个人中心| | 帮助
高级检索
当前位置： 首页 > 详情页

An ordinal radiomic model to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma based on low-dose computed tomography in lung cancer screening

| 导出 | |

文献详情

资源类型：
WOS体系：
Pubmed体系：

收录情况： ◇ SCIE

作者：
Li Yong[1] * ; Liu Jieke[1] * ; Yang Xi[1] ; Wang Ai[1] ; Zang Chi[1] ; Wang Lu[1] ; He Changjiu[1] ; Lin Libo[1] ; Qing Haomiao[1] ; Ren Jing[1] ; Zhou Peng[1] ;
机构： [1]Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No. 55, Section 4, South Renmin Road, Chengdu 610041, Sichuan, China
出处：
DOI： 10.1007/s00330-023-09453-y
ISSN：

摘要：
To construct a radiomic model of low-dose CT (LDCT) to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma (IPA) and compare its diagnostic performance with quantitative-semantic model and radiologists.A total of 682 pulmonary nodules were divided into the primary cohort (181 grade 1; 254 grade 2; 64 grade 3) and validation cohort (69 grade 1; 99 grade 2; 15 grade 3) according to scanners. The radiomic and quantitative-semantic models were built using ordinal logistic regression. The diagnostic performance of the models and radiologists was assessed by the area under the curve (AUC) of the receiver operating characteristic curve and accuracy.The radiomic model demonstrated excellent diagnostic performance in the validation cohort (AUC, 0.900 (95%CI: 0.847-0.939) for Grade 1 vs. Grade 2/Grade 3; AUC, 0.929 (95%CI: 0.882-0.962) for Grade 1/Grade 2 vs. Grade 3; accuracy, 0.803 (95%CI: 0.737-0.857)). No significant difference in diagnostic performance was found between the radiomic model and radiological expert (AUC, 0.840 (95%CI: 0.779-0.890) for Grade 1 vs. Grade 2/Grade 3, p = 0.130; AUC, 0.852 (95%CI: 0.793-0.900) for Grade 1/Grade 2 vs. Grade 3, p = 0.170; accuracy, 0.743 (95%CI: 0.673-0.804), p = 0.079), but the radiomic model outperformed the quantitative-semantic model and inexperienced radiologists (all p < 0.05).The radiomic model of LDCT can be used to predict the differentiation grade of IPA in lung cancer screening, and its diagnostic performance is comparable to that of radiological expert.• Early identifying the novel differentiation grade of invasive non-mucinous pulmonary adenocarcinoma may provide guidance for further surveillance, surgical strategy, or more adjuvant treatment. • The diagnostic performance of the radiomic model is comparable to that of a radiological expert and superior to that of the quantitative-semantic model and inexperienced radiologists. • The radiomic model of low-dose CT can be used to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma in lung cancer screening.© 2023. The Author(s), under exclusive licence to European Society of Radiology.

基金：
This study has received funding from the National Natural Science Foundation of China (82202141), the Sichuan Science and Technology Program (2021YFS0075, 2021YFS0225), and the Chengdu Science and Technology Program (2021-YF05-01507-SN).
语种：
外文
被引次数：
WOS：
PubmedID：
中科院(CAS)分区：
出版当年[2023]版：
大类 | 2 区 医学
小类 | 2 区 核医学
最新[2023]版：
大类 | 2 区 医学
小类 | 2 区 核医学
JCR分区：
出版当年[2023]版：
Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
最新[2023]版：
Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

影响因子： 最新[2023版] 最新五年平均 出版当年[2023版] 出版当年五年平均 出版前一年[2022版]

第一作者：
第一作者机构： [1]Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No. 55, Section 4, South Renmin Road, Chengdu 610041, Sichuan, China
共同第一作者：
通讯作者：
推荐引用方式(GB/T 7714)：
Li Yong,Liu Jieke,Yang Xi,et al.An ordinal radiomic model to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma based on low-dose computed tomography in lung cancer screening[J].EUROPEAN RADIOLOGY.2023,33(5):3072-3082.doi:10.1007/s00330-023-09453-y.
APA：
Li Yong,Liu Jieke,Yang Xi,Wang Ai,Zang Chi...&Zhou Peng.(2023).An ordinal radiomic model to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma based on low-dose computed tomography in lung cancer screening.EUROPEAN RADIOLOGY,33,(5)
MLA：
Li Yong,et al."An ordinal radiomic model to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma based on low-dose computed tomography in lung cancer screening".EUROPEAN RADIOLOGY 33..5(2023):3072-3082

资源点击量：52808 今日访问量：2 总访问量：4561 更新日期：2025-01-01 建议使用谷歌、火狐浏览器 常见问题

版权所有©2020 四川省肿瘤医院 技术支持：重庆聚合科技有限公司 地址：成都市人民南路四段55号