Stomach Neoplasms
Conditions
Keywords
Diagnosis, Prognosis
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.
Interventions
RADIATIONThe whole abdomen contrast-enhanced CT scan
All the participants were measured by the whole abdomen contrast-enhanced CT scan.
OTHERH&E stained sections and slides
HE pathological examination was performed on all specimens of enrolled patients.
Sponsors
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
First Hospital of China Medical University
Study design
Observational model
CASE_ONLY
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 79 Years
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. |
| 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. |
| 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| 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. |
| Survival status | 1 day | To analyze the survival status of patients with gastric cancer, involving dead and alive. |
Countries
China
Outcome results
None listed