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Deep learning-based AI model for signet-ring cell carcinoma diagnosis and chemotherapy response prediction in gastric cancer

文献类型：期刊论文

作者Li, Cong1,2; Qin, Yun3; Zhang, Wei-Han4,5,6; Jiang, Hanyu3; Song, Bin3; Bashir, Mustafa R.7,8,9; Xu, Heng10; Duan, Ting3; Fang, Mengjie1,2; Zhong, Lianzhen1,2
刊名MEDICAL PHYSICS
出版日期2022-02-01
页码12
关键词chemotherapy deep learning diagnosis signet-ring cell carcinoma survival
ISSN号0094-2405
DOI10.1002/mp.15437
通讯作者Dong, Di(di.dong@ia.ac.cn) ; Hu, Zhenhua(zhenhua.hu@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Hu, Jian-Kun(hujkwch@126.com)
英文摘要Purpose We aimed to develop a noninvasive artificial intelligence (AI) model to diagnose signet-ring cell carcinoma (SRCC) of gastric cancer (GC) and identify patients with SRCC who could benefit from postoperative chemotherapy based on preoperative contrast-enhanced computed tomography (CT). Methods A total of 855 GC patients with 855 single GCs were included, of which 249 patients were diagnosed as SRCC by histopathologic examinations. The AI model was generated with clinical, handcrafted radiomic, and deep learning features. Model diagnostic performance was measured by area under the receiver operating characteristic curve (AUC), sensitivity, and specificity, while predictive performance was measured by Kaplan-Meier curves. Results In the test cohort (n = 257), the AUC, sensitivity, and specificity of our AI model for diagnosing SRCC were 0.786 (95% CI: 0.721-0.845), 77.3%, and 69.2%, respectively. For the entire cohort, patients with AI-predicted high risk had a significantly shorter median OS compared with those with low risk (median overall survival [OS], 38.8 vs. 64.2 months, p = 0.009). Importantly, in pathologically confirmed advanced SRCC patients, AI-predicted high-risk status was indicative of a shorter overall survival (median overall survival [OS], 31.0 vs. 54.4 months, p = 0.036) and marked chemotherapy resistance, whereas AI-predicted low-risk status had substantial chemotherapy benefit (median OS [without vs. with chemotherapy], 26.0 vs. not reached, p = 0.013). Conclusions The CT-based AI model demonstrated good performance for diagnosing SRCC, stratifying patient prognosis, and predicting chemotherapy responses. Advanced SRCC patients with AI-predicted low-risk status may benefit substantially from adjuvant chemotherapy.
WOS关键词CLINICOPATHOLOGICAL CHARACTERISTICS ; PROGNOSIS ; SURVIVAL ; IMAGES ; DECODE
资助项目National Key R&D Program of China[2017YFC1309100] ; National Key R&D Program of China[2017YFA0205200] ; National Natural Science Foundation of China[91959130] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[81902437] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[61622117] ; National Natural Science Foundation of China[81671759] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[91959205] ; Beijing Natural Science Foundation[L182061] ; Beijing Natural Science Foundation[Z20J00105] ; Beijing Natural Science Foundation[JQ19027] ; Beijing Nova Program[Z181100006218046] ; 1.3.5 Project for Disciplines of Excellence, West China Hospital, Sichuan University[ZYJC21006] ; West China Hospital, Sichuan University[2018HXBH010] ; China Postdoctoral Science Foundation[2019M653418] ; China Postdoctoral Science Foundation[2020T130449] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB 38040200] ; Scientific Instrument Developing Project of the Chinese Academy of Sciences[YZ201672] ; Youth Innovation Promotion Association CAS[2017175]
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000748903200001
出版者WILEY
资助机构National Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Beijing Nova Program ; 1.3.5 Project for Disciplines of Excellence, West China Hospital, Sichuan University ; West China Hospital, Sichuan University ; China Postdoctoral Science Foundation ; Strategic Priority Research Program of Chinese Academy of Sciences ; Scientific Instrument Developing Project of the Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS
源URL[http://ir.ia.ac.cn/handle/173211/47325]  
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; Hu, Zhenhua; Tian, Jie; Hu, Jian-Kun
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing Key Lab Mol Imaging,State Key Lab Managem, Beijing, Peoples R China
3.Sichuan Univ, West China Hosp, Dept Radiol, Chengdu, Sichuan, Peoples R China
4.Sichuan Univ, West China Hosp, Dept Gastrointestinal Surg, 37 Guoxue Alley, Chengdu 610041, Sichuan, Peoples R China
5.Sichuan Univ, West China Hosp, Lab Gastr Canc, State Key Lab Biotherapy, 37 Guoxue Alley, Chengdu 610041, Sichuan, Peoples R China
6.Collaborat Innovat Ctr Biotherapy, Chengdu, Sichuan, Peoples R China
7.Duke Univ, Med Ctr, Dept Radiol, Durham, NC 27710 USA
8.Duke Univ, Ctr Adv Magnet Resonance Dev, Med Ctr, Durham, NC USA
9.Duke Univ, Dept Med Gastroenterol, Med Ctr, Durham, NC USA
10.Sichuan Univ, West China Hosp, Precis Med Ctr, Dept Lab Med,State Key Lab Biotherapy, Chengdu, Sichuan, Peoples R China
推荐引用方式
GB/T 7714		 Li, Cong,Qin, Yun,Zhang, Wei-Han,et al. Deep learning-based AI model for signet-ring cell carcinoma diagnosis and chemotherapy response prediction in gastric cancer[J]. MEDICAL PHYSICS,2022:12.
APA Li, Cong.,Qin, Yun.,Zhang, Wei-Han.,Jiang, Hanyu.,Song, Bin.,...&Hu, Jian-Kun.(2022).Deep learning-based AI model for signet-ring cell carcinoma diagnosis and chemotherapy response prediction in gastric cancer.MEDICAL PHYSICS,12.
MLA Li, Cong,et al."Deep learning-based AI model for signet-ring cell carcinoma diagnosis and chemotherapy response prediction in gastric cancer".MEDICAL PHYSICS (2022):12.

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来源：自动化研究所

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