A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study

doi:10.1177/1758835920971416

CORC > 自动化研究所 > 中国科学院自动化研究所 > 中国科学院分子影像重点实验室

	A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study
	Zhang, Fan 5,7; Zhong, Lian-Zhen 1,3; Zhao, Xun 1,3; Dong, Di 1,3; Yao, Ji-Jin 5,7; Wang, Si-Yang 7; Liu, Ye 9; Zhu, Ding 9; Wang, Yin 8; Wang, Guo-Jie 8
刊名	THERAPEUTIC ADVANCES IN MEDICAL ONCOLOGY
	2020-12-01
卷号	12 页码:12
关键词	digital pathology multi-scale features nasopharyngeal carcinoma radiomics survival analysis
ISSN号	1758-8340
DOI	10.1177/1758835920971416
通讯作者	Wei, Jiang(weijiang@glmc.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Shan, Hong(shanhong@mail.sysu.edu.cn)
英文摘要	Background: To explore the prognostic value of radiomics-based and digital pathology-based imaging biomarkers from macroscopic magnetic resonance imaging (MRI) and microscopic whole-slide images for patients with nasopharyngeal carcinoma (NPC). Methods: We recruited 220 NPC patients and divided them into training (n = 132), internal test (n = 44), and external test (n = 44) cohorts. The primary endpoint was failure-free survival (FFS). Radiomic features were extracted from pretreatment MRI and selected and integrated into a radiomic signature. The histopathological signature was extracted from whole-slide images of biopsy specimens using an end-to-end deep-learning method. Incorporating two signatures and independent clinical factors, a multi-scale nomogram was constructed. We also tested the correlation between the key imaging features and genetic alternations in an independent cohort of 16 patients (biological test cohort). Results: Both radiomic and histopathologic signatures presented significant associations with treatment failure in the three cohorts (C-index: 0.689-0.779, all p < 0.050). The multi-scale nomogram showed a consistent significant improvement for predicting treatment failure compared with the clinical model in the training (C-index: 0.817 versus 0.730, p < 0.050), internal test (C-index: 0.828 versus 0.602, p < 0.050) and external test (C-index: 0.834 versus 0.679, p < 0.050) cohorts. Furthermore, patients were stratified successfully into two groups with distinguishable prognosis (log-rank p < 0.0010) using our nomogram. We also found that two texture features were related to the genetic alternations of chromatin remodeling pathways in another independent cohort. Conclusion: The multi-scale imaging features showed a complementary value in prognostic prediction and may improve individualized treatment in NPC.
资助项目	National Key R&D Program of China[2018YFC0910600] ; National Key R&D Program of China[2017YFA0205200] ; 2016 Guangdong special support program outstanding talent project ; 2017 Zhuhai High-level Health Team Project ; National Natural Science Foundation of China[81620108017] ; National Natural Science Foundation of China[81901699] ; National Natural Science Foundation of China[82022036] ; National Natural Science Foundation of China[91959130] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81930053] ; Beijing Natural Science Foundation[L182061] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB 38040200] ; Youth Innovation Promotion Association CAS[2017175]
WOS关键词	CANCER ; SURVIVAL ; FEATURES
WOS研究方向	Oncology
语种	英语
出版者	SAGE PUBLICATIONS LTD
WOS记录号	WOS:000600037600001
资助机构	National Key R&D Program of China ; 2016 Guangdong special support program outstanding talent project ; 2017 Zhuhai High-level Health Team Project ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Strategic Priority Research Program of Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/42792]
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	Wei, Jiang; Tian, Jie; Shan, Hong
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 2.Guilin Med Univ, Dept Radiat Oncol, Affiliated Hosp, Guilin 541000, Guangxi Provinc, Peoples R China 3.Chinese Acad Sci, CAS Key Lab Mol Imaging, Beijing Key Lab Mol Imaging, State Key Lab Management & Control Complex Syst,I, Beijing, Peoples R China 4.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China 5.Sun Yat Sen Univ, Guangdong Prov Key Lab Biomed Imaging, Affiliated Hosp 5, Zhuhai 519000, Guangdong, Peoples R China 6.Sun Yat Sen Univ, Dept Intervent Med, Affiliated Hosp 5, 52 Meihua East Rd, Zhuhai 519000, Guangdong, Peoples R China 7.Sun Yat Sen Univ, Dept Head & Neck Oncol, Affiliated Hosp 5, Ctr Canc, Zhuhai, Guangdong, Peoples R China 8.Sun Yat Sen Univ, Dept Radiol, Affiliated Hosp 5, Zhuhai, Guangdong, Peoples R China 9.Sun Yat Sen Univ, Dept Pathol, Affiliated Hosp 5, Zhuhai, Guangdong, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Fan,Zhong, Lian-Zhen,Zhao, Xun,et al. A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study[J]. THERAPEUTIC ADVANCES IN MEDICAL ONCOLOGY,2020,12:12.
APA	Zhang, Fan.,Zhong, Lian-Zhen.,Zhao, Xun.,Dong, Di.,Yao, Ji-Jin.,...&Shan, Hong.(2020).A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study.THERAPEUTIC ADVANCES IN MEDICAL ONCOLOGY,12,12.
MLA	Zhang, Fan,et al."A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study".THERAPEUTIC ADVANCES IN MEDICAL ONCOLOGY 12(2020):12.