Ostrom, Q. T. et al. CBTRUS statistical report: Primary brain and other central nervous system tumors diagnosed in the United States in 2010–2014. Neuro-Oncology 19, 1–88 (2017).
Omuro, A. & DeAngelis, L. M. Glioblastoma and other malignant gliomas: A clinical review. Jama 310, 1842–1850 (2013).
Li, H., He, Y., Huang, L., Luo, H. & Zhu, X. The nomogram model predicting overall survival and guiding clinical decision in patients with glioblastoma based on the SEER database. Front. Oncol. 10, 1051 (2020).
Poon, M. T., Sudlow, C. L., Figueroa, J. D. & Brennan, P. M. Longer-term (≥ 2 years) survival in patients with glioblastoma in population-based studies pre-and post-2005: A systematic review and meta-analysis. Sci. Rep. 10, 11622 (2020).
Stupp, R. et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. NEJM 352, 987–996 (2005).
Bi, W. L. & Beroukhim, R. Beating the odds: Extreme long-term survival with glioblastoma. Neuro-Oncology 16, 1159–1160 (2014).
Shastry, K. A. & Sanjay, H. A. Machine learning for bioinformatics. In Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications (eds Srinivasa, K. G. et al.) 25–39 (Springer, 2020).
Zade, A. E., Haghighi, S. S. & Soltani, M. Deep neural networks for neuro-oncology: Towards patient individualized design of chemo-radiation therapy for Glioblastoma patients. J. Biomed. Inform. 127, 104006 (2022).
Sorayaie Azar, A. et al. Application of machine learning techniques for predicting survival in ovarian cancer. BMC Med. Inform. Decis. Mak. 22, 345 (2022).
Al-Husseini, M. J. et al. Prior malignancy impact on survival outcomes of glioblastoma multiforme; population-based study. Int. J. Neurosci. 129, 447–454 (2019).
Senders, J. T. et al. An online calculator for the prediction of survival in glioblastoma patients using classical statistics and machine learning. Neurosurgery 86, E184 (2020).
Samara, K. A., Al Aghbari, Z. & Abusafia, A. GLIMPSE: A glioblastoma prognostication model using ensemble learning—a surveillance, epidemiology, and end results study. Health Inf. Sci. Syst. 9, 1–13 (2021).
Bakirarar, B., Egemen, E., Dere, Ü. A. & Yakar, F. Machine learning model to identify prognostic factors in glioblastoma: A SEER-based analysis. Pamukkale Med J. 16, 338–348 (2022).
Doppalapudi, S., Qiu, R. G. & Badr, Y. Lung cancer survival period prediction and understanding: Deep learning approaches. Int. J. Med. Inform. 148, 104371 (2021).
Ryu, S. M., Seo, S. W. & Lee, S. H. Novel prognostication of patients with spinal and pelvic chondrosarcoma using deep survival neural networks. BMC Med. Inform. Decis. Mak. 20, 1–10 (2020).
Jajroudi, M. et al. Prediction of survival in thyroid cancer using data mining technique. TCRT 13, 353–359 (2014).
Mourad, M. et al. Machine learning and feature selection applied to SEER data to reliably assess thyroid cancer prognosis. Sci. Rep. 10, 5176 (2020).
Tewarie, I. A. et al. Survival prediction of glioblastoma patients—are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential. Neurosurg. Rev. 44, 2047–2057 (2021).
Liu, Z. Y. et al. Competing risk model to determine the prognostic factors and treatment strategies for elderly patients with glioblastoma. Sci. Rep. 11, 9321 (2021).
Goldman, D. A. et al. Lack of survival advantage among re-resected elderly glioblastoma patients: a SEER-Medicare study. Neuro-Oncol. Adv. 3, vdaa159 (2021).
Thumma, S. R. et al. Effect of pretreatment clinical factors on overall survival in glioblastoma multiforme: A surveillance epidemiology and end results (SEER) population analysis. World J. Surg. Onc. 10, 1–12 (2012).
Farahani, H. A., Rahiminezhad, A. & Same, L. A comparison of partial least squares (PLS) and ordinary least squares (OLS) regressions in predicting of couples mental health based on their communicational patterns. Procedia Soc. Behav. Sci. 5, 1459–1463 (2010).
Judkins, D. R. & Porter, K. E. Robustness of ordinary least squares in randomized clinical trials. Stat. Med. 35, 1763–1773 (2016).
Doane, D. P. & Seward, L. E. Measuring skewness: A forgotten statistic?. J. Stat. Educ. https://doi.org/10.1080/10691898.2011.11889611 (2011).
Chawla, N. V., Bowyer, K. W., Hall, L. O. & Kegelmeyer, W. P. SMOTE: Synthetic minority over-sampling technique. JAIR 16, 321–357 (2002).
Blagus, R. & Lusa, L. SMOTE for high-dimensional class-imbalanced data. BMC Bioinform. 14, 1–16 (2013).
Branco, P., Torgo, L., & Ribeiro, R. P. SMOGN: A pre-processing approach for imbalanced regression. In First international workshop on learning with imbalanced domains: Theory and applications, 36–50 (2017).
Huang, J. & Ling, C. X. Using AUC and accuracy in evaluating learning algorithms. IEEE Trans. Knowl. Data Eng. 17, 299–310 (2005).
Mandrekar, J. N. Receiver operating characteristic curve in diagnostic test assessment. JTO 5, 1315–1316 (2010).
Sidey-Gibbons, J. A. & Sidey-Gibbons, C. J. Machine learning in medicine: a practical introduction. BMC Med. Res. Methodol. 19, 1–18 (2019).
Deng, X., Liu, Q., Deng, Y. & Mahadevan, S. An improved method to construct basic probability assignment based on the confusion matrix for classification problem. Inf. Sci. 340, 250–261 (2016).
Shalev-Shwartz, S. & Ben-David, S. Understanding Machine Learning: From Theory to Algorithms (Cambridge University Press, 2014).
Rikan, S. B., Azar, A. S., Ghafari, A., Mohasefi, J. B. & Pirnejad, H. COVID-19 diagnosis from routine blood tests using artificial intelligence techniques. Biomed. Signal Process. Control. 72, 103263 (2022).
Wong, H. B. & Lim, G. H. Measures of diagnostic accuracy: sensitivity, specificity PPV and NPV. Proc. Singap. Healthc. 20, 316–318 (2011).
Parikh, R., Mathai, A., Parikh, S., Sekhar, G. C. & Thomas, R. Understanding and using sensitivity, specificity and predictive values. Indian J. Ophthalmol. 56, 45 (2008).
Chen, T., & Guestrin, C. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining 785–794 (2016).
Kristjanpoller, W., Michell, K. & Minutolo, M. C. A causal framework to determine the effectiveness of dynamic quarantine policy to mitigate COVID-19. Appl. Soft Comput. 104, 107241 (2021).
Chicco, D., Warrens, M. J. & Jurman, G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ. Comput. Sci. 7, e623 (2021).
Miles, J. R-squared, adjusted R-squared. Encycl. Stat. Behav. Sci. https://doi.org/10.1002/0470013192.bsa526 (2005).
Royston, P., Moons, K. G., Altman, D. G. & Vergouwe, Y. Prognosis and prognostic research: developing a prognostic model. Bmj 338, B604 (2009).
Mackillop, W. J. The importance of prognosis in cancer medicine. TNM Online Preprint at https://doi.org/10.1002/0471463736.tnmp01.pub2 (2006).
Harrell, F. E., Califf, R. M., Pryor, D. B., Lee, K. L. & Rosati, R. A. Evaluating the yield of medical tests. Jama 247, 2543–2546 (1982).
Wang, W. et al. An effective tool for predicting survival in breast cancer patients with de novo lung metastasis: Nomograms constructed based on SEER. Front. surg. 9, 939132 (2023).
Longato, E., Vettoretti, M. & Di Camillo, B. A practical perspective on the concordance index for the evaluation and selection of prognostic time-to-event models. J. Biomed. Inform. 108, 103496 (2020).
Kim, M. et al. Glioblastoma as an age-related neurological disorder in adults. Neuro-Oncol. Adv. 3, vdab125 (2021).
Li, S. W. et al. Prognostic factors influencing clinical outcomes of glioblastoma multiforme. Chin. Med. J. 122, 1245–1249 (2009).
Wen, J., Chen, W., Zhu, Y. & Zhang, P. Clinical features associated with the efficacy of chemotherapy in patients with glioblastoma (GBM): A surveillance, epidemiology, and end results (SEER) analysis. BMC Cancer 21, 1–10 (2021).
Villà, S., Balañà, C. & Comas, S. Radiation and concomitant chemotherapy for patients with glioblastoma multiforme. Chin. J. Cancer 33, 25 (2014).
Buckner, J. C. Factors influencing survival in high-grade gliomas. In Seminars in oncology 10–14 (2003).
Brodbelt, A. et al. Glioblastoma in england: 2007–2011. EJC 51, 533–542 (2015).
Moncada-Torres, A., van Maaren, M. C., Hendriks, M. P., Siesling, S. & Geleijnse, G. Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival. Sci. Rep. 11, 6968 (2021).
Currie, C. J. et al. Mortality after incident cancer in people with and without type 2 diabetes: Impact of metformin on survival. Diabetes Care 35, 299–304 (2012).
Surveillance Research Program: Surveillance, Epidemiology, and End Results (SEER) Program (www.seer.cancer.gov) SEER*Stat Database: Incidence—SEER 18 Regs Custom Data (with additional treatment fields). in Linked To County Attributes – Total US 1969–2017 (1975).
SEER incidence data, 1975–2020. SEER https://seer.cancer.gov/data/.
Che, W. Q. et al. How to use the Surveillance, Epidemiology, and End Results (SEER) data: Research design and methodology. Mil. Med. Res. 10, 50 (2023).
Mack, C., Su, Z., & Westreich, D. Managing missing data in patient registries: addendum to registries for evaluating patient outcomes: a user’s guide, (2018).
Scheffer, J. Dealing with missing data, (2002).
Rado, O., Ali, N., Sani, H. M., Idris, A. & Neagu, D. Performance analysis of feature selection methods for classification of healthcare datasets. In Advances in Intelligent Systems and Computing (ed. Kacprzyk, J.) 929–938 (Springer, 2019).
Laios, A. et al. Feature selection is critical for 2-year prognosis in advanced stage high grade serous ovarian cancer by using machine learning. Cancer Control 28, 10732748211044678 (2021).
Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V. & Fotiadis, D. I. Machine learning applications in cancer prognosis and prediction. CSBJ 13, 8–17 (2015).
