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 nomogram was constructed. We also tested the correlation between the key imaging features and genetic alternations in an independent cohort of 16 patients (biological test cohort).

Results

Both radiomic and histopathologic signatures presented significant associations with treatment failure in the three cohorts (C-index: 0.689-0.779, all p?versus 0.730, p?versus 0.602, p?versus 0.679, p?p?ConclusionThe multi-scale imaging features showed a complementary value in prognostic prediction and may improve individualized treatment in NPC.

SUBMITTER: Zhang F 

PROVIDER: S-EPMC7739087 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature