CASIA OpenIR  > 中国科学院分子影像重点实验室
Deep learning-based AI model for signet-ring cell carcinoma diagnosis and chemotherapy response prediction in gastric cancer
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; Meng, Lingwei1,2; Dong, Di1,2; Hu, Zhenhua1,2; Tian, Jie1,2,11,12; Hu, Jian-Kun4,5,6
Source PublicationMEDICAL PHYSICS
ISSN0094-2405
2022-02-01
Pages12
Abstract

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.

Keywordchemotherapy deep learning diagnosis signet-ring cell carcinoma survival
DOI10.1002/mp.15437
WOS KeywordCLINICOPATHOLOGICAL CHARACTERISTICS ; PROGNOSIS ; SURVIVAL ; IMAGES ; DECODE
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational 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
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000748903200001
PublisherWILEY
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/47326
Collection中国科学院分子影像重点实验室
Corresponding AuthorDong, Di; Hu, Zhenhua; Tian, Jie; Hu, Jian-Kun
Affiliation1.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
11.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med & Engn, Beijing, Peoples R China
12.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian, Shaanxi, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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