Proceedings of ICIS SGCR-WIRES 2024, held jointly with the 23rd International Cancer Imaging Society Annual Conference, collaborating with the Singapore Radiological Society and College of Radiologists Singapore
Objectives/ Teaching Points:To study the impact of preoperative prostate artery embolization (PAE) on intraoperative blood loss during transurethral resection of the prostate (TURP) for large prostates (exceeding 80 cc). Material(s) and Method(s):A prospective, surgeon-blinded, randomized controlled trial was performed at a single tertiary centre.Patients with prostate volumes over 80 cc who needed TURP were randomly allocated (1:1) to receive preoperative prostatic artery embolization followed by TURP (Group Aintervention) or TURP alone (Group B-control).The primary outcome measured the postoperative drop in haemoglobin levels, as a surrogate for blood loss.Secondary outcomes studied included resection efficiency (weight of resected tissue per minute) and the rate of postoperative complications.Results: There were 10 patients in each group.The median prostate volumes were 119 mL for Group A and 140 mL for Group B, with median preoperative haemoglobin levels of 13.3 g/dL (IQR 12.5-14.3g/dL) in Group A and 14.4 g/dL (IQR 10.1-15.2g/dL) in Group B. The decrease in postoperative haemoglobin was significantly greater in Group B compared to Group A (-1.4 g/dL vs + 0.5 g/dL, p = 0.015).There were no significant differences between the groups in terms of the weight of resected prostate tissue (52 g vs 73 g, p = 0.089) and resection efficiency (0.7 g/min vs 0.6 g/min, p = 0.853).Two patients in Group B needed a red blood cell transfusion, compared to one patient in Group A (p = 1.000).One patient from each group required an additional surgery for haemostasis. Conclusions:Preoperative PAE significantly decreased TURP blood loss in men with large prostates.
Publication Details
- Journal
- Cancer Imaging
- Published
- 2024-09-12
- DOI
- https://doi.org/10.1186/s40644-024-00751-2
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.81