Under-representation for Female Pelvis Cancers in Commercial Auto-segmentation Solutions and Open-source Imaging Datasets.

aimArtificial 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

methodsInquiries 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.

resultsAll 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).

conclusionCommercial 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.