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Udvikling af et multimodalt AI-system til GIST-behandling

3. oktober 2026 opdateret af: Qun Zhao

Udvikling og validering af en multimodal kunstig intelligensmodel, der integrerer CT-radiomik, patomik og kliniske funktioner til diagnostik, risikostratificering og genotypeforudsigelse af gastrointestinale stromale tumorer

Background: Gastrointestinal Stromal Tumors (GISTs) are the most common mesenchymal tumors of the gastrointestinal tract. Accurate pre-operative diagnosis, risk stratification, and genotyping are critical for determining the appropriate surgical approach and targeted therapy (such as Imatinib). However, current methods often rely on invasive postoperative pathology and expensive genetic testing.

Study Objective: The purpose of this study is to develop and validate a multimodal Artificial Intelligence (AI) model that integrates clinical data, CT radiomics (imaging features), and pathomics (digital pathology features) to improve the precision of GIST management.

Study Design: This is a prospective, observational study. The researchers will recruit patients with suspected gastric submucosal tumors who are scheduled for surgery or biopsy at The Fourth Hospital of Hebei Medical University.

Core Tasks: The AI model will be trained to perform three specific tasks:

Diagnosis: Distinguish GISTs from other non-GIST mesenchymal tumors (e.g., leiomyomas, schwannomas).

Risk Assessment: Stratify GISTs into risk categories (e.g., Low vs. High risk) to predict malignant potential.

Genotyping: Predict specific gene mutations (e.g., KIT or PDGFRA mutations) to guide immunotherapy or targeted therapy.

Methodology: Patient data (CT scans, pathology slides, and clinical history) will be collected and analyzed by the AI system. The AI's predictions will be compared against the "Gold Standard" results derived from postoperative pathological examination and Next-Generation Sequencing (NGS). This study is non-interventional; the AI results will not affect the standard of care received by the patients.

Studieoversigt

Status

Afsluttet

Betingelser

Intervention / Behandling

Undersøgelsestype

Observationel

Tilmelding (Faktiske)

300

Kontakter og lokationer

Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.

Studiesteder

    • Anhui
      • Fuyang, Anhui, Kina, 236003
        • The Fifth Affiliated Hospital of Anhui Medical University
    • Hebei
      • Baoding, Hebei, Kina, 071030
        • Baoding Central Hospital
      • Cangzhou, Hebei, Kina, 061000
        • Cangzhou People's Hospital
      • Hengshui, Hebei, Kina, 053099
        • Hengshui People's Hospital
      • Shijiazhuang, Hebei, Kina, 050011
        • Shijiazhuang People's Hospital
      • Xingtai, Hebei, Kina, 054000
        • The Second Affiliated Hospital of Xingtai Medical College
    • Hubei
      • Wuhan, Hubei, Kina, 430065
        • Renmin Hospital of Wuhan University
    • Hunan
      • Hengyang, Hunan, Kina, 421001
        • The First Affiliated Hospital of University of South China
    • Jiangsu
      • Nanjing, Jiangsu, Kina, 210002
        • Jinling Hospital

Deltagelseskriterier

Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.

Berettigelseskriterier

Aldre berettiget til at studere

18 år og ældre (Voksen, Ældre voksen)

Tager imod sunde frivillige

N/A

Køn, der er berettiget til at studere

Alle

Prøveudtagningsmetode

Ikke-sandsynlighedsprøve

Studiebefolkning

Patients presenting with gastric submucosal tumors (SMTs) who are admitted to the Department of Gastrointestinal Surgery at The Fourth Hospital of Hebei Medical University for surgical or endoscopic treatment. The cohort includes patients with subsequently pathologically confirmed GISTs and other mesenchymal tumors (e.g., leiomyoma, schwannoma).

Beskrivelse

Inclusion Criteria:

Age ≥ 18 years, gender not limited.

Clinical diagnosis of gastric submucosal tumor (SMT) or suspected gastrointestinal stromal tumor (GIST) based on gastroscopy or ultrasound.

Scheduled for surgical resection or endoscopic biopsy at the study center.

Standard preoperative contrast-enhanced CT scans are available (performed within 2 weeks prior to surgery).

Patients or their legal guardians have signed the informed consent form.

Exclusion Criteria:

Received neoadjuvant therapy (e.g., Imatinib, chemotherapy, or radiotherapy) prior to surgery/biopsy.

Poor quality of CT images (e.g., severe motion artifacts) affecting radiomics analysis.

Insufficient tissue samples for pathological diagnosis or genetic testing.

Confirmed diagnosis of other primary malignancies.

Incomplete clinical data or lost to follow-up immediately after surgery.

Studieplan

Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

  • Observationsmodeller: COHORT
  • Tidsperspektiver: PROSPECTIVE

Hvad måler undersøgelsen?

Primære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Diagnostic Accuracy of the AI Model for Distinguishing GIST from Non-GIST Tumors
Tidsramme: Up to 30 days post-surgery

The diagnostic accuracy is calculated as the proportion of correctly classified patients (GIST vs. Non-GIST) by the multimodal AI model, compared to the gold standard postoperative pathological diagnosis.

Up to 30 days post-surgery

Sekundære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Concordance Rate between AI-predicted Risk Grade and Pathological Modified NIH Criteria
Tidsramme: Up to 30 days post-surgery

The proportion of patients whose risk category (Very Low/Low vs. Intermediate/High) predicted by the AI model matches the actual risk grade determined by postoperative pathology according to the modified National Institutes of Health (NIH) criteria. This will be reported as a percentage (0-100%)

Up to 30 days post-surgery
Sensitivity and Specificity of the AI Model in Predicting KIT/PDGFRA Gene Mutations
Tidsramme: Up to 30 days post-surgery

The AI model's performance in identifying specific mutations (e.g., KIT exon 11, PDGFRA) compared to the results of Next-Generation Sequencing (NGS). Data will be reported as percentages with 95% confidence intervals.

Up to 30 days post-surgery
Area Under the Receiver Operating Characteristic Curve (AUC) for All Tasks
Tidsramme: Up to 30 days post-surgery

The AUC values will be calculated to evaluate the overall performance of the AI model in diagnosis, risk stratification, and genotype prediction. Sensitivity and Specificity will also be reported.

Up to 30 days post-surgery

Samarbejdspartnere og efterforskere

Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.

Sponsor

Datoer for undersøgelser

Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.

Studer store datoer

Studiestart (Faktiske)

1. marts 2026

Primær færdiggørelse (Faktiske)

1. juli 2026

Studieafslutning (Faktiske)

1. juli 2026

Datoer for studieregistrering

Først indsendt

12. februar 2026

Først indsendt, der opfyldte QC-kriterier

5. marts 2026

Først opslået (Faktiske)

6. marts 2026

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

6. oktober 2026

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

3. oktober 2026

Sidst verificeret

1. marts 2026

Mere information

Begreber relateret til denne undersøgelse

Andre undersøgelses-id-numre

  • GIST-DX-RISK-GEN-01

Plan for individuelle deltagerdata (IPD)

Planlægger du at dele individuelle deltagerdata (IPD)?

UBESLUTET

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