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Letter to the Editor
©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Feb 15, 2025; 17(2): 101379
Published online Feb 15, 2025. doi: 10.4251/wjgo.v17.i2.101379
Advancements and challenges in esophageal carcinoma prognostic models: A comprehensive review and future directions
Jia Chen, Qi-Chang Xing
Jia Chen, Qi-Chang Xing, Department of Clinical Pharmacy, Xiangtan Central Hospital, Xiangtan 411100, Hunan Province, China
Qi-Chang Xing, The Affiliated Hospital, Hunan University, Changsha 410082, Hunan Province, China
Co-first authors: Jia Chen and Qi-Chang Xing.
Author contributions: Xing QC wrote the original draft; Chen J contributed to conceptualization, writing, reviewing and editing; Xing QC and Chen J participated in drafting the manuscript; All authors have read and approved the final version of the manuscript.
Supported by the Scientific Research Program of Hunan Provincial Health Commission, No. B202313018450.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Qi-Chang Xing, MD, Research Assistant, Department of Clinical Pharmacy, Xiangtan Central Hospital, No. 120 Heping Road, Xiangtan 411100, Hunan Province, China. 67324457@qq.com
Received: September 12, 2024
Revised: November 4, 2024
Accepted: November 22, 2024
Published online: February 15, 2025
Processing time: 127 Days and 22.8 Hours
Core Tip

Core Tip: Clinical prediction model has great development space and practical value in the medical field. Despite significant efforts to explore the prognosis of esophageal carcinoma, current prognostic models remain imperfect. Traditional predictive models, such as Cox proportional hazards regression and logistic regression, are widely used but often lack effective evaluation mechanisms to determine their optimal performance. Moreover, due to limitations in sample size and predictive factors, the reproducibility of these models is poor, which severely restricts their broad application in clinical practice. Therefore, it is necessary to further explore and select more appropriate analytical methods to construct more accurate and reliable predictive models, thereby better serving clinical needs.