Summary
In this single institution retrospective study, we reviewed 950 consecutive patients with prostate adenocarcinoma receiving definitive radiotherapy between 2011 and 2019, and identified among them 112 patients with anatomic variations (edge cases) seen on simulation CT and/or MRI imaging. These variations included hip arthroplasty, prostate median lobe hypertrophy, so-called “droopy” seminal vesicles, presence of a urinary catheter, and others. Three commercially available solutions for pelvic anatomy autosegmentation were applied to treatment planning CT scans to generate segmented volumes of the prostate, rectum, bladder and bilateral femoral heads. To quantify the accuracy of software-generated contours, Dice similarity coefficients were calculated for each software-generated contour with a comparator clinician-delineated reference contour.
Results: On average, deep learning autosegmentation outperformed both atlas-based and model-based methods across both normal and edge case cohorts. However, all methods exhibited lower mean DSC for edge cases compared to the normal cohort, recapitulating certain known algorithm limitations (e.g. in the context of hip prostheses) and identifying novel areas of poor performance.
Conclusions: Anatomic variation may present challenges to commercial autosegmentation solutions, and such edge cases should be considered both prior to and throughout any clinical implementation.
Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
Hospital/Institution Name city, state, country - Special thanks to First Last Names, degree PhD, MD, etc from the Department of xxxxxx, Additional Names from same location.
Data Access
Data Type | Download all or Query/Filter | License |
---|---|---|
Images and Radiation Therapy Structures (DICOM, 17 GB) | (Download requires NBIA Data Retriever) |
Click the Versions tab for more info about data releases.
Detailed Description
Image Statistics | Radiology Image Statistics |
---|---|
Modalities | CT, RTSTRUCT |
Number of Patients | 131 |
Number of Studies | 131 |
Number of Series | 262 |
Number of Images | 23490 |
Images Size (GB) | 17 |
Citations & Data Usage Policy
Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:
Data Citation
Thompson, R., Kanwar, A., Merz, B., Cohen, E., Fisher, H., Rana, S., Claunch, C., & Hung, A. (2023). Stress-Testing Pelvic Autosegmentation Algorithms Using Anatomical Edge Cases (Prostate Anatomical Edge Cases) (Version 1) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/QSTF-ST65
Publication Citation
Kanwar, A., Merz, B., Claunch, C., Rana, S., Hung, A., & Thompson, R. F. (2023). Stress-testing pelvic autosegmentation algorithms using anatomical edge cases. In Physics and Imaging in Radiation Oncology (Vol. 25, p. 100413). Elsevier BV. https://doi.org/10.1016/j.phro.2023.100413
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
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 the TCIA Helpdesk.
Version 1 (Current): Updated 2023/mm/dd
Data Type | Download all or Query/Filter | License |
---|---|---|
Images and Radiation Therapy Structures (DICOM, 17 GB) | (Download requires the NBIA Data Retriever) |