A deep learning algorithm with good prediction efficacy for cancer-specific survival in osteosarcoma: A retrospective study.
<h4>Objective</h4>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 supp...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0286841 |
| _version_ | 1797668340701855744 |
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| author | Yang Liu Lang Xie Dingxue Wang Kaide Xia |
| author_facet | Yang Liu Lang Xie Dingxue Wang Kaide Xia |
| author_sort | Yang Liu |
| collection | DOAJ |
| description | <h4>Objective</h4>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.<h4>Methods</h4>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).<h4>Results</h4>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.<h4>Conclusions</h4>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. |
| first_indexed | 2024-03-11T20:27:45Z |
| format | Article |
| id | doaj.art-e7d900c38cab4238955f305bf566d0a6 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-11T20:27:45Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-e7d900c38cab4238955f305bf566d0a62023-10-02T12:18:23ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01189e028684110.1371/journal.pone.0286841A deep learning algorithm with good prediction efficacy for cancer-specific survival in osteosarcoma: A retrospective study.Yang LiuLang XieDingxue WangKaide Xia<h4>Objective</h4>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.<h4>Methods</h4>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).<h4>Results</h4>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.<h4>Conclusions</h4>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.https://doi.org/10.1371/journal.pone.0286841 |
| spellingShingle | Yang Liu Lang Xie Dingxue Wang Kaide Xia A deep learning algorithm with good prediction efficacy for cancer-specific survival in osteosarcoma: A retrospective study. PLoS ONE |
| title | A deep learning algorithm with good prediction efficacy for cancer-specific survival in osteosarcoma: A retrospective study. |
| title_full | A deep learning algorithm with good prediction efficacy for cancer-specific survival in osteosarcoma: A retrospective study. |
| title_fullStr | A deep learning algorithm with good prediction efficacy for cancer-specific survival in osteosarcoma: A retrospective study. |
| title_full_unstemmed | A deep learning algorithm with good prediction efficacy for cancer-specific survival in osteosarcoma: A retrospective study. |
| title_short | A deep learning algorithm with good prediction efficacy for cancer-specific survival in osteosarcoma: A retrospective study. |
| title_sort | deep learning algorithm with good prediction efficacy for cancer specific survival in osteosarcoma a retrospective study |
| url | https://doi.org/10.1371/journal.pone.0286841 |
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