Deep Learning for the Prediction of the Survival of Midline Diffuse Glioma with an H3K27M Alteration
Background: The prognosis of diffuse midline glioma (DMG) patients with H3K27M (H3K27M-DMG) alterations is poor; however, a model that encourages accurate prediction of prognosis for such lesions on an individual basis remains elusive. We aimed to construct an H3K27M-DMG survival model based on Deep...
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MDPI AG
2023-10-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/13/10/1483 |
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| author | Bowen Huang Tengyun Chen Yuekang Zhang Qing Mao Yan Ju Yanhui Liu Xiang Wang Qiang Li Yinjie Lei Yanming Ren |
| author_facet | Bowen Huang Tengyun Chen Yuekang Zhang Qing Mao Yan Ju Yanhui Liu Xiang Wang Qiang Li Yinjie Lei Yanming Ren |
| author_sort | Bowen Huang |
| collection | DOAJ |
| description | Background: The prognosis of diffuse midline glioma (DMG) patients with H3K27M (H3K27M-DMG) alterations is poor; however, a model that encourages accurate prediction of prognosis for such lesions on an individual basis remains elusive. We aimed to construct an H3K27M-DMG survival model based on DeepSurv to predict patient prognosis. Methods: Patients recruited from a single center were used for model training, and patients recruited from another center were used for external validation. Univariate and multivariate Cox regression analyses were used to select features. Four machine learning models were constructed, and the consistency index (C-index) and integrated Brier score (IBS) were calculated. We used the receiver operating characteristic curve (ROC) and area under the receiver operating characteristic (AUC) curve to assess the accuracy of predicting 6-month, 12-month, 18-month and 24-month survival rates. A heatmap of feature importance was used to explain the results of the four models. Results: We recruited 113 patients in the training set and 23 patients in the test set. We included tumor size, tumor location, Karnofsky Performance Scale (KPS) score, enhancement, radiotherapy, and chemotherapy for model training. The accuracy of DeepSurv prediction is highest among the four models, with C-indexes of 0.862 and 0.811 in the training and external test sets, respectively. The DeepSurv model had the highest AUC values at 6 months, 12 months, 18 months and 24 months, which were 0.970 (0.919–1), 0.950 (0.877–1), 0.939 (0.845–1), and 0.875 (0.690–1), respectively. We designed an interactive interface to more intuitively display the survival probability prediction results provided by the DeepSurv model. Conclusion: The DeepSurv model outperforms traditional machine learning models in terms of prediction accuracy and robustness, and it can also provide personalized treatment recommendations for patients. The DeepSurv model may provide decision-making assistance for patients in formulating treatment plans in the future. |
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| last_indexed | 2024-03-10T21:23:29Z |
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| spelling | doaj.art-5eb55f1ef13f44c28df0fa559781ef802023-11-19T15:53:35ZengMDPI AGBrain Sciences2076-34252023-10-011310148310.3390/brainsci13101483Deep Learning for the Prediction of the Survival of Midline Diffuse Glioma with an H3K27M AlterationBowen Huang0Tengyun Chen1Yuekang Zhang2Qing Mao3Yan Ju4Yanhui Liu5Xiang Wang6Qiang Li7Yinjie Lei8Yanming Ren9Department of Neurosurgery, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu 610041, ChinaDepartment of Neurosurgery, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu 610041, ChinaDepartment of Neurosurgery, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu 610041, ChinaDepartment of Neurosurgery, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu 610041, ChinaDepartment of Neurosurgery, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu 610041, ChinaDepartment of Neurosurgery, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu 610041, ChinaDepartment of Neurosurgery, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu 610041, ChinaDepartment of Neurosurgery, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu 610041, ChinaCollege of Electronics and Information Engineering, Sichuan University, Chengdu 610065, ChinaDepartment of Neurosurgery, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu 610041, ChinaBackground: The prognosis of diffuse midline glioma (DMG) patients with H3K27M (H3K27M-DMG) alterations is poor; however, a model that encourages accurate prediction of prognosis for such lesions on an individual basis remains elusive. We aimed to construct an H3K27M-DMG survival model based on DeepSurv to predict patient prognosis. Methods: Patients recruited from a single center were used for model training, and patients recruited from another center were used for external validation. Univariate and multivariate Cox regression analyses were used to select features. Four machine learning models were constructed, and the consistency index (C-index) and integrated Brier score (IBS) were calculated. We used the receiver operating characteristic curve (ROC) and area under the receiver operating characteristic (AUC) curve to assess the accuracy of predicting 6-month, 12-month, 18-month and 24-month survival rates. A heatmap of feature importance was used to explain the results of the four models. Results: We recruited 113 patients in the training set and 23 patients in the test set. We included tumor size, tumor location, Karnofsky Performance Scale (KPS) score, enhancement, radiotherapy, and chemotherapy for model training. The accuracy of DeepSurv prediction is highest among the four models, with C-indexes of 0.862 and 0.811 in the training and external test sets, respectively. The DeepSurv model had the highest AUC values at 6 months, 12 months, 18 months and 24 months, which were 0.970 (0.919–1), 0.950 (0.877–1), 0.939 (0.845–1), and 0.875 (0.690–1), respectively. We designed an interactive interface to more intuitively display the survival probability prediction results provided by the DeepSurv model. Conclusion: The DeepSurv model outperforms traditional machine learning models in terms of prediction accuracy and robustness, and it can also provide personalized treatment recommendations for patients. The DeepSurv model may provide decision-making assistance for patients in formulating treatment plans in the future.https://www.mdpi.com/2076-3425/13/10/1483diffuse midline gliomaH3K27M alterationmachine learningDeepSurvsurvival model |
| spellingShingle | Bowen Huang Tengyun Chen Yuekang Zhang Qing Mao Yan Ju Yanhui Liu Xiang Wang Qiang Li Yinjie Lei Yanming Ren Deep Learning for the Prediction of the Survival of Midline Diffuse Glioma with an H3K27M Alteration Brain Sciences diffuse midline glioma H3K27M alteration machine learning DeepSurv survival model |
| title | Deep Learning for the Prediction of the Survival of Midline Diffuse Glioma with an H3K27M Alteration |
| title_full | Deep Learning for the Prediction of the Survival of Midline Diffuse Glioma with an H3K27M Alteration |
| title_fullStr | Deep Learning for the Prediction of the Survival of Midline Diffuse Glioma with an H3K27M Alteration |
| title_full_unstemmed | Deep Learning for the Prediction of the Survival of Midline Diffuse Glioma with an H3K27M Alteration |
| title_short | Deep Learning for the Prediction of the Survival of Midline Diffuse Glioma with an H3K27M Alteration |
| title_sort | deep learning for the prediction of the survival of midline diffuse glioma with an h3k27m alteration |
| topic | diffuse midline glioma H3K27M alteration machine learning DeepSurv survival model |
| url | https://www.mdpi.com/2076-3425/13/10/1483 |
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