Jiafu Yang

Loading metrics Open Access Peer-reviewed Research Article Citation: Yang J, Wang S, Chen X, Liu L, Li Y (2026) Development of an exploratory prediction model for preoperative CK19 expression in esophageal cancer driven by radiomics and machine learning. PLoS One 21(6): e0350744. https://doi.org/10.1371/journal.pone.0350744 Editor: Lorenzo Faggioni, University of Pisa, ITALY Received: July 2, 2025; Accepted: May 18, 2026; Published: June 1, 2026 Copyright: © 2026 Yang et al.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.

1. Introduction Autonomous driving has emerged as a prominent topic for research in recent years [1,2,3,4,5,6,7,8]. Trajectory tracking is a fundamental component of autonomous driving technology that has garnered a lot of interest [9,10,11,12,13,14]. The trajectory tracking reference trajectory is contingent upon the time parameter, necessitating simultaneous control of a vehicle’s longitudinal and lateral motion.