Gastric AdenocarcinomaNCT07849608
Silent Evaluation of DeepTRG for Assessing Tumor Response After Preoperative Treatment in Stomach and Gastroesophageal Junction Cancer
An observational study of Gastric Adenocarcinoma and Gastroesophageal Junction Adenocarcinoma, run by Qun Zhao at 1 site in China.
Trial summary
This study will evaluate whether an artificial intelligence (AI) system called DeepTRG can reliably assess cancer tissue removed during surgery after treatment given before surgery. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Gastric Adenocarcinoma, Gastroesophageal Junction Adenocarcinoma
- What is being tested?
- AI-Based Digital Pathology Assessment of Treatment Response (DeepTRG)
- How many people
- 200 participants
- Where
- 1 siteChina
- Study dates
- 1 Sep 2026to 30 Dec 2026
- How long will I take part?
- 2 Years
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Adult, Older adult
Study population: Consecutive adults with histologically confirmed gastric or Siewert type II or III gastroesophageal junction adenocarcinoma who have received neoadjuvant treatment and are scheduled for surgical resection with curative intent at the Fourth Hospital of Hebei Medical University, China. Eligible patients will be approached consecutively during the recruitment period, before postoperative pathological response is known. Routinely collected primary tumor and regional lymph node specimens will be evaluated. DeepTRG will operate in silent mode, with its outputs withheld from routine reporting pathologists and treating clinicians. Technical failures and unassessable cases will be retained in the feasibility analysis.
Who may be able to join
- Age 18 years or older.
- Histologically confirmed adenocarcinoma of the stomach or gastroesophageal junction (Siewert type II or III), diagnosed before neoadjuvant treatment.
- Received neoadjuvant anticancer treatment before planned surgical resection.
- Scheduled to undergo surgical resection with curative intent at the Fourth Hospital of Hebei Medical University, with routine pathological examination of the primary tumor site and regional lymph nodes.
- Written informed consent obtained in accordance with the ethics-approved study protocol.
Who cannot join
- Non-adenocarcinoma malignancies, including squamous cell carcinoma, lymphoma, gastrointestinal stromal tumor, or neuroendocrine carcinoma.
- Surgery for recurrent gastric or gastroesophageal junction cancer.
- Previous inclusion of the patient's tissue or data in DeepTRG model training, grading-rule development, or parameter tuning.
- Patients will not be excluded because of a complete pathological response, positive surgical margins, poor-quality or unavailable slides, digitization failure, algorithm failure, or uncertain or unassessable DeepTRG results. Technical limitations identified after enrollment will be recorded and retained in the feasibility analysis. Patients who do not proceed to surgical resection will be documented in the study flow and will not contribute to the specimen-based primary outcome denominator.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
the Fourth Hospital of Hebei Medical University
Shijiazhuang, None Selected, 050011
Ping'an Ding, 031186095363, ding_ping_an@hebmu.edu.cn
About this study
Official title: Prospective Observational Study of the Feasibility and Accuracy of DeepTRG for Automated Pathological Response Assessment After Neoadjuvant Therapy in Gastric and Gastroesophageal Junction Adenocarcinoma: A Single-Center Silent Validation Study
This study will evaluate whether an artificial intelligence (AI) system called DeepTRG can reliably assess cancer tissue removed during surgery after treatment given before surgery. It will include adults with stomach cancer or cancer at the junction between the stomach and the food pipe who have received treatment before surgery.
Pathologists are doctors who examine tissue to diagnose disease. After cancer surgery, they assess how much living cancer remains and whether cancer is present in nearby lymph nodes. DeepTRG analyzes digital images of tissue slides to measure remaining cancer and describe tissue changes, such as scarring and inflammation. It combines information from the original tumor site and lymph nodes to produce a structured assessment.
Read more
The main question is:
- How often can DeepTRG complete the required assessment and produce a clear result without a person correcting the AI analysis?
The study will also examine:
- How closely the system's measurements agree with assessments made by independent expert pathologists.
- How accurately it detects remaining cancer, including small amounts of cancer and cancer in lymph nodes.
- How long the analysis takes and how often it fails or produces an uncertain result.
- Whether its performance differs across treatment types and cancer tissue types.
This study will take place at the Fourth Hospital of Hebei Medical University in China. Eligible patients will be enrolled consecutively. Researchers will use tissue collected during the patients' planned surgery. No additional surgery or biopsy is required for this study.
DeepTRG will run in the background in "silent mode." The doctors responsible for the patients' routine pathology reports and treatment decisions will not receive its results. Patients will continue to receive usual care. The AI model and its grading rules will be fixed before validation begins.
Independent expert pathologists will assess the tissue without seeing the AI results. Researchers will then compare the expert assessments, routine pathology reports, and DeepTRG outputs. Cases with poor-quality images, failed analyses, or uncertain outputs will remain part of the assessment of how reliably the system works.
Researchers will also collect information about subsequent treatment, cancer recurrence, and survival during follow-up. The initial analysis will focus on feasibility and measurement performance. A separate future study would be needed to determine whether using DeepTRG in clinical care improves doctors' decisions or patient outcomes.
- Being tested
- AI-Based Digital Pathology Assessment of Treatment Response (DeepTRG)
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Based Digital Pathology Assessment of Treatment Response (DeepTRG)Diagnostic test
DeepTRG is an AI-based digital pathology system applied to whole-slide images of routinely collected hematoxylin and eosin-stained tissue from the primary tumor site and regional lymph nodes after neoadjuvant therapy and surgical resection. It quantifies residual viable tumor and treatment-related tissue changes and generates a patient-level pathological response grade with quality and uncertainty indicators. The model and grading rules will be locked before prospective validation. The system will operate in silent mode: outputs will be stored for research and withheld from treating clinicians and routine reporting pathologists. Results will be compared with routine pathology reports and independent expert assessments. Processing time, analysis failures, and uncertain outputs will be recorded. No additional biopsy or change in treatment will be required.
What is being measured
The primary outcome is the main question the study is designed to answer.
Patient-Level Complete Automated Assessment Rate
Percentage of enrolled patients undergoing surgical resection for whom DeepTRG generates a complete patient-level pathological response assessment within the specified assessment window, without manual correction of AI-generated segmentations, measurements, or grades. A successful assessment must include the final DeepTRG grade and all required primary tumor and regional lymph node outputs, meet prespecified quality criteria, and have no unresolved uncertainty requiring manual adjudication. The AI model, grading rules, required outputs, and quality criteria will be locked before enrollment. The denominator will include all enrolled patients undergoing eligible surgical resection, including those with unavailable or inadequate slides, digitization failures, processing failures, or unassessable or uncertain outputs. These cases will be counted as unsuccessful assessments. The rate will be reported with a 95% confidence interval.
Measured: Within 14 days after receipt of the surgical specimen by the pathology department.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Sep 2026
- Study start1 Sep 2026
- Primary completion30 Dec 2026 (estimated)
- Study completion30 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gastric AdenocarcinomaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 200 participants (estimated)
- Target duration
- 2 Years
Sponsor and investigators
- Lead sponsor
- Qun Zhao
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Qun Zhao, Hebei Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07849608
- Sponsor's protocol ID
- DeepTRG-SV-001
- Status last verified
- Sep 2026
- First submitted
- 24 Sep 2026
- Last updated
- 30 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 30 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prospective Observational Study of the Feasibility and Accuracy of DeepTRG for Automated Pathological Response Assessment After Neoadjuvant Therapy in Gastric and Gastroesophageal Junction Adenocarcinoma: A Single-Center Silent Validation Study
- ClinicalTrials.gov ID
- NCT07849608
- Acronym / sponsor ID
- DeepTRG-SV-001
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Qun ZhaoUniversity, hospital or other
- Study start
- 1 Sep 2026 (actual)
- Primary completion
- 30 Dec 2026 (estimated)
- Study completion
- 30 Dec 2026 (estimated)
- First posted
- 30 Sep 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This study will evaluate whether an artificial intelligence (AI) system called DeepTRG can reliably assess cancer tissue removed during surgery after treatment given before surgery. It will include adults with stomach cancer or cancer at the junction between the stomach and the food pipe who have received treatment before surgery.
Pathologists are doctors who examine tissue to diagnose disease. After cancer surgery, they assess how much living cancer remains and whether cancer is present in nearby lymph nodes. DeepTRG analyzes digital images of tissue slides to measure remaining cancer and describe tissue changes, such as scarring and inflammation. It combines information from the original tumor site and lymph nodes to produce a structured assessment.
The main question is:
- How often can DeepTRG complete the required assessment and produce a clear result without a person correcting the AI analysis?
The study will also examine:
- How closely the system's measurements agree with assessments made by independent expert pathologists.
- How accurately it detects remaining cancer, including small amounts of cancer and cancer in lymph nodes.
- How long the analysis takes and how often it fails or produces an uncertain result.
- Whether its performance differs across treatment types and cancer tissue types.
This study will take place at the Fourth Hospital of Hebei Medical University in China. Eligible patients will be enrolled consecutively. Researchers will use tissue collected during the patients' planned surgery. No additional surgery or biopsy is required for this study.
DeepTRG will run in the background in "silent mode." The doctors responsible for the patients' routine pathology reports and treatment decisions will not receive its results. Patients will continue to receive usual care. The AI model and its grading rules will be fixed before validation begins.
Independent expert pathologists will assess the tissue without seeing the AI results. Researchers will then compare the expert assessments, routine pathology reports, and DeepTRG outputs. Cases with poor-quality images, failed analyses, or uncertain outputs will remain part of the assessment of how reliably the system works.
Researchers will also collect information about subsequent treatment, cancer recurrence, and survival during follow-up. The initial analysis will focus on feasibility and measurement performance. A separate future study would be needed to determine whether using DeepTRG in clinical care improves doctors' decisions or patient outcomes.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 2 Years
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Consecutive adults with histologically confirmed gastric or Siewert type II or III gastroesophageal junction adenocarcinoma who have received neoadjuvant treatment and are scheduled for surgical resection with curative intent at the Fourth Hospital of Hebei Medical University, China. Eligible patients will be approached consecutively during the recruitment period, before postoperative pathological response is known. Routinely collected primary tumor and regional lymph node specimens will be evaluated. DeepTRG will operate in silent mode, with its outputs withheld from routine reporting pathologists and treating clinicians. Technical failures and unassessable cases will be retained in the feasibility analysis.
Key inclusion criteria (5 listed)
- Age 18 years or older.
- Histologically confirmed adenocarcinoma of the stomach or gastroesophageal junction (Siewert type II or III), diagnosed before neoadjuvant treatment.
- Received neoadjuvant anticancer treatment before planned surgical resection.
- Scheduled to undergo surgical resection with curative intent at the Fourth Hospital of Hebei Medical University, with routine pathological examination of the primary tumor site and regional lymph nodes.
- Written informed consent obtained in accordance with the ethics-approved study protocol.
Key exclusion criteria (4 listed)
- Non-adenocarcinoma malignancies, including squamous cell carcinoma, lymphoma, gastrointestinal stromal tumor, or neuroendocrine carcinoma.
- Surgery for recurrent gastric or gastroesophageal junction cancer.
- Previous inclusion of the patient's tissue or data in DeepTRG model training, grading-rule development, or parameter tuning.
- Patients will not be excluded because of a complete pathological response, positive surgical margins, poor-quality or unavailable slides, digitization failure, algorithm failure, or uncertain or unassessable DeepTRG results. Technical limitations identified after enrollment will be recorded and retained in the feasibility analysis. Patients who do not proceed to surgical resection will be documented in the study flow and will not contribute to the specimen-based primary outcome denominator.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-Based Digital Pathology Assessment of Treatment Response (DeepTRG)DeepTRG is an AI-based digital pathology system applied to whole-slide images of routinely collected hematoxylin and eosin-stained tissue from the primary tumor site and regional lymph nodes after neoadjuvant therapy and surgical resection. It quantifies residual viable tumor and treatment-related tissue changes and generates a patient-level pathological response grade with quality and uncertainty indicators. The model and grading rules will be locked before prospective validation. The system will operate in silent mode: outputs will be stored for research and withheld from treating clinicians and routine reporting pathologists. Results will be compared with routine pathology reports and independent expert assessments. Processing time, analysis failures, and uncertain outputs will be recorded. No additional biopsy or change in treatment will be required. | Diagnostic test | – |
Primary outcome
Patient-Level Complete Automated Assessment Rate
Time frame: Within 14 days after receipt of the surgical specimen by the pathology department.
Percentage of enrolled patients undergoing surgical resection for whom DeepTRG generates a complete patient-level pathological response assessment within the specified assessment window, without manual correction of AI-generated segmentations, measurements, or grades. A successful assessment must include the final DeepTRG grade and all required primary tumor and regional lymph node outputs, meet prespecified quality criteria, and have no unresolved uncertainty requiring manual adjudication. The AI model, grading rules, required outputs, and quality criteria will be locked before enrollment. The denominator will include all enrolled patients undergoing eligible surgical resection, including those with unavailable or inadequate slides, digitization failures, processing failures, or unassessable or uncertain outputs. These cases will be counted as unsuccessful assessments. The rate will be reported with a 95% confidence interval.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Sep 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Gastric Adenocarcinoma and Gastroesophageal Junction Adenocarcinoma, run by Qun Zhao at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 30 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Ping'an Ding (031186095363, ding_ping_an@hebmu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Patient-Level Complete Automated Assessment Rate (time frame: Within 14 days after receipt of the surgical specimen by the pathology department.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Sep 2026.