Under-representation for female pelvis cancers in commercial auto-segmentation solutions and open-source imaging datasets Journal Article

   


Authors: Thor, M.; Williams, V.; Hajj, C.; Cervino, L.; Veeraraghavan, H.; Elguindi, S.; Tyagi, N.; Shukla-Dave, A.; Moran, J. M.
Article Title: Under-representation for female pelvis cancers in commercial auto-segmentation solutions and open-source imaging datasets
Abstract: 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. © 2024 The Royal College of Radiologists
Keywords: clinical article; cancer localization; hysterectomy; pelvis; computer assisted tomography; breast; radiation; radiotherapy; prostate; radiologist; mammography; algorithm; artificial intelligence; imaging; community; segmentation; pelvis cancer; qualitative analysis; women; men; psoas muscle; workflow; organs at risk; iliacus muscle; ejaculatory duct; cancer; human; male; female; article; medical physicist; deep learning; parametrectomy; implementation science; ai; segmentation algorithm; auto segmentation; gender inequality; coccygeus muscle; auto; gyn; open source research
Journal Title: Clinical Oncology
Volume: 38
ISSN: 0936-6555
Publisher: Elsevier Science, Inc.  
Date Published: 2025-02-01
Start Page: 103651
Language: English
DOI: 10.1016/j.clon.2024.10.003
PUBMED: 39837727
PROVIDER: scopus
PMCID: PMC11849395
DOI/URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217386709&doi=10.1016%2fj.clon.2024.10.003&partnerID=40&md5=434dbd799a2d540f5fea4c44e5229c31
Notes: Article -- MSK Cancer Center Support Grant (P30 CA008748) acknowledged in PubMed and PDF -- MSK corresponding author is M. Thor -- Source: Scopus
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MSK Authors
  1. Amita Dave
    136 Dave
  2. Carla Hajj
    164 Hajj
  3. Harini Veeraraghavan
    159 Veeraraghavan
  4. Neelam Tyagi
    151 Tyagi
  5. Maria Elisabeth Thor
    148 Thor
  6. Sharif Elguindi
    29 Elguindi
  7. Laura Isabel Cervino Arriba
    135 Cervino Arriba
  8. Jean Marie Moran
    48 Moran
  9. Vonetta Michelle Williams
    23 Williams
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