Osteosarcoma is the most common type of bone cancer that occurs in adolescents in the age of 10 to 14 years. The dataset is composed of Hematoxylin and eosin (H&E) stained osteosarcoma histology images. The data was collected by a team of clinical scientists at University of Texas Southwestern Medical Center, Dallas. Archival samples for 50 patients treated at Children’ s Medical Center, Dallas, between 1995 and 2015, were used to create this dataset. Four patients (out of 50) were selected by pathologists based on diversity of tumor specimens after surgical resection. The images are labelled as Non-Tumor, Viable Tumor and Necrosis according to the predominant cancer type in each image. The annotation was performed by two medical experts. All images were divided between two pathologists for the annotation activity. Each image had a single annotation as any given image was annotated by only one pathologist. The dataset consists of 1144 images of size 1024 X 1024 at 10X resolution with the following distribution: 536 (47%) non-tumor images, 263 (23%) necrotic tumor images and 345 (30%) viable tumor tiles.
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Number of Patients
Number of Images
|Images Size (MB)||196|
Citations & Data Usage Policy
These collections are freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. Questions may be directed to firstname.lastname@example.org. Please be sure to acknowledge both this data set and TCIA in publications by including the following citations in your work:
Leavey, P., Sengupta, A., Rakheja, D., Daescu, O., Arunachalam, H. B., & Mishra, R. (2019). Osteosarcoma data from UT Southwestern/UT Dallas for Viable and Necrotic Tumor Assessment [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2019.bvhjhdas
1) Mishra, Rashika, et al. "Histopathological diagnosis for viable and non-viable tumor prediction for osteosarcoma using convolutional neural network." International Symposium on Bioinformatics Research and Applications. Springer, Cham, 2017.
2) Arunachalam, Harish Babu, et al. "Computer aided image segmentation and classification for viable and non-viable tumor identification in osteosarcoma." PACIFIC SYMPOSIUM ON BIOCOMPUTING 2017. 2017.
3) Mishra, Rashika, et al. "Convolutional Neural Network for Histopathological Analysis of Osteosarcoma." Journal of Computational Biology 25.3 (2018): 313-325.
4) Leavey, Patrick, et al. "Implementation of Computer-Based Image Pattern Recognition Algorithms to Interpret Tumor Necrosis; a First Step in Development of a Novel Biomarker in Osteosarcoma." PEDIATRIC BLOOD & CANCER. Vol. 64. 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY, 2017.
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7
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