Benign uterine mass—discrimination from leiomyosarcoma by a preoperative risk score: a multicenter cohort study
Discrimination of uterine leiomyosarcoma (LMS) and leiomyoma (LM) prior to surgery by basic preoperative characteristics and development of a preoperative leiomyosarcoma score.
A predominantly prospective cohort of 826 patients with LM from a clinical institution and an outpatient center was included in the study. Further a predominantly retrospective cohort of 293 patients with LMS was included from the counseling database of the German Clinical Center of Excellence for Genital Sarcoma and Mixed Tumors (DKSM, University Medicine Greifswald, Germany). We analyzed and compared anamnestic, epidemiological and clinical findings between both cohorts. Tenfold cross-validated logistic regression and random forest was performed on the 80% training set. The preoperative LMS score (pLMS) was developed based on logistic regression and independently evaluated by analyzing the area under the receiver operating characteristic curve (AUC) with the 20% test set.
In the LMS cohort, 63.1% had initially surgery for presumed LM and only 39.6% of endometrial biopsies revealed LMS. Key features for LMS discrimination were found to be bleeding symptoms: intermenstrual bleeding [RRc = 2.71, CI = (1.90–3.49), p < 0.001], hypermenorrhea [RRc = 0.28, CI = (0.15–0.50), p < 0.001], dysmenorrhea [RRc = 0.22, CI = (0.10–0.51), p < 0.001], postmenstrual bleeding [RRc = 2.08, CI = (1.30–2.75), p < 0.001], suspicious sonography [RRc = 1.21, CI = (1.19–1.22), p < 0.001] and the tumor diameter (each centimeter difference: β = 0.24, SD = 0.04, p < 0.001). pLMS achieved a mean cross-validated AUC of 0.969 (SD = 0.019) in the training set and an AUC of 0.968 in the test set.
The presented score is based on basic clinical characteristics and allows the prediction of LMS prior to a planned surgery of a uterine mass. In case pLMS is between − 3 and + 1, we suggest subsequent diagnostics, such as endometrial biopsy, color Doppler sonography, LDH measurement, MRI and transcervical biopsy.
KeywordsLeiomyoma Leiomyosarcoma Malignancy Discrimination Leiomyosarcoma score Logistic regression
GK contributed to project development, data collection, data analysis and interpretation and manuscript writing. P-AH, KD, AL, MK, CR, HB, DT, HL, JK, MK, KE, ME and MZ helped in project development, data collection, manuscript editing. MV, NN and LK contributed to data analysis and interpretation, manuscript writing/editing.
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
All procedures performed in this study were in accordance with the ethical standards of the local institutional research committee of the University Medicine Greifswald (Identifier: BB 034/19) and with the 1964 Helsinki Declaration and its later amendments.
All patients included in this study gave written consent to data collection and the use of personal records for research.
- 4.Nugent W, Engelke G, Reicke S, Söder R, Hennefründ J, Hasskamp T et al (2015) Laparoscopic supracervical hysterectomy or myomectomy with power morcellation: risk of uterine leiomyosarcomas. A retrospective trial including 35.161 women in Germany. J Minim Invasive Gynecol 22:S2–S3CrossRefGoogle Scholar
- 20.Dennis K, Lehr A (2017) Charakterisierung von Leiomyomen und deren Varianten sowie atypischen glattmuskulären Tumoren und uterinen Sarkomen anhand von 4040 Leiomyom Operationen im Rahmen des Forschungsprojekts „Charakterisierung von Leiomyomen und uterinen Sarkomen“zur Erstellung eines Sarkom-Risiko Scores für den klinischen Alltag. Dissertation, University of GreifswaldGoogle Scholar
- 28.Cullen Alison C, Frey HC (1999) Probabilistic techniques in exposure assessment: a handbook for dealing with variability and uncertainty in models and inputs. Springer, BerlinGoogle Scholar
- 33.Plasvsic SK, Patham B, Honemeyer U, Kurjak A (2011) Uterine lesions: advances in ultrasound diagnosis. In: Kurjak A, Chervenak FA (eds) Donald school textbook of ultrasound in obstetrics and gynecology, 3rd edn. Jaypee Brothers Medical Publishers Ltd, New Delhi, pp 770–787Google Scholar