Search

Background US shear-wave elastography (SWE) and vibration-controlled transient elastography (VCTE) enable assessment of liver stiffness, an indicator of fibrosis severity. However, limited reproducibility data restrict their use in clinical trials. Purpose To estimate SWE and VCTE measurement variability in nonalcoholic fatty liver disease (NAFLD) within and across systems to support clinical trial diagnostic enrichment and clinical interpretation of longitudinal liver stiffness. Materials and Methods This prospective, observational, cross-sectional study (March 2021 to November 2021) enrolled adults with NAFLD, stratified according to the Fibrosis-4 (FIB-4) index (≤1.3, >1.3 and <2.67, ≥2.67), at two sites to assess SWE with five US systems and VCTE with one system. Each participant underwent 12 elastography examinations over two separate days within 1 week, with each day's examinations conducted by a different operator. VCTE and SWE measurements were reported in units of meters per second. The primary end point was the different-day, different-operator reproducibility coefficient (RDC DDDO ) pooled across systems for SWE and individually for VCTE. Secondary end points included system-specific RDC DDDO , same-day, same-operator repeatability coefficient (RC SDSO ), and between-system same-day, same-operator reproducibility coefficient. The planned sample provided 80% power to detect a pooled RDC DDDO of less than 35%, the prespecified performance threshold. Results A total of 40 participants (mean age, 60 years ± 10 [SD]; 24 women) with low ( n = 17), intermediate ( n = 15), and high ( n = 8) FIB-4 scores were enrolled. RDC DDDO was 30.7% (95% upper bound, 34.4%) for SWE and 35.6% (95% upper bound, 43.9%) for VCTE. SWE system-specific RDC DDDO varied from 24.2% to 34.3%. The RC SDSO was 21.0% for SWE (range, 13.9%-35.0%) and 19.6% for VCTE. The SWE between-system same-day, same-operator reproducibility coefficient was 52.7%. Conclusion SWE met the prespecified threshold, RDC DDDO less than 35%, with VCTE having a higher RDC DDDO . SWE variability was higher between different systems. These estimates advance liver US-based noninvasive test qualification by (a) defining expected variability, (b) establishing that serial examination variability is lower when performed with the same system, and (c) informing clinical trial design. ClinicalTrials.gov Identifier NCT04828551 © RSNA, 2024 Supplemental material is available for this article.

Rationale and Objectives: Evidence is building in support of the clinical utility of atherosclerotic plaque imaging by computed tomography angiography (CTA). There is increasing organized activity to embrace non-calcified plaque (NCP) as a formally defined biomarker for clinical trials, and high-risk plaque (HRP) for clinical care, as the most relevant measures for the field to advance and worthy of community efforts to validate. Yet the ability to assess the quantitative performance of any given specific solution to make these measurements or classifications is not available. Vendors use differing definitions, assessment metrics, and validation data sets to describe their offerings without clinician users having the capability to make objective assessments of accuracy and precision and how this affects diagnostic confidence., Materials and Methods: The QIBA Profile for Atherosclerosis Biomarkers by CTA was created by the Quantitative Imaging Biomarkers Alliance (QIBA) to improve objectivity and decrease the variability of noninvasive plaque phenotyping. The Profile provides claims on the accuracy and precision of plaque measures individually and when combined., Results: Individual plaque morphology measurements are evaluated in terms of bias (accuracy), slope (consistency of the bias across the measurement range, needed for measurements of change), and variability. The multiparametric plaque stability phenotype is evaluated in terms of agreement with expert pathologists. The Profile is intended for a broad audience, including those engaged in discovery science, clinical trials, and patient care., Conclusion: This report provides a rationale and overview of the Profile claims and how to comply with the Profile in research and clinical practice., Summary Statement: This article summarizes objective means to validate the analytical performance of non-calcified plaque (NCP), other emerging plaque morphology measurements, and multiparametric histology-defined high-risk plaque (HRP), as outlined in the QIBA Profile for Atherosclerosis Biomarkers by CTA., Competing Interests: Declaration of Competing Interest The nature of the work is a standards-based activity undertaken by a cross-section of academic, practicing, and industry professionals. The RSNA maintains a comprehensive vetting and representation of conflict statements, but in short, no conflicts are noted that undermine the integrity and openness of the work. The first author Andrew J. Buckler is a founder and shareholder of Elucid Bioimaging Inc., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background: Artificial intelligence (AI) systems can potentially aid the diagnostic pathway of prostate cancer by alleviating the increasing workload, preventing overdiagnosis, and reducing the dependence on experienced radiologists. We aimed to investigate the performance of AI systems at detecting clinically significant prostate cancer on MRI in comparison with radiologists using the Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS 2.1) and the standard of care in multidisciplinary routine practice at scale., Methods: In this international, paired, non-inferiority, confirmatory study, we trained and externally validated an AI system (developed within an international consortium) for detecting Gleason grade group 2 or greater cancers using a retrospective cohort of 10 207 MRI examinations from 9129 patients. Of these examinations, 9207 cases from three centres (11 sites) based in the Netherlands were used for training and tuning, and 1000 cases from four centres (12 sites) based in the Netherlands and Norway were used for testing. In parallel, we facilitated a multireader, multicase observer study with 62 radiologists (45 centres in 20 countries; median 7 [IQR 5-10] years of experience in reading prostate MRI) using PI-RADS (2.1) on 400 paired MRI examinations from the testing cohort. Primary endpoints were the sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC) of the AI system in comparison with that of all readers using PI-RADS (2.1) and in comparison with that of the historical radiology readings made during multidisciplinary routine practice (ie, the standard of care with the aid of patient history and peer consultation). Histopathology and at least 3 years (median 5 [IQR 4-6] years) of follow-up were used to establish the reference standard. The statistical analysis plan was prespecified with a primary hypothesis of non-inferiority (considering a margin of 0·05) and a secondary hypothesis of superiority towards the AI system, if non-inferiority was confirmed. This study was registered at ClinicalTrials.gov, NCT05489341., Findings: Of the 10 207 examinations included from Jan 1, 2012, through Dec 31, 2021, 2440 cases had histologically confirmed Gleason grade group 2 or greater prostate cancer. In the subset of 400 testing cases in which the AI system was compared with the radiologists participating in the reader study, the AI system showed a statistically superior and non-inferior AUROC of 0·91 (95% CI 0·87-0·94; p<0·0001), in comparison to the pool of 62 radiologists with an AUROC of 0·86 (0·83-0·89), with a lower boundary of the two-sided 95% Wald CI for the difference in AUROC of 0·02. At the mean PI-RADS 3 or greater operating point of all readers, the AI system detected 6·8% more cases with Gleason grade group 2 or greater cancers at the same specificity (57·7%, 95% CI 51·6-63·3), or 50·4% fewer false-positive results and 20·0% fewer cases with Gleason grade group 1 cancers at the same sensitivity (89·4%, 95% CI 85·3-92·9). In all 1000 testing cases where the AI system was compared with the radiology readings made during multidisciplinary practice, non-inferiority was not confirmed, as the AI system showed lower specificity (68·9% [95% CI 65·3-72·4] vs 69·0% [65·5-72·5]) at the same sensitivity (96·1%, 94·0-98·2) as the PI-RADS 3 or greater operating point. The lower boundary of the two-sided 95% Wald CI for the difference in specificity (-0·04) was greater than the non-inferiority margin (-0·05) and a p value below the significance threshold was reached (p<0·001)., Interpretation: An AI system was superior to radiologists using PI-RADS (2.1), on average, at detecting clinically significant prostate cancer and comparable to the standard of care. Such a system shows the potential to be a supportive tool within a primary diagnostic setting, with several associated benefits for patients and radiologists. Prospective validation is needed to test clinical applicability of this system., Funding: Health~Holland and EU Horizon 2020., Competing Interests: Declaration of interests NAO provides statistical consultation to Siemens Healthineers, Takeda, and Qure, and serves as a committee member of the Eastern Cooperative Oncology Group–American College of Radiology Imaging Network, the Tomosynthesis Mammographic Imaging Screening Trial, and the National Cancer Institute's Clinical Imaging Steering Committee (Bethesda, MD, USA). AB has been a consultant and advisor for Astellas and Bayer; board membership, officer, and trustee for Glactone Pharma, and LIDDS Pharma; has received lecture honoraria for Accord, Astellas, AstraZeneca, Bayer, Ipsen, Janssen, and Merck; has participated in trials run by Astellas, Ferring, and Janssen; and holds stock in Glactone Pharma, LIDDS Pharma, Noviga, and WntResearch. BvG holds stocks in and is a founder of Thirona. JKC has received research funding from GE Healthcare and Genentech and is the co-inventor of software that has been licensed to Siloam Vision. JKC has equity ownership in Siloam Vision. GS has been an advisory board member of Exact Imaging and Angiogenesis and has received lecture honorarium from Hitachi. OR has received funding for travel expenses from Philips Medical Systems. ARP has received research funding from Siemens Healthineers, holds stocks in Lucida Medical, and has received lecture honoraria for Siemens Healthineers and Bayer. HH has received research funding from Siemens Healthineers and Canon Medical Systems. GV has been a clinical advisory board member of AGFA Healthcare. VK has received lecture honoraria on prostate cancer diagnosis from the European Association of Urology and Singapore Urology Association and has received research funding from Prostate Cancer UK and the John Black Charitable Foundation. DB has received lecture honorarium from Bayer Vital and holds stocks in NVIDIA, Microsoft, and MSCI-World ETF. RvdB has been an advisory board member for Janssen; has received lecture honoraria from Amgen, Astellas, Ipsen, Janssen, and MSD; has received research support from Astellas and Janssen; and has participated in trials run by Janssen. All other authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.)

Purpose: Distinguishing radiation necrosis from tumor progression among patients with brain metastases previously treated with stereotactic radiosurgery represents a common diagnostic challenge. We performed a prospective pilot study to determine whether PET/CT with 18 F-fluciclovine, a widely available amino acid PET radiotracer, repurposed intracranially, can accurately diagnose equivocal lesions., Methods: Adults with brain metastases previously treated with radiosurgery presenting with a follow-up tumor-protocol MRI brain equivocal for radiation necrosis versus tumor progression underwent an 18 F-fluciclovine PET/CT of the brain within 30 days. The reference standard for final diagnosis consisted of clinical follow-up until multidisciplinary consensus or tissue confirmation., Results: Of 16 patients imaged from 7/2019 to 11/2020, 15 subjects were evaluable with 20 lesions (radiation necrosis, n = 16; tumor progression, n = 4). Higher SUV max statistically significantly predicted tumor progression (AUC = 0.875; p = 0.011). Lesion SUV mean (AUC = 0.875; p = 0.018), SUV peak (AUC = 0.813; p = 0.007), and SUV peak -to-normal-brain (AUC = 0.859; p = 0.002) also predicted tumor progression, whereas SUV max -to-normal-brain (p = 0.1) and SUV mean -to-normal-brain (p = 0.5) did not. Qualitative visual scores were significant predictors for readers 1 (AUC = 0.750; p < 0.001) and 3 (AUC = 0.781; p = 0.045), but not for reader 2 (p = 0.3). Visual interpretations were significant predictors for reader 1 (AUC = 0.898; p = 0.012) but not for reader 2 (p = 0.3) or 3 (p = 0.2)., Conclusions: In this prospective pilot study of patients with brain metastases previously treated with radiosurgery presenting with a contemporary MRI brain with a lesion equivocal for radiation necrosis versus tumor progression, 18 F-fluciclovine PET/CT repurposed intracranially demonstrated encouraging diagnostic accuracy, supporting the pursuit of larger clinical trials which will be necessary to establish diagnostic criteria and performance., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: VERDICT (Vascular, Extracellular, Restricted Diffusion for Cytometry in Tumours) MRI is a multi b-value, variable diffusion time DWI sequence that allows generation of ADC maps from different b-value and diffusion time combinations. The aim was to assess precision of prostate ADC measurements from varying b-value combinations using VERDICT and determine which protocol provides the most repeatable ADC., Materials and Methods: Forty-one men (median age: 67.7 years) from a prior prospective VERDICT study (April 2016-October 2017) were analysed retrospectively. Men who were suspected of prostate cancer and scanned twice using VERDICT were included. ADC maps were formed using 5b-value combinations and the within-subject standard deviations (wSD) were calculated per ADC map. Three anatomical locations were analysed per subject: normal TZ (transition zone), normal PZ (peripheral zone), and index lesions. Repeated measures ANOVAs showed which b-value range had the lowest wSD, Spearman correlation and generalized linear model regression analysis determined whether wSD was related to ADC magnitude and ROI size., Results: The mean lesion ADC for b 0 b 1500 had the lowest wSD in most zones (0.18-0.58x10 -4 mm 2 /s). The wSD was unaffected by ADC magnitude (Lesion: p = 0.064, TZ: p = 0.368, PZ: p = 0.072) and lesion Likert score (p = 0.95). wSD showed a decrease with ROI size pooled over zones (p = 0.019, adjusted regression coefficient = -1.6x10 -3 , larger ROIs for TZ versus PZ versus lesions). ADC maps formed with a maximum b-value of 500 s/mm 2 had the largest wSDs (1.90-10.24x10 -4 mm 2 /s)., Conclusion: ADC maps generated from b 0 b 1500 have better repeatability in normal TZ, normal PZ, and index lesions., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

In diagnostic medicine, the true disease status of a patient is often represented on an ordinal scale-for example, cancer stage (0, I, II, III, or IV) or coronary artery disease severity measured using the Coronary Artery Disease Reporting and Data System (CAD-RADS) scale (none, minimal, mild, moderate, severe, or occluded). With advances in quantitation of diagnostic images and in artificial intelligence (AI), both supervised and unsupervised algorithms are being developed to help physicians correctly grade disease. Most of the diagnostic accuracy literature deals with binary disease status (disease present or absent); however, tests diagnosing ordinal-scaled diseases should not be reduced to a binary status just to simplify diagnostic accuracy testing. In this paper, we propose different characterizations of ordinal-scale accuracy for different clinical use scenarios, along with methods for comparing tests. In the simplest scenario, just the proportion of correct grades is considered; other scenarios address the magnitude and direction of misgrading; and at the other extreme, a weighted accuracy measure with weights based on the relative costs of different types of misgrading is presented. The various scenarios are illustrated using a coronary artery disease example where the accuracy of AI algorithms in providing patients with the correct CAD-RADS grade is assessed., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Combinations of multiple quantitative imaging biomarkers (QIBs) are often able to predict the likelihood of an event of interest such as death or disease recurrence more effectively than single imaging measurements can alone. The development of such multiparametric quantitative imaging and evaluation of its fitness of use differs from the analogous processes for individual QIBs in several key aspects. A computational procedure to combine the QIB values into a model output must be specified. The output must also be reproducible and be shown to have reasonably strong ability to predict the risk of an event of interest. Attention must be paid to statistical issues not often encountered in the single QIB scenario, including overfitting and bias in the estimates of model performance. This is the fourth in a five-part series on statistical methodology for assessing the technical performance of multiparametric quantitative imaging. Considerations for data acquisition are discussed and recommendations from the literature on methodology to construct and evaluate QIB-based models for risk prediction are summarized. The findings in the literature upon which these recommendations are based are demonstrated through simulation studies. The concepts in this manuscript are applied to a real-life example involving prediction of major adverse cardiac events using automated plaque analysis., (Published by Elsevier Inc.)

This paper is the fifth in a five-part series on statistical methodology for performance assessment of multi-parametric quantitative imaging biomarkers (mpQIBs) for radiomic analysis. Radiomics is the process of extracting visually imperceptible features from radiographic medical images using data-driven algorithms. We refer to the radiomic features as data-driven imaging markers (DIMs), which are quantitative measures discovered under a data-driven framework from images beyond visual recognition but evident as patterns of disease processes irrespective of whether or not ground truth exists for the true value of the DIM. This paper aims to set guidelines on how to build machine learning models using DIMs in radiomics and to apply and report them appropriately. We provide a list of recommendations, named RANDAM (an abbreviation of "Radiomic ANalysis and DAta Modeling"), for analysis, modeling, and reporting in a radiomic study to make machine learning analyses in radiomics more reproducible. RANDAM contains five main components to use in reporting radiomics studies: design, data preparation, data analysis and modeling, reporting, and material availability. Real case studies in lung cancer research are presented along with simulation studies to compare different feature selection methods and several validation strategies., (Copyright © 2022 The Association of University Radiologists. All rights reserved.)

A standardized approach to acquiring amyloid PET images increases their value as disease and drug response biomarkers. Most 18 F PET amyloid brain scans often are assessed only visually (per regulatory labels), with a binary decision indicating the presence or absence of Alzheimer disease amyloid pathology. Minimizing technical variance allows precise, quantitative SUV ratios (SUVRs) for early detection of β-amyloid plaques and allows the effectiveness of antiamyloid treatments to be assessed with serial studies. Methods: The Quantitative Imaging Biomarkers Alliance amyloid PET biomarker committee developed and validated a profile to characterize and reduce the variability of SUVRs, increasing statistical power for these assessments. Results: On achieving conformance, sites can justify a claim that brain amyloid burden reflected by the SUVR is measurable to a within-subject coefficient of variation of no more than 1.94% when the same radiopharmaceutical, scanner, acquisition, and analysis protocols are used. Conclusion: This overview explains the claim, requirements, barriers, and potential future developments of the profile to achieve precision in clinical and research amyloid PET imaging., (© 2023 by the Society of Nuclear Medicine and Molecular Imaging.)

Multiparameter quantitative imaging incorporates anatomical, functional, and/or behavioral biomarkers to characterize tissue, detect disease, identify phenotypes, define longitudinal change, or predict outcome. Multiple imaging parameters are sometimes considered separately but ideally are evaluated collectively. Often, they are transformed as Likert interpretations, ignoring the correlations of quantitative properties that may result in better reproducibility or outcome prediction. In this paper we present three use cases of multiparameter quantitative imaging: i) multidimensional descriptor, ii) phenotype classification, and iii) risk prediction. A fourth application based on data-driven markers from radiomics is also presented. We describe the technical performance characteristics and their metrics common to all use cases, and provide a structure for the development, estimation, and testing of multiparameter quantitative imaging. This paper serves as an overview for a series of individual articles on the four applications, providing the statistical framework for multiparameter imaging applications in medicine., (Copyright © 2022 The Association of University Radiologists. All rights reserved.)

Purpose: Many practices have implemented support services to assist radiologists with noninterpretive tasks; however, little research has been performed to assess the overall effect of these services. The purpose of this study was to evaluate the effect of a team of imaging service navigators (ISNs) incorporated into a practice on (1) number of communications, (2) time saved by radiologists, and (3) radiologist satisfaction with the service., Methods: The numbers and types of reports dictated by radiologists were captured for 6-month periods before and after ISN implementation. Communication rates before and after implementation were then calculated. The amount of perceived time savings using the ISN team and satisfaction with the service were assessed through pre- and postimplementation surveys of participating radiologists. Mean and median time savings and satisfaction rates were calculated., Results: The overall communication rate increased from 2.196% before ISNs to 3.278% after ISNs (49% increase; 95% confidence interval, 47%-52%). Communication rates increased among all communication subtypes (critical, urgent, routine, and actionable), with the highest increases in urgent (94%) and actionable (75%) findings. Before implementation, radiologists reported spending 39 min on average per day on communications tasks, with only 33% of radiologists indicating that the communication process was efficient. After implementation, radiologists reported mean time savings of 28 min (95% confidence interval, 19.9-35.1), and 82% of radiologists indicated a positive or highly positive view of the ISN service., Conclusions: After ISN implementation, communication rates increased and radiologists reported spending less time performing communications. Most radiologists were satisfied with the service., (Copyright © 2022 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Objective: To describe the protocol of a multi-vendor, multi-site quantitative MRI study for knee post-traumatic osteoarthritis (PTOA), and to present preliminary results of cartilage degeneration using MR T 1ρ and T 2 imaging 10 years after anterior cruciate ligament reconstruction (ACLR)., Design: This study involves three sites and two MR platforms. The patients are from a nested cohort (termed as Onsite cohort) within the Multicenter Orthopaedic Outcomes Network (MOON) cohort 10 years after ACLR. Phantoms and controls were scanned for evaluating reproducibility. Cartilage was automatically segmented, and T 1ρ and T 2 were compared between operated, contralateral, and control knees., Results: Sixty-eight ACL-reconstructed patients and 20 healthy controls were included. In phantoms, the intra-site coefficients of variation (CVs) of repeated scans ranged 1.8-2.1% for T 1ρ and 1.3-1.7% for T 2 . The inter-site CVs ranged 1.6-2.1% for T 1ρ and 1.1-1.4% for T 2 . In human subjects, the intra-site scan/rescan CVs ranged 2.2-3.5% for T 1ρ and 2.6-4.9% for T 2 for the six major compartments. In patients, operated knees showed significantly higher T 1ρ and T 2 values mainly in medial femoral condyle, medial tibia and trochlear cartilage compared with contralateral knees, and showed significantly higer T 1ρ  and T 2 values in all six compartments compared to healthy control knees. The patient contralateral knees showed higher T 1ρ and T 2 values mainly in the lateral femoral condyle, lateral tibia, trochlear, and patellar cartilage compared to healthy control knees., Conclusion: A platform and workflow with rigorous quality control has been established for a multi-vendor multi-site quantitative MRI study in evaluating PTOA 10 years after ACLR. Our preliminary report suggests significant cartilage matrix changes in both operated and contralateral knees compared with healthy control knees., Competing Interests: Conflict of interest The authors have no conflicts of interest to disclose., (Copyright © 2022 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.)

The design and analysis of multireader multicase (MRMC) studies are quite challenging. These studies differ from most medical studies because they need a reference standard and sampling from two populations (ie, reader and patient populations). They are quite expensive to conduct, requiring a good deal of readers' time for image interpretation. One common problem is the use of imperfect reference standards, often correlated with the test or tests being evaluated. Another common issue is oversimplification of the multidimensional MRMC data. In this study, the fundamentals of MRMC study design and analysis are reviewed. The goal is to provide investigators with a guide to the fundamentals of MRMC design and analysis, with references to more detailed discussions. In addition, readers are updated on newer areas of research, including correction for studies with multiple diagnostic accuracy end points and adjustment for location bias., (© RSNA, 2022.)

Rationale and Objectives: In diagnostic accuracy studies, cases in which a reader does not see the condition of interest are often given the same score for ROC analysis (e.g. confidence score of 0%). However, many of these cases can be further distinguished and doing so may result in more robust ROC results., Materials and Methods: We examined two recent, real-world studies which used different procedures to encourage readers to further distinguish subjects who appear to be without the condition of interest. For each study, we calculated the results under two conditions. In the "zeros distinguished" (ZD) condition, we incorporated the confidence scores collected to further distinguish the normal-looking subjects. In the "zeros not distinguished" (ZND) condition, we disregarded these scores and simply gave the unit of analysis a score of zero whenever the reader did not identify the condition of interest in that unit. We compared the two conditions on (1) coverage of the ROC space and (2) discrepancy between parametric and nonparametric results., Results: Compared to the ZND condition, coverage of the ROC space was improved in the ZD condition for all ROC curves in both studies. In the first study, there was a significant reduction in the discrepancy between parametric and nonparametric results (median discrepancy in ZND vs ZD condition: 0.033 vs 0.011, p = 0.012). A similar reduction was not seen in the second study, though the discrepancies were very low in both conditions (0.003 vs 0.006, p = 0.313)., Conclusion: Prompting readers to further distinguish cases in which they do not see the condition of interest may result in more robust ROC results, though further exploration of this topic is warranted., (Copyright © 2021 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Rationale and Objectives: A critical performance metric for any quantitative imaging biomarker is its ability to reliably generate similar values on repeat testing. This is known as the repeatability of the biomarker, and it is used to determine the minimum detectable change needed in order to show that a change over time is real change and not just due to measurement error. Test-retest studies are the classic approach for estimating repeatability; however, these studies can be infeasible when the imaging is expensive, time-consuming, invasive, or requires contrast agents. The objective of this study was to develop and test a method for estimating repeatability without a test-retest study., Materials and Methods: We present a statistical method for estimating repeatability and testing whether an imaging method meets a specified criterion for repeatability in the absence of a test-retest study. The new method is applicable for the particular situation where a reference standard is available. A Monte Carlo simulation study was conducted to evaluate the performance of the new method., Results: The proposed estimator is unbiased, and hypothesis tests with the new estimator have nominal type I error rate and power similar to a test-retest study. We considered the situation where the reference standard provides the true value, as well as when the reference standard itself has various magnitudes of measurement error. An example from CT imaging biomarkers of atherosclerosis illustrates the new method., Conclusion: Precision of a QIB can be measured without a test-retest study in the situation where a reference standard is available., (Copyright © 2021 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Longitudinal and behavioral preclinical animal studies generate complex data, which may not be well matched to statistical approaches common in this literature. Analyses that do not adequately account for complexity may result in overly optimistic study conclusions, with consequences for reproducibility and translational decision-making. Recent work interrogating methodological shortcomings in animal research has not yet comprehensively investigated statistical shortcomings in the analysis of complex longitudinal and behavioral data. To this end, the current cross-sectional meta-research study rigorously reviewed published mouse or rat controlled experiments for motor rehabilitation in three neurologic conditions to evaluate statistical choices and reporting. Medline via PubMed was queried in February 2020 for English-language articles published January 1, 2017- December 31, 2019. Included were articles that used rat or mouse models of stroke, Parkinson's disease, or traumatic brain injury, employed a therapeutic controlled experimental design to determine efficacy, and assessed at least one functional behavioral assessment or global evaluation of function. 241 articles from 99 journals were evaluated independently by a team of nine raters. Articles were assessed for statistical handling of non-independence, animal attrition, outliers, ordinal data, and multiplicity. Exploratory analyses evaluated whether transparency or statistical choices differed as a function of journal factors. A majority of articles failed to account for sources of non-independence in the data (74-93%) and/or did not analytically account for mid-treatment animal attrition (78%). Ordinal variables were often treated as continuous (37%), outliers were predominantly not mentioned (83%), and plots often concealed the distribution of the data (51%) Statistical choices and transparency did not differ with regards to journal rank or reporting requirements. Statistical misapplication can result in invalid experimental findings and inadequate reporting obscures errors. Clinician-scientists evaluating preclinical work for translational promise should be mindful of commonplace errors. Interventions are needed to improve statistical decision-making in preclinical behavioral neurosciences research., Competing Interests: the authors have declared that no competing interests exist.

Background: Patients undergoing anterior cruciate ligament reconstruction (ACLR) are at an increased risk for posttraumatic osteoarthritis (PTOA). While we have previously shown that meniscal treatment with ACLR predicts more radiographic PTOA at 2 to 3 years postoperatively, there are a limited number of similar studies that have assessed cartilage directly with magnetic resonance imaging (MRI)., Hypothesis: Meniscal repair or partial meniscectomy at the time of ACLR independently predicts more articular cartilage damage on 2- to 3-year postoperative MRI compared with a healthy meniscus or a stable untreated tear., Study Design: Cohort study; Level of evidence, 2., Methods: A consecutive series of patients undergoing ACLR from 1 site within the prospective, nested Multicenter Orthopaedic Outcomes Network (MOON) cohort underwent bilateral knee MRI at 2 to 3 years postoperatively. Patients were aged <36 years without previous knee injuries, were injured while playing sports, and had no history of concomitant ligament surgery or contralateral knee surgery. MRI scans were graded by a board-certified musculoskeletal radiologist using the modified MRI Osteoarthritis Knee Score (MOAKS). A proportional odds logistic regression model was built to predict a MOAKS-based cartilage damage score (CDS) relative to the contralateral control knee for each compartment as well as for the whole knee, pooled by meniscal treatment, while controlling for sex, age, body mass index, baseline Marx activity score, and baseline operative cartilage grade. For analysis, meniscal injuries surgically treated with partial meniscectomy or meniscal repair were grouped together., Results: The cohort included 60 patients (32 female; median age, 18.7 years). Concomitant meniscal treatment at the time of index ACLR was performed in 17 medial menisci (13 meniscal repair and 4 partial meniscectomy) and 27 lateral menisci (3 meniscal repair and 24 partial meniscectomy). Articular cartilage damage was worse in the ipsilateral reconstructed knee ( P < .001). A meniscal injury requiring surgical treatment with ACLR predicted a worse CDS for medial meniscal treatment (medial compartment CDS: P = .005; whole joint CDS: P < .001) and lateral meniscal treatment (lateral compartment CDS: P = .038; whole joint CDS: P = .863). Other predictors of a worse relative CDS included age for the medial compartment ( P < .001), surgically observed articular cartilage damage for the patellofemoral compartment ( P = .048), and body mass index ( P = .007) and age ( P = .020) for the whole joint., Conclusion: A meniscal injury requiring surgical treatment with partial meniscectomy or meniscal repair at the time of ACLR predicted worse articular cartilage damage on MRI at 2 to 3 years after surgery. Further research is required to differentiate between the effects of partial meniscectomy and meniscal repair.

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 T 1 . 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., (© 2021 International Society for Magnetic Resonance in Medicine.)

Background: Significant aortic regurgitation (AR) leads to left ventricular (LV) remodeling; however, little data exist regarding sex-based differences in LV remodeling in this setting. We sought to compare LV remodeling and AR severity, assessed by echocardiography and cardiovascular magnetic resonance (CMR), to discern sex-based differences., Methods: Patients with ≥ moderate chronic AR by echocardiography who underwent CMR within 90 days between December 2005 and October 2015 were included. Nonlinear regression models were built to assess the effect of AR regurgitant fraction (RF) on LV remodeling. A generalized linear model and Bland Altman analyses were constructed to evaluate differences between CMR and echocardiography. Referral for surgical intervention based on symptoms and LV remodeling was evaluated., Results: Of the 243 patients (48.3 ± 16.6 years, 58 (24%) female), 119 (49%) underwent surgical intervention with a primary indication of severe AR, 97 (82%) men, 22 (18%) women. Significant sex differences in LV remodeling emerged on CMR. Women demonstrated significantly smaller LV end-diastolic volume index (LVEDVI) (96.8 ml/m 2 vs 125.6 ml/m 2 , p < 0.001), LV end-systolic volume index (LVESVI) (41.1 vs 54.5 ml/m 2 , p < 0.001), blunted LV dilation in the setting of increasing AR severity (LVEDVI p value < 0.001, LVESVI p value 0.011), and LV length indexed (8.32 vs 9.69 cm, p < 0.001). On Bland Altman analysis, a significant interaction with sex and LV diameters was evident, demonstrating a significant increase in the difference between CMR and echocardiography measurements as the LV enlarged in women: LVEDVI (p = 0.006), LVESVI (p < 0.001), such that echocardiographic measurements increasingly underestimated LV diameters in women as the LV enlarged. LV length was higher for males with a linear effect from RF (p < 0.001), with LV length increasing at a higher rate with increasing RF for males compared to females (two-way interaction with sex p = 0.005). Sphericity volume index was higher for men after adjusting for a relative wall thickness (p = 0.033)., Conclusions: CMR assessment of chronic AR revealed significant sex differences in LV remodeling and significant echocardiographic underestimation of LV dilation, particularly in women. Defining optimal sex-based CMR thresholds for surgical referral should be further developed., Trial Registration: NA., (© 2022. The Author(s).)

Objective: Glucose concentrations used in current cell culture methods are a significant departure from physiological glucose levels. The study focuses on comparing the effects of glucose concentrations on primary human progenitors (connective tissue progenitors [CTPs]) used for cartilage repair., Design: Cartilage- (Outerbridge grade 1, 2, 3; superficial and deep zone cartilage), infrapatellar fatpad-, synovium-, and periosteum-derived cells were obtained from 63 patients undergoing total knee arthroplasty and cultured simultaneously in fresh chondrogenic media containing 25 mM glucose (HGL) or 5 mM glucose (NGL) for pairwise comparison. Automated ASTM-based quantitative image analysis was used to determine colony-forming efficiency (CFE), effective proliferation rates (EPR), and sulfated-proteoglycan (GAG-ECM) staining of the CTPs across tissue sources., Results: HGL resulted in increased cell cultures with CFE = 0 compared with NGL in all tissue sources ( P = 0.049). The CFE in NGL was higher than HGL for superficial cartilage ( P < 0.001), and contrary for synovium-derived CTPs ( P = 0.046) when CFE > 0. EPR of the CTPs did not differ between the media in the 6-day assay time period ( P = 0.082). The GAG-ECM area of the CTPs and their progeny was increased in presence of HGL ( P = 0.027)., Conclusion: Glucose concentration is critical to progenitor's physiology and should be taken into account in the setting of protocols for clinical or in vitro cell expansion strategies.

MRI-based cartilage compositional analysis shows biochemical and microstructural changes at early stages of osteoarthritis before changes become visible with structural MRI sequences and arthroscopy. This could help with early diagnosis, risk assessment, and treatment monitoring of osteoarthritis. Spin-lattice relaxation time constant in rotating frame (T1ρ) and T2 mapping are the MRI techniques best established for assessing cartilage composition. Only T2 mapping is currently commercially available, which is sensitive to water, collagen content, and orientation of collagen fibers, whereas T1ρ is more sensitive to proteoglycan content. Clinical application of cartilage compositional imaging is limited by high variability and suboptimal reproducibility of the biomarkers, which was the motivation for creating the Quantitative Imaging Biomarkers Alliance (QIBA) Profile for cartilage compositional imaging by the Musculoskeletal Biomarkers Committee of the QIBA. The profile aims at providing recommendations to improve reproducibility and to standardize cartilage compositional imaging. The QIBA Profile provides two complementary claims (summary statements of the technical performance of the quantitative imaging biomarkers that are being profiled) regarding the reproducibility of biomarkers. First, cartilage T1ρ and T2 values are measurable at 3.0-T MRI with a within-subject coefficient of variation of 4%-5%. Second, a measured increase or decrease in T1ρ and T2 of 14% or more indicates a minimum detectable change with 95% confidence. If only an increase in T1ρ and T2 values is expected (progressive cartilage degeneration), then an increase of 12% represents a minimum detectable change over time. The QIBA Profile provides recommendations for clinical researchers, clinicians, and industry scientists pertaining to image data acquisition, analysis, and interpretation and assessment procedures for T1ρ and T2 cartilage imaging and test-retest conformance. This special report aims to provide the rationale for the proposed claims, explain the content of the QIBA Profile, and highlight the future needs and developments for MRI-based cartilage compositional imaging for risk prediction, early diagnosis, and treatment monitoring of osteoarthritis., (© RSNA, 2021 See also the editorial by Kijowski in this issue.)

Artificial intelligence-based methods are showing promise in medical imaging applications. There is substantial interest in clinical translation of these methods, requiring that they be evaluated rigorously. We lay out a framework for objective task-based evaluation of artificial intelligence methods. We provide a list of available tools to conduct this evaluation. We outline the important role of physicians in conducting these evaluation studies. The examples in this article are proposed in the context of PET scans with a focus on evaluating neural network-based methods. However, the framework is also applicable to evaluate other medical imaging modalities and other types of artificial intelligence methods., Competing Interests: Disclosure Nancy Obuchowski is a statistician for the Quantitative Imaging Biomarkers Alliance (QIBA). Other authors have no relevant financial disclosures., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Objectives: Proton density fat fraction (PDFF) is a validated biomarker of tissue fat quantification. However, validation has been limited to single-center or multi-center series using non-FDA-approved software. Thus, we assess the bias, linearity, and long-term reproducibility of PDFF obtained using commercial PDFF packages from several vendors., Methods: Over 35 months, 438 subjects and 16 volunteers from a multi-center observational trial underwent PDFF MRI measurements using a 3-T MR system from one of three different vendors or a 1.5-T system from one vendor. Fat-water phantom sets were measured as part of each subject's examination. Manual region-of-interest measurements on the %fat image, then cross-sectional bias, linearity, and long-term reproducibility were assessed., Results: Three hundred ninety-two phantom measurements were evaluable (90%). Bias ranged from 2.4 to - 3.8% for the lowest to the highest weight %fat. Regression fits of PDFF against synthesis weight %fat showed negligible non-linear effects and a linear slope of 0.94 (95% confidence interval: 0.938, 0.947). We observed significant vendor (p < 0.001) and field strength (p < 0.001) differences in bias and longitudinal variability. When the results were pooled across sites, vendors, and field strengths, the estimated reproducibility coefficient was 6.93% (95% CI: 6.25%, 7.81%)., Conclusions: This study demonstrated good linearity, accuracy, and reproducibility for all investigated manufacturers and field strengths. However, significant vendor-dependent and field strength-dependent bias were found. While longitudinal PDFF measurements may be made using different field strength or vendor MR systems, if the MR system is not the same, based on these results, only PDFF changes ≥ 7% can be considered a true difference., Key Points: • Phantom fat fraction (PDFF) MRI measurements over 35 months demonstrated good linearity, accuracy, and reproducibility for the vendor systems investigated. • Non-linear effects were negligible (linear slope of 0.94) over 0-100% fat; however, significant vendor (p < 0.001) and field strength (p<0.001) differences in bias and longitudinal variability were identified. Bias ranged from 2.4 to - 3.8% for 0-100 weight% fat, respectively. • Measurement bias could affect the accuracy of PDFF in clinical use. As the reproducibility coefficient was 6.93%, only greater changes in % fat can be considered true differences when making longitudinal PDFF measurements on different MR systems., (© 2021. European Society of Radiology.)

Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. KEY POINTS: • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory., (© 2021. The Author(s).)

Objectives: The aims of this study were to characterize the interplay between mixed aortic valve disease (MAVD) phenotypes (defined by concomitant severities of aortic stenosis and aortic regurgitation) and left ventricular global longitudinal strain (LV-GLS), and to assess the prognostic utility of LV-GLS in MAVD., Background: Little is known about the way LV-GLS separates MAVD phenotypes and if it is associated with their outcomes., Methods: This observational cohort study evaluated 783 consecutive adult patients with left ventricular ejection fraction ≥50% and MAVD, which was defined as coexisting with at least moderate aortic stenosis and at least moderate aortic regurgitation. We measured the conventional echocardiographic variables and average LV-GLS from apical long, 2- and 4-chamber views. The primary endpoint was all-cause mortality., Results: Mean age of patients was 69 ± 15 years, and 58% were male. Mean LV-GLS was -14.7 ± 2.9%. In total, 458 patients (59%) underwent aortic valve replacement at a median period of 50 days (25th to 75th percentile range: 6 to 560 days). During a median follow-up period of 5.6 years (25th to 75th percentile range: 1.8 to 9.4 years), 391 patients (50%) died. When stratified patients into tertiles according to LV-GLS values, patients with worse LV-GLS had worse outcomes (p < 0.001). LV-GLS was independently associated with mortality (hazard ratio: 1.09; 95% confidential intervals: 1.04 to 1.14; p < 0.001), with the relationship between LV-GLS and mortality being linear., Conclusions: LV-GLS is associated with all-cause mortality. LV-GLS may be useful for risk stratification in patients with MAVD., Competing Interests: Funding Support and Author Disclosures The authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2021 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Objective: To determine whether a simulated low-dose metal artifact reduction (MAR) CT technique is comparable with a clinical dose MAR technique for shoulder arthroplasty evaluation., Materials and Methods: Two shoulder arthroplasties in cadavers and 25 shoulder arthroplasties in patients were scanned using a clinical dose (140 kVp, 300 qrmAs); cadavers were also scanned at half dose (140 kVp, 150 qrmAs). Images were reconstructed using a MAR CT algorithm at full dose and a noise-insertion algorithm simulating 50% dose reduction. For the actual and simulated half-dose cadaver scans, differences in SD for regions of interest were assessed, and streak artifact near the arthroplasty was graded by 3 blinded readers. Simulated half-dose scans were compared with full-dose scans in patients by measuring differences in implant position and by comparing readers' grades of periprosthetic osteolysis and muscle atrophy., Results: The mean difference in SD between actual and simulated half-dose methods was 2.42 HU (95% CI [1.4, 3.4]). No differences in streak artifact grades were seen in 13/18 (72.2%) comparisons in cadavers. In patients, differences in implant position measurements were within 1° or 1 mm in 149/150 (99.3%) measurements. The inter-reader agreement rates were nearly identical when readers were using full-dose (77.3% [232/300] for osteolysis and 76.9% [173/225] for muscle atrophy) and simulated half-dose (76.7% [920/1200] for osteolysis and 74.0% [666/900] for muscle atrophy) scans., Conclusion: A simulated half-dose MAR CT technique is comparable both quantitatively and qualitatively with a standard-dose technique for shoulder arthroplasty evaluation, demonstrating that this technique could be used to reduce dose in arthroplasty imaging.

Purpose: To compare the bias and inherent reliability of the quantitative (T 1 and T 2 ) 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 T 1 and T 2 relaxation values (vendor-provided) for each of the 14 vials in T 1 and T 2 arrays. MRF-SSFP scans repeated over 30 days on GE 1.5 and 3.0 T scanners at three collaborative centers. MRF estimated T 1, and T 2 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 T 1 values, vials #1-8 (T 1 relaxation time between 2033 and 184 ms) exhibited a wCV less than 3% and 4%, respectively, on 3.0 and 1.5 T scanners. T 2 values in vials #1-8 (T 2 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 T 1 (R 2  = 0.9960 and 0.9963 at center-1 and center-2 on 3.0 T scanner, and R 2  = 0.9951 and 0.9988 at center-1 and center-3 on 1.5 T scanner) compared to T 2 (R 2  = 0.9971 and 0.9972 at center-1 and center-2 on 3.0 T scanner, and R 2  = 0.9815 and 0.9754 at center-1 and center-3 on 1.5 T scanner). Bland-Altman (BA) analysis showed MRF based T 1 and T 2 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 T 1 values were 21.34: -50.00: 92.69, 21.32: -47.29: 89.94 ms, and for T 2 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 T 1 values were 26.54: -53.41: 106.50, 9.997: -51.94: 71.94 ms, and for T 2 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 T 1 and T 2 values (R 2  = 0.9969 and 0.9977) showed a similar trend as we observed between vendor-provided and MRF estimated T 1 and T 2 values (R 2  = 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 T 1 values and except one vial for T 2 . All the vials are within the LOA between GS and MRF except one vial in T 1 and T 2 array. The wCV for reproducibility was <3% for both T 1 and T 2 values in vials #1-8, for all the 14 vials, wCV calculated for reproducibility was <4% for T 1 values and <5% for T 2 ., Conclusion: This study shows that MRF is highly repeatable (wCV <4% for T 1 and <7% for T 2 ) and reproducible (wCV < 3% for both T 1 and T 2 ) in certain vials (vials #1-8)., (© 2021 American Association of Physicists in Medicine.)

Purpose: To evaluate the reproducibility and predicted clinical outcomes of CT-based quantitative lung density measurements using standard fixed-dose (FD) and reduced-dose (RD) scans., Materials and Methods: In this retrospective analysis of prospectively acquired data, 1205 participants (mean age, 65 years ± 9 [standard deviation]; 618 men) enrolled in the COPDGene study who underwent FD and RD CT image acquisition protocols between November 2014 and July 2017 were included. Of these, the RD scans of 640 participants were also reconstructed using iterative reconstruction (IR). Median filtering was applied to the RD scans (RD-MF) to investigate an alternative noise reduction strategy. CT attenuation at the 15th percentile of the lung CT histogram (Perc15) was computed for all image types (FD, RD, RD-MF, and RD-IR). Reproducibility coefficients were calculated to quantify the measurement differences between FD and RD scans. The ability of Perc15 to predict chronic obstructive pulmonary disease (COPD) diagnosis and exacerbation frequency was investigated using receiver operating characteristic analysis., Results: The Perc15 reproducibility coefficients with and without volume adjustment were as follows: RD, 29.43 HU ± 0.62 versus 32.81 HU ± 1.70; RD-MF, 7.42 HU ± 0.42 versus 19.40 HU ± 2.65; and RD-IR, 7.10 HU ± 0.52 versus 22.46 HU ± 3.91. Receiver operating characteristic curve analysis indicated that Perc15 on volume-adjusted FD and RD scans were both predictive for COPD diagnosis (area under the receiver operating characteristic curve [AUC]: FD, 0.724 ± 0.045; RD, 0.739 ± 0.045) and for having one or more exacerbation per year (AUCs: FD, 0.593 ± 0.068; RD, 0.589 ± 0.066). Similar trends were observed when volume adjustment was not applied., Conclusion: A combination of volume adjustment and noise reduction filtering improved the reproducibility of lung density measurements computed using serial FD and RD CT scans. Supplemental material is available for this article. © RSNA, 2021., Competing Interests: Disclosures of Conflicts of Interest: C.R.H. Activities related to the present article: institution receives grant from NIH NHLBI (1R01HL150023-01). Activities not related to the present article: author is employee and stock option holder at Imbio. Other relationships: disclosed no relevant relationships. A.S.O. disclosed no relevant relationships. N.A.O. disclosed no relevant relationships. J.P.C. Activities related to the present article: institution received grant form COPD Foundation for data analysis. Activities not related to the present article: author employed by Thirona (Head of R&D); author is shareholder in Thirona. Other relationships: disclosed no relevant relationships. D.A.L. Activities related to the present article: institution received grant from NHLBI (funding for COPDGene study). Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships. S.M.H. Activities related to the present article: institution received grant from NHLBI. Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships., (2021 by the Radiological Society of North America, Inc.)

Background/aims: Quantitative imaging biomarkers have the potential to detect change in disease early and noninvasively, providing information about the diagnosis and prognosis of a patient, aiding in monitoring disease, and informing when therapy is effective. In clinical trials testing new therapies, there has been a tendency to ignore the variability and bias in quantitative imaging biomarker measurements. Unfortunately, this can lead to underpowered studies and incorrect estimates of the treatment effect. We illustrate the problem when non-constant measurement bias is ignored and show how treatment effect estimates can be corrected., Methods: Monte Carlo simulation was used to assess the coverage of 95% confidence intervals for the treatment effect when non-constant bias is ignored versus when the bias is corrected for. Three examples are presented to illustrate the methods: doubling times of lung nodules, rates of change in brain atrophy in progressive multiple sclerosis clinical trials, and changes in proton-density fat fraction in trials for patients with nonalcoholic fatty liver disease., Results: Incorrectly assuming that the measurement bias is constant leads to 95% confidence intervals for the treatment effect with reduced coverage (<95%); the coverage is especially reduced when the quantitative imaging biomarker measurements have good precision and/or there is a large treatment effect. Estimates of the measurement bias from technical performance validation studies can be used to correct the confidence intervals for the treatment effect., Conclusion: Technical performance validation studies of quantitative imaging biomarkers are needed to supplement clinical trial data to provide unbiased estimates of the treatment effect.

Background: Cardiac computed tomography (CT) is emerging as an adjunctive modality to echocardiography in the evaluation of infective endocarditis (IE) and surgical planning. CT studies in IE have, however, focused on its diagnostic rather than prognostic utility, the latter of which is important in high-risk diseases like IE. We evaluated the associations between cardiac CT and transesophageal echocardiography (TEE) findings and adverse outcomes after IE surgery., Methods: Of 833 consecutive patients with surgically proven IE during May 1, 2014 to May 1, 2019, at Cleveland Clinic, 155 underwent both preoperative ECG-gated contrast-enhanced CT and TEE. Multivariable analyses were performed to identify CT and TEE biomarkers that predict adverse outcomes after IE surgery, adjusting for EuroSCORE II (European System for Cardiac operative Risk Evaluation II)., Results: CT and TEE were positive for IE in 123 (75.0%) and 124 (75.6%) of patients, respectively. Thirty-day mortality occurred in 3 (1.9%) patients and composite mortality or morbidities in 72 (46.5%). Pseudoaneurysm or abscess detected on TEE was the only imaging biomarker to show independent association with composite mortality or morbidities in-hospital, with odds ratio (95% CI) of 3.66 (1.76-7.59), P =0.001. There were 17 late deaths, and both pseudoaneurysm or abscess detected on CT and fistula detected on CT were the only independent predictors of total mortality during follow-up, with hazards ratios (95% CI) of 3.82 (1.25-11.7), P <0.001 and 9.84 (1.89-51.0), P =0.007, respectively., Conclusions: We identified cardiac CT and TEE features that predicted separate adverse outcomes after IE surgery. Imaging biomarkers can play important roles incremental to conventional clinical factors for risk stratification in patients undergoing IE surgery.

Background: Knee osteoarthritis (OA) severity is a predictor of outcomes after arthroscopic partial meniscectomy (APM). Magnetic resonance imaging (MRI) grading of OA is predictive of postoperative outcomes; this prospective study assessed whether radiographic grading is also predictive of outcomes., Methods: Patients who underwent APM between February 2015 and January 2016, underwent radiography and MRI ≤6 months before surgery, and had outcomes from the surgery date and one year later were included. Surgical failure was defined as <10-point improvement in the Knee Osteoarthritis Outcome Score pain subscore. Radiographs were evaluated using Kellgren-Lawrence (KL) grading and continuous and ordinal minimum joint space width (mJSW) measurements; cartilage loss on MRI was evaluated using a modified Outerbridge system. Predictive abilities were estimated using area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CIs)., Results: The study cohort included 66 knees from 64 patients (32 women; mean age, 57.1 years; range, 45-77). Radiographic grading was not predictive of outcomes (KL, AUC = 0.541 [95% CI: 0.358, 0.724]; continuous mJSW, AUC = 0.482 [95% CI: 0.305, 0.659]; ordinal mJSW, AUC = 0.534 [95% CI: 0.433, 0.634]). Comparison of radiographs showing no joint space narrowing (KL grade 0-2) with corresponding MR images demonstrated that 48% of radiographs missed a clinically significant lesion (modified Outerbridge grade ≥ 3). MRI grading was predictive of outcomes (AUC = 0.720 [95% CI: 0.581, 0.859])., Conclusions: Radiographic grading of OA is not predictive of outcomes after APM; radiographs may miss clinically significant lesions. For outcome prediction, MRI should be used., Competing Interests: Declaration of competing interest Dr. Spindler reports royalties from commercial product nPhase during the conduct of the study. He reports funding for research from Smith + Nephew Endoscopy; funding for research from DonJoy Orthopedics; royalties from the NFL; consulting fees from Cytori, DePuy Mitek, Samumed, Flexion Therapeutics; and funding for research from NIH/NIAMSR01 AR053684 and NIH/NIAMSR01 AR074131 outside the submitted work. Dr. Jones reports research support from the NIH; consulting income (for participation in advisory board) from Samumed; and publishing income (for curriculum development) from the Journal of Bone and Joint Surgery. He is on the editorial board for Orthopedic Journal of Sports Medicine; and has committee membership on the Research Development Committee of American Academy of Orthopedic Surgeons. The other authors declare no competing financial interests/personal relationships., (Copyright © 2020 Elsevier B.V. All rights reserved.)

OBJECTIVE. The purpose of this study was to compare a combined dual-energy CT (DECT) and single-energy CT (SECT) metal artifact reduction technique with a SECT metal artifact reduction technique for detecting lesions near an arthroplasty in a phantom model. MATERIALS AND METHODS. Two CT phantoms with a cobalt chromium sphere attached to a titanium rod, simulating an arthroplasty, within a background of soft-tissue attenuation containing spherical lesions (range, 10-20 mm) around the head and stem of different attenuations from the background (range of attenuation, 10-70 HU) were scanned with a single CT scanner individually (unilateral) and together (bilateral) with the following three dose-equivalent techniques: the currently used clinical protocol (140 kVp, 300 Reference mAs); 100 kVp; and DECT (100 kVp and 150 kVp with a tin filter). Three radiologists reviewed the datasets to identify lesions. Nonparametric AUC was estimated for each reader with each technique. Multireader ANOVA was performed to compare AUCs. Multiple-variable logistic regression analysis was used to identify factors affecting sensitivity and specificity. RESULTS. Accuracy was lower ( p < 0.001) for the DECT 130-keV technique than for the 100-, 70-, and 140-kVp techniques. Sensitivity was higher with unilateral arthroplasties ( p = 0.037), with greater contrast differences from background ( p < 0.001), and with the SECT 100-kVp technique versus other techniques ( p < 0.001). The difference in specificities of modalities was not statistically significant ( p = 0.148). CONCLUSION. Combining DECT and SECT techniques does not provide additional benefits for lesion detection as opposed to using SECT alone.

OBJECTIVE. Cartilage loss on preoperative knee MRI is a predictor of poor outcomes after arthroscopic partial meniscectomy. The purpose of this study was to compare the ability to predict outcomes after arthroscopic partial meniscectomy with a clinically used modified Outerbridge system versus a semiquantitative MRI Osteoarthritis Knee Score system for grading cartilage loss. MATERIALS AND METHODS. Patients who underwent preoperative knee MRI within 6 months of arthroscopic partial meniscectomy and who had outcomes available from the time of surgery and 1 year later were eligible for inclusion. Cases were evaluated by two radiologists and one radiology fellow with the use of both grading systems. The accuracy of each system in discriminating between surgical success and failure was estimated using the ROC curve (AUC) with 95% CIs. A Wald test was used to assess noninferiority of the clinical grading system. Interreader agreement regarding the accuracy of the grading systems in predicting outcomes was also compared. RESULTS. A total of 78 patients (38 women and 40 men; mean age, 56.6 years) were included in the study. A prediction model using clinical grading (AUC = 0.695; 95% CI, 0.566-0.824) was noninferior ( p = 0.047) to a model using MRI Osteoarthritis Knee Score grading (AUC = 0.683; 95% CI, 0.539-0.827). Both MRI prediction models performed better than a model using demographic characteristics only (AUC = 0.667; 95% CI, 0.522-0.812). Inter-reader agreement with clinical grading (80.8%) was higher than that with MRI Osteoarthritis Knee Score grading (65.0%; p = 0.012). CONCLUSION. A clinically used system to grade cartilage loss on MRI is as effective as a semiquantitative system for predicting outcomes after arthroscopic partial meniscectomy, while also offering improved interreader agreement.

Objectives: This study sought to evaluate the role of cardiac magnetic resonance (CMR) for the quantification of ischemic mitral regurgitation (IMR) and myocardial infarct size (MIS) in patients with ischemic cardiomyopathy (ICM). This study also sought to explore the interaction between IMR severity and MIS and its association with outcomes in patients with ICM., Background: IMR occurs secondary to a disease of the left ventricle and is associated with poor outcomes. The role of CMR for the evaluation and risk stratification of patients with ICM and IMR remains uncertain., Methods: Consecutive patients with ICM who underwent baseline CMR were included. MIS was quantified on late gadolinium enhancement imaging as the proportion of left ventricular mass. IMR was quantified with CMR by calculating the mitral regurgitant fraction (MRFraction). Cox proportional hazards models were built to assess the association of IMR and MIS quantification with the combined endpoint of all-cause death or heart transplant., Results: We evaluated 578 patients (mean age: 62 ± 11 years, 76% males). The mean left ventricular ejection fraction was 25 ± 11%, with an MIS of 24 ± 16% and MRFraction of 18 ± 17%. Over a median follow-up time of 4.9 years, 198 (34%) patients experienced death or cardiac transplant. On multivariable analysis, after comprehensive medical risk score, subsequent revascularization, implantable cardioverter-defibrillator insertion, and surgical mitral valve intervention were controlled for, the interaction of IMR severity and MIS emerged as a powerful predictor of adverse outcomes (p = 0.008). For patients with significant IMR (MRFraction: ≥35%), the hazard ratio comparing moderate MIS (15% to 29%) versus small MIS (<15%) was 1.51 (0.57 to 3.98), and the hazard ratio comparing large MIS (≥30%) versus small MIS was 5.41 (2.34 to 12.7)., Conclusions: Risk associated with IMR is more comprehensively described as an interaction between IMR severity and MIS. Further studies in patients IMR using comprehensive CMR evaluation are needed to verify whether this approach can improve patient selection and procedural outcomes to address IMR., (Copyright © 2020. Published by Elsevier Inc.)

The Quantitative Imaging Biomarkers Alliance (QIBA) Profile for fluorodeoxyglucose (FDG) PET/CT imaging was created by QIBA to both characterize and reduce the variability of standardized uptake values (SUVs). The Profile provides two complementary claims on the precision of SUV measurements. First, tumor glycolytic activity as reflected by the maximum SUV (SUV max ) is measurable from FDG PET/CT with a within-subject coefficient of variation of 10%-12%. Second, a measured increase in SUV max of 39% or more, or a decrease of 28% or more, indicates that a true change has occurred with 95% confidence. Two applicable use cases are clinical trials and following individual patients in clinical practice. Other components of the Profile address the protocols and conformance standards considered necessary to achieve the performance claim. The Profile is intended for use by a broad audience; applications can range from discovery science through clinical trials to clinical practice. The goal of this report is to provide a rationale and overview of the FDG PET/CT Profile claims as well as its context, and to outline future needs and potential developments., (© RSNA, 2020 See also the editorial by Ulaner in this issue.)

Background: Cell-based therapy for cartilage repair is a promising approach and is becoming an established technique. Yet, there is no consensus on the optimal cell source., Purpose: To provide a donor-matched quantitative comparison of the connective tissue progenitors (CTPs) derived from cartilage (Outerbridge grade 1-3 [G1-2-3]), bone marrow aspirate concentrate (BMC), infrapatellar fat pad (IPFP), synovium, and periosteum with respect to (1) cell concentration ([Cell], cells/mL), (2) CTP prevalence (P CTP , colonies per million cells), and (3) biological performance based on in vitro proliferation potential (cells per colony) colony density, and differentiation potential (expression of negatively charged extracellular matrix: glycosaminoglycan-rich extra cellular matrix [GAG-ECM])., Study Design: Descriptive laboratory study., Methods: Tissues were obtained from 10 patients undergoing total knee arthroplasty (mean age, 59 years; women, n = 6). Automated quantitative colony-forming unit analysis was used to compare [Cell], P CTP , and CTP biological performance across tissue sources., Results: [Cell] was highest in grade 3 cartilage ( P = .002) and BMC ( P = .001). Median P CTP was highest in IPFP ( P = .001), synovium ( P = .003), and G1-2 cartilage ( P = .02). Proliferation was highest in synovium-derived CTPs ( P < .001). Median colony density was highest in G1-2-3 ( P < .001). Median GAG-ECM was highest in G1-2-3 ( P < .001). Within each patient, CTPs derived from all tissues were highly heterogeneous in biological performance as determined by cells per colony, density, and GAG-ECM., Conclusion: Tissue sources differ in [Cell], P CTP , and biological attributes. The data presented in this study suggest that cartilage (G1-2-3) is the preferred tissue source for cartilage repair based on P CTP and GAG-ECM, followed by synovium, IPFP, BMC, and periosteum. However, due to the heterogeneous mixture of CTPs within each tissue source, there exists a subset of CTPs with biological performance similar to G1-2-3 cartilage, particularly in synovium and IPFP. Performance-based clonal selection and expansion of preferred CTPs and their progeny will potentially lead to improved cell population with predictive future., Clinical Relevance: Optimal tissue regeneration strategies will require informed decisions regarding which of the available tissue sources to use. Optimizing cell sourcing in any tissue may require separation of CTPs with preferred attributes from those with less desirable attributes. The heterogeneity manifest in the early stage of colony formation represents an opportunity for performance-based clone selection for clinical cell processing and manufacturing.

OBJECTIVE. A case series analysis and meta-analysis were performed to assess the efficacy of stenting for inferior vena cava (IVC) stenosis after liver transplant; a secondary analysis assessed demographic factors as potential predictors of all-cause mortality. MATERIALS AND METHODS. Liver transplant recipients treated for symptomatic IVC stenosis at a major medical center from 1996 to 2017 were assessed. The main medical databases were searched for studies evaluating stenting in liver transplant recipients with IVC stenosis. Cox proportional hazards regression analysis was used to determine predictors of survival (age, sex, reason for transplant, stent size and number, publication year). Univariate and multivariable models were constructed. Because patients in the case series and meta-analysis had similar demographics and outcomes, the results were pooled. RESULTS. The case series included 40 patients (31 treated with stents; nine, without stents). Meta-analysis of 5277 records identified 17 eligible studies involving 73 patients. Stenting was effective in resolving the gradient in 100% of patients and in relieving symptoms in 85% of patients. Primary stent patency at latest follow-up (median, 556 days) was seen in 113 of 118 stents (96%; some patients had multiple stents). Reason for transplant was the only significant predictor of all-cause mortality; patients with hepatocellular carcinoma had a higher hazard of death than those undergoing transplant for other reasons (hazard ratio = 3.23; 95% CI, 1.40-7.42; p = 0.006). CONCLUSION. Stenting for IVC stenosis after liver transplant is clinically effective and durable, with 96% of stents showing long-term patency and 85% of patients experiencing symptom relief.

Purpose : There is a clinical need to better characterize tissue sources being used for stem cell therapies. This study focuses on comparison of cells and connective tissue progenitors (CTPs) derived from native human infrapatellar fatpad (IPFP), synovium (SYN), and periosteum (PERI). Materials and Methods : IPFP, SYN, PERI were harvested from twenty-eight patients undergoing arthroplasty. CTPs were quantitatively characterized using automated colony-forming-unit assay to compare total nucleated cell concentration-[Cell], cells/mg; prevalence-(P CTP ), CTPs/million nucleated cells; CTP concentration-[CTP], CTPs/mg; proliferation and differentiation potential; and correlate outcomes with patient's age and gender. Results : [Cell] did not differ between IPFP, SYN, and PERI. P CTP was influenced by age and gender: patients >60 years, IPFP and SYN had higher P CTP than PERI ( p < 0.001) and females had higher P CTP in IPFP ( p < 0.001) and SYN ( p = 0.001) than PERI. [CTP] was influenced by age: patients <50 years, SYN ( p = 0.0165) and PERI ( p < 0.001) had higher [CTP] than IPFP; patients between 60 and 69 years, SYN ( p < 0.001) had higher [CTP] than PERI; patients >70 years, IPFP ( p = 0.006) had higher [CTP] than PERI. In patients >60 years, proliferation potential of CTPs differed significantly (SYN>IPFP>PERI); however, differentiation potentials were comparable between all three tissue sources. Conclusion : SYN and IPFP may serve as a preferred tissue source for patients >60 years, and PERI along with SYN and IPFP may serve as a preferred tissue source for patients <60 years for cartilage repair. However, the heterogeneity among the CTPs in any given tissue source suggests performance-based selection might be useful to optimize cell-sourcing strategies to improve efficacy of cellular therapies for cartilage repair.

Purpose: Technological advancements have greatly expanded the field of cardiac electrophysiology, requiring greater demands on imaging systems and potentially delivering higher radiation doses to patients and operators. With little contemporary research on occupational and patient radiation risk in the electrophysiology laboratory, the aim of this study was to analyze radiation doses, including occupational fetal doses, over approximately the last decade. We benchmarked the occupational data to our patient radiation dose data to allow for comparison and to put into perspective the associated radiation risks., Methods: Occupational radiation dosimetry analyzed included data from an 11-year period for physicians, a 7-year period for nurses, and a 9-year period for fetal doses. Patient-related dose metrics over an 8-year period were also analyzed., Results: In the physician and nursing groups, there was a nearly 70% decrease in the average occupational radiation doses over the given periods. Within the electrophysiology department, the average fetal occupational doses were very low, close to 0 μSv. The average reference point air kerma per patient for all electrophysiology procedures decreased from nearly 600 mGy/procedure in 2010 to just over 100 mGy/procedure in 2017., Conclusions: Patient and occupational radiation doses in our laboratories significantly decreased over the periods analyzed as a result of clinical and technical staff efforts as well as advances in imaging technology. The radiation-related risk to individuals working in our electrophysiology laboratories, including pregnant women, is very low. Data reported herein could be used by other institutions to evaluate their occupational and patient radiation safety practices.

Artificial intelligence, as applied to medical images to detect, rule out, diagnose, and stage disease, has seen enormous growth over the last few years. There are multiple use cases of AI algorithms in medical imaging: first-reader (or concurrent) mode, second-reader mode, triage mode, and more recently prescreening mode as when an AI algorithm is applied to the worklist of images to identify obvious negative cases so that human readers do not need to review them and can focus on interpreting the remaining cases. In this paper we describe the statistical considerations for designing a study to test a new AI prescreening algorithm for identifying normal lung cancer screening CTs. We contrast agreement vs. accuracy studies, and retrospective vs. prospective designs. We evaluate various test performance metrics with respect to their sensitivity to changes in the AI algorithm's performance, as well as to shifts in reader behavior to a revised worklist. We consider sample size requirements for testing the AI prescreening algorithm.

Objective: Determine the prognostic impact of scar quantification (scar %) by cardiac magnetic resonance (CMR) in predicting heart failure admission, death and left ventricular (LV) function improvement following cardiac resynchronisation therapy (CRT), after controlling for the presence of left bundle branch block (LBBB), QRS duration (QRSd) and LV lead tip location and polarity., Methods: Consecutive patients who underwent CMR between 2002 and 2014 followed by CRT were included. The primary endpoint was death or heart failure admission. The secondary endpoint was change in ejection fraction (EF) ≥3 months after CRT. Cox proportional hazards, linear regression models and change in the area under the receiver operating characteristic curve (AUC) were used., Results: A total of 84 patients were included (63% male, 51% with ischaemic cardiomyopathy). After adjusting for clinical factors, presence of LBBB and QRSd and LV lead tip location and polarity, greater scar % remained associated with a higher risk for clinical events (HR=1.06; 95% CI 1.02 to 1.10; p<0.001) and a smaller improvement in EF (slope: -0.61%; 95% CI -0.93% to 0.29%; p<0.001). When adding scar % to QRSd and LBBB, there was significant improvement in predicting clinical events at 3 years (AUC increased to 0.831 from 0.638; p=0.027) and EF increase ≥10% (AUC 0.869 from 0.662; p=0.007)., Conclusion: Scar quantification by CMR has an incremental value in predicting response to CRT, in terms of heart failure admission, death and EF improvement, independent of the presence of LBBB, QRSd, LV lead tip location and polarity., Competing Interests: Competing interests: None declared.

Rationale and Objectives: To evaluate a new approach to establish compliance of segmentation tools with the computed tomography volumetry profile of the Quantitative Imaging Biomarker Alliance (QIBA); and determine the statistical exchangeability between real and simulated lesions through an international challenge., Materials and Methods: The study used an anthropomorphic phantom with 16 embedded physical lesions and 30 patient cases from the Reference Image Database to Evaluate Therapy Response with pathologically confirmed malignancies. Hybrid datasets were generated by virtually inserting simulated lesions corresponding to physical lesions into the phantom datasets using one projection-domain-based method (Method 1), two image-domain insertion methods (Methods 2 and 3), and simulated lesions corresponding to real lesions into the Reference Image Database to Evaluate Therapy Response dataset (using Method 2). The volumes of the real and simulated lesions were compared based on bias (measured mean volume differences between physical and virtually inserted lesions in phantoms as quantified by segmentation algorithms), repeatability, reproducibility, equivalence (phantom phase), and overall QIBA compliance (phantom and clinical phase)., Results: For phantom phase, three of eight groups were fully QIBA compliant, and one was marginally compliant. For compliant groups, the estimated biases were -1.8 ± 1.4%, -2.5 ± 1.1%, -3 ± 1%, -1.8 ± 1.5% (±95% confidence interval). No virtual insertion method showed statistical equivalence to physical insertion in bias equivalence testing using Schuirmann's two one-sided test (±5% equivalence margin). Differences in repeatability and reproducibility across physical and simulated lesions were largely comparable (0.1%-16% and 7%-18% differences, respectively). For clinical phase, 7 of 16 groups were QIBA compliant., Conclusion: Hybrid datasets yielded conclusions similar to real computed tomography datasets where phantom QIBA compliant was also compliant for hybrid datasets. Some groups deemed compliant for simulated methods, not for physical lesion measurements. The magnitude of this difference was small (<5.4%). While technical performance is not equivalent, they correlate, such that, volumetrically simulated lesions could potentially serve as practical proxies., (Copyright © 2018 The Association of University Radiologists. All rights reserved.)

Diagnostic systems designed to detect possibly multiple lesions per patient (e.g. multiple polyps during CT colonoscopy) are often evaluated in "free-response" studies that allow for diagnostic responses unconstrained in their number and locations. Analysis of free-response studies requires extensions of the traditional receiver operating characteristic (ROC) analysis, which are termed free-response ROC (FROC) methodology. Despite substantial developments in this area, FROC tools and approaches are much more cumbersome than traditional ROC methods. Alternative approaches that use well-known ROC tools (e.g. ROI-ROC) require defining and physically delineating regions of interest (ROI) and combine FROC data within ROIs. We propose an approach that allows analyzing FROC data using conventional ROC tools without delineating the actual ROIs or reducing data. The design parameters of FROC study are used to make FROC data analyzable using ROC tools and to calibrate the corresponding FROC and ROC curves on both conceptual and numerical levels. Differences in the performance indices of the nonparametric FROC and the new approach are shown to be asymptotically negligible and typically rather small in practice. Data from a large multi-reader study of colon cancer detection are used to illustrate the new approach.

OBJECTIVE. The purpose of this study was to test the hypothesis whether two-view wide-angle digital breast tomosynthesis (DBT) can replace full-field digital mammography (FFDM) for breast cancer detection. SUBJECTS AND METHODS. In a multireader multicase study, bilateral two-view FFDM and bilateral two-view wide-angle DBT images were independently viewed for breast cancer detection in two reading sessions separated by more than 1 month. From a pool of 764 patients undergoing screening and diagnostic mammography, 330 patient-cases were selected. The endpoints were the mean ROC AUC for the reader per breast (breast level), ROC AUC per patient (subject level), noncancer recall rates, sensitivity, and specificity. RESULTS. Twenty-nine of 31 readers performed better with DBT than FFDM regardless of breast density. There was a statistically significant improvement in readers' mean diagnostic accuracy with DBT. The subject-level AUC increased from 0.765 (standard error [SE], 0.027) for FFDM to 0.835 (SE, 0.027) for DBT ( p = 0.002). Breast-level AUC increased from 0.818 (SE, 0.019) for FFDM to 0.861 (SE, 0.019) for DBT ( p = 0.011). The noncancer recall rate per patient was reduced by 19% with DBT ( p < 0.001). Masses and architectural distortions were detected more with DBT ( p < 0.001); calcifications trended lower ( p = 0.136). Accuracy for detection of invasive cancers was significantly greater with DBT ( p < 0.001). CONCLUSION. Reader performance in breast cancer detection is significantly higher with wide-angle two-view DBT independent of FFDM, verifying the robustness of DBT as a sole view. However, results of perception studies in the vision sciences support the inclusion of an overview image.

Physiological properties of tumors can be measured both in vivo and noninvasively by diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging. Although these techniques have been used for more than two decades to study tumor diffusion, perfusion, and/or permeability, the methods and studies on how to reduce measurement error and bias in the derived imaging metrics is still lacking in the literature. This is of paramount importance because the objective is to translate these quantitative imaging biomarkers (QIBs) into clinical trials, and ultimately in clinical practice. Standardization of the image acquisition using appropriate phantoms is the first step from a technical performance standpoint. The next step is to assess whether the imaging metrics have clinical value and meet the requirements for being a QIB as defined by the Radiological Society of North America's Quantitative Imaging Biomarkers Alliance (QIBA). The goal and mission of QIBA and the National Cancer Institute Quantitative Imaging Network (QIN) initiatives are to provide technical performance standards (QIBA profiles) and QIN tools for producing reliable QIBs for use in the clinical imaging community. Some of QIBA's development of quantitative diffusion-weighted imaging and dynamic contrast-enhanced QIB profiles has been hampered by the lack of literature for repeatability and reproducibility of the derived QIBs. The available research on this topic is scant and is not in sync with improvements or upgrades in MRI technology over the years. This review focuses on the need for QIBs in oncology applications and emphasizes the importance of the assessment of their reproducibility and repeatability. Level of Evidence: 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;49:e101-e121., (© 2018 International Society for Magnetic Resonance in Medicine.)

The aim of this study was to establish the repeatability measures of quantitative Gaussian and non-Gaussian diffusion metrics using diffusion-weighted imaging (DWI) data from phantoms and patients with head-and-neck and papillary thyroid cancers. The Quantitative Imaging Biomarker Alliance (QIBA) DWI phantom and a novel isotropic diffusion kurtosis imaging phantom were scanned at 3 different sites, on 1.5T and 3T magnetic resonance imaging systems, using standardized multiple b-value DWI acquisition protocol. In the clinical component of this study, a total of 60 multiple b-value DWI data sets were analyzed for test-retest, obtained from 14 patients (9 head-and-neck squamous cell carcinoma and 5 papillary thyroid cancers). Repeatability of quantitative DWI measurements was assessed by within-subject coefficient of variation (wCV%) and Bland-Altman analysis. In isotropic diffusion kurtosis imaging phantom vial with 2% ceteryl alcohol and behentrimonium chloride solution, the mean apparent diffusion (D app × 10 -3 mm 2 /s) and kurtosis (K app , unitless) coefficient values were 1.02 and 1.68 respectively, capturing in vivo tumor cellularity and tissue microstructure. For the same vial, D app and K app mean wCVs (%) were ≤1.41% and ≤0.43% for 1.5T and 3T across 3 sites. For pretreatment head-and-neck squamous cell carcinoma, apparent diffusion coefficient, D, D*, K, and f mean wCVs (%) were 2.38%, 3.55%, 3.88%, 8.0%, and 9.92%, respectively; wCVs exhibited a higher trend for papillary thyroid cancers. Knowledge of technical precision and bias of quantitative imaging metrics enables investigators to properly design and power clinical trials and better discern between measurement variability versus biological change.