1. Development of a Multi-Institutional Prediction Model for Three-Year Survival Status in Patients with Uterine Leiomyosarcoma (AGOG11-022/QCGC1302 Study)
- Author
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Deborah Smith, Charlotte Ka-Lun Leung, Chiung-Ru Lai, Lan-Yan Yang, Nancy Wah-Fun Yuen, Wai-Mei Pong, Judy Ying-Wah Yuk, K.Y. Tse, Kit-sheung Chan, Ming-Shyen Yen, Chyong-Huey Lai, Margaret C. Cummings, Angel Chao, Hei-Yu Lau, Shir-Hwa Ueng, Wai-Hon Li, Annie N.Y. Cheung, Richard Wing-Cheuk Wong, Alice Ngot-Htain Chan, Philip P.C. Ip, Hung Yao, Hextan Y.S. Ngan, Hoi-Fong Hui, Andreas Obermair, and Carmen Ka Man Choi
- Subjects
Leiomyosarcoma ,Oncology ,Cancer Research ,medicine.medical_specialty ,Multivariate analysis ,Binomial regression ,Cross-validation ,Article ,uterine leiomyosarcoma ,03 medical and health sciences ,0302 clinical medicine ,Internal medicine ,medicine ,Adjuvant therapy ,RC254-282 ,030219 obstetrics & reproductive medicine ,business.industry ,Univariate ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,medicine.disease ,prediction model ,030220 oncology & carcinogenesis ,Akaike information criterion ,business ,Predictive modelling - Abstract
Simple Summary Uterine leiomyosarcoma is an aggressive tumor and the current staging system cannot differentiate the patients into different prognostic groups. This leads to difficulty in predicting the patients’ outcomes and planning for adjuvant therapy. We aimed to develop a prediction model that can predict the chance of survival by the third year. In this article, we had used different statistical tests to identify five readily available clinicopathologic parameters to build the prediction model. Internal validation was performed with satisfactory accuracy. Such a prediction model might help to predict survival outcome, and guide future research on the treatment modality. 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.
- Published
- 2021
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