Udvikling af et multimodalt AI-system til GIST-behandling
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
Status
Betingelser
Betingelser
Intervention / Behandling
Intervention / Behandling
Undersøgelsestype
Undersøgelsestype
Tilmelding (Faktiske)
Tilmelding
Kontakter og lokationer
Studiesteder
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Anhui
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Fuyang, Anhui, Kina, 236003
- The Fifth Affiliated Hospital of Anhui Medical University
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Hebei
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Baoding, Hebei, Kina, 071030
- Baoding Central Hospital
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Cangzhou, Hebei, Kina, 061000
- Cangzhou People's Hospital
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Hengshui, Hebei, Kina, 053099
- Hengshui People's Hospital
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Shijiazhuang, Hebei, Kina, 050011
- Shijiazhuang People's Hospital
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Xingtai, Hebei, Kina, 054000
- The Second Affiliated Hospital of Xingtai Medical College
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Hubei
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Wuhan, Hubei, Kina, 430065
- Renmin Hospital of Wuhan University
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Hunan
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Hengyang, Hunan, Kina, 421001
- The First Affiliated Hospital of University of South China
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Jiangsu
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Nanjing, Jiangsu, Kina, 210002
- Jinling Hospital
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Deltagelseskriterier
Berettigelseskriterier
Berettigelseskriterier
Aldre berettiget til at studere
Tager imod sunde frivillige
Køn, der er berettiget til at studere
Prøveudtagningsmetode
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
Hvordan er undersøgelsen tilrettelagt?
Design detaljer
- Observationsmodeller: COHORT
- Tidsperspektiver: PROSPECTIVE
Hvad måler undersøgelsen?
Primære resultatmål
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
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
Sponsor
Sponsor
Datoer for undersøgelser
Studer store datoer
Studiestart (Faktiske)
Studiestart
Primær færdiggørelse (Faktiske)
Primær færdiggørelse
Studieafslutning (Faktiske)
Studieafslutning
Datoer for studieregistrering
Først indsendt
Først indsendt
Først indsendt, der opfyldte QC-kriterier
Først indsendt, der opfyldte QC-kriterier
Først opslået (Faktiske)
Først opslået
Opdateringer af undersøgelsesjournaler
Sidste opdatering sendt (Faktiske)
Sidste opdatering sendt
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
Sidst verificeret
Sidst verificeret
Mere information
Begreber relateret til denne undersøgelse
Yderligere relevante MeSH-vilkår
Andre undersøgelses-id-numre
Andre undersøgelses-id-numre
- GIST-DX-RISK-GEN-01
Plan for individuelle deltagerdata (IPD)
Planlægger du at dele individuelle deltagerdata (IPD)?
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