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

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

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Stomach Neoplasms
• Intervention / Treatment: Radiation: The whole abdomen contrast-enhanced CT scan; Other: H&E stained sections and slides

• Study Type: Observational
• Enrollment (Actual): 3300

Contacts and Locations

Study Locations

• China
  • Jiangsu
    • Changzhou, Jiangsu, China, 213001
      • The fourth People's Hospital of Changzhou
  • Liaoning
    • Chaoyang, Liaoning, China, 122099
      • Chaoyang Central Hospital
    • Fushun, Liaoning, China, 113012
      • The General Hospital of Fushun Mining Bureau
    • Jinzhou, Liaoning, China, 121012
      • First Hospital of Jinzhou Medical University
    • Shenyang, Liaoning, China, 110000
      • The First Affiliated Hospital of China Medical University
  • Shandong
    • Ji'nan, Shandong, China, 250033
      • The Second Hospital of Shandong University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 79 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

3000 gastric cancer patients will participate in the phase I study, they will be divided into training group and internal validation group. 300 gastric cancer patients in five other medical centers will form the external validation group.

Description

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 Plan

How is the study designed?

Design Details

• Observational Models: Case-Only
• Time Perspectives: Retrospective

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Training Group; Based on the inclusion criteria, 2000 gastric cancer patients will be recruited in the analysis. And a model will be constructed based on deep learning.	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.
Internal Validation Group; Based on the inclusion criteria, 1000 gastric cancer patients will be recruited in this group to verify the sensitivity and specificity of the constructed model.	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.
External Validation Group; Based on the inclusion criteria, 300 gastric cancer patients from 5 other medical centers will be recruited in this group to verify the sensitivity and specificity of the constructed model.	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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Maximum diameter of tumor	To measure the maximum diameter of tumor on preoperative enhanced abdominal CT of patients with gastric cancer.	1 day
Growth pattern	To assess the growth pattern on preoperative enhanced abdominal CT of patients with gastric cancer, including endophytic, exophytic and mixed.	1 day
Enhancement pattern	To assess the enhancement pattern on preoperative enhanced abdominal CT of patients with gastric cancer, including homogeneous and heterogeneous.	1 day
Enhancement degree	To assess the enhancement degree on preoperative enhanced abdominal CT of patients with gastric cancer, including hypoenhancement, isoenhancement and hyperenhancement.	1 day
Nucleus size	To obtain the nucleus size of postoperative H&E stained sections and slides of gastric cancer by deep learning.	1 day
Nucleus shape	To obtain the nucleus shape of postoperative H&E stained sections and slides of gastric cancer by deep learning.	1 day
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
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Survival status	To analyze the survival status of patients with gastric cancer, involving dead and alive.	1 day
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
Recurrence/metastasis	To calculate the days to recurrence/metastasis of patients with gastric cancer.	1 day

Collaborators and Investigators

• Sponsor: First Hospital of China Medical University
• Collaborators: The Second Hospital of Shandong University; Chaoyang Central Hospital; The General Hospital of Fushun Mining Bureau; The fourth People's Hospital of Changzhou; First Hospital of Jinzhou Medical University
• Investigators: Principal Investigator: Kai Li, MD, First Hospital of China Medical University

Study record dates

Study Major Dates

• Study Start (Actual): July 1, 2021
• Primary Completion (Anticipated): January 31, 2022
• Study Completion (Anticipated): December 31, 2024

Study Registration Dates

• First Submitted: August 1, 2021
• First Submitted That Met QC Criteria: August 4, 2021
• First Posted (Actual): August 11, 2021

Study Record Updates

• Last Update Posted (Actual): August 11, 2021
• Last Update Submitted That Met QC Criteria: August 4, 2021
• Last Verified: August 1, 2021

More Information

Terms related to this study

• Keywords: Prognosis; Diagnosis
• Additional Relevant MeSH Terms: Digestive System Diseases; Neoplasms; Neoplasms by Site; Gastrointestinal Neoplasms; Digestive System Neoplasms; Gastrointestinal Diseases; Stomach Diseases; Stomach Neoplasms
• Other Study ID Numbers: FirstHCMU_DL_oncology

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No