CT and MRI in Prediction of Response in Patients With Gastric Cancer Following Neoadjuvant Chemotherapy and/or Immunotherapy

Sponsor
The First Hospital of Jilin University (Other)
Overall Status
Not yet recruiting
CT.gov ID
NCT04913896
Collaborator
(none)
200
24

Study Details

Study Description

Brief Summary

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.

Condition or Disease Intervention/Treatment Phase
  • Drug: PD-1 inhibitor

Detailed Description

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.

Study Design

Study Type:
Observational [Patient Registry]
Anticipated Enrollment :
200 participants
Observational Model:
Cohort
Time Perspective:
Prospective
Official Title:
Clinical Study of CT and MRI in Prediction of Response in Patients With Gastric Cancer Following Neoadjuvant Chemotherapy and/or Immunotherapy
Anticipated Study Start Date :
Jun 1, 2021
Anticipated Primary Completion Date :
Oct 1, 2022
Anticipated Study Completion Date :
Jun 1, 2023

Arms and Interventions

Arm Intervention/Treatment
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.

Drug: PD-1 inhibitor
SOX regimen for 3 cycles and/or PD-1 inhibitor before surgery
Other Names:
  • Oxaliplatin
  • Tiggio
  • Outcome Measures

    Primary Outcome Measures

    1. Predictive value of CT and MRI after the neoadjuvant treatment for developing a pCR at surgery [up to 2 year]

      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).

    Secondary Outcome Measures

    1. Predictive value of CT and MRI after the neoadjuvant treatment for pathologic T staging [up to 2 year]

      To evaluate the T staging of gastric cancer treated with neoadjuvant treatment through CT and MRI.

    2. Predictive value of CT and MRI after the neoadjuvant treatment for pathologic response according to the Tumor Regression Grading (TRG) [up to 2 year]

      Pathological tumour regression grading (Mandard criterion): from 1 to 5 grading.

    3. Prediction model based on CT and MRI of response in AGC [up to 2 year]

      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.

    Eligibility Criteria

    Criteria

    Ages Eligible for Study:
    18 Years to 80 Years
    Sexes Eligible for Study:
    All
    Accepts Healthy Volunteers:
    No
    Inclusion Criteria:
    1. Age 18 Years to 80 Years

    2. Consecutive patients with preoperative pathologically confirmed AGC by endoscopy and preoperative imaging data (CT/MRI) were included.

    3. Clinical staging Ⅱ-Ⅲ according to the UICC/AJCC 8th guideline for gastric cancer without distant metastasis.

    4. Suitable for pre-operative chemotherapy, immunotherapy and surgical resection

    5. No contraindications for CT/MRI examination.

    6. Eastern Cooperative Oncology Group (ECOG) performance status 0-2.

    7. The patients participate in this study with informed consent.

    Exclusion Criteria:
    1. Patients with a history of previous chemotherapy or immunotherapy.

    2. The patients couldn't perform MSCT or MR scanning or artefacts affect the evaluation.

    3. The patients are extremely anxious and uncooperative about surgery or neoadjuvant therapy.

    4. The patients refuse to participate in the project.

    5. Pregnancy, lactation or inadequate contraception

    6. Pacemaker or implanted defibrillator

    7. Patients with a history of psychological illness or condition such as to interfere with the patient's ability to understand requirements of the study.

    8. Other situations considered by investigators, which not meet the inclusion criteria.

    Contacts and Locations

    Locations

    No locations specified.

    Sponsors and Collaborators

    • The First Hospital of Jilin University

    Investigators

    • Principal Investigator: quan wang, MD, The First Hospital of Jilin University

    Study Documents (Full-Text)

    None provided.

    More Information

    Publications

    None provided.
    Responsible Party:
    The First Hospital of Jilin University
    ClinicalTrials.gov Identifier:
    NCT04913896
    Other Study ID Numbers:
    • STARS-GC03
    First Posted:
    Jun 4, 2021
    Last Update Posted:
    Jun 4, 2021
    Last Verified:
    Apr 1, 2021
    Individual Participant Data (IPD) Sharing Statement:
    No
    Plan to Share IPD:
    No
    Studies a U.S. FDA-regulated Drug Product:
    No
    Studies a U.S. FDA-regulated Device Product:
    No
    Additional relevant MeSH terms:

    Study Results

    No Results Posted as of Jun 4, 2021