Evaluating Biparametric Versus Multiparametric Magnetic Resonance Imaging for Diagnosing Clinically Significant Prostate Cancer: An International, Paired, Noninferiority, Confirmatory Observer Study.

BACKGROUND AND OBJECTIVE: Biparametric magnetic resonance imaging (bpMRI), excluding dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI), is a potential replacement for multiparametric MRI (mpMRI) in diagnosing clinically significant prostate cancer (csPCa). An extensive international multireader multicase observer study was conducted to assess the noninferiority of bpMRI to mpMRI in csPCa diagnosis. METHODS: An observer study was conducted with 400 mpMRI examinations from four European centers, excluding examinations with prior prostate treatment or csPCa (Gleason grade [GG] ≥2) findings. Readers assessed bpMRI and mpMRI sequentially, assigning lesion-specific Prostate Imaging Reporting and Data System (PI-RADS) scores (3-5) and a patient-level suspicion score (0-100). The noninferiority of patient-level bpMRI versus mpMRI csPCa diagnosis was evaluated using the area under the receiver operating curve (AUROC) alongside the sensitivity and specificity at PI-RADS ≥3 with a 5% margin. The secondary outcomes included insignificant prostate cancer (GG1) diagnosis, diagnostic evaluations at alternative risk thresholds, decision curve analyses (DCAs), and subgroup analyses considering reader expertise. Histopathology and ≥3 yr of follow-up were used for the reference standard. KEY FINDINGS AND LIMITATIONS: Sixty-two readers (45 centers and 20 countries) participated. The prevalence of csPCa was 33% (133/400); bpMRI and mpMRI showed similar AUROC values of 0.853 (95% confidence interval [CI], 0.819-0.887) and 0.859 (95% CI, 0.826-0.893), respectively, with a noninferior difference of -0.6% (95% CI, -1.2% to 0.1%, p < 0.001). At PI-RADS ≥3, bpMRI and mpMRI had sensitivities of 88.6% (95% CI, 84.8-92.3%) and 89.4% (95% CI, 85.8-93.1%), respectively, with a noninferior difference of -0.9% (95% CI, -1.7% to 0.0%, p < 0.001), and specificities of 58.6% (95% CI, 52.3-63.1%) and 57.7% (95% CI, 52.3-63.1%), respectively, with a noninferior difference of 0.9% (95% CI, 0.0-1.8%, p < 0.001). At alternative risk thresholds, mpMRI increased sensitivity at the expense of reduced specificity. DCA demonstrated the highest net benefit for an mpMRI pathway in cancer-averse scenarios, whereas a bpMRI pathway showed greater benefit for biopsy-averse scenarios. A subgroup analysis indicated limited additional benefit of DCE MRI for nonexperts. Limitations included that biopsies were conducted based on mpMRI imaging, and reading was performed in a sequential order. CONCLUSIONS AND CLINICAL IMPLICATIONS: It has been found that bpMRI is noninferior to mpMRI in csPCa diagnosis at AUROC, along with the sensitivity and specificity at PI-RADS ≥3, showing its value in individuals without prior csPCa findings and prostate treatment. Additional randomized prospective studies are required to investigate the generalizability of outcomes.

Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study.

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.

18F-fluciclovine PET/CT to distinguish radiation necrosis from tumor progression for brain metastases treated with radiosurgery: results of a prospective pilot study.

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 18F-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 18F-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 SUVmax statistically significantly predicted tumor progression (AUC = 0.875; p = 0.011). Lesion SUVmean (AUC = 0.875; p = 0.018), SUVpeak (AUC = 0.813; p = 0.007), and SUVpeak-to-normal-brain (AUC = 0.859; p = 0.002) also predicted tumor progression, whereas SUVmax-to-normal-brain (p = 0.1) and SUVmean-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, 18F-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.

Test-retest repeatability of ADC in prostate using the multi b-Value VERDICT acquisition.

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 b0b1500 had the lowest wSD in most zones (0.18-0.58x10-4 mm2/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/mm2 had the largest wSDs (1.90-10.24x10-4 mm2/s). CONCLUSION: ADC maps generated from b0b1500 have better repeatability in normal TZ, normal PZ, and index lesions.

Comparing the Accuracy of Diagnostic Tests When Disease Is Characterized by an Ordinal Scale.

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.

Multiparametric Data-driven Imaging Markers: Guidelines for Development, Application and Reporting of Model Outputs in Radiomics.

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.

Meniscal Treatment as a Predictor of Worse Articular Cartilage Damage on MRI at 2 Years After ACL Reconstruction: The MOON Nested Cohort.

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.

Sex-based differences in left ventricular remodeling in patients with chronic aortic regurgitation: a multi-modality study.

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/m2 vs 125.6 ml/m2, p < 0.001), LV end-systolic volume index (LVESVI) (41.1 vs 54.5 ml/m2, 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.

Development, validation, qualification, and dissemination of quantitative MR methods: Overview and recommendations by the ISMRM quantitative MR study group.

On behalf of the International Society for Magnetic Resonance in Medicine (ISMRM) Quantitative MR Study Group, this article provides an overview of considerations for the development, validation, qualification, and dissemination of quantitative MR (qMR) methods. This process is framed in terms of two central technical performance properties, i.e., bias and precision. Although qMR is confounded by undesired effects, methods with low bias and high precision can be iteratively developed and validated. For illustration, two distinct qMR methods are discussed throughout the manuscript: quantification of liver proton-density fat fraction, and cardiac T1 . These examples demonstrate the expansion of qMR methods from research centers toward widespread clinical dissemination. The overall goal of this article is to provide trainees, researchers, and clinicians with essential guidelines for the development and validation of qMR methods, as well as an understanding of necessary steps and potential pitfalls for the dissemination of quantitative MR in research and in the clinic.

Complementary Diagnostic and Prognostic Contributions of Cardiac Computed Tomography for Infective Endocarditis Surgery.

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.

Radiographic evaluation of knee osteoarthritis in predicting outcomes after arthroscopic partial meniscectomy.

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.

Comparison of Clinical and Semiquantitative Cartilage Grading Systems in Predicting Outcomes After Arthroscopic Partial Meniscectomy.

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.

The QIBA Profile for FDG PET/CT as an Imaging Biomarker Measuring Response to Cancer Therapy.

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 (SUVmax) is measurable from FDG PET/CT with a within-subject coefficient of variation of 10%-12%. Second, a measured increase in SUVmax 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.

Native-Osteoarthritic Joint Resident Stem and Progenitor Cells for Cartilage Cell-Based Therapies: A Quantitative Comparison With Respect to Concentration and Biological Performance.

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 (PCTP, 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], PCTP, and CTP biological performance across tissue sources. RESULTS: [Cell] was highest in grade 3 cartilage (P = .002) and BMC (P = .001). Median PCTP 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], PCTP, 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 PCTP 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.

Stenting for Inferior Vena Cava Stenosis After Liver Transplant.

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.

Statistical considerations for testing an AI algorithm used for prescreening lung CT images.

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.

Can Digital Breast Tomosynthesis Replace Full-Field Digital Mammography? A Multireader, Multicase Study of Wide-Angle Tomosynthesis.

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.

Repeatability of Quantitative Diffusion-Weighted Imaging Metrics in Phantoms, Head-and-Neck and Thyroid Cancers: Preliminary Findings.

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 (Dapp × 10-3 mm2/s) and kurtosis (Kapp, unitless) coefficient values were 1.02 and 1.68 respectively, capturing in vivo tumor cellularity and tissue microstructure. For the same vial, Dapp and Kapp 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.

Statistical Considerations for Planning Clinical Trials with Quantitative Imaging Biomarkers.

As imaging technologies and treatment options continue to advance, imaging outcome measures are becoming increasingly utilized as the basis of making major decisions in new drug development and clinical practice. Quantitative imaging biomarkers (QIBs) are now commonly used for subject selection, response assessment, and safety monitoring. Although quantitative measurements can have many advantages compared with subjective, qualitative endpoints, it is important to recognize that QIBs are measured with error. This study uses Monte Carlo simulation to examine the impact of measurement error on a variety of clinical trial designs as well as to test proposed adjustments for measurement error. The focus is on some of the QIBs currently being studied by the Quantitative Imaging Biomarkers Alliance. The results show that the ability of QIBs to discriminate between health states and predict patient outcome is attenuated by measurement error; however, the known technical performance characteristics of QIBs can be used to adjust study sample size, control the misinterpretation rate of imaging findings, and establish statistically valid decision thresholds. We conclude that estimates of the precision and bias of a QIB are important for properly designing clinical trials and establishing the level of imaging standardization required.

Evaluation of Simulated Lesions as Surrogates to Clinical Lesions for Thoracic CT Volumetry: The Results of an International Challenge.

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.