Back

Selective Removal of Endometriotic Lesions Using CUSA Clarity in Ovarian Endometriomas: A Case-Based Histopathological Study

Kanda, T.; Hosono, T.; Sato, A.; Maeda, Y.; Yasoshima, I.; Sakai, Y.; Kasama, H.; Kayahashi, K.; Kagami, K.; Iizuka, T.; Abiko, K.

2025-10-15 obstetrics and gynecology
10.1101/2025.10.12.25334539 medRxiv
Show abstract

ObjectiveTo evaluate the feasibility of selective removal of endometriotic lesions using the Cavitron Ultrasonic Surgical Aspirator (CUSA(R) Clarity) in ovarian endometriomas, with a focus on histological preservation of normal ovarian tissue. MethodsWe analyzed tissue from a woman in her early 30s who underwent laparoscopic surgery for an ovarian endometrioma measuring approximately 7 cm after preoperative dienogest therapy. Resected cyst wall specimens were divided into five parts, each assigned to a Tissue Select(R) setting (0-4). Samples were scraped with CUSA, followed by histological and immunohistochemical evaluation (H&E, Sirius Red, CK7, CD10). ResultsEndometriotic lesions (epithelial and stromal cells) were effectively removed across all settings. At higher Tissue Select settings (3-4), preservation of surrounding tissue was superior, with minimal vacuolization compared to lower settings (0-2). Primordial follicles were observed approximately 600 m beneath the surface, highlighting the importance of limiting cavitation depth. ConclusionCUSA Clarity enabled selective removal of endometriotic lesions with relative preservation of normal ovarian tissue, particularly at higher Tissue Select settings. This novel approach may represent a fertility-preserving alternative to cystectomy or laser ablation in the management of ovarian endometriomas. Further studies are warranted.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.