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Radiomic Nomogram Improves Preoperative T Category Accuracy in Locally Advanced Laryngeal Carcinoma

文献类型：期刊论文

作者Wang, Fei1; Zhang, Bin1,6; Wu, Xiangjun4,5; Liu, Lizhi3; Fang, Jin1; Chen, Qiuying1,6; Li, Minmin1,6; Chen, Zhuozhi1,6; Li, Yueyue1; Dong, Di4,5
刊名FRONTIERS IN ONCOLOGY
出版日期2019-10-15
卷号9页码:8
关键词advanced laryngeal cancer computed tomography radiomics T category nomogram
ISSN号2234-943X
DOI10.3389/fonc.2019.01064
通讯作者Dong, Di(di.dong@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Zhang, Shuixing(shui7515@126.com)
英文摘要Surgical decision-making on advanced laryngeal carcinoma is heavily depended on the identification of preoperative T category (T3 vs. T4), which is challenging for surgeons. A T category prediction radiomics (TCPR) model would be helpful for subsequent surgery. A total of 211 patients with locally advanced laryngeal cancer who had undergone total laryngectomy were randomly classified into the training cohort (n = 150) and the validation cohort (n = 61). We extracted 1,390 radiomic features from the contrast-enhanced computed tomography images. Interclass correlation coefficient and the least absolute shrinkage and selection operator (LASSO) analyses were performed to select features associated with pathology-confirmed T category. Eight radiomic features were found associated with preoperative T category. The radiomic signature was constructed by Support Vector Machine algorithm with the radiomic features. We developed a nomogram incorporating radiomic signature and T category reported by experienced radiologists. The performance of the model was evaluated by the area under the curve (AUC). The T category reported by radiologists achieved an AUC of 0.775 (95% CI: 0.667-0.883); while the radiomic signature yielded a significantly higher AUC of 0.862 (95% CI: 0.772-0.952). The predictive performance of the nomogram incorporating radiomic signature and T category reported by radiologists further improved, with an AUC of 0.892 (95% CI: 0.811-0.974). Consequently, for locally advanced laryngeal cancer, the TCPR model incorporating radiomic signature and T category reported by experienced radiologists have great potential to be applied for individual accurate preoperative T category. The TCPR model may benefit decision-making regarding total laryngectomy or larynx-preserving treatment.
WOS关键词COMPUTED-TOMOGRAPHY FINDINGS ; SQUAMOUS-CELL CARCINOMA ; CANCER ; PRESERVATION ; CT ; IMAGES
资助项目National Natural Science Foundation of China[81571664] ; National Natural Science Foundation of China[81871323] ; National Natural Science Foundation of China[81801665] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of Guangdong Province[2018B030311024] ; Scientific Research General Project of Guangzhou Science Technology and Innovation Commission[201707010328] ; China Postdoctoral Science Foundation[2016M600145] ; Beijing Natural Science Foundation[L182061]
WOS研究方向Oncology
语种英语
WOS记录号WOS:000497835800001
出版者FRONTIERS MEDIA SA
资助机构National Natural Science Foundation of China ; National Natural Science Foundation of Guangdong Province ; Scientific Research General Project of Guangzhou Science Technology and Innovation Commission ; China Postdoctoral Science Foundation ; Beijing Natural Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/29359]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Dong, Di; Tian, Jie; Zhang, Shuixing
作者单位1.Jinan Univ, Affiliated Hosp 1, Dept Radiol, Guangzhou, Guangdong, Peoples R China
2.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China
3.Sun Yat Sen Univ, Collaborat Innovat Ctr Canc Med, State Key Lab Oncol South China,Canc Ctr, Guangdong Key Lab Nasopharyngeal Carcinoma Diag &, Guangzhou, Guangdong, Peoples R China
4.Univ Chinese Acad Sci, Coll Artificial Intelligence, Beijing, Peoples R China
5.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
6.Jinan Univ, Clin Med Coll 1, Guangzhou, Guangdong, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Fei,Zhang, Bin,Wu, Xiangjun,et al. Radiomic Nomogram Improves Preoperative T Category Accuracy in Locally Advanced Laryngeal Carcinoma[J]. FRONTIERS IN ONCOLOGY,2019,9:8.
APA Wang, Fei.,Zhang, Bin.,Wu, Xiangjun.,Liu, Lizhi.,Fang, Jin.,...&Zhang, Shuixing.(2019).Radiomic Nomogram Improves Preoperative T Category Accuracy in Locally Advanced Laryngeal Carcinoma.FRONTIERS IN ONCOLOGY,9,8.
MLA Wang, Fei,et al."Radiomic Nomogram Improves Preoperative T Category Accuracy in Locally Advanced Laryngeal Carcinoma".FRONTIERS IN ONCOLOGY 9(2019):8.

入库方式： OAI收割

来源：自动化研究所

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