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Development of a Multi-Institutional Prediction Model for Three-Year Survival Status in Patients with Uterine Leiomyosarcoma (AGOG11-022/QCGC1302 Study).
Tse, Ka-Yu; Wong, Richard Wing-Cheuk; Chao, Angel; Ueng, Shir-Hwa; Yang, Lan-Yan; Cummings, Margaret; Smith, Deborah; Lai, Chiung-Ru; Lau, Hei-Yu; Yen, Ming-Shyen; Cheung, Annie Nga-Yin; Leung, Charlotte Ka-Lun; Chan, Kit-Sheung; Chan, Alice Ngot-Htain; Li, Wai-Hon; Choi, Carmen Ka-Man; Pong, Wai-Mei; Hui, Hoi-Fong; Yuk, Judy Ying-Wah; Yao, Hung; Yuen, Nancy Wah-Fun; Obermair, Andreas; Lai, Chyong-Huey; Ip, Philip Pun-Ching; Ngan, Hextan Yuen-Sheung.
Afiliación
  • Tse KY; Department of Obstetrics and Gynaecology, the University of Hong Kong, Pokfulam, Hong Kong.
  • Wong RW; Department of Pathology, Pamela Youde Nethersole Eastern Hospital, Chai Wan, Hong Kong.
  • Chao A; Department of Obstetrics and Gynaecology, Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan.
  • Ueng SH; Linkou Medical Center, Chang Gung University, Taoyuan 33305, Taiwan.
  • Yang LY; Department of Pathology, Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan.
  • Cummings M; Clinical Trial Center, Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan.
  • Smith D; Pathology Queensland, the Royal Brisbane and Women's Hospital, Herston, QLD 4029, Australia.
  • Lai CR; Centre for Clinical Research, University of Queensland, Herston, QLD 4029, Australia.
  • Lau HY; Pathology Queensland, the Royal Brisbane and Women's Hospital, Herston, QLD 4029, Australia.
  • Yen MS; Centre for Clinical Research, University of Queensland, Herston, QLD 4029, Australia.
  • Cheung AN; Department of Pathology and Laboratory Medicine, Taipei Veterans General Hospital, Taipei 11217, Taiwan.
  • Leung CK; Department of Obstetrics and Gynaecology, Taipei Veterans General Hospital, Taipei 11217, Taiwan.
  • Chan KS; Department of Obstetrics and Gynaecology, Taipei Veterans General Hospital, Taipei 11217, Taiwan.
  • Chan AN; Department of Pathology, the University of Hong Kong, Pokfulam, Hong Kong.
  • Li WH; Department of Pathology, North District Hospital, Sheung Shui, Hong Kong.
  • Choi CK; Department of Obstetrics and Gynaecology, Kwong Wah Hospital, Mong Kok, Hong Kong.
  • Pong WM; Department of Pathology, Kwong Wah Hospital, Mong Kok, Hong Kong.
  • Hui HF; Department of Obstetrics and Gynaecology, Queen Elizabeth Hospital, Yau Ma Tei, Hong Kong.
  • Yuk JY; Department of Obstetrics and Gynaecology, Tseung Kwan O Hospital, Tseung Kwan O, Hong Kong.
  • Yao H; Department of Pathology, Tseung Kwan O Hospital, Tseung Kwan O, Hong Kong.
  • Yuen NW; Department of Obstetrics and Gynaecology, Tuen Mun Hospital, Tuen Mun, Hong Kong.
  • Obermair A; Department of Obstetrics and Gynaecology, Princess Margaret Hospital, Lai Chi Kok, Hong Kong.
  • Lai CH; Department of Pathology, Princess Margaret Hospital, Lai Chi Kok, Hong Kong.
  • Ip PP; Department of Pathology, Caritas Medical Centre, Sham Shui Po, Hong Kong.
  • Ngan HY; Centre for Clinical Research, University of Queensland, Herston, QLD 4029, Australia.
Cancers (Basel) ; 13(10)2021 May 14.
Article en En | MEDLINE | ID: mdl-34069227
ABSTRACT

BACKGROUND:

The existing staging systems of uterine leiomyosarcoma (uLMS) cannot classify the patients into four non-overlapping prognostic groups. This study aimed to develop a prediction model to predict the three-year survival status of uLMS.

METHODS:

In total, 201 patients with uLMS who had been treated between June 1993 and January 2014, were analyzed. Potential prognostic indicators were identified by univariate models followed by multivariate analyses. Prediction models were constructed by binomial regression with 3-year survival status as a binary outcome, and the final model was validated by internal cross-validation.

RESULTS:

Nine potential parameters, including age, log tumor diameter, log mitotic count, cervical involvement, parametrial involvement, lymph node metastasis, distant metastasis, tumor circumscription and lymphovascular space invasion were identified. 110 patients had complete data to build the prediction models. Age, log tumor diameter, log mitotic count, distant metastasis, and circumscription were significantly correlated with the 3-year survival status. The final model with the lowest Akaike's Information Criterion (117.56) was chosen and the cross validation estimated prediction accuracy was 0.745.

CONCLUSION:

We developed a prediction model for uLMS based on five readily available clinicopathologic parameters. This might provide a personalized prediction of the 3-year survival status and guide the use of adjuvant therapy, a cancer surveillance program, and future studies.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Cancers (Basel) Año: 2021 Tipo del documento: Article País de afiliación: Hong Kong

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Cancers (Basel) Año: 2021 Tipo del documento: Article País de afiliación: Hong Kong
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