Suspicion Score Research Articles

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

Background and objectiveBiparametric 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. MethodsAn 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 limitationsSixty-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 implicationsIt 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.

Diagnostic performance of radiologists in distinguishing post-COVID-19 residual abnormalities from interstitial lung abnormalities

Abstract Objective Distinguishing post-COVID-19 residual abnormalities from interstitial lung abnormalities (ILA) on CT can be challenging if clinical information is limited. This study aimed to evaluate the diagnostic performance of radiologists in distinguishing post-COVID-19 residual abnormalities from ILA. Methods This multi-reader, multi-case study included 60 age- and sex-matched subjects with chest CT scans. There were 40 cases of ILA (20 fibrotic and 20 non-fibrotic) and 20 cases of post-COVID-19 residual abnormalities. Fifteen radiologists from multiple nations with varying levels of experience independently rated suspicion scores on a 5-point scale to distinguish post-COVID-19 residual abnormalities from fibrotic ILA or non-fibrotic ILA. Interobserver agreement was assessed using the weighted κ value, and the scores of individual readers were compared with the consensus of all readers. Receiver operating characteristic curve analysis was conducted to evaluate the diagnostic performance of suspicion scores for distinguishing post-COVID-19 residual abnormalities from ILA and for differentiating post-COVID-19 residual abnormalities from both fibrotic and non-fibrotic ILA. Results Radiologists’ diagnostic performance for distinguishing post-COVID-19 residual abnormalities from ILA was good (area under the receiver operating characteristic curve (AUC) range, 0.67–0.92; median AUC, 0.85) with moderate agreement (κ = 0.56). The diagnostic performance for distinguishing post-COVID-19 residual abnormalities from non-fibrotic ILA was lower than that from fibrotic ILA (median AUC = 0.89 vs. AUC = 0.80, p = 0.003). Conclusion Radiologists demonstrated good diagnostic performance and moderate agreement in distinguishing post-COVID-19 residual abnormalities from ILA, but careful attention is needed to avoid misdiagnosing them as non-fibrotic ILA. Key Points QuestionHow good are radiologists at differentiating interstitial lung abnormalities (ILA) from changes related to COVID-19 infection? FindingsRadiologists had a median AUC of 0.85 in distinguishing post-COVID-19 abnormalities from ILA with moderate agreement (κ = 0.56). Clinical relevanceRadiologists showed good diagnostic performance and moderate agreement in distinguishing post-COVID-19 residual abnormalities from ILA; nonetheless, caution is needed in distinguishing residual abnormalities from non-fibrotic ILA.

Outlier Detection in Auditing: Integrating Unsupervised Learning within a Multilevel Framework for General Ledger Analysis

ABSTRACT Auditors traditionally use sampling techniques to examine general ledger (GL) data, which suffer from sampling risks. Hence, recent research proposes full-population testing techniques, such as suspicion scoring, which rely on auditors’ judgment to recognize possible risk factors and develop corresponding risk filters to identify abnormal transactions. Thus, when auditors miss potential problems, the related transactions are not likely to be identified. This paper uses unsupervised outlier detection methods, which require no prior knowledge about outliers in a dataset, to identify outliers in GL data and tests whether auditors can gain new insights from those identified outliers. A framework called the Multilevel Outlier Detection Framework (MODF) is proposed to identify outliers at the transaction level, account level, and combination-by-variable level. Experiments with one real and one synthetic GL dataset demonstrate that the MODF can help auditors to gain new insights about GL data. Data Availability: The real dataset used in the experiment is not publicly available due to privacy policies. JEL Classifications: M410, M42.

Clinical Characterization of Patients with Venous Thromboembolic Disease in 2 Reference Centers in El Salvador

Introduction. During the last decade, venous thromboembolic disease (VTE) has become increasingly important due to its high impact on morbidity and mortality in patients. Suspicion scores together with the diversification of diagnostic methods have allowed rapid and timely diagnosis. Although the annual incidence of VTE is estimated to be approximately 1 in 1,000 people, the risk in certain groups is significantly higher. For the United States, the statistics do not vary significantly. It is estimated that DVT and PE affect between 350,000 and 600,000 people a year, causing between 100,000 and 300,000 deaths. For El Salvador, there are no clinical data related to risk and development factors. Aim. To describe the clinical characteristics of patients with venous thromboembolic disease in 2 reference centers of the Salvadoran Social Security Institute in 2020. Methods. This study is retrospective, observational, cross-sectional and descriptive. All patients with a first-time or recurrent diagnosis that were documented by venous Doppler of the lower and/or upper limbs, computed tomography angiography, magnetic resonance imaging and/or venography at the General Hospital, Medical Surgical and Oncological Hospital of Social Security were included. diagnosed in the internal medicine hospitalization area and in outpatient consultation for the first time in the Hematology Service during the period established between January and December 2020. Results. Of the total of 138 registered patients, the final study population that met all the inclusion criteria was 88, of which the majority corresponded to the female gender in 56.8% (n=50), with a mean age for all of them. the cases of 55 years (range 23 to 88 years). Regarding the associated comorbidities, the 2 main ones were arterial hypertension 52.3% (n=46) and diabetes mellitus 25% (n=22), the most prevalent risk factor being the use of OACs with 9.1% (n=8 ). The most frequent anatomical location was the left lower limb with 45.2% (n=46) of the cases and at the time of diagnosis the initial treatment of all patients with thrombosis was with low molecular weight heparins in 51.1% (n=45). ). VTE in any of its entities continues to be a disease predominantly in adults and women, which is favored by various modifiable and sometimes preventable risk factors. Conclusions. VTE in any of its entities continues to be a disease predominantly in adults and women, which is favored by various modifiable and sometimes preventable risk factors. The main anatomical location of thrombosis is in the lower limbs, mainly on the left side, and in the lungs in main branches. Regardless of the location, mortality is similar in deep vein thrombosis and pulmonary embolism.

PI-RR: The Prostate Imaging for Recurrence Reporting System for MRI Assessment of Local Prostate Cancer Recurrence After Radiation Therapy or Radical Prostatectomy-A Review.

The purpose of this article is to review clinical application of the Prostate Imaging for Recurrence Reporting (PI-RR) system. This system, released in 2021, represents international consensus-based guidelines for the acquisition, interpretation, and reporting of multiparametric MRI performed to detect locally recurrent prostate cancer after radiation therapy or radical prostatectomy. The system reduces variability through use of a standardized and structured reporting approach whereby the overall level of suspicion of recurrence is classified on a 5-point scale. The overall suspicion score is derived from 5-point scales for assessing DWI and dynamic contrast-enhanced (DCE) imaging. Separate scales for both DWI and DCE imaging are provided for evaluation after radiation therapy and after radical prostatectomy. These scales account for the relation between detected abnormalities and the location of the primary tumor on pretreatment imaging. T2-weighted imaging is also assessed on a 5-point scale and is useful for anatomic imaging but does not influence the overall score. Initial retrospective studies have shown promising results with respect to the reproducibility and accuracy of PI-RR in detecting locally recurrent tumor.

PD38-12 NATURAL HISTORY OF SMALL INDEX LESIONS ON MULTIPARAMETRIC MRI: IMPLICATIONS FOR ACTIVE SURVEILLANCE

You have accessJournal of UrologyCME1 Apr 2023PD38-12 NATURAL HISTORY OF SMALL INDEX LESIONS ON MULTIPARAMETRIC MRI: IMPLICATIONS FOR ACTIVE SURVEILLANCE Anjali Pillai, Zoe Blake, Daniel R. Nemirovsky, Jacob J. Enders, Neil Mendhiratta, Alexander P. Kenigsberg, Michael B. Rothberg, Jibriel Noun, Daniel Nethala, Sandeep Gurram, Bradford J. Wood, Baris Turkbey, and Peter A. Pinto Anjali PillaiAnjali Pillai More articles by this author , Zoe BlakeZoe Blake More articles by this author , Daniel R. NemirovskyDaniel R. Nemirovsky More articles by this author , Jacob J. EndersJacob J. Enders More articles by this author , Neil MendhirattaNeil Mendhiratta More articles by this author , Alexander P. KenigsbergAlexander P. Kenigsberg More articles by this author , Michael B. RothbergMichael B. Rothberg More articles by this author , Jibriel NounJibriel Noun More articles by this author , Daniel NethalaDaniel Nethala More articles by this author , Sandeep GurramSandeep Gurram More articles by this author , Bradford J. WoodBradford J. Wood More articles by this author , Baris TurkbeyBaris Turkbey More articles by this author , and Peter A. PintoPeter A. Pinto More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003336.12AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Multiparametric magnetic resonance imaging (mpMRI) has demonstrated utility as an adjunct to active surveillance (AS). However, the optimal time interval between sequential MRIs is not standardized. This aims to analyze index lesion growth rates to potentially inform modern AS imaging practice. METHODS: A prospectively maintained database of patients on AS at our institution was queried for patients with ≥2 mpMRIs with no index lesion or index lesions ≤7 mm, as well as a subset of patients with no index lesion or index lesions ≤5 mm. Both cohorts were limited to patients with paired MRI-targeted biopsy showing benign or Gleason grade group (GG) 1 disease. For MRIs preceding PI-RADS scoring, an internal Likert suspicion scoring system was converted into PI-RADS scores. Imaging intervals were calculated between serial mpMRIs and growth rate was calculated with two-tailed t-tests to assess differences between initial and most recent lesion sizes measured on mpMRI. RESULTS: Between 2003-2021, patients on AS with ≥2 mpMRIs with small index lesions measuring ≤7 mm (n=123) or <5 mm (n=65) were identified (Table 1). Lesions in both the ≤7 mm (p=.015) and ≤5 mm (p=.003) cohorts demonstrated a significant change between first and last mpMRI. For the ≤7 mm cohort, average overall growth rate was 0.44±2.4 mm/year over a mean imaging interval of 1.27±0.09 years. For the ≤5 mm cohort, the average overall growth rate was 0.70±3.05 mm/year over a mean imaging interval of 1.31±0.13 years. There were 11 lesions that grew over 5 mm in the first year after initial mpMRI, however, univariate analysis of age, PSA, PI-RADS, and GG did not identify these variables as significant predictors of growth. The majority of lesions (84.6%) grew <1 mm/year (Figure 1). CONCLUSIONS: Small MRI lesions in patients on AS demonstrate minimal growth between follow up mpMRIs. This may indicate that these lesions can be followed longer surveillance intervals. Further studies are needed to prospectively identify the small subset of lesions that demonstrate more rapid growth. Source of Funding: N/A © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e998 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Anjali Pillai More articles by this author Zoe Blake More articles by this author Daniel R. Nemirovsky More articles by this author Jacob J. Enders More articles by this author Neil Mendhiratta More articles by this author Alexander P. Kenigsberg More articles by this author Michael B. Rothberg More articles by this author Jibriel Noun More articles by this author Daniel Nethala More articles by this author Sandeep Gurram More articles by this author Bradford J. Wood More articles by this author Baris Turkbey More articles by this author Peter A. Pinto More articles by this author Expand All Advertisement PDF downloadLoading ...

Validation of a suspicion index to identify patients at risk for hereditary angioedema.

Hereditary angioedema (HAE) is a genetic condition characterized by dysregulation of the contact (kallikrein-bradykinin) pathway, leading to recurrent episodes of angioedema. This project sought to determine whether a suspicion index screening tool using electronic health record (EHR) data can identify patients with an increased likelihood of a diagnosis of HAE. A suspicion index screening tool for HAE was created and validated by using known patients with HAE from the medical literature as well as positive and negative controls from HAE-focused centers. Through the use of key features of medical and family history, a series of logistic regression models for 5 known genetic causes of HAE were created. Top variables populated the digital suspicion scoring system and were run against deidentified EHR data. Patients at 2 diverse sites were categorized as being at increased, possible, or no increased risk of HAE. Prediction scoring using the strongest 13 variables on the "real-world" EHR-positive control data identified all but 1 patient with C1 inhibitor deficiency and patient with non-C1 inhibitor deficiency without false-positive results. The 2 missed patients had no documented family history of HAE in their EHR. When the prediction scoring variables were expanded to 25, the screening algorithm approached 100% sensitivity and specificity. The 25-variable algorithm run on general population EHR data identified 26 patients at the medical centers as being at increased risk for HAE. These results suggest that development, validation, and implementation of suspicion index screening tools can be useful to aid providers in identifying patients with rare genetic conditions.

Axumin (18F-Fluciclovine) PET imaging in men exhibiting no clinically significant cancer on initial negative biopsy of PI-RADS 4 and 5 regions of interest.

PurposeThe objective of the study was to determine whether Axumin (18F-Fluciclovine) PET/MRI informs the decision to perform an early repeat biopsy of PI-RADS 4/5 region of interest (ROI) exhibiting no clinically significant prostate cancer (csPCa) on initial biopsy.MethodsThis prospective study enrolled men with at least one PI-RADS 4/5 ROI on multi-parametric MRI and no csPCa on prior biopsy defined as Gleason grade group (GGG) > 1. All men underwent an Axumin PET/MRI and only-persistent PI-RADS > 2 ROI were advised to undergo a repeat biopsy. A PET cancer suspicion score (PETCSS) was internally developed to stratify PET avid lesions according to their suspicion of harboring csPCa.The sensitivity, specificity, positive (PPV) and negative predictive value (NPV) of the PETCSS for predicting csPCa were assessed. Relative risk was calculated to analyze the association of baseline variables with csPCa on repeat biopsy.ResultsThirty-eight ROI on 36 enrolled men were analyzed. Fourteen (36.8%) were downgraded to PI-RADS 1/2 and were not subjected to repeat biopsy. Thirteen (92.9%) of these downgraded scans also exhibited low-risk PETCSS. Overall, 18/22 (81.2%) subjects underwent a repeat per protocol biopsy. Of the 20 ROI subjected to repeat biopsy, eight (40%) were found to harbour csPCa.The sensitivity, specificity, PPV and NPV of the PETCSS were 50, 50, 40, and 60%, respectively. No predictor of csPCa was found in the risk analysis.ConclusionOur pilot study showed that both MRI and PET sequences have limited performance for identifying those persistently suspicious PI-RADS 4/5 ROI that are found to harbor csPCa on repeat biopsy.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00345-022-04172-3.

Detraque: Dynamic execution tracing techniques for automatic fault localization of hardware design code.

In an error-prone development process, the ability to localize faults is a crucial one. Generally speaking, detecting and repairing errant behavior at an early stage of the development cycle considerably reduces costs and development time. The debugging of the Verilog program takes much time to read the waveform and capture the signal, and in many cases, problem-solving relies heavily on experienced developers. Most existing Verilog fault localization methods utilize the static analysis method to find faults. However, using static analysis methods exclusively may result in some types of faults being inevitably ignored. The use of dynamic analysis could help resolve this issue. Accordingly, in this work, we propose a new fault localization approach for Verilog, named Detraque. After obtaining dynamic execution through test cases, Detraque traces these executions to localize faults; subsequently, it can determine the likelihood of any Verilog statement being faulty and sort the statements in descending order by suspicion score. Through conducting empirical research on real Verilog programs with 61 faulty versions, Detraque can achieve an EXAM score of 18.3%. Thus, Detraque is verified as able to improve Verilog fault localization effectiveness when used as a supplement to static analysis methods.

Augmenting prostate magnetic resonance imaging reporting to incorporate diagnostic recommendations based upon clinical risk calculators.

Risk calculators have offered a viable tool for clinicians to stratify patients at risk of prostate cancer (PCa) and to mitigate the low sensitivity and specificity of screening prostate specific antigen (PSA). While initially based on clinical and demographic data, incorporation of multiparametric magnetic resonance imaging (MRI) and the validated prostate imaging reporting and data system suspicion scoring system has standardized and improved risk stratification beyond the use of PSA and patient parameters alone. Biopsy-naïve patients with lower risk profiles for harboring clinically significant PCa are often subjected to uncomfortable, invasive, and potentially unnecessary prostate biopsy procedures. Incorporating risk calculator data into prostate MRI reports can broaden the role of radiologists, improve communication with clinicians primarily managing these patients, and help guide clinical care in directing the screening, detection, and risk stratification of PCa.

Audit of cancer yields after prostate MRI using both the PI-RADS version 2 and Likert scoring systems

To audit diagnostic yields of the updated magnetic resonance imaging (MRI)-directed prostate cancer diagnostic service according to Prostate Imaging - Reporting and Data System (PI-RADS) version 2 and Likert assessments, comparing outcomes of the two scoring systems. Consecutive men with suspected cancer undergoing prostate MRI were included. Biopsy rates and histological diagnostic yields of all and International Society of Urological Pathology Grade Group (ISUP GG) ≥2 cancers according to PI-RADS and Likert assessment categories were documented and outcomes compared. Of 326 men (91% biopsy naive), 177 (54%) underwent transrectal (n=119) or transperineal (n=58) ultrasound-guided biopsies; 92% with negative MRI avoided immediate biopsies following multidisciplinary team (MDT) review. All cancer and ISUP GG≥2 cancer-detection rates increased with increasing suspicion scores. Prospective paired PI-RADS and Likert scoring was undertaken in 323/326 studies, with 87% concordance rate. High concordance between PI-RADS and Likert scores was observed in negative MRI (99%) and score 5 (96%). High discordance was demonstrated in the PI-RADS 4 group (32% with PI-RADS 4 categories up-risked to Likert 5). All cancer and ISUP GG≥2 cancer detection rates for MRI score ≥3 were 78% and 63%, and for MRI score ≥4 were 75% and 61%, respectively for both PI-RADS and Likert scoring systems. Most men with negative prostate MRI can avoid immediate biopsies following MDT review. Performance of PI-RADS and Likert scoring systems in clinically significant cancer detection after positive MRI is equivalent. Discordance between PI-RADS and Likert systems seems mostly confined to PI-RADS 4 categories.

The Prevalence and Impact of Fake News on COVID-19 Vaccination in Taiwan: Retrospective Study of Digital Media.

BackgroundVaccination is an important intervention to prevent the incidence and spread of serious diseases. Many factors including information obtained from the internet influence individuals’ decisions to vaccinate. Misinformation is a critical issue and can be hard to detect, although it can change people's minds, opinions, and decisions. The impact of misinformation on public health and vaccination hesitancy is well documented, but little research has been conducted on the relationship between the size of the population reached by misinformation and the vaccination decisions made by that population. A number of fact-checking services are available on the web, including the Islander news analysis system, a free web service that provides individuals with real-time judgment on web news. In this study, we used such services to estimate the amount of fake news available and used Google Trends levels to model the spread of fake news. We quantified this relationship using official public data on COVID-19 vaccination in Taiwan.ObjectiveIn this study, we aimed to quantify the impact of the magnitude of the propagation of fake news on vaccination decisions.MethodsWe collected public data about COVID-19 infections and vaccination from Taiwan's official website and estimated the popularity of searches using Google Trends. We indirectly collected news from 26 digital media sources, using the news database of the Islander system. This system crawls the internet in real time, analyzes the news, and stores it. The incitement and suspicion scores of the Islander system were used to objectively judge news, and a fake news percentage variable was produced. We used multivariable linear regression, chi-square tests, and the Johnson-Neyman procedure to analyze this relationship, using weekly data.ResultsA total of 791,183 news items were obtained over 43 weeks in 2021. There was a significant increase in the proportion of fake news in 11 of the 26 media sources during the public vaccination stage. The regression model revealed a positive adjusted coefficient (β=0.98, P=.002) of vaccine availability on the following week's vaccination doses, and a negative adjusted coefficient (β=–3.21, P=.04) of the interaction term on the fake news percentage with the Google Trends level. The Johnson-Neiman plot of the adjusted effect for the interaction term showed that the Google Trends level had a significant negative adjustment effect on vaccination doses for the following week when the proportion of fake news exceeded 39.3%.ConclusionsThere was a significant relationship between the amount of fake news to which the population was exposed and the number of vaccination doses administered. Reducing the amount of fake news and increasing public immunity to misinformation will be critical to maintain public health in the internet age.

Analysis of Diagnostic Modalities in Hospital-admitted Patients Evaluated for COVID-19.

To assess the diagnostic performance of reverse transcriptase polymerase chain reaction (RT-PCR), low-dose chest computed tomography (CT), and serological testing, alone and in combinations, as well as routine inflammatory markers in patients evaluated for COVID-19 during the first wave in early 2020. We retrospectively analyzed data of all patients who were admitted to the emergency department due to fever and/or respiratory symptoms. CT scans were rated using the COVID-19 Reporting and Data System (CO-RADS) suspicion score. True disease status (COVID-19 - positive vs. negative) was adjudicated by two independent clinicians. Receiver-operating characteristic curves and areas under the curves were calculated for inflammatory markers. Sensitivities and specificities were calculated for RT-PCR, CT, and serology alone, as well as the combinations of RT-PCR+CT, RT-PCR+serology, CT+serology, and all three modalities. Of 221 patients with a median age of 72 years, 113 were classified as COVID-19 positive. Among 180 patients from which data on CT and RT-PCR were available, RT-PCR had the highest sensitivity to detect COVID-19 (0.87; 95%CI=0.78-0.93). Notably, the addition of CT in the analysis increased sensitivity to 0.89 (95%CI=0.8-0.94), but lowered specificity from 1 (95%CI=0.96-1) to 0.9 (95%CI=0.83-0.95). The combination of RT-PCR, CT and serology (n=60 patients with complete dataset) yielded a sensitivity of 0.83 (95%CI=0.61-0.94) and specificity of 0.86 (95%CI=0.72-0.93). RT-PCR was the best single test in patients evaluated for COVID-19. Conversely, the routine performance of chest CT adds little sensitivity and decreases specificity.

Evaluation of COVID-19-related psychological distress in parents of children referring to a child and adolescent psychiatry outpatient clinic: a university hospital sample

Aims We aimed to determine parents’ levels of anxiety and fear and suspicion in relation to the COVID-19 period and the possible predictive factors for these variables in families admitted to a child and adolescent psychiatry outpatient clinic of a university hospital in Turkey. In this way, it is hoped to contribute to the identification of priority target groups in psychosocial support services. Method The research study group consisted of 600 patients aged 0–18 years and their parents who were referred to a child and adolescent psychiatry outpatient clinic of a university hospital. Parents completed the COVID-19-related psychological distress (CORPD) scale and the data recording form developed specifically for this study. All cases included in the study were examined by a child and adolescent psychiatrist for diagnostic evaluation and their current diagnoses were recorded. Results Parents of children with any psychiatric diagnosis had higher suspicion scores on the CORPD scale and parents of children with high COVID-19-related anxiety also had higher anxiety and fear scores on the same scale. Logistic regression analysis revealed that negative changes in family relationships increased the risk of higher maternal COVID-19-related suspicion. Conclusions The most important factors associated with parental high CORPD levels were negative changes in family relationships and children with high COVID-19-related anxiety. Children’s high COVID-19-related anxiety levels were associated with living with a single parent or separation from parents, negative changes in family relationships, previous COVID-19 infection in the family, and changes in daily routines.

MP26-14 RISK STRATIFICATION BY PROSTATE HEALTH INDEX PRIOR TO MRI-ULTRASOUND FUSION TARGETED BIOPSY

You have accessJournal of UrologyProstate Cancer: Detection & Screening II (MP26)1 Sep 2021MP26-14 RISK STRATIFICATION BY PROSTATE HEALTH INDEX PRIOR TO MRI-ULTRASOUND FUSION TARGETED BIOPSY Benjamin Press, Ghazal Khajir, Olamide Olawoyin, Michael Leapman, and Preston Sprenkle Benjamin Press More articles by this author , Ghazal KhajirGhazal Khajir More articles by this author , Olamide OlawoyinOlamide Olawoyin More articles by this author , Michael LeapmanMichael Leapman More articles by this author , and Preston SprenklePreston Sprenkle More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002023.14AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Prostate Health Index (phi) was FDA approved in 2012 for men with PSA between 4 - 10 ng/mL to predict the likelihood of prostate cancer (PCa). We sought to determine whether a combination of index phi and magnetic resonance imaging (MRI) suspicion score (PIRADS) could further optimize detection of PCa on MRI ultrasound fusion-targeted biopsy (MRF-TB). We also sought to evaluate the predictive ability of phi density (phiD) to detect PCa. METHODS: Between June 2015 and December 2020, 303 men obtained phi testing prior to MRF-TB at our institution. Findings on prostate biopsy, including Grade Group (GG), PIRADS, and phi were prospectively recorded. Cancer detection rates were evaluated at 2 different cutoffs for phi proposed in the literature. Interaction of PIRADS and phi were also evaluated at those cutoffs. Multivariate analysis and ROC curves were used to determine the predictive ability of phi and phiD to detect ≥GG2 PCa. RESULTS: phi levels above 27 (47.8% vs 16.0%, p=0.002) and 36 (52.8% vs 19.1%, p<0.001) were associated with increased detection of ≥GG2 PCa. Among men with PIRADS ≥ 3, a higher phi remained predictive of ≥GG2 PCa using a cutoff of 27 (60.29 vs 25.00%, p=0.006), and 36 (64.61 vs. 28.57%, p<0.001) (Table 1). On multivariate analysis, only phi cutoff of 36 was associated with increased detection of ≥GG2 PCa for all PSA levels (OR 4.02, p<0.001) and PSA 4 - 10 ng/mL (OR 4.22, p<0.001). phiD was associated with increased detection of ≥GG2 PCa on multivariate analysis (OR 2.026, p<0.001). phiD also had a higher discriminatory ability to predict ≥GG2 PCa (AUC=0.725) compared to phi cutoffs of 36 (AUC=0.62) and 27 (AUC=0.549) (Figure 1). CONCLUSIONS: Our institutional data supports the use of a phi cutoff of > 36. Given the high NPV for ≥GG2 PCa of phi with negative MRI, its use in conjunction with MRI appears to appropriately risk stratify men and may help avoid unnecessary biopsy. We also advocate for the increased use of phiD when evaluating the necessity of prostate biopsy given its increased ability to predict ≥GG2 PCa when compared to widely accepted cutoffs for phi. Source of Funding: None © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e468-e468 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Benjamin Press More articles by this author Ghazal Khajir More articles by this author Olamide Olawoyin More articles by this author Michael Leapman More articles by this author Preston Sprenkle More articles by this author Expand All Advertisement Loading ...

Assessment of Axillary Lymph Nodes for Metastasis on Ultrasound Using Artificial Intelligence.

The purpose of this study was to evaluate an artificial intelligence (AI) system for the classification of axillary lymph nodes on ultrasound compared to radiologists. Ultrasound images of 317 axillary lymph nodes from patients referred for ultrasound guided fine needle aspiration or core needle biopsy and corresponding pathology findings were collected. Lymph nodes were classified into benign and malignant groups with histopathological result serving as the reference. Google Cloud AutoML Vision (Mountain View, CA) was used for AI image classification. Three experienced radiologists also classified the images and gave a level of suspicion score (1-5). To test the accuracy of AI, an external testing dataset of 64 images from 64 independent patients was evaluated by three AI models and the three readers. The diagnostic performance of AI and the humans were then quantified using receiver operating characteristics curves. In the complete set of 317 images, AutoML achieved a sensitivity of 77.1%, positive predictive value (PPV) of 77.1%, and an area under the precision recall curve of 0.78, while the three radiologists showed a sensitivity of 87.8% ± 8.5%, specificity of 50.3% ± 16.4%, PPV of 61.1% ± 5.4%, negative predictive value (NPV) of 84.1% ± 6.6%, and accuracy of 67.7% ± 5.7%. In the three external independent test sets, AI and human readers achieved sensitivity of 74.0% ± 0.14% versus 89.9% ± 0.06% (p = .25), specificity of 64.4% ± 0.11% versus 50.1 ± 0.20% (p = .22), PPV of 68.3% ± 0.04% versus 65.4 ± 0.07% (p = .50), NPV of 72.6% ± 0.11% versus 82.1% ± 0.08% (p = .33), and accuracy of 69.5% ± 0.06% versus 70.1% ± 0.07% (p = .90), respectively. These preliminary results indicate AI has comparable performance to trained radiologists and could be used to predict the presence of metastasis in ultrasound images of axillary lymph nodes.

Impact of Artificial Intelligence Decision Support Using Deep Learning on Breast Cancer Screening Interpretation with Single-View Wide-Angle Digital Breast Tomosynthesis.

Background The high volume of data in digital breast tomosynthesis (DBT) and the lack of agreement on how to best implement it in screening programs makes its use challenging. Purpose To compare radiologist performance when reading single-view wide-angle DBT images with and without an artificial intelligence (AI) system for decision and navigation support. Materials and Methods A retrospective observer study was performed with bilateral mediolateral oblique examinations and corresponding synthetic two-dimensional images acquired between June 2016 and February 2018 with a wide-angle DBT system. Fourteen breast screening radiologists interpreted 190 DBT examinations (90 normal, 26 with benign findings, and 74 with malignant findings), with the reference standard being verified by using histopathologic analysis or at least 1 year of follow-up. Reading was performed in two sessions, separated by at least 4 weeks, with a random mix of examinations being read with and without AI decision and navigation support. Forced Breast Imaging Reporting and Data System (categories 1-5) and level of suspicion (1-100) scores were given per breast by each reader. The area under the receiver operating characteristic curve (AUC) and the sensitivity and specificity were compared between conditions by using the public-domain iMRMC software. The average reading times were compared by using the Wilcoxon signed rank test. Results The 190 women had a median age of 54 years (range, 48-63 years). The examination-based reader-averaged AUC was higher when interpreting results with AI support than when reading unaided (0.88 [95% CI: 0.84, 0.92] vs 0.85 [95% CI: 0.80, 0.89], respectively; P = .01). The average sensitivity increased with AI support (64 of 74, 86% [95% CI: 80%, 92%] vs 60 of 74, 81% [95% CI: 74%, 88%]; P = .006), whereas no differences in the specificity (85 of 116, 73.3% [95% CI: 65%, 81%] vs 83 of 116, 71.6% [95% CI: 65%, 78%]; P = .48) or reading time (48 seconds vs 45 seconds; P = .35) were detected. Conclusion Using a single-view digital breast tomosynthesis (DBT) and artificial intelligence setup could allow for a more effective screening program with higher performance, especially in terms of an increase in cancers detected, than using single-view DBT alone. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Chan and Helvie in this issue.

The Role of Computed Tomography in the Management of Patients with Suspected COVID-19 – A Nigerian Study

Background: Computed tomography (CT) of the chest is a useful diagnostic adjunct in the management of COVID-19. It has proven useful in areas where ready access to real-time reverse transcriptase polymerase chain reaction (PCR) is not readily available. Objectives: This study sought to evaluate the chest CT findings of patients suspected with having COVID-19 on presenting at the emergency room. Materials and Methods: This is a retrospective study done in Cedarcrest Hospitals, Abuja, with patient information pooled from patients' electronic medical records from April to December 2020. Patients were selected based on suspicion of COVID-19 infection. Suspicion was stratified into high, moderate, and low using an in-house clinical suspicion score called the Cedarcrest Emergency COVID-19 Risk Assessment Tool. Patients with background pulmonary pathology or chest trauma were excluded. Plain chest CT scans were performed to evaluate for COVID-19 pneumonia. Results: CT imaging increased the clinical suspicion of COVID-19. However, no statistically significant relationship was found between the positive CT findings and the PCR results. Conclusion: Chest CT should be correlated with clinical findings and laboratory results for patient evaluation.

Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study

ObjectivesDigital breast tomosynthesis (DBT) increases sensitivity of mammography and is increasingly implemented in breast cancer screening. However, the large volume of images increases the risk of reading errors and reading time. This study aims to investigate whether the accuracy of breast radiologists reading wide-angle DBT increases with the aid of an artificial intelligence (AI) support system. Also, the impact on reading time was assessed and the stand-alone performance of the AI system in the detection of malignancies was compared to the average radiologist.MethodsA multi-reader multi-case study was performed with 240 bilateral DBT exams (71 breasts with cancer lesions, 70 breasts with benign findings, 339 normal breasts). Exams were interpreted by 18 radiologists, with and without AI support, providing cancer suspicion scores per breast. Using AI support, radiologists were shown examination-based and region-based cancer likelihood scores. Area under the receiver operating characteristic curve (AUC) and reading time per exam were compared between reading conditions using mixed-models analysis of variance.ResultsOn average, the AUC was higher using AI support (0.863 vs 0.833; p = 0.0025). Using AI support, reading time per DBT exam was reduced (p < 0.001) from 41 (95% CI = 39–42 s) to 36 s (95% CI = 35– 37 s). The AUC of the stand-alone AI system was non-inferior to the AUC of the average radiologist (+0.007, p = 0.8115).ConclusionsRadiologists improved their cancer detection and reduced reading time when evaluating DBT examinations using an AI reading support system.Key Points• Radiologists improved their cancer detection accuracy in digital breast tomosynthesis (DBT) when using an AI system for support, while simultaneously reducing reading time.• The stand-alone breast cancer detection performance of an AI system is non-inferior to the average performance of radiologists for reading digital breast tomosynthesis exams.• The use of an AI support system could make advanced and more reliable imaging techniques more accessible and could allow for more cost-effective breast screening programs with DBT.

A Novel Deep Learning Based Computer-Aided Diagnosis System Improves the Accuracy and Efficiency of Radiologists in Reading Biparametric Magnetic Resonance Images of the Prostate: Results of a Multireader, Multicase Study.

The aim of this study was to evaluate the effect of a deep learning based computer-aided diagnosis (DL-CAD) system on radiologists' interpretation accuracy and efficiency in reading biparametric prostate magnetic resonance imaging scans. We selected 100 consecutive prostate magnetic resonance imaging cases from a publicly available data set (PROSTATEx Challenge) with and without histopathologically confirmed prostate cancer. Seven board-certified radiologists were tasked to read each case twice in 2 reading blocks (with and without the assistance of a DL-CAD), with a separation between the 2 reading sessions of at least 2 weeks. Reading tasks were to localize and classify lesions according to Prostate Imaging Reporting and Data System (PI-RADS) v2.0 and to assign a radiologist's level of suspicion score (scale from 1-5 in 0.5 increments; 1, benign; 5, malignant). Ground truth was established by consensus readings of 3 experienced radiologists. The detection performance (receiver operating characteristic curves), variability (Fleiss κ), and average reading time without DL-CAD assistance were evaluated. The average accuracy of radiologists in terms of area under the curve in detecting clinically significant cases (PI-RADS ≥4) was 0.84 (95% confidence interval [CI], 0.79-0.89), whereas the same using DL-CAD was 0.88 (95% CI, 0.83-0.94) with an improvement of 4.4% (95% CI, 1.1%-7.7%; P = 0.010). Interreader concordance (in terms of Fleiss κ) increased from 0.22 to 0.36 (P = 0.003). Accuracy of radiologists in detecting cases with PI-RADS ≥3 was improved by 2.9% (P = 0.10). The median reading time in the unaided/aided scenario was reduced by 21% from 103 to 81 seconds (P < 0.001). Using a DL-CAD system increased the diagnostic accuracy in detecting highly suspicious prostate lesions and reduced both the interreader variability and the reading time.