Worldwide cancer data [Internet]. 2022 [cited 2023 Nov 20]. Available from: https://www.wcrf.org/cancer-trends/worldwide-cancer-data/

Oerlemans, S., Mols, F., Issa, D. E., Pruijt, J. H., Peters, W. G., Lybeert, M., Zijlstra, W., Coebergh, J. W., & van de Poll-Franse, L. V. (2013). A high level of fatigue among long-term survivors of non-Hodgkin’s lymphoma: Results from the longitudinal population-based PROFILES registry in the south of the Netherlands. Haematologica, 98(3), 479.

Article  PubMed  PubMed Central  Google Scholar 

de Rooij, B. H., Oerlemans, S., van Deun, K., Mols, F., de Ligt, K. M., Husson, O., Ezendam, N. P., Hoedjes, M., van de Poll-Franse, L. V., & Schoormans, D. (2021). Symptom clusters in 1330 survivors of 7 cancer types from the PROFILES registry: A network analysis. Cancer, 127(24), 4665–4674.

Article  PubMed  Google Scholar 

Poort, H., de Rooij, B. H., Uno, H., Weng, S., Ezendam, N. P., van de Poll-Franse, L., & Wright, A. A. (2020). Patterns and predictors of cancer-related fatigue in ovarian and endometrial cancers: 1-year longitudinal study. Cancer, 126(15), 3526–3533.

Article  PubMed  CAS  Google Scholar 

Oertelt-Prigione, S., de Rooij, B. H., Mols, F., Oerlemans, S., Husson, O., Schoormans, D., Haanen, J. B., & van de Poll-Franse, L. V. (2021). Sex-differences in symptoms and functioning in> 5000 cancer survivors: Results from the PROFILES registry. European Journal of Cancer., 1(156), 24–34.

Article  Google Scholar 

Di Meglio, A., Havas, J., Soldato, D., Presti, D., Martin, E., Pistilli, B., Menvielle, G., Dumas, A., Charles, C., Everhard, S., & Martin, A. L. (2022). Development, and validation of a predictive model of severe fatigue after breast cancer diagnosis: Toward a personalized framework in survivorship care. Journal of Clinical Oncology., 40(10), 1111–1123.

Article  PubMed  PubMed Central  Google Scholar 

Aaronson, N. K., Ahmedzai, S., Bergman, B., Bullinger, M., Cull, A., Duez, N. J., Filiberti, A., Flechtner, H., Fleishman, S. B., de Haes, J. C. J. M., Kaasa, S., Klee, M., Osoba, D., Razavi, D., Rofe, P. B., Schraub, S., Sneeuw, K., Sullivan, M., & Takeda, F. (1993). The European organization for research and treatment of cancer QLQ-C30: A quality-of-life instrument for use in international clinical trials in oncology. JNCI Journal of the National Cancer Institute, 85(5), 365–376.

Article  PubMed  CAS  Google Scholar 

Singer, S., Wollbrück, D., Wulke, C., Dietz, A., Klemm, E., Oeken, J., Meister, E. F., Gudziol, H., Bindewald, J., & Schwarz, R. (2009). Validation of the for QLQ-C30 and EORTC QLQ-H&N35 in patients with laryngeal cancer after surgery. Head & Neck: Journal for the Sciences and Specialties of the Head and Neck, 31(1), 64–76.

Article  Google Scholar 

Arraras, J. I., Arias, F., Tejedor, M., Pruja, E., Marcos, M., Martínez, E., & Valerdi, J. (2002). The EORTC QLQ-C30 (version 3.0) quality of life questionnaire: validation study for Spain with head and neck cancer patients. Psycho-Oncology: Journal of the Psychological Social and Behavioral Dimensions of Cancer., 11(3), 249–256.

Article  CAS  Google Scholar 

Giesinger, J. M., Loth, F. L., Aaronson, N. K., Arraras, J. I., Caocci, G., Efficace, F., Groenvold, M., van Leeuwen, M., Petersen, M. A., Ramage, J., & Tomaszewski, K. A. (2020). Thresholds for clinical importance were established to improve interpretation of the EORTC QLQ-C30 in clinical practice and research. Journal of clinical epidemiology., 1(118), 1–8.

Article  Google Scholar 

Vickers, A. J., Cronin, A. M., Kattan, M. W., Gonen, M., Scardino, P. T., Milowsky, M. I., Dalbagni, G., & Bochner, B. H. (2009). International bladder cancer nomogram consortium clinical benefits of a multivariate prediction model for bladder cancer: A decision analytic approach. Cancer, 115(23), 5460–5469.

Article  PubMed  Google Scholar 

Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 1(13), 8–17.

Article  Google Scholar 

Cruz, J. A., & Wishart, D. S. (2006). Applications of machine learning in cancer prediction and prognosis. Cancer Informatics, 2, 117693510600200030.

Article  Google Scholar 

Li, J., Zhou, Z., Dong, J., Fu, Y., Li, Y., Luan, Z., & Peng, X. (2021). Predicting breast cancer 5-year survival using machine learning: A systematic review. PLoS ONE, 16(4), e0250370.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Shi, H. Y., Tsai, J. T., Chen, Y. M., Culbertson, R., Chang, H. T., & Hou, M. F. (2012). Predicting two-year quality of life after breast cancer surgery using artificial neural network and linear regression models. Breast Cancer Research and Treatment, 135, 221–229.

Article  PubMed  Google Scholar 

Huber, M., Kurz, C., & Leidl, R. (2019). Predicting patient-reported outcomes following hip and knee replacement surgery using supervised machine learning. BMC Medical Informatics and Decision Making, 19(1), 1–3.

Article  Google Scholar 

Valdes, G., Simone, C. B., II., Chen, J., Lin, A., Yom, S. S., Pattison, A. J., Carpenter, C. M., & Solberg, T. D. (2017). Clinical decision support of radiotherapy treatment planning: A data-driven machine learning strategy for patient-specific dosimetric decision making. Radiotherapy and Oncology, 125(3), 392–397.

Article  PubMed  Google Scholar 

Courtier, N., Gambling, T., Enright, S., Barrett-Lee, P., Abraham, J., & Mason, M. D. (2013). A prognostic tool to predict fatigue in women with early-stage breast cancer undergoing radiotherapy. The Breast, 22(4), 504–509.

Article  PubMed  CAS  Google Scholar 

Beenhakker, L., Wijlens, K. A., Witteveen, A., Heins, M., Korevaar, J. C., de Ligt, K. M., Bode, C., Vollenbroek-Hutten, M. M., & Siesling, S. (2023). Development of machine learning models to predict cancer-related fatigue in Dutch breast cancer survivors up to 15 years after diagnosis. Journal of Cancer Survivorship, 7, 1–4.

Google Scholar 

Ma, B., Meng, F., Yan, G., Yan, H., Chai, B., & Song, F. (2020). Diagnostic classification of cancers using extreme gradient boosting algorithm and multi-omics data. Computers in Biology and Medicine, 1(121), 103761.

Article  Google Scholar 

van de Poll-Franse, L. V., Horevoorts, N., Schoormans, D., Beijer, S., Ezendam, N. P., Husson, O., Oerlemans, S., Schagen, S. B., Hageman, G. J., Van Deun, K., & van den Hurk, C. (2022). Measuring clinical, biological, and behavioral variables to elucidate trajectories of patient-reported outcomes: The PROFILES registry. JNCI: Journal of the National Cancer Institute, 114(6), 800–807.

Article  PubMed  PubMed Central  Google Scholar 

van de Poll-Franse, L. V., Horevoorts, N., van Eenbergen, M., Denollet, J., Roukema, J. A., Aaronson, N. K., Vingerhoets, A., Coebergh, J. W., de Vries, J., Essink-Bot, M. L., Mols, F., Profiles Registry Group. (2011). The Patient Reported Outcomes Following Initial treatment and Long-term Evaluation of Survivorship registry: scope, rationale, and design of infrastructure for the study of physical and psychosocial outcomes in cancer survivorship cohorts. European Journal of Cancer., 47(14), 2188–2194.

Article  PubMed  Google Scholar 

Burbach, J. P. M., Kurk, S. A., Coebergh van den Braak, R. R. J., Dik, V. K., May, A. M., Meijer, G. A., Punt, C. J. A., Vink, G. R., Los, M., Hoogerbrugge, N., Huijgens, P. C., Ijzermans, J. N. M., Kuipers, E. J., de Noo, M. E., Pennings, J. P., van der Velden, A. M. T., Verhoef, C., Siersema, P. D., van Oijen, M. G. H., … Koopman, M. (2016). Prospective Dutch colorectal cancer cohort: an infrastructure for long-term observational, prognostic, predictive and (randomized) intervention research. Acta Oncologica, 55(11), 1273–1280.

Article  PubMed  CAS  Google Scholar 

van de Poll-Franse, L. V., Nicolaije, K. A., Vos, M. C., Pijnenborg, J. M., Boll, D., Husson, O., Ezendam, N. P., Boss, E. A., Hermans, R. H., Engelhart, K. C., & Haartsen, J. E. (2011). The impact of a cancer survivorship care Plan on gynaecological cancer patient and health care provider reported outcomes (ROGY Care): Study protocol for a pragmatic cluster randomized controlled trial. Trials, 12, 1–8.

Google Scholar 

Ripping, T. M., Kiemeney, L. A., van Hoogstraten, L. M. C., Witjes, J. A., & Aben, K. K. H. (2020). Insight into bladder cancer care: study protocol of a large nationwide prospective cohort study (BlaZIB). BMC Cancer, 20, 455–463.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Vernooij, R. W. C. R., Jansen, H., Somford, D. M., Kiemeney, L. A., van Andel, G., Wijsman, B. P., Busstra, M. B., van Moorselaar, R. J., Wijnen, E. M., & Pos, F. J. (2020). Urinary incontinence and erectile dysfunction in patients with localized or locally advanced prostate cancer: A nationwide observational study. Urologic Oncology: Seminars and Original Investigations, 38(9), 735–752.

Article  Google Scholar 

What is R? [Internet]. [cited 2024 Feb 24]. Available from: https://www.r-project.org/about.html

Wickham H, François R, Henry L, Müller K, Wickham MH. Package ‘dplyr’. A Grammar of Data Manipulation. R package version. 2019 Feb 15;8

Charlson, M. E., Pompei, P., Ales, K. L., et al. (1987). A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. Journal of Chronic Diseases, 40, 373–383.

Article  PubMed  CAS  Google Scholar 

Sterne, J. A., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G., Wood, A. M., & Carpenter, J. R. (2009). Multiple imputation for missing data in epidemiological and clinical research: Potential and pitfalls. BMJ, 29, 338.

Google Scholar 

Mayer, M. Package ‘missRanger’ [Internet]. 2023 [cited 2023Oct24]. Available from: https://cran.r-project.org/web/packages/missRanger/missRanger.pdf

Stekhoven, D. J., & Bühlmann, P. (2012). MissForest—non-parametric missing value imputation for mixed-type data. Bioinformatics, 28(1), 112–118.

Article  PubMed  CAS  Google Scholar 

Karri, R., Chen, Y. P., & Drummond, K. J. (2022). Using machine learning to predict health-related quality of life outcomes in patients with low grade glioma, meningioma, and acoustic neuroma. PLoS ONE, 17(5), e0267931.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Lou, S. J., Hou, M. F., Chang, H. T., Lee, H. H., Chiu, C. C., Yeh, S. C., & Shi, H. Y. (2021). Breast cancer surgery 10-year survival prediction by machine learning: A large prospective cohort study. Biology, 11(1), 47.

Article  PubMed  PubMed Central  Google Scholar