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 e

SUBMITTER: Peng Y 

PROVIDER: S-EPMC7298832 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature