Nomograms to predict overall survival for patients with endometrial carcinosarcoma.

Abstract

455 Dear editor, we read a very interesting article entitled “a personalized nomogram for predicting 3-year overall survival of patients with uterine carcinosarcoma in a tertiary care hospital in Southern Thailand” by Nanthamongkolkul et al. [1]. The authors presented their retrospective study analyzing a cohort of patients with uterine carcinosarcoma and how body mass index (BMI), International Federation of Gynecology and Obstetrics (FIGO) stage, and adjuvant chemotherapy can affect the 3-year overall survival. The authors combined three parameters to form their nomogram; one presenting the individual’s performance status (BMI), one presenting the tumor’s aggressiveness (FIGO stage), and one presenting the medical approach (adjuvant chemotherapy). They aimed to form a nomogram in which these parameters could predict the 3-year overall survival of patients with uterine carcinosarcoma. Recently, Chen et al. [2] in their multivariate analysis showed that age, race, year of diagnosis, FIGO stage, and treatment type can also be associated with survival and formed a similar nomogram with a good predictive capacity. Another study formed a nomogram predicting the overall survival of patients with ovarian carcinosarcoma including the following parameters: age, grade, tumor site, surgery, and chemotherapy [3]. We would like to ask the authors whether the addition of patients’ age and surgery might improve the prognostic performance of their nomogram. Moreover, Gao et al. [4] revealed in their study that log odds of metastatic lymph nodes (LODDS) has a better predicting accuracy compared to the number of positive lymph nodes and lymph node ratio. They suggest that a nomogram based on LODDS can offer an accurate predictive model of the overall survival of patients with uterine carcinosarcoma [4]. A possible modification of the nomogram by the team from Thailand might include such parameters (age, surgery, and LODDS). Would such additions improve their predictive model? Once again, we would like to thank the authors for their study.