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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 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.

Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease worldwide, and prevalence of NAFLD is increasing owing to upward trends of obesity and diabetes (24042449). Nonalcoholic steatohepatitis (NASH) is an advanced stage of NAFLD with a higher risk of progression to complications related to cirrhosis, is currently the second most common indication for liver transplantation in the United States, and is expected to become the leading cause in the near future (24768810, 25461851, 25920090). Despite the large societal burden of NASH, there is no safe, effective medication approved by the United States Food and Drug Administration (FDA) (27646933). Most clinical trials for NASH require as an eligibility criterion the presence of ‘at-risk NASH’, defined as diagnosed NASH, NAFLD activity score (NAS) ≥ 4 and significant fibrosis (F≥2). The rationale is that these patients are at substantial risk for progression to complications of cirrhosis, and so might benefit from participating in NASH drug development clinical trials. By comparison, patients without NASH, with NAS ≤ 3, or with F

International audience; An important need exists for strategies to perform rigorous objective clinical-task-based evaluation of artificial intelligence (AI) algorithms for nuclear medicine. To address this need, we propose a 4-class framework to evaluate AI algorithms for promise, technical task-specific efficacy, clinical decision making, and postdeployment efficacy. We provide best practices to evaluate AI algorithms for each of these classes. Each class of evaluation yields a claim that provides a descriptive performance of the AI algorithm. Key best practices are tabulated as the RELAINCE (Recommendations for EvaLuation of AI for NuClear medicinE) guidelines. The report was prepared by the Society of Nuclear Medicine and Molecular Imaging AI Task Force Evaluation team, which consisted of nuclear-medicine physicians, physicists, computational imaging scientists, and representatives from industry and regulatory agencies.

Additional file 1: Table S1. Echocardiographic and CMR Data Indexed by BSA and Stratified Based on Aortic Valve Morphology Table S2. Echocardiographic and CMR Data Indexed by Height and Stratified Based on Aortic Valve Morphology

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.

Background Proton density fat fraction (PDFF) estimated by using chemical shift-encoded (CSE) MRI is an accepted imaging biomarker of hepatic steatosis. This work aims to promote standardized use of CSE MRI to estimate PDFF. Purpose To assess the accuracy of CSE MRI methods for estimating PDFF by determining the linearity and range of bias observed in a phantom. Materials and Methods In this prospective study, a commercial phantom with 12 vials of known PDFF values were shipped across nine U.S. centers. The phantom underwent 160 independent MRI examinations on 27 1.5-T and 3.0-T systems from three vendors. Two three-dimensional CSE MRI protocols with minimal T1 bias were included: vendor and standardized. Each vendor's confounder-corrected complex or hybrid magnitude-complex based reconstruction algorithm was used to generate PDFF maps in both protocols. The Siemens reconstruction required a configuration change to correct for water-fat swaps in the phantom. The MRI PDFF values were compared with the known PDFF values by using linear regression with mixed-effects modeling. The 95% CIs were calculated for the regression slope (ie, proportional bias) and intercept (ie, constant bias) and compared with the null hypothesis (slope = 1, intercept = 0). Results Pooled regression slope for estimated PDFF values versus phantom-derived reference PDFF values was 0.97 (95% CI: 0.96, 0.98) in the biologically relevant 0%-47.5% PDFF range. The corresponding pooled intercept was -0.27% (95% CI: -0.50%, -0.05%). Across vendors, slope ranges were 0.86-1.02 (vendor protocols) and 0.97-1.0 (standardized protocol) at 1.5 T and 0.91-1.01 (vendor protocols) and 0.87-1.01 (standardized protocol) at 3.0 T. The intercept ranges (absolute PDFF percentage) were -0.65% to 0.18% (vendor protocols) and -0.69% to -0.17% (standardized protocol) at 1.5 T and -0.48% to 0.10% (vendor protocols) and -0.78% to -0.21% (standardized protocol) at 3.0 T. Conclusion Proton density fat fraction estimation derived from three-dimensional chemical shift-encoded MRI in a commercial phantom was accurate across vendors, imaging centers, and field strengths, with use of the vendors' product acquisition and reconstruction software. © RSNA, 2021 See also the editorial by Dyke in this issue.

A Correction to this paper has been published

Artificial intelligence (AI)-based methods are showing promise in multiple medical-imaging applications. Thus, there is substantial interest in clinical translation of these methods, requiring in turn, that they be evaluated rigorously. In this paper, our goal is to lay out a framework for objective task-based evaluation of AI methods. We will also provide a list of tools available in the literature to conduct this evaluation. Further, we outline the important role of physicians in conducting these evaluation studies. The examples in this paper will be proposed in the context of PET with a focus on neural-network-based methods. However, the framework is also applicable to evaluate other medical-imaging modalities and other types of AI methods.

Supplemental material, sj-pdf-1-ajs-10.1177_0363546521995182 for The Clinical Radiographic Incidence of Posttraumatic Osteoarthritis 10 Years After Anterior Cruciate Ligament Reconstruction: Data From the MOON Nested Cohort by The MOON Group in The American Journal of Sports Medicine

NeuroQuant (NQ) is an FDA approved software that performs automatic MRI quantitative volumetric analysis. There is a paucity of literature investigating its clinical utility in a real patient population. This study aims to compare the accuracy of NQ relative to neuroradiologists in detecting MRI signs of hippocampal sclerosis (HS). Methods: We included 144 adult patients with no prior brain surgery, who underwent a temporal lobe resection for epilepsy at Cleveland Clinic from 2010 to 2017. The “gold standard” for the presence of HS was defined by pathology (as confirmed by expert neuropathologist review). Nonspecific findings on pathology were classified as no HS. MRI was acquired on a 3T scanner. An epilepsy neuroradiologist reviewed the pre-operative MRI and classified patients as having HS or not. Questionable signs of HS were classified as no-HS. After de-identifying the MRIs, we used NQ to quantify the volume of the eventually resected hippocampus. The results were given in percentile (based on age- and gender-matched normative cohort controls). Percentiles below 5 were considered abnormal. We used a McNemar’s paired proportions test to compare the specificity and sensitivity of identifying HS between NQ and neuroradiologist reports. Results: There were no differences between the HS group versus no-HS defined by pathology, but for age (Table 1). Distribution of hippocampus volumes (in percentile) is described in table 1. Sensitivity, specificity, positive predictive values, and negative predictive values are presented in Table 2. We found that NQ has the same specificity (p=1) but lower sensitivity when compared to neuroradiologists (69% vs 93%; p

Purpose To determine the linearity, bias, and precision of hepatic proton density fat fraction (PDFF) measurements by using magnetic resonance (MR) imaging across different field strengths, imager manufacturers, and reconstruction methods. Materials and Methods This meta-analysis was performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A systematic literature search identified studies that evaluated the linearity and/or bias of hepatic PDFF measurements by using MR imaging (hereafter, MR imaging-PDFF) against PDFF measurements by using colocalized MR spectroscopy (hereafter, MR spectroscopy-PDFF) or the precision of MR imaging-PDFF. The quality of each study was evaluated by using the Quality Assessment of Studies of Diagnostic Accuracy 2 tool. De-identified original data sets from the selected studies were pooled. Linearity was evaluated by using linear regression between MR imaging-PDFF and MR spectroscopy-PDFF measurements. Bias, defined as the mean difference between MR imaging-PDFF and MR spectroscopy-PDFF measurements, was evaluated by using Bland-Altman analysis. Precision, defined as the agreement between repeated MR imaging-PDFF measurements, was evaluated by using a linear mixed-effects model, with field strength, imager manufacturer, reconstruction method, and region of interest as random effects. Results Twenty-three studies (1679 participants) were selected for linearity and bias analyses and 11 studies (425 participants) were selected for precision analyses. MR imaging-PDFF was linear with MR spectroscopy-PDFF (R2 = 0.96). Regression slope (0.97; P < .001) and mean Bland-Altman bias (-0.13%; 95% limits of agreement: -3.95%, 3.40%) indicated minimal underestimation by using MR imaging-PDFF. MR imaging-PDFF was precise at the region-of-interest level, with repeatability and reproducibility coefficients of 2.99% and 4.12%, respectively. Field strength, imager manufacturer, and reconstruction method each had minimal effects on reproducibility. Conclusion MR imaging-PDFF has excellent linearity, bias, and precision across different field strengths, imager manufacturers, and reconstruction methods. © RSNA, 2017 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on October 2, 2017.

Supplemental Material, GSJ739610_suppl_mat for Spine Surgeon Treatment Variability: The Impact on Costs by Matthew D. Alvin, Daniel Lubelski, Ridwan Alam, Seth K. Williams, Nancy A. Obuchowski, Michael P. Steinmetz, Jeffrey C. Wang, Alfred J. Melillo, Amit Pahwa, Edward C. Benzel, Michael T. Modic, Robert Quencer, and Thomas E. Mroz in Global Spine Journal

Objectives: ACL reconstruction effectively restores knee stability and allows a return to athletic activities after ACL injury, but patients are still at higher risk of developing post-traumatic OA. Patient reported outcomes from the Multicenter Orthopaedic Outcomes Network (MOON) prospective longitudinal cohort of over 1500 patients undergoing ACL reconstruction showed no increase in OA symptoms (KOOS subscale) at 2 or 6 years after surgery. Therefore, identification of structural changes of OA that may precede the onset of symptoms is of critical importance for determining risk factors for the initiation and progression of post-traumatic OA in addition to measuring the effectiveness of potential disease-modifying treatments. One structural measure of OA is radiographic joint space width (JSW). We previously demonstrated that meniscus treatment and age predict narrower medial compartment JSW. Methods: 335 patients from the MOON cohort (154 males, 181 females, median age 18 years at the time of surgery) were recruited at a minimum of 2 years following surgery for on-site evaluations including bilateral metatarsophalangeal joint (MTP) radiographs to assess JSW. To minimize bias related to pre-existing knee injury or OA, subjects were 35 years or younger, were injured playing a sport, had primary ACL reconstruction without prior meniscus or articular cartilage surgery, did not undergo subsequent ACL revision, and had a surgically normal contralateral knee. Radiographic JSW was measured in the lateral compartment of both knees using a validated semiautomated method. The association of age, sex, BMI, meniscus treatment, and articular cartilage treatment with lateral compartment JSW differences (JSD) between the reconstructed and normal knees was examined using multivariable generalized linear models. The Holm-Bonferroni method was used to account for multiple comparisons. Results: The mean lateral compartment JSW was 7.73 mm and (95% CI 7.61-7.85 mm) for ACL reconstructed knees and 7.82 mm (95% CI 7.71-7.94 mm) for contralateral normal knees, with a mean JSD of 0.10 mm (p

To evaluate whether the presence of facial photographs obtained at the point-of-care of portable radiography leads to increased detection of wrong-patient errors.In this institutional review board-approved study, 166 radiograph-photograph combinations were obtained from 30 patients. Consecutive radiographs from the same patients resulted in 83 unique pairs (ie, a new radiograph and prior, comparison radiograph) for interpretation. To simulate wrong-patient errors, mismatched pairs were generated by pairing radiographs from different patients chosen randomly from the sample. Ninety radiologists each interpreted a unique randomly chosen set of 10 radiographic pairs, containing up to 10% mismatches (ie, error pairs). Radiologists were randomly assigned to interpret radiographs with or without photographs. The number of mismatches was identified, and interpretation times were recorded.Ninety radiologists with 21 ± 10 (mean ± standard deviation) years of experience were recruited to participate in this observer study. With the introduction of photographs, the proportion of errors detected increased from 31% (9 of 29) to 77% (23 of 30; P = .006). The odds ratio for detection of error with photographs to detection without photographs was 7.3 (95% confidence interval: 2.29-23.18). Observer qualifications, training, or practice in cardiothoracic radiology did not influence sensitivity for error detection. There is no significant difference in interpretation time for studies without photographs and those with photographs (60 ± 22 vs. 61 ± 25 seconds; P = .77).In this observer study, facial photographs obtained simultaneously with portable chest radiographs increased the identification of any wrong-patient errors, without substantial increase in interpretation time. This technique offers a potential means to increase patient safety through correct patient identification.

Multireader imaging trials often use a factorial design, in which study patients undergo testing with all imaging modalities and readers interpret the results of all tests for all patients. A drawback of this design is the large number of interpretations required of each reader. Split-plot designs have been proposed as an alternative, in which one or a subset of readers interprets all images of a sample of patients, while other readers interpret the images of other samples of patients. In this paper, the authors compare three methods of analysis for the split-plot design.Three statistical methods are presented: the Obuchowski-Rockette method modified for the split-plot design, a newly proposed marginal-mean analysis-of-variance approach, and an extension of the three-sample U-statistic method. A simulation study using the Roe-Metz model was performed to compare the type I error rate, power, and confidence interval coverage of the three test statistics.The type I error rates for all three methods are close to the nominal level but tend to be slightly conservative. The statistical power is nearly identical for the three methods. The coverage of 95% confidence intervals falls close to the nominal coverage for small and large sample sizes.The split-plot multireader, multicase study design can be statistically efficient compared to the factorial design, reducing the number of interpretations required per reader. Three methods of analysis, shown to have nominal type I error rates, similar power, and nominal confidence interval coverage, are available for this study design.

We describe a step-by-step procedure for estimating power and sample size for planned multireader receiver operating characteristic (ROC) studies that will be analyzed using either the Dorfman-Berbaum-Metz (DBM) or Obuchowski-Rockette (OR) method. This procedure updates previous approaches by incorporating recent methodological developments and unifies the approaches by allowing inputs to be conjectured parameter values or outputs from either a DBM or OR pilot-study analysis.Power computations are described in a step-by-step procedure and the theoretical basis for the procedure is described. Updates include using the currently recommended denominator degrees of freedom, accounting for different pilot and planned study normal-to-abnormal case ratios, and a new method for computing the OR test-by-reader variance component.Using a real dataset we illustrate how to compute the power for two planned studies, one having the same normal-to-abnormal case ratio as the pilot study and the other having a different ratio. In a simulation study, we show that the proposed procedure gives mean power estimates close to the true power.Application of the updated procedure is straightforward. It is important that pilot data be comparable to the planned study with respect to the modalities, reader expertise, and case selection. Variability of the power estimates warrants further investigation.

BACKGROUND AND PURPOSE: There is a wide range of normal variation in sphenoid sinus development, especially in the size of the lateral recesses. The purpose of this study was to determine imaging characteristics that may help differentiate between opacification of a developmentally asymmetric lateral recess and a true expansile lesion of the sphenoid sinus. METHODS: Coronal CT was performed in seven patients with expansile or erosive benign lesions of the sphenoid sinus, and results were compared to a control population of 72 subjects with unopacified sphenoid sinuses. The degree of asymmetry of lateral recess development was assessed with particular attention to the separation of vidian's canal and the foramen rotundum (vidian-rotundum distance). The images were also examined for evidence of: erosion, defined as loss of the normal thin bony margin on at least two contiguous sections; apparent thinning of the sinus wall, defined as a focal apparent decrease in thickness again on at least two contiguous sections; and for vidian's canal or foramen rotundum rim erosion or flattening. RESULTS: Of the seven patients with expansile lesions, vidian's canal margin erosion was present in seven, unequivocal sinus expansion in three, wall erosion in three, wall thinning in three, erosion of the foramen rotundum in two, and flattening in the foramen rotundum in four. Forty-one of the 72 controls had lateral recess formation, 28 of which were asymmetric. The distance between vidian's canal and the foramen rotundum (vidian-rotundum distance) relied on the presence or absence of pneumatization, with a significantly larger distance in the presence of greater wing pneumatization. Examination of 24 controls revealed apparent thinning of the sinus wall, typically at the carotid groove, but no flattening, thinning, or erosion of the vidian canal or of the foramen rotundum. CONCLUSION: Examination of controls and patients with expansile or erosive lesions of the sphenoid sinus revealed side-to-side asymmetry in the development of the sinus and lateral recess, making subtle expansion difficult to assess. Furthermore, variability in the vidian-rotundum distance correlated with degree of pneumatization, and did not necessarily reflect expansion. Thus, in the absence of gross sinus wall erosion, flattening or erosion of the rims of vidian's canal or the foramen rotundum provides the most specific evidence of an expansile or erosive process within the sinus.