Multi-center and Multi-modal Deep Learning Study of Gastric Cancer
Study Details
Study Description
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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
Study Design
Arms and Interventions
| Arm | 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.
|
Outcome Measures
Primary Outcome Measures
- Maximum diameter of tumor [1 day]
To measure the maximum diameter of tumor on preoperative enhanced abdominal CT of patients with gastric cancer.
- Growth pattern [1 day]
To assess the growth pattern on preoperative enhanced abdominal CT of patients with gastric cancer, including endophytic, exophytic and mixed.
- Enhancement pattern [1 day]
To assess the enhancement pattern on preoperative enhanced abdominal CT of patients with gastric cancer, including homogeneous and heterogeneous.
- 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.
- Nucleus shape [1 day]
To obtain the nucleus shape 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
- Survival status [1 day]
To analyze the survival status of patients with gastric cancer, involving dead and alive.
- 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.
- Recurrence/metastasis [1 day]
To calculate the days to recurrence/metastasis of patients with gastric cancer.
Eligibility Criteria
Criteria
Inclusion Criteria:
-
The diagnosis of gastric cancer was confirmed by pathology;
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Preoperative enhanced abdominal CT;
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Available detailed clinical and pathological data;
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Integrated follow-up data.
Exclusion Criteria:
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The patients had severe underlying disease;
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Overall survival was less than 3 months;
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No detailed information could be collected.
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | The fourth People's Hospital of Changzhou | Changzhou | Jiangsu | China | 213001 |
| 2 | Chaoyang Central Hospital | Chaoyang | Liaoning | China | 122099 |
| 3 | The General Hospital of Fushun Mining Bureau | Fushun | Liaoning | China | 113012 |
| 4 | First Hospital of Jinzhou Medical University | Jinzhou | Liaoning | China | 121012 |
| 5 | The First Affiliated Hospital of China Medical University | Shenyang | Liaoning | China | 110000 |
| 6 | The Second Hospital of Shandong University | Ji'nan | Shandong | China | 250033 |
Sponsors and Collaborators
- First Hospital of China Medical University
- 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 Documents (Full-Text)
None provided.More Information
Publications
None provided.- FirstHCMU_DL_oncology