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This is a prospective and observational clinical study for seeking out a better way to predict the pathologic complete response (pCR) in patients with advanced gastric cancer (AGC) based on the post-neoadjuvant treatment Magnetic Resonance Imaging (MRI) and CT data. This study will help the surgeons to better formulate treatment regimens for gastric cancer in the clinical practice.
With the gradual development of neoadjuvant immunotherapy and/or chemotherapy in the clinic, the pCR has become more and more accessible in the AGC. Preoperative accurate prediction of pCR is of great clinical significance. The contrast-enhanced CT and 3.0T MRI were carried out in patients within 1 week prior to commencing neoadjuvant treatment, as well as 1 week within surgery after the completion of neoadjuvant treatment, respectively. Based on the information extracted from the CT/MRI, the clinical completed response (cCR) and the clinical T staging were compared with pCR, pathologic T staging. The pathologic results were considered as the golden standard. With the ROC curve analysis, the diagnosis coincidence rate, sensitivity and specificity were assessed. The AI prediction model would be constructed and trained. The depth convolution neural network based on contrast-enhanced CT and multi-modal MR quantitative images which can automatically mine key images characterization, combined with imaging features and histopathologic response, could further help to improve the prediction of response of gastric cancer treated with systematic therapy. The abdominal contrast-enhanced CT will focus on parameters: Local T Staging, nodal status, diameter, according to RECIST 1.1. MRI T2 (1-3mm slice as per NS Radiology protocol and ESGAR guideline) will focus on parameters: DWI & ADC value (preferably on a single camera with reproducible ADC value), Local T Staging, MRF involvement, EMVI, nodal status, MR volumetry, and desmoplastic reaction.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Single Group Assignment | Patients with AGC who underwent neoadjuvant immunotherapy and/or chemotherapy would recieve MRI and CT examination before and after 3 cycles treatment. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| PD-1 inhibitor | Drug | SOX regimen for 3 cycles and/or PD-1 inhibitor before surgery |
|
| Measure | Description | Time Frame |
|---|---|---|
| Predictive value of CT and MRI after the neoadjuvant treatment for developing a pCR at surgery | Predictive value of CT and MRI after the neoadjuvant treatment for developing a pathologic complete response at surgery (Grade 0 - no viable cancer cells seen in the resection specimen). | up to 2 year |
| Measure | Description | Time Frame |
|---|---|---|
| Predictive value of CT and MRI after the neoadjuvant treatment for pathologic T staging | To evaluate the T staging of gastric cancer treated with neoadjuvant treatment through CT and MRI. | up to 2 year |
| Predictive value of CT and MRI after the neoadjuvant treatment for pathologic response according to the Tumor Regression Grading (TRG) |
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Inclusion Criteria:
Exclusion Criteria:
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Patients with endoscopically biopsy-proven gastric cancer will receive preoperative neoadjuvant immunotherapy and/or chemotherapy.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| quan wang, MD | Contact | 15843073207 | wquan@jlu.edu.cn |
| Name | Affiliation | Role |
|---|---|---|
| quan wang, MD | The First Hospital of Jilin University | Principal Investigator |
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Surgically removed tumor tissue
Pathological tumour regression grading (Mandard criterion): from 1 to 5 grading. |
| up to 2 year |
| Prediction model based on CT and MRI of response in AGC | To construct a model, a depth convolution neural network based on contrast-enhanced CT and multi-modal MR quantitative images which can automatically mine key images characterization, combined with imaging features and histopathologic response, could further help to improve the prediction of response of gastric/rectal cancer treated with systematic therapy. | up to 2 year |
| ID | Term |
|---|---|
| D013274 | Stomach Neoplasms |
| ID | Term |
|---|---|
| D005770 | Gastrointestinal Neoplasms |
| D004067 | Digestive System Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D004066 | Digestive System Diseases |
| D005767 | Gastrointestinal Diseases |
| D013272 | Stomach Diseases |
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| ID | Term |
|---|---|
| D000082082 | Immune Checkpoint Inhibitors |
| D000077150 | Oxaliplatin |
| ID | Term |
|---|---|
| D045504 | Molecular Mechanisms of Pharmacological Action |
| D020228 | Pharmacologic Actions |
| D020164 | Chemical Actions and Uses |
| D000074322 | Antineoplastic Agents, Immunological |
| D000970 | Antineoplastic Agents |
| D045506 | Therapeutic Uses |
| D056831 | Coordination Complexes |
| D009930 | Organic Chemicals |
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