Comparison of Traditional Radiomics, Deep Learning Radiomics and Fusion Methods for Axillary Lymph Node Metastasis Prediction in Breast Cancer

无线电技术 乳腺癌 腋窝淋巴结 人工智能 医学 淋巴结 深度学习 磁共振成像 机器学习 淋巴结转移 放射科 计算机科学 转移 癌症 内科学
作者
Xue Li,Lifeng Yang,Xiong Jiao
出处
期刊:Academic Radiology [Elsevier BV]
卷期号:30 (7): 1281-1287 被引量:31
标识
DOI:10.1016/j.acra.2022.10.015
摘要

Rationale and Objectives

Accurate identification of axillary lymph node (ALN) status in breast cancer patients is important for determining treatment options and avoiding axillary overtreatments. Our study aims to comprehensively compare the performance of the traditional radiomics model, deep learning radiomics model, and the fusion models in evaluating breast cancer ALN status based on dynamic contrast-enhanced-magnetic resonance imaging (DCE-MRI) images.

Materials and Methods

The handcrafted radiomics features and deep features were extracted from 3062 DCE-MRI images. The feature selection was performed by applying mutual information and feature recursive elimination algorithms. The traditional radiomics model and deep learning radiomics model were built using the optimal features and machine learning classifiers, respectively. The fusion models for distinguishing axillary lymph node status were constructed using two fusion strategies. The performance of the models with MRI-reported lymphadenopathy or suspicious nodes to evaluate axillary lymph node status was also compared.

Results

The decision fusion model, with the integration of the radiomics features and deep learning features at the decision level, achieved an area under the curve (AUC) of 0.91 (95% confidence interval (CI): 0.879-0.937), which was higher than that of the traditional radiomics model and deep learning radiomics model. The results of the decision fusion model with clinical characteristic yielded an AUC of 0.93 (95% CI: 0.899-0.951), which was also superior to other models incorporating clinical characteristic.

Conclusion

This study demonstrates the effectiveness of the fusion models for predicting axillary lymph node metastasis in breast cancer.
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
852应助Chenzr采纳,获得10
1秒前
好运连连完成签到 ,获得积分10
1秒前
happy完成签到 ,获得积分10
2秒前
科研通AI5应助逆行的百合采纳,获得20
3秒前
麻辣修勾完成签到 ,获得积分10
5秒前
6秒前
6秒前
pengyh8完成签到 ,获得积分10
6秒前
7秒前
LSY完成签到 ,获得积分10
8秒前
会飞的猪qq完成签到,获得积分10
8秒前
晨晨lili完成签到,获得积分10
8秒前
10秒前
10秒前
12秒前
lizhongxin发布了新的文献求助10
13秒前
14秒前
ddddd发布了新的文献求助10
14秒前
14秒前
15秒前
lalala完成签到 ,获得积分10
16秒前
Come_On_luguo发布了新的文献求助10
16秒前
赘婿应助称心嫣娆采纳,获得10
16秒前
77发布了新的文献求助10
16秒前
curryif发布了新的文献求助10
17秒前
Akim应助八零采纳,获得10
17秒前
zys发布了新的文献求助10
17秒前
18秒前
ED应助lizhongxin采纳,获得10
19秒前
亿眼万年完成签到,获得积分10
20秒前
curryif完成签到,获得积分10
25秒前
25秒前
史淼荷发布了新的文献求助10
25秒前
25秒前
hyhyhyhy发布了新的文献求助10
26秒前
29秒前
科研通AI5应助Stting采纳,获得30
30秒前
淡定发布了新的文献求助10
30秒前
八零发布了新的文献求助10
31秒前
共享精神应助露亮采纳,获得10
31秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
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 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
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 3989797
求助须知:如何正确求助?哪些是违规求助? 3531914
关于积分的说明 11255516
捐赠科研通 3270597
什么是DOI,文献DOI怎么找? 1805008
邀请新用户注册赠送积分活动 882181
科研通“疑难数据库(出版商)”最低求助积分说明 809190