Locally Advanced Gastric Carcinoma
Conditions
Keywords
Gastric cancer, Neoadjuvant chemotherapy, Immunotherapy, Targeted therapy
Brief summary
In this study, investigators utilize a radiopathomics integrated Artificial Intelligence (AI) supportive system to predict tumor response to neoadjuvant chemoradiotherapy (nCRT) before its administration for patients with locally advanced gastric cancer (LAGC). By the system, the postoperative tumor regression grade (TRG) of the participants will be identified based on the radiopathomics features extracted from the pre-nCRT Enhanced CT and biopsy images. The ability to predict TRG will be validated in this multicenter, prospective clinical study.
Detailed description
This is a multicenter, prospective, observational clinical study for validation of a radiopathomics artificial intelligence (AI) system. Patients who have been diagnosed with gastric adenocarcinoma by pathology and defined as clinical stage II-IVa without distant metastasis by enhanced CT scan will be enrolled from the Second Affiliated Hospital of Zhejiang University, the First Affiliated Hospital of Zhejiang University, Shangyu People's Hospital of Shaoxing City and Zhejiang Cancer Institute & Hospital. All participants should adhere to a highly standardized treatment protocol, which involves receiving either 2-4 courses of standard neoadjuvant chemotherapy based on 5-FU + platinum, or 2-4 courses of neoadjuvant chemotherapy based on 5-FU + platinum combined with trastuzumab, or 2-4 courses of neoadjuvant chemotherapy based on 5-FU + platinum combined with anti-PD-L1 therapy. Following the neoadjuvant treatment protocol, participants will undergo a D2 radical gastrectomy for gastric cancer. The enhanced CT and biopsy examination should be completed before the nCRT and the images will be subjected to the manual delineation of the tumor regions of interest (ROI) by experienced radiologists and pathologists. Subsequently, the enhanced CT and biopsy images outlined will be used in the radiological pathology AI system to generate predicted responses (predicted postoperative TRG grading) for individual patients, while actual responses (confirmed postoperative TRG grading) will be diagnosed in surgical resection specimens. Through comparisons of the predicted responses and true pathologic responses, investigators calculate the prediction accuracy, specificity, sensitivity as well as the Area Under Curve (AUC) of Receiver Operating Characteristic (ROC) curves. The aim of this study is to verify the high accuracy and robustness of the radiological pathology AI system in predicting postoperative TRG grading in individuals before nCRT, which will promote further precise treatment of locally advanced cancer patients.
Interventions
None listed
Sponsors
Zhejiang University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 75 Years
Healthy volunteers
No
Inclusion criteria
1. Pathological diagnosis of gastric adenocarcinoma 2. Gastric cancer CT evaluation is clinical stage II-IVa (≥ T3, and/or lymph node positive), with or without local tissue or organ invasion, and no distant metastasis. 3. Acceptance criteria for 2-4 courses of 5-FU+platinum neoadjuvant chemotherapy regimen, or 2-4 courses of 5-FU+platinum neoadjuvant chemotherapy combined with trastuzumab regimen, or 2-4 courses of 5-FU+platinum neoadjuvant chemotherapy combined with anti-PD-L1 treatment regimen. 4. D2 gastric cancer radical surgery after neoadjuvant therapy 5. Digital images of enhanced CT images and HE stained gastroscopy biopsy sections before neoadjuvant therapy are available. 6. Complete clinical diagnosis and treatment information, as well as expression information of targeted and immunotherapy related molecular markers.
Exclusion criteria
1. Has a history of other tumors. 2. Insufficient imaging quality of CT or biopsy slides, unable to obtain features. 3. Unable to extract molecular information related to research from organizational samples. 4. Interruption of neoadjuvant therapy course for any reason.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the radiopathomics artificial intelligence model | baseline | Calculate the area under the receiver operating characteristic (ROC) curve (AUC) of the artificial intelligence model for radiomics to predict the postoperative pathological TRG grading index in LAGC patients treated with nCRT. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The specificity of the radiopathomics artificial intelligence model | baseline | the specificity of artificial intelligence models for radiomics in predicting postoperative TRG grading in LAGC patients treated with nCRT. |
| The sensitivity of the radiopathomics artificial intelligence model | baseline | The sensitivity of artificial intelligence models for radiomics in predicting postoperative TRG grading in LAGC patients treated with nCRT. |
Countries
China
Contacts
Primary ContactJian Chen
Outcome results
None listed