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

doi:10.3389/fonc.2019.01064

	Radiomic Nomogram Improves Preoperative T Category Accuracy in Locally Advanced Laryngeal Carcinoma
	Wang, Fei 1; Zhang, Bin 1,2; Wu, Xiangjun 3,4; Liu, Lizhi 5; Fang, Jin 1; Chen, Qiuying 1,2; Li, Minmin 1,2; Chen, Zhuozhi 1,2; Li, Yueyue 1; Dong, Di3,4 ; Tian, Jie 3,4,6; Zhang, Shuixing 1
发表期刊	FRONTIERS IN ONCOLOGY
ISSN	2234-943X
	2019-10-15
卷号	9 页码:8
通讯作者	Dong, Di(di.dong@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Zhang, Shuixing(shui7515@126.com)
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.
关键词	advanced laryngeal cancer computed tomography radiomics T category nomogram
DOI	10.3389/fonc.2019.01064
关键词[WOS]	COMPUTED-TOMOGRAPHY FINDINGS ; SQUAMOUS-CELL CARCINOMA ; CANCER ; PRESERVATION ; CT ; IMAGES
收录类别	SCI
语种	英语
资助项目	Beijing Natural Science Foundation[L182061] ; China Postdoctoral Science Foundation[2016M600145] ; Scientific Research General Project of Guangzhou Science Technology and Innovation Commission[201707010328] ; National Natural Science Foundation of Guangdong Province[2018B030311024] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[81801665] ; National Natural Science Foundation of China[81871323] ; National Natural Science Foundation of China[81571664] ; 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]
项目资助者	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
WOS研究方向	Oncology
WOS类目	Oncology
WOS记录号	WOS:000497835800001
出版者	FRONTIERS MEDIA SA
七大方向——子方向分类	医学影像处理与分析
引用统计	被引频次：22[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/29360
专题	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.Jinan Univ, Clin Med Coll 1, Guangzhou, Guangdong, Peoples R China 3.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China 4.Univ Chinese Acad Sci, Coll Artificial Intelligence, Beijing, Peoples R China 5.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 6.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China
通讯作者单位	中国科学院自动化研究所
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.