Letter to the Editor
Copyright ©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.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
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
Abstract

In this article, we comment on the article published by Yu et al. By employing LASSO regression and Cox proportional hazard models, the article identified nine significant variables affecting survival, including body mass index, Karnofsky performance status, and tumor-node-metastasis staging. We firmly concur with Yu et al regarding the vital significance of clinical prediction models (CPMs), including logistic regression and Cox regression for assessment in esophageal carcinoma (EC). However, the nomogram's limitations and the complexities of integrating genetic factors pose challenges. The integration of immunological data with advanced statistics offers new research directions. High-throughput sequencing and big data, facilitated by machine learning, have revolutionized cancer research but require substantial computational resources. The future of CPMs in EC depends on leveraging these technologies to improve predictive accuracy and clinical application, addressing the need for larger datasets, patient-reported outcomes, and regular updates for clinical relevance.

Keywords: Predictive model; Machine learning; Esophageal carcinoma; Survival rate; Factors

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.