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Enaud R, Frison E, Missonnier S, Fischer A, de Ledinghen V, Perez P, Bui S, Fayon M, Chateil JF, Lamireau T. Cystic fibrosis and noninvasive liver fibrosis assessment methods in children. Pediatr Res 2022; 91:223-229. [PMID: 33731812 DOI: 10.1038/s41390-021-01427-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 01/27/2021] [Accepted: 02/08/2021] [Indexed: 01/31/2023]
Abstract
BACKGROUND Noninvasive assessments of liver fibrosis are currently used to evaluate cystic fibrosis (CF)-related liver disease. However, there is scarce data regarding their repeatability and reproducibility, especially in children with CF. The present study aimed to evaluate the repeatability and reproducibility of transient elastography (TE) (FibroScan®) and point shear-wave elastography using virtual touch quantification (pSWE VTQ) in children with CF. METHODS TE and pSWE VTQ were performed in 56 children with CF by two different operators. Analysis of repeatability and reproducibility was available in 33 patients for TE and 46 patients for pSWE VTQ. Intra- and interobserver agreement were assessed using the intraclass correlation coefficient (ICC) and their 95% confidence interval (CI), and Bland and Altman graphs. RESULTS For TE, ICC was 0.91 (0.83-0.95) for intraobserver agreement and 0.92 (95% CI: 0.86-0.96) for interobserver agreement. For pSWE VTQ, ICC was 0.83 (0.72-0.90) for intraobserver agreement and 0.67 (0.48-0.80) for interobserver agreement. CONCLUSIONS Both technics can be proposed in the follow-up of patients, according to their availability in CF centers. IMPACT This study shows that TE and pSWE VTQ are reliable methods to evaluate liver fibrosis in children with CF. This study shows for the first time that TE and pSWE VTQ are both repeatable and reproducible in children with CF. These data indicate that both TE and pSWE VTQ can be proposed for the follow-up of patients with CF, according to their availability in each CF center.
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Affiliation(s)
- Raphael Enaud
- Pediatric Hepatology and Gastroenterology Unit, Bordeaux University Hospital, Pellegrin-Enfants Hospital, Bordeaux, France.,Bordeaux University Hospital, Pellegrin-Enfants Hospital, Pediatric Cystic Fibrosis-Center (CRCM), Bordeaux, France.,INSERM, Centre de Recherche Cardio-thoracique de Bordeaux (U1045), University of Bordeaux, Bordeaux, France
| | - Eric Frison
- Unité de soutien méthodologique à la recherche clinique et épidémiologique, Service d'information médicale, Pôle Santé Publique, Bordeaux University Hospital, Bordeaux, France.,Clinical Investigation Centre (CIC 1401), Bordeaux University Hospital, Bordeaux, France
| | - Sophie Missonnier
- Pediatric Imaging Unit, Bordeaux University Hospital, Pellegrin-Enfants Hospital, Bordeaux, France
| | - Aude Fischer
- Pediatric Hepatology and Gastroenterology Unit, Bordeaux University Hospital, Pellegrin-Enfants Hospital, Bordeaux, France
| | - Victor de Ledinghen
- Hepatology Unit, Bordeaux University Hospital, Haut-Lévêque Hospital, Pessac, France
| | - Paul Perez
- Unité de soutien méthodologique à la recherche clinique et épidémiologique, Service d'information médicale, Pôle Santé Publique, Bordeaux University Hospital, Bordeaux, France.,Clinical Investigation Centre (CIC 1401), Bordeaux University Hospital, Bordeaux, France
| | - Stéphanie Bui
- Bordeaux University Hospital, Pellegrin-Enfants Hospital, Pediatric Cystic Fibrosis-Center (CRCM), Bordeaux, France
| | - Michael Fayon
- Bordeaux University Hospital, Pellegrin-Enfants Hospital, Pediatric Cystic Fibrosis-Center (CRCM), Bordeaux, France.,INSERM, Centre de Recherche Cardio-thoracique de Bordeaux (U1045), University of Bordeaux, Bordeaux, France.,Clinical Investigation Centre (CIC 1401), Bordeaux University Hospital, Bordeaux, France
| | - Jean-François Chateil
- Pediatric Imaging Unit, Bordeaux University Hospital, Pellegrin-Enfants Hospital, Bordeaux, France.,CRMSB (UMR 5536), University of Bordeaux/CNRS, Bordeaux, France
| | - Thierry Lamireau
- Pediatric Hepatology and Gastroenterology Unit, Bordeaux University Hospital, Pellegrin-Enfants Hospital, Bordeaux, France. .,Clinical Investigation Centre (CIC 1401), Bordeaux University Hospital, Bordeaux, France.
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Paulsen SJ, Mitcham TM, Pan CS, Long J, Grigoryan B, Sazer DW, Harlan CJ, Janson KD, Pagel MD, Miller JS, Bouchard RR. Projection-based stereolithography for direct 3D printing of heterogeneous ultrasound phantoms. PLoS One 2021; 16:e0260737. [PMID: 34882719 PMCID: PMC8659365 DOI: 10.1371/journal.pone.0260737] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 11/16/2021] [Indexed: 01/17/2023] Open
Abstract
Modern ultrasound (US) imaging is increasing its clinical impact, particularly with the introduction of US-based quantitative imaging biomarkers. Continued development and validation of such novel imaging approaches requires imaging phantoms that recapitulate the underlying anatomy and pathology of interest. However, current US phantom designs are generally too simplistic to emulate the structure and variability of the human body. Therefore, there is a need to create a platform that is capable of generating well-characterized phantoms that can mimic the basic anatomical, functional, and mechanical properties of native tissues and pathologies. Using a 3D-printing technique based on stereolithography, we fabricated US phantoms using soft materials in a single fabrication session, without the need for material casting or back-filling. With this technique, we induced variable levels of stable US backscatter in our printed materials in anatomically relevant 3D patterns. Additionally, we controlled phantom stiffness from 7 to >120 kPa at the voxel level to generate isotropic and anisotropic phantoms for elasticity imaging. Lastly, we demonstrated the fabrication of channels with diameters as small as 60 micrometers and with complex geometry (e.g., tortuosity) capable of supporting blood-mimicking fluid flow. Collectively, these results show that projection-based stereolithography allows for customizable fabrication of complex US phantoms.
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Affiliation(s)
- Samantha J. Paulsen
- Department of Bioengineering, Rice University, Houston, TX, United States of America
| | - Trevor M. Mitcham
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
- The University of Texas MD Anderson Cancer Center Graduate School of Biomedical Sciences, Houston, TX, United States of America
| | - Charlene S. Pan
- Department of Bioengineering, Rice University, Houston, TX, United States of America
| | - James Long
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
| | - Bagrat Grigoryan
- Department of Bioengineering, Rice University, Houston, TX, United States of America
| | - Daniel W. Sazer
- Department of Bioengineering, Rice University, Houston, TX, United States of America
| | - Collin J. Harlan
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
- The University of Texas MD Anderson Cancer Center Graduate School of Biomedical Sciences, Houston, TX, United States of America
| | - Kevin D. Janson
- Department of Bioengineering, Rice University, Houston, TX, United States of America
| | - Mark D. Pagel
- The University of Texas MD Anderson Cancer Center Graduate School of Biomedical Sciences, Houston, TX, United States of America
- Department of Cancer Systems Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
| | - Jordan S. Miller
- Department of Bioengineering, Rice University, Houston, TX, United States of America
- * E-mail: (RRB); (JSM)
| | - Richard R. Bouchard
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
- The University of Texas MD Anderson Cancer Center Graduate School of Biomedical Sciences, Houston, TX, United States of America
- * E-mail: (RRB); (JSM)
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Weingärtner S, Desmond KL, Obuchowski NA, Baessler B, Zhang Y, Biondetti E, Ma D, Golay X, Boss MA, Gunter JL, Keenan KE, Hernando D. Development, validation, qualification, and dissemination of quantitative MR methods: Overview and recommendations by the ISMRM quantitative MR study group. Magn Reson Med 2021; 87:1184-1206. [PMID: 34825741 DOI: 10.1002/mrm.29084] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/20/2021] [Accepted: 10/27/2021] [Indexed: 12/26/2022]
Abstract
On behalf of the International Society for Magnetic Resonance in Medicine (ISMRM) Quantitative MR Study Group, this article provides an overview of considerations for the development, validation, qualification, and dissemination of quantitative MR (qMR) methods. This process is framed in terms of two central technical performance properties, i.e., bias and precision. Although qMR is confounded by undesired effects, methods with low bias and high precision can be iteratively developed and validated. For illustration, two distinct qMR methods are discussed throughout the manuscript: quantification of liver proton-density fat fraction, and cardiac T1 . These examples demonstrate the expansion of qMR methods from research centers toward widespread clinical dissemination. The overall goal of this article is to provide trainees, researchers, and clinicians with essential guidelines for the development and validation of qMR methods, as well as an understanding of necessary steps and potential pitfalls for the dissemination of quantitative MR in research and in the clinic.
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Affiliation(s)
- Sebastian Weingärtner
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Kimberly L Desmond
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Nancy A Obuchowski
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio, USA
| | - Bettina Baessler
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Yuxin Zhang
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA.,Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Emma Biondetti
- Department of Neuroscience, Imaging and Clinical Sciences, D'Annunzio University of Chieti and Pescara, Chieti, Italy
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Xavier Golay
- Brain Repair & Rehabilitation, Institute of Neurology, University College London, United Kingdom.,Gold Standard Phantoms Limited, Rochester, United Kingdom
| | - Michael A Boss
- Center for Research and Innovation, American College of Radiology, Philadelphia, Pennsylvania, USA
| | | | - Kathryn E Keenan
- National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Diego Hernando
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA.,Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
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Sarcopenia: Diagnosis and Management, State of the Art and Contribution of Ultrasound. J Clin Med 2021; 10:jcm10235552. [PMID: 34884255 PMCID: PMC8658070 DOI: 10.3390/jcm10235552] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 11/18/2021] [Accepted: 11/26/2021] [Indexed: 12/15/2022] Open
Abstract
Age-related muscle loss is a phenomenon that has been extensively studied in recent decades. Sarcopenia is a multisystem disease, which predisposes to muscle weakness and frailty. At around 50 years of age, an individual begins to lose muscle strength, although this becomes more evident after 70. Sarcopenia is a condition typically found in older adults but can also affect younger people. Sarcopenia is a preventable and treatable condition. In past years, methods and tools to recognize the condition early have been researched. For the development of therapeutic interventions, agreement on diagnosis is fundamental. In recent years, a possible role of ultrasonography in the diagnosis of sarcopenia has been evaluated, compared with the best-known techniques.
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105
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Cunha GM, Fowler KJ. Automated Liver Segmentation for Quantitative MRI Analysis. Radiology 2021; 302:355-356. [PMID: 34783598 DOI: 10.1148/radiol.2021212306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Guilherme Moura Cunha
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Seattle, WA 98195-0005 (G.M.C.); and Liver Imaging Group, Department of Radiology, University of California San Diego, La Jolla, Calif (K.J.F.)
| | - Kathryn J Fowler
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Seattle, WA 98195-0005 (G.M.C.); and Liver Imaging Group, Department of Radiology, University of California San Diego, La Jolla, Calif (K.J.F.)
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106
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Magnetic Resonance Imaging-Based Radiomics Models to Predict Early Extrapancreatic Necrosis in Acute Pancreatitis. Pancreas 2021; 50:1368-1375. [PMID: 35041335 DOI: 10.1097/mpa.0000000000001935] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
OBJECTIVE The aim of the study was to investigate radiomics models based on magnetic resonance imaging (MRI) for predicting early extrapancreatic necrosis (EXPN) in acute pancreatitis. METHODS Radiomics features were extracted from T2-weighted images of extrapancreatic collections and late arterial-phase images of the pancreatic parenchyma for 135 enrolled patients (94 in the primary cohort, including 47 EXPN patients and 41 in the validation cohort, including 20 EXPN patients). The optimal features after dimension reduction were used for radiomics modeling through a support vector machine. A clinical model, the MR severity index score, and extrapancreatic inflammation on MRI were evaluated. RESULTS Twelve optimal features from the extrapancreatic collection images and 10 from the pancreatic parenchyma images were selected for modeling. The pancreatic parenchyma-based and extrapancreatic collection-based radiomics models showed good predictive accuracy in both the training and validation cohorts. The areas under the curve of the extrapancreatic collection-based radiomics model (0.969 and 0.976) were consistent with those of the pancreatic parenchyma-based model (0.931 and 0.921) for both cohorts and better than those of the clinical model and imaging scores for both cohorts. CONCLUSIONS The MRI-based radiomics models of both the extrapancreatic collections and the pancreatic parenchyma had excellent predictive performance for early EXPN.
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107
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Kendrick J, Francis R, Hassan GM, Rowshanfarzad P, Jeraj R, Kasisi C, Rusanov B, Ebert M. Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies. Front Oncol 2021; 11:771787. [PMID: 34790581 PMCID: PMC8591174 DOI: 10.3389/fonc.2021.771787] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 10/11/2021] [Indexed: 12/21/2022] Open
Abstract
Metastatic Prostate Cancer (mPCa) is associated with a poor patient prognosis. mPCa spreads throughout the body, often to bones, with spatial and temporal variations that make the clinical management of the disease difficult. The evolution of the disease leads to spatial heterogeneity that is extremely difficult to characterise with solid biopsies. Imaging provides the opportunity to quantify disease spread. Advanced image analytics methods, including radiomics, offer the opportunity to characterise heterogeneity beyond what can be achieved with simple assessment. Radiomics analysis has the potential to yield useful quantitative imaging biomarkers that can improve the early detection of mPCa, predict disease progression, assess response, and potentially inform the choice of treatment procedures. Traditional radiomics analysis involves modelling with hand-crafted features designed using significant domain knowledge. On the other hand, artificial intelligence techniques such as deep learning can facilitate end-to-end automated feature extraction and model generation with minimal human intervention. Radiomics models have the potential to become vital pieces in the oncology workflow, however, the current limitations of the field, such as limited reproducibility, are impeding their translation into clinical practice. This review provides an overview of the radiomics methodology, detailing critical aspects affecting the reproducibility of features, and providing examples of how artificial intelligence techniques can be incorporated into the workflow. The current landscape of publications utilising radiomics methods in the assessment and treatment of mPCa are surveyed and reviewed. Associated studies have incorporated information from multiple imaging modalities, including bone scintigraphy, CT, PET with varying tracers, multiparametric MRI together with clinical covariates, spanning the prediction of progression through to overall survival in varying cohorts. The methodological quality of each study is quantified using the radiomics quality score. Multiple deficits were identified, with the lack of prospective design and external validation highlighted as major impediments to clinical translation. These results inform some recommendations for future directions of the field.
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Affiliation(s)
- Jake Kendrick
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Roslyn Francis
- Medical School, University of Western Australia, Crawley, WA, Australia
- Department of Nuclear Medicine, Sir Charles Gairdner Hospital, Perth, WA, Australia
| | - Ghulam Mubashar Hassan
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Pejman Rowshanfarzad
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Robert Jeraj
- Department of Medical Physics, University of Wisconsin, Madison, WI, United States
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
| | - Collin Kasisi
- Department of Nuclear Medicine, Sir Charles Gairdner Hospital, Perth, WA, Australia
| | - Branimir Rusanov
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Martin Ebert
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Perth, WA, Australia
- 5D Clinics, Claremont, WA, Australia
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Mirón Mombiela R, Vucetic J, Monllor P, Cárdenas-Herrán JS, Taltavull de La Paz P, Borrás C. Diagnostic Performance of Muscle Echo Intensity and Fractal Dimension for the Detection of Frailty Phenotype. ULTRASONIC IMAGING 2021; 43:337-352. [PMID: 34238072 DOI: 10.1177/01617346211029656] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
To determine the relationship between muscle echo intensity (EI) and fractal dimension (FD), and the diagnostic performance of both ultrasound parameters for the identification of frailty phenotype. A retrospective interpretation of ultrasound scans from a previous cohort (November 2014-February 2015) was performed. The sample included healthy participants <60 years old, and participants ≥60 divided into robust, pre-frail, and frail groups according to Fried frailty criteria. A region of interest of the rectus femoris from the ultrasound scan was segmented, and histogram function was applied to obtain EI. For fractal analysis, images were processed using two-dimensional box-counting techniques to calculate FD. Statistical analyses were performed with diagnostic performance tests. A total of 102 participants (mean age 63 ± 16, 57 men) were evaluated. Muscle fractal dimension correlated with EI (r = .38, p < .01) and showed different pattern in the scatter plots when participants were grouped by non-frail (control + robust) and frail (pre-frail + frail). The diagnostic accuracy for EI to categorize frailty was of 0.69 (95%CI: 0.59-0.78, p = .001), with high intra-rater (ICC: 0.98, 95%CI: 0.98-0.99); p < .001) and inter-rater (ICC: 0.89, 95%CI: 0.75-0.95; p < .001) reliability and low measurement error for both parameters (EI: -0.18, LOA95%: -10.8 to 10.5; FD: 0.00, LOA95%: -0.09 to 0.10) in arbitrary units. The ROC curve combining both parameters was not better than EI alone (p = .18). Muscle FD correlated with EI and showed different patterns according to frailty phenotype, with EI outperforming FD as a possible diagnostic tool for frailty.
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Affiliation(s)
- Rebeca Mirón Mombiela
- Consorcio Hospital General Universitario de Valencia, Valencia, Spain
- Department of Physiology, Faculty of Medicine, University of Valencia, Valencia, Spain
- Radiologisk Afdeling, Herlev Hospital, Herlev, Denmark
| | - Jelena Vucetic
- Consorcio Hospital General Universitario de Valencia, Valencia, Spain
- Department of Physiology, Faculty of Medicine, University of Valencia, Valencia, Spain
- Hospital ICOT Ciudad de Telde, Calle Míster Blisse, Telde, Spain
| | - Paloma Monllor
- Department of Physiology, Faculty of Medicine, University of Valencia, Valencia, Spain
| | | | | | - Consuelo Borrás
- Department of Physiology, Faculty of Medicine, University of Valencia, Valencia, Spain
- INCLIVA Health Research Institute, Valencia, Spain
- Center for Biomedical Network Research on Frailty and Healthy Aging (CIBERFES), CIBER-ISCIII, Spain
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109
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Crop F, Comte P, Le Tinier F, Pasquier D, Mirabel X. Evaluation of an ultrasound bladder scanner in supine and standing position. J Appl Clin Med Phys 2021; 22:194-202. [PMID: 34676959 PMCID: PMC8664133 DOI: 10.1002/acm2.13424] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 08/25/2021] [Accepted: 08/30/2021] [Indexed: 11/09/2022] Open
Abstract
PURPOSE This study examined the performance of a bladder volume measuring device, the BladderScan (BS) BVI9400. The use of the BS offers the possibility of assessing the bladder volume before positioning the patient and performing the daily image-guided radiotherapy procedure. Patients often cannot lie down before entering the treatment vault. Therefore, the BS was also assessed in a standing position. METHODS The repeatability precision was first evaluated, which is the variability of immediate repeated measures of the BS with same operator and subject. This was followed by the reproducibility precision of the BS in which the operator and subjects differ. Finally, the trueness was evaluated in terms of fixed and proportional bias of the results by applying weighted least-squares fitting. Note that 53 and 85 patient measurements were carried out in supine and standing position, respectively, each consisting of three repeated BS measurements. These were compared with the computed tomography (CT)-delineated bladder volume. RESULTS Repeatability was dependent on measurement value (heteroscedasticity) with σrepeatability (BS) = ±15 cm3 ± 10%. However, the total agreement between BS and CT was low with the 95% limits of agreement (LOAs) exceeding ±200 cm3 due to poor patient reproducibility and presence of fixed and proportional bias. Only in the best case of male patients in the supine position, three BS measurements, and correction for the fixed and proportional bias, 95% LOAs of [-147, +114] cm3 were obtained between CT and BS. CONCLUSION The agreement of the BVI9400 BS with CT was found to be too low for radiotherapy applications.
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Affiliation(s)
- Frederik Crop
- Department of Medical Physics, Centre Oscar Lambret, Lille, France
| | - Pauline Comte
- Department of Medical Physics, Centre Oscar Lambret, Lille, France
| | - Florence Le Tinier
- Academic Department of Radiotherapy, Centre Oscar Lambret, Lille, France
| | - David Pasquier
- Academic Department of Radiotherapy, Centre Oscar Lambret, Lille, France
| | - Xavier Mirabel
- Academic Department of Radiotherapy, Centre Oscar Lambret, Lille, France
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Abstract
Biological allometries, such as the scaling of metabolism to mass, are hypothesized to result from natural selection to maximize how vascular networks fill space yet minimize internal transport distances and resistance to blood flow. Metabolic scaling theory argues two guiding principles—conservation of fluid flow and space-filling fractal distributions—describe a diversity of biological networks and predict how the geometry of these networks influences organismal metabolism. Yet, mostly absent from past efforts are studies that directly, and independently, measure metabolic rate from respiration and vascular architecture for the same organ, organism, or tissue. Lack of these measures may lead to inconsistent results and conclusions about metabolism, growth, and allometric scaling. We present simultaneous and consistent measurements of metabolic scaling exponents from clinical images of lung cancer, serving as a first-of-its-kind test of metabolic scaling theory, and identifying potential quantitative imaging biomarkers indicative of tumor growth. We analyze data for 535 clinical PET-CT scans of patients with non-small cell lung carcinoma to establish the presence of metabolic scaling between tumor metabolism and tumor volume. Furthermore, we use computer vision and mathematical modeling to examine predictions of metabolic scaling based on the branching geometry of the tumor-supplying blood vessel networks in a subset of 56 patients diagnosed with stage II-IV lung cancer. Examination of the scaling of maximum standard uptake value with metabolic tumor volume, and metabolic tumor volume with gross tumor volume, yield metabolic scaling exponents of 0.64 (0.20) and 0.70 (0.17), respectively. We compare these to the value of 0.85 (0.06) derived from the geometric scaling of the tumor-supplying vasculature. These results: (1) inform energetic models of growth and development for tumor forecasting; (2) identify imaging biomarkers in vascular geometry related to blood volume and flow; and (3) highlight unique opportunities to develop and test the metabolic scaling theory of ecology in tumors transitioning from avascular to vascular geometries.
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111
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Xue C, Yuan J, Lo GG, Chang ATY, Poon DMC, Wong OL, Zhou Y, Chu WCW. Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review. Quant Imaging Med Surg 2021; 11:4431-4460. [PMID: 34603997 DOI: 10.21037/qims-21-86] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 05/17/2021] [Indexed: 12/13/2022]
Abstract
Radiomics research is rapidly growing in recent years, but more concerns on radiomics reliability are also raised. This review attempts to update and overview the current status of radiomics reliability research in the ever expanding medical literature from the perspective of a single reliability metric of intraclass correlation coefficient (ICC). To conduct this systematic review, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. After literature search and selection, a total of 481 radiomics studies using CT, PET, or MRI, covering a wide range of subject and disease types, were included for review. In these highly heterogeneous studies, feature reliability to image segmentation was much more investigated than reliability to other factors, such as image acquisition, reconstruction, post-processing, and feature quantification. The reported ICCs also suggested high radiomics feature reliability to image segmentation. Image acquisition was found to introduce much more feature variability than image segmentation, in particular for MRI, based on the reported ICC values. Image post-processing and feature quantification yielded different levels of radiomics reliability and might be used to mitigate image acquisition-induced variability. Some common flaws and pitfalls in ICC use were identified, and suggestions on better ICC use were given. Due to the extremely high study heterogeneities and possible risks of bias, the degree of radiomics feature reliability that has been achieved could not yet be safely synthesized or derived in this review. More future researches on radiomics reliability are warranted.
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Affiliation(s)
- Cindy Xue
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China.,Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Jing Yuan
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Gladys G Lo
- Department of Diagnostic & Interventional Radiology, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Amy T Y Chang
- Comprehensive Oncology Centre, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Darren M C Poon
- Comprehensive Oncology Centre, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Oi Lei Wong
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Yihang Zhou
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Winnie C W Chu
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
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112
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Scapicchio C, Gabelloni M, Barucci A, Cioni D, Saba L, Neri E. A deep look into radiomics. LA RADIOLOGIA MEDICA 2021; 126:1296-1311. [PMID: 34213702 PMCID: PMC8520512 DOI: 10.1007/s11547-021-01389-x] [Citation(s) in RCA: 170] [Impact Index Per Article: 56.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 06/15/2021] [Indexed: 11/29/2022]
Abstract
Radiomics is a process that allows the extraction and analysis of quantitative data from medical images. It is an evolving field of research with many potential applications in medical imaging. The purpose of this review is to offer a deep look into radiomics, from the basis, deeply discussed from a technical point of view, through the main applications, to the challenges that have to be addressed to translate this process in clinical practice. A detailed description of the main techniques used in the various steps of radiomics workflow, which includes image acquisition, reconstruction, pre-processing, segmentation, features extraction and analysis, is here proposed, as well as an overview of the main promising results achieved in various applications, focusing on the limitations and possible solutions for clinical implementation. Only an in-depth and comprehensive description of current methods and applications can suggest the potential power of radiomics in fostering precision medicine and thus the care of patients, especially in cancer detection, diagnosis, prognosis and treatment evaluation.
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Affiliation(s)
- Camilla Scapicchio
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy.
| | - Michela Gabelloni
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Andrea Barucci
- CNR-IFAC Institute of Applied Physics "N. Carrara", 50019, Sesto Fiorentino, Italy
| | - Dania Cioni
- Academic Radiology, Department of Surgical, Medical, Molecular Pathology and Emergency Medicine, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Monserrato (Cagliari),Cagliari, Italy
| | - Emanuele Neri
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy
- Italian Society of Medical and Interventional Radiology, SIRM Foundation, Via della Signora 2, 20122, Milano, Italy
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113
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Hernando D, Zhang Y, Pirasteh A. Quantitative diffusion MRI of the abdomen and pelvis. Med Phys 2021; 49:2774-2793. [PMID: 34554579 DOI: 10.1002/mp.15246] [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: 06/06/2021] [Revised: 08/05/2021] [Accepted: 09/15/2021] [Indexed: 12/14/2022] Open
Abstract
Diffusion MRI has enormous potential and utility in the evaluation of various abdominal and pelvic disease processes including cancer and noncancer imaging of the liver, prostate, and other organs. Quantitative diffusion MRI is based on acquisitions with multiple diffusion encodings followed by quantitative mapping of diffusion parameters that are sensitive to tissue microstructure. Compared to qualitative diffusion-weighted MRI, quantitative diffusion MRI can improve standardization of tissue characterization as needed for disease detection, staging, and treatment monitoring. However, similar to many other quantitative MRI methods, diffusion MRI faces multiple challenges including acquisition artifacts, signal modeling limitations, and biological variability. In abdominal and pelvic diffusion MRI, technical acquisition challenges include physiologic motion (respiratory, peristaltic, and pulsatile), image distortions, and low signal-to-noise ratio. If unaddressed, these challenges lead to poor technical performance (bias and precision) and clinical outcomes of quantitative diffusion MRI. Emerging and novel technical developments seek to address these challenges and may enable reliable quantitative diffusion MRI of the abdomen and pelvis. Through systematic validation in phantoms, volunteers, and patients, including multicenter studies to assess reproducibility, these emerging techniques may finally demonstrate the potential of quantitative diffusion MRI for abdominal and pelvic imaging applications.
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Affiliation(s)
- Diego Hernando
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Yuxin Zhang
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Ali Pirasteh
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
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114
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Accuracy and repeatability of QRAPMASTER and MRF-vFA. Magn Reson Imaging 2021; 83:196-207. [PMID: 34506911 DOI: 10.1016/j.mri.2021.09.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 09/03/2021] [Accepted: 09/05/2021] [Indexed: 11/23/2022]
Abstract
Our purpose is to evaluate bias and repeatability of the quantitative MRI sequences QRAPMASTER, based on steady-state imaging, and variable Flip Angle MRF (MRF-VFA), based on the transient response. Both techniques are assessed with a standardized phantom and five volunteers on 1.5 T and 3 T clinical scanners. All scans were repeated eight times in consecutive weeks. In the phantom, the mean bias±95% confidence interval for T1 values with QRAPMASTER was 10 ± 10% on 1.5 T and 4 ± 13% on 3.0 T. The mean bias for T1 values with MRF-vFA was 21 ± 17% on 1.5 T and 9 ± 9% on 3.0 T. For T2 values the mean bias with QRAPMASTER was 12 ± 3% on 1.5 T and 23 ± 1% on 3.0 T. For T2 values the mean bias with MRF-vFA was 17 ± 1% on 1.5 T and 19 ± 2% on 3.0 T. QRAPMASTER estimated lower T1 and T2 values than MRF-vFA. Repeatability was good with low coefficients of variation (CoV). Mean CoV ± 95% confidence interval for T1 were 3.2 ± 0.4% on 1.5 T and 4.5 ± 0.8% on 3.0 T with QRAPMASTER and 2.7% ± 0.2% on 1.5 T and 2.5 ± 0.2% on 3.0 T with MRF-vFA. For T2 were 3.3 ± 1.9% on 1.5 T and 3.2 ± 0.6% on 3.0 T with QRAPMASTER and 2.0 ± 0.4% on 1.5 T and 5.7 ± 1.0% on 3.0 T with MRF-vFA. The in-vivo T1 and T2 are in the range of values previously reported by other authors. The in-vivo mean CoV ± 95% confidence interval in gray matter were for T1 1.7 ± 0.2% using QRAPMASTER and 0.7 ± 0.5% using MRF-vFA and for T2 were 0.9 ± 0.4% using QRAPMASTER and 2.4 ± 0.5% using MRF-vFA. In white matter were for T1 0.9 ± 0.3% using QRAPMASTER and 1.3 ± 1.1% using MRF-vFA and for T2 were 0.7 ± 0.4% using QRAPMASTER and 2.4 ± 0.4% using MRF-vFA. A GLM analysis showed that the variations in T1 and T2 mainly depend on the field strength and the subject, but not on the follow-up repetition in different days. This confirms the high repeatability of QRAPMASTER and MRF-vFA. In summary, QRAPMASTER and MRF-vFA on both systems were highly repeatable with moderate accuracy, providing results comparable to standard references. While repeatability was similar for both methods, QRAPMASTER was more accurate. QRAPMASTER is a tested commercial product but MRF-vFA is 4.77 times faster, which would ease the inclusion of quantitative relaxometry.
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115
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Busoni S, Bock M, Chmelik M, Colgan N, De Bondt T, Hanson LG, Israel M, Kugel H, Maieron M, Mazzoni LN, Seimenis I, Vestergaard P. ADDENDUM to EFOMP Policy statement No.14 "The role of the Medical Physicist in the management of safety within the magnetic resonance imaging environment: EFOMP recommendations". Phys Med 2021; 89:303-305. [PMID: 34492497 DOI: 10.1016/j.ejmp.2021.07.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
Affiliation(s)
- S Busoni
- Medical Physics Department, Firenze University Hospital - AOU Careggi, Firenze, Italy.
| | - M Bock
- Radiology - Medical Physics Dpt., University Medical Center Freiburg, Freiburg, Germany
| | - M Chmelik
- Department of Technical Disciplines in Health Care, Faculty of Health Care, University of Prešov, Prešov, Slovakia
| | - N Colgan
- Galway University Hospital, National University of Ireland Galway, Galway, Ireland
| | - T De Bondt
- Vinçotte - Controlatom, Vilvoorde, Belgium; De Bondt Consulting BV, Antwerp, Belgium
| | - L G Hanson
- Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark; Section for Magnetic Resonance, DTU Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - M Israel
- Department of Hygiene, Medical Ecology and Professional Diseases, Medical University - Pleven, Bulgaria; Department of Physical Factors, National Centre of Public Health and Analyses, Sofia, Bulgaria
| | - H Kugel
- University Clinic for Radiology, University Hospital Muenster, Muenster, Germany
| | - M Maieron
- Health Physics Department, Azienda Sanitaria Universitaria Integrata di Udine, Udine, Italy
| | - L N Mazzoni
- Health Physics Unit, AUSL Toscana Centro, Pistoia, Italy
| | - I Seimenis
- School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - P Vestergaard
- Indkøb & Medicoteknik, Region Midtjylland, Aarhus, Denmark
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116
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Wang C, Padgett KR, Su MY, Mellon EA, Maziero D, Chang Z. Multi-parametric MRI (mpMRI) for treatment response assessment of radiation therapy. Med Phys 2021; 49:2794-2819. [PMID: 34374098 DOI: 10.1002/mp.15130] [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: 03/03/2021] [Revised: 06/23/2021] [Accepted: 06/28/2021] [Indexed: 11/11/2022] Open
Abstract
Magnetic resonance imaging (MRI) plays an important role in the modern radiation therapy (RT) workflow. In comparison with computed tomography (CT) imaging, which is the dominant imaging modality in RT, MRI possesses excellent soft-tissue contrast for radiographic evaluation. Based on quantitative models, MRI can be used to assess tissue functional and physiological information. With the developments of scanner design, acquisition strategy, advanced data analysis, and modeling, multiparametric MRI (mpMRI), a combination of morphologic and functional imaging modalities, has been increasingly adopted for disease detection, localization, and characterization. Integration of mpMRI techniques into RT enriches the opportunities to individualize RT. In particular, RT response assessment using mpMRI allows for accurate characterization of both tissue anatomical and biochemical changes to support decision-making in monotherapy of radiation treatment and/or systematic cancer management. In recent years, accumulating evidence have, indeed, demonstrated the potentials of mpMRI in RT response assessment regarding patient stratification, trial benchmarking, early treatment intervention, and outcome modeling. Clinical application of mpMRI for treatment response assessment in routine radiation oncology workflow, however, is more complex than implementing an additional imaging protocol; mpMRI requires additional focus on optimal study design, practice standardization, and unified statistical reporting strategy to realize its full potential in the context of RT. In this article, the mpMRI theories, including image mechanism, protocol design, and data analysis, will be reviewed with a focus on the radiation oncology field. Representative works will be discussed to demonstrate how mpMRI can be used for RT response assessment. Additionally, issues and limits of current works, as well as challenges and potential future research directions, will also be discussed.
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Affiliation(s)
- Chunhao Wang
- Department of Radiation Oncology, Duke University, Durham, North Carolina, USA
| | - Kyle R Padgett
- Department of Radiation Oncology, University of Miami, Miami, Florida, USA.,Department of Radiology, University of Miami, Miami, Florida, USA
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, California, USA.,Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Eric A Mellon
- Department of Radiation Oncology, University of Miami, Miami, Florida, USA
| | - Danilo Maziero
- Department of Radiation Oncology, University of Miami, Miami, Florida, USA
| | - Zheng Chang
- Department of Radiation Oncology, Duke University, Durham, North Carolina, USA
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117
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Sitek A, Ahn S, Asma E, Chandler A, Ihsani A, Prevrhal S, Rahmim A, Saboury B, Thielemans K. Artificial Intelligence in PET: An Industry Perspective. PET Clin 2021; 16:483-492. [PMID: 34353746 DOI: 10.1016/j.cpet.2021.06.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) imaging applications. AI has the ability to enhance and optimize all aspects of the PET imaging chain from patient scheduling, patient setup, protocoling, data acquisition, detector signal processing, reconstruction, image processing, and interpretation. AI poses industry-specific challenges which will need to be addressed and overcome to maximize the future potentials of AI in PET. This article provides an overview of these industry-specific challenges for the development, standardization, commercialization, and clinical adoption of AI and explores the potential enhancements to PET imaging brought on by AI in the near future. In particular, the combination of on-demand image reconstruction, AI, and custom-designed data-processing workflows may open new possibilities for innovation which would positively impact the industry and ultimately patients.
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Affiliation(s)
- Arkadiusz Sitek
- Sano Centre for Computational Medicine, Nawojki 11 Street, Kraków 30-072, Poland.
| | - Sangtae Ahn
- GE Research, 1 Research Circle KWC-1310C, Niskayuna, NY 12309, USA
| | - Evren Asma
- Canon Medical Research, 706 N Deerpath Drive, Vernon Hills, IL 60061, USA
| | - Adam Chandler
- Global Scientific Collaborations Group, United Imaging Healthcare, America, 9230 Kirby Drive, Houston, TX 77054, USA
| | - Alvin Ihsani
- NVIDIA, 2 Technology Park Drive, Westford, MA 01886, USA
| | - Sven Prevrhal
- Philips Research Europe, Röntgenstr. 22, Hamburg 22335, Germany
| | - Arman Rahmim
- Department of Radiology, University of British Columbia, BC Cancer, BC Cancer Research Institute, 675 West 10th Avenue, Office 6-112, Vancouver, British Columbia V5Z 1L3, Canada; Department of Physics, University of British Columbia, BC Cancer, BC Cancer Research Institute, 675 West 10th Avenue, Office 6-112, Vancouver, British Columbia V5Z 1L3, Canada
| | - Babak Saboury
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, USA; Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA
| | - Kris Thielemans
- Institute of Nuclear Medicine, University College London, UCL Hospital Tower 5, 235 Euston Road, London NW1 2BU, UK; Algorithms and Software Consulting Ltd, 10 Laneway, London SW15 5HX, UK
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118
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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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119
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Keenan KE, Gimbutas Z, Dienstfrey A, Stupic KF, Boss MA, Russek SE, Chenevert TL, Prasad PV, Guo J, Reddick WE, Cecil KM, Shukla-Dave A, Aramburu Nunez D, Shridhar Konar A, Liu MZ, Jambawalikar SR, Schwartz LH, Zheng J, Hu P, Jackson EF. Multi-site, multi-platform comparison of MRI T1 measurement using the system phantom. PLoS One 2021; 16:e0252966. [PMID: 34191819 PMCID: PMC8244851 DOI: 10.1371/journal.pone.0252966] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 05/26/2021] [Indexed: 11/19/2022] Open
Abstract
Recent innovations in quantitative magnetic resonance imaging (MRI) measurement methods have led to improvements in accuracy, repeatability, and acquisition speed, and have prompted renewed interest to reevaluate the medical value of quantitative T1. The purpose of this study was to determine the bias and reproducibility of T1 measurements in a variety of MRI systems with an eye toward assessing the feasibility of applying diagnostic threshold T1 measurement across multiple clinical sites. We used the International Society of Magnetic Resonance in Medicine/National Institute of Standards and Technology (ISMRM/NIST) system phantom to assess variations of T1 measurements, using a slow, reference standard inversion recovery sequence and a rapid, commonly-available variable flip angle sequence, across MRI systems at 1.5 tesla (T) (two vendors, with number of MRI systems n = 9) and 3 T (three vendors, n = 18). We compared the T1 measurements from inversion recovery and variable flip angle scans to ISMRM/NIST phantom reference values using Analysis of Variance (ANOVA) to test for statistical differences between T1 measurements grouped according to MRI scanner manufacturers and/or static field strengths. The inversion recovery method had minor over- and under-estimations compared to the NMR-measured T1 values at both 1.5 T and 3 T. Variable flip angle measurements had substantially greater deviations from the NMR-measured T1 values than the inversion recovery measurements. At 3 T, the measured variable flip angle T1 for one vendor is significantly different than the other two vendors for most of the samples throughout the clinically relevant range of T1. There was no consistent pattern of discrepancy between vendors. We suggest establishing rigorous quality control procedures for validating quantitative MRI methods to promote confidence and stability in associated measurement techniques and to enable translation of diagnostic threshold from the research center to the entire clinical community.
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Affiliation(s)
- Kathryn E. Keenan
- National Institute of Standards and Technology, Boulder, Colorado, United State of America
- * E-mail:
| | - Zydrunas Gimbutas
- National Institute of Standards and Technology, Boulder, Colorado, United State of America
| | - Andrew Dienstfrey
- National Institute of Standards and Technology, Boulder, Colorado, United State of America
| | - Karl F. Stupic
- National Institute of Standards and Technology, Boulder, Colorado, United State of America
| | - Michael A. Boss
- American College of Radiology, Center for Research and Innovation, Philadelphia, Pennsylvania, United State of America
| | - Stephen E. Russek
- National Institute of Standards and Technology, Boulder, Colorado, United State of America
| | | | - P. V. Prasad
- NorthShore University Health System, Evanston, Illinois, United State of America
| | - Junyu Guo
- St. Jude Children’s Research Hospital, Memphis, Tennessee, United State of America
| | - Wilburn E. Reddick
- St. Jude Children’s Research Hospital, Memphis, Tennessee, United State of America
| | - Kim M. Cecil
- Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine Cincinnati, Ohio, United State of America
| | - Amita Shukla-Dave
- Memorial Sloan Kettering Cancer Center, New York, New York, United State of America
| | - David Aramburu Nunez
- Memorial Sloan Kettering Cancer Center, New York, New York, United State of America
| | | | - Michael Z. Liu
- Columbia University Medical Center, New York, New York, United State of America
| | | | | | - Jie Zheng
- Washington University in St. Louis, St. Louis, Missouri, United State of America
| | - Peng Hu
- University of California, Los Angeles, California, United State of America
| | - Edward F. Jackson
- University of Wisconsin, Madison, Wisconsin, United State of America
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120
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Chianca V, Cuocolo R, Albano D. Editorial for "Quantification of Bone Marrow Fat Fraction and Iron by MRI for Distinguishing Aplastic Anemia and Myelodysplastic Syndromes". J Magn Reson Imaging 2021; 54:1761-1762. [PMID: 34131985 DOI: 10.1002/jmri.27778] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 05/24/2021] [Indexed: 01/10/2023] Open
Affiliation(s)
- Vito Chianca
- Clinica di Radiologia EOC IIMSI, Lugano, Switzerland.,Dipartimento di Radiologia, Ospedale Evangelico Betania, Naples, Italy
| | - Renato Cuocolo
- Dipartimento di Medicina Clinica e Chirurgia, Università degli Studi di Napoli "Federico II", Naples, Italy.,Laboratory of Augmented Reality for Health Monitoring (ARHeMLab), Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli "Federico II", Naples, Italy
| | - Domenico Albano
- Unità di Radiologia Diagnostica ed Interventistica, IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.,Sezione di Scienze Radiologiche, Dipartimento di Biomedicina, Neuroscienze e Diagnostica Avanzata, Università degli Studi di Palermo, Palermo, Italy
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121
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Pang Y, Malyarenko DI, Amouzandeh G, Barberi E, Cole M, Vom Endt A, Peeters J, Tan ET, Chenevert TL. Empirical validation of gradient field models for an accurate ADC measured on clinical 3T MR systems in body oncologic applications. Phys Med 2021; 86:113-120. [PMID: 34107440 PMCID: PMC8268998 DOI: 10.1016/j.ejmp.2021.05.030] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 04/28/2021] [Accepted: 05/21/2021] [Indexed: 12/20/2022] Open
Abstract
PURPOSE To empirically corroborate vendor-provided gradient nonlinearity (GNL) characteristics and demonstrate efficient GNL bias correction for human brain apparent diffusion coefficient (ADC) across 3T MR systems and spatial locations. METHODS Spatial distortion vector fields (DVF) were mapped in 3D using a surface fiducial array phantom for individual gradient channels on three 3T MR platforms from different vendors. Measured DVF were converted into empirical 3D GNL tensors and compared with their theoretical counterparts derived from vendor-provided spherical harmonic (SPH) coefficients. To illustrate spatial impact of GNL on ADC, diffusion weighted imaging using three orthogonal gradient directions was performed on a volunteer brain positioned at isocenter (as a reference) and offset superiorly by 10-17 cm (>10% predicted GNL bias). The SPH tensor-based GNL correction was applied to individual DWI gradient directions, and derived ADC was compared with low-bias reference for human brain white matter (WM) ROIs. RESULTS Empiric and predicted GNL errors were comparable for all three studied 3T MR systems, with <1.0% differences in the median and width of spatial histograms for individual GNL tensor elements. Median (±width) of ADC (10-3mm2/s) histograms measured at isocenter in WM reference ROIs from three MR systems were: 0.73 ± 0.11, 0.71 ± 0.14, 0.74 ± 0.17, and at off-isocenters (before versus after GNL correction) were respectively 0.63 ± 0.14 versus 0.72 ± 0.11, 0.53 ± 0.16 versus 0.74 ± 0.18, and 0.65 ± 0.16 versus 0.76 ± 0.18. CONCLUSION The phantom-based spatial distortion measurements validated vendor-provided gradient fields, and accurate WM ADC was recovered regardless of spatial locations and clinical MR platforms using system-specific tensor-based GNL correction for routine DWI.
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Affiliation(s)
- Yuxi Pang
- Radiology, University of Michigan, Ann Arbor, MI, United States.
| | | | | | - Enzo Barberi
- Modus Medical Devices Inc., London, ON, CA, Canada
| | - Michael Cole
- Modus Medical Devices Inc., London, ON, CA, Canada
| | | | | | - Ek T Tan
- Radiology and Imaging, Hospital for Special Surgery, New York, NY, United States
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122
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Usumura M, Kishimoto R, Ishii K, Hotta E, Kershaw J, Higashi T, Obata T, Suga M. Longitudinal stability of a multimodal visco-elastic polyacrylamide gel phantom for magnetic resonance and ultrasound shear-wave elastography. PLoS One 2021; 16:e0250667. [PMID: 34019551 PMCID: PMC8139483 DOI: 10.1371/journal.pone.0250667] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 04/08/2021] [Indexed: 11/22/2022] Open
Abstract
We evaluated the long-term stability of a newly developed viscoelastic phantom made of polyacrylamide (PAAm) gel for magnetic resonance elastography (MRE) and ultrasound-based shear-wave elastography (US SWE). The stiffness of the cylindrical phantom was measured at 0, 13 and 18 months. Storage and loss moduli were measured with MRE, and shear-wave speed (SWS) was measured with US SWE. Long-term stability was evaluated in accordance with the Quantitative Imaging Biomarker Alliance (QIBA) profiles for each modality. The initial storage and loss moduli of the phantom were 5.01±0.22 and 1.11±0.15 respectively, and SWS was 2.57±0.04 m/s. The weight of the phantom decreased by 0.6% over the 18 months. When measured with MRE, the stiffness of the phantom decreased and changes to the storage and loss moduli were -3.0% and -4.6% between 0 and 13 months, and -4.3% and 0.0% between 0 and 18 months. The US measurements found that SWS decreased by 2.4% over the first 13 months and 3.6% at 18 months. These changes were smaller than the tolerances specified in the QIBA profiles, so the viscoelastic PAAm gel phantom fulfilled the condition for long-term stability. This new phantom has the potential to be used as a quality assurance and quality control phantom for MRE and US SWE.
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Affiliation(s)
- Masashi Usumura
- Department of Medical Engineering, Graduate School of Science and Engineering, Chiba University, Chiba, Japan
| | - Riwa Kishimoto
- QST Hospital, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
| | - Koki Ishii
- Department of Medical Engineering, Graduate School of Science and Engineering, Chiba University, Chiba, Japan
| | - Eika Hotta
- QST Hospital, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
| | - Jeff Kershaw
- Applied MRI Research, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
| | - Tatsuya Higashi
- Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
| | - Takayuki Obata
- Applied MRI Research, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
| | - Mikio Suga
- Center for Frontier Medical Engineering, Chiba University, Chiba, Japan
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123
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McGee KP, Hwang KP, Sullivan DC, Kurhanewicz J, Hu Y, Wang J, Li W, Debbins J, Paulson E, Olsen JR, Hua CH, Warner L, Ma D, Moros E, Tyagi N, Chung C. Magnetic resonance biomarkers in radiation oncology: The report of AAPM Task Group 294. Med Phys 2021; 48:e697-e732. [PMID: 33864283 PMCID: PMC8361924 DOI: 10.1002/mp.14884] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 03/24/2021] [Accepted: 03/28/2021] [Indexed: 12/16/2022] Open
Abstract
A magnetic resonance (MR) biologic marker (biomarker) is a measurable quantitative characteristic that is an indicator of normal biological and pathogenetic processes or a response to therapeutic intervention derived from the MR imaging process. There is significant potential for MR biomarkers to facilitate personalized approaches to cancer care through more precise disease targeting by quantifying normal versus pathologic tissue function as well as toxicity to both radiation and chemotherapy. Both of which have the potential to increase the therapeutic ratio and provide earlier, more accurate monitoring of treatment response. The ongoing integration of MR into routine clinical radiation therapy (RT) planning and the development of MR guided radiation therapy systems is providing new opportunities for MR biomarkers to personalize and improve clinical outcomes. Their appropriate use, however, must be based on knowledge of the physical origin of the biomarker signal, the relationship to the underlying biological processes, and their strengths and limitations. The purpose of this report is to provide an educational resource describing MR biomarkers, the techniques used to quantify them, their strengths and weakness within the context of their application to radiation oncology so as to ensure their appropriate use and application within this field.
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Affiliation(s)
- Kiaran P McGee
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ken-Pin Hwang
- Department of Imaging Physics, Division of Diagnostic Imaging, MD Anderson Cancer Center, University of Texas, Houston, Texas, USA
| | - Daniel C Sullivan
- Department of Radiology, Duke University, Durham, North Carolina, USA
| | - John Kurhanewicz
- Department of Radiology, University of California, San Francisco, California, USA
| | - Yanle Hu
- Department of Radiation Oncology, Mayo Clinic, Scottsdale, Arizona, USA
| | - Jihong Wang
- Department of Radiation Oncology, MD Anderson Cancer Center, University of Texas, Houston, Texas, USA
| | - Wen Li
- Department of Radiation Oncology, University of Arizona, Tucson, Arizona, USA
| | - Josef Debbins
- Department of Radiology, Barrow Neurologic Institute, Phoenix, Arizona, USA
| | - Eric Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Jeffrey R Olsen
- Department of Radiation Oncology, University of Colorado Denver - Anschutz Medical Campus, Denver, Colorado, USA
| | - Chia-Ho Hua
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | | | - Daniel Ma
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, USA
| | - Eduardo Moros
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, USA
| | - Neelam Tyagi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Caroline Chung
- Department of Radiation Oncology, MD Anderson Cancer Center, University of Texas, Houston, Texas, USA
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Abstract
The 2016 World Health Organization brain tumor classification is based on genomic and molecular profile of tumor tissue. These characteristics have improved understanding of the brain tumor and played an important role in treatment planning and prognostication. There is an ongoing effort to develop noninvasive imaging techniques that provide insight into tissue characteristics at the cellular and molecular levels. This article focuses on the molecular characteristics of gliomas, transcriptomic subtypes, and radiogenomic studies using semantic and radiomic features. The limitations and future directions of radiogenomics as a standalone diagnostic tool also are discussed.
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Affiliation(s)
- Chaitra Badve
- Department of Radiology, Division of Neuroradiology, University Hospitals Cleveland Medical Center, BSH 5056, 11100 Euclid Avenue, Cleveland, OH 44106, USA.
| | - Sangam Kanekar
- Department of Radiology and Neurology, Division of Neuroradiology, Penn State College of Medicine, Penn State Milton Hershey Medical Center, Mail Code H066 500, University Drive, Hershey, PA 17033, USA
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Wang JH, Wahid KA, van Dijk LV, Farahani K, Thompson RF, Fuller CD. Radiomic biomarkers of tumor immune biology and immunotherapy response. Clin Transl Radiat Oncol 2021; 28:97-115. [PMID: 33937530 PMCID: PMC8076712 DOI: 10.1016/j.ctro.2021.03.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 03/20/2021] [Accepted: 03/24/2021] [Indexed: 02/08/2023] Open
Abstract
Immunotherapies are leading to improved outcomes for many cancers, including those with devastating prognoses. As therapies like immune checkpoint inhibitors (ICI) become a mainstay in treatment regimens, many concurrent challenges have arisen - for instance, delineating clinical responders from non-responders. Predicting response has proven to be difficult given a lack of consistent and accurate biomarkers, heterogeneity of the tumor microenvironment (TME), and a poor understanding of resistance mechanisms. For the most part, imaging data have remained an untapped, yet abundant, resource to address these challenges. In recent years, quantitative image analyses have highlighted the utility of medical imaging in predicting tumor phenotypes, prognosis, and therapeutic response. These studies have been fueled by an explosion of resources in high-throughput mining of image features (i.e. radiomics) and artificial intelligence. In this review, we highlight current progress in radiomics to understand tumor immune biology and predict clinical responses to immunotherapies. We also discuss limitations in these studies and future directions for the field, particularly if high-dimensional imaging data are to play a larger role in precision medicine.
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Affiliation(s)
- Jarey H. Wang
- Medical Scientist Training Program, Baylor College of Medicine, Houston, TX, United States
| | - Kareem A. Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Lisanne V. van Dijk
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Keyvan Farahani
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, United States
| | - Reid F. Thompson
- Department of Radiation Medicine, Oregon Health & Science University, Portland, OR, United States
| | - Clifton David Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
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Hajjo R, Sabbah DA, Bardaweel SK, Tropsha A. Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML). Diagnostics (Basel) 2021; 11:742. [PMID: 33919342 PMCID: PMC8143297 DOI: 10.3390/diagnostics11050742] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 04/09/2021] [Accepted: 04/12/2021] [Indexed: 02/06/2023] Open
Abstract
The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types.
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Affiliation(s)
- Rima Hajjo
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan;
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, The University of North Carlina at Chapel Hill, Chapel Hill, NC 27599, USA;
- National Center for Epidemics and Communicable Disease Control, Amman 11118, Jordan
| | - Dima A. Sabbah
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan;
| | - Sanaa K. Bardaweel
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Jordan, Amman 11942, Jordan;
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, The University of North Carlina at Chapel Hill, Chapel Hill, NC 27599, USA;
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Shridhar Konar A, Qian E, Geethanath S, Buonincontri G, Obuchowski NA, Fung M, Gomez P, Schulte R, Cencini M, Tosetti M, Schwartz LH, Shukla-Dave A. Quantitative imaging metrics derived from magnetic resonance fingerprinting using ISMRM/NIST MRI system phantom: An international multicenter repeatability and reproducibility study. Med Phys 2021; 48:2438-2447. [PMID: 33690905 DOI: 10.1002/mp.14833] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 02/04/2021] [Accepted: 02/23/2021] [Indexed: 11/10/2022] Open
Abstract
PURPOSE To compare the bias and inherent reliability of the quantitative (T1 and T2 ) imaging metrics generated from the magnetic resonance fingerprinting (MRF) technique using the ISMRM/NIST system phantom in an international multicenter setting. METHOD ISMRM/NIST MRI system phantom provides standard reference T1 and T2 relaxation values (vendor-provided) for each of the 14 vials in T1 and T2 arrays. MRF-SSFP scans repeated over 30 days on GE 1.5 and 3.0 T scanners at three collaborative centers. MRF estimated T1, and T2 values averaged over 30 days were compared with the phantom vendor-provided and spin-echo (SE) based convention gold standard (GS) method. Repeatability and reproducibility were characterized by the within-case coefficient of variation (wCV) of the MRF data acquired over 30 days, along with the biases. RESULT For the wide ranges of MRF estimated T1 values, vials #1-8 (T1 relaxation time between 2033 and 184 ms) exhibited a wCV less than 3% and 4%, respectively, on 3.0 and 1.5 T scanners. T2 values in vials #1-8 (T2 relaxation, 1044-45 ms) have shown wCV to be <7% on both 3.0 and 1.5 T scanners. A stronger linear correlation overall for T1 (R2 = 0.9960 and 0.9963 at center-1 and center-2 on 3.0 T scanner, and R2 = 0.9951 and 0.9988 at center-1 and center-3 on 1.5 T scanner) compared to T2 (R2 = 0.9971 and 0.9972 at center-1 and center-2 on 3.0 T scanner, and R2 = 0.9815 and 0.9754 at center-1 and center-3 on 1.5 T scanner). Bland-Altman (BA) analysis showed MRF based T1 and T2 values were within the limit of agreement (LOA) except for one data point. The mean difference or bias and 95% lower bound (LB) and upper bound (UB) LOA are reported in the format; mean bias: 95% LB LOA: 95% UB LOA. The biases for T1 values were 21.34: -50.00: 92.69, 21.32: -47.29: 89.94 ms, and for T2 values were -19.88: -42.37: 2.61, -19.06: -43.58: 5.45 ms on 3.0 T scanner at center-1 and center-2, respectively. Similarly, on 1.5 T scanner biases for T1 values were 26.54: -53.41: 106.50, 9.997: -51.94: 71.94 ms, and for T2 values were -23.84: -135.40: 87.76, -37.30: 134.30: 59.73 ms at center-1 and center-3, respectively. Additionally, the correlation between the SE based GS and MRF estimated T1 and T2 values (R2 = 0.9969 and 0.9977) showed a similar trend as we observed between vendor-provided and MRF estimated T1 and T2 values (R2 = 0.9963 and 0.9972). In addition to correlation, BA analysis showed that all the vials are within the LOA between the GS and vendor-provided for the T1 values and except one vial for T2 . All the vials are within the LOA between GS and MRF except one vial in T1 and T2 array. The wCV for reproducibility was <3% for both T1 and T2 values in vials #1-8, for all the 14 vials, wCV calculated for reproducibility was <4% for T1 values and <5% for T2 . CONCLUSION This study shows that MRF is highly repeatable (wCV <4% for T1 and <7% for T2 ) and reproducible (wCV < 3% for both T1 and T2 ) in certain vials (vials #1-8).
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Affiliation(s)
- Amaresha Shridhar Konar
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Enlin Qian
- Columbia Magnetic Resonance Research Center, Columbia University in the City of New York, New York, NY, 10027, USA
| | - Sairam Geethanath
- Columbia Magnetic Resonance Research Center, Columbia University in the City of New York, New York, NY, 10027, USA
| | - Guido Buonincontri
- Imago7 Foundation and IRCCS Stella Maris Foundation, Pisa, PI, 56128, Italy
| | - Nancy A Obuchowski
- Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, OH, 44106, USA
| | | | - Pedro Gomez
- Munich School of Bioengineering, Technical University of Munich, Munich, BY, 85748, Germany
| | | | - Matteo Cencini
- Imago7 Foundation and IRCCS Stella Maris Foundation, Pisa, PI, 56128, Italy
| | - Michela Tosetti
- Imago7 Foundation and IRCCS Stella Maris Foundation, Pisa, PI, 56128, Italy
| | - Lawrence H Schwartz
- Department of Radiology, Columbia University Irving Medical Center and New York Presbyterian Hospital, New York, NY, 10032, USA
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.,Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
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Xu P, Xue Y, Schoepf UJ, Varga-Szemes A, Griffith J, Yacoub B, Zhou F, Zhou C, Yang Y, Xing W, Zhang L. Radiomics: The Next Frontier of Cardiac Computed Tomography. Circ Cardiovasc Imaging 2021; 14:e011747. [PMID: 33722057 DOI: 10.1161/circimaging.120.011747] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Radiomics uses advanced image analysis to extract massive amounts of quantitative information from digital images, which is not otherwise distinguishable to the human eye. The mined data can be used to explore and establish new and undiscovered correlations between these imaging features and clinical end points. Cardiac computed tomography (CT) is a first-line imaging modality for evaluating coronary artery disease and has a primary role in the assessment of cardiac structures. Conventional interpretation of cardiac CT images relies mostly on subjective and qualitative analysis, as well as basic geometric quantification. To date, some proof-of-concept studies have demonstrated the feasibility and diagnostic performance of cardiac CT radiomics analysis. This review describes the current literature on radiomics in cardiac CT and discusses its advantages, challenges, and future directions. Although much evidences are needed in this field, cardiac CT radiomics has a lot to offer to patients and physicians with potential to define cardiac disease phenotypes on imaging with higher precision.
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Affiliation(s)
- Pengpeng Xu
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Jiangsu, China (P.X., F.Z., C.Z., L.Z.)
| | - Yi Xue
- Department of Diagnostic Radiology, Jinling Hospital, the First School of Clinical Medicine, Southern Medical University, Nanjing, Jiangsu Province, China (Y.X., Y.Y., L.Z.)
| | - U Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., A.V.-S., J.G., B.Y.)
| | - Akos Varga-Szemes
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., A.V.-S., J.G., B.Y.)
| | - Joseph Griffith
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., A.V.-S., J.G., B.Y.)
| | - Basel Yacoub
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (U.J.S., A.V.-S., J.G., B.Y.)
| | - Fan Zhou
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Jiangsu, China (P.X., F.Z., C.Z., L.Z.)
| | - Changsheng Zhou
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Jiangsu, China (P.X., F.Z., C.Z., L.Z.)
| | - Yuting Yang
- Department of Diagnostic Radiology, Jinling Hospital, the First School of Clinical Medicine, Southern Medical University, Nanjing, Jiangsu Province, China (Y.X., Y.Y., L.Z.)
| | - Wei Xing
- Department of Radiology, Third Affiliated Hospital of Soochow University and Changzhou First People's Hospital, Jiangsu, China (W.X.)
| | - Longjiang Zhang
- Department of Diagnostic Radiology, Jinling Hospital, the First School of Clinical Medicine, Southern Medical University, Nanjing, Jiangsu Province, China (Y.X., Y.Y., L.Z.)
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Booth TC, Thompson G, Bulbeck H, Boele F, Buckley C, Cardoso J, Dos Santos Canas L, Jenkinson D, Ashkan K, Kreindler J, Huskens N, Luis A, McBain C, Mills SJ, Modat M, Morley N, Murphy C, Ourselin S, Pennington M, Powell J, Summers D, Waldman AD, Watts C, Williams M, Grant R, Jenkinson MD. A Position Statement on the Utility of Interval Imaging in Standard of Care Brain Tumour Management: Defining the Evidence Gap and Opportunities for Future Research. Front Oncol 2021; 11:620070. [PMID: 33634034 PMCID: PMC7900557 DOI: 10.3389/fonc.2021.620070] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 01/06/2021] [Indexed: 12/19/2022] Open
Abstract
OBJECTIV E To summarise current evidence for the utility of interval imaging in monitoring disease in adult brain tumours, and to develop a position for future evidence gathering while incorporating the application of data science and health economics. METHODS Experts in 'interval imaging' (imaging at pre-planned time-points to assess tumour status); data science; health economics, trial management of adult brain tumours, and patient representatives convened in London, UK. The current evidence on the use of interval imaging for monitoring brain tumours was reviewed. To improve the evidence that interval imaging has a role in disease management, we discussed specific themes of data science, health economics, statistical considerations, patient and carer perspectives, and multi-centre study design. Suggestions for future studies aimed at filling knowledge gaps were discussed. RESULTS Meningioma and glioma were identified as priorities for interval imaging utility analysis. The "monitoring biomarkers" most commonly used in adult brain tumour patients were standard structural MRI features. Interval imaging was commonly scheduled to provide reported imaging prior to planned, regular clinic visits. There is limited evidence relating interval imaging in the absence of clinical deterioration to management change that alters morbidity, mortality, quality of life, or resource use. Progression-free survival is confounded as an outcome measure when using structural MRI in glioma. Uncertainty from imaging causes distress for some patients and their caregivers, while for others it provides an important indicator of disease activity. Any study design that changes imaging regimens should consider the potential for influencing current or planned therapeutic trials, ensure that opportunity costs are measured, and capture indirect benefits and added value. CONCLUSION Evidence for the value, and therefore utility, of regular interval imaging is currently lacking. Ongoing collaborative efforts will improve trial design and generate the evidence to optimise monitoring imaging biomarkers in standard of care brain tumour management.
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Affiliation(s)
- Thomas C. Booth
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
- Department of Neuroradiology, King’s College Hospital NHS Foundation Trust, London, United Kingdom
| | - Gerard Thompson
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | | | - Florien Boele
- Leeds Institute of Medical Research at St James’s, St James’s University Hospital, Leeds, United Kingdom
- Faculty of Medicine and Health, Leeds Institute of Health Sciences, University of Leeds, Leeds, United Kingdom
| | | | - Jorge Cardoso
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Liane Dos Santos Canas
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | | | - Keyoumars Ashkan
- Department of Neurosurgery, King’s College Hospital NHS Foundation Trust, London, United Kingdom
| | | | - Nicky Huskens
- The Tessa Jowell Brain Cancer Mission, London, United Kingdom
| | - Aysha Luis
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Catherine McBain
- Department of Oncology, Christie Hospital NHS Foundation Trust, Manchester, United Kingdom
| | - Samantha J. Mills
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Nick Morley
- Department of Radiology, Wales Research and Diagnostic PET Imaging Centre, Cardiff University School of Medicine, Cardiff, United Kingdom
| | - Caroline Murphy
- King’s College Trials Unit, King’s College London, London, United Kingdom
| | - Sebastian Ourselin
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Mark Pennington
- King’s Health Economics, King’s College London, London, United Kingdom
| | - James Powell
- Department of Oncology, Velindre Cancer Centre, Cardiff, United Kingdom
| | - David Summers
- Department of Neuroradiology, Western General Hospital, Edinburgh, United Kingdom
| | - Adam D. Waldman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Colin Watts
- Birmingham Brain Cancer Program, University of Birmingham, Birmingham, United Kingdom
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
| | - Matthew Williams
- Department of Neuro-oncology, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Robin Grant
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Michael D. Jenkinson
- Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
- Department of Neurosurgery, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
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Lehmann N, Aye N, Kaufmann J, Heinze HJ, Düzel E, Ziegler G, Taubert M. Longitudinal Reproducibility of Neurite Orientation Dispersion and Density Imaging (NODDI) Derived Metrics in the White Matter. Neuroscience 2021; 457:165-185. [PMID: 33465411 DOI: 10.1016/j.neuroscience.2021.01.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 01/04/2021] [Accepted: 01/05/2021] [Indexed: 02/06/2023]
Abstract
Diffusion-weighted magnetic resonance imaging (DWI) is undergoing constant evolution with the ambitious goal of developing in-vivo histology of the brain. A recent methodological advancement is Neurite Orientation Dispersion and Density Imaging (NODDI), a histologically validated multi-compartment model to yield microstructural features of brain tissue such as geometric complexity and neurite packing density, which are especially useful in imaging the white matter. Since NODDI is increasingly popular in clinical research and fields such as developmental neuroscience and neuroplasticity, it is of vast importance to characterize its reproducibility (or reliability). We acquired multi-shell DWI data in 29 healthy young subjects twice over a rescan interval of 4 weeks to assess the within-subject coefficient of variation (CVWS), between-subject coefficient of variation (CVBS) and the intraclass correlation coefficient (ICC), respectively. Using these metrics, we compared regional and voxel-by-voxel reproducibility of the most common image analysis approaches (tract-based spatial statistics [TBSS], voxel-based analysis with different extents of smoothing ["VBM-style"], ROI-based analysis). We observed high test-retest reproducibility for the orientation dispersion index (ODI) and slightly worse results for the neurite density index (NDI). Our findings also suggest that the choice of analysis approach might have significant consequences for the results of a study. Collectively, the voxel-based approach with Gaussian smoothing kernels of ≥4 mm FWHM and ROI-averaging yielded the highest reproducibility across NDI and ODI maps (CVWS mostly ≤3%, ICC mostly ≥0.8), respectively, whilst smaller kernels and TBSS performed consistently worse. Furthermore, we demonstrate that image quality (signal-to-noise ratio [SNR]) is an important determinant of NODDI metric reproducibility. We discuss the implications of these results for longitudinal and cross-sectional research designs commonly employed in the neuroimaging field.
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Affiliation(s)
- Nico Lehmann
- Faculty of Human Sciences, Institute III, Department of Sport Science, Otto von Guericke University, Zschokkestraße 32, 39104 Magdeburg, Germany; Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany.
| | - Norman Aye
- Faculty of Human Sciences, Institute III, Department of Sport Science, Otto von Guericke University, Zschokkestraße 32, 39104 Magdeburg, Germany
| | - Jörn Kaufmann
- Department of Neurology, Otto von Guericke University, Leipziger Straße 44, 39120 Magdeburg, Germany
| | - Hans-Jochen Heinze
- Department of Neurology, Otto von Guericke University, Leipziger Straße 44, 39120 Magdeburg, Germany; Germany German Center for Neurodegenerative Diseases (DZNE), Leipziger Straße 44, 39120 Magdeburg, Germany; Center for Behavioral and Brain Science (CBBS), Otto von Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany; Leibniz-Institute for Neurobiology (LIN), Brenneckestraße 6, 39118 Magdeburg, Germany
| | - Emrah Düzel
- Germany German Center for Neurodegenerative Diseases (DZNE), Leipziger Straße 44, 39120 Magdeburg, Germany; Center for Behavioral and Brain Science (CBBS), Otto von Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany; Institute of Cognitive Neurology and Dementia Research, Otto von Guericke University, Leipziger Straße 44, 39120 Magdeburg, Germany; Institute of Cognitive Neuroscience, University College London, Alexandra House, 17-19 Queen Square, Bloomsbury, London, WC1N 3AZ, UK
| | - Gabriel Ziegler
- Germany German Center for Neurodegenerative Diseases (DZNE), Leipziger Straße 44, 39120 Magdeburg, Germany; Institute of Cognitive Neurology and Dementia Research, Otto von Guericke University, Leipziger Straße 44, 39120 Magdeburg, Germany
| | - Marco Taubert
- Faculty of Human Sciences, Institute III, Department of Sport Science, Otto von Guericke University, Zschokkestraße 32, 39104 Magdeburg, Germany; Center for Behavioral and Brain Science (CBBS), Otto von Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany
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Hasenstab K, Cunha GM, Ichikawa S, Dehkordy SF, Lee MH, Kim SJ, Schlein A, Covarrubias Y, Sirlin CB, Fowler KJ. CNN color-coded difference maps accurately display longitudinal changes in liver MRI-PDFF. Eur Radiol 2021; 31:5041-5049. [PMID: 33449180 DOI: 10.1007/s00330-020-07649-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 11/24/2020] [Accepted: 12/18/2020] [Indexed: 01/19/2023]
Abstract
OBJECTIVES To assess the feasibility of a CNN-based liver registration algorithm to generate difference maps for visual display of spatiotemporal changes in liver PDFF, without needing manual annotations. METHODS This retrospective exploratory study included 25 patients with suspected or confirmed NAFLD, who underwent PDFF-MRI at two time points at our institution. PDFF difference maps were generated by applying a CNN-based liver registration algorithm, then subtracting follow-up from baseline PDFF maps. The difference maps were post-processed by smoothing (5 cm2 round kernel) and applying a categorical color scale. Two fellowship-trained abdominal radiologists and one radiology resident independently reviewed difference maps to visually determine segmental PDFF change. Their visual assessment was compared with manual ROI-based measurements of each Couinaud segment and whole liver PDFF using intraclass correlation (ICC) and Bland-Altman analysis. Inter-reader agreement for visual assessment was calculated (ICC). RESULTS The mean patient age was 49 years (12 males). Baseline and follow-up PDFF ranged from 2.0 to 35.3% and 3.5 to 32.0%, respectively. PDFF changes ranged from - 20.4 to 14.1%. ICCs against the manual reference exceeded 0.95 for each reader, except for segment 2 (2 readers ICC = 0.86-0.91) and segment 4a (reader 3 ICC = 0.94). Bland-Altman limits of agreement were within 5% across all three readers. Inter-reader agreement for visually assessed PDFF change (whole liver and segmental) was excellent (ICCs > 0.96), except for segment 2 (ICC = 0.93). CONCLUSIONS Visual assessment of liver segmental PDFF changes using a CNN-generated difference map strongly agreed with manual estimates performed by an expert reader and yielded high inter-reader agreement. KEY POINTS • Visual assessment of longitudinal changes in quantitative liver MRI can be performed using a CNN-generated difference map and yields strong agreement with manual estimates performed by expert readers.
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Affiliation(s)
- Kyle Hasenstab
- Liver Imaging Group, Department of Radiology, University of California, San Diego, La Jolla, CA, USA.
- Department of Mathematics and Statistics, San Diego State University, San Diego, CA, USA.
| | - Guilherme Moura Cunha
- Liver Imaging Group, Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | | | - Soudabeh Fazeli Dehkordy
- Liver Imaging Group, Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - Min Hee Lee
- Soonchunhyang University Bucheon Hospital, Gyeonggi-do, South Korea
| | - Soo Jin Kim
- National Cancer Center, Republic of Korea, Gyeonggi-do, South Korea
| | - Alexandra Schlein
- Liver Imaging Group, Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - Yesenia Covarrubias
- Liver Imaging Group, Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - Claude B Sirlin
- Liver Imaging Group, Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - Kathryn J Fowler
- Liver Imaging Group, Department of Radiology, University of California, San Diego, La Jolla, CA, USA
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McHugh DJ, Porta N, Little RA, Cheung S, Watson Y, Parker GJM, Jayson GC, O’Connor JPB. Image Contrast, Image Pre-Processing, and T 1 Mapping Affect MRI Radiomic Feature Repeatability in Patients with Colorectal Cancer Liver Metastases. Cancers (Basel) 2021; 13:E240. [PMID: 33440685 PMCID: PMC7826650 DOI: 10.3390/cancers13020240] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 01/01/2021] [Accepted: 01/05/2021] [Indexed: 01/25/2023] Open
Abstract
Imaging biomarkers require technical, biological, and clinical validation to be translated into robust tools in research or clinical settings. This study contributes to the technical validation of radiomic features from magnetic resonance imaging (MRI) by evaluating the repeatability of features from four MR sequences: pre-contrast T1- and T2-weighted images, pre-contrast quantitative T1 maps (qT1), and contrast-enhanced T1-weighted images. Fifty-one patients with colorectal cancer liver metastases were scanned twice, up to 7 days apart. Repeatability was quantified using the intraclass correlation coefficient (ICC) and repeatability coefficient (RC), and the impact of non-Gaussian feature distributions and image normalisation was evaluated. Most radiomic features had non-Gaussian distributions, but Box-Cox transformations enabled ICCs and RCs to be calculated appropriately for an average of 97% of features across sequences. ICCs ranged from 0.30 to 0.99, with volume and other shape features tending to be most repeatable; volume ICC > 0.98 for all sequences. 19% of features from non-normalised images exhibited significantly different ICCs in pair-wise sequence comparisons. Normalisation tended to increase ICCs for pre-contrast T1- and T2-weighted images, and decrease ICCs for qT1 maps. RCs tended to vary more between sequences than ICCs, showing that evaluations of feature performance depend on the chosen metric. This work suggests that feature-specific repeatability, from specific combinations of MR sequence and pre-processing steps, should be evaluated to select robust radiomic features as biomarkers in specific studies. In addition, as different repeatability metrics can provide different insights into a specific feature, consideration of the appropriate metric should be taken in a study-specific context.
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Affiliation(s)
- Damien J. McHugh
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PL, UK; (D.J.M.); (R.A.L.); (S.C.); (Y.W.); (G.C.J.)
- Quantitative Biomedical Imaging Laboratory, The University of Manchester, Manchester M13 9PL, UK
| | - Nuria Porta
- Clinical Trials and Statistics Unit, Institute of Cancer Research, London SW3 6JB, UK;
| | - Ross A. Little
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PL, UK; (D.J.M.); (R.A.L.); (S.C.); (Y.W.); (G.C.J.)
- Quantitative Biomedical Imaging Laboratory, The University of Manchester, Manchester M13 9PL, UK
| | - Susan Cheung
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PL, UK; (D.J.M.); (R.A.L.); (S.C.); (Y.W.); (G.C.J.)
- Quantitative Biomedical Imaging Laboratory, The University of Manchester, Manchester M13 9PL, UK
| | - Yvonne Watson
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PL, UK; (D.J.M.); (R.A.L.); (S.C.); (Y.W.); (G.C.J.)
- Quantitative Biomedical Imaging Laboratory, The University of Manchester, Manchester M13 9PL, UK
| | - Geoff J. M. Parker
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK;
- Bioxydyn Ltd., Manchester M15 6SZ, UK
| | - Gordon C. Jayson
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PL, UK; (D.J.M.); (R.A.L.); (S.C.); (Y.W.); (G.C.J.)
- Department of Medical Oncology, The Christie Hospital, Manchester M20 4BX, UK
| | - James P. B. O’Connor
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PL, UK; (D.J.M.); (R.A.L.); (S.C.); (Y.W.); (G.C.J.)
- Quantitative Biomedical Imaging Laboratory, The University of Manchester, Manchester M13 9PL, UK
- Department of Radiology, The Christie Hospital, Manchester M20 4BX, UK
- Division of Radiotherapy and Imaging, Institute of Cancer Research, London SW3 6JB, UK
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Lee J, Lee JY, Oh SW, Chung MS, Park JE, Moon Y, Jeon HJ, Moon WJ. Evaluation of Reproducibility of Brain Volumetry between Commercial Software, Inbrain and Established Research Purpose Method, FreeSurfer. J Clin Neurol 2021; 17:307-316. [PMID: 33835753 PMCID: PMC8053534 DOI: 10.3988/jcn.2021.17.2.307] [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: 07/27/2020] [Revised: 02/02/2021] [Accepted: 02/02/2021] [Indexed: 01/18/2023] Open
Abstract
Background and Purpose We aimed to determine the intermethod reproducibility between the commercial software Inbrain (MIDAS IT) and the established research-purpose method FreeSurfer, as well as the effect of MRI resolution and the pathological condition of subjects on their intermethod reproducibility. Methods This study included 45 healthy volunteers and 85 patients with mild cognitive impairment (MCI). In 43 of the 85 patients with MCI, three-dimensional, T1-weighted MRI data were obtained at an in-plane resolution of 1.2 mm. The data of the remaining 42 patients with MCI and the healthy volunteers were obtained at an in-plane resolution of 1.0 mm. The within-subject coefficient of variation (CoV), intraclass correlation coefficient (ICC), and effect size were calculated, and means were compared using paired t-tests. The parameters obtained at 1.0-mm and 1.2-mm resolutions in patients with MCI were compared to evaluate the effect of the in-plane resolution on the intermethod reproducibility. The parameters obtained at a 1.0-mm in-plane resolution in patients with MCI and healthy volunteers were used to analyze the effect of subject condition on intermethod reproducibility. Results Overall the two methods showed excellent reproducibility across all regions of the brain (CoV=0.5–3.9, ICC=0.93 to >0.99). In the subgroup of healthy volunteers, the intermethod reliability was only good in some regions (frontal, temporal, cingulate, and insular). The intermethod reproducibility was better in the 1.0-mm group than the 1.2-mm group in all regions other than the nucleus accumbens. Conclusions Inbrain and FreeSurfer showed good-to-excellent intermethod reproducibility for volumetric measurements. Nevertheless, some noticeable differences were found based on subject condition, image resolution, and brain region.
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Affiliation(s)
- Jungbin Lee
- Department of Radiology, Soonchunghyang University Bucheon Hospital, Bucheon, Korea
| | - Ji Young Lee
- Department of Radiology, Hanyang University Medical Center, Seoul, Korea
| | - Se Won Oh
- Department of Radiology, Soonchunhyang University Cheonan Hospital, Cheonan, Korea
| | - Mi Sun Chung
- Department of Radiology, Chung-Ang University Hospital, Seoul, Korea
| | - Ji Eun Park
- Department of Radiology, Asan Medical Center, Seoul, Korea
| | - Yeonsil Moon
- Department of Neurology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea
| | - Hong Jun Jeon
- Department of Psychiatry, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea
| | - Won Jin Moon
- Department of Radiology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea.
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Könik A, O'Donoghue JA, Wahl RL, Graham MM, Van den Abbeele AD. Theranostics: The Role of Quantitative Nuclear Medicine Imaging. Semin Radiat Oncol 2021; 31:28-36. [PMID: 33246633 DOI: 10.1016/j.semradonc.2020.07.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Theranostics is a precision medicine discipline that integrates diagnostic nuclear medicine imaging with radionuclide therapy in a manner that provides both a tumor phenotype and personalized therapy to patients with cancer using radiopharmaceuticals aimed at the same target-specific biological pathway or receptor. The aim of quantitative nuclear medicine imaging is to plan the alpha or beta-emitting therapy based on an accurate 3-dimensional representation of the in-vivo distribution of radioactivity concentration within the tumor and normal organs/tissues in a noninvasive manner. In general, imaging may be either based on positron emission tomography (PET) or single photon emission computed tomography (SPECT) invariably in combination with X-ray CT (PET/CT; SPECT/CT) or, to a much lesser extent, MRI. PET and SPECT differ in terms of the radionuclides and physical processes that give rise to the emission of high energy photons, as well as the sets of technologies involved in their detection. Using a variety of standardized quantitative parameters, system calibration, patient preparation, imaging acquisition and reconstruction protocols, and image analysis protocols, an accurate quantification of the tracer distribution can be obtained, which helps prescribe the therapeutic dose for each patient.
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Affiliation(s)
- Arda Könik
- Department of Imaging, Dana-Farber Cancer Institute, Boston, MA.
| | - Joseph A O'Donoghue
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Richard L Wahl
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University in St Louis School of Medicine, St. Louis, MO
| | - Michael M Graham
- Past Director of Nuclear Medicine, Roy J and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA
| | - Annick D Van den Abbeele
- Department of Imaging, Dana-Farber Cancer Institute, Boston, MA; Division of Cancer Imaging, Mass General Brigham, Boston, MA; Dana-Farber Cancer Institute and Mass General Brigham, Boston, MA; Center for Biomedical Imaging in Oncology, Dana-Farber Cancer Institute, Boston, MA; Tumor Imaging Metrics Core, Dana-Farber/Harvard Cancer Center, Boston, MA
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Therapeutic response monitoring after targeted therapy in an orthotopic rat model of hepatocellular carcinoma using contrast-enhanced ultrasound: Focusing on inter-scanner, and inter-operator reproducibility. PLoS One 2020; 15:e0244304. [PMID: 33362203 PMCID: PMC7757904 DOI: 10.1371/journal.pone.0244304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 12/03/2020] [Indexed: 11/30/2022] Open
Abstract
Purpose To assess therapeutic response monitoring after targeted therapy in an orthotopic rat model of hepatocellular carcinoma (HCC) using CEUS with focusing on inter-scanner and inter-operator reproducibility. Materials and methods For reproducibility, CEUS was performed using two different US scanners by two operators in sixteen rat models of HCC. Using perfusion analysis software (VueBox ®), eleven parameters were collected, and intra-class correlation coefficient (ICC) was used to analyze reproducibility. Then seventeen rat models of HCC were divided into treatment group (n = 8, 30 mg/kg/day sorafenib for five days) and control group (n = 9). CEUS was performed at baseline and 14 days after first treatment, and changes of perfusion parameters were analyzed. Results In treatment group, CEUS perfusion parameters showed a significant change. The peak enhancement (PE, 2.50 x103±1.68 x103 vs 5.55x102±4.65x102, p = 0.010) and wash-in and wash out AUC (WiWoAUC, 1.07x105±6.48 x104 vs 2.65x104±2.25x104, p = 0.009) had significantly decreased two weeks after treatment. On the contrary, control group did not show a significant change, including PE (1.15 x103±7.53x102 vs 9.43x102± 7.81 x102, p = 0.632) and WiWoAUC (5.09 x104±3.25x104 vs 5.92 x104±3.20x104, p = 0.646). For reproducibility, the various degrees of inter-scanner reproducibility were from poor to good (ICC: <0.01–0.63). However, inter-operator reproducibility of important perfusion parameters, including WiAUC, WoAUC, and WiWoAUC, ranged from fair to excellent (ICC: 0.59–0.93) in a different scanner. Conclusion Our results suggest that CEUS is useful for assessment of the treatment response after targeted therapy and with fair to excellent inter-operator reproducibility.
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Bagher-Ebadian H, Chetty IJ. Technical Note: ROdiomiX: A validated software for radiomics analysis of medical images in radiation oncology. Med Phys 2020; 48:354-365. [PMID: 33169367 DOI: 10.1002/mp.14590] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 10/27/2020] [Accepted: 11/03/2020] [Indexed: 12/19/2022] Open
Abstract
PURPOSE This study introduces an in-house-designed software platform (ROdiomiX) for the radiomics analysis of medical images in radiation oncology. ROdiomiX is a MATLAB-based framework for the computation of radiomic features and feature aggregation techniques in compliance with the Image-Biomarker-Standardization-Initiative (IBSI) guidelines, which includes preprocessing protocols and quantitative benchmark results for analysis of computational phantom images. METHODS AND MATERIALS The ROdiomiX software system consists of a series of computation cores implemented on the basis of the guidelines proposed by the IBSI. It is capable of quantitative computation of the following 11 different feature categories: Local-Intensity, Intensity-Histogram, Intensity-Based-Statistical, Intensity-Volume-Histogram, Gray-Level-Co-occurrence, Gray-Level-Run-Length, Gray-Level-Size-Zone, Gray-Level-Distance-Zone, Neighborhood-Grey-Tone-Difference, Neighboring-Grey-Level-Dependence, and Morphological feature. ROdiomiX was validated against benchmark values for the IBSI 3D digital phantom, as well as one designed in-house (HFH). The intraclass correlation coefficient (ICC) for estimating the degree of absolute agreement between ROdiomiX computation and benchmark values for different features at the 95% confidence level (CL) was used for comparison. RESULTS Among the 11 feature categories with 149 total features including 10 different feature aggregation methods (following the IBSI guidelines), the percent difference between absolute feature values computed by the ROdiomiX software and benchmark values reported for IBSI and HFH digital phantoms were 0.14% + 0.43% and 0.11% + 0.27%, respectively. The ICC values were >0.997 for all ten feature categories for both the IBSI and HFH digital phantoms. CONCLUSION The authors successfully developed a platform for computation of quantitative radiomic features. The image preprocessing and computational software cores were designed following the procedures specified by the IBSI. Benchmarking testing was in excellent agreement against the IBSI- and HFH-designed computational phantoms.
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Affiliation(s)
- Hassan Bagher-Ebadian
- Department of Radiation Oncology, Henry Ford Health System, 2799 West Grand Blvd, Detroit, MI, 48202, USA
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Health System, 2799 West Grand Blvd, Detroit, MI, 48202, USA
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Alis D, Yergin M, Asmakutlu O, Topel C, Karaarslan E. The influence of cardiac motion on radiomics features: radiomics features of non-enhanced CMR cine images greatly vary through the cardiac cycle. Eur Radiol 2020; 31:2706-2715. [PMID: 33051731 DOI: 10.1007/s00330-020-07370-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 10/02/2020] [Indexed: 11/29/2022]
Abstract
OBJECTIVES The cardiac cycle might impair the reproducibility of radiomics features of cardiac magnetic resonance (CMR) cine images, yet this issue has not been addressed in the previous research. We aim to evaluate whether radiomics features of CMR cine images vary during the cardiac cycle and investigate the reproducibility of radiomics features of CMR cine images. METHODS This retrospective study enrolled 59 healthy adults who underwent CMR examination. Two observers segmented the myocardium on a 4D stack of three consecutive mid-ventricular short-axis cine images covering the cardiac cycle. A total of 352 radiomics features were extracted. The coefficient of variation and intraclass correlation coefficient were used to assess the feature variability through the cycle and inter-observer reproducibility, respectively. RESULTS Approximately 55% of radiomics features showed large variability through the cardiac cycle. The original features showed more variability than the Laplacian of Gaussian-filtered features (73.8% vs. 48%). The features of 4D stack cine images had a higher proportion of reproducible features (92.0%, 87.7%, and 76.1%) compared with the end-diastolic (77.8%, 62.2%, and 41.7%) and the end-systolic images (81.5%, 74.1%, and 58.8%) for intraclass correlation cut-off values of 30.80, > 0.85, and > 0.90, respectively. CONCLUSIONS Radiomics features of CMR cine images greatly vary during the cardiac cycle. The radiomics features of 4D stack of cine images are more robust compared with end-diastolic and end-systolic cine images in terms of reproducibility. The impact of the cardiac cycle on the reproducibility of the features should be considered when employing CMR cine images radiomics. KEY POINTS • There is limited evidence on the impact of cardiac motion on radiomics features of CMR cine images and the reproducibility of the radiomics features of CMR cine images. • Radiomics features of non-enhanced CMR cine images greatly vary during the cardiac cycle, and the number of "reproducible" features shows significant variations according to the cardiac phases. • The impact of cardiac cycle on the reproducibility of the radiomics features should be considered when employing CMR cine images radiomics.
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Affiliation(s)
- Deniz Alis
- Department of Radiology, Acibadem Mehmet Ali Aydinlar University School of Medicine, Istanbul, Turkey.
| | - Mert Yergin
- Department of Software Engineering and Applied Sciences, Bahcesehir University, Istanbul, Turkey
| | - Ozan Asmakutlu
- Department of Radiology, Istanbul Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training and Research Hospital, Halkali, Istanbul, Turkey
| | - Cagdas Topel
- Department of Radiology, Istanbul Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training and Research Hospital, Halkali, Istanbul, Turkey
| | - Ercan Karaarslan
- Department of Radiology, Acibadem Mehmet Ali Aydinlar University School of Medicine, Istanbul, Turkey
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Zopfs D, Graffe J, Reimer RP, Schäfer S, Persigehl T, Maintz D, Borggrefe J, Haneder S, Lennartz S, Große Hokamp N. Quantitative distribution of iodinated contrast media in body computed tomography: data from a large reference cohort. Eur Radiol 2020; 31:2340-2348. [PMID: 32997173 PMCID: PMC7979665 DOI: 10.1007/s00330-020-07298-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 07/27/2020] [Accepted: 09/15/2020] [Indexed: 12/14/2022]
Abstract
OBJECTIVES Dual-energy computed tomography allows for an accurate and reliable quantification of iodine. However, data on physiological distribution of iodine concentration (IC) is still sparse. This study aims to establish guidance for IC in abdominal organs and important anatomical landmarks using a large cohort of individuals without radiological tumor burden. METHODS Five hundred seventy-one oncologic, portal venous phase dual-layer spectral detector CT studies of the chest and abdomen without tumor burden at time point of imaging confirmed by > 3-month follow-up were included. ROI were placed in parenchymatous organs (n = 25), lymph nodes (n = 6), and vessels (n = 3) with a minimum of two measurements per landmark. ROI were placed on conventional images and pasted to iodine maps to retrieve absolute IC. Normalization to the abdominal aorta was conducted to obtain iodine perfusion ratios. Bivariate regression analysis, t tests, and ANOVA with Tukey-Kramer post hoc test were used for statistical analysis. RESULTS Absolute IC showed a broad scatter and varied with body mass index, between different age groups and between the sexes in parenchymatous organs, lymph nodes, and vessels (range 0.0 ± 0.0 mg/ml-6.6 ± 1.3 mg/ml). Unlike absolute IC, iodine perfusion ratios did not show dependency on body mass index; however, significant differences between the sexes and age groups persisted, showing a tendency towards decreased perfusion ratios in elderly patients (e.g., liver 18-44 years/≥ 64 years: 0.50 ± 0.11/0.43 ± 0.10, p ≤ 0.05). CONCLUSIONS Distribution of IC obtained from a large-scale cohort is provided. As significant differences between sexes and age groups were found, this should be taken into account when obtaining quantitative iodine concentrations and applying iodine thresholds. KEY POINTS • Absolute iodine concentration showed a broad variation and differed between body mass index, age groups, and between the sexes in parenchymatous organs, lymph nodes, and vessels. • The iodine perfusion ratios did not show dependency on body mass index while significant differences between sexes and age groups persisted. • Provided guidance values may serve as reference when aiming to differentiate healthy and abnormal tissue based on iodine perfusion ratios.
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Affiliation(s)
- David Zopfs
- University Cologne, Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, Cologne, Germany
| | - Josefine Graffe
- Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Robert Peter Reimer
- University Cologne, Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, Cologne, Germany
| | | | - Thorsten Persigehl
- University Cologne, Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, Cologne, Germany
| | - David Maintz
- University Cologne, Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, Cologne, Germany
| | - Jan Borggrefe
- University Cologne, Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, Cologne, Germany
| | - Stefan Haneder
- University Cologne, Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, Cologne, Germany
| | - Simon Lennartz
- University Cologne, Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, Cologne, Germany.,Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Nils Große Hokamp
- University Cologne, Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, Cologne, Germany.
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Visser JJ, Goergen SK, Klein S, Noguerol TM, Pickhardt PJ, Fayad LM, Omoumi P. The Value of Quantitative Musculoskeletal Imaging. Semin Musculoskelet Radiol 2020; 24:460-474. [DOI: 10.1055/s-0040-1710356] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
AbstractMusculoskeletal imaging is mainly based on the subjective and qualitative analysis of imaging examinations. However, integration of quantitative assessment of imaging data could increase the value of imaging in both research and clinical practice. Some imaging modalities, such as perfusion magnetic resonance imaging (MRI), diffusion MRI, or T2 mapping, are intrinsically quantitative. But conventional morphological imaging can also be analyzed through the quantification of various parameters. The quantitative data retrieved from imaging examinations can serve as biomarkers and be used to support diagnosis, determine patient prognosis, or monitor therapy.We focus on the value, or clinical utility, of quantitative imaging in the musculoskeletal field. There is currently a trend to move from volume- to value-based payments. This review contains definitions and examines the role that quantitative imaging may play in the implementation of value-based health care. The influence of artificial intelligence on the value of quantitative musculoskeletal imaging is also discussed.
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Affiliation(s)
- Jacob J. Visser
- Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Stacy K. Goergen
- Department of Imaging, Monash Imaging, Clayton, Victoria, Australia
- School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
| | - Stefan Klein
- Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | | | - Perry J. Pickhardt
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Laura M. Fayad
- The Russell H. Morgan Department of Radiology & Radiological Science, The Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Patrick Omoumi
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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Bianchini L, Santinha J, Loução N, Figueiredo M, Botta F, Origgi D, Cremonesi M, Cassano E, Papanikolaou N, Lascialfari A. A multicenter study on radiomic features from T 2 -weighted images of a customized MR pelvic phantom setting the basis for robust radiomic models in clinics. Magn Reson Med 2020; 85:1713-1726. [PMID: 32970859 DOI: 10.1002/mrm.28521] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 08/25/2020] [Accepted: 08/26/2020] [Indexed: 12/11/2022]
Abstract
PURPOSE To investigate the repeatability and reproducibility of radiomic features extracted from MR images and provide a workflow to identify robust features. METHODS T2 -weighted images of a pelvic phantom were acquired on three scanners of two manufacturers and two magnetic field strengths. The repeatability and reproducibility of features were assessed by the intraclass correlation coefficient and the concordance correlation coefficient, respectively, and by the within-subject coefficient of variation, considering repeated acquisitions with and without phantom repositioning, and with different scanner and acquisition parameters. The features showing intraclass correlation coefficient or concordance correlation coefficient >0.9 were selected, and their dependence on shape information (Spearman's ρ > 0.8) analyzed. They were classified for their ability to distinguish textures, after shuffling voxel intensities of images. RESULTS From 944 two-dimensional features, 79.9% to 96.4% showed excellent repeatability in fixed position across all scanners. A much lower range (11.2% to 85.4%) was obtained after phantom repositioning. Three-dimensional extraction did not improve repeatability performance. Excellent reproducibility between scanners was observed in 4.6% to 15.6% of the features, at fixed imaging parameters. In addition, 82.4% to 94.9% of the features showed excellent agreement when extracted from images acquired with echo times 5 ms apart, but decreased with increasing echo-time intervals, and 90.7% of the features exhibited excellent reproducibility for changes in pulse repetition time. Of nonshape features, 2.0% was identified as providing only shape information. CONCLUSION We showed that radiomic features are affected by MRI protocols and propose a general workflow to identify repeatable, reproducible, and informative radiomic features to ensure robustness of clinical studies.
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Affiliation(s)
- Linda Bianchini
- Department of Physics, Università degli Studi di Milano and INSTM RU, Milan, Italy
| | - João Santinha
- Computational Clinical Imaging Group, Center for the Unknown (CCU), Champalimaud Foundation, Lisbon, Portugal.,Instituto de Telecomunicações, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
| | | | - Mário Figueiredo
- Instituto de Telecomunicações, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
| | - Francesca Botta
- Medical Physics Unit, IEO, European Institute of Oncology IRCSS, Milan, Italy
| | - Daniela Origgi
- Medical Physics Unit, IEO, European Institute of Oncology IRCSS, Milan, Italy
| | - Marta Cremonesi
- Radiation Research Unit, IEO, European Institute of Oncology IRCSS, Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO, European Institute of Oncology IRCSS, Milan, Italy
| | - Nikolaos Papanikolaou
- Computational Clinical Imaging Group, Center for the Unknown (CCU), Champalimaud Foundation, Lisbon, Portugal
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141
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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: 45] [Impact Index Per Article: 11.3] [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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Tozzi L, Fleming SL, Taylor ZD, Raterink CD, Williams LM. Test-retest reliability of the human functional connectome over consecutive days: identifying highly reliable portions and assessing the impact of methodological choices. Netw Neurosci 2020; 4:925-945. [PMID: 33615097 PMCID: PMC7888485 DOI: 10.1162/netn_a_00148] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 05/13/2020] [Indexed: 11/21/2022] Open
Abstract
Countless studies have advanced our understanding of the human brain and its organization by using functional magnetic resonance imaging (fMRI) to derive network representations of human brain function. However, we do not know to what extent these "functional connectomes" are reliable over time. In a large public sample of healthy participants (N = 833) scanned on two consecutive days, we assessed the test-retest reliability of fMRI functional connectivity and the consequences on reliability of three common sources of variation in analysis workflows: atlas choice, global signal regression, and thresholding. By adopting the intraclass correlation coefficient as a metric, we demonstrate that only a small portion of the functional connectome is characterized by good (6-8%) to excellent (0.08-0.14%) reliability. Connectivity between prefrontal, parietal, and temporal areas is especially reliable, but also average connectivity within known networks has good reliability. In general, while unreliable edges are weak, reliable edges are not necessarily strong. Methodologically, reliability of edges varies between atlases, global signal regression decreases reliability for networks and most edges (but increases it for some), and thresholding based on connection strength reduces reliability. Focusing on the reliable portion of the connectome could help quantify brain trait-like features and investigate individual differences using functional neuroimaging.
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Affiliation(s)
- Leonardo Tozzi
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Scott L. Fleming
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Zachary D. Taylor
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Cooper D. Raterink
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Leanne M. Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
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van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging-"how-to" guide and critical reflection. Insights Imaging 2020; 11:91. [PMID: 32785796 PMCID: PMC7423816 DOI: 10.1186/s13244-020-00887-2] [Citation(s) in RCA: 561] [Impact Index Per Article: 140.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 06/22/2020] [Indexed: 02/06/2023] Open
Abstract
Radiomics is a quantitative approach to medical imaging, which aims at enhancing the existing data available to clinicians by means of advanced mathematical analysis. Through mathematical extraction of the spatial distribution of signal intensities and pixel interrelationships, radiomics quantifies textural information by using analysis methods from the field of artificial intelligence. Various studies from different fields in imaging have been published so far, highlighting the potential of radiomics to enhance clinical decision-making. However, the field faces several important challenges, which are mainly caused by the various technical factors influencing the extracted radiomic features.The aim of the present review is twofold: first, we present the typical workflow of a radiomics analysis and deliver a practical "how-to" guide for a typical radiomics analysis. Second, we discuss the current limitations of radiomics, suggest potential improvements, and summarize relevant literature on the subject.
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Affiliation(s)
- Janita E van Timmeren
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Davide Cester
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Hatem Alkadhi
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Bettina Baessler
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.
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Goodkin O, Pemberton HG, Vos SB, Prados F, Das RK, Moggridge J, De Blasi B, Bartlett P, Williams E, Campion T, Haider L, Pearce K, Bargallό N, Sanchez E, Bisdas S, White M, Ourselin S, Winston GP, Duncan JS, Cardoso J, Thornton JS, Yousry TA, Barkhof F. Clinical evaluation of automated quantitative MRI reports for assessment of hippocampal sclerosis. Eur Radiol 2020; 31:34-44. [PMID: 32749588 PMCID: PMC7755617 DOI: 10.1007/s00330-020-07075-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 05/07/2020] [Accepted: 07/15/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVES Hippocampal sclerosis (HS) is a common cause of temporal lobe epilepsy. Neuroradiological practice relies on visual assessment, but quantification of HS imaging biomarkers-hippocampal volume loss and T2 elevation-could improve detection. We tested whether quantitative measures, contextualised with normative data, improve rater accuracy and confidence. METHODS Quantitative reports (QReports) were generated for 43 individuals with epilepsy (mean age ± SD 40.0 ± 14.8 years, 22 men; 15 histologically unilateral HS; 5 bilateral; 23 MR-negative). Normative data was generated from 111 healthy individuals (age 40.0 ± 12.8 years, 52 men). Nine raters with different experience (neuroradiologists, trainees, and image analysts) assessed subjects' imaging with and without QReports. Raters assigned imaging normal, right, left, or bilateral HS. Confidence was rated on a 5-point scale. RESULTS Correct designation (normal/abnormal) was high and showed further trend-level improvement with QReports, from 87.5 to 92.5% (p = 0.07, effect size d = 0.69). Largest magnitude improvement (84.5 to 93.8%) was for image analysts (d = 0.87). For bilateral HS, QReports significantly improved overall accuracy, from 74.4 to 91.1% (p = 0.042, d = 0.7). Agreement with the correct diagnosis (kappa) tended to increase from 0.74 ('fair') to 0.86 ('excellent') with the report (p = 0.06, d = 0.81). Confidence increased when correctly assessing scans with the QReport (p < 0.001, η2p = 0.945). CONCLUSIONS QReports of HS imaging biomarkers can improve rater accuracy and confidence, particularly in challenging bilateral cases. Improvements were seen across all raters, with large effect sizes, greatest for image analysts. These findings may have positive implications for clinical radiology services and justify further validation in larger groups. KEY POINTS • Quantification of imaging biomarkers for hippocampal sclerosis-volume loss and raised T2 signal-could improve clinical radiological detection in challenging cases. • Quantitative reports for individual patients, contextualised with normative reference data, improved diagnostic accuracy and confidence in a group of nine raters, in particular for bilateral HS cases. • We present a pre-use clinical validation of an automated imaging assessment tool to assist clinical radiology reporting of hippocampal sclerosis, which improves detection accuracy.
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Affiliation(s)
- Olivia Goodkin
- Centre for Medical Image Computing (CMIC), University College London, London, UK. .,Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - Hugh G Pemberton
- Centre for Medical Image Computing (CMIC), University College London, London, UK.,Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sjoerd B Vos
- Centre for Medical Image Computing (CMIC), University College London, London, UK.,Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Epilepsy Society MRI Unit, Chalfont St Peter, UK
| | - Ferran Prados
- Centre for Medical Image Computing (CMIC), University College London, London, UK.,Universitat Oberta de Catalunya, Barcelona, Spain
| | - Ravi K Das
- Clinical, Educational and Health Psychology, University College London, London, UK
| | - James Moggridge
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Bianca De Blasi
- Department of Medical Physics and Bioengineering, University College London, London, UK
| | - Philippa Bartlett
- Epilepsy Society MRI Unit, Chalfont St Peter, UK.,Department of Clinical and Experimental Epilepsy, University College London, London, UK
| | - Elaine Williams
- Wellcome Trust Centre for Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Thomas Campion
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Lukas Haider
- Department of Biomedical Imaging and Image Guided Therapy, Medical University of Vienna, Vienna, Austria.,NMR Research Unit, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Kirsten Pearce
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Nuria Bargallό
- Radiology Department, Hospital Clínic de Barcelona and Magnetic Resonance Image Core Facility, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain
| | - Esther Sanchez
- Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Sotirios Bisdas
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Mark White
- Digital Services, University College London Hospital, London, UK
| | - Sebastien Ourselin
- Department of Medical Physics and Bioengineering, University College London, London, UK.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Gavin P Winston
- Epilepsy Society MRI Unit, Chalfont St Peter, UK.,Department of Clinical and Experimental Epilepsy, University College London, London, UK.,Department of Medicine, Division of Neurology, Queen's University, Kingston, Ontario, Canada
| | - John S Duncan
- Epilepsy Society MRI Unit, Chalfont St Peter, UK.,Department of Clinical and Experimental Epilepsy, University College London, London, UK
| | - Jorge Cardoso
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - John S Thornton
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Tarek A Yousry
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Frederik Barkhof
- Centre for Medical Image Computing (CMIC), University College London, London, UK.,Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK.,Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
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145
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CT Phantom Evaluation of 67,392 American College of Radiology Accreditation Examinations: Implications for Opportunistic Screening of Osteoporosis Using CT. AJR Am J Roentgenol 2020; 216:447-452. [PMID: 32755177 DOI: 10.2214/ajr.20.22943] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
OBJECTIVE. The purpose of this study was to investigate whether systematic bias in attenuation measurements occurs among CT scanners made by four major manufacturers and the relevance of this bias regarding opportunistic screening for osteoporosis. MATERIALS AND METHODS. Data on attenuation measurement accuracy were acquired using the American College of Radiology (ACR) accreditation phantom and were evaluated in a blinded fashion for four CT manufacturers (8500 accreditation submissions for manufacturer A; 18,575 for manufacturer B; 8278 for manufacturer C; and 32,039 for manufacturer D). The attenuation value for water, acrylic (surrogate for trabecular bone), and Teflon (surrogate for cortical bone; Chemours) materials for an adult abdominal CT technique (120 kV, 240 mA, standard reconstruction algorithm) was used in the analysis. Differences in attenuation value across all manufacturers were assessed using the Kruskal-Wallis test followed by a post hoc test for pairwise comparisons. RESULTS. The mean attenuation value for water ranged from -0.3 to 2.7 HU, with highly significant differences among all manufacturers (p < 0.001). For the trabecular bone surrogate, differences in attenuation values across all manufacturers were also highly significant (p < 0.001), with mean values of 120.9 (SD, 3.5), 124.6 (3.3), 126.9 (4.4), and 123.9 (3.4) HU for manufacturers A, B, C, and D, respectively. For the cortical bone surrogate, differences in attenuation values across all manufacturers were also highly significant (p < 0.001), with mean values of 939.0 (14.2), 874.3 (13.3), 897.6 (11.3), and 912.7 (13.4) HU for manufacturers A, B, C, and D, respectively. CONCLUSION. CT scanners made by different manufacturers show systematic offsets in attenuation measurement when compared with each other. Knowledge of these off-sets is useful for optimizing the accuracy of opportunistic diagnosis of osteoporosis.
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Evaluation of the Reproducibility of Bolus Transit Quantification With Contrast-Enhanced Ultrasound Across Multiple Scanners and Analysis Software Packages—A Quantitative Imaging Biomarker Alliance Study. Invest Radiol 2020; 55:643-656. [DOI: 10.1097/rli.0000000000000702] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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147
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Leroi L, Gras V, Boulant N, Ripart M, Poirion E, Santin MD, Valabregue R, Mauconduit F, Hertz‐Pannier L, Le Bihan D, Rochefort L, Vignaud A. Simultaneous proton density, T
1
, T
2
, and flip‐angle mapping of the brain at 7 T using multiparametric 3D SSFP imaging and parallel‐transmission universal pulses. Magn Reson Med 2020; 84:3286-3299. [DOI: 10.1002/mrm.28391] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 05/28/2020] [Accepted: 06/02/2020] [Indexed: 12/15/2022]
Affiliation(s)
- Lisa Leroi
- Université Paris‐Saclay, CEA, CNRS, BAOBAB, NeuroSpin Gif‐sur‐Yvette France
| | - Vincent Gras
- Université Paris‐Saclay, CEA, CNRS, BAOBAB, NeuroSpin Gif‐sur‐Yvette France
| | - Nicolas Boulant
- Université Paris‐Saclay, CEA, CNRS, BAOBAB, NeuroSpin Gif‐sur‐Yvette France
| | - Mathilde Ripart
- Université Paris‐Saclay, CEA, CNRS, BAOBAB, NeuroSpin Gif‐sur‐Yvette France
| | - Emilie Poirion
- Université Paris‐Saclay, CEA, CNRS, BAOBAB, NeuroSpin Gif‐sur‐Yvette France
- ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Universités, UPMC Université Paris 06 UMR S1127, Institut du Cerveau et de la Moelle Épinière Paris France
| | - Mathieu D. Santin
- ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Universités, UPMC Université Paris 06 UMR S1127, Institut du Cerveau et de la Moelle Épinière Paris France
- CENIR, ICM, Hôpital Pitié‐Salpêtrière Paris France
| | - Romain Valabregue
- ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Universités, UPMC Université Paris 06 UMR S1127, Institut du Cerveau et de la Moelle Épinière Paris France
- CENIR, ICM, Hôpital Pitié‐Salpêtrière Paris France
| | - Franck Mauconduit
- Université Paris‐Saclay, CEA, CNRS, BAOBAB, NeuroSpin Gif‐sur‐Yvette France
| | | | - Denis Le Bihan
- Université Paris‐Saclay, CEA, CNRS, BAOBAB, NeuroSpin Gif‐sur‐Yvette France
| | - Ludovic Rochefort
- Aix‐Marseille University, CNRS, CRMBM (Center for Magnetic Resonance in Biology and Medicine‐UMR 7339) Marseille France
| | - Alexandre Vignaud
- Université Paris‐Saclay, CEA, CNRS, BAOBAB, NeuroSpin Gif‐sur‐Yvette France
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Panayides AS, Amini A, Filipovic ND, Sharma A, Tsaftaris SA, Young A, Foran D, Do N, Golemati S, Kurc T, Huang K, Nikita KS, Veasey BP, Zervakis M, Saltz JH, Pattichis CS. AI in Medical Imaging Informatics: Current Challenges and Future Directions. IEEE J Biomed Health Inform 2020; 24:1837-1857. [PMID: 32609615 PMCID: PMC8580417 DOI: 10.1109/jbhi.2020.2991043] [Citation(s) in RCA: 105] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine.
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149
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Pinkert MA, Cox BL, Dai B, Hall TJ, Eliceiri KW. 3-D-Printed Registration Phantom for Combined Ultrasound and Optical Imaging of Biological Tissues. ULTRASOUND IN MEDICINE & BIOLOGY 2020; 46:1808-1814. [PMID: 32340797 PMCID: PMC7293928 DOI: 10.1016/j.ultrasmedbio.2020.03.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 03/10/2020] [Accepted: 03/12/2020] [Indexed: 05/04/2023]
Abstract
Efforts to develop quantitative ultrasound biomarkers would benefit from comparisons between ultrasound data and higher-resolution images of the tissue microstructure, such as from optical microscopy. However, only a few studies have used these methods for multiscale imaging because it is difficult to register low-resolution (>100 μm) ultrasound images to high-resolution microscopy images. To address this need, we have designed a 3-D-printed registration phantom that is made of a hard fluorescent resin, fits into a glass-bottom dish and can be used to calculate a coordinate system transform between ultrasound and optical microscopy. We report the phantom design, a registration protocol and an example registration using 18.5-MHz ultrasound and second harmonic generation microscopy. We evaluate the registration precision, achieving standard deviations smaller than the ultrasound resolution across all axes, and illustrate on a mouse mammary gland that this method yields results superior to those of manual landmark registration.
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Affiliation(s)
- Michael A Pinkert
- Morgridge Institute for Research, 330 N Orchard St, Madison, WI, United States of America, 53715
- University of Wisconsin Madison, Laboratory for Optical and Computational Instrumentation, 1675 Observatory Drive, Madison, WI, United States of America, 53706
- University of Wisconsin Madison, Department of Medical Physics, 1111 Highland Ave, Madison, WI, United States of America, 53705
| | - Benjamin L Cox
- University of Wisconsin Madison, Laboratory for Optical and Computational Instrumentation, 1675 Observatory Drive, Madison, WI, United States of America, 53706
- University of Wisconsin Madison, Department of Medical Physics, 1111 Highland Ave, Madison, WI, United States of America, 53705
| | - Bing Dai
- University of Wisconsin Madison, Laboratory for Optical and Computational Instrumentation, 1675 Observatory Drive, Madison, WI, United States of America, 53706
| | - Timothy J Hall
- University of Wisconsin Madison, Department of Medical Physics, 1111 Highland Ave, Madison, WI, United States of America, 53705
- Co-corresponding Author: Timothy J. Hall, , Phone: +1 608-265-9459
| | - Kevin W Eliceiri
- Morgridge Institute for Research, 330 N Orchard St, Madison, WI, United States of America, 53715
- University of Wisconsin Madison, Laboratory for Optical and Computational Instrumentation, 1675 Observatory Drive, Madison, WI, United States of America, 53706
- University of Wisconsin Madison, Department of Medical Physics, 1111 Highland Ave, Madison, WI, United States of America, 53705
- University of Wisconsin Madison, Department of Biomedical Engineering, 1550 Engineering Dr, Madison, WI 53706
- Co-corresponding Author: Kevin W. Eliceiri, , Phone: +1 608-263-6288
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150
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Mahmutovic Persson I, Falk Håkansson H, Örbom A, Liu J, von Wachenfeldt K, Olsson LE. Imaging Biomarkers and Pathobiological Profiling in a Rat Model of Drug-Induced Interstitial Lung Disease Induced by Bleomycin. Front Physiol 2020; 11:584. [PMID: 32636756 PMCID: PMC7317035 DOI: 10.3389/fphys.2020.00584] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Accepted: 05/11/2020] [Indexed: 02/06/2023] Open
Abstract
A large number of systemically administered drugs have the potential to cause drug-induced interstitial lung disease (DIILD). We aim to characterize a model of DIILD in the rat and develop imaging biomarkers (IBs) for detection and quantification of DIILD. In this study, Sprague-Dawley rats received one single dose of intratracheal (i.t.) bleomycin and were longitudinally imaged at day 0, 3, 7, 14, 21, and 28 post dosing, applying the imaging techniques magnetic resonance imaging (MRI) and positron emission tomography (PET)/computed tomography (CT). Bronchoalveolar lavage fluid (BALF) was analyzed for total protein and inflammatory cells. Lungs were saved for further evaluation by gene analysis using quantitative-PCR and by histology. Lung sections were stained with Masson's-Trichrome staining and evaluated by modified Ashcroft score. Gene expression profiling of inflammatory and fibrotic markers was performed on lung tissue homogenates. Bleomycin induced significant increase in total protein concentration and total cell count in bronchoalveolar lavage (BAL), peaking at day 3 (p > 0.001) and day 7 (p > 0.001) compared to control, respectively. Lesions measured by MRI and PET signal in the lungs of bleomycin challenged rats were significantly increased during days 3-14, peaking at day 7. Two subgroups of animals were identified as low- and high-responders by their different change in total lung volume. Both groups showed signs of inflammation initially, while at later time points, the low-responder group recovered toward control, and the high-responder group showed sustained lung volume increase, and significant increase of lesion volume (p < 0.001) compared to control. Lastly, important inflammatory and pro-fibrotic markers were assessed from lung tissue, linking observed imaging pathological changes to gene expression patterns. In conclusion, bleomycin-induced lung injury is an adequate animal model for DIILD studies and for translational lung injury assessment by MRI and PET imaging. The scenario comprised disease responses, with different fractions of inflammation and fibrosis. Thereby, this study improved the understanding of imaging and biological biomarkers in DIILD and lung injury.
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Affiliation(s)
- Irma Mahmutovic Persson
- Department of Medical Radiation Physics, Institution of Translational Medicine, Faculty of Medicine, Lund University, Malmö, Sweden
| | | | - Anders Örbom
- Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | | | | | - Lars E Olsson
- Department of Medical Radiation Physics, Institution of Translational Medicine, Faculty of Medicine, Lund University, Malmö, Sweden.,TRISTAN-IMI Consortium (Translational Imaging in Drug Safety Assessment-Innovative Medicines Initiative)
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