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Data Access

Click the Download  button to save a ".tcia" manifest file button to save files to your computer, which you must open with the  NBIA Data Retriever .

Note:   Please contact help@cancerimagingarchive.net  with any questions regarding usage.

Collections Used in this Third Party Analysis
Below is a list of the Collections used in these analyses:

• TCGA-LGG

Detailed Description

Data resulting from this experiment is available in the following formats:

• DICOM image format
• Processed NIFTI images with segmentations and radiomic features

Citations & Data Usage Policy 

Other Publications Using This Data

TCIA maintains a list of publications that leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.

• Aliotta, E., Dutta, S. W., Feng, X., Tustison, N. J., Batchala, P. P., Schiff, D., . . . Patel, S. H. (2020). Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign. J Neurooncol, 149(2), 325-335. doi:https://doi.org/10.1007/s11060-020-03611-8
• Astaraki, M., Wang, C., Carrizo, G., Toma-Dasu, I., & Smedby, Ö. (2020). Multimodal Brain Tumor Segmentation with Normal Appearance Autoencoder. Paper presented at the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Shenzhen, China.
• Bhadani, S., Mitra, S., & Banerjee, S. (2020). Fuzzy volumetric delineation of brain tumor and survival prediction. Soft Computing, 24(17), 13115-13134. doi:10.1007/s00500-020-04728-8
  Chan, H.-W., Weng, Y.-T., & Huang, T.-Y. (2020). Automatic Classification of Brain Tumor Types with the MRI Scans and Histopathology Images. Paper presented at the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries Shenzhen, China,.
• Chen, M., Wu, Y., & Wu, J. (2020). Aggregating Multi-scale Prediction Based on 3D U-Net in Brain Tumor Segmentation. Paper presented at the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. , Shenzhen, China.
• Ge, C. (2020). Machine Learning Methods for Image Analysis in Medical Applications From Alzheimer’s Disease, Brain Tumors, to Assisted Living. (Ph. D. Dissertation), Chalmers University of Technology, Göteborg, Sweden. Retrieved from https://research.chalmers.se/publication/517576 10.7937/K9/TCIA.2017.KLXWJJ1Q; 10.7937/K9/TCIA.2017.GJQ7R0EF database.
• Rafi, A., Ali, J., Akram, T., Fiaz, K., Raza Shahid, A., Raza, B., & Mustafa Madni, T. (2020, March 18-19, 2020). U-Net Based Glioblastoma Segmentation with Patient’s Overall Survival Prediction. Paper presented at the ISICS: International Symposium on Intelligent Computing Systems, Sharjah, United Arab Emirates.
• Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., . . . Bakas, S. (2020). Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci Rep, 10(1), 12598. doi:https://doi.org/10.1038/s41598-020-69250-1
• Thakur, S., Doshi, J., Pati, S., Rathore, S., Sako, C., Bilello, M., . . . Bakas, S. (2020). Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training. Neuroimage, 220, 117081. doi:https://doi.org/10.1016/j.neuroimage.2020.117081
• Tunga, P. (2019). Extraction of Tumor in Brain MRI using Support Vector Machine and Performance Evaluation. Visvesvaraya Technological University Journal of Engineering Sciences and Management, 1(3), 1-8.
• Zhang, X., Liu, S., Zhao, X., Shi, X., Li, J., Guo, J., . . . Zhang, X. (2020). Magnetic resonance imaging-based radiomic features for extrapolating infiltration levels of immune cells in lower-grade gliomas. Strahlentherapie und Onkologie, 196(10), 913-921. doi:10.1007/s00066-020-01584-1

Version 1 (Current): 2017/07/17