End-to-end deep learning model for the diagnosis and segmentation of primary retroperitoneal neoplasm: a multicenter cohort study.

ABSTRACT:

Background

Primary retroperitoneal neoplasms (PRNs) are a diverse group of tumors that pose significant diagnostic challenges. Currently, no multicenter-validated diagnostic model exists for multiple PRN types based on computed tomography (CT) images. This study aimed to develop and validate an end-to-end deep learning model, REMIND (REtroperitoneal neoplasMs artificial-INtelligence Diagnosis), for the accurate diagnosis and segmentation of PRNs using enhanced CT images.

Methods

Patients from 12 Chinese centers between January 2012 and June 2024 were involved in this study. The dataset comprised patients with histologically confirmed PRNs, including seven types of PRNs: dedifferentiated liposarcoma, well-differentiated liposarcoma, leiomyosarcoma, ganglioneuroma, lymphoma, s

SUBMITTER: Feng X 

PROVIDER: S-EPMC12508580 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature