Three-dimensional volumetric rendering on augmented reality headsets for ovarian cancer cytoreduction planning: A Memorial Sloan Kettering Cancer Center Team Ovary study Journal Article


Authors: Kahn, R. M.; Murphy, K.; Patel, T.; Yeoshoua, E.; Tian, E.; Finch, L.; Zhou, Q.; Iasonos, A.; Booth, P.; Honeyman, J. N.; Schefflein, J.; Crouch, M.; Kaza, S.; Broach, V.; Gardner, G. J.; Long Roche, K.; Sonoda, Y.; Abu-Rustum, N. R.; Chi, D. S.
Article Title: Three-dimensional volumetric rendering on augmented reality headsets for ovarian cancer cytoreduction planning: A Memorial Sloan Kettering Cancer Center Team Ovary study
Abstract: Objective: New three-dimensional (3D) augmented reality technology represents an opportunity to improve presurgical planning. This study aimed to measure the accuracy of 3D volumetric rendering on augmented reality headsets to predict extent of disease prior to ovarian cancer cytoreductive surgery. Methods: This single-institution prospective study took place from 03/01/2024 to 10/01/2024. Utilizing Medivis 3D augmented reality headsets, investigators reviewed volumetric renderings for patients with suspected advanced ovarian cancer prior to scheduled surgery and filled out a survey predicting presence of disease based on anatomic site. Pathology records were later reviewed to confirm the presence of disease. Statistical analyses included Cohen's kappa coefficient, sensitivity/specificity, and positive/negative predictive value measurements. Results: We included 15 patients: 9 (60 %) with interval cytoreduction and 6 (40 %) with primary cytoreduction. For procedure, 14 (93 %) had complete gross resection and 1 (7 %) suboptimal cytoreduction (>1 cm of residual disease). Using pathology results as the gold standard for each anatomic site, the 3D headset demonstrated accuracy of 100 % for omentum and pelvic lymph nodes; 93 % for para-aortic lymph nodes, right diaphragm, rectum, and liver; 87 % for small mesentery; and 80 % for small bowel serosa, spleen, and left diaphragm (P > 0.05 for all). Conclusion: The use of preoperative 3D volumetric rendering on augmented reality headsets to predict the extent of ovarian cancer spread showed high agreement with pathology across all anatomic sites studied. Additional research is needed to assess the potential role of this technology in improving surgical planning and patient outcomes. © 2025 Elsevier Inc.
Keywords: adult; clinical article; controlled study; human tissue; treatment outcome; aged; cancer staging; nuclear magnetic resonance imaging; antineoplastic agent; paraaortic lymph node; pelvis lymph node; prospective study; sensitivity and specificity; ovarian cancer; cytoreductive surgery; accuracy; computer assisted tomography; ovary cancer; spleen; cohort analysis; pathology; histology; cancer center; liver; feasibility study; preoperative period; ovary carcinoma; ovary metastasis; neoadjuvant chemotherapy; small intestine; diaphragm; mesentery; predictive value; serosa; rectum; kappa statistics; omentum; international federation of gynecology and obstetrics; ovarian carcinosarcoma; three-dimensional imaging; human; female; article; two-dimensional imaging; gynecologic oncologist; anatomical location; patient triage; neoplasms by histologic type; three-dimensional rendering; dedifferentiated ovary carcinoma
Journal Title: Gynecologic Oncology
Volume: 196
ISSN: 0090-8258
Publisher: Elsevier Inc.  
Date Published: 2025-05-01
Start Page: 107
End Page: 112
Language: English
DOI: 10.1016/j.ygyno.2025.03.040
PROVIDER: scopus
PUBMED: 40187023
PMCID: PMC12118044
DOI/URL:
Notes: Article -- MSK Cancer Center Support Grant (P30 CA008748) acknowledged in PDF -- MSK corresponding author is Dennis Chi -- Source: Scopus
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MSK Authors
  1. Ginger J Gardner
    270 Gardner
  2. Dennis S Chi
    707 Chi
  3. Yukio Sonoda
    472 Sonoda
  4. Qin Zhou
    253 Zhou
  5. Alexia Elia Iasonos
    362 Iasonos
  6. Paul R Booth
    15 Booth
  7. Vance Andrew Broach
    115 Broach
  8. Kristie Anne Murphy
    5 Murphy
  9. Ryan Matthew Kahn
    40 Kahn
  10. Lindsey Adams Finch
    11 Finch
  11. Tulsi Patel
    2 Patel
  12. Emily Tian
    2 Tian
  13. Michael Wayne Crouch
    1 Crouch
  14. Sameer Kaza
    1 Kaza