Clinical trial NCT06078930: Development of Novel Gastric Cancer Screening and Diagnosis Technologies Using Tongue Imaging and Study of Tongue Image Changes Mechanisms
Official title: A Multi-center Observational Clinical Study on the Development of Artificial Intelligence-Based Gastric Cancer Screening and Diagnosis Technology Using Multi-Omics Analysis of Tongue Imaging and Tongue Coating
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Gastric Cancer
Tongue Images
Diagnosis
Lead sponsor: Zhejiang Cancer Hospital
Sponsor class: OTHER
Collaborators: name: Sichuan Cancer Hospital and Research Institute
name: The First Affiliated Hospital of Zhejiang Chinese Medical University
name: Wenzhou Medical University
name: Fujian Cancer Hospital
name: RenJi Hospital
name: Tongde Hospital of Zhejiang
name: Kecheng District People's Hospital
name: Yueyang Central Hospital
name: Shandong Cancer Hospital and Institute
name: Shanxi Province Cancer Hospital
name: Liaoning Cancer Hospital & Institute
name: Harbin Cancer Hospital
name: Yuhang District People's Hospital
name: Hainan Cancer Hospital
name: Zigong Fourth People's Hospital
name: Henan University of Science and Technology
name: Shangyu People's Hospital of Shaoxing City
name: Red Cross Hospital, Hangzhou, China
name: Hangzhou Normal University Affiliated Hospital
name: Xiaoshan District First People's Hospital
name: Xianju County People's Hospital
name: Lin'an District First People's Hospital
name: First Affiliated Hospital of Wenzhou Medical University
name: Wenzhou Central Hospital
name: Ruian City People's Hospital
name: Leqing City People's Hospital
name: The Affiliated Hospital of Medical School of Ningbo University
name: Cixi City People's Hospital
name: Mingzhou Hospital, Zhejiang University
name: Fourth Hospital of Ningbo City
name: Fenghua District People's Hospital
name: Fenghua Traditional Chinese Medicine Hospital, Ningbo City
name: Xikou Hospital, Fenghua District, Ningbo City
name: Chengdu University of Traditional Chinese Medicine
name: Guang'an Hospital, Affiliated to Chengdu University of Traditional Chinese Medicine
name: Jiangsu Cancer Institute & Hospital
name: Jiangsu Provincial People's Hospital
name: The First People's Hospital of Changzhou
name: The First Affiliated Hospital of Soochow University
name: Jiangsu Provincial Hospital of Traditional Chinese Medicine
name: Zidong Campus of Jiangsu Provincial Hospital of TCM
name: Wuxi Traditional Chinese Medicine Hospital
name: First Affiliated Hospital of Harbin Medical University
name: The First Affiliated Hospital of Anhui Medical University
name: Peking University Cancer Hospital & Institute
name: Xijing Hospital
name: Guangdong Provincial Hospital of Traditional Chinese Medicine
name: Xiangya Hospital of Central South University
name: Yunnan Cancer Hospital
name: Chongqing University Cancer Hospital
name: Gansu Cancer Hospital
name: Fudan University
name: Cancer Institute and Hospital, Chinese Academy of Medical Sciences
name: First Affiliated Hospital of Guangxi Medical University
name: Tianjin Medical University Cancer Institute and Hospital
name: Huzhou Central Hospital
name: Zhoushan Hospital, Zhejiang
First posted: 2023-10-12
Enrollment: 100000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 90
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Histologically or cytologically confirmed gastric cancer, and no prior chemotherapy, radiotherapy, biotherapy, immunotherapy or other anti-tumor treatment for malignant tumor. Gastric cancer patients and healthy participants should not have the following conditions: the use of glucocorticoids and antibiotics in the past three months, a history of long-term medication, as well as oral diseases such as tooth and gum diseases.

Brief summary: In this study, the investigators will prospectively recruit 100,000 individuals, including gastric cancer patients who have not undergone any anti-tumor treatment and non-gastric cancer participants. The investigators will construct a diagnostic model for malignant tumors based on the combination of tongue imaging, tongue coating, saliva, gastric juice, and fecal multi-omics data (including metagenomics, proteomics, metabolomics, etc.). Additionally, the study will explore the relationship between oral, gastric, and intestinal microbiota and the development of malignant tumors.

Detailed description: Patients with gastric cancer were recruited through outpatient clinics, endoscopy units, and inpatient departments of various centers, with all patients requiring a histopathological diagnosis. Non-gastric cancer participants were sourced from community health check-ups, outpatient clinics, and endoscopy units affiliated with these centers.

Eligibility criteria: Inclusion Criteria for Gastric Cancer Patients:

* 18≤age≤90
* Histologically or cytologically confirmed gastric cancer
* No prior chemotherapy, radiotherapy, biotherapy, immunotherapy or other anti-tumor treatment for gastric cancer
* Subject volunteers to join the study, Signs informed consent, has good compliance and can cooperate with follow-up

Exclusion Criteria for Gastric Cancer Patients:

* Two or more kinds of malignant tumors at the same time
* Gastric cancer that has been treated by chemotherapy, radiotherapy, biotherapy, immunotherapy or other anti-tumor therapy
* The use of glucocorticoids and antibiotics in the past three months
* History of long-term medication
* Presence of oral diseases such as tooth and gum diseases
* Subjects who had other factors that might force them to terminate the research ahead of time, such as the development of other severe disease (including mental disease) that required combined treatment, seriously abnormal laboratory examination value, and family or social factors that might affect the subject safety or experimental data collection, as judged by the researchers

Inclusion Criteria for Healthy Participants:

* 18≤age≤90
* Willingness to participate in the study, signing an informed consent form, and demonstrating good compliance

Exclusion Criteria for Healthy Participants:

* History of malignant tumors
* The use of glucocorticoids and antibiotics in the past three months
* History of long-term medication
* Presence of oral diseases such as tooth and gum diseases
* Subjects who had other factors that might force them to terminate the research ahead of time, such as the development of other severe disease (including mental disease) that required combined treatment, seriously abnormal laboratory examination value, and family or social factors that might affect the subject safety or experimental data collection, as judged by the researchers

Primary outcomes:
measure: The differences in tongue images, tongue coating, saliva, gastric juice, and fecal samples between patients with gastric cancer and healthy individuals will be systematically analyzed.
timeFrame: 5 years
measure: The differences of the clinical information between patients with gastric cancer and healthy participants.
timeFrame: 5 years
measure: Overall Survival (OS)
timeFrame: 5 years
Secondary outcomes:
measure: Disease Free Survival (DFS)
timeFrame: 5 years
Other outcomes:
Locations:
city: Hangzhou
country: China
facility: Zhejiang Cancer Hospital
state: Zhejiang
Study officials:
References:
citation: Yuan L, Yang L, Zhang S, Xu Z, Qin J, Shi Y, Yu P, Wang Y, Bao Z, Xia Y, Sun J, He W, Chen T, Chen X, Hu C, Zhang Y, Dong C, Zhao P, Wang Y, Jiang N, Lv B, Xue Y, Jiao B, Gao H, Chai K, Li J, Wang H, Wang X, Guan X, Liu X, Zhao G, Zheng Z, Yan J, Yu H, Chen L, Ye Z, You H, Bao Y, Cheng X, Zhao P, Wang L, Zeng W, Tian Y, Chen M, You Y, Yuan G, Ruan H, Gao X, Xu J, Xu H, Du L, Zhang S, Fu H, Cheng X. Development of a tongue image-based machine learning tool for the diagnosis of gastric cancer: a prospective multicentre clinical cohort study. EClinicalMedicine. 2023 Feb 6;57:101834. doi: 10.1016/j.eclinm.2023.101834. eCollection 2023 Mar.
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citation: Li MY, Zhu DJ, Xu W, Lin YJ, Yung KL, Ip AWH. Application of U-Net with Global Convolution Network Module in Computer-Aided Tongue Diagnosis. J Healthc Eng. 2021 Nov 18;2021:5853128. doi: 10.1155/2021/5853128. eCollection 2021.