Multi-center and Multi-modal Deep Learning Study of Gastric Cancer

Stomach Neoplasms Clinical Trial

Official title:

Multi-center and Multi-modal Deep Learning Study of Diagnosis, Therapeutic Outcome and Prognosis of Gastric Cancer

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT05001321
• Other study ID #: FirstHCMU_DL_oncology
• Status: Active, not recruiting
• Start date: July 1, 2021
• Est. completion date: December 31, 2024

Study information

• Verified date: August 2021
• Source: First Hospital of China Medical University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

To assist postoperative pathological diagnosis and classification of gastric cancer by machine learning; To improve the accuracy of pathological diagnosis of gastric cancer by machine learning; To predict the effectiveness of treatment for gastric cancer by deep learning; To construct a model to predict the survival of gastric cancer patients by multimodal deep learning.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 3300
• Est. completion date: December 31, 2024
• Est. primary completion date: January 31, 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 79 Years

Eligibility

Inclusion Criteria:

• The diagnosis of gastric cancer was confirmed by pathology;

• Preoperative enhanced abdominal CT;

• Available detailed clinical and pathological data;

• Integrated follow-up data.

Exclusion Criteria:

• The patients had severe underlying disease;

• Overall survival was less than 3 months;

• No detailed information could be collected.

Related Conditions & MeSH terms

• Stomach Neoplasms

Intervention

Radiation:
• The whole abdomen contrast-enhanced CT scan
All the participants were measured by the whole abdomen contrast-enhanced CT scan.

Other:
• H&E stained sections and slides
HE pathological examination was performed on all specimens of enrolled patients.

Locations

Country	Name	City	State
China	The fourth People's Hospital of Changzhou	Changzhou	Jiangsu
China	Chaoyang Central Hospital	Chaoyang	Liaoning
China	The General Hospital of Fushun Mining Bureau	Fushun	Liaoning
China	The Second Hospital of Shandong University	Ji'nan	Shandong
China	First Hospital of Jinzhou Medical University	Jinzhou	Liaoning
China	The First Affiliated Hospital of China Medical University	Shenyang	Liaoning

Sponsors (6)

Lead Sponsor	Collaborator
First Hospital of China Medical University	Chaoyang Central Hospital, First Hospital of Jinzhou Medical University, The fourth People's Hospital of Changzhou, The General Hospital of Fushun Mining Bureau, The Second Hospital of Shandong University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Maximum diameter of tumor	To measure the maximum diameter of tumor on preoperative enhanced abdominal CT of patients with gastric cancer.	1 day
Primary	Growth pattern	To assess the growth pattern on preoperative enhanced abdominal CT of patients with gastric cancer, including endophytic, exophytic and mixed.	1 day
Primary	Enhancement pattern	To assess the enhancement pattern on preoperative enhanced abdominal CT of patients with gastric cancer, including homogeneous and heterogeneous.	1 day
Primary	Enhancement degree	To assess the enhancement degree on preoperative enhanced abdominal CT of patients with gastric cancer, including hypoenhancement, isoenhancement and hyperenhancement.	1 day
Primary	Nucleus size	To obtain the nucleus size of postoperative H&E stained sections and slides of gastric cancer by deep learning.	1 day
Primary	Nucleus shape	To obtain the nucleus shape of postoperative H&E stained sections and slides of gastric cancer by deep learning.	1 day
Primary	Distribution of pixel intensity	To obtain the distribution of pixel intensity of postoperative H&E stained sections and slides of gastric cancer by deep learning.	1 day
Primary	Texture of nuclei	To obtain the texture of nuclei of postoperative H&E stained sections and slides of gastric cancer by deep learning.	1 day
Secondary	Survival status	To analyze the survival status of patients with gastric cancer, involving dead and alive.	1 day
Secondary	Overall survival	To calculate the overall survival of patients with gastric cancer based on days to death and days to last follow-up.	1 day
Secondary	Recurrence/metastasis	To calculate the days to recurrence/metastasis of patients with gastric cancer.	1 day