Differential Radiomics‐Based Signature Predicts Lung Cancer Risk Accounting for Imaging Parameters in NLST Cohort

人工智能 特征选择 支持向量机 Boosting(机器学习) 计算机科学 无线电技术 交叉验证 医学 接收机工作特性 肺癌 模式识别(心理学) 机器学习 肿瘤科
作者
Leyla Ebrahimpour,Philippe Desprès,Venkata Manem
出处
期刊:Cancer Medicine [Wiley]
卷期号:13 (20)
标识
DOI:10.1002/cam4.70359
摘要

ABSTRACT Objective Lung cancer remains the leading cause of cancer‐related mortality worldwide, with most cases diagnosed at advanced stages. Hence, there is a need to develop effective predictive models for early detection. This study aims to investigate the impact of imaging parameters and delta radiomic features from temporal scans on lung cancer risk prediction. Methods Using the National Lung Screening Trial (NLST) within a nested case–control study involving 462 positive screenings, radiomic features were extracted from temporal computed tomography (CT) scans and harmonized with ComBat method to adjust variations in slice thickness category (TC) and reconstruction kernel type (KT). Both harmonized and non‐harmonized features from baseline (T0), delta features between T0 and a year later (T1), and combined T0 and delta features were utilized for the analysis. Feature reduction was done using LASSO, followed by five feature selection (FS) methods and nine machine learning (ML) models, evaluated with 5‐fold cross‐validation repeated 10 times. Synthetic Minority Oversampling Technique (SMOTE) was applied to address class imbalances for lung cancer risk prediction. Results Models using delta features outperformed baseline features, with SMOTE consistently boosting performance when using combination of baseline and delta features. TC‐based harmonized features improved performance with SMOTE, but overall, harmonization did not significantly enhance the model performance. The highest test score of 0.76 was achieved in three scenarios: delta features with a Gradient Boosting (GB) model (TC‐based harmonization and MultiSurf FS); and T0 + delta features, with both a Support Vector Classifier (SVC) model (KT‐based harmonization and F ‐test FS), and an XGBoost (XGB) model (TC‐based harmonization and Mutual Information (MI) FS), all using SMOTE. Conclusions This study underscores the significance of delta radiomic features and balanced datasets to improve lung cancer prediction. While our findings are based on a subsample of NLST data, they provide a valuable foundation for further exploration. Further research is needed to assess the impact of harmonization on imaging‐derived models. Future investigations should explore advanced harmonization techniques and additional imaging parameters to develop robust radiomics‐based biomarkers of lung cancer risk.

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
曲蔚然完成签到 ,获得积分10
刚刚
1秒前
你帅你有理完成签到,获得积分10
2秒前
wy完成签到,获得积分10
2秒前
xxxy完成签到,获得积分10
3秒前
海洋发布了新的文献求助10
3秒前
4秒前
木子水告发布了新的文献求助30
5秒前
excellent_shit完成签到,获得积分10
7秒前
7秒前
太阳当空赵完成签到,获得积分10
9秒前
9秒前
张再在完成签到 ,获得积分10
10秒前
我真是不理解完成签到,获得积分10
11秒前
黄勇杰发布了新的文献求助10
11秒前
工藤新一发布了新的文献求助10
11秒前
安然僧应助小陶采纳,获得10
14秒前
乐乐应助陶1122采纳,获得10
16秒前
Owen应助听说外面下雨了采纳,获得10
17秒前
打打应助log采纳,获得10
19秒前
Yuan88发布了新的文献求助10
19秒前
21秒前
Hazel发布了新的文献求助10
22秒前
1234给故意的姿的求助进行了留言
23秒前
25秒前
佳佳发布了新的文献求助20
26秒前
李子敬发布了新的文献求助10
28秒前
陶1122发布了新的文献求助10
30秒前
31秒前
明亮咖啡发布了新的文献求助10
31秒前
32秒前
33秒前
34秒前
英姑应助yyyyxxxg采纳,获得10
34秒前
36秒前
fang应助yyyyxxxg采纳,获得10
37秒前
陶1122完成签到,获得积分10
37秒前
冯大夫发布了新的文献求助10
39秒前
Liufgui应助天天小女孩采纳,获得20
41秒前
寒冷的樱桃完成签到 ,获得积分10
41秒前
高分求助中
【重要!!请各位用户详细阅读此贴】科研通的精品贴汇总(请勿应助) 10000
Semantics for Latin: An Introduction 1055
Plutonium Handbook 1000
Three plays : drama 1000
Robot-supported joining of reinforcement textiles with one-sided sewing heads 600
北师大毕业论文 基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 510
Cochrane Handbook for Systematic Reviews ofInterventions(current version) 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 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4103513
求助须知:如何正确求助?哪些是违规求助? 3641221
关于积分的说明 11538535
捐赠科研通 3349869
什么是DOI,文献DOI怎么找? 1840540
邀请新用户注册赠送积分活动 907555
科研通“疑难数据库(出版商)”最低求助积分说明 824725