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Static Digital Twins for Surgical Planning in Complex Gynecologic Cancer Recurrence: A Proof-of-Concept Feasibility

Francesco Mezzapesa1, Barbara Bortolani2, Elisabetta Pia Bilancia1

  • 1Department of Medical and Surgical Sciences (DIMEC) (Drs. Mezzapesa, Bilancia, Genovesi, Freyrie, De Iaco, and Perrone), University of Bologna, Bologna, Italy; Division of Oncologic Gynecology (Drs. Mezzapesa, Bilancia, Genovesi, Freyrie, De Iaco, and Perrone), IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.

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Summary

Static digital twins are feasible for complex gynecologic cancer surgeries, enhancing planning and intraoperative safety. These 3D models improve anatomical understanding and team coordination, leading to positive surgical outcomes.

Keywords:
Computer-assisted surgeryDigital twinGynecologic cancersRelapseSurgery

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Area of Science:

  • Oncology
  • Surgical Innovation
  • Medical Imaging

Background:

  • Recurrent gynecologic cancers present complex surgical challenges.
  • Advanced imaging and modeling are crucial for intricate resections.

Purpose of the Study:

  • To assess the feasibility and surgical impact of static digital twins in complex gynecologic cancer recurrence surgeries.
  • To evaluate the role of 3D models in preoperative planning and intraoperative navigation.

Main Methods:

  • Prospective feasibility study involving four patients with recurrent gynecologic malignancy.
  • High-resolution imaging and static digital twin reconstruction using Mimics Medical® software.
  • Multidisciplinary team evaluation of model concordance and surgical impact.

Main Results:

  • All surgeries completed successfully without intraoperative complications.
  • Static digital twins facilitated enhanced preoperative planning, identification of anatomical variants, and team coordination.
  • High concordance between 3D models and surgical findings, positively influencing intraoperative decision-making.

Conclusions:

  • Static digital twins are feasible and effective for surgical planning in complex gynecologic cancer recurrences.
  • These models improve anatomical understanding, interdisciplinary coordination, and intraoperative safety.
  • Further research is warranted to confirm impact on outcomes and workflow efficiency.