Predicting Gastric Cancer Response to Chemo With Multimodal AI Model
A Radio-Pathomic Multimodal Machine Learning Model for Predicting Pathological Complete Response to Neoadjuvant Chemotherapy in Advanced Gastric Cancer: A Retrospective Observational Study
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Yonghe Chen, MD
- Phone Number: +86 135 6038 6150
- Email: chenyhe@mail2.sysu.edu.cn
Study Contact Backup
- Name: Junsheng Peng, MD
- Phone Number: +86 13802963578
- Email: pengjsh@mail.sysu.edu.cn
Study Locations
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Guangdong
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Guangzhou, Guangdong, China, 510655
- Recruiting
- The Sixth Affiliated Hospital, Sun Yat-sen University
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Contact:
- Xiangen Lu, Master
- Phone Number: +86 20 3837 9764
- Email: zslyllb@mail.sysu.edu.cn
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- patients with histologically confirmed adenocarcinoma of the stomach or esophagogastric junction who received NAC and radical gastrectomy;
- patients who underwent abdominal multidetector computed tomography (CT) inspection, gastroscope, and tumor tissue biopsy before any intervention started;
- Lesions that are assessable according to The Response Evaluation Criteria in Solid Tumors Version 1.1
Exclusion Criteria:
- Patients with indistinguishable tumor lesions on the CT images due to insufficient filling of the stomach during the CT inspection;
- patients without indistinguishable tumor cell on the pathological slides due to inadequate sampling;
- patients with insufficient data.
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Neoadjuvant chemotherapy with radical tumor resection surgery
(i) Patients with indistinguishable tumor lesions on the CT images due to insufficient filling of the stomach during the CT inspection; (ii) patients without indistinguishable tumor cell on the pathological slides due to inadequate sampling; (iii) patients with insufficient data.
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All patients were pathologically diagnosed as advanced gastric cancer, all receive neoadjuvant chemotherapy, after the completion of neoadjuvant chemotherapy, all patients receive radical tumor resection surgery (partial gastrectomy or total gastrectomy, as proper).
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Pathological Complete Response
Time Frame: Assessed within 30 days after radical resection surgery.
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Pathological complete response (pCR) was defined as no viable cells remained in the primary tumor lesions and the dissected lymph nodes.
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Assessed within 30 days after radical resection surgery.
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Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Principal Investigator: Junsheng Peng, MD, The Sixth Affiliated Hospital, Sun Yat-sen University
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- E2021088
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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