Literature DB >> 35121810

A novel morphology-based risk stratification model for stage I uterine leiomyosarcoma: an analysis of 203 cases.

David B Chapel1,2, Aarti Sharma3,4, Ricardo R Lastra4, Livia Maccio5, Emma Bragantini5, Gian Franco Zannoni6, Suzanne George7, Bradley J Quade3, Carlos Parra-Herran3, Marisa R Nucci3.   

Abstract

Uterine leiomyosarcoma is the most common uterine mesenchymal malignancy. The majority present at stage I, and clinical outcomes vary widely. However, no widely accepted risk stratification system for stage I uterine leiomyosarcoma is currently available. We studied 17 routinely evaluated clinicopathologic parameters in 203 stage I uterine leiomyosarcoma from three institutions to generate a novel risk stratification model for these tumors. Mitoses >25 per 2.4 mm2 (10 high-power fields), atypical mitoses, coagulative necrosis, lymphovascular invasion, and serosal abutment were significantly associated with disease-free and disease-specific survival in univariate and multivariate analyses. These prognostic parameters were each scored as binary ("yes" or "no") variables and fitted to a single optimized algebraic risk model:Risk score = (coagulative necrosis)(1) + (mitoses > 25 per 2.4 mm2)(2) + (atypical mitoses)(2) + (lymphovascular invasion)(3) + (serosal abutment)(5)By logistic regression, the risk model was significantly associated with 5-year disease-free (AUC = 0.9270) and 5-year disease-specific survival (AUC = 0.8517). Internal and external validation substantiated the model. The continuous score (range, 0-13) was optimally divided into 3 risk groups with distinct 5-year disease-free and disease-specific survival: low risk (0-2 points), intermediate risk (3-5 points), and high risk (6-13 points) groups. Our novel risk model performed significantly better than alternative uterine leiomyosarcoma risk stratification systems in predicting 5-year disease-free and disease-specific survival in stage I tumors. A simplified risk model, omitting terms for serosal abutment and lymphovascular invasion, can be accurately applied to myomectomy or morcellated specimens. We advocate routine application of this novel risk model in stage I uterine leiomyosarcoma to facilitate patient counseling and proper risk stratification for clinical trials.
© 2022. The Author(s), under exclusive licence to United States & Canadian Academy of Pathology.

Entities:  

Mesh:

Year:  2022        PMID: 35121810     DOI: 10.1038/s41379-022-01011-z

Source DB:  PubMed          Journal:  Mod Pathol        ISSN: 0893-3952            Impact factor:   7.842


  44 in total

1.  A nomogram to predict postresection 5-year overall survival for patients with uterine leiomyosarcoma.

Authors:  Oliver Zivanovic; Lindsay M Jacks; Alexia Iasonos; Mario M Leitao; Robert A Soslow; Emanuela Veras; Dennis S Chi; Nadeem R Abu-Rustum; Richard R Barakat; Murray F Brennan; Martee L Hensley
Journal:  Cancer       Date:  2011-07-12       Impact factor: 6.860

2.  Incidence patterns of soft tissue sarcomas, regardless of primary site, in the surveillance, epidemiology and end results program, 1978-2001: An analysis of 26,758 cases.

Authors:  Jorge R Toro; Lois B Travis; Hongyu Julian Wu; Kangmin Zhu; Christopher D M Fletcher; Susan S Devesa
Journal:  Int J Cancer       Date:  2006-12-15       Impact factor: 7.396

3.  Analysis of clinicopathologic prognostic factors for 157 uterine sarcomas and evaluation of a grading score validated for soft tissue sarcoma.

Authors:  P Pautier; C Genestie; A Rey; P Morice; B Roche; C Lhommé; C Haie-Meder; P Duvillard
Journal:  Cancer       Date:  2000-03-15       Impact factor: 6.860

4.  Uterine Sarcoma: Analysis of 13,089 Cases Based on Surveillance, Epidemiology, and End Results Database.

Authors:  Mona Hosh; Sarah Antar; Ahmed Nazzal; Mahmoud Warda; Ahmed Gibreel; Basel Refky
Journal:  Int J Gynecol Cancer       Date:  2016-07       Impact factor: 3.437

5.  Incidence of soft tissue sarcoma and beyond: a population-based prospective study in 3 European regions.

Authors:  Giuseppe Mastrangelo; Jean-Michel Coindre; Françoise Ducimetière; Angelo Paolo Dei Tos; Emanuela Fadda; Jean-Yves Blay; Alessandra Buja; Ugo Fedeli; Luca Cegolon; Alvise Frasson; Dominique Ranchère-Vince; Cristina Montesco; Isabelle Ray-Coquard; Carlo Riccardo Rossi
Journal:  Cancer       Date:  2012-04-19       Impact factor: 6.860

6.  Uterine sarcomas in Norway. A histopathological and prognostic survey of a total population from 1970 to 2000 including 419 patients.

Authors:  Vera M Abeler; Odd Røyne; Steinar Thoresen; Håvard E Danielsen; Jahn M Nesland; Gunnar B Kristensen
Journal:  Histopathology       Date:  2009-02       Impact factor: 5.087

7.  External validation of a prognostic nomogram for overall survival in women with uterine leiomyosarcoma.

Authors:  Alexia Iasonos; Emily Z Keung; Oliver Zivanovic; Rosanna Mancari; Michele Peiretti; Marisa Nucci; Suzanne George; Nicoletta Colombo; Silvestro Carinelli; Martee L Hensley; Chandrajit P Raut
Journal:  Cancer       Date:  2013-03-01       Impact factor: 6.860

8.  Predictive value of FIGO and AJCC staging systems in patients with uterine leiomyosarcoma.

Authors:  Chandrajit P Raut; Marisa R Nucci; Qian Wang; Judith Manola; Monica M Bertagnolli; George D Demetri; Jeffrey A Morgan; Michael G Muto; Christopher D M Fletcher; Suzanne George
Journal:  Eur J Cancer       Date:  2009-07-31       Impact factor: 9.162

9.  Surveillance, epidemiology, and end results analysis of 2677 cases of uterine sarcoma 1989-1999.

Authors:  Sandra E Brooks; Min Zhan; Timothy Cote; Claudia R Baquet
Journal:  Gynecol Oncol       Date:  2004-04       Impact factor: 5.482

10.  Stage-specific outcomes of patients with uterine leiomyosarcoma: a comparison of the international Federation of gynecology and obstetrics and american joint committee on cancer staging systems.

Authors:  Oliver Zivanovic; Mario M Leitao; Alexia Iasonos; Lindsay M Jacks; Qin Zhou; Nadeem R Abu-Rustum; Robert A Soslow; Margrit M Juretzka; Dennis S Chi; Richard R Barakat; Murray F Brennan; Martee L Hensley
Journal:  J Clin Oncol       Date:  2009-03-02       Impact factor: 44.544

View more
  1 in total

1.  A Novel Deep Learning-Based Mitosis Recognition Approach and Dataset for Uterine Leiomyosarcoma Histopathology.

Authors:  Talat Zehra; Sharjeel Anjum; Tahir Mahmood; Mahin Shams; Binish Arif Sultan; Zubair Ahmad; Najah Alsubaie; Shahzad Ahmed
Journal:  Cancers (Basel)       Date:  2022-08-03       Impact factor: 6.575

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.