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1283 3D-printing the cancer: a new frontier of surgical planning for complex pelvic resections
  1. Maureen Byrne1,
  2. Tulsi Patel2 and
  3. Vance Broach1
  1. 1Gynecology Service, Memorial Sloan Kettering Cancer Center, New York, USA
  2. 2Memorial Sloan Kettering Cancer Center, New York, USA


Introduction/Background In this video we will highlight the development and utility of a 3D printed model rendered from a preoperative CT scan to aid in the surgical planning for resection of a complex pelvic mass. Our patient is a 62 year old with a history of FIGO Stage IB Leiomyosarcoma status post total abdominal hysterectomy and bilateral salpingectomy performed five years prior at an outside institution. She presented to her local gynecologic oncologist complaining of abdominal discomfort and a new 5cm pelvic mass was appreciated on exam.

Methodology The mass was closely related to numerous anatomic structures in the pelvis, including the bladder, rectum, iliac vessels, ureter, and vaginal cuff. To further clarify the extent of surgical resection necessitated and plan for surgical personnel and approach, a 3D-model of the pelvic mass was created.

Results The final 3D-printed model helped to elucidate the mass’ relationship with nearby structures, specifically which structures had to be taken and where. The model demonstrated almost complete obliteration of the right anterior iliac artery, necessitating resection of the artery close to its origin. However, the model also revealed that the mass was abutting but not invading the right external iliac artery and vein, allowing them to be spared. On CT there was initial concern for obturator nerve involvement, and while the nerve was pulled in towards the mass on the model, it was separate from the tumor. This was confirmed intra-operatively and the nerve was successfully spared.

Conclusion Radical resection of the mass was successful and the mass was removed en-bloc from the pelvis. The 3D-model allowed for coordination of surgical personnel, ensuring that colleagues from urology and vascular surgery were available if necessary. The model successfully identified structures needing resection and those that could be spared pre-operartively, facilitating a safer, well-planned surgery.

Disclosures None.

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