Silent Evaluation of DeepTRG for Assessing Tumor Response After Preoperative Treatment in Stomach and Gastroesophageal Junction Cancer
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.
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.
Studieoversikt
Status
Intervensjon / Behandling
Studietype
Registrering (Antatt)
Kontakter og plasseringer
Studiekontakt
- Navn: Ping'an Ding
- Telefonnummer: 031186095363
- E-post: ding_ping_an@hebmu.edu.cn
Studiesteder
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-
None Selected
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Shijiazhuang, None Selected, Kina, 050011
- Rekruttering
- the Fourth Hospital of Hebei Medical University
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Ta kontakt med:
- Ping'an Ding
- Telefonnummer: 031186095363
- E-post: ding_ping_an@hebmu.edu.cn
-
-
Deltakelseskriterier
Kvalifikasjonskriterier
Alder som er kvalifisert for studier
Tar imot friske frivillige
Kjønn som er kvalifisert for studier
Prøvetakingsmetode
Studiepopulasjon
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.
Beskrivelse
Inclusion Criteria:
- 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.
Exclusion Criteria:
- 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.
Studieplan
Hvordan er studiet utformet?
Designdetaljer
- Observasjonsmodeller: COHORT
- Tidsperspektiver: PROSPECTIVE
Hva måler studien?
Primære resultatmål
Resultatmål |
Tiltaksbeskrivelse |
Tidsramme |
|---|---|---|
|
Patient-Level Complete Automated Assessment Rate
Tidsramme: 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. |
Within 14 days after receipt of the surgical specimen by the pathology department.
|
Samarbeidspartnere og etterforskere
Sponsor
Studierekorddatoer
Studer hoveddatoer
Studiestart (Faktiske)
Primær fullføring (Antatt)
Studiet fullført (Antatt)
Datoer for studieregistrering
Først innsendt
Først innsendt som oppfylte QC-kriteriene
Først lagt ut (Faktiske)
Oppdateringer av studieposter
Sist oppdatering lagt ut (Faktiske)
Siste oppdatering sendt inn som oppfylte QC-kriteriene
Sist bekreftet
Mer informasjon
Begreper knyttet til denne studien
Andre studie-ID-numre
- DeepTRG-SV-001
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