Novel Machine Learning Model to Predict Interval of Oral Cancer Recurrence for Surveillance Stratification

Abstract

Objective(s) We aimed to develop a machine learning (ML) model to accurately predict the timing of oral squamous cell carcinoma (OSCC) recurrence across four 1‐year intervals. Methods Patients with surgically treated OSCC between 2012–2018 were retrospectively identified from the Yale‐New Haven Health system tumor registry. Patients with known recurrence or minimum follow‐up of 24 months from surgery were included. Patients were classified into one of five levels: four 1‐year intervals and one level for no recurrence (within 4 years of surgery). Three sets of data inputs (comprehensive, feature selection, nomogram) were combined with 4 ML architectures (logistic regression, decision tree (DT), support vector machine (SVM), artificial neural network classifiers) yielding 12 models in total. Models were primarily evaluated using mean absolute error (MAE), lower values indicating better prediction of 1‐year interval recurrence. Secondary outcomes included accuracy, weighted precision, and weighted recall. Results 389 patients met inclusion criteria: 102 (26.2%) recurred within 48 months of surgery. Median follow‐up time was 25 months (IQR: 15–37.5) for patients with recurrence and 44 months (IQR: 32–57) for patients without recurrence. MAE of 0.654% and 80.8% accuracy were achieved on a 15‐variable feature selection input by 2 ML models: DT and SVM classifiers. Conclusions To our knowledge, this is the first study to leverage multiclass ML models to predict time to OSCC recurrence. We developed a model using feature selection data input that reliably predicted recurrence within 1‐year intervals. Precise modeling of recurrence timing has the potential to personalize surveillance protocols in the future to enhance early detection and reduce extraneous healthcare costs. Level of Evidence 3 Laryngoscope, 133:1652–1659, 2023

Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review

“…Among these, three studies also evaluated traditional linear models, with the AUROC for corresponding ML models ranging from 0.67 to 0.88, while for linear models including logistic regression, it ranged from 0.68 to 0.73 [ 64 , 79 , 84 ]. Additionally, six studies provided F-1 scores for ML models, with values ranging from 0.53 to 0.89 [ 57 , 59 , 62 , 69 , 79 , 84 ]. Of these, three studies included F1-scores for linear models as well, with the corresponding ML models’ F1-scores ranging from 0.53 to 0.89 and linear models’ scores from 0.30 to 0.87 [ 69 , 79 , 84 ].…”

“…Additionally, six studies provided F-1 scores for ML models, with values ranging from 0.53 to 0.89 [ 57 , 59 , 62 , 69 , 79 , 84 ]. Of these, three studies included F1-scores for linear models as well, with the corresponding ML models’ F1-scores ranging from 0.53 to 0.89 and linear models’ scores from 0.30 to 0.87 [ 69 , 79 , 84 ]. Besides the oral cavity, other sites either had no or only one related study, so their results could not be consolidated.…”

Prediction of postoperative recurrence of oral cancer by artificial intelligence model: Multilayer perceptron

“…The research on establishing a machine learning model to predict the recurrence time of oral squamous cell carcinoma not only verified the view that postoperative recurrence of oral cancer is mainly concentrated in the first 2 years after surgery but also confirmed that the predictive performance of the AI machine learning model is better than that of other models; the results supported the view that the prediction of characteristic variables is more accurate. 36 Using individual characteristic variables to establish a prediction model can make each case more characteristic, but screening more factors related to postoperative recurrence for comprehensive variable prediction is expected to provide more comprehensive prediction results.…”