From the Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A planta 7, 46026 Valencia, Spain (D.V.C., M.F.P., L.C.A., A.G.M., L.M.B.); Área Clínica de Imagen Médica (D.V.C., C.S.N., L.M.B.) and Department of Pediatric Oncology (B.M.d.l.H., A.C.), Hospital Universitari i Politècnic La Fe, Valencia, Spain; Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain (A.J.P., A.A.B.); Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain (J.M.C.S.); St. Anna Children’s Cancer Research Institute, Vienna, Austria (U.P., S.T.M., R.L.); Division of Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy (E.N.); and Department of Pediatric Oncology and Hematology, University Children’s Hospital of Cologne, Medical 18 Faculty, University of Cologne, Cologne, Germany (B.H.)
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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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5
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Katsoulakis E, Wang Q, Wu H, Shahriyari L, Fletcher R, Liu J, Achenie L, Liu H, Jackson P, Xiao Y, Syeda-Mahmood T, Tuli R, Deng J. Digital twins for health: a scoping review. NPJ Digit Med 2024; 7:77. [PMID: 38519626 PMCID: PMC10960047 DOI: 10.1038/s41746-024-01073-0] [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: 08/22/2023] [Accepted: 03/07/2024] [Indexed: 03/25/2024] Open
Abstract
The use of digital twins (DTs) has proliferated across various fields and industries, with a recent surge in the healthcare sector. The concept of digital twin for health (DT4H) holds great promise to revolutionize the entire healthcare system, including management and delivery, disease treatment and prevention, and health well-being maintenance, ultimately improving human life. The rapid growth of big data and continuous advancement in data science (DS) and artificial intelligence (AI) have the potential to significantly expedite DT research and development by providing scientific expertise, essential data, and robust cybertechnology infrastructure. Although various DT initiatives have been underway in the industry, government, and military, DT4H is still in its early stages. This paper presents an overview of the current applications of DTs in healthcare, examines consortium research centers and their limitations, and surveys the current landscape of emerging research and development opportunities in healthcare. We envision the emergence of a collaborative global effort among stakeholders to enhance healthcare and improve the quality of life for millions of individuals worldwide through pioneering research and development in the realm of DT technology.
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Affiliation(s)
- Evangelia Katsoulakis
- VA Informatics and Computing Infrastructure, Salt Lake City, UT, 84148, USA
- Department of Radiation Oncology, University of South Florida, Tampa, FL, 33606, USA
| | - Qi Wang
- Department of Mathematics, University of South Carolina, Columbia, SC, 29208, USA
| | - Huanmei Wu
- Department of Health Services Administration and Policy, Temple University, Philadelphia, PA, 19122, USA
| | - Leili Shahriyari
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA, 01003, USA
| | - Richard Fletcher
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, 02139, USA
| | - Jinwei Liu
- Department of Computer and Information Sciences, Florida A&M University, Tallahassee, FL, 32307, USA
| | - Luke Achenie
- Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24060, USA
| | - Hongfang Liu
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Pamela Jackson
- Precision Neurotherapeutics Innovation Program & Department of Neurosurgery, Mayo Clinic, Phoenix, AZ, 85003, USA
| | - Ying Xiao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | - Richard Tuli
- Department of Radiation Oncology, University of South Florida, Tampa, FL, 33606, USA
| | - Jun Deng
- Department of Therapeutic Radiology, Yale University, New Haven, CT, 06510, USA.
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6
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Kelly BS, Mathur P, McGuinness G, Dillon H, Lee EH, Yeom KW, Lawlor A, Killeen RP. A Radiomic "Warning Sign" of Progression on Brain MRI in Individuals with MS. AJNR Am J Neuroradiol 2024; 45:236-243. [PMID: 38216299 DOI: 10.3174/ajnr.a8104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 11/08/2023] [Indexed: 01/14/2024]
Abstract
BACKGROUND AND PURPOSE MS is a chronic progressive, idiopathic, demyelinating disorder whose diagnosis is contingent on the interpretation of MR imaging. New MR imaging lesions are an early biomarker of disease progression. We aimed to evaluate a machine learning model based on radiomics features in predicting progression on MR imaging of the brain in individuals with MS. MATERIALS AND METHODS This retrospective cohort study with external validation on open-access data obtained full ethics approval. Longitudinal MR imaging data for patients with MS were collected and processed for machine learning. Radiomics features were extracted at the future location of a new lesion in the patients' prior MR imaging ("prelesion"). Additionally, "control" samples were obtained from the normal-appearing white matter for each participant. Machine learning models for binary classification were trained and tested and then evaluated the external data of the model. RESULTS The total number of participants was 167. Of the 147 in the training/test set, 102 were women and 45 were men. The average age was 42 (range, 21-74 years). The best-performing radiomics-based model was XGBoost, with accuracy, precision, recall, and F1-score of 0.91, 0.91, 0.91, and 0.91 on the test set, and 0.74, 0.74, 0.74, and 0.70 on the external validation set. The 5 most important radiomics features to the XGBoost model were associated with the overall heterogeneity and low gray-level emphasis of the segmented regions. Probability maps were produced to illustrate potential future clinical applications. CONCLUSIONS Our machine learning model based on radiomics features successfully differentiated prelesions from normal-appearing white matter. This outcome suggests that radiomics features from normal-appearing white matter could serve as an imaging biomarker for progression of MS on MR imaging.
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Affiliation(s)
- Brendan S Kelly
- From the Department of Radiology (B.S.K., G.M., H.D., R.P.K.), St. Vincent's University Hospital, Dublin, Ireland
- Insight Centre for Data Analytics (B.S.K., P.M., A.L.), University College Dublin, Dublin, Ireland
- Wellcome Trust and Health Research Board (B.S.K.), Irish Clinical Academic Training, Dublin, Ireland
- School of Medicine (B.S.K.), University College Dublin, Dublin, Ireland
| | - Prateek Mathur
- Insight Centre for Data Analytics (B.S.K., P.M., A.L.), University College Dublin, Dublin, Ireland
| | - Gerard McGuinness
- From the Department of Radiology (B.S.K., G.M., H.D., R.P.K.), St. Vincent's University Hospital, Dublin, Ireland
| | - Henry Dillon
- From the Department of Radiology (B.S.K., G.M., H.D., R.P.K.), St. Vincent's University Hospital, Dublin, Ireland
| | - Edward H Lee
- Lucille Packard Children's Hospital at Stanford (E.H.L., K.W.Y.), Stanford, California
| | - Kristen W Yeom
- Lucille Packard Children's Hospital at Stanford (E.H.L., K.W.Y.), Stanford, California
| | - Aonghus Lawlor
- Insight Centre for Data Analytics (B.S.K., P.M., A.L.), University College Dublin, Dublin, Ireland
| | - Ronan P Killeen
- From the Department of Radiology (B.S.K., G.M., H.D., R.P.K.), St. Vincent's University Hospital, Dublin, Ireland
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7
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Brancato V, Esposito G, Coppola L, Cavaliere C, Mirabelli P, Scapicchio C, Borgheresi R, Neri E, Salvatore M, Aiello M. Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine. J Transl Med 2024; 22:136. [PMID: 38317237 PMCID: PMC10845786 DOI: 10.1186/s12967-024-04891-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 01/14/2024] [Indexed: 02/07/2024] Open
Abstract
Advancements in data acquisition and computational methods are generating a large amount of heterogeneous biomedical data from diagnostic domains such as clinical imaging, pathology, and next-generation sequencing (NGS), which help characterize individual differences in patients. However, this information needs to be available and suitable to promote and support scientific research and technological development, supporting the effective adoption of the precision medicine approach in clinical practice. Digital biobanks can catalyze this process, facilitating the sharing of curated and standardized imaging data, clinical, pathological and molecular data, crucial to enable the development of a comprehensive and personalized data-driven diagnostic approach in disease management and fostering the development of computational predictive models. This work aims to frame this perspective, first by evaluating the state of standardization of individual diagnostic domains and then by identifying challenges and proposing a possible solution towards an integrative approach that can guarantee the suitability of information that can be shared through a digital biobank. Our analysis of the state of the art shows the presence and use of reference standards in biobanks and, generally, digital repositories for each specific domain. Despite this, standardization to guarantee the integration and reproducibility of the numerical descriptors generated by each domain, e.g. radiomic, pathomic and -omic features, is still an open challenge. Based on specific use cases and scenarios, an integration model, based on the JSON format, is proposed that can help address this problem. Ultimately, this work shows how, with specific standardization and promotion efforts, the digital biobank model can become an enabling technology for the comprehensive study of diseases and the effective development of data-driven technologies at the service of precision medicine.
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Affiliation(s)
| | - Giuseppina Esposito
- Bio Check Up S.R.L, 80121, Naples, Italy
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131, Naples, Italy
| | | | | | - Peppino Mirabelli
- UOS Laboratori di Ricerca e Biobanca, AORN Santobono-Pausilipon, Via Teresa Ravaschieri, 8, 80122, Naples, Italy
| | - Camilla Scapicchio
- Academic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy
| | - Rita Borgheresi
- Academic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy
| | - Emanuele Neri
- Academic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy
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8
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Jahangiri L. Predicting Neuroblastoma Patient Risk Groups, Outcomes, and Treatment Response Using Machine Learning Methods: A Review. Med Sci (Basel) 2024; 12:5. [PMID: 38249081 PMCID: PMC10801560 DOI: 10.3390/medsci12010005] [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: 11/04/2023] [Revised: 12/28/2023] [Accepted: 01/03/2024] [Indexed: 01/23/2024] Open
Abstract
Neuroblastoma, a paediatric malignancy with high rates of cancer-related morbidity and mortality, is of significant interest to the field of paediatric cancers. High-risk NB tumours are usually metastatic and result in survival rates of less than 50%. Machine learning approaches have been applied to various neuroblastoma patient data to retrieve relevant clinical and biological information and develop predictive models. Given this background, this study will catalogue and summarise the literature that has used machine learning and statistical methods to analyse data such as multi-omics, histological sections, and medical images to make clinical predictions. Furthermore, the question will be turned on its head, and the use of machine learning to accurately stratify NB patients by risk groups and to predict outcomes, including survival and treatment response, will be summarised. Overall, this study aims to catalogue and summarise the important work conducted to date on the subject of expression-based predictor models and machine learning in neuroblastoma for risk stratification and patient outcomes including survival, and treatment response which may assist and direct future diagnostic and therapeutic efforts.
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Affiliation(s)
- Leila Jahangiri
- School of Science and Technology, Nottingham Trent University, Clifton Site, Nottingham NG11 8NS, UK;
- Division of Cellular and Molecular Pathology, Addenbrookes Hospital, University of Cambridge, Cambridge CB2 0QQ, UK
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9
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Borau C, Wertheim KY, Hervas-Raluy S, Sainz-DeMena D, Walker D, Chisholm R, Richmond P, Varella V, Viceconti M, Montero A, Gregori-Puigjané E, Mestres J, Kasztelnik M, García-Aznar JM. A multiscale orchestrated computational framework to reveal emergent phenomena in neuroblastoma. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 241:107742. [PMID: 37572512 DOI: 10.1016/j.cmpb.2023.107742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 07/19/2023] [Accepted: 07/31/2023] [Indexed: 08/14/2023]
Abstract
Neuroblastoma is a complex and aggressive type of cancer that affects children. Current treatments involve a combination of surgery, chemotherapy, radiotherapy, and stem cell transplantation. However, treatment outcomes vary due to the heterogeneous nature of the disease. Computational models have been used to analyse data, simulate biological processes, and predict disease progression and treatment outcomes. While continuum cancer models capture the overall behaviour of tumours, and agent-based models represent the complex behaviour of individual cells, multiscale models represent interactions at different organisational levels, providing a more comprehensive understanding of the system. In 2018, the PRIMAGE consortium was formed to build a cloud-based decision support system for neuroblastoma, including a multi-scale model for patient-specific simulations of disease progression. In this work we have developed this multi-scale model that includes data such as patient's tumour geometry, cellularity, vascularization, genetics and type of chemotherapy treatment, and integrated it into an online platform that runs the simulations on a high-performance computation cluster using Onedata and Kubernetes technologies. This infrastructure will allow clinicians to optimise treatment regimens and reduce the number of costly and time-consuming clinical trials. This manuscript outlines the challenging framework's model architecture, data workflow, hypothesis, and resources employed in its development.
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Affiliation(s)
- C Borau
- Multiscale in Mechanical and Biological Engineering (M2BE), Aragon Institute of Engineering Research (I3A), Mechanical Engineering Department, University of Zaragoza, Zaragoza, Spain.
| | - K Y Wertheim
- Department of Computer Science and InsigneoInstitute for In Silico Medicine, University of Sheffield, Sheffield, United Kingdom; Centre of Excellence for Data Science, Artificial Intelligence and Modelling and School of Computer Science, University of Hull, Kingston upon Hull, United Kingdom
| | - S Hervas-Raluy
- Multiscale in Mechanical and Biological Engineering (M2BE), Aragon Institute of Engineering Research (I3A), Mechanical Engineering Department, University of Zaragoza, Zaragoza, Spain
| | - D Sainz-DeMena
- Multiscale in Mechanical and Biological Engineering (M2BE), Aragon Institute of Engineering Research (I3A), Mechanical Engineering Department, University of Zaragoza, Zaragoza, Spain
| | - D Walker
- Department of Computer Science and InsigneoInstitute for In Silico Medicine, University of Sheffield, Sheffield, United Kingdom
| | - R Chisholm
- Department of Computer Science and InsigneoInstitute for In Silico Medicine, University of Sheffield, Sheffield, United Kingdom
| | - P Richmond
- Department of Computer Science and InsigneoInstitute for In Silico Medicine, University of Sheffield, Sheffield, United Kingdom
| | - V Varella
- Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Bologna, Italy; Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
| | - M Viceconti
- Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Bologna, Italy; Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
| | - A Montero
- Chemotargets SL, Baldiri Reixac 4, Parc Cientific de Barcelona (PCB), Barcelona, Spain
| | - E Gregori-Puigjané
- Chemotargets SL, Baldiri Reixac 4, Parc Cientific de Barcelona (PCB), Barcelona, Spain
| | - J Mestres
- Chemotargets SL, Baldiri Reixac 4, Parc Cientific de Barcelona (PCB), Barcelona, Spain
| | - M Kasztelnik
- ACC Cyfronet, AGH University of Science and Technology, Kraków, Poland
| | - J M García-Aznar
- Multiscale in Mechanical and Biological Engineering (M2BE), Aragon Institute of Engineering Research (I3A), Mechanical Engineering Department, University of Zaragoza, Zaragoza, Spain
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10
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Babarenda Gamage TP, Elsayed A, Lin C, Wu A, Feng Y, Yu J, Gao L, Wijenayaka S, Nash MP, Doyle AJ, Nickerson DP. Vision for the 12 LABOURS Digital Twin Platform . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083471 DOI: 10.1109/embc40787.2023.10341138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Clinical translation of personalised computational physiology workflows and digital twins can revolutionise healthcare by providing a better understanding of an individual's physiological processes and any changes that could lead to serious health consequences. However, the lack of common infrastructure for developing these workflows and digital twins has hampered the realisation of this vision. The Auckland Bioengineering Institute's 12 LABOURS project aims to address these challenges by developing a Digital Twin Platform to enable researchers to develop and personalise computational physiology models to an individual's health data in clinical workflows. This will allow clinical trials to be more efficiently conducted to demonstrate the efficacy of these personalised clinical workflows. We present a demonstration of the platform's capabilities using publicly available data and an existing automated computational physiology workflow developed to assist clinicians with diagnosing and treating breast cancer. We also demonstrate how the platform facilitates the discovery and exploration of data and the presentation of workflow results as part of clinical reports through a web portal. Future developments will involve integrating the platform with health systems and remote-monitoring devices such as wearables and implantables to support home-based healthcare. Integrating outputs from multiple workflows that are applied to the same individual's health data will also enable the generation of their personalised digital twin.Clinical Relevance- The proposed 12 LABOURS Digital Twin Platform will enable researchers to 1) more efficiently conduct clinical trials to assess the efficacy of their computational physiology workflows and support the clinical translation of their research; 2) reuse primary and derived data from these workflows to generate novel workflows; and 3) generate personalised digital twins by integrating the outputs of different computational physiology workflows.
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11
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Kondylakis H, Kalokyri V, Sfakianakis S, Marias K, Tsiknakis M, Jimenez-Pastor A, Camacho-Ramos E, Blanquer I, Segrelles JD, López-Huguet S, Barelle C, Kogut-Czarkowska M, Tsakou G, Siopis N, Sakellariou Z, Bizopoulos P, Drossou V, Lalas A, Votis K, Mallol P, Marti-Bonmati L, Alberich LC, Seymour K, Boucher S, Ciarrocchi E, Fromont L, Rambla J, Harms A, Gutierrez A, Starmans MPA, Prior F, Gelpi JL, Lekadir K. Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects. Eur Radiol Exp 2023; 7:20. [PMID: 37150779 PMCID: PMC10164664 DOI: 10.1186/s41747-023-00336-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 03/02/2023] [Indexed: 05/09/2023] Open
Abstract
Artificial intelligence (AI) is transforming the field of medical imaging and has the potential to bring medicine from the era of 'sick-care' to the era of healthcare and prevention. The development of AI requires access to large, complete, and harmonized real-world datasets, representative of the population, and disease diversity. However, to date, efforts are fragmented, based on single-institution, size-limited, and annotation-limited datasets. Available public datasets (e.g., The Cancer Imaging Archive, TCIA, USA) are limited in scope, making model generalizability really difficult. In this direction, five European Union projects are currently working on the development of big data infrastructures that will enable European, ethically and General Data Protection Regulation-compliant, quality-controlled, cancer-related, medical imaging platforms, in which both large-scale data and AI algorithms will coexist. The vision is to create sustainable AI cloud-based platforms for the development, implementation, verification, and validation of trustable, usable, and reliable AI models for addressing specific unmet needs regarding cancer care provision. In this paper, we present an overview of the development efforts highlighting challenges and approaches selected providing valuable feedback to future attempts in the area.Key points• Artificial intelligence models for health imaging require access to large amounts of harmonized imaging data and metadata.• Main infrastructures adopted either collect centrally anonymized data or enable access to pseudonymized distributed data.• Developing a common data model for storing all relevant information is a challenge.• Trust of data providers in data sharing initiatives is essential.• An online European Union meta-tool-repository is a necessity minimizing effort duplication for the various projects in the area.
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Affiliation(s)
| | | | | | - Kostas Marias
- FORTH-ICS, FORTH-ICS, N. Plastira 100, Heraklion, Crete, Greece
| | | | | | | | | | | | | | | | | | - Gianna Tsakou
- MAGGIOLI S.P.A., Research and Development Lab, Marousi, Greece
| | - Nikolaos Siopis
- Centre of Research & Technology - Hellas, Information Technologies Institute, Thermi - Thessaloniki, Greece
| | - Zisis Sakellariou
- Centre of Research & Technology - Hellas, Information Technologies Institute, Thermi - Thessaloniki, Greece
| | - Paschalis Bizopoulos
- Centre of Research & Technology - Hellas, Information Technologies Institute, Thermi - Thessaloniki, Greece
| | - Vicky Drossou
- Centre of Research & Technology - Hellas, Information Technologies Institute, Thermi - Thessaloniki, Greece
| | - Antonios Lalas
- Centre of Research & Technology - Hellas, Information Technologies Institute, Thermi - Thessaloniki, Greece
| | - Konstantinos Votis
- Centre of Research & Technology - Hellas, Information Technologies Institute, Thermi - Thessaloniki, Greece
| | - Pedro Mallol
- La Fe Health Research Institute, Valencia, Spain
| | | | | | | | | | | | - Lauren Fromont
- European Genome-Phenome Archive, Centre for Genomic Regulation, Barcelona, Spain
| | - Jordi Rambla
- European Genome-Phenome Archive, Centre for Genomic Regulation, Barcelona, Spain
| | | | | | | | - Fred Prior
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
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12
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Hervas-Raluy S, Wirthl B, Guerrero PE, Robalo Rei G, Nitzler J, Coronado E, Font de Mora Sainz J, Schrefler BA, Gomez-Benito MJ, Garcia-Aznar JM, Wall WA. Tumour growth: An approach to calibrate parameters of a multiphase porous media model based on in vitro observations of Neuroblastoma spheroid growth in a hydrogel microenvironment. Comput Biol Med 2023; 159:106895. [PMID: 37060771 DOI: 10.1016/j.compbiomed.2023.106895] [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: 01/13/2023] [Revised: 03/09/2023] [Accepted: 04/09/2023] [Indexed: 04/17/2023]
Abstract
To unravel processes that lead to the growth of solid tumours, it is necessary to link knowledge of cancer biology with the physical properties of the tumour and its interaction with the surrounding microenvironment. Our understanding of the underlying mechanisms is however still imprecise. We therefore developed computational physics-based models, which incorporate the interaction of the tumour with its surroundings based on the theory of porous media. However, the experimental validation of such models represents a challenge to its clinical use as a prognostic tool. This study combines a physics-based model with in vitro experiments based on microfluidic devices used to mimic a three-dimensional tumour microenvironment. By conducting a global sensitivity analysis, we identify the most influential input parameters and infer their posterior distribution based on Bayesian calibration. The resulting probability density is in agreement with the scattering of the experimental data and thus validates the proposed workflow. This study demonstrates the huge challenges associated with determining precise parameters with usually only limited data for such complex processes and models, but also demonstrates in general how to indirectly characterise the mechanical properties of neuroblastoma spheroids that cannot feasibly be measured experimentally.
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Affiliation(s)
- Silvia Hervas-Raluy
- Multiscale in Mechanical and Biological Engineering, Department of Mechanical Engineering, University of Zaragoza, Aragon Institute for Engineering Research (I3A), Maria de Luna 3, Zaragoza, 50018, Spain.
| | - Barbara Wirthl
- Institute for Computational Mechanics, Technical University of Munich, TUM School of Engineering and Design, Department of Engineering Physics & Computation, Boltzmannstraße 15, Garching b. Munich, 85748, Germany
| | - Pedro E Guerrero
- Multiscale in Mechanical and Biological Engineering, Department of Mechanical Engineering, University of Zaragoza, Aragon Institute for Engineering Research (I3A), Maria de Luna 3, Zaragoza, 50018, Spain
| | - Gil Robalo Rei
- Institute for Computational Mechanics, Technical University of Munich, TUM School of Engineering and Design, Department of Engineering Physics & Computation, Boltzmannstraße 15, Garching b. Munich, 85748, Germany
| | - Jonas Nitzler
- Institute for Computational Mechanics, Technical University of Munich, TUM School of Engineering and Design, Department of Engineering Physics & Computation, Boltzmannstraße 15, Garching b. Munich, 85748, Germany; Professorship for Data-Driven Materials Modeling, Technical University of Munich, TUM School of Engineering and Design, Department of Engineering Physics & Computation, Boltzmannstraße 15, Garching b. Munich, 85748, Germany
| | - Esther Coronado
- Clinical and Translational Oncology Research Group, Instituto de Investigación La Fe,, Fernando Abril Martorell 106, Valencia, 46026, Spain
| | - Jaime Font de Mora Sainz
- Clinical and Translational Oncology Research Group, Instituto de Investigación La Fe,, Fernando Abril Martorell 106, Valencia, 46026, Spain
| | - Bernhard A Schrefler
- Department of Civil, Environmental and Architectural Engineering, University of Padua, Marzolo 9, Padua, 35131, Italy; Institute for Advanced Study, Technical University of Munich, Boltzmannstraße 15, Garching b. Munich, 85748, Germany
| | - Maria Jose Gomez-Benito
- Multiscale in Mechanical and Biological Engineering, Department of Mechanical Engineering, University of Zaragoza, Aragon Institute for Engineering Research (I3A), Maria de Luna 3, Zaragoza, 50018, Spain
| | - Jose Manuel Garcia-Aznar
- Multiscale in Mechanical and Biological Engineering, Department of Mechanical Engineering, University of Zaragoza, Aragon Institute for Engineering Research (I3A), Maria de Luna 3, Zaragoza, 50018, Spain
| | - Wolfgang A Wall
- Institute for Computational Mechanics, Technical University of Munich, TUM School of Engineering and Design, Department of Engineering Physics & Computation, Boltzmannstraße 15, Garching b. Munich, 85748, Germany
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Italia M, Wertheim KY, Taschner-Mandl S, Walker D, Dercole F. Mathematical Model of Clonal Evolution Proposes a Personalised Multi-Modal Therapy for High-Risk Neuroblastoma. Cancers (Basel) 2023; 15:cancers15071986. [PMID: 37046647 PMCID: PMC10093626 DOI: 10.3390/cancers15071986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/15/2023] [Accepted: 03/24/2023] [Indexed: 03/29/2023] Open
Abstract
Neuroblastoma is the most common extra-cranial solid tumour in children. Despite multi-modal therapy, over half of the high-risk patients will succumb. One contributing factor is the one-size-fits-all nature of multi-modal therapy. For example, during the first step (induction chemotherapy), the standard regimen (rapid COJEC) administers fixed doses of chemotherapeutic agents in eight two-week cycles. Perhaps because of differences in resistance, this standard regimen results in highly heterogeneous outcomes in different tumours. In this study, we formulated a mathematical model comprising ordinary differential equations. The equations describe the clonal evolution within a neuroblastoma tumour being treated with vincristine and cyclophosphamide, which are used in the rapid COJEC regimen, including genetically conferred and phenotypic drug resistance. The equations also describe the agents’ pharmacokinetics. We devised an optimisation algorithm to find the best chemotherapy schedules for tumours with different pre-treatment clonal compositions. The optimised chemotherapy schedules exploit the cytotoxic difference between the two drugs and intra-tumoural clonal competition to shrink the tumours as much as possible during induction chemotherapy and before surgical removal. They indicate that induction chemotherapy can be improved by finding and using personalised schedules. More broadly, we propose that the overall multi-modal therapy can be enhanced by employing targeted therapies against the mutations and oncogenic pathways enriched and activated by the chemotherapeutic agents. To translate the proposed personalised multi-modal therapy into clinical use, patient-specific model calibration and treatment optimisation are necessary. This entails a decision support system informed by emerging medical technologies such as multi-region sequencing and liquid biopsies. The results and tools presented in this paper could be the foundation of this decision support system.
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Affiliation(s)
- Matteo Italia
- Department of Electronic, Information, and Bioengineering, Politecnico di Milano, 20133 Milano, Italy
- Correspondence:
| | - Kenneth Y. Wertheim
- Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield S10 2TN, UK
- Department of Computer Science, University of Sheffield, Sheffield S10 2TN, UK
- Centre of Excellence for Data Science, Artificial Intelligence, and Modelling, University of Hull, Kingston upon Hull HU6 7RX, UK
- School of Computer Science, University of Hull, Kingston upon Hull HU6 7RX, UK
| | | | - Dawn Walker
- Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield S10 2TN, UK
- Department of Computer Science, University of Sheffield, Sheffield S10 2TN, UK
| | - Fabio Dercole
- Department of Electronic, Information, and Bioengineering, Politecnico di Milano, 20133 Milano, Italy
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14
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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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15
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Cerdá-Alberich L, Solana J, Mallol P, Ribas G, García-Junco M, Alberich-Bayarri A, Marti-Bonmati L. MAIC-10 brief quality checklist for publications using artificial intelligence and medical images. Insights Imaging 2023; 14:11. [PMID: 36645542 PMCID: PMC9842808 DOI: 10.1186/s13244-022-01355-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 12/20/2022] [Indexed: 01/17/2023] Open
Abstract
The use of artificial intelligence (AI) with medical images to solve clinical problems is becoming increasingly common, and the development of new AI solutions is leading to more studies and publications using this computational technology. As a novel research area, the use of common standards to aid AI developers and reviewers as quality control criteria will improve the peer review process. Although some guidelines do exist, their heterogeneity and extension advocate that more explicit and simple schemes should be applied on the publication practice. Based on a review of existing AI guidelines, a proposal which collects, unifies, and simplifies the most relevant criteria was developed. The MAIC-10 (Must AI Criteria-10) checklist with 10 items was implemented as a guide to design studies and evaluate publications related to AI in the field of medical imaging. Articles published in Insights into Imaging in 2021 were selected to calculate their corresponding MAIC-10 quality score. The mean score was found to be 5.6 ± 1.6, with critical items present in most articles, such as "Clinical need", "Data annotation", "Robustness", and "Transparency" present in more than 80% of papers, while improvements in other areas were identified. MAIC-10 was also observed to achieve the highest intra-observer reproducibility when compared to other existing checklists, with an overall reduction in terms of checklist length and complexity. In summary, MAIC-10 represents a short and simple quality assessment tool which is objective, robust and widely applicable to AI studies in medical imaging.
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Affiliation(s)
- Leonor Cerdá-Alberich
- grid.84393.350000 0001 0360 9602Clinical Medical Imaging Area and Biomedical Imaging Research Group (GIBI230-PREBI), Hospital Universitario y Politécnico La Fe – Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | - Jimena Solana
- grid.84393.350000 0001 0360 9602Clinical Medical Imaging Area and Biomedical Imaging Research Group (GIBI230-PREBI), Hospital Universitario y Politécnico La Fe – Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | - Pedro Mallol
- grid.84393.350000 0001 0360 9602Clinical Medical Imaging Area and Biomedical Imaging Research Group (GIBI230-PREBI), Hospital Universitario y Politécnico La Fe – Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | - Gloria Ribas
- grid.84393.350000 0001 0360 9602Clinical Medical Imaging Area and Biomedical Imaging Research Group (GIBI230-PREBI), Hospital Universitario y Politécnico La Fe – Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | - Miguel García-Junco
- grid.84393.350000 0001 0360 9602Clinical Medical Imaging Area and Biomedical Imaging Research Group (GIBI230-PREBI), Hospital Universitario y Politécnico La Fe – Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | - Angel Alberich-Bayarri
- grid.84393.350000 0001 0360 9602Clinical Medical Imaging Area and Biomedical Imaging Research Group (GIBI230-PREBI), Hospital Universitario y Politécnico La Fe – Instituto de Investigación Sanitaria La Fe, Valencia, Spain ,Quantitative Imaging Biomarkers in Medicine, Quibim SL, Valencia, Spain
| | - Luis Marti-Bonmati
- grid.84393.350000 0001 0360 9602Clinical Medical Imaging Area and Biomedical Imaging Research Group (GIBI230-PREBI), Hospital Universitario y Politécnico La Fe – Instituto de Investigación Sanitaria La Fe, Valencia, Spain
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16
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Liao J, Li X, Gan Y, Han S, Rong P, Wang W, Li W, Zhou L. Artificial intelligence assists precision medicine in cancer treatment. Front Oncol 2023; 12:998222. [PMID: 36686757 PMCID: PMC9846804 DOI: 10.3389/fonc.2022.998222] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 11/22/2022] [Indexed: 01/06/2023] Open
Abstract
Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the same drugs or surgical methods in patients with the same tumor may have different curative effects, leading to the need for more accurate treatment methods for tumors and personalized treatments for patients. The precise treatment of tumors is essential, which renders obtaining an in-depth understanding of the changes that tumors undergo urgent, including changes in their genes, proteins and cancer cell phenotypes, in order to develop targeted treatment strategies for patients. Artificial intelligence (AI) based on big data can extract the hidden patterns, important information, and corresponding knowledge behind the enormous amount of data. For example, the ML and deep learning of subsets of AI can be used to mine the deep-level information in genomics, transcriptomics, proteomics, radiomics, digital pathological images, and other data, which can make clinicians synthetically and comprehensively understand tumors. In addition, AI can find new biomarkers from data to assist tumor screening, detection, diagnosis, treatment and prognosis prediction, so as to providing the best treatment for individual patients and improving their clinical outcomes.
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Affiliation(s)
- Jinzhuang Liao
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xiaoying Li
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Yu Gan
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Shuangze Han
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Pengfei Rong
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
| | - Wei Wang
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
| | - Wei Li
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
| | - Li Zhou
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,Department of Pathology, The Xiangya Hospital of Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
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17
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Yang Y, Zhang Y, Li Y. Artificial intelligence applications in pediatric oncology diagnosis. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:157-169. [PMID: 36937318 PMCID: PMC10017189 DOI: 10.37349/etat.2023.00127] [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: 10/17/2022] [Accepted: 12/30/2022] [Indexed: 03/04/2023] Open
Abstract
Artificial intelligence (AI) algorithms have been applied in abundant medical tasks with high accuracy and efficiency. Physicians can improve their diagnostic efficiency with the assistance of AI techniques for improving the subsequent personalized treatment and surveillance. AI algorithms fundamentally capture data, identify underlying patterns, achieve preset endpoints, and provide decisions and predictions about real-world events with working principles of machine learning and deep learning. AI algorithms with sufficient graphic processing unit power have been demonstrated to provide timely diagnostic references based on preliminary training of large amounts of clinical and imaging data. The sample size issue is an inevitable challenge for pediatric oncology considering its low morbidity and individual heterogeneity. However, this problem may be solved in the near future considering the exponential advancements of AI algorithms technically to decrease the dependence of AI operation on the amount of data sets and the efficiency of computing power. For instance, it could be a feasible solution by shifting convolutional neural networks (CNNs) from adults and sharing CNN algorithms across multiple institutions besides original data. The present review provides important insights into emerging AI applications for the diagnosis of pediatric oncology by systematically overviewing of up-to-date literature.
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Affiliation(s)
- Yuhan Yang
- Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Yimao Zhang
- Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Yuan Li
- Laboratory of Digestive Surgery, State Key Laboratory of Biotherapy and Cancer Center, Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
- Correspondence: Yuan Li, Laboratory of Digestive Surgery, State Key Laboratory of Biotherapy and Cancer Center, Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
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18
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Varella V, Quintela BDM, Kasztelnik M, Viceconti M. Effect of particularisation size on the accuracy and efficiency of a multiscale tumours' growth model. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3657. [PMID: 36282099 DOI: 10.1002/cnm.3657] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 10/05/2022] [Indexed: 06/16/2023]
Abstract
In silico, medicine models are frequently used to represent a phenomenon across multiples space-time scales. Most of these multiscale models require impracticable execution times to be solved, even using high performance computing systems, because typically each representative volume element in the upper scale model is coupled to an instance of the lower scale model; this causes a combinatory explosion of the computational cost, which increases exponentially as the number of scales to be modelled increases. To attenuate this problem, it is a common practice to interpose between the two models a particularisation operator, which maps the upper-scale model results into a smaller number of lower-scale models, and an operator, which maps the fewer results of the lower-scale models on the whole space-time homogenisation domain of upper-scale model. The aim of this study is to explore what is the simplest particularisation / homogenisation scheme that can couple a model aimed to predict the growth of a whole solid tumour (neuroblastoma) to a tissue-scale model of the cell-tissue biology with an acceptable approximation error and a viable computational cost. Using an idealised initial dataset with spatial gradients representative of those of real neuroblastomas, but small enough to be solved without any particularisation, we determined the approximation error and the computational cost of a very simple particularisation strategy based on binning. We found that even such simple algorithm can significantly reduce the computational cost with negligible approximation errors.
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Affiliation(s)
- Vinicius Varella
- Department of Industrial Engineering, Alma Mater Studiorum, University of Bologna, Bologna, Italy
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
| | - Barbara de M Quintela
- Departamento de Ciencia da Computacao, Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil
| | - Marek Kasztelnik
- Academic Computer Center Cyfronet AGH, University of Science and Technology, Krakow, Poland
| | - Marco Viceconti
- Department of Industrial Engineering, Alma Mater Studiorum, University of Bologna, Bologna, Italy
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
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19
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Veiga-Canuto D, Cerdà-Alberich L, Sangüesa Nebot C, Martínez de las Heras B, Pötschger U, Gabelloni M, Carot Sierra JM, Taschner-Mandl S, Düster V, Cañete A, Ladenstein R, Neri E, Martí-Bonmatí L. Comparative Multicentric Evaluation of Inter-Observer Variability in Manual and Automatic Segmentation of Neuroblastic Tumors in Magnetic Resonance Images. Cancers (Basel) 2022; 14:cancers14153648. [PMID: 35954314 PMCID: PMC9367307 DOI: 10.3390/cancers14153648] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/21/2022] [Accepted: 07/26/2022] [Indexed: 02/05/2023] Open
Abstract
Simple Summary Tumor segmentation is a key step in oncologic imaging processing and is a time-consuming process usually performed manually by radiologists. To facilitate it, there is growing interest in applying deep-learning segmentation algorithms. Thus, we explore the variability between two observers performing manual segmentation and use the state-of-the-art deep learning architecture nnU-Net to develop a model to detect and segment neuroblastic tumors on MR images. We were able to show that the variability between nnU-Net and manual segmentation is similar to the inter-observer variability in manual segmentation. Furthermore, we compared the time needed to manually segment the tumors from scratch with the time required for the automatic model to segment the same cases, with posterior human validation with manual adjustment when needed. Abstract Tumor segmentation is one of the key steps in imaging processing. The goals of this study were to assess the inter-observer variability in manual segmentation of neuroblastic tumors and to analyze whether the state-of-the-art deep learning architecture nnU-Net can provide a robust solution to detect and segment tumors on MR images. A retrospective multicenter study of 132 patients with neuroblastic tumors was performed. Dice Similarity Coefficient (DSC) and Area Under the Receiver Operating Characteristic Curve (AUC ROC) were used to compare segmentation sets. Two more metrics were elaborated to understand the direction of the errors: the modified version of False Positive (FPRm) and False Negative (FNR) rates. Two radiologists manually segmented 46 tumors and a comparative study was performed. nnU-Net was trained-tuned with 106 cases divided into five balanced folds to perform cross-validation. The five resulting models were used as an ensemble solution to measure training (n = 106) and validation (n = 26) performance, independently. The time needed by the model to automatically segment 20 cases was compared to the time required for manual segmentation. The median DSC for manual segmentation sets was 0.969 (±0.032 IQR). The median DSC for the automatic tool was 0.965 (±0.018 IQR). The automatic segmentation model achieved a better performance regarding the FPRm. MR images segmentation variability is similar between radiologists and nnU-Net. Time leverage when using the automatic model with posterior visual validation and manual adjustment corresponds to 92.8%.
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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; (L.C.-A.); (L.M.-B.)
- Área Clínica de Imagen Médica, Hospital Universitario y Politécnico La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026 Valencia, Spain;
- Correspondence:
| | - 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; (L.C.-A.); (L.M.-B.)
| | - 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;
| | - 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; (B.M.d.l.H.); (A.C.)
| | - Ulrike Pötschger
- St. Anna Children’s Cancer Research Institute, Zimmermannplatz 10, 1090 Vienna, Austria; (U.P.); (S.T.-M.); (V.D.); (R.L.)
| | - Michela Gabelloni
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma, 67, 56126 Pisa, Italy; (M.G.); (E.N.)
| | - 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;
| | - Sabine Taschner-Mandl
- St. Anna Children’s Cancer Research Institute, Zimmermannplatz 10, 1090 Vienna, Austria; (U.P.); (S.T.-M.); (V.D.); (R.L.)
| | - Vanessa Düster
- St. Anna Children’s Cancer Research Institute, Zimmermannplatz 10, 1090 Vienna, Austria; (U.P.); (S.T.-M.); (V.D.); (R.L.)
| | - Adela Cañete
- Unidad de Oncohematología Pediátrica, Hospital Universitario y Politécnico La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026 Valencia, Spain; (B.M.d.l.H.); (A.C.)
| | - Ruth Ladenstein
- St. Anna Children’s Cancer Research Institute, Zimmermannplatz 10, 1090 Vienna, Austria; (U.P.); (S.T.-M.); (V.D.); (R.L.)
| | - Emanuele Neri
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma, 67, 56126 Pisa, Italy; (M.G.); (E.N.)
| | - 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; (L.C.-A.); (L.M.-B.)
- Área Clínica de Imagen Médica, Hospital Universitario y Politécnico La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026 Valencia, Spain;
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Kondylakis H, Ciarrocchi E, Cerda-Alberich L, Chouvarda I, Fromont LA, Garcia-Aznar JM, Kalokyri V, Kosvyra A, Walker D, Yang G, Neri E. Position of the AI for Health Imaging (AI4HI) network on metadata models for imaging biobanks. Eur Radiol Exp 2022; 6:29. [PMID: 35773546 PMCID: PMC9247122 DOI: 10.1186/s41747-022-00281-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 04/20/2022] [Indexed: 11/10/2022] Open
Abstract
A huge amount of imaging data is becoming available worldwide and an incredible range of possible improvements can be provided by artificial intelligence algorithms in clinical care for diagnosis and decision support. In this context, it has become essential to properly manage and handle these medical images and to define which metadata have to be considered, in order for the images to provide their full potential. Metadata are additional data associated with the images, which provide a complete description of the image acquisition, curation, analysis, and of the relevant clinical variables associated with the images. Currently, several data models are available to describe one or more subcategories of metadata, but a unique, common, and standard data model capable of fully representing the heterogeneity of medical metadata has not been yet developed. This paper reports the state of the art on metadata models for medical imaging, the current limitations and further developments, and describes the strategy adopted by the Horizon 2020 "AI for Health Imaging" projects, which are all dedicated to the creation of imaging biobanks.
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Affiliation(s)
| | - Esther Ciarrocchi
- grid.5395.a0000 0004 1757 3729Department of Translational Research, University of Pisa, Pisa, Italy
| | | | - Ioanna Chouvarda
- grid.4793.90000000109457005Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Lauren A. Fromont
- grid.11478.3b0000 0004 1766 3695Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | | | - Varvara Kalokyri
- grid.5395.a0000 0004 1757 3729Department of Translational Research, University of Pisa, Pisa, Italy
| | - Alexandra Kosvyra
- grid.4793.90000000109457005Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dawn Walker
- grid.11835.3e0000 0004 1936 9262Department of Computer Science and Insigneo Institute of in silico Medicine, University of Sheffield, Sheffield, UK
| | - Guang Yang
- grid.7445.20000 0001 2113 8111National Heart and Lung Institute, Imperial College London, London, UK
| | - Emanuele Neri
- grid.5395.a0000 0004 1757 3729Department of Translational Research, University of Pisa, Pisa, Italy
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21
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Baeza-Delgado C, Cerdá Alberich L, Carot-Sierra JM, Veiga-Canuto D, Martínez de Las Heras B, Raza B, Martí-Bonmatí L. A practical solution to estimate the sample size required for clinical prediction models generated from observational research on data. Eur Radiol Exp 2022; 6:22. [PMID: 35641659 PMCID: PMC9156610 DOI: 10.1186/s41747-022-00276-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 04/12/2022] [Indexed: 12/23/2022] Open
Abstract
Background Estimating the required sample size is crucial when developing and validating clinical prediction models. However, there is no consensus about how to determine the sample size in such a setting. Here, the goal was to compare available methods to define a practical solution to sample size estimation for clinical predictive models, as applied to Horizon 2020 PRIMAGE as a case study. Methods Three different methods (Riley’s; “rule of thumb” with 10 and 5 events per predictor) were employed to calculate the sample size required to develop predictive models to analyse the variation in sample size as a function of different parameters. Subsequently, the sample size for model validation was also estimated. Results To develop reliable predictive models, 1397 neuroblastoma patients are required, 1060 high-risk neuroblastoma patients and 1345 diffuse intrinsic pontine glioma (DIPG) patients. This sample size can be lowered by reducing the number of variables included in the model, by including direct measures of the outcome to be predicted and/or by increasing the follow-up period. For model validation, the estimated sample size resulted to be 326 patients for neuroblastoma, 246 for high-risk neuroblastoma, and 592 for DIPG. Conclusions Given the variability of the different sample sizes obtained, we recommend using methods based on epidemiological data and the nature of the results, as the results are tailored to the specific clinical problem. In addition, sample size can be reduced by lowering the number of parameter predictors, by including direct measures of the outcome of interest.
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Affiliation(s)
- Carlos Baeza-Delgado
- Biomedical Imaging Research Group (GIBI230-PREBI) at La Fe Health Research Institute and the Imaging La Fe node of the Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), Valencia, Spain
| | - Leonor Cerdá Alberich
- Biomedical Imaging Research Group (GIBI230-PREBI) at La Fe Health Research Institute and the Imaging La Fe node of the Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), Valencia, Spain
| | - José Miguel Carot-Sierra
- Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València, Valencia, Spain
| | - Diana Veiga-Canuto
- Radiology Department, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | | | - Ben Raza
- Biomedical Imaging Research Group (GIBI230-PREBI) at La Fe Health Research Institute and the Imaging La Fe node of the Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), Valencia, Spain.,Pediatric Oncology Department, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Luis Martí-Bonmatí
- Biomedical Imaging Research Group (GIBI230-PREBI) at La Fe Health Research Institute and the Imaging La Fe node of the Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), Valencia, Spain.
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22
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de Melo Quintela B, Hervas-Raluy S, Manuel Garcia Aznar J, Walker D, Wertheim KY, Viceconti M. A Theoretical Analysis of the Scale Separation in a Model to Predict Solid Tumour Growth. J Theor Biol 2022; 547:111173. [DOI: 10.1016/j.jtbi.2022.111173] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 03/27/2022] [Accepted: 05/19/2022] [Indexed: 11/27/2022]
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23
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Bonmatí LM, Miguel A, Suárez A, Aznar M, Beregi JP, Fournier L, Neri E, Laghi A, França M, Sardanelli F, Penzkofer T, Lambin P, Blanquer I, Menzel M, Seymour K, Figueiras S, Krischak K, Martínez R, Mirsky Y, Yang G, Alberich-Bayarri Á. CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools. Front Oncol 2022; 12:742701. [PMID: 35280732 PMCID: PMC8913333 DOI: 10.3389/fonc.2022.742701] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 01/28/2022] [Indexed: 12/13/2022] Open
Abstract
The CHAIMELEON project aims to set up a pan-European repository of health imaging data, tools and methodologies, with the ambition to set a standard and provide resources for future AI experimentation for cancer management. The project is a 4 year long, EU-funded project tackling some of the most ambitious research in the fields of biomedical imaging, artificial intelligence and cancer treatment, addressing the four types of cancer that currently have the highest prevalence worldwide: lung, breast, prostate and colorectal. To allow this, clinical partners and external collaborators will populate the repository with multimodality (MR, CT, PET/CT) imaging and related clinical data. Subsequently, AI developers will enable a multimodal analytical data engine facilitating the interpretation, extraction and exploitation of the information stored at the repository. The development and implementation of AI-powered pipelines will enable advancement towards automating data deidentification, curation, annotation, integrity securing and image harmonization. By the end of the project, the usability and performance of the repository as a tool fostering AI experimentation will be technically validated, including a validation subphase by world-class European AI developers, participating in Open Challenges to the AI Community. Upon successful validation of the repository, a set of selected AI tools will undergo early in-silico validation in observational clinical studies coordinated by leading experts in the partner hospitals. Tool performance will be assessed, including external independent validation on hallmark clinical decisions in response to some of the currently most important clinical end points in cancer. The project brings together a consortium of 18 European partners including hospitals, universities, R&D centers and private research companies, constituting an ecosystem of infrastructures, biobanks, AI/in-silico experimentation and cloud computing technologies in oncology.
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Affiliation(s)
- Luis Martí Bonmatí
- Medical Imaging Department, La Fe University and Polytechnic Hospital & Biomedical Imaging Research Group Grupo de Investigación Biomédica en Imagen (GIBI2) at La Fe University and Polytechnic Hospital and Health Research Institute, Valencia, Spain,*Correspondence: Luis Martí Bonmatí,
| | - Ana Miguel
- Medical Imaging Department, La Fe University and Polytechnic Hospital & Biomedical Imaging Research Group Grupo de Investigación Biomédica en Imagen (GIBI2) at La Fe University and Polytechnic Hospital and Health Research Institute, Valencia, Spain
| | | | | | | | - Laure Fournier
- Collège des enseignants en radiologie de France, Paris, France
| | - Emanuele Neri
- Diagnostic Radiology 3, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Andrea Laghi
- Medicina Traslazionale e Oncologia, Sant Andrea Sapienza Rome, Rome, Italy
| | - Manuela França
- Department of Radiology, Centro Hospitalar Universitário do Porto, Porto, Portugal
| | - Francesco Sardanelli
- Servizio di Diagnostica per Immagini, “Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Policlinico San Donato, Milanese, Italy
| | - Tobias Penzkofer
- Department of Radiology, CHARITÉ-Universitätsmedizin Berlin, Berlin, Germany
| | - Phillipe Lambin
- Department of Precision Medicine, Maastricht University, Maastricht, Netherlands
| | - Ignacio Blanquer
- Computing Science Department, Universitat Politècnica de València, València, Spain
| | - Marion I. Menzel
- GE Healthcare, München, Germany,Department of Physics, Technical University of Munich, Garching, Germany
| | | | | | - Katharina Krischak
- European Institute for Biomedical Imaging Research, EIBIR gemeinnützige GmbH, Vienna, Austria
| | - Ricard Martínez
- Departamento de Derecho Constitucional, Ciencia Política y Administración, Universitat de València, València, Spain
| | - Yisroel Mirsky
- Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
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24
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Bridging gaps between images and data: a systematic update on imaging biobanks. Eur Radiol 2022; 32:3173-3186. [PMID: 35001159 DOI: 10.1007/s00330-021-08431-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 10/01/2021] [Accepted: 10/22/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND OBJECTIVE The systematic collection of medical images combined with imaging biomarkers and patient non-imaging data is the core concept of imaging biobanks, a key element for fuelling the development of modern precision medicine. Our purpose is to review the existing image repositories fulfilling the criteria for imaging biobanks. METHODS Pubmed, Scopus and Web of Science were searched for articles published in English from January 2010 to July 2021 using a combination of the terms: "imaging" AND "biobanks" and "imaging" AND "repository". Moreover, the Community Research and Development Information Service (CORDIS) database ( https://cordis.europa.eu/projects ) was searched using the terms: "imaging" AND "biobanks", also including collections, projects, project deliverables, project publications and programmes. RESULTS Of 9272 articles retrieved, only 54 related to biobanks containing imaging data were finally selected, of which 33 were disease-oriented (61.1%) and 21 population-based (38.9%). Most imaging biobanks were European (26/54, 48.1%), followed by American biobanks (20/54, 37.0%). Among disease-oriented biobanks, the majority were focused on neurodegenerative (9/33, 27.3%) and oncological diseases (9/33, 27.3%). The number of patients enrolled ranged from 240 to 3,370,929, and the imaging modality most frequently involved was MRI (40/54, 74.1%), followed by CT (20/54, 37.0%), PET (13/54, 24.1%), and ultrasound (12/54, 22.2%). Most biobanks (38/54, 70.4%) were accessible under request. CONCLUSIONS Imaging biobanks can serve as a platform for collecting, sharing and analysing medical images integrated with imaging biomarkers, biological and clinical data. A relatively small proportion of current biobanks also contain images and can thus be classified as imaging biobanks. KEY POINTS • Imaging biobanks are a powerful tool for large-scale collection and processing of medical images integrated with imaging biomarkers and patient non-imaging data. • Most imaging biobanks retrieved were European, disease-oriented and accessible under user request. • While many biobanks have been developed so far, only a relatively small proportion of them are imaging biobanks.
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25
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Scapicchio C, Gabelloni M, Barucci A, Cioni D, Saba L, Neri E. A deep look into radiomics. LA RADIOLOGIA MEDICA 2021; 126:1296-1311. [PMID: 34213702 PMCID: PMC8520512 DOI: 10.1007/s11547-021-01389-x] [Citation(s) in RCA: 170] [Impact Index Per Article: 56.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 06/15/2021] [Indexed: 11/29/2022]
Abstract
Radiomics is a process that allows the extraction and analysis of quantitative data from medical images. It is an evolving field of research with many potential applications in medical imaging. The purpose of this review is to offer a deep look into radiomics, from the basis, deeply discussed from a technical point of view, through the main applications, to the challenges that have to be addressed to translate this process in clinical practice. A detailed description of the main techniques used in the various steps of radiomics workflow, which includes image acquisition, reconstruction, pre-processing, segmentation, features extraction and analysis, is here proposed, as well as an overview of the main promising results achieved in various applications, focusing on the limitations and possible solutions for clinical implementation. Only an in-depth and comprehensive description of current methods and applications can suggest the potential power of radiomics in fostering precision medicine and thus the care of patients, especially in cancer detection, diagnosis, prognosis and treatment evaluation.
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Affiliation(s)
- Camilla Scapicchio
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy.
| | - Michela Gabelloni
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Andrea Barucci
- CNR-IFAC Institute of Applied Physics "N. Carrara", 50019, Sesto Fiorentino, Italy
| | - Dania Cioni
- Academic Radiology, Department of Surgical, Medical, Molecular Pathology and Emergency Medicine, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Monserrato (Cagliari),Cagliari, Italy
| | - Emanuele Neri
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy
- Italian Society of Medical and Interventional Radiology, SIRM Foundation, Via della Signora 2, 20122, Milano, Italy
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26
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Fernández Patón M, Cerdá Alberich L, Sangüesa Nebot C, Martínez de Las Heras B, Veiga Canuto D, Cañete Nieto A, Martí-Bonmatí L. MR Denoising Increases Radiomic Biomarker Precision and Reproducibility in Oncologic Imaging. J Digit Imaging 2021; 34:1134-1145. [PMID: 34505958 DOI: 10.1007/s10278-021-00512-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 07/24/2021] [Accepted: 08/17/2021] [Indexed: 12/25/2022] Open
Abstract
Several noise sources, such as the Johnson-Nyquist noise, affect MR images disturbing the visualization of structures and affecting the subsequent extraction of radiomic data. We evaluate the performance of 5 denoising filters (anisotropic diffusion filter (ADF), curvature flow filter (CFF), Gaussian filter (GF), non-local means filter (NLMF), and unbiased non-local means (UNLMF)), with 33 different settings, in T2-weighted MR images of phantoms (N = 112) and neuroblastoma patients (N = 25). Filters were discarded until the most optimal solutions were obtained according to 3 image quality metrics: peak signal-to-noise ratio (PSNR), edge-strength similarity-based image quality metric (ESSIM), and noise (standard deviation of the signal intensity of a region in the background area). The selected filters were ADFs and UNLMs. From them, 107 radiomics features preservation at 4 progressively added noise levels were studied. The ADF with a conductance of 1 and 2 iterations standardized the radiomic features, improving reproducibility and quality metrics.
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Affiliation(s)
- 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.
| | - Leonor Cerdá Alberich
- 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, 46026, Valencia, Spain
| | - Blanca Martínez de Las Heras
- Unidad de Oncohematología Pediátrica, Hospital Universitario Y Politécnico La Fe, Avenida Fernando Abril Martorell, 106, 46026, Valencia, Spain
| | - Diana Veiga Canuto
- Área Clínica de Imagen Médica, Hospital Universitario Y Politécnico La Fe, Avenida Fernando Abril Martorell, 106, 46026, Valencia, Spain
| | - Adela Cañete Nieto
- Unidad de Oncohematología Pediátrica, Hospital Universitario Y Politécnico La Fe, Avenida Fernando Abril Martorell, 106, 46026, Valencia, Spain
| | - 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, 46026, Valencia, Spain
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27
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Scapicchio C, Gabelloni M, Forte SM, Alberich LC, Faggioni L, Borgheresi R, Erba P, Paiar F, Marti-Bonmati L, Neri E. DICOM-MIABIS integration model for biobanks: a use case of the EU PRIMAGE project. Eur Radiol Exp 2021; 5:20. [PMID: 33977357 PMCID: PMC8113005 DOI: 10.1186/s41747-021-00214-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 03/26/2021] [Indexed: 12/19/2022] Open
Abstract
PRIMAGE is a European Commission-financed project dealing with medical imaging and artificial intelligence aiming to create an imaging biobank in oncology. The project includes a task dedicated to the interoperability between imaging and standard biobanks. We aim at linking Digital imaging and Communications in Medicine (DICOM) metadata to the Minimum Information About BIobank data Sharing (MIABIS) standard of biobanking. A very first integration model based on the fusion of the two existing standards, MIABIS and DICOM, has been developed. The fundamental method was that of expanding the MIABIS core to the imaging field, adding DICOM metadata derived from CT scans of 18 paediatric patients with neuroblastoma. The model was developed with the relational database management system Structured Query Language. The integration data model has been built as an Entity Relationship Diagram, commonly used to organise data within databases. Five additional entities have been linked to the “Image Collection” subcategory in order to include the imaging metadata more specific to the particular type of data: Body Part Examined, Modality Information, Dataset Type, Image Analysis, and Registration Parameters. The model is a starting point for the expansion of MIABIS with further DICOM metadata, enabling the inclusion of imaging data in biorepositories.
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Affiliation(s)
- Camilla Scapicchio
- Department of Translational Research on New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy.
| | - Michela Gabelloni
- Department of Translational Research on New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Sara Maria Forte
- Department of Translational Research on New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Leonor Cerdá Alberich
- Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute, Valencia, Spain
| | - Lorenzo Faggioni
- Department of Translational Research on New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Rita Borgheresi
- Department of Translational Research on New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Paola Erba
- Department of Translational Research on New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Fabiola Paiar
- Department of Translational Research on New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Luis Marti-Bonmati
- Medical Imaging Department, La Fe University and Polytechnic Hospital & Biomedical Imaging Research Group (GIBI230), La Fe University and Polytechnic Hospital and Health Research Institute, Valencia, Spain
| | - Emanuele Neri
- Department of Translational Research on New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
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28
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Bubak M, Czechowicz K, Gubała T, Hose DR, Kasztelnik M, Malawski M, Meizner J, Nowakowski P, Wood S. The EurValve model execution environment. Interface Focus 2021; 11:20200006. [PMID: 33343876 DOI: 10.1098/rsfs.2020.0006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/23/2020] [Indexed: 01/14/2023] Open
Abstract
The goal of this paper is to present a dedicated high-performance computing (HPC) infrastructure which is used in the development of a so-called reduced-order model (ROM) for simulating the outcomes of interventional procedures which are contemplated in the treatment of valvular heart conditions. Following a brief introduction to the problem, the paper presents the design of a model execution environment, in which representative cases can be simulated and the parameters of the ROM fine-tuned to enable subsequent deployment of a decision support system without further need for HPC. The presentation of the system is followed by information concerning its use in processing specific patient cases in the context of the EurValve international collaboration.
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Affiliation(s)
- M Bubak
- Department of Computer Science, AGH University of Science and Technology, Kraków, Poland.,ACC Cyfronet AGH University of Science and Technology, Kraków, Poland.,Sano Centre for Computational Medicine, Kraków, Poland
| | - K Czechowicz
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - T Gubała
- ACC Cyfronet AGH University of Science and Technology, Kraków, Poland.,Sano Centre for Computational Medicine, Kraków, Poland
| | - D R Hose
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - M Kasztelnik
- ACC Cyfronet AGH University of Science and Technology, Kraków, Poland.,Sano Centre for Computational Medicine, Kraków, Poland
| | - M Malawski
- Department of Computer Science, AGH University of Science and Technology, Kraków, Poland.,ACC Cyfronet AGH University of Science and Technology, Kraków, Poland.,Sano Centre for Computational Medicine, Kraków, Poland
| | - J Meizner
- ACC Cyfronet AGH University of Science and Technology, Kraków, Poland.,Sano Centre for Computational Medicine, Kraków, Poland
| | - P Nowakowski
- ACC Cyfronet AGH University of Science and Technology, Kraków, Poland.,Sano Centre for Computational Medicine, Kraków, Poland
| | - S Wood
- Medical Physics and Clinical Engineering, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
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29
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Radiologists and Clinical Trials: Part 1 The Truth About Reader Disagreements. Ther Innov Regul Sci 2021; 55:1111-1121. [PMID: 34228319 PMCID: PMC8259547 DOI: 10.1007/s43441-021-00316-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Accepted: 06/18/2021] [Indexed: 02/06/2023]
Abstract
The debate over human visual perception and how medical images should be interpreted have persisted since X-rays were the only imaging technique available. Concerns over rates of disagreement between expert image readers are associated with much of the clinical research and at times driven by the belief that any image endpoint variability is problematic. The deeper understanding of the reasons, value, and risk of disagreement are somewhat siloed, leading, at times, to costly and risky approaches, especially in clinical trials. Although artificial intelligence promises some relief from mistakes, its routine application for assessing tumors within cancer trials is still an aspiration. Our consortium of international experts in medical imaging for drug development research, the Pharma Imaging Network for Therapeutics and Diagnostics (PINTAD), tapped the collective knowledge of its members to ground expectations, summarize common reasons for reader discordance, identify what factors can be controlled and which actions are likely to be effective in reducing discordance. Reinforced by an exhaustive literature review, our work defines the forces that shape reader variability. This review article aims to produce a singular authoritative resource outlining reader performance's practical realities within cancer trials, whether they occur within a clinical or an independent central review.
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30
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Davendralingam N, Sebire NJ, Arthurs OJ, Shelmerdine SC. Artificial intelligence in paediatric radiology: Future opportunities. Br J Radiol 2021; 94:20200975. [PMID: 32941736 PMCID: PMC7774693 DOI: 10.1259/bjr.20200975] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 09/04/2020] [Indexed: 12/13/2022] Open
Abstract
Artificial intelligence (AI) has received widespread and growing interest in healthcare, as a method to save time, cost and improve efficiencies. The high-performance statistics and diagnostic accuracies reported by using AI algorithms (with respect to predefined reference standards), particularly from image pattern recognition studies, have resulted in extensive applications proposed for clinical radiology, especially for enhanced image interpretation. Whilst certain sub-speciality areas in radiology, such as those relating to cancer screening, have received wide-spread attention in the media and scientific community, children's imaging has been hitherto neglected.In this article, we discuss a variety of possible 'use cases' in paediatric radiology from a patient pathway perspective where AI has either been implemented or shown early-stage feasibility, while also taking inspiration from the adult literature to propose potential areas for future development. We aim to demonstrate how a 'future, enhanced paediatric radiology service' could operate and to stimulate further discussion with avenues for research.
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Affiliation(s)
- Natasha Davendralingam
- Department of Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
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