Development of a prognostic nomogram for ocular melanoma: a SEER population-based study (2000–2021)
IntroductionOcular melanoma (OM) is a rare but lethal subtype of melanoma. This study develops a prognostic nomogram for OM using machine learning and internal validation techniques, aiming to improve prognosis prediction and clinical decision-making.MethodsIndependent prognostic variables were iden...
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Frontiers Media S.A.
2025-01-01
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1494925/full |
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| author | Miyun Zheng Miyun Zheng Maodong Xu Maodong Xu Mengxing You Mengxing You Zhiqing Huang Zhiqing Huang |
| author_facet | Miyun Zheng Miyun Zheng Maodong Xu Maodong Xu Mengxing You Mengxing You Zhiqing Huang Zhiqing Huang |
| author_sort | Miyun Zheng |
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| description | IntroductionOcular melanoma (OM) is a rare but lethal subtype of melanoma. This study develops a prognostic nomogram for OM using machine learning and internal validation techniques, aiming to improve prognosis prediction and clinical decision-making.MethodsIndependent prognostic variables were identified using univariate and multivariate COX proportional hazard regression models. Significant variables were then incorporated into the nomogram. The predictive accuracy of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA), and 10-fold cross-validation. The performance of the nomogram was compared with that of a machine learning model.ResultsThirteen variables, including age, sex, tumor site, histologic subtype, stage, basal diameter size, tumor thickness, liver metastasis, first malignant primary indicator, marital status, and treatment modalities (surgery/radiotherapy/chemotherapy) were identified as independent prognostic factors for overall survival (OS) and were included in the nomogram (all P < 0.05). The nomogram showed a concordance index of 0.712. The areas under the curve (AUC) for predicting 3-, 5-, and 10-year survival rates were 0.749, 0.734, and 0.730, respectively. Calibration plots for 3-, 5-, and 10-year survival were in close agreement with the ideal predictions, and DCA indicated a superior net benefit. The average AUC from 10-fold cross-validation was 0.725. The machine-learning model identified liver metastasis as the most significant predictor of survival, followed by age, radiotherapy, stage, and other factors that were incorporated into the nomogram. The machine-learning model achieved a predictive AUC score of 0.750.ConclusionsA robust nomogram incorporating 13 significant clinicopathological variables was developed. The combined use of ROC curve analysis, calibration plots, DCA, 10-fold cross-validation, and machine learning confirmed the strong predictive performance of the nomogram for survival outcomes in patients with OM. |
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| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Medicine |
| spelling | doaj-art-444111b51bf34865b883c2bfc251cff02025-01-29T06:45:44ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-01-011210.3389/fmed.2025.14949251494925Development of a prognostic nomogram for ocular melanoma: a SEER population-based study (2000–2021)Miyun Zheng0Miyun Zheng1Maodong Xu2Maodong Xu3Mengxing You4Mengxing You5Zhiqing Huang6Zhiqing Huang7Department of Ophthalmology, The First Hospital of Putian City, Putian, ChinaThe School of Clinical Medicine, Fujian Medical University, Fuzhou, ChinaDepartment of Ophthalmology, The First Hospital of Putian City, Putian, ChinaThe School of Clinical Medicine, Fujian Medical University, Fuzhou, ChinaThe School of Clinical Medicine, Fujian Medical University, Fuzhou, ChinaDepartment of Medical Oncology, The First Hospital of Putian City, Putian, ChinaDepartment of Ophthalmology, The First Hospital of Putian City, Putian, ChinaThe School of Clinical Medicine, Fujian Medical University, Fuzhou, ChinaIntroductionOcular melanoma (OM) is a rare but lethal subtype of melanoma. This study develops a prognostic nomogram for OM using machine learning and internal validation techniques, aiming to improve prognosis prediction and clinical decision-making.MethodsIndependent prognostic variables were identified using univariate and multivariate COX proportional hazard regression models. Significant variables were then incorporated into the nomogram. The predictive accuracy of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA), and 10-fold cross-validation. The performance of the nomogram was compared with that of a machine learning model.ResultsThirteen variables, including age, sex, tumor site, histologic subtype, stage, basal diameter size, tumor thickness, liver metastasis, first malignant primary indicator, marital status, and treatment modalities (surgery/radiotherapy/chemotherapy) were identified as independent prognostic factors for overall survival (OS) and were included in the nomogram (all P < 0.05). The nomogram showed a concordance index of 0.712. The areas under the curve (AUC) for predicting 3-, 5-, and 10-year survival rates were 0.749, 0.734, and 0.730, respectively. Calibration plots for 3-, 5-, and 10-year survival were in close agreement with the ideal predictions, and DCA indicated a superior net benefit. The average AUC from 10-fold cross-validation was 0.725. The machine-learning model identified liver metastasis as the most significant predictor of survival, followed by age, radiotherapy, stage, and other factors that were incorporated into the nomogram. The machine-learning model achieved a predictive AUC score of 0.750.ConclusionsA robust nomogram incorporating 13 significant clinicopathological variables was developed. The combined use of ROC curve analysis, calibration plots, DCA, 10-fold cross-validation, and machine learning confirmed the strong predictive performance of the nomogram for survival outcomes in patients with OM.https://www.frontiersin.org/articles/10.3389/fmed.2025.1494925/fullocular melanomamachine learningSHAPSEERprognosisnomogram |
| spellingShingle | Miyun Zheng Miyun Zheng Maodong Xu Maodong Xu Mengxing You Mengxing You Zhiqing Huang Zhiqing Huang Development of a prognostic nomogram for ocular melanoma: a SEER population-based study (2000–2021) Frontiers in Medicine ocular melanoma machine learning SHAP SEER prognosis nomogram |
| title | Development of a prognostic nomogram for ocular melanoma: a SEER population-based study (2000–2021) |
| title_full | Development of a prognostic nomogram for ocular melanoma: a SEER population-based study (2000–2021) |
| title_fullStr | Development of a prognostic nomogram for ocular melanoma: a SEER population-based study (2000–2021) |
| title_full_unstemmed | Development of a prognostic nomogram for ocular melanoma: a SEER population-based study (2000–2021) |
| title_short | Development of a prognostic nomogram for ocular melanoma: a SEER population-based study (2000–2021) |
| title_sort | development of a prognostic nomogram for ocular melanoma a seer population based study 2000 2021 |
| topic | ocular melanoma machine learning SHAP SEER prognosis nomogram |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1494925/full |
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