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OVARIAN-BEVACIZUMAB-RESPONSE

The Cancer Imaging Archive

Ovarian Bevacizumab Response | A dataset of histopathological whole slide images for classification of Treatment effectiveness to ovarian cancer

DOI: 10.7937/TCIA.985G-EY35 | Data Citation Required | Image Collection

Location Species Subjects Data Types Cancer Types Size Supporting Data Status Updated
Ovary Human 78 Histopathology Ovarian Cancer 253.8GB Clinical Public, Complete 2023/04/26

Summary

 

Despite the progress made during the last two decades in the surgery and chemotherapy of ovarian cancer, more than 70% of advanced patients are with recurrent cancer and decease. Bevacizumab has been recently approved by FDA as a monotherapy for advanced ovarian cancer in combination with chemotherapy. Considering the cost, potential toxicity, and finding that only a portion of patients will benefit from these drugs, the identification of a new predictive method for the treatment of ovarian cancer remains an urgent unmet medical need. Prediction of therapeutic effects and individualization of therapeutic strategies are critical, but to the authors' best knowledge, there are no effective biomarkers that can be used to predict patient response to bevacizumab treatment for ovarian cancer. This dataset helps researchers to explore and develop methods to predict the therapeutic effect of patients with epithelial ovarian cancer to bevacizumab.

The dataset consists of de-identified 288 hematoxylin and eosin (H&E) stained whole slides with clinical information from 78 patients. The slides were collected from the tissue bank of the Tri-Service General Hospital and the National Defense Medical Center, Taipei, Taiwan. Whole Slide Images (WSIs) were acquired with a digital slide scanner (Leica AT2) with a 20x objective lens. The dimension of the ovarian cancer slides is 54342x41048 in pixels and 27.34 x 20.66mm on average. The bevacizumab treatment is effective in 162 and invalid in 126 of the dataset.  Ethical approvals have been obtained from the research ethics committee of the Tri-Service General Hospital (TSGHIRB No.1-107-05-171 and No.B202005070), and the data were de-identified and used for a retrospective study without impacting patient care.

The clinicopathologic characteristics of patients were recorded by the data managers of the Gynecologic Oncology Center. Age, pre- and post-treatment serum CA-125 concentrations, histologic subtype, and recurrence, and survival status were recorded. A tumor, which is resistant to bevacizumab therapy, is defined as a measurable regrowth of the tumor or as a serum CA-125 concentration more than twice the value of the upper limit of normal during the treatment course for the bevacizumab therapy (i.e., the patient had the detectable disease or elevated CA-125 level following cytoreductive surgery combine with carboplatin/paclitaxel plus bevacizumab). A tumor, which is sensitive to bevacizumab therapy, is defined as no measurable regrowth of the tumor or as a serum CA-125 concentration under than twice the value of the upper limit of normal during the treatment course for the bevacizumab therapy.

This dataset is further described in the following publications:

Data Access

Version 2: Updated

Update: 2 files (414056O.svs and 414056P.svs) removed from folder e12.
1 file (220725D.svs) moved from folder in4 to e12.
Metadata spreadsheet “Final CA125 data.xlsx” updated with clinical information previously missing.

Title Data Type Format Access Points Subjects Studies Series Images License
Tissue Slide Images Histopathology SVS
Download requires IBM-Aspera-Connect plugin
78 285 CC BY 4.0
Clinical data: serum cancer antigen 125 data XLS CC BY 4.0
Clinical data: Final patient list XLS CC BY 4.0

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

Wang, C.-W., Chang, C.-C., Lo, S.-C., Lin, Y.-J., Liou, Y.-A., Hsu, P.-C., Lee, Y.-C., & Chao, T.-K. (2021). A dataset of histopathological whole slide images for classification of Treatment effectiveness to ovarian cancer (Ovarian Bevacizumab Response) (Version 2) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.985G-EY35

Detailed Description

Slides include 162 effective and 126 invalid images.

Acknowledgements

This research study is supported by the Ministry of Science and Technology of Taiwan, under a grant (MOST-108-2221-E-011-070, MOST109-2221-E-011-018-MY3, 110-2321-B-016 -002), Tri-Service General Hospital, Taipei, Taiwan (TSGH-C-108086, TSGH-D-109094, TSGH-D-110036, and TSGH-801GB111010), and Tri-Service General Hospital-National Taiwan University of Science and Technology (TSGH-NTUST-111-05).

Additional Publications Related to this Work

The Collection authors suggest the below will give context to this dataset:

  • Wang, CW., Chang, CC., Khalil, M.A. et al. Histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer. Sci Data 9, 25 (2022). https://doi.org/10.1038/s41597-022-01127-6

Other Publications Using this Data

TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.

Publication Citation

Wang, C.-W., Chang, C.-C., Lee, Y.-C., Lin, Y.-J., Lo, S.-C., Hsu, P.-C., Liou, Y.-A., Wang, C.-H., & Chao, T.-K. (2022). Weakly Supervised Deep Learning for Prediction of Treatment Effectiveness on Ovarian Cancer from Histopathology Images. In Computerized Medical Imaging and Graphics (p. 102093). Elsevier BV. https://doi.org/10.1016/j.compmedimag.2022.102093

TCIA Citation

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7

Previous Versions

Version 1: Updated 2021/05/24

Title Data Type Format Access Points Studies Series Images License
Tissue Slide Images SVS
Download requires IBM-Aspera-Connect plugin
Clinical Data XLS