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Keenan KE, Jordanova KV, Ogier SE, Tamada D, Bruhwiler N, Starekova J, Riek J, McCracken PJ, Hernando D. Phantoms for Quantitative Body MRI: a review and discussion of the phantom value. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01181-8. [PMID: 38896407 DOI: 10.1007/s10334-024-01181-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 05/18/2024] [Accepted: 06/11/2024] [Indexed: 06/21/2024]
Abstract
In this paper, we review the value of phantoms for body MRI in the context of their uses for quantitative MRI methods research, clinical trials, and clinical imaging. Certain uses of phantoms are common throughout the body MRI community, including measuring bias, assessing reproducibility, and training. In addition to these uses, phantoms in body MRI methods research are used for novel methods development and the design of motion compensation and mitigation techniques. For clinical trials, phantoms are an essential part of quality management strategies, facilitating the conduct of ethically sound, reliable, and regulatorily compliant clinical research of both novel MRI methods and therapeutic agents. In the clinic, phantoms are used for development of protocols, mitigation of cost, quality control, and radiotherapy. We briefly review phantoms developed for quantitative body MRI, and finally, we review open questions regarding the most effective use of a phantom for body MRI.
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Affiliation(s)
- Kathryn E Keenan
- Physical Measurement Laboratory, National Institute of Standards and Technology, NIST, 325 Broadway, Boulder, CO, 80305, USA.
| | - Kalina V Jordanova
- Physical Measurement Laboratory, National Institute of Standards and Technology, NIST, 325 Broadway, Boulder, CO, 80305, USA
| | - Stephen E Ogier
- Physical Measurement Laboratory, National Institute of Standards and Technology, NIST, 325 Broadway, Boulder, CO, 80305, USA
- Department of Physics, University of Colorado Boulder, Boulder, CO, USA
| | | | - Natalie Bruhwiler
- Physical Measurement Laboratory, National Institute of Standards and Technology, NIST, 325 Broadway, Boulder, CO, 80305, USA
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Smucny J, Wylie KP, Lesh TA, Carter CS, Tregellas JR. Whole-brain intrinsic functional connectivity predicts symptoms and functioning in early psychosis. J Psychiatr Res 2024; 175:411-417. [PMID: 38781675 DOI: 10.1016/j.jpsychires.2024.05.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/13/2024] [Accepted: 05/16/2024] [Indexed: 05/25/2024]
Abstract
Theories of psychotic illness suggest that abnormal intrinsic functional connectivity may explain its characteristic positive and disorganization symptoms as well as lead to impaired general functioning. Here we used resting state functional magnetic resonance imaging (fMRI) to evaluate associations between these symptoms and the degree to which global connectivity is abnormal in early psychosis (EP). Eighty-six healthy controls (HCs) and 108 individuals with EP with resting state fMRI data were included in primary analyses. The EP group included 83 participants with schizophrenia-spectrum disorders and 25 with bipolar disorder type I with psychotic features. A global intrinsic connectivity "similarity index" for each EP individual was determined by calculating its correlation with the average HC connectivity matrix extracted using Schaefer atlases of multiple parcellations (100, 200, 300, and 400 region parcellations). As hypothesized, connectivity similarity with the average HC matrix was negatively associated with Brief Psychiatric Rating Scale total score, Scale for the Assessment of Positive Symptoms total score, and disorganization symptoms. Similarity was also positively associated with Global Assessment of Functioning score. Results were not driven by sex or diagnosis effects and were consistent across parcellation schemes. These results support the hypothesis that changes in whole-brain connectivity patterns are associated with psychosis symptoms and support the use of functional connectivity as a biomarker for these symptoms in EP.
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Affiliation(s)
- Jason Smucny
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, USA.
| | - Korey P Wylie
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, USA
| | - Tyler A Lesh
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, USA
| | - Cameron S Carter
- Department of Psychiatry and Human Behavior, University of California, Irvine, USA
| | - Jason R Tregellas
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, USA; Research Service, Rocky Mountain Regional VA Medical Center, USA
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Eertink JJ, Bahce I, Waterton JC, Huisman MC, Boellaard R, Wunder A, Thiele A, Menke-van der Houven van Oordt CW. The development process of 'fit-for-purpose' imaging biomarkers to characterize the tumor microenvironment. Front Med (Lausanne) 2024; 11:1347267. [PMID: 38818386 PMCID: PMC11138661 DOI: 10.3389/fmed.2024.1347267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 04/24/2024] [Indexed: 06/01/2024] Open
Abstract
Immune-based treatment approaches are successfully used for the treatment of patients with cancer. While such therapies can be highly effective, many patients fail to benefit. To provide optimal therapy choices and to predict treatment responses, reliable biomarkers for the assessment of immune features in patients with cancer are of significant importance. Biomarkers (BM) that enable a comprehensive and repeatable assessment of the tumor microenvironment (TME), the lymphoid system, and the dynamics induced by drug treatment can fill this gap. Medical imaging, notably positron emission tomography (PET) and magnetic resonance imaging (MRI), providing whole-body imaging BMs, might deliver such BMs. However, those imaging BMs must be well characterized as being 'fit for purpose' for the intended use. This review provides an overview of the key steps involved in the development of 'fit-for-purpose' imaging BMs applicable in drug development, with a specific focus on pharmacodynamic biomarkers for assessing the TME and its modulation by immunotherapy. The importance of the qualification of imaging BMs according to their context of use (COU) as defined by the Food and Drug Administration (FDA) and National Institutes of Health Biomarkers, EndpointS, and other Tools (BEST) glossary is highlighted. We elaborate on how an imaging BM qualification for a specific COU can be achieved.
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Affiliation(s)
- Jakoba J. Eertink
- Department of Medical Oncology, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, Netherlands
| | - Idris Bahce
- Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, Netherlands
- Department of Pulmonary Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - John C. Waterton
- Centre for Imaging Sciences, University of Manchester, Manchester, United Kingdom
| | - Marc C. Huisman
- Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Ronald Boellaard
- Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Andreas Wunder
- Department of Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach and der Riss, Germany
| | - Andrea Thiele
- Department of Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach and der Riss, Germany
| | - Catharina W. Menke-van der Houven van Oordt
- Department of Medical Oncology, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, Netherlands
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Bäuerle T, Dietzel M, Pinker K, Bonekamp D, Zhang KS, Schlemmer HP, Bannas P, Cyran CC, Eisenblätter M, Hilger I, Jung C, Schick F, Wegner F, Kiessling F. Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma. ROFO-FORTSCHR RONTG 2024; 196:354-362. [PMID: 37944934 DOI: 10.1055/a-2175-4446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
BACKGROUND Imaging biomarkers are quantitative parameters from imaging modalities, which are collected noninvasively, allow conclusions about physiological and pathophysiological processes, and may consist of single (monoparametric) or multiple parameters (bi- or multiparametric). METHOD This review aims to present the state of the art for the quantification of multimodal and multiparametric imaging biomarkers. Here, the use of biomarkers using artificial intelligence will be addressed and the clinical application of imaging biomarkers in breast and prostate cancers will be explained. For the preparation of the review article, an extensive literature search was performed based on Pubmed, Web of Science and Google Scholar. The results were evaluated and discussed for consistency and generality. RESULTS AND CONCLUSION Different imaging biomarkers (multiparametric) are quantified based on the use of complementary imaging modalities (multimodal) from radiology, nuclear medicine, or hybrid imaging. From these techniques, parameters are determined at the morphological (e. g., size), functional (e. g., vascularization or diffusion), metabolic (e. g., glucose metabolism), or molecular (e. g., expression of prostate specific membrane antigen, PSMA) level. The integration and weighting of imaging biomarkers are increasingly being performed with artificial intelligence, using machine learning algorithms. In this way, the clinical application of imaging biomarkers is increasing, as illustrated by the diagnosis of breast and prostate cancers. KEY POINTS · Imaging biomarkers are quantitative parameters to detect physiological and pathophysiological processes.. · Imaging biomarkers from multimodality and multiparametric imaging are integrated using artificial intelligence algorithms.. · Quantitative imaging parameters are a fundamental component of diagnostics for all tumor entities, such as for mammary and prostate carcinomas.. CITATION FORMAT · Bäuerle T, Dietzel M, Pinker K et al. Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma. Fortschr Röntgenstr 2024; 196: 354 - 362.
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Affiliation(s)
- Tobias Bäuerle
- Institute of Radiology, University Medical Center Erlangen, Germany
| | - Matthias Dietzel
- Institute of Radiology, University Medical Center Erlangen, Germany
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, United States
| | - David Bonekamp
- Department of Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Kevin S Zhang
- Department of Radiology, German Cancer Research Center, Heidelberg, Germany
| | | | - Peter Bannas
- Institute of Diagnostic and Interventional Radiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Clemens C Cyran
- Institute of Radiology, University Medical Center München (LMU), München, Germany
| | - Michel Eisenblätter
- Diagnostische und Interventionelle Radiologie, Universitätsklinikum OWL, Universität Bielefeld Campus Klinikum Lippe, 32756 Detmold, Germany
| | - Ingrid Hilger
- Experimental Radiology, University Medical Center Jena, Germany
| | - Caroline Jung
- Institute of Diagnostic and Interventional Radiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Fritz Schick
- Experimental Radiology, University Medical Center Tübingen, Germany
| | - Franz Wegner
- Department of Radiology, University Hospital Schleswig-Holstein Campus Lübeck, Germany
| | - Fabian Kiessling
- Experimental Molecular Imaging, University Medical Center Aachen, Germany
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Herndon RC. Functional information guided adaptive radiation therapy. Front Oncol 2024; 13:1251937. [PMID: 38250556 PMCID: PMC10798040 DOI: 10.3389/fonc.2023.1251937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 12/06/2023] [Indexed: 01/23/2024] Open
Abstract
Introduction Functional informaton is introduced as the mechanism to adapt cancer therapies uniquely to individual patients based on changes defined by qualified tumor biomarkers. Methods To demonstrate the methodology, a tumor volume biomarker model, characterized by a tumor volume reduction rate coefficient, is used to adapt a tumor cell survival bioresponse radiotherapy model in terms of therapeutic radiation dose. Tumor volume, acquired from imaging data, serves as a surrogate measurement for tumor cell death, but the biomarker model derived from this data cannot be used to calculate the radiation dose absorbed by the target tumor. However, functional information does provide a mathematical connection between the tumor volume biomarker model and the tumor cell survival bioresponse model by quantifying both data sets in the units of information, thus creating an analytic conduit from bioresponse to biomarker. Results The information guided process for individualized dose adaptations using information values acquired from the tumor cell survival bioresponse model and the tumor volume biomarker model are presented in detailed form by flowchart and tabular data. Clinical data are used to generate a presentation that assists investigator application of the information guided methodology to adaptive cancer therapy research. Conclusions Information guided adaptation of bioresponse using surrogate data is extensible across multiple research fields because functional information mathematically connects disparate bioresponse and biomarker data sets. Thus, functional information offers adaptive cancer therapy by mathematically connecting immunotherapy, chemotherapy, and radiotherapy cancer treatment processes to implement individualized treatment plans.
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Affiliation(s)
- R. Craig Herndon
- Hillman Cancer Center, Radiation Oncology, University of Pittsburgh Medical Center, Williamsport, PA, United States
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Zhong J, Liu X, Hu Y, Xing Y, Ding D, Ge X, Song Y, Wang S, Chen L, Zhu Y, Lu W, Zhang H, Yao W. Robustness of Quantitative Diffusion Metrics from Four Models: A Prospective Study on the Influence of Scan-Rescans, Voxel Size, Coils, and Observers. J Magn Reson Imaging 2023. [PMID: 38112305 DOI: 10.1002/jmri.29192] [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: 09/29/2023] [Revised: 12/01/2023] [Accepted: 12/02/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Quantitative diffusion metrics provide additional microstructural information of diseases. The robustness of quantitative diffusion metrics should be established before clinical application. PURPOSE To evaluate the variability and reproducibility of quantitative diffusion MRI metrics. STUDY TYPE Prospective. POPULATION 14 volunteers (7 men; median age, range, 28, 26-59 years). FIELD STRENGTH/SEQUENCE 3.0-T/Diffusion spectrum imaging. ASSESSMENT Brain MRI studies were performed four times per subject: involving different combinations of coil types and voxel sizes. Regions of interest of 13 brain anatomical sites were drawn by one observer twice and another observer once to allow interobserver and intraobserver reproducibility assessment. Twenty-five quantitative metrics were calculated using four diffusion models. STATISTICAL TESTS The variability was evaluated with coefficients of variation (CV), and quartile coefficient of dispersion (QCD). The reproducibility was assessed with intraclass correlation coefficient (ICC), and concordance correlation coefficient (CCC). Wilcoxon signed rank test was used to compare the influence of factors on robustness of quantitative diffusion metrics. A two-tailed P < 0.05 was considered statistically significant. RESULTS The variability of quantitative diffusion metrics showed CV of 2.4%-68.2%, and QCD of 0.6%-48.2%, respectively. The reproducibility of scans using 20-channel coils with voxels of 2 × 2 × 2 mm3 and 3 × 3 × 3 mm3 , respectively (ICC 0.03-0.84, CCC 0.03-0.84) was significantly worse than that of repeated scans using a 20-channel coil with a voxel size of 2 × 2 × 2 mm3 (ICC of 0.74-0.97, CCC 0.74-0.97) and that of scans using 20- and 64-channel coils, respectively, with a voxel size of 2 × 2 × 2 mm3 (ICC 0.59-0.95, CCC 0.59-0.95). The intraobserver reproducibility (ICC 0.49-0.94, CCC 0.49-0.94) was significantly better than the interobserver reproducibility (ICC 0.28-0.91, CCC 0.28-0.91). DATA CONCLUSION Our study indicated that the voxel size has a greater influence on the reproducibility of quantitative diffusion metrics than scan-rescans and coils. The reproducibility within one observer was higher than that between two observers. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xianwei Liu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yangfan Hu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yue Xing
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Defang Ding
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiang Ge
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yang Song
- MR Scientific Marketing, Siemens Healthineers Ltd, Shanghai, China
| | - Silian Wang
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Liwei Chen
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ying Zhu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenjie Lu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Chauvie S, Mazzoni LN, O’Doherty J. A Review on the Use of Imaging Biomarkers in Oncology Clinical Trials: Quality Assurance Strategies for Technical Validation. Tomography 2023; 9:1876-1902. [PMID: 37888741 PMCID: PMC10610870 DOI: 10.3390/tomography9050149] [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: 08/16/2023] [Revised: 10/10/2023] [Accepted: 10/13/2023] [Indexed: 10/28/2023] Open
Abstract
Imaging biomarkers (IBs) have been proposed in medical literature that exploit images in a quantitative way, going beyond the visual assessment by an imaging physician. These IBs can be used in the diagnosis, prognosis, and response assessment of several pathologies and are very often used for patient management pathways. In this respect, IBs to be used in clinical practice and clinical trials have a requirement to be precise, accurate, and reproducible. Due to limitations in imaging technology, an error can be associated with their value when considering the entire imaging chain, from data acquisition to data reconstruction and subsequent analysis. From this point of view, the use of IBs in clinical trials requires a broadening of the concept of quality assurance and this can be a challenge for the responsible medical physics experts (MPEs). Within this manuscript, we describe the concept of an IB, examine some examples of IBs currently employed in clinical practice/clinical trials and analyze the procedure that should be carried out to achieve better accuracy and reproducibility in their use. We anticipate that this narrative review, written by the components of the EFOMP working group on "the role of the MPEs in clinical trials"-imaging sub-group, can represent a valid reference material for MPEs approaching the subject.
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Affiliation(s)
- Stephane Chauvie
- Medical Physics Division, Santa Croce e Carle Hospital, 12100 Cuneo, Italy;
| | | | - Jim O’Doherty
- Siemens Medical Solutions, Malvern, PA 19355, USA;
- Department of Radiology & Radiological Sciences, Medical University of South Carolina, Charleston, SC 20455, USA
- Radiography & Diagnostic Imaging, University College Dublin, D04 C7X2 Dublin, Ireland
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Marti-Aguado D, Ten-Esteve A, Baracaldo-Silva CM, Crespo A, Coello E, Merino-Murgui V, Fernandez-Paton M, Alfaro-Cervello C, Sánchez-Martín A, Bauza M, Jimenez-Pastor A, Perez-Girbes A, Benlloch S, Pérez-Rojas J, Puglia V, Ferrández A, Aguilera V, Latorre M, Monton C, Escudero-García D, Bosch-Roig I, Alberich-Bayarri Á, Marti-Bonmati L. Pancreatic steatosis and iron overload increases cardiovascular risk in non-alcoholic fatty liver disease. Front Endocrinol (Lausanne) 2023; 14:1213441. [PMID: 37600695 PMCID: PMC10436077 DOI: 10.3389/fendo.2023.1213441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 07/18/2023] [Indexed: 08/22/2023] Open
Abstract
Objective To assess the prevalence of pancreatic steatosis and iron overload in non-alcoholic fatty liver disease (NAFLD) and their correlation with liver histology severity and the risk of cardiometabolic diseases. Method A prospective, multicenter study including NAFLD patients with biopsy and paired Magnetic Resonance Imaging (MRI) was performed. Liver biopsies were evaluated according to NASH Clinical Research Network, hepatic iron storages were scored, and digital pathology quantified the tissue proportionate areas of fat and iron. MRI-biomarkers of fat fraction (PDFF) and iron accumulation (R2*) were obtained from the liver and pancreas. Different metabolic traits were evaluated, cardiovascular disease (CVD) risk was estimated with the atherosclerotic CVD score, and the severity of iron metabolism alteration was determined by grading metabolic hiperferritinemia (MHF). Associations between CVD, histology and MRI were investigated. Results In total, 324 patients were included. MRI-determined pancreatic iron overload and moderate-to severe steatosis were present in 45% and 25%, respectively. Liver and pancreatic MRI-biomarkers showed a weak correlation (r=0.32 for PDFF, r=0.17 for R2*). Pancreatic PDFF increased with hepatic histologic steatosis grades and NASH diagnosis (p<0.001). Prevalence of pancreatic steatosis and iron overload increased with the number of metabolic traits (p<0.001). Liver R2* significantly correlated with MHF (AUC=0.77 [0.72-0.82]). MRI-determined pancreatic steatosis (OR=3.15 [1.63-6.09]), and iron overload (OR=2.39 [1.32-4.37]) were independently associated with high-risk CVD. Histologic diagnosis of NASH and advanced fibrosis were also associated with high-risk CVD. Conclusion Pancreatic steatosis and iron overload could be of utility in clinical decision-making and prognostication of NAFLD.
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Affiliation(s)
- David Marti-Aguado
- Digestive Disease Department, Clinic University Hospital, INCLIVA Health Research Institute, Valencia, Spain
- Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute, and Imaging La Fe node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), Valencia, Spain
| | - Amadeo Ten-Esteve
- Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute, and Imaging La Fe node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), Valencia, Spain
- Department of Technologies for Health and Well-Being, Polytechnic University of Valencia, Valencia, Spain
| | | | - Ana Crespo
- Digestive Disease Department, Hospital Arnau de Vilanova, Valencia, Spain
| | - Elena Coello
- Hepatology and Liver Transplantation Unit, La Fe University and Polytechnic Hospital, Valencia, Spain
| | - Víctor Merino-Murgui
- Digestive Disease Department, Clinic University Hospital, INCLIVA Health Research Institute, Valencia, Spain
| | - Matias Fernandez-Paton
- Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute, and Imaging La Fe node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), Valencia, Spain
| | - Clara Alfaro-Cervello
- Pathology Department, Clinic University Hospital, INCLIVA Health Research Institute, Valencia, Spain
- Faculty of Medicine, University of Valencia, Valencia, Spain
| | - Alba Sánchez-Martín
- Pathology Department, Clinic University Hospital, INCLIVA Health Research Institute, Valencia, Spain
| | - Mónica Bauza
- Pathology Department, La Fe University and Polytechnic Hospital, Valencia, Spain
| | - Ana Jimenez-Pastor
- Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute, and Imaging La Fe node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), Valencia, Spain
- Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain
| | | | - Salvador Benlloch
- Digestive Disease Department, Hospital Arnau de Vilanova, Valencia, Spain
- CIBERehd, Centro de Investigación Biomédica en Red en Enfermedades Hepáticas y Digestivas, Instituto de Salud Carlos III, Madrid, Spain
| | - Judith Pérez-Rojas
- Pathology Department, La Fe University and Polytechnic Hospital, Valencia, Spain
| | - Víctor Puglia
- Pathology Department, Hospital Arnau de Vilanova, Valencia, Spain
| | - Antonio Ferrández
- Pathology Department, Clinic University Hospital, INCLIVA Health Research Institute, Valencia, Spain
- Faculty of Medicine, University of Valencia, Valencia, Spain
| | - Victoria Aguilera
- Hepatology and Liver Transplantation Unit, La Fe University and Polytechnic Hospital, Valencia, Spain
- CIBERehd, Centro de Investigación Biomédica en Red en Enfermedades Hepáticas y Digestivas, Instituto de Salud Carlos III, Madrid, Spain
| | - Mercedes Latorre
- Hepatology Unit, Consorcio Hospital General Universitario de Valencia, Valencia, Spain
| | - Cristina Monton
- Digestive Disease Department, Clinic University Hospital, INCLIVA Health Research Institute, Valencia, Spain
| | - Desamparados Escudero-García
- Digestive Disease Department, Clinic University Hospital, INCLIVA Health Research Institute, Valencia, Spain
- Faculty of Medicine, University of Valencia, Valencia, Spain
| | - Ignacio Bosch-Roig
- Universitat Politècnica de València, Institute of Telecommunications and Multimedia Applications (iTEAM), Valencia, Spain
| | - Ángel Alberich-Bayarri
- Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute, and Imaging La Fe node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), Valencia, Spain
- Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain
| | - Luis Marti-Bonmati
- Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute, and Imaging La Fe node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), Valencia, Spain
- Radiology Department, La Fe University and Polytechnic Hospital, Valencia, Spain
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9
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Street JS, Pandit AS, Toma AK. Predicting vasospasm risk using first presentation aneurysmal subarachnoid hemorrhage volume: A semi-automated CT image segmentation analysis using ITK-SNAP. PLoS One 2023; 18:e0286485. [PMID: 37262041 DOI: 10.1371/journal.pone.0286485] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 05/17/2023] [Indexed: 06/03/2023] Open
Abstract
PURPOSE Cerebral vasospasm following aneurysmal subarachnoid hemorrhage (aSAH) is a significant complication associated with poor neurological outcomes. We present a novel, semi-automated pipeline, implemented in the open-source medical imaging analysis software ITK-SNAP, to segment subarachnoid blood volume from initial CT head (CTH) scans and use this to predict future radiological vasospasm. METHODS 42 patients were admitted between February 2020 and December 2021 to our tertiary neurosciences center, and whose initial referral CTH scan was used for this retrospective cohort study. Blood load was segmented using a semi-automated random forest classifier and active contour evolution implemented in ITK-SNAP. Clinical data were extracted from electronic healthcare records in order to fit models aimed at predicting radiological vasospasm risk. RESULTS Semi-automated segmentations demonstrated excellent agreement with manual, expert-derived volumes (mean Dice coefficient = 0.92). Total normalized blood volume, extracted from CTH images at first presentation, was significantly associated with greater odds of later radiological vasospasm, increasing by approximately 7% for each additional cm3 of blood (OR = 1.069, 95% CI: 1.021-1.120; p < .005). Greater blood volume was also significantly associated with vasospasm of a higher Lindegaard ratio, of longer duration, and a greater number of discrete episodes. Total blood volume predicted radiological vasospasm with a greater accuracy as compared to the modified Fisher scale (AUC = 0.86 vs 0.70), and was of independent predictive value. CONCLUSION Semi-automated methods provide a plausible pipeline for the segmentation of blood from CT head images in aSAH, and total blood volume is a robust, extendable predictor of radiological vasospasm, outperforming the modified Fisher scale. Greater subarachnoid blood volume significantly increases the odds of subsequent vasospasm, its time course and its severity.
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Affiliation(s)
- James S Street
- Department of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, London, United Kingdom
| | - Anand S Pandit
- Victor Horsley Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, Queen Square, London, United Kingdom
- High-Dimensional Neurology, Institute of Neurology, University College London, London, United Kingdom
| | - Ahmed K Toma
- Victor Horsley Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, Queen Square, London, United Kingdom
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10
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Chiu FY, Yen Y. Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives. Biomark Res 2023; 11:35. [PMID: 36991494 DOI: 10.1186/s40364-023-00476-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 03/16/2023] [Indexed: 03/31/2023] Open
Abstract
Biomarker discovery and development are popular for detecting the subtle diseases. However, biomarkers are needed to be validated and approved, and even fewer are ever used clinically. Imaging biomarkers have a crucial role in the treatment of cancer patients because they provide objective information on tumor biology, the tumor's habitat, and the tumor's signature in the environment. Tumor changes in response to an intervention complement molecular and genomic translational diagnosis as well as quantitative information. Neuro-oncology has become more prominent in diagnostics and targeted therapies. The classification of tumors has been actively updated, and drug discovery, and delivery in nanoimmunotherapies are advancing in the field of target therapy research. It is important that biomarkers and diagnostic implements be developed and used to assess the prognosis or late effects of long-term survivors. An improved realization of cancer biology has transformed its management with an increasing emphasis on a personalized approach in precision medicine. In the first part, we discuss the biomarker categories in relation to the courses of a disease and specific clinical contexts, including that patients and specimens should both directly reflect the target population and intended use. In the second part, we present the CT perfusion approach that provides quantitative and qualitative data that has been successfully applied to the clinical diagnosis, treatment and application. Furthermore, the novel and promising multiparametric MR imageing approach will provide deeper insights regarding the tumor microenvironment in the immune response. Additionally, we briefly remark new tactics based on MRI and PET for converging on imaging biomarkers combined with applications of bioinformatics in artificial intelligence. In the third part, we briefly address new approaches based on theranostics in precision medicine. These sophisticated techniques merge achievable standardizations into an applicatory apparatus for primarily a diagnostic implementation and tracking radioactive drugs to identify and to deliver therapies in an individualized medicine paradigm. In this article, we describe the critical principles for imaging biomarker characterization and discuss the current status of CT, MRI and PET in finiding imaging biomarkers of early disease.
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Affiliation(s)
- Fang-Ying Chiu
- Center for Cancer Translational Research, Tzu Chi University, Hualien City, 970374, Taiwan.
- Center for Brain and Neurobiology Research, Tzu Chi University, Hualien City, 970374, Taiwan.
- Teaching and Research Headquarters for Sustainable Development Goals, Tzu Chi University, Hualien City, 970374, Taiwan.
| | - Yun Yen
- Center for Cancer Translational Research, Tzu Chi University, Hualien City, 970374, Taiwan.
- Ph.D. Program for Cancer Biology and Drug Discovery, Taipei Medical University, Taipei City, 110301, Taiwan.
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei City, 110301, Taiwan.
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei City, 110301, Taiwan.
- Cancer Center, Taipei Municipal WanFang Hospital, Taipei City, 116081, Taiwan.
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11
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van Tuijl RJ, Pham SDT, Ruigrok YM, Biessels GJ, Velthuis BK, Zwanenburg JJM. Reliability of velocity pulsatility in small vessels on 3Tesla MRI in the basal ganglia: a test-retest study. MAGMA (NEW YORK, N.Y.) 2023; 36:15-23. [PMID: 36166103 PMCID: PMC9992040 DOI: 10.1007/s10334-022-01042-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/08/2022] [Accepted: 09/08/2022] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Recent work showed the feasibility of measuring velocity pulsatility in the perforating arteries at the level of the BG using 3T MRI. However, test-retest measurements have not been performed, yet. This study assessed the test-retest reliability of 3T MRI blood flow velocity measurements in perforating arteries in the BG. MATERIALS AND METHODS Two-dimensional phase-contrast cardiac gated (2D-PC) images were acquired for 35 healthy controls and repeated with and without repositioning. 2D-PC images were processed and analyzed, to assess the number of detected perforating arteries (Ndetected), mean blood flow velocity (Vmean), and velocity pulsatility index (vPI). Paired t-tests and Bland-Altman plots were used to compare variance in outcome parameters with and without repositioning, and limits of agreement (LoA) were calculated. RESULTS The LoA was smallest for Vmean (35%) and highest for vPI (79%). Test-retest reliability was similar with and without repositioning of the subject. DISCUSSION We found similar LoA with and without repositioning indicating that the measurement uncertainty is dominated by scanner and physiological noise, rather than by planning. This enables to study hemodynamic parameters in perforating arteries at clinically available scanners, provided sufficiently large sample sizes are used to mitigate the contribution of scanner- and physiological noise.
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Affiliation(s)
- Rick J van Tuijl
- Center for Image Sciences, Radiology Department, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Stanley D T Pham
- Center for Image Sciences, Radiology Department, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Ynte M Ruigrok
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, Utrecht, Netherlands
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, Utrecht, Netherlands
| | - Birgitta K Velthuis
- Department of Radiology University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Jaco J M Zwanenburg
- Center for Image Sciences, Radiology Department, University Medical Center Utrecht, Utrecht, The Netherlands
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12
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Clinical Applications of B-Flow Ultrasound: A Scoping Review of the Literature. Diagnostics (Basel) 2023; 13:diagnostics13030397. [PMID: 36766502 PMCID: PMC9914334 DOI: 10.3390/diagnostics13030397] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/17/2023] [Accepted: 01/19/2023] [Indexed: 01/24/2023] Open
Abstract
Coded excitation ultrasound investigations have the potential to augment the resolution, increase the efficiency, and expand the possibilities of noninvasive diagnostic imaging. B-Flow ultrasound, a type of digitally encoded imaging, was developed more than 20 years ago with the aim to optimize the visualization of blood flow. It has been investigated for a plethora of applications so far. A scoping review regarding its clinical applications was conducted based on a systematic literature research. B-Flow has been investigated in various anatomic locations and pathologies. However, previous research is limited by small sample sizes, the rare occurrence of elaborate study designs, the reliance on subjective reports and qualitative data, as well as several potential biases. While results are in general promising, it should therefore still be considered an emerging technology. Nevertheless, the limitations can be addressed in future research and the potential to expand its applications make B-Flow an interesting candidate for further investigations.
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13
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Ponsiglione A, Stanzione A, Spadarella G, Baran A, Cappellini LA, Lipman KG, Van Ooijen P, Cuocolo R. Ovarian imaging radiomics quality score assessment: an EuSoMII radiomics auditing group initiative. Eur Radiol 2023; 33:2239-2247. [PMID: 36303093 PMCID: PMC9935717 DOI: 10.1007/s00330-022-09180-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 07/26/2022] [Accepted: 09/18/2022] [Indexed: 11/27/2022]
Abstract
OBJECTIVE To evaluate the methodological rigor of radiomics-based studies using noninvasive imaging in ovarian setting. METHODS Multiple medical literature archives (PubMed, Web of Science, and Scopus) were searched to retrieve original studies focused on computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), or positron emission tomography (PET) radiomics for ovarian disorders' assessment. Two researchers in consensus evaluated each investigation using the radiomics quality score (RQS). Subgroup analyses were performed to assess whether the total RQS varied according to first author category, study aim and topic, imaging modality, and journal quartile. RESULTS From a total of 531 items, 63 investigations were finally included in the analysis. The studies were greatly focused (94%) on the field of oncology, with CT representing the most used imaging technique (41%). Overall, the papers achieved a median total RQS 6 (IQR, -0.5 to 11), corresponding to a percentage of 16.7% of the maximum score (IQR, 0-30.6%). The scoring was low especially due to the lack of prospective design and formal validation of the results. At subgroup analysis, the 4 studies not focused on oncological topic showed significantly lower quality scores than the others. CONCLUSIONS The overall methodological rigor of radiomics studies in the ovarian field is still not ideal, limiting the reproducibility of results and potential translation to clinical setting. More efforts towards a standardized methodology in the workflow are needed to allow radiomics to become a viable tool for clinical decision-making. KEY POINTS • The 63 included studies using noninvasive imaging for ovarian applications were mostly focused on oncologic topic (94%). • The included investigations achieved a median total RQS 6 (IQR, -0.5 to 11), indicating poor methodological rigor. • The RQS was low especially due to the lack of prospective design and formal validation of the results.
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Affiliation(s)
- Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.
| | - Gaia Spadarella
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Agah Baran
- Department of Diagnostic and Interventional Radiology, University of Cologne, Cologne, Germany
| | | | - Kevin Groot Lipman
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Peter Van Ooijen
- Department of Radiation Oncology, University Medical Center Groningen, Groningen, The Netherlands
- Machine Learning Lab, Data Science Center in Health, University Medical Center Groningen, Groningen, the Netherlands
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
- Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy
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Gündüz N, Eser MB, Yıldırım A, Kabaalioğlu A. Radiomics improves the utility of ADC for differentiation between renal oncocytoma and chromophobe renal cell carcinoma: Preliminary findings. Actas Urol Esp 2022; 46:167-177. [PMID: 35216964 DOI: 10.1016/j.acuroe.2022.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 01/17/2021] [Accepted: 04/18/2021] [Indexed: 06/14/2023]
Abstract
OBJECTIVE Differentiation between renal oncocytoma (RON) and chromophobe renal cell carcinoma (chRCC) remains challenging. We aimed to assess the accurate apparent diffusion coefficient (ADC) radiomics features in differentiating these tumors. MATERIALS AND METHODS This single-center retrospective study included 14 patients with histopathologically proven RON (n = 6) and chRCC (n = 8) who underwent magnetic resonance imaging. Features were extracted from ADC maps. Features with an intraclass correlation coefficient >0.90, an intergroup p < 0.01 and interrater differences with normal distribution underwent agreement and receiver operating characteristic curve analyses. RESULTS Overall, 6 features qualified for further analysis and Bland-Altman plots revealed acceptable agreement for all. Only 1 first order feature and 5 high order texture features successfully predicted RON with more than 90% sensitivities and specificities more than 80%. CONCLUSION Squared mean ADC and certain gray level run length matrix features extracted by radiomics of ADC mapping provide quite high diagnostic precision in terms of distinguishing between RON and chRCC.
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Affiliation(s)
- N Gündüz
- Istanbul Medeniyet University, Faculty of Medicine, Department of Radiology, Istanbul, Turkey.
| | - M B Eser
- Istanbul Medeniyet University, Prof. Dr. Süleyman Yalçın City Hospital, Department of Radiology, Istanbul, Turkey
| | - A Yıldırım
- Istanbul Medeniyet University, Faculty of Medicine, Department of Urology, Istanbul, Turkey
| | - A Kabaalioğlu
- Istanbul Medeniyet University, Faculty of Medicine, Department of Radiology, Istanbul, Turkey
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15
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Ponsiglione A, Stanzione A, Cuocolo R, Ascione R, Gambardella M, De Giorgi M, Nappi C, Cuocolo A, Imbriaco M. Cardiac CT and MRI radiomics: systematic review of the literature and radiomics quality score assessment. Eur Radiol 2022; 32:2629-2638. [PMID: 34812912 DOI: 10.1007/s00330-021-08375-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/06/2021] [Accepted: 09/30/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVE To systematically review and evaluate the methodological quality of studies using magnetic resonance imaging (MRI) and computed tomography (CT) radiomics for cardiac applications. METHODS Multiple medical literature archives (PubMed, Web of Science, and EMBASE) were systematically searched to retrieve original studies focused on cardiac MRI and CT radiomics applications. Two researchers in consensus assessed each investigation using the radiomics quality score (RQS). Subgroup analyses were performed to assess whether the total RQS varied according to study aim, journal quartile, imaging modality, and first author category. RESULTS From a total of 1961 items, 53 articles were finally included in the analysis. Overall, the studies reached a median total RQS of 7 (IQR, 4-12), corresponding to a percentage score of 19.4% (IQR, 11.1-33.3%). Item scores were particularly low due to lack of prospective design, cost-effectiveness analysis, and open science. Median RQS percentage score was significantly higher in papers where the first author was a medical doctor and in those published on first quartile journals. CONCLUSIONS The overall methodological quality of radiomics studies in cardiac MRI and CT is still lacking. A higher degree of standardization of the radiomics workflow and higher publication standards for studies are required to allow its translation into clinical practice. KEY POINTS • RQS has been recently proposed for the overall assessment of the methodological quality of radiomics-based studies. • The 53 included studies on cardiac MRI and CT radiomics applications reached a median total RQS of 7 (IQR, 4-12), corresponding to a percentage of 19.4% (IQR, 11.1-33.3%). • A more standardized methodology in the radiomics workflow is needed, especially in terms of study design, validation, and open science, in order to translate the results to clinical applications.
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Affiliation(s)
- Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Renato Cuocolo
- Department of Clinical Medicine and Surgery, University of Naples Federico II, Via Pansini 5, 80131, Naples, Italy.
- Interdepartmental Research Center on Management and Innovation in Healthcare - CIRMIS, University of Naples Federico II, Naples, Italy.
| | - Raffaele Ascione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Michele Gambardella
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Marco De Giorgi
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Carmela Nappi
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Alberto Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
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16
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Vermeulen I, Isin EM, Barton P, Cillero-Pastor B, Heeren RM. Multimodal molecular imaging in drug discovery and development. Drug Discov Today 2022; 27:2086-2099. [DOI: 10.1016/j.drudis.2022.04.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/03/2022] [Accepted: 04/08/2022] [Indexed: 02/06/2023]
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17
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Gündüz N, Eser M, Yıldırım A, Kabaalioğlu A. La radiómica mejora la utilidad del ADC en la diferenciación entre el oncocitoma renal y el carcinoma cromófobo de células renales: resultados preliminares. Actas Urol Esp 2022. [DOI: 10.1016/j.acuro.2021.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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18
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Romanchikova M, Thomas SA, Dexter A, Shaw M, Partarrieau I, Smith N, Venton J, Adeogun M, Brettle D, Turpin RJ. The need for measurement science in digital pathology. J Pathol Inform 2022; 13:100157. [PMID: 36405869 PMCID: PMC9646441 DOI: 10.1016/j.jpi.2022.100157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 11/07/2022] [Indexed: 11/11/2022] Open
Abstract
Background Pathology services experienced a surge in demand during the COVID-19 pandemic. Digitalisation of pathology workflows can help to increase throughput, yet many existing digitalisation solutions use non-standardised workflows captured in proprietary data formats and processed by black-box software, yielding data of varying quality. This study presents the views of a UK-led expert group on the barriers to adoption and the required input of measurement science to improve current practices in digital pathology. Methods With an aim to support the UK's efforts in digitalisation of pathology services, this study comprised: (1) a review of existing evidence, (2) an online survey of domain experts, and (3) a workshop with 42 representatives from healthcare, regulatory bodies, pharmaceutical industry, academia, equipment, and software manufacturers. The discussion topics included sample processing, data interoperability, image analysis, equipment calibration, and use of novel imaging modalities. Findings The lack of data interoperability within the digital pathology workflows hinders data lookup and navigation, according to 80% of attendees. All participants stressed the importance of integrating imaging and non-imaging data for diagnosis, while 80% saw data integration as a priority challenge. 90% identified the benefits of artificial intelligence and machine learning, but identified the need for training and sound performance metrics.Methods for calibration and providing traceability were seen as essential to establish harmonised, reproducible sample processing, and image acquisition pipelines. Vendor-neutral data standards were seen as a "must-have" for providing meaningful data for downstream analysis. Users and vendors need good practice guidance on evaluation of uncertainty, fitness-for-purpose, and reproducibility of artificial intelligence/machine learning tools. All of the above needs to be accompanied by an upskilling of the pathology workforce. Conclusions Digital pathology requires interoperable data formats, reproducible and comparable laboratory workflows, and trustworthy computer analysis software. Despite high interest in the use of novel imaging techniques and artificial intelligence tools, their adoption is slowed down by the lack of guidance and evaluation tools to assess the suitability of these techniques for specific clinical question. Measurement science expertise in uncertainty estimation, standardisation, reference materials, and calibration can help establishing reproducibility and comparability between laboratory procedures, yielding high quality data and providing higher confidence in diagnosis.
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Affiliation(s)
- Marina Romanchikova
- National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW, United Kingdom,Corresponding author
| | - Spencer Angus Thomas
- National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW, United Kingdom
| | - Alex Dexter
- National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW, United Kingdom
| | - Mike Shaw
- National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW, United Kingdom
| | - Ignacio Partarrieau
- National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW, United Kingdom
| | - Nadia Smith
- National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW, United Kingdom
| | - Jenny Venton
- National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW, United Kingdom
| | - Michael Adeogun
- National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW, United Kingdom
| | - David Brettle
- Leeds Teaching Hospitals NHS Trust, St. James's University Hospital, Beckett Street, Leeds, West Yorkshire LS9 7TF, United Kingdom
| | - Robert James Turpin
- British Standards Institution, 389 Chiswick High Road, London W4 4AL, United Kingdom
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Duffton A, Kemp O, Devlin L, Hay L, McLoone P, Paterson C. Feasibility of DW-MRI analysis of salivary glands during head and neck radiotherapy. Tech Innov Patient Support Radiat Oncol 2021; 19:46-51. [PMID: 34527819 PMCID: PMC8430428 DOI: 10.1016/j.tipsro.2021.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/30/2021] [Accepted: 07/06/2021] [Indexed: 11/25/2022] Open
Abstract
INTRODUCTION With no effective treatment for xerostomia, there remains an unmet need to reduce radiation induced toxicity. Measuring physiological changes during RT in salivary glands using DW-MRI may predict which patients are most at risk of severe toxicity. This study evaluated the feasibility of measuring apparent diffusion coefficient (ADC) in the major salivary glands and describes the observed changes in volume and ADC during RT. METHODS Scans were acquired at baseline (MR_base) and after 10 fractions (MR_rpt). Sequences included T1 post contrast fat saturated (T1PCFS) and DW-MRI (5b values, 0-1000 s/mm2). Ipsilateral and contralateral parotid (iPG/cPG), submandibular (iSMG/cSMG) and sublingual glands (iSLG/cSLG) were delineated on T1PCFS, modified on b0 and copied to the ADC map. RESULTS 31 patients with intermediate/high risk squamous cell carcinoma (SCC) of the oropharynx were evaluated. On 124 scans, SMG and SLG delineations were successful on all; parotids were fully contoured in 90.7%. Baseline mean ADC were significantly different between each gland type (p < 0.0001). IPG and cPG volume decreased during treatment by 6.7% and 11.2%. ISMG, cSMG, iSLG and cSLG volume increased by 6.9, 0.9, 60.8 and 60.3% respectively. All structures showed an increase in mean_ADC values. For each gland the increase in ADC was statistically significant p < 0.0001. A smaller mean percentage increase in ADC was observed in the group experiencing a higher grade (2 or > ) of toxicity. CONCLUSION It is feasible to measure volume and ADC of the salivary glands prior to and during RT for HNC. Early data suggests a lower rise in ADC during treatment is associated with more severe late xerostomia.
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Affiliation(s)
- Aileen Duffton
- Department of Radiotherapy, The Beatson West of Scotland Cancer Centre, Glasgow G12 0YN, United Kingdom
| | - Olivia Kemp
- School of Medicine, Veterinary and Life Science, University of Glasgow, Glasgow, United Kingdom
| | - Lynsey Devlin
- Department of Radiotherapy, The Beatson West of Scotland Cancer Centre, Glasgow G12 0YN, United Kingdom
| | - Lisa Hay
- Department of Radiotherapy, The Beatson West of Scotland Cancer Centre, Glasgow G12 0YN, United Kingdom
| | - Philip McLoone
- Institute of Health & Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Claire Paterson
- Department of Clinical Oncology, The Beatson West of Scotland Cancer Centre, Glasgow G12 0YN, United Kingdom
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Clancy K, Dadashzadeh ER, Handzel R, Rieser C, Moses JB, Rosenblum L, Wu S. Machine learning for the prediction of pathologic pneumatosis intestinalis. Surgery 2021; 170:797-805. [PMID: 33926706 PMCID: PMC8405549 DOI: 10.1016/j.surg.2021.03.049] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/19/2021] [Accepted: 03/22/2021] [Indexed: 11/18/2022]
Abstract
BACKGROUND The radiographic finding of pneumatosis intestinalis can indicate a spectrum of underlying processes ranging from a benign finding to a life-threatening condition. Although radiographic pneumatosis intestinalis is relatively common, there is no validated clinical tool to guide surgical management. METHODS Using a retrospective cohort of 300 pneumatosis intestinalis cases from a single institution, we developed 3 machine learning models for 2 clinical tasks: (1) the distinction of benign from pathologic pneumatosis intestinalis cases and (2) the determination of patients who would benefit from an operation. The 3 models are (1) an imaging model based on radiomic features extracted from computed tomography scans, (2) a clinical model based on clinical variables, and (3) a combination model using both the imaging and clinical variables. RESULTS The combination model achieves an area under the curve of 0.91 (confidence interval: 0.87-0.94) for task I and an area under the curve of 0.84 (confidence interval: 0.79-0.88) for task II. The combination model significantly (P < .05) outperforms the imaging model and the clinical model for both tasks. The imaging model achieves an area under the curve of 0.72 (confidence interval: 0.57-0.87) for task I and 0.68 (confidence interval: 0.61-0.74) for task II. The clinical model achieves an area under the curve of 0.87 (confidence interval: 0.83-0.91) for task I and 0.76 (confidence interval: 0.70-0.81) for task II. CONCLUSION This study suggests that combined radiographic and clinical features can identify pathologic pneumatosis intestinalis and aid in patient selection for surgery. This tool may better inform the surgical decision-making process for patients with pneumatosis intestinalis.
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Affiliation(s)
- Kadie Clancy
- Department of Computer Science, University of Pittsburgh, PA
| | | | - Robert Handzel
- Department of Surgery, University of Pittsburgh Medical Center, PA
| | - Caroline Rieser
- Department of Surgery, University of Pittsburgh Medical Center, PA
| | - J B Moses
- Department of Surgery, University of Pittsburgh Medical Center, PA
| | - Lauren Rosenblum
- Department of Surgery, University of Pittsburgh Medical Center, PA
| | - Shandong Wu
- Departments of Radiology, Biomedical Informatics, and Bioengineering, University of Pittsburgh, PA.
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Abstract
The central role of MRI in neuro-oncology is undisputed. The technique is used, both in clinical practice and in clinical trials, to diagnose and monitor disease activity, support treatment decision-making, guide the use of focused treatments and determine response to treatment. Despite recent substantial advances in imaging technology and image analysis techniques, clinical MRI is still primarily used for the qualitative subjective interpretation of macrostructural features, as opposed to quantitative analyses that take into consideration multiple pathophysiological features. However, the field of quantitative imaging and imaging biomarker development is maturing. The European Imaging Biomarkers Alliance (EIBALL) and Quantitative Imaging Biomarkers Alliance (QIBA) are setting standards for biomarker development, validation and implementation, as well as promoting the use of quantitative imaging and imaging biomarkers by demonstrating their clinical value. In parallel, advanced imaging techniques are reaching the clinical arena, providing quantitative, commonly physiological imaging parameters that are driving the discovery, validation and implementation of quantitative imaging and imaging biomarkers in the clinical routine. Additionally, computational analysis techniques are increasingly being used in the research setting to convert medical images into objective high-dimensional data and define radiomic signatures of disease states. Here, I review the definition and current state of MRI biomarkers in neuro-oncology, and discuss the clinical potential of quantitative image analysis techniques.
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Bruixola G, Remacha E, Jiménez-Pastor A, Dualde D, Viala A, Montón JV, Ibarrola-Villava M, Alberich-Bayarri Á, Cervantes A. Radiomics and radiogenomics in head and neck squamous cell carcinoma: Potential contribution to patient management and challenges. Cancer Treat Rev 2021; 99:102263. [PMID: 34343892 DOI: 10.1016/j.ctrv.2021.102263] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 07/06/2021] [Accepted: 07/23/2021] [Indexed: 12/12/2022]
Abstract
The application of imaging biomarkers in oncology is still in its infancy, but with the expansion of radiomics and radiogenomics a revolution is expected in this field. This may be of special interest in head and neck cancer, since it can promote precision medicine and personalization of treatment by overcoming several intrinsic obstacles in this pathology. Our goal is to provide the medical oncologist with the basis to approach these disciplines and appreciate their main uses in clinical research and clinical practice in the medium term. Aligned with this objective we analyzed the most relevant studies in the field, also highlighting novel opportunities and current challenges.
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Affiliation(s)
- Gema Bruixola
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain
| | - Elena Remacha
- Quantitative Imaging Biomarkers in Medicine (QUIBIM SL), Valencia, Spain
| | - Ana Jiménez-Pastor
- Quantitative Imaging Biomarkers in Medicine (QUIBIM SL), Valencia, Spain
| | - Delfina Dualde
- Department of Radiology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain
| | - Alba Viala
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain
| | - Jose Vicente Montón
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain
| | - Maider Ibarrola-Villava
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain; CIBERONC, Instituto de Salud Carlos III, Madrid, Spain
| | | | - Andrés Cervantes
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain; CIBERONC, Instituto de Salud Carlos III, Madrid, Spain.
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Halligan S, Menu Y, Mallett S. Why did European Radiology reject my radiomic biomarker paper? How to correctly evaluate imaging biomarkers in a clinical setting. Eur Radiol 2021; 31:9361-9368. [PMID: 34003349 PMCID: PMC8589811 DOI: 10.1007/s00330-021-07971-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 03/06/2021] [Accepted: 03/31/2021] [Indexed: 12/23/2022]
Abstract
This review explains in simple terms, accessible to the non-statistician, general principles regarding the correct research methods to develop and then evaluate imaging biomarkers in a clinical setting, including radiomic biomarkers. The distinction between diagnostic and prognostic biomarkers is made and emphasis placed on the need to assess clinical utility within the context of a multivariable model. Such models should not be restricted to imaging biomarkers and must include relevant disease and patient characteristics likely to be clinically useful. Biomarker utility is based on whether its addition to the basic clinical model improves diagnosis or prediction. Approaches to both model development and evaluation are explained and the need for adequate amounts of representative data stressed so as to avoid underpowering and overfitting. Advice is provided regarding how to report the research correctly. KEY POINTS: • Imaging biomarker research is common but methodological errors are encountered frequently that may mean the research is not clinically useful. • The clinical utility of imaging biomarkers is best assessed by their additive effect on multivariable models based on clinical factors known to be important. • The data used to develop such models should be sufficient for the number of variables investigated and the model should be evaluated, preferably using data unrelated to development.
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Affiliation(s)
- Steve Halligan
- Centre for Medical Imaging, University College London UCL, 43-45 Foley Street, London, W1W 7TS, UK.
| | - Yves Menu
- Department of Diagnostic and Interventional Radiology, Saint Antoine Hospital, APHP-Sorbonne University, Paris, France
| | - Sue Mallett
- Centre for Medical Imaging, University College London UCL, 43-45 Foley Street, London, W1W 7TS, UK
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Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma. Cancers (Basel) 2021; 13:cancers13040722. [PMID: 33578746 PMCID: PMC7916478 DOI: 10.3390/cancers13040722] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 02/01/2021] [Accepted: 02/06/2021] [Indexed: 02/07/2023] Open
Abstract
Simple Summary Glioblastoma (GBM) is the most malignant primary brain tumor, for which improving patient outcome is limited by a substantial amount of tumor heterogeneity. Magnetic resonance imaging (MRI) in combination with machine learning offers the possibility to collect qualitative and quantitative imaging features which can be used to predict patient prognosis and relevant tumor markers which can aid in selecting the right treatment. This study showed that combining these MRI features with clinical features has the highest prognostic value for GBM patients; this model performed similarly in an independent GBM cohort, showing its reproducibility. The prediction of tumor markers showed promising results in the training set but not could be validated in the independent dataset. This study shows the potential of using MRI to predict prognosis and tumor markers, but further optimization and prospective studies are warranted. Abstract Glioblastoma (GBM) is the most malignant primary brain tumor for which no curative treatment options exist. Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase (IDH)-wild type GBM. Predictive models for IDH-mutation, 06-methylguanine-DNA-methyltransferase (MGMT)-methylation and epidermal growth factor receptor (EGFR) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. This could be reproduced after external validation (C-index 0.711 95% CI 0.64–0.78) and used to stratify Kaplan–Meijer curves in two survival groups (p-value < 0.001). The predictive models performed significantly in the external validation for EGFR amplification (area-under-the-curve (AUC) 0.707, 95% CI 0.582–8.25) and MGMT-methylation (AUC 0.667, 95% CI 0.522–0.82) but not for IDH-mutation (AUC 0.695, 95% CI 0.436–0.927). The integrated clinical and imaging prognostic model was shown to be robust and of potential clinical relevance. The prediction of molecular markers showed promising results in the training set but could not be validated after external validation in a clinically relevant manner. Overall, these results show the potential of combining clinical features with imaging features for prognostic and predictive models in GBM, but further optimization and larger prospective studies are warranted.
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Choida V, Hall-Craggs M, Jebson BR, Fisher C, Leandro M, Wedderburn LR, Ciurtin C. Biomarkers of Response to Biologic Therapy in Juvenile Idiopathic Arthritis. Front Pharmacol 2021; 11:635823. [PMID: 33603671 PMCID: PMC7884612 DOI: 10.3389/fphar.2020.635823] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 12/31/2020] [Indexed: 01/22/2023] Open
Abstract
Background: Juvenile idiopathic arthritis (JIA) is the most common chronic inflammatory arthritis of childhood, characterized by various clinical phenotypes associated with variable prognosis. Significant progress has been achieved with the use of biologic treatments, which specifically block pro-inflammatory molecules involved in the disease pathogenesis. The most commonly used biologics in JIA are monoclonal antibodies and recombinant proteins targeting interleukins 1 (IL-1) and 6 (IL-6), and tumor necrosis factor α (TNF-α). Several biomarkers have been investigated in JIA. Aims: To assess the level of evidence available regarding the role of biomarkers in JIA related to guiding clinical and therapeutic decisions, providing disease prognostic information, facilitating disease activity monitoring and assessing biologic treatment response in JIA, as well as propose new strategies for biologic therapy-related biomarker use in JIA. Methods: We searched PubMed for relevant literature using predefined key words corresponding to several categories of biomarkers to assess their role in predicting and assessing biologic treatment response and clinical remission in JIA. Results: We reviewed serological, cellular, genetic, transcriptomic and imaging biomarkers, to identify candidates that are both well-established and widely used, as well as newly investigated in JIA on biologic therapy. We evaluated their role in management of JIA as well as identified the unmet needs for new biomarker discovery and better clinical applications. Conclusion: Although there are no ideal biomarkers in JIA, we identified serological biomarkers with potential clinical utility. We propose strategies of combining biomarkers of response to biologics in JIA, as well as routine implementation of clinically acceptable imaging biomarkers for improved disease assessment performance.
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Affiliation(s)
- Varvara Choida
- Centre for Adolescent Rheumatology Versus Arthritis at UCL UCLH and GOSH, Division of Medicine, University College London, London, United Kingdom
- Department of Adolescent Rheumatology, University College London Hospital, London, United Kingdom
| | | | - Bethany R. Jebson
- Centre for Adolescent Rheumatology Versus Arthritis at UCL UCLH and GOSH, Division of Medicine, University College London, London, United Kingdom
- University College London Great Ormond Street Institute for Child Health, London, United Kingdom
| | - Corinne Fisher
- Centre for Adolescent Rheumatology Versus Arthritis at UCL UCLH and GOSH, Division of Medicine, University College London, London, United Kingdom
- Department of Adolescent Rheumatology, University College London Hospital, London, United Kingdom
| | - Maria Leandro
- Centre for Adolescent Rheumatology Versus Arthritis at UCL UCLH and GOSH, Division of Medicine, University College London, London, United Kingdom
- Department of Adolescent Rheumatology, University College London Hospital, London, United Kingdom
| | - Lucy R. Wedderburn
- Centre for Adolescent Rheumatology Versus Arthritis at UCL UCLH and GOSH, Division of Medicine, University College London, London, United Kingdom
- University College London Great Ormond Street Institute for Child Health, London, United Kingdom
- NIHR Biomedical Research Centre at Great Ormond Street Hospital, London, United Kingdom
| | - Coziana Ciurtin
- Centre for Adolescent Rheumatology Versus Arthritis at UCL UCLH and GOSH, Division of Medicine, University College London, London, United Kingdom
- Department of Adolescent Rheumatology, University College London Hospital, London, United Kingdom
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Testud B, Brun G, Varoquaux A, Hak JF, Appay R, Le Troter A, Girard N, Stellmann JP. Perfusion-weighted techniques in MRI grading of pediatric cerebral tumors: efficiency of dynamic susceptibility contrast and arterial spin labeling. Neuroradiology 2021; 63:1353-1366. [PMID: 33506349 DOI: 10.1007/s00234-021-02640-y] [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: 09/07/2020] [Accepted: 01/06/2021] [Indexed: 01/23/2023]
Abstract
PURPOSE Dynamic susceptibility contrast (DSC) and arterial spin labeling (ASL) perfusion MRI are applied in pediatric brain tumor grading, but their value for clinical daily practice remains unclear. We explored the ability of ASL and DSC to distinguish low- and high-grade lesions, in an unselected cohort of pediatric cerebral tumors. METHODS We retrospectively compared standard perfusion outcomes including blood volume, blood flow, and time parameters from DSC and ASL at 1.5T or 3T MRI scanners of 46 treatment-naive patients by drawing ROI via consensus by two neuroradiologists on the solid portions of every tumor. The discriminant abilities of perfusion parameters were evaluated by receiver operating characteristic (ROC) over the entire cohort and depending on the tumor location and the magnetic field. RESULTS ASL and DSC parameters showed overall low to moderate performances to distinguish low- and high-grade tumors (area under the curve: between 0.548 and 0.697). Discriminant abilities were better for tumors located supratentorially (AUC between 0.777 and 0.810) than infratentorially, where none of the metrics reached significance. We observed a better differentiation between low- and high-grade cancers at 3T than at 1.5-T. For infratentorial tumors, time parameters from DSC performed better than the commonly used metrics (AUC ≥ 0.8). CONCLUSION DSC and ASL show moderate abilities to distinguish low- and high-grade brain tumors in an unselected cohort. Absolute value of K2, TMAX, tMIP, and normalized value of TMAX of the DSC appear as an alternative to conventional parameters for infratentorial tumors. Three Tesla evaluation should be favored over 1.5-Tesla.
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Affiliation(s)
- B Testud
- Department of Diagnostic and Interventional Neuroradiology, APHM La Timone, 264 Saint Pierre Street, 13385, CEDEX 05, Marseille, France.
| | - G Brun
- Department of Diagnostic and Interventional Neuroradiology, APHM La Timone, 264 Saint Pierre Street, 13385, CEDEX 05, Marseille, France
| | - A Varoquaux
- APHM La Conception, Department of Medical Imaging, Aix Marseille Université, Marseille, France
| | - J F Hak
- Department of Diagnostic and Interventional Neuroradiology, APHM La Timone, 264 Saint Pierre Street, 13385, CEDEX 05, Marseille, France
| | - R Appay
- Department of Pathology and Neuropathology, APHM La Timone, Marseille, France.,Aix-Marseille Univ, CNRS, INP, Inst Neurophysiopathol, Marseille, France
| | - A Le Troter
- Aix-Marseille Univ, CNRS, CRMBM, UMR 7339, Marseille, France.,APHM La Timone, CEMEREM, Marseille, France
| | - N Girard
- Department of Diagnostic and Interventional Neuroradiology, APHM La Timone, 264 Saint Pierre Street, 13385, CEDEX 05, Marseille, France.,Aix-Marseille Univ, CNRS, CRMBM, UMR 7339, Marseille, France
| | - J P Stellmann
- Aix-Marseille Univ, CNRS, CRMBM, UMR 7339, Marseille, France.,APHM La Timone, CEMEREM, Marseille, France
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Quantitative Imaging and Radiomics in Multiple Myeloma: A Potential Opportunity? ACTA ACUST UNITED AC 2021; 57:medicina57020094. [PMID: 33494449 PMCID: PMC7912483 DOI: 10.3390/medicina57020094] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 01/19/2021] [Accepted: 01/19/2021] [Indexed: 12/25/2022]
Abstract
Multiple Myeloma (MM) is the second most common type of hematological disease and, although it is rare among patients under 40 years of age, its incidence rises in elderly subjects. MM manifestations are usually identified through hyperCalcemia, Renal failure, Anaemia, and lytic Bone lesions (CRAB). In particular, the extent of the bone disease is negatively related to a decreased quality of life in patients and, in general, bone disease in MM increases both morbidity and mortality. The detection of lytic bone lesions on imaging, especially computerized tomography (CT) and Magnetic Resonance Imaging (MRI), is becoming crucial from the clinical viewpoint to separate asymptomatic from symptomatic MM patients and the detection of focal lytic lesions in these imaging data is becoming relevant even when no clinical symptoms are present. Therefore, radiology is pivotal in the staging and accurate management of patients with MM even in early phases of the disease. In this review, we describe the opportunities offered by quantitative imaging and radiomics in multiple myeloma. At the present time there is still high variability in the choice between various imaging methods to study MM patients and high variability in image interpretation with suboptimal agreement among readers even in tertiary centers. Therefore, the potential of medical imaging for patients affected by MM is still to be completely unveiled. In the coming years, new insights to study MM with medical imaging will derive from artificial intelligence (AI) and radiomics usage in different bone lesions and from the wide implementations of quantitative methods to report CT and MRI. Eventually, medical imaging data can be integrated with the patient’s outcomes with the purpose of finding radiological biomarkers for predicting the prognostic flow and therapeutic response of the disease.
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Mohammadi S, Callaghan MF. Towards in vivo g-ratio mapping using MRI: Unifying myelin and diffusion imaging. J Neurosci Methods 2021; 348:108990. [PMID: 33129894 PMCID: PMC7840525 DOI: 10.1016/j.jneumeth.2020.108990] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 09/21/2020] [Accepted: 10/20/2020] [Indexed: 12/19/2022]
Abstract
BACKGROUND The g-ratio, quantifying the comparative thickness of the myelin sheath encasing an axon, is a geometrical invariant that has high functional relevance because of its importance in determining neuronal conduction velocity. Advances in MRI data acquisition and signal modelling have put in vivo mapping of the g-ratio, across the entire white matter, within our reach. This capacity would greatly increase our knowledge of the nervous system: how it functions, and how it is impacted by disease. NEW METHOD This is the second review on the topic of g-ratio mapping using MRI. RESULTS This review summarizes the most recent developments in the field, while also providing methodological background pertinent to aggregate g-ratio weighted mapping, and discussing pitfalls associated with these approaches. COMPARISON WITH EXISTING METHODS Using simulations based on recently published data, this review reveals caveats to the state-of-the-art calibration methods that have been used for in vivo g-ratio mapping. It highlights the need to estimate both the slope and offset of the relationship between these MRI-based markers and the true myelin volume fraction if we are really to achieve the goal of precise, high sensitivity g-ratio mapping in vivo. Other challenges discussed in this review further evidence the need for gold standard measurements of human brain tissue from ex vivo histology. CONCLUSIONS We conclude that the quest to find the most appropriate MRI biomarkers to enable in vivo g-ratio mapping is ongoing, with the full potential of many novel techniques yet to be investigated.
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Affiliation(s)
- Siawoosh Mohammadi
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Martina F Callaghan
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, UK
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Marti-Aguado D, Rodríguez-Ortega A, Mestre-Alagarda C, Bauza M, Valero-Pérez E, Alfaro-Cervello C, Benlloch S, Pérez-Rojas J, Ferrández A, Alemany-Monraval P, Escudero-García D, Monton C, Aguilera V, Alberich-Bayarri Á, Serra MÁ, Marti-Bonmati L. Digital pathology: accurate technique for quantitative assessment of histological features in metabolic-associated fatty liver disease. Aliment Pharmacol Ther 2021; 53:160-171. [PMID: 32981113 DOI: 10.1111/apt.16100] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 05/24/2020] [Accepted: 09/05/2020] [Indexed: 12/19/2022]
Abstract
BACKGROUND Histological evaluation of metabolic-associated fatty liver disease (MAFLD) biopsies is subjective, descriptive and with interobserver variability. AIMS To examine the relationship between different histological features (fibrosis, steatosis, inflammation and iron) measured with automated whole-slide quantitative digital pathology and corresponding semiquantitative scoring systems, and the distribution of digital pathology measurements across Fatty Liver Inhibition of Progression (FLIP) algorithm and Steatosis, Activity and Fibrosis (SAF) scoring system METHODS: We prospectively included 136 consecutive patients who underwent liver biopsy for MAFLD at three Spanish centres (January 2017-January 2020). Biopsies were scored by two blinded pathologists according to the Non-alcoholic Steatohepatitis (NASH) Clinical Research Network system for fibrosis staging, the FLIP/SAF classification for steatosis and inflammation grading and Deugnier score for iron grading. Proportionate areas of collagen, fat, inflammatory cells and iron deposits were measured with computer-assisted digital image analysis. A test-retest experiment was performed for precision repeatability evaluation. RESULTS Digital pathology showed strong correlation with fibrosis (r = 0.79; P < 0.001), steatosis (r = 0.85; P < 0.001) and iron (r = 0.70; P < 0.001). Performance was lower when assessing the degree of inflammation (r = 0.35; P < 0.001). NASH cases had a higher proportion of collagen and fat compared to non-NASH cases (P < 0.005), whereas inflammation and iron quantification did not show significant differences between categories. Repeatability evaluation showed that all the coefficients of variation were ≤1.1% and all intraclass correlation coefficient values were ≥0.99, except those of collagen. CONCLUSION Digital pathology allows an automated, precise, objective and quantitative assessment of MAFLD histological features. Digital analysis measurements show good concordance with pathologists´ scores.
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Prayer F, Hofmanninger J, Weber M, Kifjak D, Willenpart A, Pan J, Röhrich S, Langs G, Prosch H. Variability of computed tomography radiomics features of fibrosing interstitial lung disease: A test-retest study. Methods 2020; 188:98-104. [PMID: 32891727 DOI: 10.1016/j.ymeth.2020.08.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 08/27/2020] [Accepted: 08/31/2020] [Indexed: 02/08/2023] Open
Abstract
OBJECTIVES To investigate the intra- and inter-scanner repeatability and reproducibility of CT radiomics features (RF) of fibrosing interstitial lung disease (fILD). METHODS For this prospective, IRB-approved test-retest study, CT data of sixty fILD patients were acquired. Group A (n = 30) underwent one repeated CT scan on a single scanner. Group B (n = 30) was scanned using two different CT scanners. All CT data were reconstructed using different reconstruction kernels (soft, intermediate, sharp) and slice thicknesses (one and three millimeters), resulting in twelve datasets per patient. Following ROI placement in fibrotic lung tissue, 86 RF were extracted. Intra- and inter-scanner RF repeatability and reproducibility were assessed by calculating intraclass correlation coefficients (ICCs) for corresponding kernels and slice thicknesses, and between lung-specific and non-lung-specific reconstruction parameters. Furthermore, test-retest lung volumes were compared. RESULTS Test-retest demonstrated a majority of RF is highly repeatable for all reconstruction parameter combinations. Intra-scanner reproducibility was negatively affected by reconstruction kernel changes, and further reduced by slice thickness alterations. Inter-scanner reproducibility was highly variable, reconstruction parameter-specific, and greatest if either soft kernels and three-millimeter slice thickness, or lung-specific reconstruction parameters were used for both scans. Test-retest lung volumes showed no significant difference. CONCLUSION CT RF of fILD are highly repeatable for constant reconstruction parameters in a single scanner. Intra- and inter-scanner reproducibility are severely impacted by alterations in slice thickness more than reconstruction kernel, and are reconstruction parameter-specific. These findings may facilitate CT data and RF selection and assessment in future fILD radiomics studies collecting data across scanners.
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Affiliation(s)
- Florian Prayer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Johannes Hofmanninger
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Michael Weber
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Daria Kifjak
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Alexander Willenpart
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Jeanny Pan
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Sebastian Röhrich
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
| | - Helmut Prosch
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
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Zhong J, Hu Y, Si L, Jia G, Xing Y, Zhang H, Yao W. A systematic review of radiomics in osteosarcoma: utilizing radiomics quality score as a tool promoting clinical translation. Eur Radiol 2020; 31:1526-1535. [PMID: 32876837 DOI: 10.1007/s00330-020-07221-w] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 07/12/2020] [Accepted: 08/21/2020] [Indexed: 12/11/2022]
Abstract
OBJECTIVES To assess the methodological quality and risk of bias in radiomics studies investigating diagnosis, therapy response, and survival of patients with osteosarcoma. METHODS In this systematic review, literatures on radiomics in osteosarcoma were included and assessed for methodological quality through the radiomics quality score (RQS). The risk of bias and concern of application was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. A meta-analysis of studies focusing on predicting osteosarcoma response to neoadjuvant chemotherapy was performed. RESULTS Twelve radiomics studies exploring osteosarcoma were identified, and five were included in meta-analysis. The RQS reached an average of 20.4% (6.92 of 36) with good inter-rater agreement (ICC 0.95, 95% CI 0.85-0.99). Four studies validated results with an internal dataset, none of which used external dataset; one study was prospectively designed, and another one shared part of the dataset. The risk of bias and concern of application were mainly related to index test aspect. The meta-analysis showed a diagnostic odds ratio of 43.68 (95%CI 13.5-141.31) for predicting response to neoadjuvant chemotherapy with high heterogeneity and low methodological quality. CONCLUSIONS The overall scientific quality of included studies is insufficient; however, radiomics remains a promising technology for predicting treatment response, which might guide therapeutic decision-making and related to prognosis. Improvements in study design, validation, and open science needs to be made to demonstrate the generalizability of findings and to achieve clinical applications. Widespread application of RQS, pre-trained RQS scoring procedure, and modification of RQS in response to clinical needs are necessary. KEY POINTS • Limited radiomics studies were established in osteosarcoma with mean RQS of 20.4%, commonly due to unvalidated results, retrospective study design, and absence of open science. • Meta-analysis of radiomics studies predicting osteosarcoma response to neoadjuvant chemotherapy showed high diagnostic odds ratio 43.68, while high heterogeneity and low methodological quality were the main concerns. • A previously trained data extraction instrument allowed reaching moderate inter-rater agreement in RQS applications, while RQS still needs improvement to become a wide adaptive tool in reviews of radiomics studies, in routine self-check before manuscript submitting and in study design.
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Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Changning District, Shanghai, 200050, China
| | - Yangfan Hu
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Xuhui District, Shanghai, 200233, China
| | - Liping Si
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Changning District, Shanghai, 200050, China
| | - Geng Jia
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Xuhui District, Shanghai, 200233, China
| | - Yue Xing
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Xuhui District, Shanghai, 200233, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin 2nd Road, Huangpu District, Shanghai, 200025, China.
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Changning District, Shanghai, 200050, China.
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