In:
PLOS ONE, Public Library of Science (PLoS), Vol. 18, No. 9 ( 2023-9-28), p. e0286841-
Abstract:
Successful prognosis is crucial for the management and treatment of osteosarcoma (OSC). This study aimed to predict the cancer-specific survival rate in patients with OSC using deep learning algorithms and classical Cox proportional hazard models to provide data to support individualized treatment of patients with OSC. Methods Data on patients diagnosed with OSC from 2004 to 2017 were obtained from the Surveillance, Epidemiology, and End Results database. The study sample was then divided randomly into a training cohort and a validation cohort in the proportion of 7:3. The DeepSurv algorithm and the Cox proportional hazard model were chosen to construct prognostic models for patients with OSC. The prediction efficacy of the model was estimated using the concordance index (C-index), the integrated Brier score (IBS), the root mean square error (RMSE), and the mean absolute error (SME). Results A total of 3218 patients were randomized into training and validation groups (n = 2252 and 966, respectively). Both DeepSurv and Cox models had better efficacy in predicting cancer-specific survival (CSS) in OSC patients (C-index 〉 0.74). In the validation of other metrics, DeepSurv did not have superiority over the Cox model in predicting survival in OSC patients. Conclusions After validation, our CSS prediction model for patients with OSC based on the DeepSurv algorithm demonstrated satisfactory prediction efficacy and provided a convenient webpage calculator.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0286841
DOI:
10.1371/journal.pone.0286841.g001
DOI:
10.1371/journal.pone.0286841.g002
DOI:
10.1371/journal.pone.0286841.g003
DOI:
10.1371/journal.pone.0286841.g004
DOI:
10.1371/journal.pone.0286841.t001
DOI:
10.1371/journal.pone.0286841.s001
DOI:
10.1371/journal.pone.0286841.s002
DOI:
10.1371/journal.pone.0286841.r001
DOI:
10.1371/journal.pone.0286841.r002
DOI:
10.1371/journal.pone.0286841.r003
DOI:
10.1371/journal.pone.0286841.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2267670-3
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