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Crowds Cure Cancer (https://www.crowds-cure.org) 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 https://doi.org/10.7937/K9/TCIA.2018.OW73VLO2.
For RSNA 2018, the activity the application was re-designed to promote more comprehensive data collection and increase 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. The amount of data being annotated was also significantly increased to include CT scans from 13 TCIA collections: Anti-PD-1_Lung, Anti-PD-1_MELANOMA, CPTAC-CCRCC, CPTAC-GBM, CPTAC-HNSCC, CPTAC-PDA, CPTAC-UCEC, NSCLC Radiogenomics, TCGA-BLCA, TCGA-COAD, TCGA-HNSC, TCGA-LUSC, TCGA-UCEC.
During 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. Data will be The data is being released in DICOM Structured Report and CSV formats for analysis by the community. The application is available on GitHub https://github.com/crowds-cure/cancer and an enhanced version will be exhibited at RSNA 2019.
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- Source DICOM scans annotated by participants
- CSV representation of crowd measurements
- DICOM-SR representation of crowd measurements Clinical Data
- Note: Because all subjects were pulled from The Cancer Genome Atlas cohorts clinical data was available through the NCI Genomic Data Commons. A CSV dump of that data is provided here for convenience.