TY - JOUR
T1 - Development of a multi-institutional prediction model for three-year survival status in patients with uterine leiomyosarcoma (Agog11-022/qcgc1302 study)
AU - Tse, Ka Yu
AU - Wong, Richard Wing Cheuk
AU - Chao, Angel
AU - Ueng, Shir Hwa
AU - Yang, Lan Yan
AU - Cummings, Margaret
AU - Smith, Deborah
AU - Lai, Chiung Ru
AU - Lau, Hei Yu
AU - Yen, Ming Shyen
AU - Cheung, Annie Nga Yin
AU - Leung, Charlotte Ka Lun
AU - Chan, Kit Sheung
AU - Chan, Alice Ngot Htain
AU - Li, Wai Hon
AU - Choi, Carmen Ka Man
AU - Pong, Wai Mei
AU - Hui, Hoi Fong
AU - Yuk, Judy Ying Wah
AU - Yao, Hung
AU - Yuen, Nancy Wah Fun
AU - Obermair, Andreas
AU - Lai, Chyong Huey
AU - Ip, Philip Pun Ching
AU - Ngan, Hextan Yuen Sheung
N1 - Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/5/2
Y1 - 2021/5/2
N2 - 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. Con-clusion: 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.
AB - 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. Con-clusion: 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.
KW - Prediction model
KW - Uterine leiomyosarcoma
UR - http://www.scopus.com/inward/record.url?scp=85105735317&partnerID=8YFLogxK
U2 - 10.3390/cancers13102378
DO - 10.3390/cancers13102378
M3 - 文章
AN - SCOPUS:85105735317
SN - 2072-6694
VL - 13
JO - Cancers
JF - Cancers
IS - 10
M1 - 2378
ER -