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

Active, not recruiting

• Conditions: Stomach Neoplasms

• Registration Number: NCT05001321
• Lead Sponsor: First Hospital of China Medical University

Brief 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.

Detailed Description

Not available

Study Timeline

• Study Start: 2021-07-01
• Status Verified: 2021-08
• Study First Submitted: 2021-08-01
• Study First Submitted QC: 2021-08-04
• Last Update Submitted: 2021-08-04
• Study First Posted: 2021-08-11
• Last Update Posted: 2021-08-11
• Primary Completion: 2022-01-31
• Study Completion: 2024-12-31

Recruitment & Eligibility

• Status: ACTIVE_NOT_RECRUITING
• Sex: All
• Target Recruitment: 3300

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Growth pattern	1 day	To assess the growth pattern on preoperative enhanced abdominal CT of patients with gastric cancer, including endophytic, exophytic and mixed.
Nucleus shape	1 day	To obtain the nucleus shape of postoperative H\&E stained sections and slides of gastric cancer by deep learning.
Enhancement pattern	1 day	To assess the enhancement pattern on preoperative enhanced abdominal CT of patients with gastric cancer, including homogeneous and heterogeneous.
Maximum diameter of tumor	1 day	To measure the maximum diameter of tumor on preoperative enhanced abdominal CT of patients with gastric cancer.
Enhancement degree	1 day	To assess the enhancement degree on preoperative enhanced abdominal CT of patients with gastric cancer, including hypoenhancement, isoenhancement and hyperenhancement.
Nucleus size	1 day	To obtain the nucleus size of postoperative H\&E stained sections and slides of gastric cancer by deep learning.
Distribution of pixel intensity	1 day	To obtain the distribution of pixel intensity of postoperative H\&E stained sections and slides of gastric cancer by deep learning.
Texture of nuclei	1 day	To obtain the texture of nuclei of postoperative H\&E stained sections and slides of gastric cancer by deep learning.

Secondary Outcome Measures

Name	Time	Method
Survival status	1 day	To analyze the survival status of patients with gastric cancer, involving dead and alive.
Recurrence/metastasis	1 day	To calculate the days to recurrence/metastasis of patients with gastric cancer.
Overall survival	1 day	To calculate the overall survival of patients with gastric cancer based on days to death and days to last follow-up.

Trial Locations

Locations (6)

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

First Hospital of Jinzhou Medical University; 🇨🇳 Jinzhou, Liaoning, China

The First Affiliated Hospital of China Medical University; 🇨🇳 Shenyang, Liaoning, China

The Second Hospital of Shandong University; 🇨🇳 Ji'nan, Shandong, China