Machine learning in laryngeal cancer: a pilot study to predict oncological outcomes and the role of adverse features

Background: Laryngeal carcinoma (LC) remains a significant economic and emotional problem to the healthcare system and severe social morbidity. New tools as Machine Learning could allow clinicians to develop accurate and reproducible treatments. Methods: This study aims to evaluate the performance of a ML-algorithm in predicting 1- and 3-year overall survival (OS) in a cohort of patients surgical treated for LC. Moreover, the impact of different adverse features on prognosis will be investigated. Data was collected on oncological FU of 132 patients. A retrospective review was performed to create a dataset of 23 variables for each patient. Results: The decision-tree algorithm is highly effective in predicting the prognosis, with a 95% accuracy in predicting the 1-year survival and 82.5% in 3-year survival; The measured AUC area is 0.886 at 1-year Test and 0.871 at 3-years Test. The measured AUC area is 0.917 at 1-year Training set and 0.964 at 3-years Training set. Factors that affected 1yOS are: LNR, type of surgery, and subsite. The most significant variables at 3yOS are: number of metastasis, perineural invasion and Grading. Conclusions: The integration of ML in medical practices could revolutionize our approach on cancer pathology.