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Purpose: To develop a model that simulates radiologist assessments and use it to explore whether pairing readers based on their individual performance characteristics could optimize screening performance. Methods: Logistic regression models were designed and used to model individual radiologist assessments. For model evaluation, model-predicted individual performance metrics and paired disagreement rates were compared against the observed data using Pearson correlation coefficients. The logistic regression models were subsequently used to simulate different screening programs with reader pairing based on individual true-positive rates (TPR) and/or false-positive rates (FPR). For this, retrospective results from breast cancer screening programs employing double reading in Sweden, England, and Norway were used. Outcomes of random pairing were compared against those composed of readers with similar and opposite TPRs/FPRs, with positive assessments defined by either reader flagging an examination as abnormal. Results: The analysis data sets consisted of 936,621 (Sweden), 435,281 (England), and 1,820,053 (Norway) examinations. There was good agreement between the model-predicted and observed radiologists' TPR and FPR (r ≥ 0.969). Model-predicted negative-case disagreement rates showed high correlations (r ≥ 0.709), whereas positive-case disagreement rates had lower correlation levels due to sparse data (r ≥ 0.532). Pairing radiologists with similar FPR characteristics (Sweden: 4.50% [95% confidence interval: 4.46%–4.54%], England: 5.51% [5.47%–5.56%], Norway: 8.03% [7.99%–8.07%]) resulted in significantly lower FPR than with random pairing (Sweden: 4.74% [4.70%–4.78%], England: 5.76% [5.71%–5.80%], Norway: 8.30% [8.26%–8.34%]), reducing examinations sent to consensus/arbitration while the TPR did not change significantly. Other pairing strategies resulted in equal or worse performance than random pairing. Conclusions: Logistic regression models accurately predicted screening mammography assessments and helped explore different radiologist pairing strategies. Pairing readers with similar modeled FPR characteristics reduced the number of examinations unnecessarily sent to consensus/arbitration without significantly compromising the TPR. Highlights: A logistic-regression model can be derived that accurately predicts individual and paired reader performance during mammography screening reading. Pairing screening mammography radiologists with similar false-positive characteristics reduced false-positive rates with no significant loss in true positives and may reduce the number of examinations unnecessarily sent to consensus/arbitration. [ABSTRACT FROM AUTHOR]

Background: Although artificial intelligence (AI) has potential in the field of screening of breast cancer, there are still issues. It is vital to make sure AI does not overlook cancer or cause needless recalls. The aim of this work was to investigate the effectiveness of indulging AI in combination with one radiologist in the routine double reading of mammography for breast cancer screening. The study prospectively analyzed 32,822 screening mammograms. Reading was performed in a blind-paired style by (i) two radiologists and (ii) one radiologist paired with AI. A heatmap and abnormality scoring percentage were provided by AI for abnormalities detected on mammograms. Negative mammograms and benign-looking lesions that were not biopsied were confirmed by a 2-year follow-up. Results: Double reading by the radiologist and AI detected 1324 cancers (6.4%); on the other side, reading by two radiologists revealed 1293 cancers (6.2%) and presented a relative proportion of 1·02 (p < 0·0001). At the recall stage, suspicion and biopsy recommendation were more presented by the AI plus one radiologist combination than by the two radiologists. The interpretation of the mammogram by AI plus only one radiologist showed a sensitivity of 94.03%, a specificity of 99.75%, a positive predictive value of 96.571%, a negative predictive value of 99.567%, and an accuracy of 99.369% (from 99.252 to 99.472%). The positive likelihood ratio was 387.260, negative likelihood ratio was 0.060, and AUC "area under the curve" was 0.969 (0.967–0.971). Conclusions: AI could be used as an initial reader for the evaluation of screening mammography in routine workflow. Implementation of AI enhanced the opportunity to reduce false negative cases and supported the decision to recall or biopsy. [ABSTRACT FROM AUTHOR]

Abstract Background Although artificial intelligence (AI) has potential in the field of screening of breast cancer, there are still issues. It is vital to make sure AI does not overlook cancer or cause needless recalls. The aim of this work was to investigate the effectiveness of indulging AI in combination with one radiologist in the routine double reading of mammography for breast cancer screening. The study prospectively analyzed 32,822 screening mammograms. Reading was performed in a blind-paired style by (i) two radiologists and (ii) one radiologist paired with AI. A heatmap and abnormality scoring percentage were provided by AI for abnormalities detected on mammograms. Negative mammograms and benign-looking lesions that were not biopsied were confirmed by a 2-year follow-up. Results Double reading by the radiologist and AI detected 1324 cancers (6.4%); on the other side, reading by two radiologists revealed 1293 cancers (6.2%) and presented a relative proportion of 1·02 (p

Objective The use of artificial intelligence has potential in assisting many aspects of imaging interpretation. We undertook a prospective service evaluation from March to October 2022 of Mammography Intelligent Assessment (MIA) operating "silently" within our Breast Screening Service, with a view to establishing its performance in the local population and setting. This evaluation addressed the performance of standalone MIA vs conventional double human reading of mammograms. Methods MIA analyzed 8779 screening events over an 8-month period. The MIA outcome did not influence the decisions made on the clinical pathway. Cases were reviewed approximately 6 weeks after the screen reading decision when human reading and/or MIA indicated a recall. Results There were 146 women with positive concordance between human reading and MIA (human reader and MIA recalled) in whom 58 breast cancers were detected. There were 270 women with negative discordance (MIA no recall, human reader recall) for whom 19 breast cancers and 1 breast lymphoma were detected, with 1 cancer being an incidental finding at assessment. Six hundred and four women had positive discordance (MIA recall, human reader no recall) in whom 2 breast cancers were detected at review. The breast cancers demonstrated a wide spectrum of mammographic features, sites, sizes, and pathologies, with no statistically significant difference in features between the negative discordant and positive concordant cases. Conclusion Of 79 breast cancers identified by human readers, 18 were not identified by MIA, and these had no specific features or site to suggest a systematic error for MIA analysis of 2D screening mammograms. [ABSTRACT FROM AUTHOR]

Objectives: To analyze discordant and false-negatives of double reading digital breast tomosynthesis (DBT) versus digital mammography (DM) including reading times in the Oslo Tomosynthesis Screening Trial (OTST), and reclassify these in a retrospective reader study as missed, minimal sign, or true-negatives. Methods: The prospective OTST comparing double reading DBT vs. DM had paired design with four parallel arms: DM, DM + computer aided detection, DBT + DM, and DBT + synthetic mammography. Eight radiologists interpreted images in batches using a 5-point scale. Reading time was automatically recorded. A retrospective reader study including four radiologists classified screen-detected cancers with at least one false-negative score and screening examinations of interval cancers as negative, non-specific minimal sign, significant minimal sign, and missed; the two latter groups are defined "actionable." Statistics included chi-square, Fisher's exact, McNemar's, and Mann–Whitney U tests. Results: Discordant rate (cancer missed by one reader) for screen-detected cancers was overall comparable (DBT (31% [71/227]) and DM (30% [52/175]), p =.81), significantly lower at DBT for spiculated cancers (DBT, 19% [20/106] vs. DM, 36% [38/106], p =.003), but high (28/49 = 57%, p = 0.001) for DBT-only detected spiculated cancers. Reading time and sensitivity varied among readers. False-negative DBT-only detected spiculated cancers had shorter reading time than true-negatives in 46% (13/28). Retrospective evaluation classified the following DBT exams "actionable": three missed by both readers, 95% (39/41) of discordant cancers detected by both modes, all 30 discordant DBT-only cancers, 25% (13/51) of interval cancers. Conclusions: Discordant rate was overall comparable for DBT and DM, significantly lower at DBT for spiculated cancers, but high for DBT-only detected spiculated lesions. Most false-negative screen-detected DBT were classified as "actionable." Clinical relevance statement: Retrospective evaluation of false-negative interpretations from the Oslo Tomosynthesis Screening Trial shows that most discordant and several interval cancers could have been detected at screening. This underlines the potential for modern AI-based reading aids and triage, as high-volume screening is a demanding task. Key Points: • Digital breast tomosynthesis (DBT) screening is more sensitive and has higher specificity compared to digital mammography screening, but high-volume DBT screening is a demanding task which can result in high discordance rate among readers. • Independent double reading DBT screening had overall comparable discordance rate as digital mammography, lower for spiculated masses seen on both modalities, and higher for small spiculated cancer seen only on DBT. • Almost all discordant digital breast tomosynthesis-detected cancers (72 of 74) and 25% (13 of 51) of the interval cancers in the Oslo Tomosynthesis Screening Trial were retrospectively classified as actionable and could have been detected by the readers. [ABSTRACT FROM AUTHOR]

Abstract Background Artificial intelligence (AI) systems are proposed as a replacement of the first reader in double reading within mammography screening. We aimed to assess cancer detection accuracy of an AI system in a Danish screening population. Methods We retrieved a consecutive screening cohort from the Region of Southern Denmark including all participating women between Aug 4, 2014, and August 15, 2018. Screening mammograms were processed by a commercial AI system and detection accuracy was evaluated in two scenarios, Standalone AI and AI-integrated screening replacing first reader, with first reader and double reading with arbitration (combined reading) as comparators, respectively. Two AI-score cut-off points were applied by matching at mean first reader sensitivity (AIsens) and specificity (AIspec). Reference standard was histopathology-proven breast cancer or cancer-free follow-up within 24 months. Coprimary endpoints were sensitivity and specificity, and secondary endpoints were positive predictive value (PPV), negative predictive value (NPV), recall rate, and arbitration rate. Accuracy estimates were calculated using McNemar’s test or exact binomial test. Results Out of 272,008 screening mammograms from 158,732 women, 257,671 (94.7%) with adequate image data were included in the final analyses. Sensitivity and specificity were 63.7% (95% CI 61.6%-65.8%) and 97.8% (97.7-97.8%) for first reader, and 73.9% (72.0-75.8%) and 97.9% (97.9-98.0%) for combined reading, respectively. Standalone AIsens showed a lower specificity (-1.3%) and PPV (-6.1%), and a higher recall rate (+ 1.3%) compared to first reader (p < 0.0001 for all), while Standalone AIspec had a lower sensitivity (-5.1%; p < 0.0001), PPV (-1.3%; p = 0.01) and NPV (-0.04%; p = 0.0002). Compared to combined reading, Integrated AIsens achieved higher sensitivity (+ 2.3%; p = 0.0004), but lower specificity (-0.6%) and PPV (-3.9%) as well as higher recall rate (+ 0.6%) and arbitration rate (+ 2.2%; p < 0.0001 for all). Integrated AIspec showed no significant difference in any outcome measures apart from a slightly higher arbitration rate (p < 0.0001). Subgroup analyses showed higher detection of interval cancers by Standalone AI and Integrated AI at both thresholds (p < 0.0001 for all) with a varying composition of detected cancers across multiple subgroups of tumour characteristics. Conclusions Replacing first reader in double reading with an AI could be feasible but choosing an appropriate AI threshold is crucial to maintaining cancer detection accuracy and workload.

Objective: To evaluate the effectiveness of a new strategy for using artificial intelligence (AI) as supporting reader for the detection of breast cancer in mammography-based double reading screening practice. Methods: Large-scale multi-site, multi-vendor data were used to retrospectively evaluate a new paradigm of AI-supported reading. Here, the AI served as the second reader only if it agrees with the recall/no-recall decision of the first human reader. Otherwise, a second human reader made an assessment followed by the standard clinical workflow. The data included 280 594 cases from 180 542 female participants screened for breast cancer at seven screening sites in two countries and using equipment from four hardware vendors. The statistical analysis included non-inferiority and superiority testing of cancer screening performance and evaluation of the reduction in workload, measured as arbitration rate and number of cases requiring second human reading. Results: Artificial intelligence as a supporting reader was found to be superior or noninferior on all screening metrics compared with human double reading while reducing the number of cases requiring second human reading by up to 87% (245 395/280 594). Compared with AI as an independent reader, the number of cases referred to arbitration was reduced from 13% (35 199/280 594) to 2% (5056/280 594). Conclusion: The simulation indicates that the proposed workflow retains screening performance of human double reading while substantially reducing the workload. Further research should study the impact on the second human reader because they would only assess cases in which the AI prediction and first human reader disagree. [ABSTRACT FROM AUTHOR]

This article analyzes how the Communist Party of Spain (PCE) interpreted the Republican period from a double point of view. The PCE fought against the Republic because considered a bourgeois republic that had to be destroyed by a workers' revolution that culminated in a proletariat dictatorship. The sectarianism and scarce social support began to be overcome from 1934 and, especially, from 1935 with the Popular Front tactics from the Communist International. In addition to this, the PCE was included in the electoral coalition of the Popular Front in February 1936: the PCE was presented as a popular party more than a worker party. The Civil War facilitated its social and political penetration as a popular front party. The exile times created a myth in the Republican period, focused on the years of the war. The PCE interpreted the Republican period from the interests of the foreign policy of the URSS. For this reason, it was interpreted as a national and international struggle against fascism aggression, except in the period between the German-Soviet Pact and the Nazi attack on the USSR.

OBJECTIVES: The objectives of this study were to estimate the proportion of radiology reports that were changed during double reading and to assess the potential clinical impact of these changes. MATERIAL AND METHODS: Rates of double reading in radiology department were explored in survey issued to consultant radiologists, covering practice of double reading, department guidelines and quality improvement work. The responses of consultant radiologists, grouped according to workplace, were used to validate management responses about working hours consumed by double reading. The clinical importance of changes to radiology reports was estimated retrospectively. We collected pairs of preliminary and final reports from 1500 double read examinations (CT, MRI, X ray, ultrasound). Exploratory analysis of associations between clinically important changes and characteristics of patients, examinations, and readers was performed with multivariate logistic regression. We also constructed two random effects models to test for clustering of clinically important report changes in separate examinations read by the same radiologist. RESULTS: We found double reading rate of 21% for CT and MRI, 17% for X ray images, 15% for ultrasound and 5% for mammograms. All exams read by consultants, consuming an estimated 20-25% of consultant working hours. By modality double reading rates were highest for Magnetic Resonance Imaging (MRI) (47%) and CT (33%), intermediate for X-ray (24%) and fluoroscopy (23%), and lowest for ultrasonography (16%) and intervention (16%). Chest radiologists and sonologists made more clinically important changes than other second readers. The severity of the radiological findings was increased in 30% of the clinically important changes. Double reading caused upto 25% increase in time consumed for reporting. CONCLUSION: Double reading has a major impact on workflow and output directly by consuming working hours. The rates of clinically important changes to radiology reports following double reading indicate that some quality assurance of radiological interpretation is warranted. [ABSTRACT FROM AUTHOR]

Simple Summary: According to guidelines from the European Association for the Study of the Liver (EASL) and American Association for the Study of Liver Diseases (AASLD), abdominal ultrasound (US) is recommended for surveillance of hepatocellular carcinoma (HCC) in high-risk patients. However, US is limited as a surveillance modality for various reasons. Magnetic resonance imaging (MRI) is generally considered a better modality for detection of early HCC, but too elaborate in a surveillance setting. Consequently, abbreviated MRI (AMRI) protocols are investigated for surveillance purposes. The aim of our study was to evaluate the potential of non-contrast-enhanced AMRI (NC-AMRI) for surveillance of HCC, using multiple readers to investigate inter-observer agreement and the added value of double reading. We found that NC-AMRI presents a valuable screening tool for HCC and that double reading improves the sensitivity and specificity of HCC detection. Purpose: To evaluate NC-AMRI for the detection of HCC in high-risk patients. Methods: Patients who underwent yearly contrast-enhanced MRI (i.e., full MRI protocol) of the liver were included retrospectively. For all patients, the sequences that constitute the NC-AMRI protocol, namely diffusion-weighted imaging (DWI), T2-weighted (T2W) imaging with fat saturation, and T1-weighted (T1W) in-phase and opposed-phase imaging, were extracted, anonymized, and uploaded to a separate research server and reviewed independently by three radiologists with different levels of experience. Reader I and III held a mutual training session. Levels of suspicion of HCC per patient were compared and the sensitivity, specificity, and area under the curve (AUC) using the Mann–Whitney U test were calculated. The reference standard was a final diagnosis based on full liver MRI and clinical follow-up information. Results: Two-hundred-and-fifteen patients were included, 36 (16.7%) had HCC and 179 (83.3%) did not. The level of agreement between readers was reasonable to good and concordant with the level of expertise and participation in a mutual training session. Receiver operating characteristics (ROC) analysis showed relatively high AUC values (range 0.89–0.94). Double reading showed increased sensitivity of 97.2% and specificity of 87.2% compared with individual results (sensitivity 80.1%–91.7%–97.2%; specificity 91.1%–72.1%–82.1%). Only one HCC (2.8%) was missed by all readers. Conclusion: NC-AMRI presents a good potential surveillance imaging tool for the detection of HCC in high-risk patients. The best results are achieved with two observers after a mutual training session. [ABSTRACT FROM AUTHOR]

Objective: To examine the breast cancer detection rate by single reading of an experienced radiologist supported by an artificial intelligence (AI) system, and compare with two-dimensional full-field digital mammography (2D-FFDM) double reading. Materials and methods: Images (3D-tomosynthesis) of 161 biopsy-proven cancers were re-read by the AI algorithm and compared to the results of first human reader, second human reader and consensus following double reading in screening. Detection was assessed in subgroups by tumour type, breast density and grade, and at two operating points, referred to as a lower and a higher sensitivity threshold. Results: The AI algorithm method gave similar results to double-reading 2D-FFDM, and the detection rate was significantly higher compared to single-reading 2D-FFDM. At the lower sensitivity threshold, the algorithm was significantly more sensitive than reader A (97.5% vs. 89.4%, p = 0.02), non-significantly more sensitive than reader B (97.5% vs. 94.4%, p = 0.2) and non-significantly less sensitive than the consensus from double reading (97.5% vs. 99.4%, p = 0.2). At the higher sensitivity threshold, the algorithm was significantly more sensitive than reader A (99.4% vs. 89.4%, p < 0.001) and reader B (99.4% vs. 94.4%, p = 0.02) and identical to the consensus sensitivity (99.7% in both cases, p = 1.0). There were no significant differences in the detection capability of the AI system by tumour type, grading and density. Conclusion: In this proof of principle study, we show that sensitivity using single reading with a suitable AI algorithm is non-inferior to that of standard of care using 2D mammography with double reading, when tomosynthesis is the primary screening examination. [ABSTRACT FROM AUTHOR]

Since its inception in the late 2000s, supporters of Quentin Meillassoux tended to oppose the movement he founded, speculative materialism, to Derrida and the Derridean community. The arc of Meillassoux's later publications, however, did not support that opposition, especially when it became clear that he neither dismissed correlationism nor set aside literary concerns. These approaches might not be as incompatible as has been supposed. Still, if any affinity is to be established, a first need is to reassess the core of Meillassoux's argument in After Finitude, which has continued to influence his publications. In this article, I attempt not merely to comment upon Meillassoux's argument, but to justify it from within, and so to demonstrate its logic. Then, using a method of re-reading that will be familiar to many, I will attempt to show how that logic reveals speculative materialism and Derridean concerns to be better treated as logical affiliates. Doing so will not presuppose agreement, but will set the stage for more thorough comparisons in future. [ABSTRACT FROM AUTHOR]

Background The study was conducted to evaluate the performance of a state‐of‐the‐art commercial deep learning‐based computer‐aided diagnosis (DL‐CAD) system for detecting and characterizing pulmonary nodules. Methods Pulmonary nodules in 346 healthy subjects (male: female = 221:125, mean age 51 years) from a lung cancer screening program conducted from March to November 2017 were screened using a DL‐CAD system and double reading independently, and their performance in nodule detection and characterization were evaluated. An expert panel combined the results of the DL‐CAD system and double reading as the reference standard. Results The DL‐CAD system showed a higher detection rate than double reading, regardless of nodule size (86.2% vs. 79.2%; P < 0.001): nodules ≥ 5 mm (96.5% vs. 88.0%; P = 0.008); nodules < 5 mm (84.3% vs. 77.5%; P < 0.001). However, the false positive rate (per computed tomography scan) of the DL‐CAD system (1.53, 529/346) was considerably higher than that of double reading (0.13, 44/346; P < 0.001). Regarding nodule characterization, the sensitivity and specificity of the DL‐CAD system for distinguishing solid nodules > 5 mm (90.3% and 100.0%, respectively) and ground‐glass nodules (100.0% and 96.1%, respectively) were close to that of double reading, but dropped to 55.5% and 93%, respectively, when discriminating part solid nodules. Conclusion Our DL‐CAD system detected significantly more nodules than double reading. In the future, false positive findings should be further reduced and characterization accuracy improved.

Background: The goal of this study was to determine whether the detection of histologically confirmed cases of cervical high‐grade squamous intraepithelial lesions or worse (HSIL+) can be increased by having each liquid‐based cytology (LBC) slide read by 2 cytotechnologists as part of routine screening. Methods: Over 36,212 women aged 30 to 64 years participated in the Forwarding Research for Improved Detection and Access for Cervical Cancer Screening and Triage (FRIDA) Study in Mexico between 2013 and 2016. For each participant, 2 cervical samples were collected at the same clinic visit, one to test for high‐risk human papillomavirus (hrHPV) and the other for LBC, which was used to triage those with a hrHPV positive result. LBC slides were evaluated by 7 cytotechnologists, with each slide read independently by 2 blinded cytotechnologists. All women with atypical cells of undetermined significance or a worse result were referred to colposcopy for further evaluation and diagnosis. Three pathologists evaluated the biopsy specimens to confirm the final HSIL+ diagnosis. The HSIL+ detection rates for the single versus double reading were estimated and compared. Results: A total of 3,914 women with a positive hrHPV result were triaged with LBC. The first and second cytology readings resulted in 43 HSIL+ cases detected; the double‐reading strategy detected 9 additional HSIL+ cases, resulting in a total of 52 HSIL+ cases. The HSIL+ detection rate increased from 10.99/1000 with a single reading to 13.29/1000 with the double‐reading strategy (P =.004). Conclusion: A 20.9% increase in HSIL+ cases detected was achieved with a double reading of the LBC slides in this sample of hrHPV‐positive women. Over 36,212 FRIDA Study participants provided 2 cervical samples during the same clinic visit to test for high‐risk human papillomavirus (hrHPV), and liquid‐based cytology (LBC) was used to triage women with an hrHPV‐positive result. The detection of histologically confirmed cases of cervical high‐grade squamous intraepithelial lesions or worse increased nearly 21% by having each LBC slide read by 2 cytotechnologists as part of the routine work of the cytological laboratory. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: To establish the efficacy of pairing readers randomly and evaluate the merits of developing optimal pairing methodologies.Materials and Methods: Sensitivity, specificity, and proportion correct were computed for three different case sets that were independently read by 16 radiologists. Performance of radiologists as single readers was compared to expected double reading performance. We theoretically evaluated all possible pairing methodologies. Bootstrap resampling methods were used for statistical analyses.Results: Significant improvements in expected performance for double versus single reading (ie, delta performance) were shown for all performance measures and case-sets (p ≤ .003), with overall delta performance across all theoretically possible pairing schemes (n = 10,395) ranging between .05 and .08. Delta performance for the 20 best pairing schemes was significant (p < .001) and ranged between .07 and .10. Delta performance for 20 random pairing schemes was also significant (p ≤ .003) and ranged between .05 and .08. Delta performance for the 20 worst pairing schemes ranged between .03 and .06, reaching significance in delta proportion correct (p ≤ .021) for all three case-sets and in delta specificity for two case-sets (p ≤ .033) but not for a third case-set (p = .131), and not reaching significance in delta sensitivity for any of the three case-sets (.098 ≥ p ≥ .067).Conclusion: Significant benefits accrue from double reading, and while random reader pairing achieves most double reading benefits, a strategic pairing approach may maximize the benefits of double reading. [ABSTRACT FROM AUTHOR]

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To assess the performance of the second reading of chest compute tomography (CT) examinations by expert radiologists in patients with discordance between the reverse transcription real-time fluorescence polymerase chain reaction (RT-PCR) test for COVID-19 viral pneumonia and the CT report. Three hundred and seventy-eight patients were included in this retrospective study (121 women and 257 men; 71 years median age, with a range of 29–93 years) and subjected to RT-PCR tests for suspicious COVID-19 infection. All patients were subjected to CT examination in order to evaluate the pulmonary disease involvement by COVID-19. CT images were reviewed first by two radiologists who identified COVID-19 typical CT patterns and then reanalyzed by another two radiologists using a CT structured report for COVID-19 diagnosis. Weighted k values were used to evaluate the inter-reader agreement. The median temporal window between RT-PCRs execution and CT scan was zero days with a range of (−9, 11) days. The RT-PCR test was positive in 328/378 (86.8%). Discordance between RT-PCR and CT findings for viral pneumonia was revealed in 60 cases. The second reading changed the CT diagnosis in 16/60 (26.7%) cases contributing to an increase the concordance with the RT-PCR. Among these 60 cases, eight were false negative with positive RT-PCR, and 36 were false positive with negative RT-PCR. Sensitivity, specificity, positive predictive value and negative predictive value of CT were respectively of 97.3%, 53.8%, 89.0%, and 88.4%. Double reading of CT scans and expert second readers could increase the diagnostic confidence of radiological interpretation in COVID-19 patients.

Purpose: The currently recommended double reading of all screening mammography examinations is an economic burden for screening programs. The sensitivity of screening is higher for women with low breast density than for women with high density. One may therefore ask whether single reading could replace double reading at least for women with low density. We addressed this question using data from a screening program where the radiologists coded their readings independently.Methods: Data include all screening mammography examinations in the Capital Region of Denmark from 1 November 2012 to 31 December 2013. Outcome of screening was assessed by linkage to the Danish Pathology Register. We calculated sensitivity, specificity, number of interval cancers, and false positive-tests per 1000 screened women by both single reader and consensus BI-RADS density code.Results: In total 54,808 women were included. The overall sensitivity of double reading was 72%, specificity was 97.6%, 3 women per 1000 screened experienced an interval cancer, and 24 a false-positive test. Across all BI-RADS density codes, single reading consistently decreased sensitivity as compared with consensus reading. The same was true for specificity, apart from results across BI-RADS density codes set by reader 2.Conclusions: Single reading decreased sensitivity as compared with double reading across all BI-RADS density codes. This included results based on consensus BI-RADS density codes. This means that replacement of double with single reading would have negative consequences for the screened women, even if density could be assessed automatically calibrated to the usual consensus level. [ABSTRACT FROM AUTHOR]