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Deep learning signatures reveal multiscale intratumor heterogeneity associated with biological functions and survival in recurrent nasopharyngeal carcinoma

文献类型：期刊论文

作者Zhao, Xun1,5; Liang, Yu-Jing2,4; Zhang, Xu2,3; Wen, Dong-Xiang2,4; Fan, Wei2,3; Tang, Lin-Quan2,4; Dong, Di1,5; Tian, Jie1,5; Mai, Hai-Qiang2,4
刊名EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
出版日期2022-04-26
页码11
ISSN号1619-7070
关键词Recurrent nasopharyngeal carcinoma Survival analysis Radiomics Deep learning
DOI10.1007/s00259-022-05793-x
通讯作者Tang, Lin-Quan(tanglq@sysucc.org.cn) ; Dong, Di(di.dong@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Mai, Hai-Qiang(maihq@sysucc.org.cn)
英文摘要Purpose How to discriminate different risks of recurrent nasopharyngeal carcinoma (rNPC) patients and guide individual treatment has become of great importance. This study aimed to explore the associations between deep learning signatures and biological functions as well as survival in (rNPC) patients. Methods A total of 420 rNPC patients with PET/CT imaging and follow-up of overall survival (OS) were retrospectively enrolled. All patients were randomly divided into a training set (n= 269) and test set (n = 151) with a 6:4 ratio. We constructed multi-modality deep learning signatures from PET and CT images with a light-weighted deep convolutional neural network EfficienetNet-lite0 and survival loss DeepSurvLoss. An integrated nomogram was constructed incorporating clinical factors and deep learning signatures from PET/CT. Clinical nomogram and single-modality deep learning nomograms were also built for comparison. Furthermore, the association between biological functions and survival risks generated from an integrated nomogram was analyzed by RNA sequencing (RNA-seq). Results The C-index of the integrated nomogram incorporating age, rT-stage, and deep learning PET/CT signature was 0.741 (95% CI: 0.688-0.794) in the training set and 0.732 (95% CI: 0.679-0.785) in the test set. The nomogram stratified patients into two groups with high risk and low risk in both the training set and test set with hazard ratios (HR) of 4.56 (95% CI: 2.80-7.42, p < 0.001) and 4.05 (95% CI: 2.21-7.43, p < 0.001), respectively. The C-index of the integrated nomogram was significantly higher than the clinical nomogram and single-modality nomograms. When stratified by sex, N-stage, or EBV DNA, risk prediction of our integrated nomogram was valid in all patient subgroups. Further subgroup analysis showed that patients with a low-risk could benefit from surgery and re-irradiation, while there was no difference in survival rates between patients treated by chemotherapy in the high-risk and low-risk groups. RNA sequencing (RNA-seq) of data further explored the mechanism of high- and low-risk patients from the genetic and molecular level. Conclusion Our study demonstrated that PET/CT-based deep learning signatures showed satisfactory prognostic predictive performance in rNPC patients. The nomogram incorporating deep learning signatures successfully divided patients into different risks and had great potential to guide individual treatment: patients with a low-risk were supposed to be treated with surgery and re-irradiation, while for high-risk patients, the application of palliative chemotherapy may be sufficient.
WOS关键词PROGNOSTIC-FACTORS ; RADIOMIC NOMOGRAM ; NODE-METASTASIS ; REIRRADIATION
资助项目Strategic Priority Research Program of Chinese Academy of Sciences[XDB38040200] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC0908500] ; National Key R&D Program of China[2017YFC1309003] ; 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[62027901] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81425018] ; National Natural Science Foundation of China[81672868] ; National Natural Science Foundation of China[81802775] ; National Natural Science Foundation of China[82073003] ; National Natural Science Foundation of China[82002852] ; National Natural Science Foundation of China[82003267] ; National Natural Science Foundation of China[81803105] ; Beijing Natural Science Foundation[L182061] ; Youth Innovation Promotion Association CAS[Y2021049] ; Youth Innovation Promotion Association CAS[2017175] ; Sci-Tech Project Foundation of Guangzhou City[201707020039] ; Sun Yat-sen University Clinical Research 5010 Program[2016010] ; Sun Yat-sen University Clinical Research 5010 Program[201315] ; Sun Yat-sen University Clinical Research 5010 Program[2015021] ; Sun Yat-sen University Clinical Research 5010 Program[2017010] ; Sun Yat-sen University Clinical Research 5010 Program[2016013] ; Sun Yat-sen University Clinical Research 5010 Program[2019023] ; Innovative research team of high-level local universities in Shanghai[SSMU-ZLCX20180500] ; Natural Science Foundation of Guangdong Province[2017A030312003] ; Natural Science Foundation of Guangdong Province[20ykzd24] ; Natural Science Foundation of Guangdong Province for Distinguished Young Scholar[2018B030306001] ; Health & Medical Collaborative Innovation Project of Guangzhou City[201803040003] ; Pearl River S&T Nova Program of Guangzhou[201806010135] ; Planned Science and Technology Project of Guangdong Province[2019B020230002] ; Fundamental Research Funds for the Central Universities
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
出版者SPRINGER
WOS记录号WOS:000787662100001
资助机构Strategic Priority Research Program of Chinese Academy of Sciences ; National Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Youth Innovation Promotion Association CAS ; Sci-Tech Project Foundation of Guangzhou City ; Sun Yat-sen University Clinical Research 5010 Program ; Innovative research team of high-level local universities in Shanghai ; Natural Science Foundation of Guangdong Province ; Natural Science Foundation of Guangdong Province for Distinguished Young Scholar ; Health & Medical Collaborative Innovation Project of Guangzhou City ; Pearl River S&T Nova Program of Guangzhou ; Planned Science and Technology Project of Guangdong Province ; Fundamental Research Funds for the Central Universities
源URL[http://ir.ia.ac.cn/handle/173211/48377]  
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Tang, Lin-Quan; Dong, Di; Tian, Jie; Mai, Hai-Qiang
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
2.Sun Yat Sen Univ, Dept Nasopharyngeal Carcinoma, Canc Ctr, 651 Dongfeng Rd East, Guangzhou 510060, Peoples R China
3.Sun Yat Sen Univ, Dept Nucl Med, Canc Ctr, Guangzhou 510060, Peoples R China
4.Sun Yat Sen Univ, Collaborat Innovat Ctr Canc Med, State Key Lab Oncol South China,Canc Ctr, Guangdong Key Lab Nasopharyngeal Carcinoma Diag &, Guangzhou 510060, Peoples R China
5.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing Key Lab Mol Imaging,State Key Lab Managem, 95 Zhongguancun East Rd, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Zhao, Xun,Liang, Yu-Jing,Zhang, Xu,et al. Deep learning signatures reveal multiscale intratumor heterogeneity associated with biological functions and survival in recurrent nasopharyngeal carcinoma[J]. EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING,2022:11.
APA Zhao, Xun.,Liang, Yu-Jing.,Zhang, Xu.,Wen, Dong-Xiang.,Fan, Wei.,...&Mai, Hai-Qiang.(2022).Deep learning signatures reveal multiscale intratumor heterogeneity associated with biological functions and survival in recurrent nasopharyngeal carcinoma.EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING,11.
MLA Zhao, Xun,et al."Deep learning signatures reveal multiscale intratumor heterogeneity associated with biological functions and survival in recurrent nasopharyngeal carcinoma".EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2022):11.

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来源：自动化研究所

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