Data AccessData Type | Download all or Query/Filter | License |
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Images (DICOM, 6.6GB) |
(Download requires the NBIA Data Retriever) | | Lung3 clinical (xls, 32 kb) | | |
Click the Versions tab for more info about data releases. Additional Resources for this DatasetThe following external resources have been made available by the data submitters. These are not hosted or supported by TCIA, but may be useful to researchers utilizing this collection. The NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data. |
Detailed Description | Collection Statistics |
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Modalities | CT | Number of Participants | 89 | Number of Studies | 89 | Number of Series | 89 | Number of Images | 13,482 | Image Size (GB) | 6.6 |
Gene-expression DataCorresponding microarray data acquired for the imaging samples are available at National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (Link to GEO: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE58661). The patient names used to identify the cases on GEO are identical to those used in the DICOM files on TCIA and in the clinical data spreadsheet.
Clinical DataCorresponding clinical data can be found here: Lung3.metadata.xls. DICOM patients names are identical in TCIA and clinical data file. |
Citations & Data Usage Policy Aerts HJWL, Rios Velazquez E, Leijenaar RTH, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, & Lambin P. (2015). Data From NSCLC-Radiomics-Genomics. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.L4FRET6Z |
Aerts HJWL, Rios Velazquez E, Leijenaar RTH, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, & Lambin P. (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications 5, 4006 . https://doi.org/10.1038/ncomms5006 |
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: https://doi.org/10.1007/s10278-013-9622-7 |
Questions may be directed to help@cancerimagingarchive.net. Other Publications Using This DataTCIA maintains a list of publications which leverage our data. If you have a manuscript you'd like to add, please contact TCIA's Helpdesk. - Chen, L., Qi, H., Lu, D., Zhai, J., Cai, K., Wang, L., . . . Zhang, Z. (2022). Machine vision-assisted identification of the lung adenocarcinoma category and high-risk tumor area based on CT images. Patterns (N Y), 3(4), 100464. doi: https://doi.org/10.1016/j.patter.2022.100464
- Choi, J., Cho, H. H., Kwon, J., Lee, H. Y., & Park, H. (2021). A Cascaded Neural Network for Staging in Non-Small Cell Lung Cancer Using Pre-Treatment CT. Diagnostics (Basel), 11(6). doi: https://doi.org/10.3390/diagnostics11061047
- Chui, K. T., Arya, V., Band, S. S., Alhalabi, M., Liu, R. W., & Chi, H. R. (2023). Facilitating innovation and knowledge transfer between homogeneous and heterogeneous datasets: Generic incremental transfer learning approach and multidisciplinary studies. Journal of Innovation & Knowledge, 8(2). doi: https://doi.org/10.1016/j.jik.2023.100313
- Cury, S. S., de Moraes, D., Freire, P. P., de Oliveira, G., Marques, D. V. P., Fernandez, G. J., . . . Carvalho, R. F. (2019). Tumor Transcriptome Reveals High Expression of IL-8 in Non-Small Cell Lung Cancer Patients with Low Pectoralis Muscle Area and Reduced Survival. Cancers (Basel), 11(9). doi:10.3390/cancers11091251
- Erkoc, M., & Icer, S. (2022). Analysis of Computed Tomography Images of Lung Cancer Patients with The Marker Controlled Based Method. Paper presented at the 2022 Medical Technologies Congress (TIPTEKNO), Antalya, Türkiye.
- Horng, H., Singh, A., Yousefi, B., Cohen, E. A., Haghighi, B., Katz, S., . . . Shinohara, R. T. (2022). Improved generalized ComBat methods for harmonization of radiomic features. Sci Rep, 12(1), 19009. doi:https://doi.org/10.1038/s41598-022-23328-0
- Khodabakhshi, Z., Mostafaei, S., Arabi, H., Oveisi, M., Shiri, I., & Zaidi, H. (2021). Non-small cell lung carcinoma histopathological subtype phenotyping using high-dimensional multinomial multiclass CT radiomics signature. Comput Biol Med, 136, 104752. doi: https://doi.org/10.1016/j.compbiomed.2021.104752
- Lin, P., Lin, Y. Q., Gao, R. Z., Wan, W. J., He, Y., & Yang, H. (2023). Integrative radiomics and transcriptomics analyses reveal subtype characterization of non-small cell lung cancer. Eur Radiol. doi:https://doi.org/10.1007/s00330-023-09503-5
- Pastor-Serrano, O., & Perko, Z. (2022). Millisecond speed deep learning based proton dose calculation with Monte Carlo accuracy. Phys Med Biol, 67(10). doi: https://doi.org/10.1088/1361-6560/ac692e
- Patel, D., Cowan, C., & Prasanna, P. (2021). Predicting Mutation Status and Recurrence Free Survival in Non-Small Cell Lung Cancer: A Hierarchical ct Radiomics – Deep Learning Approach. Paper presented at the 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Nice, France.
- Primakov, S. P., Ibrahim, A., van Timmeren, J. E., Wu, G., Keek, S. A., Beuque, M., . . . Lambin, P. (2022). Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications, 13(1), 3423. doi: https://doi.org/10.1038/s41467-022-30841-3
- Trebeschi, S., Bodalal, Z., van Dijk, N., Boellaard, T. N., Apfaltrer, P., Tareco Bucho, T. M., . . . Beets-Tan, R. G. H. (2021). Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy. Front Oncol, 11, 637804. doi: https://doi.org/10.3389/fonc.2021.637804
- Yu, L., Tao, G., Zhu, L., Wang, G., Li, Z., Ye, J., & Chen, Q. (2019). Prediction of pathologic stage in non-small cell lung cancer using machine learning algorithm based on CT image feature analysis. BMC Cancer, 19(1), 464. doi: https://doi.org/10.1186/s12885-019-5646-9
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Version 1 (Current): Updated 2014/07/02Data Type | Download all or Query/Filter |
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Images (DICOM, 6.6GB) |
(Download requires the NBIA Data Retriever) | Lung3 clinical (CSV) | |
Additional Resources for this Dataset |
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