ECAsT: a large dataset for conversational search and an evaluation of metric robustness.

The Text REtrieval Conference Conversational assistance track (CAsT) is an annual conversational passage retrieval challenge to create a large-scale open-domain conversational search benchmarking. However, as of yet, the datasets used are small, with just more than 1,000 turns and 100 conversation topics. In the first part of this research, we address the dataset limitation by building a much larger novel multi-turn conversation dataset for conversation search benchmarking called Expanded-CAsT (ECAsT). ECAsT is built using a multi-stage solution that uses a combination of conversational query reformulation and neural paraphrasing and also includes a new model to create multi-turn paraphrases. The meaning and diversity of paraphrases are evaluated with human and automatic evaluation. Using this methodology, we produce and release to the research community a conversational search dataset that is 665% more extensive in terms of size and language diversity than is available at the time of this study, with more than 9,200 turns. The augmented dataset not only provides more data but also more language diversity to improve conversational search neural model training and testing. In the second part of the research, we use ECAsT to assess the robustness of traditional metrics for conversational evaluation used in CAsT and identify its bias toward language diversity. Results show the benefits of adding language diversity for improving the collection of pooled passages and reducing evaluation bias. We found that introducing language diversity via paraphrases returned up to 24% new passages compared to only 2% using CAsT baseline.

Who is in the sample? An analysis of real and surrogate users as participants in user study research in the information technology fields.

Background: Constructing a sample of real users as participants in user studies is considered by most researchers to be vital for the validity, usefulness, and applicability of research findings. However, how often user studies reported in information technology academic literature sample real users or surrogate users is unknown. Therefore, it is uncertain whether or not the use of surrogate users in place of real users is a widespread problem within user study practice. Objective: To determine how often user studies reported in peer-reviewed information technology literature sample real users or surrogate users as participants. Method: We analyzed 725 user studies reported in 628 peer-reviewed articles published from 2013 through 2021 in 233 unique conference and journal outlets, retrieved from the ACM Digital Library, IEEE Xplore, and Web of Science archives. To study the sample selection choices, we categorized each study as generic (i.e., users are from the general population) or targeted (i.e., users are from a specific subpopulation), and the sampled study participants as real users (i.e., from the study population) or surrogate users (i.e., other than real users). Results: Our analysis of all 725 user studies shows that roughly two-thirds (75.4%) sampled real users. However, of the targeted studies, only around half (58.4%) sampled real users. Of the targeted studies sampling surrogate users, the majority (69.7%) used students, around one-in-four (23.6%) sampled through crowdsourcing, and the remaining 6.7% of studies used researchers or did not specify who the participants were. Conclusions: Key findings are as follows: (a) the state of sampling real users in information technology research has substantial room for improvement for targeted studies; (b) researchers often do not explicitly characterize their study participants in adequate detail, which is probably the most disconcerting finding; and (c) suggestions are provided for recruiting real users, which may be challenging for researchers. Implications: The results imply a need for standard guidelines for reporting the types of users sampled for a user study. We provide a template for reporting user study sampling with examples.

Investigating toxicity changes of cross-community redditors from 2 billion posts and comments.

This research investigates changes in online behavior of users who publish in multiple communities on Reddit by measuring their toxicity at two levels. With the aid of crowdsourcing, we built a labeled dataset of 10,083 Reddit comments, then used the dataset to train and fine-tune a Bidirectional Encoder Representations from Transformers (BERT) neural network model. The model predicted the toxicity levels of 87,376,912 posts from 577,835 users and 2,205,581,786 comments from 890,913 users on Reddit over 16 years, from 2005 to 2020. This study utilized the toxicity levels of user content to identify toxicity changes by the user within the same community, across multiple communities, and over time. As for the toxicity detection performance, the BERT model achieved a 91.27% classification accuracy and an area under the receiver operating characteristic curve (AUC) score of 0.963 and outperformed several baseline machine learning and neural network models. The user behavior toxicity analysis showed that 16.11% of users publish toxic posts, and 13.28% of users publish toxic comments. However, results showed that 30.68% of users publishing posts and 81.67% of users publishing comments exhibit changes in their toxicity across different communities, indicating that users adapt their behavior to the communities' norms. Furthermore, time series analysis with the Granger causality test of the volume of links and toxicity in user content showed that toxic comments are Granger caused by links in comments.

Persona preparedness: a survey instrument for measuring the organizational readiness for deploying personas.

User-centric design within organizations is crucial for developing information technology that offers optimal usability and user experience. Personas are a central user-centered design technique that puts people before technology and helps decision makers understand the needs and wants of the end-user segments of their products, systems, and services. However, it is not clear how ready organizations are to adopt persona thinking. To address these concerns, we develop and validate the Persona Readiness Scale (PRS), a survey instrument to measure organizational readiness for personas. After a 12-person qualitative pilot study, the PRS was administered to 372 professionals across different industries to examine its reliability and validity, including 125 for exploratory factor analysis and 247 for confirmatory factor analysis. The confirmatory factor analysis indicated a good fit with five dimensions: Culture readiness, Knowledge readiness, Data and systems readiness, Capability readiness, and Goal readiness. Higher persona readiness is positively associated with the respondents' evaluations of successful persona projects. Organizations can apply the resulting 18-item scale to identify areas of improvement before initiating costly persona projects towards the overarching goal of user-centric product development. Located at the cross-section of information systems and human-computer interaction, our research provides a valuable instrument for organizations wanting to leverage personas towards more user-centric and empathetic decision making about users. Supplementary Information: The online version contains supplementary material available at 10.1007/s10799-022-00373-9.

Big Data, Small Personas: How Algorithms Shape the Demographic Representation of Data-Driven User Segments.

Derived from the notion of algorithmic bias, it is possible that creating user segments such as personas from data results in over- or under-representing certain segments (FAIRNESS), does not properly represent the diversity of the user populations (DIVERSITY), or produces inconsistent results when hyperparameters are changed (CONSISTENCY). Collecting user data on 363M video views from a global news and media organization, we compare personas created from this data using different algorithms. Results indicate that the algorithms fall into two groups: those that generate personas with low diversity-high fairness and those that generate personas with high diversity-low fairness. The algorithms that rank high on diversity tend to rank low on fairness (Spearman's correlation: -0.83). The algorithm that best balances diversity, fairness, and consistency is Spectral Embedding. The results imply that the choice of algorithm is a crucial step in data-driven user segmentation, because the algorithm fundamentally impacts the demographic attributes of the generated personas and thus influences how decision makers view the user population. The results have implications for algorithmic bias in user segmentation and creating user segments that not only consider commercial segmentation criteria but also consider criteria derived from ethical discussions in the computing community.

Measuring user interactions with websites: A comparison of two industry standard analytics approaches using data of 86 websites.

This research compares four standard analytics metrics from Google Analytics with SimilarWeb using one year's average monthly data for 86 websites from 26 countries and 19 industry verticals. The results show statistically significant differences between the two services for total visits, unique visitors, bounce rates, and average session duration. Using Google Analytics as the baseline, SimilarWeb average values were 19.4% lower for total visits, 38.7% lower for unique visitors, 25.2% higher for bounce rate, and 56.2% higher for session duration. The website rankings between SimilarWeb and Google Analytics for all metrics are significantly correlated, especially for total visits and unique visitors. The accuracy/inaccuracy of the metrics from both services is discussed from the vantage of the data collection methods employed. In the absence of a gold standard, combining the two services is a reasonable approach, with Google Analytics for onsite and SimilarWeb for network metrics. Finally, the differences between SimilarWeb and Google Analytics measures are systematic, so with Google Analytics metrics from a known site, one can reasonably generate the Google Analytics metrics for related sites based on the SimilarWeb values. The implications are that SimilarWeb provides conservative analytics in terms of visits and visitors relative to those of Google Analytics, and both tools can be utilized in a complementary fashion in situations where site analytics is not available for competitive intelligence and benchmarking analysis.

Prospective analysis of clinically significant prostate cancer detection with [18F]DCFPyL PET/MRI compared to multiparametric MRI: a comparison with the histopathology in the radical prostatectomy specimen, the ProStaPET study.

PURPOSE: Multiparametric magnetic resonance imaging (mpMRI) is a well-established imaging method for localizing primary prostate cancer (PCa) and for guiding targeted prostate biopsies. [18F]DCFPyL positron emission tomography combined with MRI (PSMA-PET/MRI) might be of additional value to localize primary PCa. The aim of this study was to assess the diagnostic performance of [18F]DCFPyL-PET/MRI vs. mpMRI in tumour localization based on histopathology after robot-assisted radical-prostatectomy (RARP), also assessing biopsy advice for potential image-guided prostate biopsies. METHODS: Thirty prospectively included patients with intermediate to high-risk PCa underwent [18F]DCFPyL-PET/MRI and mpMRI prior to RARP. Two nuclear medicine physicians and two radiologists assessed tumour localization on [18F]DCFPyL-PET/MRI and on mpMRI respectively, and gave a prostate biopsy advice (2 segments) using a 14-segment model of the prostate. The uro-pathologist evaluated the RARP specimen for clinically significant PCa (csPCa) using the same model. csPCa was defined as any PCa with Grade Group (GG) ≥ 2. The biopsy advice based on imaging was correlated with the final histology in the RARP specimen for a total-agreement analysis. An additional near-agreement correlation was performed to approximate clinical reality. RESULTS: Overall, 142 of 420 (33.8%) segments contained csPCa after pathologic examination. The segments recommended for targeted biopsy contained the highest GG PCa segment in 27/30 patients (90.0%) both for [18F]DCFPyL-PET/MRI and mpMRI. Areas under the receiver operating characteristics curves (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the total-agreement detection of csPCa per segment using [18F]DCFPyL-PET/MRI were 0.70, 50.0%, 89.9%, 71.7%, and 77.9%, respectively. These results were 0.75, 54.2%, 94.2%, 82.8%, and 80.1%, respectively, for mpMRI only. CONCLUSION: Both [18F]DCFPyL-PET/MRI and mpMRI were only partly able to detect csPCa on a per-segment basis. An accurate detection (90.0%) of the highest GG lesion at patient-level was observed when comparing both [18F]DCFPyL-PET/MRI and mpMRI biopsy advice with the histopathology in the RARP specimen. So, despite the finding that [18F]DCFPyL-PET/MRI adequately detects csPCa, it does not outperform mpMRI.

SUVs Are Adequate Measures of Lesional 18F-DCFPyL Uptake in Patients with Low Prostate Cancer Disease Burden.

In prostate cancer (PCa) patients, the tumor-to-blood ratio (TBR) has been validated as the preferred simplified method for lesional 18F-DCFPyL (a radiolabeled prostate-specific membrane antigen ligand) uptake quantification on PET. In contrast to SUVs, the TBR accounts for variability in arterial input functions caused by differences in total tumor burden between patients (the sink effect). However, TBR depends strongly on tracer uptake interval and has worse repeatability and is less applicable in clinical practice than SUVs. We investigated whether SUV could provide adequate quantification of 18F-DCFPyL uptake on PET/CT in a patient cohort with low PCa burden. Methods: In total, 116 patients with PCa undergoing 18F-DCFPyL PET/CT imaging were retrospectively included. All 18F-DCFPyL-avid lesions suspected of being PCa were semiautomatically delineated. SUVpeak was plotted against TBR for the most intense lesion of each patient. The correlation of SUVpeak and TBR was evaluated using linear regression and was stratified for patients undergoing PET/CT for primary staging, patients undergoing restaging at biochemical recurrence, and patients with metastatic castration-resistant PCa. Moreover, the correlation was evaluated as a function of tracer uptake time, prostate-specific antigen level, and PET-positive tumor volume. Results: In total, 436 lesions were delineated (median, 1 per patient; range, 1-66). SUVpeak correlated well with TBR in patients with PCa and a total tumor volume of less than 200 cm3 (R2 = 0.931). The correlation between SUV and TBR was not affected by disease setting, prostate-specific antigen level, or tumor volume. SUVpeak depended less on tracer uptake time than did TBR. Conclusion: For 18F-DCFPyL PET/CT, SUVpeak correlates strongly with TBR. Therefore, it is a valuable simplified, semiquantitative measurement in patients with low-volume PCa (<200 cm3). SUVpeak can therefore be applied in 18F-DCFPyL PET assessment as an imaging biomarker to characterize tumors and to monitor treatment outcomes.

Detection of Recurrent Prostate Cancer Using Prostate-specific Membrane Antigen Positron Emission Tomography in Patients not Meeting the Phoenix Criteria for Biochemical Recurrence After Curative Radiotherapy.

Biochemical recurrence of prostate cancer (PCa) after curative radiotherapy is defined as a prostate-specific antigen (PSA) rise of ≥2 ng/ml above the nadir ("Phoenix criteria", 2005). With the introduction of prostate-specific membrane antigen positron emission tomography (PSMA-PET), the ability to localise PCa recurrences has increased markedly. Here, we reviewed 315 patients scanned with PSMA-PET after curative radiotherapy in the Prostate Cancer Network Amsterdam (2015-2018). Sixty-three patients (20.3%) were scanned below the Phoenix threshold (PSA rise <2.0 ng/ml). In 53 of these patients (84.1%), PSMA-PET-avid lesions were detected nonetheless: 21 patients (33.3%) revealed a local recurrence as a single site of disease, 32 patients (50.8%) harboured metastatic PCa. Besides rising PSA, no predictors were identified that prompted early PSMA-PET imaging. In this communication, we report on the frequent detection of metastatic PCa with PSMA-PET in men below the Phoenix PSA threshold. These findings are a plea for re-evaluation of current diagnostic work-up for rising PSA values after radiotherapy, as early detection of recurrences might refine salvage and/or adjuvant therapies. PATIENT SUMMARY: This study reports on the unexpected detection of prostate cancer (PCa) recurrences with prostate-specific membrane antigen positron emission tomography in patients treated with radiotherapy. This calls for re-evaluation of the current criteria for recurrent PCa after radiotherapy.

Prostate Specific Membrane Antigen Positron Emission Tomography/Computerized Tomography in the Evaluation of Initial Response in Candidates Who Underwent Salvage Radiation Therapy after Radical Prostatectomy for Prostate Cancer.

PURPOSE: We assessed predictors of short-term oncologic outcomes of patients who underwent salvage radiation therapy for biochemical recurrence after robot-assisted laparoscopic radical prostatectomy without evidence of metastases on prostate specific membrane antigen positron emission tomography/computerized tomography. MATERIALS AND METHODS: We retrospectively analyzed 194 patients with biochemical recurrence after robot-assisted laparoscopic radical prostatectomy who underwent prostate specific membrane antigen positron emission tomography/computerized tomography prior to salvage radiation therapy. Patients with lymph node or distant metastases on restaging imaging or at the time of extended pelvic lymph node dissection during robot-assisted laparoscopic radical prostatectomy were excluded, as were patients who received androgen deprivation therapy during or prior to salvage radiation therapy. A multivariable logistic regression analysis was performed to assess predictors of treatment response, defined as prostate specific antigen value ≤0.1 ng/ml after salvage radiation therapy. RESULTS: Overall treatment response after salvage radiation therapy was 75% (146/194 patients). On multivariable analysis, prostate specific antigen value at initiation of salvage radiation therapy (OR 0.42, 95% CI 0.27-0.62, p <0.001), pathological T stage (pT3a vs pT2 OR 0.28, 95% CI 0.11-0.69, p=0.006; pT3b vs pT2 OR 0.26, 95% CI 0.09-0.71, p=0.009) and local recurrent disease on imaging (OR 5.53, 95% CI 1.96-18.52, p=0.003) were predictors of treatment response. CONCLUSIONS: Salvage radiation therapy in patients without evidence of metastases on prostate specific membrane antigen positron emission tomography/computerized tomography showed a good overall treatment response of 75%. Higher treatment response rates were observed in patients with lower prostate specific antigen values at initiation of salvage radiation therapy, those with local recurrent disease on imaging and those with lower pathological T stage (pT2 vs pT3a/b).

Machine learning-based analysis of [18F]DCFPyL PET radiomics for risk stratification in primary prostate cancer.

PURPOSE: Quantitative prostate-specific membrane antigen (PSMA) PET analysis may provide for non-invasive and objective risk stratification of primary prostate cancer (PCa) patients. We determined the ability of machine learning-based analysis of quantitative [18F]DCFPyL PET metrics to predict metastatic disease or high-risk pathological tumor features. METHODS: In a prospective cohort study, 76 patients with intermediate- to high-risk PCa scheduled for robot-assisted radical prostatectomy with extended pelvic lymph node dissection underwent pre-operative [18F]DCFPyL PET-CT. Primary tumors were delineated using 50-70% peak isocontour thresholds on images with and without partial-volume correction (PVC). Four hundred and eighty standardized radiomic features were extracted per tumor. Random forest models were trained to predict lymph node involvement (LNI), presence of any metastasis, Gleason score ≥ 8, and presence of extracapsular extension (ECE). For comparison, models were also trained using standard PET features (SUVs, volume, total PSMA uptake). Model performance was validated using 50 times repeated 5-fold cross-validation yielding the mean receiver-operator characteristic curve AUC. RESULTS: The radiomics-based machine learning models predicted LNI (AUC 0.86 ± 0.15, p < 0.01), nodal or distant metastasis (AUC 0.86 ± 0.14, p < 0.01), Gleason score (0.81 ± 0.16, p < 0.01), and ECE (0.76 ± 0.12, p < 0.01). The highest AUCs reached using standard PET metrics were lower than those of radiomics-based models. For LNI and metastasis prediction, PVC and a higher delineation threshold improved model stability. Machine learning pre-processing methods had a minor impact on model performance. CONCLUSION: Machine learning-based analysis of quantitative [18F]DCFPyL PET metrics can predict LNI and high-risk pathological tumor features in primary PCa patients. These findings indicate that PSMA expression detected on PET is related to both primary tumor histopathology and metastatic tendency. Multicenter external validation is needed to determine the benefits of using radiomics versus standard PET metrics in clinical practice.

Clinical verification of 18F-DCFPyL PET-detected lesions in patients with biochemically recurrent prostate cancer.

PURPOSE: Radiolabeled Prostate-Specific Membrane Antigen (PSMA) PET/CT is the current standard-of-care for lesion detection in patients with biochemically recurrent (BCR) prostate cancer (PCa). However, rigorous verification of detected lesions is not always performed in routine clinical practice. To aid future 18F-radiolabeled PSMA PET/CT interpretation, we aimed to identify clinical/imaging characteristics that increase the likelihood that a PSMA-avid lesion is malignant. MATERIALS AND METHODS: 262 patients with BCR, who underwent 18F-DCFPyL PSMA PET/CT, were retrospectively analyzed. The malignant nature of 18F-DCFPyL PET-detected lesions was verified through any of the following metrics: (1) positive histopathological examination; (2) additional positive imaging; (3) a ≥50% decrease in Prostate-Specific Antigen (PSA) following irradiation of the lesion(s). RESULTS: In 226/262 PET scans (86.3%) at least one lesion suspicious for recurrent PCa was detected ('positive scan'). In 84/226 positive scans (37.2%), at least one independent verification metric was available. PSMA PET-detected lesions were most often confirmed to be malignant (PCa) in the presence of a CT-substrate (96.5% vs. 55.6% without CT-substrate), with SUVpeak ≥3.5 (91.4% vs. 60.0% with SUVpeak<3.5), in patients with a PSA-level ≥2.0 ng/mL (83.7% vs. 65.7% in patients with PSA <2.0ng/mL) and in patients with >2 PET-positive lesions (94.1% vs. 64.2% in patients with 1-2 PET-positive lesions; p<0.001-0.03). CONCLUSIONS: In this study, the clinical verification of 18F-DCFPyL PET-positive lesions in patients with BCR was performed. Diagnostic certainty of PET-detected lesions increases in the presence of characteristic abnormalities on CT, when SUVpeak is ≥3.5, when PSA-levels exceed 2.0 ng/mL or in patients with more than two PET-positive lesions.

Topic-driven toxicity: Exploring the relationship between online toxicity and news topics.

Hateful commenting, also known as 'toxicity', frequently takes place within news stories in social media. Yet, the relationship between toxicity and news topics is poorly understood. To analyze how news topics relate to the toxicity of user comments, we classify topics of 63,886 online news videos of a large news channel using a neural network and topical tags used by journalists to label content. We score 320,246 user comments from those videos for toxicity and compare how the average toxicity of comments varies by topic. Findings show that topics like Racism, Israel-Palestine, and War & Conflict have more toxicity in the comments, and topics such as Science & Technology, Environment & Weather, and Arts & Culture have less toxic commenting. Qualitative analysis reveals five themes: Graphic videos, Humanistic stories, History and historical facts, Media as a manipulator, and Religion. We also observe cases where a typically more toxic topic becomes non-toxic and where a typically less toxic topic becomes "toxicified" when it involves sensitive elements, such as politics and religion. Findings suggest that news comment toxicity can be characterized as topic-driven toxicity that targets topics rather than as vindictive toxicity that targets users or groups. Practical implications suggest that humanistic framing of the news story (i.e., reporting stories through real everyday people) can reduce toxicity in the comments of an otherwise toxic topic.

Repeatability of Quantitative 18F-DCFPyL PET/CT Measurements in Metastatic Prostate Cancer.

Quantitative evaluation of radiolabeled prostate-specific membrane antigen (PSMA) PET scans may be used to monitor treatment response in patients with prostate cancer (PCa). To interpret longitudinal differences in PSMA uptake, the intrinsic variability of tracer uptake in PCa lesions needs to be defined. The aim of this study was to investigate the repeatability of quantitative PET/CT measurements using 18F-DCFPyL ([2-(3-(1-carboxy-5-[(6-18F-fluoro-pyridine-3-carbonyl)-amino]-pentyl)-ureido)-pentanedioic acid], a second-generation 18F-PSMA-ligand) in patients with PCa. Methods: Twelve patients with metastatic PCa were prospectively included, of whom 2 were excluded from final analyses. Patients received 2 whole-body 18F-DCFPyL PET/CT scans (median dose, 317 MBq; uptake time, 120 min) within a median of 4 d (range, 1-11 d). After semiautomatic (isocontour-based) tumor delineation, the following lesion-based metrics were derived: mean, peak, and maximum tumor-to-blood ratio; SUVmean, SUVpeak, and SUVmax normalized to body weight; tumor volume; and total lesion uptake (TLU). Additionally, patient-based total tumor volume (TTV) (sum of PSMA-positive tumor volumes) and total tumor burden (TTB) (sum of all lesion TLUs) were derived. Repeatability was analyzed using repeatability coefficients (RC) and intraclass correlation coefficients. Additionally, the effect of point-spread function (PSF) image reconstruction on the repeatability of uptake metrics was evaluated. Results: In total, 36 18F-DCFPyL PET-positive lesions were analyzed (≤5 lesions per patient). The RCs for mean, peak, and maximum tumor-to-blood ratio were 31.8%, 31.7%, and 37.3%, respectively. For SUVmean, SUVpeak, and SUVmax, the RCs were 24.4%, 25.3%, and 31.0%, respectively. All intraclass correlation coefficients were at least 0.97. Tumor volume delineations were quite repeatable, with an RC of 28.1% for individual lesion volumes and 17.0% for TTV. TTB had an RC of 23.2% and 33.4% when based on SUVmean and mean tumor-to-blood ratio, respectively. Small lesions (<4.2 cm3) had worse repeatability for volume measurements. The repeatability of SUVpeak, TLU, and all patient-level metrics was not affected by PSF reconstruction. Conclusion:18F-DCFPyL uptake measurements are quite repeatable and can be used for clinical validation in future treatment response assessment studies. Patient-based TTV may be preferred for multicenter studies because its repeatability was both high and robust to different image reconstructions.

Lesion Detection and Interobserver Agreement with Advanced Image Reconstruction for 18F-DCFPyL PET/CT in Patients with Biochemically Recurrent Prostate Cancer.

Biochemically recurrent prostate cancer (BCR) is the main indication to perform prostate-specific membrane antigen PET/CT. However, localizing BCR with prostate-specific membrane antigen PET/CT remains challenging in patients with low prostate-specific antigen (PSA) values. Here, we studied the impact of advanced PET image reconstruction methods on BCR localization and interobserver agreement with 18F-DCFPyL PET/CT scans in patients with BCR and low PSA values. Methods: Twenty-four patients with BCR and a PSA level of less than 2.0 ng/mL were included. PET images were reconstructed with 4-mm voxels and 2-mm voxels, both with and without point-spread function. All scans were interpreted by 4 nuclear medicine physicians. Additionally, PET examinations of 5 patients with primary prostate cancer and confirmed absence of lymph node metastases (after lymph node dissection) were included, to assess the risk of introducing false-positive findings when using advanced reconstruction. Calculation of BCR localization rates (scan positivity) was based on consensus among our readers (≥3 readers regarding a scan positive for BCR), as well as the individual scan interpretations of the readers. Results: In the consensus analysis, BCR localization rates were not higher using advanced reconstruction (62.5%-66.7%) than using 4-mm reconstruction (62.5%). On the basis of individual readings, however, more scans were positive using 2-mm reconstruction (74.0%; 95% confidence interval [CI], 65.0%-82.9%) (P = 0.027) and 2-mm reconstruction with point-spread function (75.0%; 95% CI, 66.2%-83.8%) (P = 0.014) than 4-mm reconstruction (65.6%; 95% CI, 56.0%-75.3%). A higher number of lesions was detected on the 2-mm scans (median, 2 lesions; interquartile range, 1-3) than the 4-mm scans (median, 1; interquartile range, 0-3; P = 0.008). The advanced reconstruction methods did not increase interobserver agreement (80.6%-84.7%), compared with the 4-mm scans (75.7%, P = 0.08-0.25). In the patients with primary prostate cancer, an equal number of false-positive lesions was observed among the different reconstruction methods (overall, n = 13). Conclusion: Applying advanced image reconstruction for 18F-DCFPyL PET/CT scans did not increase BCR localization in patients with BCR and low PSA values (reader consensus). Yet, the increased number of positive individual readings may imply that further development of image reconstruction methods holds potential to improve BCR localization. No improved interobserver agreement was observed with advanced reconstruction compared with standard 4-mm reconstruction.

Mapping online hate: A scientometric analysis on research trends and hotspots in research on online hate.

Internet and social media participation open doors to a plethora of positive opportunities for the general public. However, in addition to these positive aspects, digital technology also provides an effective medium for spreading hateful content in the form of cyberbullying, bigotry, hateful ideologies, and harassment of individuals and groups. This research aims to investigate the growing body of online hate research (OHR) by mapping general research indices, prevalent themes of research, research hotspots, and influential stakeholders such as organizations and contributing regions. For this, we use scientometric techniques and collect research papers from the Web of Science core database published through March 2019. We apply a predefined search strategy to retrieve peer-reviewed OHR and analyze the data using CiteSpace software by identifying influential papers, themes of research, and collaborating institutions. Our results show that higher-income countries contribute most to OHR, with Western countries accounting for most of the publications, funded by North American and European funding agencies. We also observed increased research activity post-2005, starting from more than 50 publications to more than 550 in 2018. This applies to a number of publications as well as citations. The hotbeds of OHR focus on cyberbullying, social media platforms, co-morbid mental disorders, and profiling of aggressors and victims. Moreover, we identified four main clusters of OHR: (1) Cyberbullying, (2) Sexual solicitation and intimate partner violence, (3) Deep learning and automation, and (4) Extremist and online hate groups, which highlight the cross-disciplinary and multifaceted nature of OHR as a field of research. The research has implications for researchers and policymakers engaged in OHR and its associated problems for individuals and society.

Simplified Methods for Quantification of 18F-DCFPyL Uptake in Patients with Prostate Cancer.

Radiolabeled prostate-specific membrane antigen (PSMA) PET has demonstrated promising results for prostate cancer (PCa) imaging. Quantification of PSMA radiotracer uptake is desired as it enables reliable interpretation of PET images, use of PSMA uptake as an imaging biomarker for tumor characterization, and evaluation of treatment effects. The aim of this study was to perform a full pharmacokinetic analysis of 2-(3-(1-carboxy-5-[(6-18F-fluoro-pyridine-3-carbonyl)-amino]-pentyl)-ureido)-pentanedioic acid (18F-DCFPyL), a second-generation 18F-labeled PSMA ligand. On the basis of the pharmacokinetic analysis (reference method), simplified methods for quantification of 18F-DCFPyL uptake were validated. Methods: Eight patients with metastasized PCa were included. Dynamic PET acquisitions were performed at 0-60 and 90-120 min after injection of a median dose of 313 MBq of 18F-DCFPyL (range, 292-314 MBq). Continuous and manual arterial blood sampling provided calibrated plasma tracer input functions. Time-activity curves were derived for each PCa metastasis, and 18F-DCFPyL kinetics were described using standard plasma input tissue-compartment models. Simplified methods for quantification of 18F-DCFPyL uptake (SUVs; tumor-to-blood ratios [TBRs]) were correlated with kinetic parameter estimates obtained from full pharmacokinetic analysis. Results: In total, 46 metastases were evaluated. A reversible 2-tissue-compartment model was preferred for 18F-DCFPyL kinetics in 59% of the metastases. The observed k4 was small, however, resulting in nearly irreversible kinetics during the course of the PET study. Hence, k4 was fixated (0.015) and net influx rate, Ki, was preferred as the reference kinetic parameter. Whole-blood TBR provided an excellent correlation with Ki from full kinetic analysis (R2 = 0.97). This TBR could be simplified further by replacing the blood samples with an image-based, single measurement of blood activity in the ascending aorta (image-based TBR, R2 = 0.96). SUV correlated poorly with Ki (R2 = 0.47 and R2 = 0.60 for SUV normalized to body weight and lean body mass, respectively), most likely because of deviant blood activity concentrations (i.e., tumor tracer input) in patients with higher tumor volumes. Conclusion:18F-DCFPyL kinetics in PCa metastases are best described by a reversible 2-tissue-compartment model. Image-based TBRs were validated as a simplified method to quantify 18F-DCFPyL uptake and might be applied to clinical, whole-body PET scans. SUV does not provide reliable quantification of 18F-DCFPyL uptake.

Healthy Tissue Uptake of 68Ga-Prostate-Specific Membrane Antigen, 18F-DCFPyL, 18F-Fluoromethylcholine, and 18F-Dihydrotestosterone.

PET is increasingly used for prostate cancer (PCa) diagnostics. Important PCa radiotracers include 68Ga-prostate-specific membrane antigen HBED-CC (68Ga-PSMA), 18F-DCFPyL, 18F-fluoromethylcholine (18F-FCH), and 18F-dihydrotestosterone (18F-FDHT). Knowledge on the variability of tracer uptake in healthy tissues is important for accurate PET interpretation, because malignancy is suspected only if the uptake of a lesion contrasts with its background. Therefore, the aim of this study was to quantify uptake variability of PCa tracers in healthy tissues and identify stable reference regions for PET interpretation. Methods: A total of 232 PCa PET/CT scans from multiple hospitals was analyzed, including 87 68Ga-PSMA scans, 50 18F-DCFPyL scans, 68 18F-FCH scans, and 27 18F-FDHT scans. Tracer uptake was assessed in the blood pool, lung, liver, bone marrow, and muscle using several SUVs (SUVmax, SUVmean, SUVpeak). Variability in uptake between patients was analyzed using the coefficient of variation (COV%). For all tracers, SUV reference ranges (95th percentiles) were calculated, which could be applicable as image-based quality control for future PET acquisitions. Results: For 68Ga-PSMA, the lowest uptake variability was observed in the blood pool (COV, 19.9%), which was significantly more stable than all other tissues (COV, 29.8%-35.2%; P = 0.001-0.024). For 18F-DCFPyL, the lowest variability was observed in the blood pool and liver (COV, 14.4% and 21.7%, respectively; P = 0.001-0.003). The least variable 18F-FCH uptake was observed in the liver, blood pool, and bone marrow (COV, 16.8%-24.2%; P = 0.001-0.012). For 18F-FDHT, low uptake variability was observed in all tissues, except the lung (COV, 14.6%-23.6%; P = 0.001-0.040). The different SUV types had limited effect on variability (COVs within 3 percentage points). Conclusion: In this multicenter analysis, healthy tissues with limited uptake variability were identified, which may serve as reference regions for PCa PET interpretation. These reference regions include the blood pool for 68Ga-PSMA and 18F-DCFPyL and the liver for 18F-FCH and 18F-FDHT. Healthy tissue SUV reference ranges are presented and applicable as image-based quality control.