Comparison of nomogram with machine learning techniques for prediction of overall survival in patients with tongue cancer

Rasheed Omobolaji Alabi; Antti A Mäkitie; Matti Pirinen; Mohammed Elmusrati; Ilmo Leivo; Alhadi Almangush

doi:10.1016/j.ijmedinf.2020.104313

Comparison of nomogram with machine learning techniques for prediction of overall survival in patients with tongue cancer

Int J Med Inform. 2021 Jan:145:104313. doi: 10.1016/j.ijmedinf.2020.104313. Epub 2020 Oct 24.

Authors

Rasheed Omobolaji Alabi¹, Antti A Mäkitie², Matti Pirinen³, Mohammed Elmusrati⁴, Ilmo Leivo⁵, Alhadi Almangush⁶

Affiliations

¹ Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland. Electronic address: rasheed.alabi@student.uwasa.fi.
² Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden.
³ Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland; Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
⁴ Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland.
⁵ University of Turku, Institute of Biomedicine, Pathology, Turku, Finland.
⁶ Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; University of Turku, Institute of Biomedicine, Pathology, Turku, Finland; Department of Pathology, University of Helsinki, Helsinki, Finland; Faculty of Dentistry, University of Misurata, Misurata, Libya.

PMID: 33142259
DOI: 10.1016/j.ijmedinf.2020.104313

Abstract

Background: The prediction of overall survival in tongue cancer is important for planning of personalized care and patient counselling.

Objectives: This study compares the performance of a nomogram with a machine learning model to predict overall survival in tongue cancer. The nomogram and machine learning model were built using a large data set from the Surveillance, Epidemiology, and End Results (SEER) program database. The comparison is necessary to provide the clinicians with a comprehensive, practical, and most accurate assistive system to predict overall survival of this patient population.

Methods: The data set used included the records of 7596 tongue cancer patients. The considered machine learning algorithms were logistic regression, support vector machine, Bayes point machine, boosted decision tree, decision forest, and decision jungle. These algorithms were mainly evaluated in terms of the areas under the receiver-operating characteristic (ROC) curve (AUC) and accuracy values. The performance of the algorithm that produced the best result was compared with a nomogram to predict overall survival in tongue cancer patients.

Results: The boosted decision-tree algorithm outperformed other algorithms. When compared with a nomogram using external validation data, the boosted decision tree produced an accuracy of 88.7% while the nomogram showed an accuracy of 60.4%. In addition, it was found that age of patient, T stage, radiotherapy, and the surgical resection were the most prominent features with significant influence on the machine learning model's performance to predict overall survival.

Conclusion: The machine learning model provides more personalized and reliable prognostic information of tongue cancer than the nomogram. However, the level of transparency offered by the nomogram in estimating patients' outcomes seems more confident and strengthened the principle of shared decision making between the patient and clinician. Therefore, a combination of a nomogram - machine learning (NomoML) predictive model may help to improve care, provides information to patients, and facilitates the clinicians in making tongue cancer management-related decisions.

Keywords: Machine learning; Nomogram; Predict; overall survival; tongue cancer.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Bayes Theorem
Humans
Machine Learning
Nomograms*
Support Vector Machine
Tongue Neoplasms*