Advances in Predictive and Prognostic Modeling for Non-Metastatic Gallbladder Adenocarcinoma Using the SEER Database

本文旨在介绍基于SEER数据库构建无远处转移的胆囊腺癌患者的预测及预后模型的研究现状及进展。首先,介绍其研究背景及其重要性,指出了该课题的提出及其现实意义。接着,系统总结了该课题的主要理论框架和研究方法。通过对国内外相关课题的深入梳理与分析,介绍了该课题在该领域的研究现状和特点,发现该课题在该领域中目前存在的一些创新,主要体现在该领域中关于其他疾病的大量文献,而该疾病的相关文献较其他疾病少之又少。最后,本文对现有研究进行了综合评述,指出了现阶段研究的不足之处,未来将树立不断完善该课题的目标。本文旨在为该疾病的医学领域的研究人员提供参考,并为其他相关研究提供一些思路和建议。This review aims to present the current research status and advancements in developing predictive and prognostic models for non-metastatic gallbladder adenocarcinoma (GBA) using the Surveillance, Epidemiology, and end results (SEER) database. Firstly, the research background and its significance are introduced, and the practical importance of this topic is pointed out. Subsequently, the theoretical frameworks and methodological approaches adopted in this field are systematically summarized. Through an in-depth analysis of global studies, we highlight the unique characteristics of existing research, notably the scarcity of disease-specific literature compared to the extensive studies on other malignancies. This article conducts a comprehensive review of the existing research, points out the shortcomings of the current studies, and sets the goal of continuously improving this subject in the future. This work serves as a comprehensive reference for researchers in gastrointestinal oncology and provides actionable insights for related studies.

Authors

Publication Details

Journal
Advances in Clinical Medicine
Published
2025-01-01
DOI
https://doi.org/10.12677/acm.2025.1561886
Primary Topic
Cholangiocarcinoma and Gallbladder Cancer Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Advances in Predictive and Prognostic Modeling for Non-Metastatic Gallbladder Adenocarcinoma Using the SEER Database

兴梅 何
Advances in Clinical Medicine
Cholangiocarcinoma and Gallbladder Cancer Studies
article

Advances in Predictive and Prognostic Modeling for Non-Metastatic Gallbladder Adenocarcinoma Using the SEER Database

兴梅 何
article en

Abstract

本文旨在介绍基于SEER数据库构建无远处转移的胆囊腺癌患者的预测及预后模型的研究现状及进展。首先,介绍其研究背景及其重要性,指出了该课题的提出及其现实意义。接着,系统总结了该课题的主要理论框架和研究方法。通过对国内外相关课题的深入梳理与分析,介绍了该课题在该领域的研究现状和特点,发现该课题在该领域中目前存在的一些创新,主要体现在该领域中关于其他疾病的大量文献,而该疾病的相关文献较其他疾病少之又少。最后,本文对现有研究进行了综合评述,指出了现阶段研究的不足之处,未来将树立不断完善该课题的目标。本文旨在为该疾病的医学领域的研究人员提供参考,并为其他相关研究提供一些思路和建议。This review aims to present the current research status and advancements in developing predictive and prognostic models for non-metastatic gallbladder adenocarcinoma (GBA) using the Surveillance, Epidemiology, and end results (SEER) database. Firstly, the research background and its significance are introduced, and the practical importance of this topic is pointed out. Subsequently, the theoretical frameworks and methodological approaches adopted in this field are systematically summarized. Through an in-depth analysis of global studies, we highlight the unique characteristics of existing research, notably the scarcity of disease-specific literature compared to the extensive studies on other malignancies. This article conducts a comprehensive review of the existing research, points out the shortcomings of the current studies, and sets the goal of continuously improving this subject in the future. This work serves as a comprehensive reference for researchers in gastrointestinal oncology and provides actionable insights for related studies.

Advances in Clinical MedicineVol. 15(06)
Good health and well-being
Openalex Percentile: Top 74%
Cholangiocarcinoma and Gallbladder Cancer Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.