BackgroundCitizen science, scientific research conducted by non-specialists, has the potential to facilitate biomedical research using available large-scale data, however validating the results is challenging. The Cell Slider is a citizen science project that intends to share images from tumors with the general public, enabling them to score tumor markers independently through an internet-based interface.MethodsFrom October 2012 to June 2014, 98,293 Citizen Scientists accessed the Cell Slider web page and scored 180,172 sub-images derived from images of 12,326 tissue microarray cores labeled for estrogen receptor (ER). We evaluated the accuracy of Citizen Scientist's ER classification, and the association between ER status and prognosis by comparing their test performance against trained pathologists.FindingsThe area under ROC curve was 0.95 (95% CI 0.94 to 0.96) for cancer cell identification and 0.97 (95% CI 0.96 to 0.97) for ER status. ER positive tumors scored by Citizen Scientists were associated with survival in a similar way to that scored by trained pathologists. Survival probability at 15 years were 0.78 (95% CI 0.76 to 0.80) for ER-positive and 0.72 (95% CI 0.68 to 0.77) for ER-negative tumors based on Citizen Scientists classification. Based on pathologist classification, survival probability was 0.79 (95% CI 0.77 to 0.81) for ER-positive and 0.71 (95% CI 0.67 to 0.74) for ER-negative tumors. The hazard ratio for death was 0.26 (95% CI 0.18 to 0.37) at diagnosis and became greater than one after 6.5 years of follow-up for ER scored by Citizen Scientists, and 0.24 (95% CI 0.18 to 0.33) at diagnosis increasing thereafter to one after 6.7 (95% CI 4.1 to 10.9) years of follow-up for ER scored by pathologists.InterpretationCrowdsourcing of the general public to classify cancer pathology data for research is viable, engages the public and provides accurate ER data. Crowdsourced classification of research data may offer a valid solution to problems of throughput requiring human input.

IntroductionReal-world evidence on glucagon-like peptide-1 receptor agonist (GLP-1 RAs) usage is emerging in different European countries but is lacking in Italy. This retrospective cohort study aimed to describe the real-world drug utilization patterns in patients initiating GLP-1 RAs for treating T2DM in Italy.MethodsAdults aged ≥ 20 years and with ≥ 1 oral antidiabetic drug (alone or in combination with insulin) other than GLP-1 RAs in the 6 months prior to initiating exenatide twice daily (exBID), exenatide once weekly (exQW), dulaglutide once weekly (DULA), liraglutide once daily (LIRA) or lixisenatide once daily (LIXI) between March and July 2016 were retrospectively identified in the Italian IMS LifeLink™ longitudinal prescriptions database (retail pharmacy data). Patients with ≥ 6-month follow-up (defined as evidence of any prescription activity) were included. Proportions of patients who remained persistent (continued treatment until discontinuation/switch) in the first 6 months and of those who discontinued or switched to a different GLP-1 RA over the entire follow-up were recorded. For each treatment, the average daily/weekly dosage (ADD/AWD) while persistent during the available follow-up was calculated.ResultsWe identified 7319 patients: 92 exBID, 970 exQW, 3368 DULA, 2573 LIRA and 316 LIXI. Across treatments, 89% patients were ≥ 50 years old, 54% were males, and the median follow-up duration ranged between 8.1 and 8.7 months. At 6 months, 35% exBID, 47% exQW, 62% DULA, 50% LIRA and 40% LIXI patients remained persistent. Over the entire follow-up, median persistence days varied from 73 (exBID) to > 300 days (DULA). The mean ± SD ADD/AWD was exBID: 17.7 ± 2.1 µg/day; exQW: 2.1 ± 0.1 mg/week; DULA: 1.5 ± 0.2 mg/week; LIRA: 1.5 ± 0.2 mg/day; LIXI: 21.0 ± 5.5 µg/day.ConclusionsThis real-world analysis suggests differences exist in persistence between patients treated with various GLP-1 RAs. Among the investigated treatments, patients prescribed exBID recorded the lowest and those prescribed DULA the highest persistence with therapy.FundingEli Lilly and Co., Indianapolis, IN, USA.Electronic supplementary materialThe online version of this article (10.1007/s13300-018-0396-2) contains supplementary material, which is available to authorized users.

Background:Academic pathology suffers from an acute and growing lack of workforce resource. This especially impacts on translational elements of clinical trials, which can require detailed analysis of thousands of tissue samples. We tested whether crowdsourcing – enlisting help from the public – is a sufficiently accurate method to score such samples.Methods:We developed a novel online interface to train and test lay participants on cancer detection and immunohistochemistry scoring in tissue microarrays. Lay participants initially performed cancer detection on lung cancer images stained for CD8, and we measured how extending a basic tutorial by annotated example images and feedback-based training affected cancer detection accuracy. We then applied this tutorial to additional cancer types and immunohistochemistry markers – bladder/ki67, lung/EGFR, and oesophageal/CD8 – to establish accuracy compared with experts. Using this optimised tutorial, we then tested lay participants' accuracy on immunohistochemistry scoring of lung/EGFR and bladder/p53 samples.Results:We observed that for cancer detection, annotated example images and feedback-based training both improved accuracy compared with a basic tutorial only. Using this optimised tutorial, we demonstrate highly accurate (>0.90 area under curve) detection of cancer in samples stained with nuclear, cytoplasmic and membrane cell markers. We also observed high Spearman correlations between lay participants and experts for immunohistochemistry scoring (0.91 (0.78, 0.96) and 0.97 (0.91, 0.99) for lung/EGFR and bladder/p53 samples, respectively).Conclusions:These results establish crowdsourcing as a promising method to screen large data sets for biomarkers in cancer pathology research across a range of cancers and immunohistochemical stains.

Background Immunohistochemistry (IHC) is often used in personalisation of cancer treatments. Analysis of large data sets to uncover predictive biomarkers by specialists can be enormously time-consuming. Here we investigated crowdsourcing as a means of reliably analysing immunostained cancer samples to discover biomarkers predictive of cancer survival. Methods We crowdsourced the analysis of bladder cancer TMA core samples through the smartphone app ‘Reverse the Odds’. Scores from members of the public were pooled and compared to a gold standard set scored by appropriate specialists. We also used crowdsourced scores to assess associations with disease-specific survival. Results Data were collected over 721 days, with 4,744,339 classifications performed. The average time per classification was approximately 15 s, with approximately 20,000 h total non-gaming time contributed. The correlation between crowdsourced and expert H-scores (staining intensity × proportion) varied from 0.65 to 0.92 across the markers tested, with six of 10 correlation coefficients at least 0.80. At least two markers (MRE11 and CK20) were significantly associated with survival in patients with bladder cancer, and a further three markers showed results warranting expert follow-up. Conclusions Crowdsourcing through a smartphone app has the potential to accurately screen IHC data and greatly increase the speed of biomarker discovery.

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