Machine Learning to Predict Delays in Adjuvant Radiation following Surgery for Head and Neck Cancer

Abstract

Objective To apply a novel methodology with machine learning (ML) to a large national cancer registry to help identify patients who are high risk for delayed adjuvant radiation. Study Design Observational cohort study. Setting National Cancer Database (NCDB). Subjects and Methods A total of 76,573 patients were identified from the NCDB who had invasive head and neck cancer and underwent surgery, followed by radiation. The model was constructed from 80% of the patient data and subsequently evaluated and scored with the remaining 20%. Permutation feature importance analysis was used to understand the weighted model construction. Results A total of 76,573 patients met inclusion and exclusion criteria. Our ML model was able to predict whether patients would start adjuvant therapy beyond 50 days after surgery with an overall accuracy of 64.41% and a precision of 58.5%. The 2 most important variables used to build the model were treating facility and urban versus rural demographics. Conclusion Statistics can provide inferences within an overall system, while ML is a novel methodology that can make predictions. We can identify patients who are “high risk” for delayed radiation using information from >75,000 patient experiences, which has the potential for a direct impact on clinical care. Our inability to achieve greater accuracy is due to limitations of the data captured by the NCDB, and we need to continue to identify new variables that are correlated with delayed radiation therapy. ML will prove to be a valuable clinical tool in years to come, but its utility is limited by available data.

Machine Learning for Predicting Complications in Head and Neck Microvascular Free Tissue Transfer

“…Other investigators have applied ML modeling to the Surveillance, Epidemiology, and End Results database with a goal of improving prognostic predictions in patients with well‐differentiated thyroid cancer 9 . Shew et al similarly used data from the NCDB and a decision tree ML model to predict delays in adjuvant radiation in those undergoing surgery for head and neck cancer 10 . Still others have applied ML algorithms to imaging data to create models that predict treatment outcomes in patients with sinonasal squamous cell carcinoma 11 .…”

Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

“…AI has also been used to support clinical diagnoses and treatments, decision-making, the Table 4. Continued prediction of prognoses [98][99][100]125,126], disease profiling, the construction of mass spectral databases [43,[127][128][129], the identification or prediction of disease progress [101,105,[107][108][109][110]130], and the confirmation of diagnoses and the utility of treatments [102][103][104]112,131]. Although many algorithms have been applied, some are not consistently reliable, and certain challenges remain.…”

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

“…29 Additionally, our group has previously used ML to predict delays in initiation of adjuvant RT in patients with head and neck cancers and to predict occult nodal metastasis in early oral SCC. 20,21 In this study, we evaluated the potential of ML to predict patients who will go on to require STL despite primary larynx-preservation treatment with RT with or without chemotherapy. There are currently no universally recognized criteria for identifying patients in whom primary larynx-preservation treatment may fail.…”

“…Our group has previously applied machine learning (ML) to make predictions about clinically important outcomes in head and neck oncology. [19][20][21] ML is a subset of artificial intelligence that allows computers to learn from historical data, gather insights, and make predictions about new data using what it has learned. 22 ML has demonstrated a high degree of accuracy and precision that exceeds the abilities of standard statistical models and expert clinical judgment to make predictions about outcomes in medicine.…”