亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Development of a weighted Alpha-Fetoprotein tumor burden score-integrated nomogram for predicting overall survival in locally ablated hepatocellular carcinoma patients

列线图 肝细胞癌 医学 肿瘤科 总体生存率 内科学 甲胎蛋白 阿尔法(金融) 外科 结构效度 患者满意度
作者
Yang Wang,Zhixia Gu,Wenying Qiao,Xiaoxue Yuan,Caixia Hu,Ronghua Jin
出处
期刊:Frontiers in Oncology [Frontiers Media SA]
卷期号:15
标识
DOI:10.3389/fonc.2025.1660569
摘要

Introduction The Weighted Alpha-Fetoprotein Tumor Burden Score (WATS) shows promise for hepatocellular carcinoma (HCC) prognosis, but its usefulness in local ablation patients is uncertain, and no validated nomograms exist for overall survival (OS) prediction. Methods This retrospective study enrolled 862 HCC patients who underwent local ablation therapy at Beijing You’an Hospital between January 1, 2015 and December 31, 2022. Participants were randomly allocated into a training cohort (n=603) and validation cohort (n=259) in a 7:3 ratio. Based on the median value of the WATS score, patients were stratified into low-risk (n=431) and high-risk (n=431) groups. The Kaplan-Meier (KM) curve was used to compare the prognosis between the two groups. Potential prognostic factors were screened via least absolute shrinkage and selection operator (Lasso) regression, followed by construction of a WATS-incorporated nomogram prediction model using Cox proportional hazards regression. The SHapley Additive exPlanations (SHAP) method was employed to interpret variable contributions within the model. Model performance was evaluated via Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Patients were stratified into low- and high-risk groups according to the nomogram scores, and KM curves were used to compare OS differences between the two groups. Results The study identified the WATS, age, history of drinking, and prealbumin as independent prognostic factors for OS, and successfully established a nomogram model for OS prediction. The ROC curves, calibration curves, and DCA all confirmed that the model possesses good discriminative ability, calibration accuracy, and clinical utility. KM curves demonstrated that the nomogram could effectively stratify patients into different risk categories with satisfactory predictive performance. Conclusion This study developed and validated a novel prognostic nomogram incorporating the WATS to assess OS in HCC patients receiving local ablation therapy. The nomogram demonstrated robust discriminative ability, enabling accurate prediction of 3-, 5-, and 8-year OS rates, thereby providing clinicians with a reliable tool for individualized risk assessment and treatment decision-making.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
hhhm完成签到 ,获得积分10
2秒前
ding应助徐甜采纳,获得10
7秒前
msn00完成签到 ,获得积分10
7秒前
本本完成签到 ,获得积分10
15秒前
我真的服了完成签到 ,获得积分10
16秒前
16秒前
薛禾完成签到,获得积分20
18秒前
19秒前
Amor完成签到,获得积分10
21秒前
感冒药完成签到 ,获得积分10
23秒前
nn发布了新的文献求助10
24秒前
hhh完成签到 ,获得积分10
26秒前
Criminology34应助大胖采纳,获得30
29秒前
xl_c完成签到,获得积分10
31秒前
34秒前
徐甜发布了新的文献求助10
38秒前
40秒前
44秒前
Tendency完成签到 ,获得积分10
45秒前
李健应助aaaaal采纳,获得10
47秒前
何文鑫完成签到,获得积分10
47秒前
jyy完成签到,获得积分10
48秒前
lan兰发布了新的文献求助10
48秒前
Abdurrahman完成签到,获得积分10
50秒前
微笑的丹南完成签到,获得积分10
53秒前
56秒前
Ashely完成签到 ,获得积分10
58秒前
1分钟前
1分钟前
1分钟前
科研通AI6应助科研通管家采纳,获得10
1分钟前
在水一方应助科研通管家采纳,获得10
1分钟前
浮游应助科研通管家采纳,获得10
1分钟前
1分钟前
星辰大海应助科研通管家采纳,获得10
1分钟前
浮游应助科研通管家采纳,获得10
1分钟前
活力尔岚应助科研通管家采纳,获得10
1分钟前
Hello应助科研通管家采纳,获得30
1分钟前
1分钟前
鹿呦完成签到 ,获得积分10
1分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
The Social Work Ethics Casebook: Cases and Commentary (revised 2nd ed.).. Frederic G. Reamer 1070
The Complete Pro-Guide to the All-New Affinity Studio: The A-to-Z Master Manual: Master Vector, Pixel, & Layout Design: Advanced Techniques for Photo, Designer, and Publisher in the Unified Suite 1000
按地区划分的1,091个公共养老金档案列表 801
The International Law of the Sea (fourth edition) 800
Teacher Wellbeing: A Real Conversation for Teachers and Leaders 600
Machine Learning for Polymer Informatics 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 物理化学 基因 遗传学 催化作用 冶金 量子力学 光电子学
热门帖子
关注 科研通微信公众号,转发送积分 5407659
求助须知:如何正确求助?哪些是违规求助? 4525171
关于积分的说明 14101365
捐赠科研通 4439018
什么是DOI,文献DOI怎么找? 2436551
邀请新用户注册赠送积分活动 1428528
关于科研通互助平台的介绍 1406604