Automated Identification of Optimal Portal Venous Phase Timing with Convolutional Neural Networks.

ABSTRACT:

Objectives

To develop a deep learning-based algorithm to automatically identify optimal portal venous phase timing (PVP-timing) so that image analysis techniques can be accurately performed on post contrast studies.

Methods

681 CT-scans (training: 479 CT-scans; validation: 202 CT-scans) from a multicenter clinical trial in patients with liver metastases from colorectal cancer were retrospectively analyzed for algorithm development and validation. An additional external validation was performed on a cohort of 228 CT-scans from gastroenteropancreatic neuroendocrine cancer patients. Image acquisition was performed according to each centers' standard CT protocol for single portal venous phase, portal venous acquisition. The reference gold standard for the classification of PVP-t

SUBMITTER: Ma J 

PROVIDER: S-EPMC9109713 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature