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

Study Overview
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Brief Summary
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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.

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

Conditions
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Stomach Neoplasms

Intervention / Treatment
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* Radiation: The whole abdomen contrast-enhanced CT scan
* Other: H&E stained sections and slides


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 79 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Training Group<br>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<br>* All the participants were measured by the whole abdomen contrast-enhanced CT scan.<br>Other: H&E stained sections and slides<br>* HE pathological examination was performed on all specimens of enrolled patients.<br>|
| Internal Validation Group<br>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<br>* All the participants were measured by the whole abdomen contrast-enhanced CT scan.<br>Other: H&E stained sections and slides<br>* HE pathological examination was performed on all specimens of enrolled patients.<br>|
| External Validation Group<br>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<br>* All the participants were measured by the whole abdomen contrast-enhanced CT scan.<br>Other: H&E stained sections and slides<br>* HE pathological examination was performed on all specimens of enrolled patients.<br>|

What is the study measuring?
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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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Diagnosis, Prognosis