A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study.

ABSTRACT:

Background

To explore the prognostic value of radiomics-based and digital pathology-based imaging biomarkers from macroscopic magnetic resonance imaging (MRI) and microscopic whole-slide images for patients with nasopharyngeal carcinoma (NPC).

Methods

We recruited 220 NPC patients and divided them into training (n = 132), internal test (n = 44), and external test (n = 44) cohorts. The primary endpoint was failure-free survival (FFS). Radiomic features were extracted from pretreatment MRI and selected and integrated into a radiomic signature. The histopathological signature was extracted from whole-slide images of biopsy specimens using an end-to-end deep-learning method. Incorporating two signatures and independent clinical factors, a multi-scale nomogra

SUBMITTER: Zhang F 

PROVIDER: S-EPMC7739087 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature