The Cancer Imaging Archive (TCIA) has procured substantial troves of image data, which could serve as valuable training sets for improving machine learning algorithms. However, these datasets lack consistent lesion annotations. To address this issue, the\u00a0Cancer Imaging Informatics Lab<\/a>\u00a0at the Frederick National Laboratory for Cancer Research (FNLCR) formed a partnership with five groups funded by the\u00a0National Cancer Institute's Informatics Technology for Cancer Research program<\/a>\u00a0to develop a web-based crowdsourcing application for gathering lesion annotations, featured at the annual meeting of the Radiological Society of North American (RSNA).<\/p> Crowds Cure Cancer (https:\/\/www.crowds-cure.org<\/a>) first exhibited at RSNA 2017 utilizing CT scans from 4 different TCIA collections. Participants were asked to make a uni-dimensional measurement of the largest lesion. There were no options to provide details regarding imaging quality (e.g., no IV contrast, motion artifact, etc.), lesion location (e.g., lung, liver, etc.) or lesion characteristics (e.g., ill-defined, ground glass, etc.), requiring additional post-collection image review. The 2017 dataset can be found at\u00a0https:\/\/doi.org\/10.7937\/K9\/TCIA.2018.OW73VLO2<\/a>.<\/p> For RSNA 2018, the application was re-designed to promote more comprehensive data collection and increased community participation. Participants were instructed to identify all metastatic disease and provide details regarding image quality, lesion location and characteristics. To provide additional incentives for participation, we improved the system by adding gamification features (e.g., reward badges), and created a leaderboard to display participant standings.\u00a0 The amount of data being annotated was also significantly increased to include CT scans from 324 patients spanning 13 TCIA collections:\u00a0\u00a0Anti-PD-1_Lung<\/a>,\u00a0\u00a0Anti-PD-1_MELANOMA<\/a>,\u00a0\u00a0The Clinical Proteomic Tumor Analysis Consortium Clear Cell Renal Cell Carcinoma Collection (CPTAC-CCRCC)<\/a>,\u00a0\u00a0The Clinical Proteomic Tumor Analysis Consortium Glioblastoma Multiforme Collection (CPTAC-GBM)<\/a>,\u00a0\u00a0The Clinical Proteomic Tumor Analysis Consortium Head and Neck Squamous Cell Carcinoma Collection (CPTAC-HNSCC)<\/a>,\u00a0\u00a0The Clinical Proteomic Tumor Analysis Consortium Pancreatic Ductal Adenocarcinoma Collection (CPTAC-PDA)<\/a>,\u00a0\u00a0The Clinical Proteomic Tumor Analysis Consortium Uterine Corpus Endometrial Carcinoma Collection (CPTAC-UCEC)<\/a>,\u00a0\u00a0NSCLC Radiogenomics<\/a>,\u00a0\u00a0The Cancer Genome Atlas Urothelial Bladder Carcinoma Collection (TCGA-BLCA)<\/a>,\u00a0\u00a0The Cancer Genome Atlas Colon Adenocarcinoma Collection (TCGA-COAD)<\/a>,\u00a0\u00a0The Cancer Genome Atlas Head-Neck Squamous Cell Carcinoma Collection (TCGA-HNSC)<\/a>,\u00a0\u00a0The Cancer Genome Atlas Lung Squamous Cell Carcinoma Collection (TCGA-LUSC)<\/a>,\u00a0\u00a0The Cancer Genome Atlas Uterine Corpus Endometrial Carcinoma Collection (TCGA-UCEC)<\/a>.\u00a0\u00a0During RSNA 2018, 4756 bi-directional measurements were obtained compared to 2345 uni-dimensional measurements in 2017. Of the 4756 measurements, 65% of the lesions were annotated with location information. The data is being released in DICOM Structured Report and CSV formats for analysis by the community. The application is available on GitHub\u00a0https:\/\/github.com\/crowds-cure\/cancer<\/a>\u00a0.<\/p>![](\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/embedded-page\/DOI\/Crowds%20Cure%20Cancer:%20Data%20collected%20at%20the%20RSNA%202018%20annual%20meeting%20(Crowds-Cure-2018)\/crowds%20cure%20booth.jpg?api=v2\")
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