Gastrointestinal Stromal TumorsNCT07454967
Development of a Multimodal AI System for GIST Management
An observational study of Gastrointestinal Stromal Tumors and Gastric Subepithelial Tumors, run by Qun Zhao at 9 sites in China.
Trial summary
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). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Gastrointestinal Stromal Tumors, Gastric Subepithelial Tumors, Gastric Leiomyoma
- What is being tested?
- Multimodal AI Analysis System
- How many people
- 300 participants
- Where
- 9 sitesChina
- Study dates
- 1 Mar 2026to 1 Jul 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
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: 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).
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
9 sites in 1 country.
9 sites in 1 country
China9 sites
Baoding Central Hospital
Baoding, Hebei, 071030
Cangzhou People's Hospital
Cangzhou, Hebei, 061000
The Fifth Affiliated Hospital of Anhui Medical University
Fuyang, Anhui, 236003
Hengshui People's Hospital
Hengshui, Hebei, 053099
The First Affiliated Hospital of University of South China
Hengyang, Hunan, 421001
Jinling Hospital
Nanjing, Jiangsu, 210002
Shijiazhuang People's Hospital
Shijiazhuang, Hebei, 050011
Renmin Hospital of Wuhan University
Wuhan, Hubei, 430065
The Second Affiliated Hospital of Xingtai Medical College
Xingtai, Hebei, 054000
About this study
Official title: Development and Validation of a Multimodal Artificial Intelligence Model Integrating CT Radiomics, Pathomics, and Clinical Features for the Diagnosis, Risk Stratification, and Genotype Prediction of Gastrointestinal Stromal Tumors
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.
Read more
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.
- Conditions
- Gastrointestinal Stromal Tumors, Gastric Subepithelial Tumors, Gastric Leiomyoma, Artificial Intelligence (AI), Multimodal Imaging
- Being tested
- Multimodal AI Analysis System
- Medical subject headings
- Gastrointestinal Stromal Tumors
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Multimodal AI Analysis SystemDiagnostic test
Also called GIST-RadPath-AI Model
CT-based multitask deep learning system (GIST-Net). Input is the routine preoperative contrast-enhanced CT only; no pathology, molecular or laboratory data are used at inference, and no extra imaging, radiation, blood sampling or biopsy is required. The tumour is segmented on the portal venous phase, and four task-specific heads output probabilities for: (1) GIST vs non-GIST submucosal lesions; (2) modified NIH risk category; (3) driver genotype (KIT exon 11/9, PDGFRA non-D842V, D842V, wild-type); (4) recurrence within 24 months after R0 resection. Steps 2-4 are conditioned on step 1. A prespecified reader component evaluates human-AI interaction: 10 radiologists of three experience levels read the same cases unaided, then re-read with model scores and attention maps after a 4-week washout, giving paired within-reader comparisons of AUC, accuracy, agreement, confidence and reading time. Observational only; outputs are blinded to treating physicians and do not affect management.
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic Accuracy of the AI Model for Distinguishing GIST from Non-GIST Tumors
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.
Measured: Up to 30 days post-surgery
Secondary outcomes (3)
Concordance Rate between AI-predicted Risk Grade and Pathological Modified NIH Criteria
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%)
Measured: Up to 30 days post-surgery
Sensitivity and Specificity of the AI Model in Predicting KIT/PDGFRA Gene Mutations
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.
Measured: Up to 30 days post-surgery
Area Under the Receiver Operating Characteristic Curve (AUC) for All Tasks
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.
Measured: Up to 30 days post-surgery
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Mar 2026
- Study start1 Mar 2026
- Primary completion1 Jul 2026
- Study completion1 Jul 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gastrointestinal Stromal TumorsStudies 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
- 300 participants (actual)
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
- NCT07454967
- Sponsor's protocol ID
- GIST-DX-RISK-GEN-01
- Status last verified
- Mar 2026
- First submitted
- 12 Feb 2026
- Last updated
- 6 Oct 2026
- Results posted
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 6 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of a Multimodal Artificial Intelligence Model Integrating CT Radiomics, Pathomics, and Clinical Features for the Diagnosis, Risk Stratification, and Genotype Prediction of Gastrointestinal Stromal Tumors
- ClinicalTrials.gov ID
- NCT07454967
- Acronym / sponsor ID
- GIST-DX-RISK-GEN-01
- Conditions
- Gastrointestinal Stromal Tumors, Gastric Subepithelial Tumors, Gastric Leiomyoma, Artificial Intelligence (AI), Multimodal Imaging
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 300 participants (actual)
- Lead sponsor
- Qun ZhaoUniversity, hospital or other
- Study start
- 1 Mar 2026 (actual)
- Primary completion
- 1 Jul 2026 (actual)
- Study completion
- 1 Jul 2026 (actual)
- First posted
- 6 Mar 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.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- 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).
Key inclusion criteria (5 listed)
- 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.
Key exclusion criteria (5 listed)
- 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.
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 |
|---|---|---|
| Multimodal AI Analysis SystemAlso: GIST-RadPath-AI ModelCT-based multitask deep learning system (GIST-Net). Input is the routine preoperative contrast-enhanced CT only; no pathology, molecular or laboratory data are used at inference, and no extra imaging, radiation, blood sampling or biopsy is required. The tumour is segmented on the portal venous phase, and four task-specific heads output probabilities for: (1) GIST vs non-GIST submucosal lesions; (2) modified NIH risk category; (3) driver genotype (KIT exon 11/9, PDGFRA non-D842V, D842V, wild-type); (4) recurrence within 24 months after R0 resection. Steps 2-4 are conditioned on step 1. A prespecified reader component evaluates human-AI interaction: 10 radiologists of three experience levels read the same cases unaided, then re-read with model scores and attention maps after a 4-week washout, giving paired within-reader comparisons of AUC, accuracy, agreement, confidence and reading time. Observational only; outputs are blinded to treating physicians and do not affect management. | Diagnostic test | – |
Primary outcome
Diagnostic Accuracy of the AI Model for Distinguishing GIST from Non-GIST Tumors
Time frame: 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.
3 secondary outcomes
Concordance Rate between AI-predicted Risk Grade and Pathological Modified NIH Criteria
Time frame: Up to 30 days post-surgery
Sensitivity and Specificity of the AI Model in Predicting KIT/PDGFRA Gene Mutations
Time frame: Up to 30 days post-surgery
Area Under the Receiver Operating Characteristic Curve (AUC) for All Tasks
Time frame: Up to 30 days post-surgery
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Oct 2026.
- Individual participant dataSharing planned: Undecided
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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. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
9 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Gastrointestinal Stromal Tumors and Gastric Subepithelial Tumors, run by Qun Zhao at 9 sites 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 5 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: finished, as of the sponsor's last update on 6 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 9 sites 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic Accuracy of the AI Model for Distinguishing GIST from Non-GIST Tumors (time frame: Up to 30 days post-surgery). 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 6 Oct 2026.