Developing a Preoperative Algorithm for the Diagnosis of Uterine Leiomyosarcoma

Lawlor, Hannah, Ward, Alexandra, Maclean, Alison, Lane, Steven, Adishesh, Meera, Taylor, Sian, DeCruze, Shandya Bridget and Hapangama, Dharani Kosala ORCID: 0000-0003-0270-0150 (2020) Developing a Preoperative Algorithm for the Diagnosis of Uterine Leiomyosarcoma. DIAGNOSTICS, 10 (10). p. 34.

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Official URL: https://www.mdpi.com/2075-4418/10/10/735

Abstract

Early diagnosis of the rare and life-threatening uterine leiomyosarcoma (LMS) is essential for prompt treatment, to improve survival. Preoperative distinction of LMS from benign leiomyoma remains a challenge, and thus LMS is often diagnosed post-operatively. This retrospective observational study evaluated the predictive diagnostic utility of 32 preoperative variables in 190 women who underwent a hysterectomy, with a postoperative diagnosis of leiomyoma (n = 159) or LMS (n = 31), at the Liverpool Women’s National Health Service (NHS) Foundation Trust, between 2010 and 2019. A total of 7 preoperative variables were associated with increased odds of LMS, including postmenopausal status (p < 0.001, OR 3.08), symptoms of pressure (p = 0.002, OR 2.7), postmenopausal bleeding (p = 0.001, OR 5.01), neutrophil count ≥7.5 × 109/L (p < 0.001, OR 5.72), haemoglobin level <118 g/L (p = 0.037, OR 2.22), endometrial biopsy results of cellular atypia or neoplasia (p = 0.001, OR 9.6), and a mass size of ≥10 cm on radiological imaging (p < 0.0001, OR 8.52). This study has identified readily available and easily identifiable preoperative clinical variables that can be implemented into clinical practice to discern those with high risk of LMS, for further specialist investigations in women presenting with symptoms of leiomyoma.

Item Type:	Article
Uncontrolled Keywords:	fibroid, leiomyoma, uterine leiomyosarcoma, uterine neoplasm, diagnosis
Depositing User:	Symplectic Admin
Date Deposited:	29 Sep 2020 08:47
Last Modified:	18 Jan 2023 23:31
DOI:	10.3390/diagnostics10100735
Open Access URL:	https://doi.org/10.3390/diagnostics10100735
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3102956