Combining texture features of whole slide images improves prognostic prediction of recurrence-free survival for cutaneous melanoma patients

Abstract

Background: Accurate prediction of recurrence-free survival (RFS) is important for the prognosis of cutaneous melanoma patients. The image-based pathological examination remains as the gold standard for diagnosis. It is of clinical interest to account for computer-aided processing of pathology image when performing prognostic analysis.Methods: We enrolled in this study a total of 152 patients from TCGA-SKCM with complete information in recurrence-related survival time, baseline variables (clinicopathologic variables, mutation status of BRAF and NRAS genes), gene expression data and whole slide image (WSI) features. We preprocessed WSI to segment global or nucleus areas, and extracted 3 types of texture features from each region. We performed cross validation and used multiple evaluation metrics including C-index and time-dependent AUC to determine the best model of predicting recurrence events. We further performed differential gene expression analysis between the higher and lower-risk groups within AJCC pathologic tumor stage III patients to explore the underlying molecular mechanisms driving risk stratification. Results: The model combining baseline variables and WSI features had the best performance among models with any other types of data integration. The prognostic risk score generated by this model could provide a higher-resolution risk stratification within pathologically-defined subgroups. We found the selected image features captured important immune-related variations, such as the aberration of expression in T cell activation and proliferation gene sets, and therefore contributed to the improved prediction. Conclusions: Our study provided a prognostic model based on the combination of baseline variables and computer-processed WSI features. This model provided more accurate prediction than models based on other types of data combination in recurrence-free survival analysis. Trail registration: This study was based on public open data from TCGA and hence the study objects were retrospectively registered.