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Under-representation for Female Pelvis Cancers in Commercial Auto-segmentation Solutions and Open-source Imaging
M Thor1, V Williams2, C Hajj2
1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, USA.
Aim:
Artificial intelligence (AI) based auto-segmentation aids radiation therapy (RT) workflows and is being adopted in clinical environments facilitated by the increased availability of commercial solutions for organs at risk (OARs). In addition, open-source imaging datasets support training for new auto-segmentation algorithms. Here, we studied if the female and male anatomies are equally represented among these solutions.
Materials And Methods:
Inquiries were sent to eight vendors regarding their clinically available OAR auto-segmentation solutions for each gender. The Cancer Imaging Archive (TCIA) was also screened for publicly available imaging datasets specific to the female and the male anatomy.
Results:
All vendors provided AI based auto-segmentation solutions for the male pelvis and female breasts, while 5/8 vendors provided solutions for the female pelvis. The female breast and the female pelvis solutions were released at a median of 0.6 years and 2.3 years, respectively, after the release of the male pelvis solutions. Among 27 TCIA datasets identified, 15 involved the female anatomy (breast: 10; pelvis: 5) and 12 involved the male pelvis but no female-specific dataset included OAR segmentations, while three male pelvis datasets included OARs (ejaculatory duct, neurovascular bundle, penile bulb and verumontanum).
Conclusion:
Commercial AI auto-segmentation solutions and open-source imaging datasets include considerably more solutions and OAR segmentations for male cancer over female cancer sites. This gender disparity is likely to propagate throughout the RT pipeline.
