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Brain-Tumor-Progression | Brain-Tumor-Progression
DOI: 10.7937/K9/TCIA.2018.15quzvnb | Data Citation Required | Image Collection
| Location | Species | Subjects | Data Types | Cancer Types | Size | Status | Updated | |
|---|---|---|---|---|---|---|---|---|
| Brain | Human | 20 | MR | Brain Cancer | Image Analyses | Limited, Complete | 2018/01/31 |
Summary
This collection includes datasets from 20 subjects with primary newly diagnosed glioblastoma who were treated with surgery and standard concomitant chemo-radiation therapy (CRT) followed by adjuvant chemotherapy. Two MRI exams are included for each patient: within 90 days following CRT completion and at progression (determined clinically, and based on a combination of clinical performance and/or imaging findings, and punctuated by a change in treatment or intervention). All image sets are in DICOM format and contain T1w (pre and post-contrast agent), FLAIR, T2w, ADC, normalized cerebral blood flow, normalized relative cerebral blood volume, standardized relative cerebral blood volume, and binary tumor masks (generated using T1w images). The perfusion images were generated from dynamic susceptibility contrast (GRE-EPI DSC) imaging following a preload of contrast agent. All of the series are co-registered with the T1+C images. The intent of this dataset is for assessing deep learning algorithm performance to predict tumor progression.
Data Access
Some data in this collection contains images that could potentially be used to reconstruct a human face. To safeguard the privacy of participants, users must sign and submit a TCIA Restricted License Agreement to help@cancerimagingarchive.net before accessing the data.
Version 1: Updated 2018/01/31
| Title | Data Type | Format | Access Points | Subjects | License | |||
|---|---|---|---|---|---|---|---|---|
| Images | MR | DICOM | Download requires NBIA Data Retriever |
20 | 40 | 383 | 8,798 | TCIA Restricted |
Citations & Data Usage Policy
Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:
Data Citation |
|
|
Schmainda KM, Prah M (2018). Data from Brain-Tumor-Progression. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2018.15quzvnb |
Related Publications
Publications by the Dataset Authors
The authors recommended this paper as the best source of additional information about this dataset:
No publications by dataset authors were found.
Research Community Publications
TCIA maintains a list of publications which leverage our data. If you have a publication you’d like to add please contact TCIA’s Helpdesk.
- Abdelazeem, R. M., Youssef, D., El-Azab, J., Hassab-Elnaby, S., & Agour, M. (2020). Three-dimensional visualization of brain tumor progression based accurate segmentation via comparative holographic projection. PLoS One, 15(7), e0236835. doi: 10.1371/journal.pone.0236835
- Adinegoro, A. F., Sutapa, G. N., Gunawan, A. A. N., Anggarani, N. K. N., Suardana, P., & Kasmawan, I. G. A. (2023). Classification and Segmentation of Brain Tumor Using EfficientNet-B7 and U-Net. Asian Journal of Research in Computer Science, 15(3), 1-9. doi: https://doi.org/10.9734/ajrcos/2023/v15i3320
- Azat, H. S., Sekeroglu, B., & Dimililer, K. (2021). A Pre-study on the Layer Number Effect of Convolutional Neural Networks in Brain Tumor Classification. Paper presented at the 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA), Kocaeli, Turkey.
- Barbu, T. (2020). Variational Quantum Denoising Technique for Medical Images. Paper presented at the 2020 International Conference on e-Health and Bioengineering (EHB), Iasi, Romania.
- Bayoumi, E., Abd-Ellah, m., Khalaf, A. A. M., & Gharieb, R. (2021). Brain Tumor Automatic Detection from MRI Images Using Transfer Learning Model with Deep Convolutional Neural Network. [الكشف التلقائي عن أورم المخ من خالل صور التصوير بالرنين المغناطيسي باستخدام نموذج نقل التعلم مع الشبكة العصبية التالفيفية العميقة]. Journal of Advanced Engineering Trends, 41(2), 19-30. doi:10.21608/jaet.2020.42896.1051
- Bell, L. C., Stokes, A. M., & Quarles, C. C. (2019). Analysis of postprocessing steps for residue function dependent dynamic susceptibility contrast (DSC)‐MRI biomarkers and their clinical impact on glioma grading for both 1.5 and 3T. Journal of Magnetic Resonance Imaging. doi:10.1002/jmri.26837
- Celik, S., & KASIM, Ö. (2020). Detection of Tumor Slice in Brain Magnetic Resonance Images by Feature Optimized Transfer Learning. Aksaray University Journal of Science and Engineering, 4(2), 187-198. doi:10.29002/asujse.820599
- De Sutter, S., Geens, W., Bossa, M., Vanbinst, A.-M., Duerinck, J., & Vandemeulebroucke, J. (2023). Probabilistic Tissue Mapping for Tumor Segmentation and Infiltration Detection of Glioma. In S. Bakas, A. Crimi, U. Baid, S. Malec, M. Pytlarz, B. Baheti, M. Zenk, & R. Dorent (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 13769, pp. 80-89). Singapore: Springer.
- Du, R., & Vardhanabhuti, V. (2020, 06-08 July 2020). 3D-RADNet: Extracting labels from DICOM metadata for training general medical domain deep 3D convolution neural networks. Paper presented at the Third Conference on Medical Imaging with Deep Learning (MIDL 2020), Montréal, QC, Canada.
- Gryska, E. (2022). Automatic tumour segmentation in brain images: moving towards clinical implementation. (Ph. D.). Sahlgrenska Academy, University of Gothenburg, Borås, Sweden. Retrieved from https://gupea.ub.gu.se/handle/2077/72056
- Isunuri, B. V., & Kakarla, J. (2023). EfficientNet and multi-path convolution with multi-head attention network for brain tumor grade classification. Computers and Electrical Engineering, 108. doi:https://doi.org/10.1016/j.compeleceng.2023.108700
- Miyata, S., Chang, C.-M., & Igarashi, T. (2022, June 26 – July 1, 2022). Trafne: A Training Framework for Non-expert Annotators with Auto Validation and Expert Feedback. Paper presented at the Artificial Intelligence in HCI: 3rd International Conference, AI-HCI 2022, Virtual Event.
- Romero-Garcia, R., Mandal, A. S., Bethlehem, R. A. I., Crespo-Facorro, B., Hart, M. G., & Suckling, J. (2022). Transcriptomic and connectomic correlates of differential spatial patterning among gliomas. BRAIN, 146(3), 1200-1211. doi:10.1093/brain/awac378
- Si, T. (2023). 2D MRI registration using glowworm swarm optimization with partial opposition-based learning for brain tumor progression. Pattern Analysis and Applications. doi:https://doi.org/10.1007/s10044-023-01153-z
- Sultan, H., Owais, M., Nam, S. H., Haider, A., Akram, R., Usman, M., & Park, K. R. (2023). MDFU-Net: Multiscale dilated features up-sampling network for accurate segmentation of tumor from heterogeneous brain data. Journal of King Saud University – Computer and Information Sciences, 35(5). doi:https://doi.org/10.1016/j.jksuci.2023.101560
- Tong, T., Huang, W., Wang, K., He, Z., Yin, L., Yang, X., . . . Tian, J. (2020). Domain Transform Network for Photoacoustic Tomography from Limited-view and Sparsely Sampled Data. Photoacoustics, 19, 100190. doi:10.1016/j.pacs.2020.100190
- van der Voort, S. R., Incekara, F., Wijnenga, M. M. J., Kapsas, G., Gahrmann, R., Schouten, J. W., . . . Klein, S. (2022). Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning. Neuro Oncol. doi:10.1093/neuonc/noac166