MCO  Study Whole Slide Image Dataset

 The MCO CRC study whole slide image collection is a substantial repository comprising 1,500 digitized tissue slides obtained from patients with colorectal cancer. Spanning the period from 1994 to 2010, the Molecular and Cellular Oncology (MCO) Study Group conducted an extensive research effort involving individuals undergoing treatment for colorectal cancer. As part of this study, a systematic collection of tissue samples, along with comprehensive clinical and pathological data, was conducted from over 1,500 individuals who had undergone surgical removal of tumors in their large bowels.

This collection features one representative tissue section from each case, meticulously stained with hematoxylin and Eosin, and subsequently scanned using an x40 objective. The resulting digitized images boast an impressive resolution that closely mirrors what is observable under an optical microscope—exceeding 100,000 dots per inch (dpi). At such a high resolution, each individual image occupies approximately 2 gigabytes of storage space, contributing to a cumulative size of 3 terabytes for the entire collection of 1,500 images within the MCO Whole Slide Image Collection.

To facilitate accessibility and utilization for research and scientific endeavors, the entirety of the MCO slide image collection is currently made available on the SREDH Platform. The dataset's origin can be traced back to the dedicated efforts of the MCO research group at the University of New South Wales (UNSW), spanning from 1993 to 2011. This invaluable resource serves as a cornerstone for researchers and medical professionals seeking to advance the understanding and diagnosis of colorectal cancer through comprehensive pathological insights.

Data access fees

• A$2,800 (Excluding taxes)

• Additional A$950, if the ethics approval is not in English 

• Payment via bank transfer to UNSW Sydney

Dataset access instructions

• Fill out the data request form to obtain access to the dataset.

• Once the request is approved, please sign and return the SREDH Consortium membership, data usage and project description forms that will be sent up on filling out the data request form above.

• Pay data access and associated fees, if applicable 

• Download the dataset from the SREDH secure server. 

• Submit progress report every 6 months until the completion of the project

Data access criteria

• Available to researchers (academic and non-academic) for non-commercial purposes

• Researchers need to have experience handling sensitive patients and training in ethics.

• Researchers are required to report biannually to the SREDH Consortium on any research outputs that arise.

• Any output that arises from this dataset needs to be reviewed by the data custodian (SREDH Consortium) before submission. 

Frequently Asked Questions

• Please refer to FAQs page.

                Selected publications

• Jiang X, Hoffmeister M, Brenner H, Muti HS, Yuan T, Foersch S, West NP, Brobeil A, Jonnagaddala J, Hawkins N, Ward RL, Brinker TJ, Saldanha OL, Ke J, Müller W, Grabsch HI, Quirke P, Truhn D, Kather JN. End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study. Lancet Digit Health. 2024 Jan;6(1):e33-e43. doi: 10.1016/S2589-7500(23)00208-X. PMID: 38123254. https://doi.org/10.1016/S2589-7500(23)00208-X

• Wagner, S. J., Reisenbüchler, D., West, N. P., Niehues, J. M., Zhu, J., Foersch, S., Veldhuizen, G. P., Quirke, P., Grabsch, H. I., van den Brandt, P. A., Hutchins, G. G. A., Richman, S. D., Yuan, T., Langer, R., Jenniskens, J. C. A., Offermans, K., Mueller, W., Gray, R., Gruber, S. B., Greenson, J. K., … Kather, J. N. (2023). Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study. Cancer cell, 41(9), 1650–1661.e4. https://doi.org/10.1016/j.ccell.2023.08.002 

• Susič, D., Syed-Abdul, S., Dovgan, E., Jonnagaddala, J., & Gradišek, A. (2023). Artificial intelligence based personalized predictive survival among colorectal cancer patients. Computer methods and programs in biomedicine, 231, 107435. https://doi.org/10.1016/j.cmpb.2023.107435 

•  Shao, Z., Dai, L., Jonnagaddala, J., Chen, Y., Wang, Y., Fang, Z., & Zhang, Y. (2023). Generalizability of Self-Supervised Training Models for Digital Pathology: A Multicountry Comparison in Colorectal Cancer. JCO clinical cancer informatics, 7, e2200178. https://doi.org/10.1200/CCI.22.00178 

• Liu, A., Li, X., Wu, H., Guo, B., Jonnagaddala, J., Zhang, H., & Xu, S. (2023). Prognostic Significance of Tumor-Infiltrating Lymphocytes Determined Using LinkNet on Colorectal Cancer Pathology Images. JCO precision oncology, 7, e2200522. https://doi.org/10.1200/PO.22.00522 

• Höhn, J., Krieghoff-Henning, E., Wies, C., Kiehl, L., Hetz, M. J., Bucher, T. C., Jonnagaddala, J., Zatloukal, K., Müller, H., Plass, M., Jungwirth, E., Gaiser, T., Steeg, M., Holland-Letz, T., Brenner, H., Hoffmeister, M., & Brinker, T. J. (2023). Colorectal cancer risk stratification on histological slides based on survival curves predicted by deep learning. NPJ precision oncology, 7(1), 98. https://doi.org/10.1038/s41698-023-00451-3  

• Guo, B., Li, X., Yang, M., Jonnagaddala, J., Zhang, H., & Xu, X. S. (2023). Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: achieving state-of-the-art predictive performance with fewer data using Swin Transformer. The journal of pathology. Clinical research, 9(3), 223–235. https://doi.org/10.1002/cjp2.312 

• Li, X., Jonnagaddala, J., Yang, S., Zhang, H., & Xu, X. S. (2022). A retrospective analysis using deep-learning models for prediction of survival outcome and benefit of adjuvant chemotherapy in stage II/III colorectal cancer. Journal of cancer research and clinical oncology, 148(8), 1955–1963. https://doi.org/10.1007/s00432-022-03976-5 

• Li, X., Jonnagaddala, J., Cen, M., Zhang, H., & Xu, S. (2022). Colorectal Cancer Survival Prediction Using Deep Distribution Based Multiple-Instance Learning. Entropy (Basel, Switzerland), 24(11), 1669. https://doi.org/10.3390/e24111669 

• Raju, A., Yao, J., Haq, M. M., Jonnagaddala, J., & Huang, J. (2020). Graph Attention multi-instance learning for accurate colorectal cancer staging. In Lecture Notes in Computer Science (pp. 529–539). https://doi.org/10.1007/978-3-030-59722-1_51

• Yao, J., Zhu, X., Jonnagaddala, J., Hawkins, N. J., & Huang, J. (2020). Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks. Medical Image Analysis, 65, 101789. https://doi.org/10.1016/j.media.2020.101789