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.

Impact of Cardiac Magnetic Resonance Left Atrial Ejection Fraction in Advanced Ischemic Cardiomyopathy.

BACKGROUND: The prognostic significance of cardiac magnetic resonance (CMR)-based left atrial ejection fraction (LAEF) is not well defined in the ischemic cardiomyopathy (ICM) cohort. OBJECTIVES: The authors sought to assess the prognostic impact of LAEF, when adjusted for left ventricular remodeling, myocardial infarct size (MIS), left atrial volume index, and functional mitral regurgitation (FMR), on outcomes in patients with advanced ICM. METHODS: ICM patients who underwent CMR were retrospectively evaluated (April 2001-December 2019). LAEF, left atrial volume index, MIS, left ventricular remodeling, and FMR were derived from CMR. The primary clinical endpoint was a composite of all-cause mortality and cardiac transplant. A baseline multivariable Cox proportional hazards regression model was constructed to assess prognostic power of LAEF. RESULTS: There were 718 patients (416 primary events) evaluated, with a median duration of follow-up of 1,763 days (4.8 years) and a mean LAEF of 36% ± 15%. On multivariable analysis, higher LAEF was independently associated with reduced risk (HR: 0.24, 95% CI: 0.12-0.48, P < 0.001), even after adjusting for FMR and MIS. The highest adjusted risk was observed in patients with an LAEF <20% and an MIS of >30% (HR: 3.20, 95% CI: 1.73-5.93). The lowest risk was in patients within the comparator group with an LAEF of >50% and a MIS of <15% (HR: 1.07, 95% CI: 0.81-1.42). CONCLUSIONS: Reduced LAEF is independently associated with increased mortality in ICM. Risk associated with declining LAEF is continuous and incremental to other risk factors for adverse outcomes in patients with ICM even after adjusting for MIS and FMR severity.

Repeatability of 18F-FDG uptake in metastatic bone lesions of breast cancer patients and implications for accrual to clinical trials.

BACKGROUND: Standard measures of response such as Response Evaluation Criteria in Solid Tumors are ineffective for bone lesions, often making breast cancer patients that have bone-dominant metastases ineligible for clinical trials with potentially helpful therapies. In this study we prospectively evaluated the test-retest uptake variability of 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG) in a cohort of breast cancer patients with bone-dominant metastases to determine response criteria. The thresholds for 95% specificity of change versus no-change were then applied to a second cohort of breast cancer patients with bone-dominant metastases. METHODS: For this study, nine patients with 38 bone lesions were imaged with 18F-FDG in the same calibrated scanner twice within 14 days. Tumor uptake was quantified by the most commonly used PET parameter, the maximum tumor voxel normalized by dose and body weight (SUVmax) and also by the mean of a 1-cc maximal uptake volume normalized by dose and lean-body-mass (SULpeak). The asymmetric repeatability coefficients with confidence intervals for SUVmax and SULpeak were used to determine the limits of 18F-FDG uptake variability. A second cohort of 28 breast cancer patients with bone-dominant metastases that had 146 metastatic bone lesions was imaged with 18F-FDG before and after standard-of-care therapy for response assessment. RESULTS: The mean relative difference of SUVmax and SULpeak in 38 bone tumors of the first cohort were 4.3% and 6.7%. The upper and lower asymmetric limits of the repeatability coefficient were 19.4% and - 16.3% for SUVmax, and 21.2% and - 17.5% for SULpeak. 18F-FDG repeatability coefficient confidence intervals resulted in the following patient stratification using SULpeak for the second patient cohort: 11-progressive disease, 5-stable disease, 7-partial response, and 1-complete response with three inevaluable patients. The asymmetric repeatability coefficients response criteria for SULpeak changed the status of 3 patients compared to the standard Positron Emission Tomography Response Criteria in Solid Tumors of ± 30% SULpeak. CONCLUSION: In evaluating bone tumor response for breast cancer patients with bone-dominant metastases using 18F-FDG SUVmax, the repeatability coefficients from test-retest studies show that reductions of more than 17% and increases of more than 20% are unlikely to be due to measurement variability. Serial 18F-FDG imaging in clinical trials investigating bone lesions in these patients, such as the ECOG-ACRIN EA1183 trial, benefit from confidence limits that allow interpretation of response.

Prospective comparison of positron emission tomography (PET)/magnetic resonance and PET/computed tomography dosimetry in hepatic malignant neoplastic disease after 90Y radioembolization treatment.

Background: 90Y radioembolization is an established treatment modality for hepatic malignancies. Successful radioembolization requires optimal dose delivery to tumors while minimizing dosages to parenchyma. Post-treatment positron emission tomography (PET)/computed tomography (CT) dosimetry is the established benchmark, whereas PET/magnetic resonance (MR) is an emerging modality. The goal of this study was to assess the intermodality agreement between PET/MR and PET/CT 90Y dosimetry. Methods: In this single-institution study, 18 patients (20 treatment sessions) with a primary or metastatic hepatic malignancy underwent both PET/MR and PET/CT after 90Y radioembolization. Patients were randomized to undergo one modality first, followed by the other. The region of interest was delineated using MR images and tumor and liver dosimetry was calculated. Intermodality agreement was assessed using the Bland-Altman method. A generalized linear model was used to assess the effect of baseline variables on intermodality dose differences. Results: PET/MR underestimated tumor and liver absorbed doses when compared to PET/CT by -3.7% (P=0.042) and -5.8% (P=0.029), respectively. A coverage probability plot demonstrated that 80% and 90% of tumor dose measurements fell within intermodality differences of 11% and 18%, respectively. PET/MR underestimated tumor dose at both low (<1 GBq) and high (>3 GBq) injected activity levels (P<0.001) by -22.3 [standard deviation (SD) =13.5] and -24.3 (SD =18.7), respectively. Conclusions: Although PET/MR significantly underestimated the absorbed dose when compared to PET/CT, the intermodality agreement was high and the degree of underestimation was better than previously reported. Intermodality differences were more pronounced at low and high injected doses. Additional studies are required to assess the clinical implications of these findings.

Repeatability of 18F-FDG uptake in metastatic bone lesions of breast cancer patients and implications for accrual to clinical trials.

BACKGROUND: Standard measures of response such as Response Evaluation Criteria in Solid Tumors are ineffective for bone lesions, often making breast cancer patients with bone-dominant metastases ineligible for clinical trials with potentially helpful therapies. In this study we prospectively evaluated the test-retest uptake variability of 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG) in a cohort of breast cancer patients with bone-dominant metastases to determine response criteria. The thresholds for 95% specificity of change versus no-change were then applied to a second cohort of breast cancer patients with bone-dominant metastases.In this study, nine patients with 38 bone lesions were imaged with 18F-FDG in the same calibrated scanner twice within 14 days. Tumor uptake was quantified as the maximum tumor voxel normalized by dose and body weight (SUVmax) and the mean of a 1-cc maximal uptake volume normalized by dose and lean-body-mass (SULpeak). The asymmetric repeatability coefficients with confidence intervals of SUVmax and SULpeak were used to determine limits of 18F-FDG uptake variability. A second cohort of 28 breast cancer patients with bone-dominant metastases that had 146 metastatic bone lesions was imaged with 18F-FDG before and after standard-of-care therapy for response assessment. RESULTS: The mean relative difference of SUVmax in 38 bone tumors of the first cohort was 4.3%. The upper and lower asymmetric limits of the repeatability coefficient were 19.4% and -16.3%, respectively. The 18F-FDG repeatability coefficient confidence intervals resulted in the following patient stratification for the second patient cohort: 11-progressive disease, 5-stable disease, 7-partial response, and 1-complete response with three inevaluable patients. The asymmetric repeatability coefficients response criteria changed the status of 3 patients compared to standard the standard Positron Emission Tomography Response Criteria in Solid Tumors of ±30% SULpeak. CONCLUSIONS: In evaluating bone tumor response for breast cancer patients with bone-dominant metastases using 18F-FDG uptake, the repeatability coefficients from test-retest studies show that reductions of more than 17% and increases of more than 20% are unlikely to be due to measurement variability. Serial 18F-FDG imaging in clinical trials investigating bone lesions from these patients, such as the ECOG-ACRIN EA1183 trial, benefit from confidence limits that allow interpretation of response.

METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII.

PURPOSE: To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies. METHODS: We conducted an online modified Delphi study with a group of international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members to identify the items to be voted; and Stage#3, four rounds of the modified Delphi exercise by panelists to determine the items eligible for the METRICS and their weights. The consensus threshold was 75%. Based on the median ranks derived from expert panel opinion and their rank-sum based conversion to importance scores, the category and item weights were calculated. RESULT: In total, 59 panelists from 19 countries participated in selection and ranking of the items and categories. Final METRICS tool included 30 items within 9 categories. According to their weights, the categories were in descending order of importance: study design, imaging data, image processing and feature extraction, metrics and comparison, testing, feature processing, preparation for modeling, segmentation, and open science. A web application and a repository were developed to streamline the calculation of the METRICS score and to collect feedback from the radiomics community. CONCLUSION: In this work, we developed a scoring tool for assessing the methodological quality of the radiomics research, with a large international panel and a modified Delphi protocol. With its conditional format to cover methodological variations, it provides a well-constructed framework for the key methodological concepts to assess the quality of radiomic research papers. CRITICAL RELEVANCE STATEMENT: A quality assessment tool, METhodological RadiomICs Score (METRICS), is made available by a large group of international domain experts, with transparent methodology, aiming at evaluating and improving research quality in radiomics and machine learning. KEY POINTS: • A methodological scoring tool, METRICS, was developed for assessing the quality of radiomics research, with a large international expert panel and a modified Delphi protocol. • The proposed scoring tool presents expert opinion-based importance weights of categories and items with a transparent methodology for the first time. • METRICS accounts for varying use cases, from handcrafted radiomics to entirely deep learning-based pipelines. • A web application has been developed to help with the calculation of the METRICS score ( https://metricsscore.github.io/metrics/METRICS.html ) and a repository created to collect feedback from the radiomics community ( https://github.com/metricsscore/metrics ).

Visceral adipose tissue volume effect in Crohn's disease using reduced exposure CT enterography.

PURPOSE: The purpose of this investigation was to assess the effect of visceral adipose tissue volume (VA) on reader efficacy in diagnosing and characterizing small bowel Crohn's disease using lower exposure CT enterography (CTE). Secondarily, we investigated the effect of lower exposure and VA on reader diagnostic confidence. METHODS: Prospective paired investigation of 256 CTE, 129 with Crohn's disease, were reconstructed at 100% and simulated 50% and 30% exposure. The senior author provided the disease classification for the 129 patients with Crohn's disease. Patient VA was measured, and exams were evaluated by six readers for presence or absence of Crohn's disease and phenotype using a 0-10-point scale. Logistic regression models assessed the effect of VA on sensitivity and specificity. RESULTS: The effect of VA on sensitivity was significantly reduced at 30% exposure (odds radio [OR]: 1.00) compared to 100% exposure (OR: 1.12) (p = 0.048). There was no statistically significant difference among the exposures with respect to the effect of visceral fat on specificity (p = 0.159). The study readers' probability of agreement with the senior author on disease classification was 60%, 56%, and 53% at 100%, 50%, and 30% exposure, respectively (p = 0.004). When detecting low severity Crohn's disease, readers' mean sensitivity was 83%, 75%, and 74% at 100%, 50%, and 30% exposure, respectively (p = 0.002). In low severity disease, sensitivity also tended to increase as visceral fat increased (ORs per 1000 cm3 increase in visceral fat: 1.32, 1.31, and 1.18, p = 0.010, 0.016, and 0.100, at 100%, 50%, and 30% exposure). CONCLUSIONS: While the interaction is complex, VA plays a role in detecting and characterizing small bowel Crohn's disease when exposure is altered, particularly in low severity disease.

The Effect of Aspiration and Corticosteroid Injection After ACL Injury on Postoperative Infection Rate.

BACKGROUND: Injecting bioactive substances into the knee is common in orthopaedic practice, and recently it has been shown to mitigate risk factors for posttraumatic osteoarthritis. Therefore, understanding the influence of these injections on postoperative infection rate is imperative. HYPOTHESIS: Postinjury aspiration and corticosteroid injection (CSI) of the knee before anterior cruciate ligament (ACL) reconstruction (ACLR) would not increase the risk of postoperative infection. STUDY DESIGN: Cohort Study; Level of evidence, 3. METHODS: All patients between the ages of 10 and 65 years who underwent primary bone-patellar tendon-bone ACLR by 1 fellowship-trained sports medicine orthopaedic surgeon between January 1, 2011, and September 8, 2020, at 1 of 2 major academic centers were evaluated for inclusion. A total of 693 patients were included, with 273 patients receiving postinjury and preoperative aspiration and CSI. A postoperative infection was defined as a patient returning to the operating room for an intra-articular washout. The intervals-measured in days-between the CSI and ACLR and between ACLR and the final follow-up were recorded. To further evaluate the infection risk in each cohort (total cohort; aspiration and injection cohort; no aspiration and injection cohort), the upper 95% confidence bound for the infection risk was calculated for each cohort. RESULTS: There were no postoperative infections in the 693 patients included in this study. The upper 95% confidence bounds were 0.4%, 1.1%, and 0.7% for the total cohort, the cohort that underwent aspiration and injection, and the cohort that did not, respectively. The median number of days between the surgical date and that of the aspiration and injection was 34 days, and the mean follow-up for the entire cohort was 337.4 days (95% CI, 307.6-367.3). CONCLUSION: Postinjury and preoperative aspiration and CSI is a safe intervention that can be used before ACLR. Future studies with larger sample sizes, longer patient follow-ups, and multiple surgeons would be helpful to both better understand infection risk and better identify the influence of CSI on preventing posttraumatic osteoarthritis.

Repeatability of MRI Biomarkers in Nonalcoholic Fatty Liver Disease: The NIMBLE Consortium.

Background There is a need for reliable noninvasive methods for diagnosing and monitoring nonalcoholic fatty liver disease (NAFLD). Thus, the multidisciplinary Non-invasive Biomarkers of Metabolic Liver disease (NIMBLE) consortium was formed to identify and advance the regulatory qualification of NAFLD imaging biomarkers. Purpose To determine the different-day same-scanner repeatability coefficient of liver MRI biomarkers in patients with NAFLD at risk for steatohepatitis. Materials and Methods NIMBLE 1.2 is a prospective, observational, single-center short-term cross-sectional study (October 2021 to June 2022) in adults with NAFLD across a spectrum of low, intermediate, and high likelihood of advanced fibrosis as determined according to the fibrosis based on four factors (FIB-4) index. Participants underwent up to seven MRI examinations across two visits less than or equal to 7 days apart. Standardized imaging protocols were implemented with six MRI scanners from three vendors at both 1.5 T and 3 T, with central analysis of the data performed by an independent reading center (University of California, San Diego). Trained analysts, who were blinded to clinical data, measured the MRI proton density fat fraction (PDFF), liver stiffness at MR elastography (MRE), and visceral adipose tissue (VAT) for each participant. Point estimates and CIs were calculated using χ2 distribution and statistical modeling for pooled repeatability measures. Results A total of 17 participants (mean age, 58 years ± 8.5 [SD]; 10 female) were included, of which seven (41.2%), six (35.3%), and four (23.5%) participants had a low, intermediate, or high likelihood of advanced fibrosis, respectively. The different-day same-scanner mean measurements were 13%-14% for PDFF, 6.6 L for VAT, and 3.15 kPa for two-dimensional MRE stiffness. The different-day same-scanner repeatability coefficients were 0.22 L (95% CI: 0.17, 0.29) for VAT, 0.75 kPa (95% CI: 0.6, 0.99) for MRE stiffness, 1.19% (95% CI: 0.96, 1.61) for MRI PDFF using magnitude reconstruction, 1.56% (95% CI: 1.26, 2.07) for MRI PDFF using complex reconstruction, and 19.7% (95% CI: 15.8, 26.2) for three-dimensional MRE shear modulus. Conclusion This preliminary study suggests that thresholds of 1.2%-1.6%, 0.22 L, and 0.75 kPa for MRI PDFF, VAT, and MRE, respectively, should be used to discern measurement error from real change in patients with NAFLD. ClinicalTrials.gov registration no. NCT05081427 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Kozaka and Matsui in this issue.

Calibration phantom-based prediction of CT lung nodule volume measurement performance.

Background: A calibration phantom-based method has been developed for predicting small lung nodule volume measurement bias and precision that is specific to a particular computed tomography (CT) scanner and acquisition protocol. Methods: The approach involves CT scanning a simple reference object with a specific acquisition protocol, analyzing the scan to estimate the fundamental imaging properties of the CT acquisition system, generating numerous simulated images of a target geometry using the fundamental imaging properties, measuring the simulated images with a standard nodule volume segmentation algorithm, and calculating bias and precision performance statistics from the resulting volume measurements. We evaluated the ability of this approach to predict volume measurement bias and precision of Teflon spheres (diameters =4.76, 6.36, and 7.94 mm) placed within an anthropomorphic chest phantom when using 3M Scotch Magic™ tape as the reference object. CT scanning of the spheres was performed with 0.625, 1.25, and 2.5 mm slice thickness and spacing. Results: The study demonstrated good agreement between predicted volumetric performance and observed volume measurement performance for both volumetric measurement bias and precision. The predicted and observed volume mean for all slice thicknesses was found to be 28% and 13% lower on average than the manufactured sphere volume, respectively. When restricted to 0.625 and 1.25 mm slice thickness scans, which are recommended for small lung nodule volume measurement, we found that the difference between predicted and observed volume coefficient of variation was less than 1.0 %. The approach also showed a resilience to varying CT image acquisition protocols, a critical capability when deploying in a real-world clinical setting. Conclusions: This is the first report of a calibration phantom-based method's ability to predict both small lung nodule volume measurement bias and precision. Volume measurement bias and precision for small lung nodules can be predicted using simple low-cost reference objects to estimate fundamental CT image characteristics and modeling and simulation techniques. The approach demonstrates an improved method for predicting task specific, clinically relevant measurement performance using advanced and fully automated image analysis techniques and low-cost reference objects.

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.

Identification of acute aortic syndromes based on cross-sectional variability of Hounsfield units.

BACKGROUND: A characteristic feature of communicating aortic dissections (CD) is the dissection flap between the true and false lumen. However, in intramural hematomas (IMH) a flap is not visible. We aimed to determine if cross-sectional HU variability allow reliable identification of aortic dissections including IMH. METHODS: We included 362 patients presenting with acute chest pain (CP) or respiratory distress (RD) and underwent contrast-enhanced CTA with or without ECG-gating. In the derivation group we included 72 CP patients with and 74 without AAS. In the validation group we included 108 CP or RD patients with and 108 without AAS. The adventitial border of the aorta was visually identified and measurements were performed at 6 locations along the ascending and descending aorta. At each cross-section 5 circular ROI measurements of HU were made and the maximum HU difference calculated. RESULTS: In the derivation and validation group the maximum difference in HUs at any one location was significantly higher for AAS subjects than controls (validation group: median = 128.5 vs. 34.0, p-value Wilcoxon two-sample test <0.001). In the validation group, the estimated AUC was 0.939 with 95% CIs of [0.906, 0.972], indicating that the maximum difference in HUs is a strong predictor of AAS (p < 0.001). CONCLUSION: Our data provide evidence that cross-sectional variability of Hounsfield Unit reliably identifies aortic dissection including IMH in dedicated ECG-gated aorta scans but also non-gated chest CTs with limited aortic contrast enhancement. These results suggest that this approach could be feasible for an automated algorithm for identification of AAS.

Performance of Wide-Angle Tomosynthesis with Synthetic Mammography in Comparison to Full Field Digital Mammography.

RATIONALE AND OBJECTIVES: The purpose of this study was to test for superiority of wide-angle digital breast tomosynthesis plus synthetic mammography (Insight 2D) in comparison to full-field digital mammography (FFDM). MATERIALS AND METHODS: In this study, twenty readers interpreted 350 screening and diagnostic cases of wide-angle digital breast tomosynthesis (DBT) plus Insight 2D and FFDM in two separate reading sessions separated by at least a 6-week washout period. Breast-level estimates of the area under the curve and sensitivity along with subject-level recall rate were measured and compared between wide-angle DBT plus Insight 2D and FFDM. The same measures were also assessed for dense breasts. A hierarchical analysis plan was used to control the study's type I error rate at 0.05. RESULTS: The mean breast-level area under the curve for distinguishing breasts with cancer from non-cancer breasts was 0.893 with DBT plus Insight 2D versus 0.837 with FFDM, showing superiority of DBT plus Insight 2D (p < 0.001). Breast-level sensitivity was significantly superior for DBT plus Insight 2D in comparison to FFDM (0.852 vs. 0.805, p = 0.043). Subject-level recall rate for DBT plus Insight 2D was significantly lower in comparison to FFDM (0.344 vs. 0.473, p < 0.001). For dense breasts, the readers' accuracy with DBT plus Insight 2D was superior to their accuracy with FFDM (0.875 vs. 0.830, p = 0.026), and their recall rate was significantly lower for DBT plus Insight 2D in comparison to FFDM (0.338 vs. 0.441, p = 0.003). CONCLUSION: Reader performance with wide-angle DBT plus Insight 2D is superior to that with FFDM, showing significantly higher breast-level accuracy and sensitivity and significantly lower recall rates.

Multiparametric Quantitative Imaging Biomarker as a Multivariate Descriptor of Health: A Roadmap.

Multiparametric quantitative imaging biomarkers (QIBs) offer distinct advantages over single, univariate descriptors because they provide a more complete measure of complex, multidimensional biological systems. In disease, where structural and functional disturbances occur across a multitude of subsystems, multivariate QIBs are needed to measure the extent of system malfunction. This paper, the first Use Case in a series of articles on multiparameter imaging biomarkers, considers multiple QIBs as a multidimensional vector to represent all relevant disease constructs more completely. The approach proposed offers several advantages over QIBs as multiple endpoints and avoids combining them into a single composite that obscures the medical meaning of the individual measurements. We focus on establishing statistically rigorous methods to create a single, simultaneous measure from multiple QIBs that preserves the sensitivity of each univariate QIB while incorporating the correlation among QIBs. Details are provided for metrological methods to quantify the technical performance. Methods to reduce the set of QIBs, test the superiority of the mp-QIB model to any univariate QIB model, and design study strategies for generating precision and validity claims are also provided. QIBs of Alzheimer's Disease from the ADNI merge data set are used as a case study to illustrate the methods described.

Imaging Service Navigators: An Approach Toward More Efficient and Effective Communications.

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.

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.

Novel Multi-Parametric Mitral Annular Calcification Score Predicts Outcomes in Mitral Valve Dysfunction.

The objective of the study was to construct a multi-parametric mitral annular calcification (MAC) score using computed tomography (CT) features for prediction of outcomes in patients undergoing mitral valve surgery. We constructed a multi-parametric MAC score, which ranges between 2 and 12, and consists of Agatston calcium score (1 point: <1000 Agatston units (AU); 2 points: 1000-<3000 AU; 3 points: 3000-5000 AU; 4 points: >5000 AU), quantitative MAC circumferential angle (1 point: <90°; 2 points: 90-<180°; 3 points: 180-<270°; 4 points: 270-360°), involvement of trigones (1 point: 1 trigone; 2 points: both trigones), and 1 point each for myocardial infiltration and left ventricular outflow tract extension/involvement of aorto-mitral curtain. The association between MAC score and clinical outcomes was evaluated. The study cohort consisted of 334 patients undergoing mitral valve surgery (128 mitral valve repairs, 206 mitral valve replacements) between January 2011 and September 2019, who had both non-contrast gated CT scan and evidence of MAC. The mean age was 72 ± 11 years, with 58% of subjects being female. MAC score was a statistically significant predictor of total operation time (P<0.001), cross-clamp time (P = 0.001) and in-hospital complications (P = 0.003). Additionally, MAC score was a significant predictor of time to all-cause death (P = 0.046). A novel multi-parametric score based on CT features allowed systematic assessment of MAC, and predicted clinical outcomes in patients with mitral valve dysfunction undergoing mitral valve surgery.