biostudies-literatureUnknown12Yan L<h4>Background</h4>Accurate prediction of prognosis is critical for therapeutic decisions in chondrosarcoma patients. Several prognostic models have been created utilizing multivariate Cox regression or binary classification-based machine learning approaches to predict the 3- and 5-year survival of patients with chondrosarcoma, but few studies have investigated the results of combining deep learning with time-to-event prediction. Compared with simplifying the prediction as a binary classification problem, modeling the probability of an event as a function of time by combining it with deep learning can provide better accuracy and flexibility.<h4>Materials and methods</h4>Patients with the diagnosis of chondrosarcoma between 2000 and 2018 were extracted from the Surveillance, Epidemiology, aFrontiers in oncology967758https://www.ebi.ac.uk/biostudies/studies/S-EPMC9442032biostudies-literatureDeep learning models for predicting the survival of patients with chondrosarcoma based on a surveillance, epidemiology, and end results analysis.PMC9442032Yan LAi FGao NChen JWeng YKang YZhao YfalseDeep learning models for predicting the survival of patients with chondrosarcoma based on a surveillance, epidemiology, and end results analysis.<h4>Background</h4>Accurate prediction of prognosis is critical for therapeutic decisions in chondrosarcoma patients. Several prognostic models have been created utilizing multivariate Cox regression or binary classification-based machine learning approaches to predict the 3- and 5-year survival of patients with chondrosarcoma, but few studies have investigated the results of combining deep learning with time-to-event prediction. Compared with simplifying the prediction as a binary classification problem, modeling the probability of an event as a function of time by combining it with deep learning can provide better accuracy and flexibility.<h4>Materials and methods</h4>Patients with the diagnosis of chondrosarcoma between 2000 and 2018 were extracted from the Surveillance, Epidemiology, a2022-01-01T00:00:00Z20222025-04-04T12:25:22.695Z2025-02-18T23:48:50.029ZS-EPMC94420323607279510.3389/fonc.2022.967758