骨肉瘤患者预后研究的发展
Advancement in Prognostic Studies among Patients Diagnosed with Osteosarcoma
DOI: 10.12677/acm.2024.143944, PDF,   
作者: 周楚雍, 向志钢*:吉首大学医学院,湖南 吉首;朱 钧*:吉首大学第四附属医院骨科,湖南 怀化
关键词: 骨肉瘤预后研究机器学习列线图Osteosarcoma Prognostic Studies Machine Learning Nomogram
摘要: 骨肉瘤是一种源自间充质细胞的恶性骨肿瘤,其特征是肿瘤细胞直接形成骨或骨样组织的增殖,通常情况下,骨肉瘤起源于骨骼,极少数情况下起源于软组织。癌症患者的预后是指患者在一定时间范围内发展为结局事件。预后研究旨在探究患者在特定基线健康状态(起点)下与未来结果(终点)之间的关联,以促进健康状况的改善。因此,预后研究结果应该是临床决策、医疗保健政策、发现和评估患者管理的新方法中不可或缺的一部分,作者根据相关的参考文献和数据总结分析了恶性骨肿瘤预后研究的发展。
Abstract: Osteosarcoma is a malignancy arising from mesenchymal cells, characterized by the direct formation of bone or bone-like tissue by tumor cells. While primarily originating from bone, there are rare instances in which osteosarcoma originates from soft tissues. The prognosis of cancer patients is defined as the occurrence of outcome events within a specific timeframe. Prognostic studies seek to investigate the relationship between patients’ initial health status and subsequent outcomes, with the goal of improving healthcare conditions. Consequently, the findings from prognostic studies should be considered vital in clinical decision-making, healthcare policies, and the identification and assessment of novel approaches in patient management. Drawing upon pertinent literature and data, the author provides a comprehensive overview of the advancements made in prognostic studies concerning malignant bone tumors.
文章引用:周楚雍, 朱钧, 向志钢. 骨肉瘤患者预后研究的发展[J]. 临床医学进展, 2024, 14(3): 2060-2065. https://doi.org/10.12677/acm.2024.143944

参考文献

[1] Messerschmitt, P.J., Garcia, R.M., Abdul-Karim, F.W., et al. (2009) Osteosarcoma. The Journal of the American Academy of Orthopaedic Surgeons, 17, 515-527. [Google Scholar] [CrossRef] [PubMed]
[2] Zhao, X., Wu, Q., Gong, X., et al. (2021) Osteosarcoma: A Review of Current and Future Therapeutic Approaches. Biomedical Engineering Online, 20, Article No. 24. [Google Scholar] [CrossRef] [PubMed]
[3] Jo, V.Y. and Fletcher, C.D. (2014) WHO Classification of Soft Tissue Tumours: An Update Based on the 2013 (4th) Edition. Pathology, 46, 95-104. [Google Scholar] [CrossRef
[4] Anderson, M.E. (2016) Update on Survival in Osteosarcoma. The Orthopedic Clinics of North America, 47, 283-292. [Google Scholar] [CrossRef] [PubMed]
[5] Marchandet, L., Lallier, M., Charrier, C., et al. (2021) Mechanisms of Resistance to Conventional Therapies for Osteosarcoma. Cancers, 13, Article 683. [Google Scholar] [CrossRef] [PubMed]
[6] Siegel, R.L., Miller, K.D. and Jemal, A. (2017) Cancer Statistics, 2017. CA: A Cancer Journal for Clinicians, 67, 7-30. [Google Scholar] [CrossRef] [PubMed]
[7] Moons, K.G., Royston, P., Vergouwe, Y., et al. (2009) Prognosis and Prognostic Research: What, Why, and How? BMJ, 338, b375. [Google Scholar] [CrossRef] [PubMed]
[8] Hemingway, H., Croft, P., Perel, P., et al. (2013) Prognosis Research Strategy (PROGRESS) 1: A Framework for Researching Clinical Outcomes. BMJ, 346, e5595. [Google Scholar] [CrossRef] [PubMed]
[9] Glare, P., Virik, K., Jones, M., et al. (2003) A Systematic Review of Physicians’ Survival Predictions in Terminally Ill Cancer Patients. BMJ, 327, 195-198. [Google Scholar] [CrossRef] [PubMed]
[10] 周支瑞, 李博. 临床预测模型构建方法学[M]. 长沙: 中南大学出版社, 2021.
[11] Nieto, F.J. and Coresh, J. (1996) Adjusting Survival Curves for Confounders: A Review and a New Method. American Journal of Epidemiology, 143, 1059-1068. [Google Scholar] [CrossRef] [PubMed]
[12] 杨乾坤, 陈通, 王巍, 等. 骨肉瘤预后相关的临床预测指标[J]. 中华肿瘤杂志, 2021, 43(5): 516-522.
[13] Edge, S.B. and Compton, C.C. (2010) The American Joint Committee on Cancer: The 7th Edition of the AJCC Cancer Staging Manual and the Future of TNM. Annals of Surgical Oncology, 17, 1471-1474. [Google Scholar] [CrossRef] [PubMed]
[14] Zhan, H., Mo, F., Zhu, M., et al. (2021) A SEER-Based Nomogram Accurately Predicts Prognosis in Ewing’s Sarcoma. Scientific Reports, 11, Article No. 22723. [Google Scholar] [CrossRef] [PubMed]
[15] Ren, H.Y., Sun, L.L., Li, H.Y., et al. (2015) Prognostic Significance of Serum Alkaline Phosphatase Level in Osteosarcoma: A Meta-Analysis of Published Data. BioMed Research International, 2015, Article ID: 160835. [Google Scholar] [CrossRef] [PubMed]
[16] Chen, J., Sun, M.X., Hua, Y.Q., et al. (2014) Prognostic Significance of Serum Lactate Dehydrogenase Level in Osteosarcoma: A Meta-Analysis. Journal of Cancer Research and Clinical Oncology, 140, 1205-1210. [Google Scholar] [CrossRef] [PubMed]
[17] Gou, B., Cao, H., Cheng, X., et al. (2019) Prognostic Value of Mean Platelet Volume to Plateletcrit Ratio in Patients with Osteosarcoma. Cancer Management and Research, 11, 1615-1621. [Google Scholar] [CrossRef
[18] Guo, T., Wei, R., Dean, D.C., et al. (2022) SMARCB1 Expression Is a Novel Diagnostic and Prognostic Biomarker for Osteosarcoma. Bioscience Reports, 42, BSR20212446. [Google Scholar] [CrossRef
[19] Kattan, M.W., Leung, D.H. and Brennan, M.F. (2002) Postoperative Nomogram for 12-Year Sarcoma-Specific Death. Journal of Clinical Oncology, 20, 791-796. [Google Scholar] [CrossRef
[20] 蔺海山, 哈巴西∙卡肯, 马超, 等. 基于SEER数据库绘制列线图分析软骨肉瘤患者预后相关因素[J]. 现代肿瘤医学, 2022, 30(7): 1292-1299.
[21] Iasonos, A., Schrag, D., Raj, G.V., et al. (2008) How to Build and Interpret a Nomogram for Cancer Prognosis. Journal of Clinical Oncology, 26, 1364-1370. [Google Scholar] [CrossRef
[22] 左怡洁, 闵安杰, 胡传宇, 等. 基于SEER数据库的口底鳞状细胞癌特异性预后分析及列线图模型构建[J]. 临床口腔医学杂志, 2021, 37(6): 339-344.
[23] 黄凯, 张长乐, 吴文涌, 等. 结肠癌列线图预后风险模型构建:基于SEER数据库的回顾性研究[J]. 安徽医科大学学报, 2021, 56(2): 299-305.
[24] 李文乐, 胡朝晖, 王永辉, 等. 基于SEER数据库脊索瘤临床预测模型的建立及验证[J]. 中国骨与关节杂志, 2021, 10(2): 85-92.
[25] Morlacco, A., Modonutti, D., Motterle, G., et al. (2021) Nomograms in Urologic Oncology: Lights and Shadows. Journal of Clinical Medicine, 10, Article 980. [Google Scholar] [CrossRef] [PubMed]
[26] Breslow, N.E. and Day, N.E. (1975) Indirect Standardization and Multiplicative Models for Rates, with Reference to the Age Adjustment of Cancer Incidence and Relative Frequency Data. Journal of Chronic Diseases, 28, 289-303. [Google Scholar] [CrossRef] [PubMed]
[27] Chen, J.H. and Asch, S.M. (2017) Machine Learning and Prediction in Medicine—Beyond the Peak of Inflated Expectations. The New England Journal of Medicine, 376, 2507-2509. [Google Scholar] [CrossRef
[28] Fernández-Delgado, M., Cernadas, E., Barro, S., et al. (2014) Do We Need Hundreds of Classifiers to Solve Real World Classification Problems. Journal of Machine Learning Research, 15, 3133-3181.
[29] Ryo, M. and Rillig, M.C. (2017) Statistically Reinforced Machine Learning for Nonlinear Patterns and Variable Interactions. Ecosphere, 8, e01976. [Google Scholar] [CrossRef
[30] Bai, B.L., Wu, Z.Y., Weng, S.J., et al. (2023) Application of Interpretable Machine Learning Algorithms to Predict Distant Metastasis in Osteosarcoma. Cancer Medicine, 12, 5025-5034. [Google Scholar] [CrossRef] [PubMed]