Machine learning methods in prediction of basal cell skin cancer recurrence after photodynamic therapy

Abstract

Objective: Verification of predictors and forecasting basal cell skin cancer recurrence (BCSC) after conducting photodynamic therapy (PDT) based on machine learning methods (ML).
Methods: The prospective study of 170 patients (117 women and 53 men) was conducted. The median age was 68 years. All patients got PDT treatment on BCSC. Potential predictors of BCSC were analyzed. Primary outcome measure was the fact of tumor development recurrence.
Results: During 4-year observation period the recurrence of the disease took place in 18 cases (10.6% of patients). Processing and analyzing data with the assistance of machine learning methods (ML) allowed to highlight the predictors connected with the development of BCSC recurrence development linearly and non linearly. There are such predictors as: 2nd stage of the process, its morphea-like form, localization in the thoracic cage area, the level of ESR and glucose in the blood. The most accurate forecast of BCSC recurrence was gotten using model based on multiple linear regression (LR). It was proved by high levels of quality indexes (the area under ROCcurve – 0.893, sensitivity – 0.849, specificity – 0.889). Predictive accuracy of the stochastic gradient boosting model (SGB) was less significant.
Conclusions. PDT is an effective BCSC treatment method. It is proved by the results of prospective observation of patients for the period of 4 years. ML methods are an informative tool to verify predictors and forecast BCSC recurrence. Forecasting models based on multiple LR demonstrate much higher accuracy compared with SGB.