ArticleFrontiers in oncology2020
Development and Validation of a Personalized Survival Prediction Model for Uterine Adenosarcoma: A Population-Based Deep Learning Study.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- A Systematic Review on Machine Learning Techniques for Survival Analysis in Cancer.Cancer medicine · 2025Pooled it
- A deep-learning-based clinical risk stratification for overall survival in adolescent and young adult women with breast cancer.Journal of cancer research and clinical oncology · 2023Article
- Development and validation of a deep learning survival model for cervical adenocarcinoma patients.BMC bioinformatics · 2023Article
- Next-Generation Sequencing Analysis of 3 Uterine Adenosarcomas with Heterogeneously Differentiated Genomic Mutations.International journal of analytical chemistry · 2023Article
- A novel risk classification system based on the eighth edition of TNM frameworks for esophageal adenocarcinoma patients: A deep learning approach.Frontiers in oncology · 2022Article
- Case Report: Uterine Adenosarcoma With Sarcomatous Overgrowth and Malignant Heterologous Elements.Frontiers in medicine · 2021Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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