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Benign uterine mass—discrimination from leiomyosarcoma by a preoperative risk score: a multicenter cohort study

  • Günter Köhler
  • Marcus VollmerEmail author
  • Neetika Nath
  • Philipp-Andreas Hessler
  • Katarina Dennis
  • Angela Lehr
  • Martina Köller
  • Christine Riechmann
  • Helena Bralo
  • Dominika Trojnarska
  • Hanka Lehnhoff
  • Johann Krichbaum
  • Manfred Krichbaum
  • Katja Evert
  • Matthias Evert
  • Marek Zygmunt
  • Lars Kaderali
Gynecologic Oncology

Abstract

Purpose

Discrimination of uterine leiomyosarcoma (LMS) and leiomyoma (LM) prior to surgery by basic preoperative characteristics and development of a preoperative leiomyosarcoma score.

Methods

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.

Results

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.

Conclusions

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.

Keywords

Leiomyoma Leiomyosarcoma Malignancy Discrimination Leiomyosarcoma score Logistic regression 

Notes

Author contributions

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.

Funding

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.

Ethical approval

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.

Informed consent

All patients included in this study gave written consent to data collection and the use of personal records for research.

Supplementary material

404_2019_5344_MOESM1_ESM.docx (470 kb)
Supplementary material 1 (DOCX 469 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Günter Köhler
    • 1
  • Marcus Vollmer
    • 2
    Email author
  • Neetika Nath
    • 2
    • 3
  • Philipp-Andreas Hessler
    • 5
  • Katarina Dennis
    • 5
  • Angela Lehr
    • 5
  • Martina Köller
    • 5
  • Christine Riechmann
    • 5
  • Helena Bralo
    • 5
  • Dominika Trojnarska
    • 1
  • Hanka Lehnhoff
    • 1
  • Johann Krichbaum
    • 6
  • Manfred Krichbaum
    • 6
  • Katja Evert
    • 4
  • Matthias Evert
    • 4
  • Marek Zygmunt
    • 1
  • Lars Kaderali
    • 2
  1. 1.Department of Obstetrics and GynecologyUniversity Medicine GreifswaldGreifswaldGermany
  2. 2.Institute of BioinformaticsUniversity Medicine GreifswaldGreifswaldGermany
  3. 3.Interfaculty Institute for Genetics and Functional GenomicsUniversity Medicine GreifswaldGreifswaldGermany
  4. 4.Institute of PathologyUniversity of RegensburgRegensburgGermany
  5. 5.Department of Gynecologic SurgeryHospital SachsenhausenFrankfurt/MainGermany
  6. 6.Outpatient Department GynmünsterVAAOMünsterGermany

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