Habitat-Based MRI Radiomics to Predict the Origin of Brain Metastasis

无线电技术 脑转移 栖息地 转移 医学 人工智能 生态学 计算机科学 生物 内科学 癌症
作者
Yiyao Sun,Peng Zhao,Mingchen Jiang,Jia Wei,Huan‐Huan Chen,Huan Wang,Yuqi Ding,Xiaoyu Wang,Juan Su,Xianzheng Sha,Chunna Yang,Dan Zhao,Bo Huang,Xiran Jiang
标识
DOI:10.2139/ssrn.4812498
摘要

Background: This study aims to explore the value of habitat-based MRI radiomics for predicting the origin of brain metastasis (BM).Methods: A primary cohort was developed with 384 patients from two centers, which comprises 734 BM lesions. An independent cohort was developed with 28 patients from a third center, which comprises 70 BM lesions. All patients underwent T1-weighted contrast-enhanced (T1-CE) and T2-weighted (T2W) MRI scans before treatment. Radiomics features were extracted from tumor active area (TAA) and peritumoral edema area (PEA) selected using the least absolute shrinkage and selection operator (LASSO) to construct radiomics signatures (Rads). The Rads were further integrated with volume of peritumoral edema (VPE) to build combined models for predicting the metastatic type of BM. Performance of the models were assessed through receiver operating characteristic (ROC) curve analysis.Findings: Rads derived from TAA and PEA both showed predictive power for identifying the origin of BM. The developed combined models generated the best performance in the training (AUCs, lung cancer (LC)/non-lung cancer (NLC) vs. small cell lung cancer (SCLC)/ non-small cell lung cancer (NSCLC) vs. breast cancer (BC)/ gastrointestinal cancer (GIC), 0.870 vs. 0.946 vs. 0.886), internal validation (AUCs, LC/NLC vs. SCLC/NSCLC vs. BC/GIC, 0.786 vs. 0.863 vs. 0.836) and external validation (AUCs, LC /NLC vs. SCLC/NSCLC vs. BC/GIC, 0.805 vs. 0.877 vs. 0.774) cohort.Interpretation: The developed habitat-based radiomics models can effectively identificat the metastatic tumor type of BM, and may be considered as a potential preoperative basis for timely treatment planning.Funding: The study was supported by the National Key R&D Program of China: BTIT (Grant NO.2022YFF1202803), and General Program from Department of Education of Liaoning Province (JYTMS20230132).Declaration of Interest: The authors declare that they have no competing interests.Ethical Approval: The ethics review boards of our hospitals granted ethical approval for this retrospective analysis, waiving the requirement for informed consent from patients.

科研通智能强力驱动
Strongly Powered by AbleSci AI
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
super小萌萌完成签到,获得积分10
刚刚
April完成签到 ,获得积分10
刚刚
雪白问兰应助科研通管家采纳,获得20
1秒前
1秒前
1秒前
小蘑菇应助科研通管家采纳,获得20
1秒前
充电宝应助科研通管家采纳,获得10
1秒前
orixero应助科研通管家采纳,获得10
1秒前
1秒前
NexusExplorer应助科研通管家采纳,获得10
1秒前
maox1aoxin应助科研通管家采纳,获得80
1秒前
小蘑菇应助科研通管家采纳,获得10
1秒前
zhong完成签到,获得积分10
1秒前
36456657应助科研通管家采纳,获得10
1秒前
100完成签到,获得积分20
1秒前
领导范儿应助科研通管家采纳,获得30
1秒前
隐形曼青应助科研通管家采纳,获得10
1秒前
顾矜应助科研通管家采纳,获得10
1秒前
1秒前
orixero应助科研通管家采纳,获得10
1秒前
控制小弟应助科研通管家采纳,获得10
1秒前
2秒前
SciGPT应助从容的幻然采纳,获得30
2秒前
无情念之完成签到,获得积分20
2秒前
YL完成签到,获得积分10
2秒前
2秒前
京言完成签到,获得积分10
2秒前
小宇发布了新的文献求助10
3秒前
3秒前
大胆的小白菜完成签到,获得积分10
3秒前
不是省油的灯完成签到,获得积分10
4秒前
小管完成签到,获得积分20
4秒前
niu1发布了新的文献求助10
4秒前
夏泽水梦完成签到,获得积分10
6秒前
老实的半山完成签到,获得积分10
6秒前
指纹抒写年轮完成签到,获得积分10
6秒前
愉快的哈密瓜完成签到,获得积分10
6秒前
小小发布了新的文献求助10
6秒前
小二郎应助成就缘分采纳,获得10
6秒前
7秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 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 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527304
求助须知:如何正确求助?哪些是违规求助? 3107454
关于积分的说明 9285518
捐赠科研通 2805269
什么是DOI,文献DOI怎么找? 1539827
邀请新用户注册赠送积分活动 716708
科研通“疑难数据库(出版商)”最低求助积分说明 709672