Predicting salvage laryngectomy in patients treated with primary nonsurgical therapy for laryngeal squamous cell carcinoma using machine learning

Background: Machine learning (ML) algorithms may predict patients who will require salvage total laryngectomy (STL) after primary radiotherapy with or without chemotherapy for laryngeal squamous cell carcinoma (SCC). Methods: Patients treated for T1-T3a laryngeal SCC were identified from the National Cancer Database. Multiple ML algorithms were trained to predict which patients would go on to require STL after primary nonsurgical treatment. Results: A total of 16 440 cases were included. The best classification performance was achieved with a gradient boosting algorithm, which achieved accuracy of 76.0% (95% CI 74.5-77.5) and area under the curve = 0.762. The most important variables used to construct the model were distance from residence to treating facility and days from diagnosis to start of treatment. Conclusion: We can identify patients likely to fail primary radiotherapy with or without chemotherapy and who will go on to require STL by applying ML techniques and argue for high-quality, multidisciplinary regionalized care.