Developing a deep learning-based imaging diagnostic framework, PVDNet, for differentiating pulmonary artery sarcoma and pulmonary thromboembolism: a multi-center observational study.

ABSTRACT:

Background

Differentiating pulmonary artery sarcoma (PAS) from pulmonary thromboembolism (PTE) based on CT pulmonary angiography (CTPA) is a big challenge, necessitating the incorporation of other methods, such as deep learning (DL). This study aimed to develop and validate a DL-based model, PVDNet, for differentiating PAS and PTE on CTPA.

Methods

This study retrospectively analyzed CTPA image datasets from the prospective CHinese pulmOnary embolism multimodality Imaging artifiCial intelligencE (CHOICE) study to develop and validate a DL model for differentiating PAS from PTE. CTPA image datasets of 952 patients (470 acute PTE [APE], 363 chronic PTE [CPE], and 119 PAS) from 15 hospitals were included. The training set comprised CTPA images from 590 patients, and the internal

SUBMITTER: Xi L 

PROVIDER: S-EPMC12417360 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature