Stomach Neoplasms Clinical Trial
Official title:
Multi-center and Multi-modal Deep Learning Study of Diagnosis, Therapeutic Outcome and Prognosis of Gastric Cancer
Verified date | August 2021 |
Source | First Hospital of China Medical University |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
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.
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. |
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 |
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 |
China,
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 |
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