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

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资源类型：
Pubmed体系：
作者：
Li Cong[1,2] * ; Qin Yun[3] * ; Zhang Wei-Han[4] * ; Jiang Hanyu[3] * ; Song Bin[3] ; Bashir Mustafa R[5,6,7] ; Xu Heng[8] ; Duan Ting[3] ; Fang Mengjie[1,2] ; Zhong Lianzhen[1,2] ; Meng Lingwei[1,2] ; Dong Di[1,2,*2] ; Hu Zhenhua[1,2,*2] ; Tian Jie[1,2,9,10,*2] ; Hu Jian-Kun[4,*1] ;
机构： [1]School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China. [2]CAS Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China. [3]Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China. [4]Department of Gastrointestinal Surgery and Laboratory of Gastric Cancer, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, China. [5]Department of Radiology, Duke University Medical Center, Durham, North Carolina. [6]Center for Advanced Magnetic Resonance Development, Duke University Medical Center, Durham, North Carolina. [7]Department of Medicine (Gastroenterology), Duke University Medical Center, Durham, North Carolina. [8]Department of Laboratory Medicine, Precision Medicine Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China. [9]Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China. [10]Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China.
出处：
DOI： 10.1002/mp.15437
ISSN：

摘要：
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).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.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).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.© 2022 American Association of Physicists in Medicine.

基金：
National Key R&D Program of China, Grant/Award Numbers: 2017YFC1309100, 2017YFA0205200; National Natural Science Foundation of China, Grant/Award Numbers:91959130, 81971776, 81902437, 81771924, 61622117, 81671759, 81930053, 81527805, 91959205; Beijing Natural Science Foundation, Grant/Award Numbers: L182061, Z20J00105, JQ19027; Beijing Nova Program, Grant/Award Number: Z181100006218046; 1.3.5 Project for Disciplines of Excellence, West China Hospital; Sichuan University, Grant/Award Number: ZYJC21006; Post-Doctor Research Project,West China Hospital, Sichuan University,Grant/Award Number: 2018HXBH010; China Postdoctoral Science Foundation, Grant/Award Numbers: 2019M653418, 2020T130449; Strategic Priority Research Program of Chinese Academy of Sciences,Grant/Award Number: XDB 38040200; Scientific Instrument Developing Project of the Chinese Academy of Sciences,Grant/Award Number: YZ201672; Youth Innovation Promotion Association CAS, Grant/Award Number: 2017175
语种：
外文
PubmedID：
中科院(CAS)分区：
出版当年[2022]版：
大类 | 3 区 医学
小类 | 3 区 核医学
最新[2023]版：
大类 | 2 区 医学
小类 | 3 区 核医学
第一作者：
第一作者机构： [1]School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China. [2]CAS Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
共同第一作者：
通讯作者：
通讯机构： [1]School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China. [2]CAS Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China. [4]Department of Gastrointestinal Surgery and Laboratory of Gastric Cancer, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, China. [9]Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China. [10]Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China. [*1]Department of Gastrointestinal Surgery and Laboratory of Gastric Cancer, State Key Laboratory of Biotherapy,West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, No. 37 Guoxue Alley, Chengdu, Sichuan 610041, China. [*2]CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Hai Dian District, Beijing 100190, 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,doi:10.1002/mp.15437.
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,,
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)

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