CT-Based Radiomics for the Preoperative Prediction of Occult Peritoneal Metastasis in Epithelial Ovarian Cancers

医学 无线电技术 逻辑回归 放射性武器 接收机工作特性 放射科 神秘的 内科学 病理 替代医学
作者
Jiaojiao Li,Jianing Zhang,Fang Wang,Juanwei Ma,Shujun Cui,Zhaoxiang Ye
出处
期刊:Academic Radiology [Elsevier]
被引量:1
标识
DOI:10.1016/j.acra.2023.11.032
摘要

Rationale and Objectives The objective of this study was to develop a comprehensive combined model for predicting occult peritoneal metastasis (OPM) in epithelial ovarian cancers (EOCs) using radiomics features derived from computed tomography (CT) and clinical-radiological predictors. Materials and Methods A total of 224 patients with EOCs were randomly divided into training dataset (N = 156) and test dataset (N = 86). Five clinical factors and seven radiological features were collected. The radiomics features were extracted from CT images of each patient. Multivariate logistic regression was employed to construct clinical and radiological models. The correlation analysis and least absolute shrinkage and selection operator algorithm were used to select radiomics features and build radiomics model. The important clinical, radiological factors, and radiomics features were integrated into a combined model by multivariate logistic regression. Receiver operating characteristics curve with area under the curve (AUC) were used to evaluate and compare predictive performance. Results Carbohydrate antigen 125 (CA-125) and human epididymal protein 4 (HE-4) were independent clinical predictors. Laterality, thickened septa and margin were independent radiological predictors. In the training dataset, the AUCs for the clinical, radiological and radiomics models in evaluating OPM were 0.759, 0.819, and 0.830, respectively. In the test dataset, the AUCs for these models were 0.846, 0.835, and 0.779, respectively. The combined model outperformed other models in both the training and the test datasets with AUCs of 0.901 and 0.912, respectively. Decision curve analysis indicated that the combined model yielded a higher net benefit compared to the other models. Conclusion The combined model, integrating radiomics features with clinical and radiological predictors exhibited improved accuracy in predicting OPM in EOCs. The objective of this study was to develop a comprehensive combined model for predicting occult peritoneal metastasis (OPM) in epithelial ovarian cancers (EOCs) using radiomics features derived from computed tomography (CT) and clinical-radiological predictors. A total of 224 patients with EOCs were randomly divided into training dataset (N = 156) and test dataset (N = 86). Five clinical factors and seven radiological features were collected. The radiomics features were extracted from CT images of each patient. Multivariate logistic regression was employed to construct clinical and radiological models. The correlation analysis and least absolute shrinkage and selection operator algorithm were used to select radiomics features and build radiomics model. The important clinical, radiological factors, and radiomics features were integrated into a combined model by multivariate logistic regression. Receiver operating characteristics curve with area under the curve (AUC) were used to evaluate and compare predictive performance. Carbohydrate antigen 125 (CA-125) and human epididymal protein 4 (HE-4) were independent clinical predictors. Laterality, thickened septa and margin were independent radiological predictors. In the training dataset, the AUCs for the clinical, radiological and radiomics models in evaluating OPM were 0.759, 0.819, and 0.830, respectively. In the test dataset, the AUCs for these models were 0.846, 0.835, and 0.779, respectively. The combined model outperformed other models in both the training and the test datasets with AUCs of 0.901 and 0.912, respectively. Decision curve analysis indicated that the combined model yielded a higher net benefit compared to the other models. The combined model, integrating radiomics features with clinical and radiological predictors exhibited improved accuracy in predicting OPM in EOCs.
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
大幅提高文件上传限制,最高150M (2024-4-1)

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
跳跃的孤云完成签到 ,获得积分10
10秒前
Ivan完成签到 ,获得积分10
15秒前
不吃了完成签到 ,获得积分10
18秒前
xiaohong完成签到 ,获得积分0
19秒前
迅速的巧曼完成签到 ,获得积分10
20秒前
20秒前
21秒前
kl完成签到 ,获得积分10
22秒前
sihaibo完成签到,获得积分10
24秒前
TOUHOUU发布了新的文献求助10
27秒前
28秒前
yfy完成签到 ,获得积分10
31秒前
生动听筠完成签到 ,获得积分10
43秒前
wangxc完成签到 ,获得积分10
44秒前
如沐完成签到 ,获得积分20
44秒前
44秒前
H-kevin.完成签到,获得积分10
46秒前
婉莹完成签到 ,获得积分0
47秒前
cripple发布了新的文献求助10
48秒前
丰盛的煎饼应助zyjsunye采纳,获得10
49秒前
TOUHOUU完成签到,获得积分10
49秒前
沉沉完成签到 ,获得积分0
50秒前
黑球发布了新的文献求助10
50秒前
今宵 别梦寒完成签到 ,获得积分10
52秒前
53秒前
Krim完成签到 ,获得积分10
54秒前
隔壁的镇长完成签到,获得积分10
56秒前
yys发布了新的文献求助10
57秒前
浮生若梦完成签到,获得积分10
58秒前
黑球完成签到,获得积分10
58秒前
燕晓啸完成签到 ,获得积分0
58秒前
epmoct完成签到 ,获得积分10
59秒前
一叶舟完成签到,获得积分10
1分钟前
李健应助黑球采纳,获得10
1分钟前
伴夏完成签到 ,获得积分10
1分钟前
徐悦完成签到,获得积分10
1分钟前
1分钟前
cripple完成签到,获得积分10
1分钟前
wowser发布了新的文献求助10
1分钟前
整齐的大开完成签到 ,获得积分10
1分钟前
高分求助中
좌파는 어떻게 좌파가 됐나:한국 급진노동운동의 형성과 궤적 2500
Sustainability in Tides Chemistry 1500
TM 5-855-1(Fundamentals of protective design for conventional weapons) 1000
Cognitive linguistics critical concepts in linguistics 800
Threaded Harmony: A Sustainable Approach to Fashion 799
Livre et militantisme : La Cité éditeur 1958-1967 500
氟盐冷却高温堆非能动余热排出性能及安全分析研究 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 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 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3052652
求助须知:如何正确求助?哪些是违规求助? 2709874
关于积分的说明 7418298
捐赠科研通 2354492
什么是DOI,文献DOI怎么找? 1246104
科研通“疑难数据库(出版商)”最低求助积分说明 605951
版权声明 595921