Abstract
BackgroundThe aim was to develop a personalized survival prediction deep learning model for adenosarcoma patients using the surveillance, epidemiology and end results (SEER) database.MethodsA total of 797 uterine adenosarcoma patients were enrolled in this study. Duplicated and useless variables were excluded, and 15 variables were selected for further analyses, including age, grade, positive lymph nodes or not, marital status, race, tumor extension, stage, and surgery or not. We created our deep survival learning (DSL) model to manipulate the data, which was randomly split into a training set (n = 519, 65%), validation set (n = 143, 18%) and testing set (n = 143, 18%). The Cox proportional hazard (CPH) model was also included comparatively. Finally, personalized survival curves were plotted for randomly selected patients.ResultsThe c-index for the CPH model was 0.726, and the Brier score was 0.17. For our deep survival learning model, we achieved a c-index of 0.774 and a Brier score of 0.14 in the external testing set. In addition, the limitations of the traditional staging system were revealed, and a personalized survival prediction system based on our risk scoring grouping was developed.ConclusionsOur study developed a deep neural network model for adenosarcoma. The performance of this model was superior to that of the traditional Cox proportional hazard model. In addition, a personalized survival prediction system was developed based on our deep survival learning model, which provided more accurate prognostic information for adenosarcoma patients.
Highlights
Adenosarcoma is a rare tumor of the female genital tract, accounting for approximately 5% of uterine sarcoma
We aimed to develop a survival prediction deep learning model for adenosarcoma patients collected from the SEER database
We used “Collaborative Stage (CS) Schema v2040+”, which was collected under the specifications of a particular schema based on site and histology, to select corpus adenosarcoma patients from 1973 to 2014
Summary
Adenosarcoma is a rare tumor of the female genital tract, accounting for approximately 5% of uterine sarcoma. Due to the low incidence and histological diversity of uterine adenosarcoma, only a few case reports and series provide data on prognostic factors and survival prediction [3]. Researchers have developed prognostication studies with different methods, including univariate analysis, multivariate analysis, multivariable Cox regression and the Kaplan-Meier method [4, 5], among which the most commonly used is multivariable Cox regression analyses. Whether these traditional methods accurately work remains debatable. The aim was to develop a personalized survival prediction deep learning model for adenosarcoma patients using the surveillance, epidemiology and end results (SEER) database
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