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 (The Cancer Genome Atlas Skin Cutaneous Melanoma project) 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 higherresolution 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.

Integration of lncRNAs, Protein-Coding Genes and Pathology Images for Detecting Metastatic Melanoma

“…Pathology imaging provides an important view of cancer tissue and has been widely used in diagnosing primary and metastatic cancers [ 67 , 68 , 69 ]. Eight cell types were segmented and counted from the pathology images and the percentage of each cell type among all the detected cells was denoted as the image feature of this cell type for the corresponding sample.…”

A deep learning model based on whole slide images to predict disease-free survival in cutaneous melanoma patients

“…The post-surgery evaluation of disease-free survival outcome has gained increasing attention for management of melanoma patients in consequence of the continuous progress in defining even more suitable therapies during the last few years. Whether WSI analysis can improve the prediction of prognostic tasks related to melanoma with respect to considering clinical data alone is currently under scrutiny within the scientific community [23][24][25]31,32 . As example, Peng et al 31 developed lasso Cox prediction models based on the integration of clinical variables, gene signature and WSI features to predict recurrence-free survival in melanoma patients.…”

“…Whether WSI analysis can improve the prediction of prognostic tasks related to melanoma with respect to considering clinical data alone is currently under scrutiny within the scientific community [23][24][25]31,32 . As example, Peng et al 31 developed lasso Cox prediction models based on the integration of clinical variables, gene signature and WSI features to predict recurrence-free survival in melanoma patients. The integration of WSI features with baseline clinical variable improved performance obtained by using clinical variable only.…”

Robust deep learning model for prognostic stratification of pancreatic ductal adenocarcinoma patients

“…In the future, we plan to include more types of data, and we are particularly interested in the whole slide images (WSIs). Studies ( Cheerla and Gevaert, 2019 ; Peng et al., 2020 ) have demonstrated that WSI data alone, as well as together with genomic data, can achieve a remarkable performance in cancer prognosis prediction. However, most pancreatic cancer-specific studies using WSI data focused on diagnosis, i.e., pancreatic cancer detection and segmentation ( Fu et al., 2021 ; Kriegsmann et al., 2021 ; Le et al, 2019 ).…”