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AzTrials

Submucosal TumorNCT07078136

Multicenter Observational Study of Multimodal AI for Upper GI Mesenchymal Tumor Diagnosis

An observational study of Submucosal Tumor and Gastrointestinal Stromal Tumor (GIST), run by Huazhong University of Science and Technology at 1 site in China.

Recruiting

Looking for participants now. Contact the study team or a site below to ask about taking part.

Sites and contacts
Official record
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Trial summary

This study develops a multimodal AI model using endoscopic ultrasound, white-light endoscopy, and clinical information to support the diagnosis of upper GI mesenchymal tumors and the risk stratification of gastric GISTs. Read more about this study

Who is it for?
Ages 18 years and older
What is being tested?
Multimodal AI Model and Expert Endoscopist Assessment
How many people
130 participants
Where
1 siteChina
Study dates
28 Jul 2025to Jun 2026
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
  • No healthy volunteers
  • Adult, Older adult

Study population: The cohort will be selected from several hospitals in China, including Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology.

Who may be able to join

  • Age ≥ 18 years old
  • Patients with an upper gastrointestinal subepithelial lesion (SEL) identified by white-light endoscopy and who have completed an endoscopic ultrasound (EUS) examination
  • Patients with a histopathological diagnosis of GIST confirmed by surgical or endoscopic resection, or other SELs confirmed by surgical resection, EUS-guided sampling, or other biopsy techniques
  • EUS image quality meets the following quality control standards
  • Equipment requirements: Olympus EU-ME2/ME1 processor (Olympus Medical Systems Corp., Tokyo, Japan); radial EUS scope (GF-UE260/GF-UE240; Olympus, Tokyo, Japan) or linear EUS scope (GF-UCT260/GF-UCT240; Olympus, Tokyo, Japan); miniature probe (UM2R/3R; Olympus, Tokyo, Japan); Pentax ARIETTA 850 processor (Pentax, Tokyo, Japan); radial EUS scope (EG-3670URK, Pentax, Tokyo, Japan); linear EUS scope (EG-3870UT, Pentax, Tokyo, Japan); Fujifilm SU-8000 or SU-9000 processor; linear EUS scope (EG-580UT, Fujifilm, Tokyo, Japan); radial EUS scope (EG-580UR, Fujifilm, Tokyo, Japan)
  • EUS images clearly showing the lesion and surrounding tissue characteristics (at least 5 images or video); must include at least one image of the maximum lesion diameter, one image showing the layer of origin, and one image demonstrating the growth pattern (intraluminal/extraluminal)
Show all 10Show fewer
  • EUS images must not contain artificial annotations, such as measurement scales, biopsy needles, Doppler signals, or elastography overlays
  • Image resolution must be at least 448 × 448 pixels
  • WLE (white-light endoscopy) image quality meets the following standards: images must clearly show the lesion location, mucosal features, and margins; at least one close-up and one distant view
  • Complete clinical data and histopathological reports must be available

Who cannot join

  • Age < 18 years old
  • Absolute contraindications for EUS examination, history of gastric surgery, pregnancy, severe comorbidities, or known allergy to anesthetic agents
  • EUS examination terminated prematurely due to esophageal stricture, obstruction, large space-occupying lesions, rapid changes in heart rate or respiratory rate, patient intolerance, or excessive residual food
  • EUS image quality does not meet the required quality control standards
  • Pathological specimens do not meet diagnostic requirements: insufficient biopsy tissue (only R0 resection specimens are accepted for the GIST group), or incomplete immunohistochemical staining (missing CD117/CD34/DOG-1 expression report for the GIST group)
  • Pathological results indicate that the lesion is a metastatic tumor originating from another site

Trial locations and contacts

1 site in 1 country. Contact the study team or a site near you to ask about taking part.

Bin Cheng contact

+8613986097542

b.cheng@tjh.tjmu.edu.cn

China1 site, 1 recruiting

  • Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology

    Wuhan, Hubei, 430030

    Bin Cheng, 13986097542, b.cheng@tjh.tjmu.edu.cn

    Recruiting

Other trials in China

About this study

Official title: Multicenter Observational Study of a Multimodal AI Model Using EUS, White-Light Endoscopy, and Clinical Data for Diagnosis of Upper GI Mesenchymal Tumors and Risk Stratification of Gastric GISTs

This study develops a multimodal AI model using endoscopic ultrasound, white-light endoscopy, and clinical information to support the diagnosis of upper GI mesenchymal tumors and the risk stratification of gastric GISTs.

Read the detailed description

This is a multicenter, observational study designed to evaluate the diagnostic performance of a multimodal artificial intelligence (AI) model for the classification of upper gastrointestinal subepithelial lesions (SELs) and risk stratification of gastric gastrointestinal stromal tumors (gGISTs). The study combines retrospective image data for training and validation with prospectively recruited cases for testing.

Endoscopic ultrasound (EUS) images, white-light endoscopy (WLE) images, and relevant clinical data will be collected according to strict image quality control criteria. The multimodal AI model integrates these inputs using a multi-branch fusion strategy. A cross-validation trial will be conducted using prospectively recruited patients' data from multiple centers to compare the diagnostic and predictive performance of endoscopists with and without AI assistance for both lesion classification and risk stratification.

According to existing literature, no multimodal AI model has yet reported diagnostic performance for classifying SELs or for risk stratification of gastric gGISTs. It is assumed that the multimodal AI model will achieve a diagnostic accuracy of 95% for classifying upper gastrointestinal SELs and 95% for gGIST risk stratification. In comparison, the diagnostic accuracy of endoscopists is approximately 73.3%-75% for differentiating GIST from non-GIST and 72.4%-78.9% for risk stratification of gGISTs . GISTs account for about 67-68% of all lesions . Using a two-sided confidence interval with α = 0.05 and β = 0.2, and considering a 20% potential dropout rate, the minimum sample size required for prospective SEL classification is 65 cases, and 88 gGIST cases for risk stratification. Since the risk stratification task requires a larger sample size and GISTs are the common target of both tasks, the final planned sample size is 130 patients with upper GI SELs, which meets the statistical requirements for all primary endpoints.

The study team will screen patients based on the inclusion and exclusion criteria, ensure that all required examinations are completed to confirm eligibility, and record pre-treatment test results. All prospective participants will provide written informed consent before any study-related examinations.

This study is purely observational. No additional interventions will be performed on participants, nor will any additional costs be incurred. Patients' access to optimal diagnostic or treatment options will not be affected. The primary potential risk is the breach of patient privacy; the research team will establish a strict data security and monitoring plan and inform participants that their data will be used for clinical research purposes.

This study is purely observational. No additional interventions will be performed on participants, nor will any additional costs be incurred. Patients' access to optimal diagnostic or treatment options will not be affected. The primary potential risk is the breach of patient privacy; the research team will establish a strict data security and monitoring plan and inform participants that their data will be used for clinical research purposes.

Each enrolled participant will undergo diagnostic assessment by both the multimodal AI model and expert endoscopists. The AI model and expert interpretation will be blinded to each other. Final diagnosis will be confirmed by histopathology. Diagnostic performance will be compared using paired analysis. All statistical tests will be two-sided, and differences will be considered statistically significant at P < 0.05. Continuous variables will be described as mean ± standard deviation. Categorical variables will be presented as counts and percentages. (1) Diagnostic Performance: The diagnostic performance of endoscopists and the AI model will be compared using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the curve (AUC). F1-score (harmonic mean) and balanced accuracy will be calculated to address class imbalance (e.g., GIST vs. other lesions). (2) Continuous Data: Comparisons with baseline values will be conducted using paired t-tests, ANOVA, or rank-sum tests as appropriate. (3) Categorical Data: Group comparisons will use Chi-square tests (including CMH Chi-square test) or Fisher's exact test. (4) Baseline Comparability: Demographic and baseline characteristics will be compared using independent t-tests or Chi-square tests to assess group balance. (5) Effectiveness Analysis: The primary effectiveness endpoint is the diagnostic accuracy for GI subepithelial lesions. The difference in proportions and Youden index will be compared using the approximate normal Z test or Chi-square test with center effect control. (6) Software: All statistical analyses will be performed using SPSS version 26.0.

Being tested
Multimodal AI Model, Expert Endoscopist Assessment
Keywords
Artificial Intelligence, Endoscopic ultrasound, white-light endoscopy
Medical subject headings
Gastrointestinal Stromal Tumors, Leiomyoma, Neurilemmoma

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

  • Multimodal AI ModelDiagnostic test

    Patients' endoscopic images, EUS images, and clinical data will be analyzed by a multimodal AI model for lesion classification and GIST risk stratification.

  • Expert Endoscopist AssessmentDiagnostic test

    Endoscopic ultrasound images will be interpreted by experienced endoscopists for comparison with the AI model.

Study groups in NCT07078136
Study groupRoleWhat they receive
All ParticipantsAll enrolled patients with upper gastrointestinal subepithelial lesions confirmed by histopathology. Each participant will undergo standard diagnostic evaluation and independent multimodal AI prediction and expert endoscopist diagnosis.
Receives: Diagnostic Test: Multimodal AI Model; Diagnostic Test: Expert Endoscopist Assessment

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. Diagnostic accuracy of a multimodal AI model for differentiating gastrointestinal stromal tumors (GISTs) from other upper gastrointestinal mesenchymal tumors

    Receiver operating characteristic (ROC) analyses, sensitivity, specificity, accuracy, positive predictive value and negative predictive value will be used to evaluate the efficacy of the model

    Measured: After the training process of the multimodal AI model is completed，on average per year

  2. Predictive accuracy of the multimodal AI model for risk stratification of GISTs

    ROC analyses, sensitivity, specificity, accuracy, positive predictive value and negative predictive value will be used to evaluate the efficacy of the model

    Measured: After the training process of the multimodal AI model is completed，on average per year

Secondary outcomes (3)

  1. Comparison of Diagnostic Accuracy Between the Multimodal AI Model and Single-Modality Models

    Receiver operating characteristic (ROC) analyses, sensitivity, specificity, accuracy, positive predictive value and negative predictive value will be used to evaluate the efficacy of the model

    Measured: After the training process of the Multimodal AI model is completed，on average per year

  2. Comparison of diagnostic accuracy between the multimodal AI model and experienced endoscopists for differentiating GISTs and non-GIST mesenchymal tumors

    The diagnostic accuracy of the AI model and expert endoscopists will be compared within the same participants, using histopathology as the gold standard. Receiver operating characteristic (ROC) analyses, sensitivity, specificity, accuracy, positive predictive value and negative predictive value will be used to evaluate the efficacy of the model

    Measured: After the testing process of the multimodal AI model is completed，on average per year

  3. Comparison of the predictive accuracy for GIST risk stratification between the multimodal AI model and experienced endoscopists

    The diagnostic accuracy of the AI model and expert endoscopists will be compared within the same participants, using histopathology as the gold standard. Receiver operating characteristic (ROC) analyses, sensitivity, specificity, accuracy, positive predictive value and negative predictive value will be used to evaluate the efficacy of the model

    Measured: After the testing process of the multimodal AI model is completed，on average per year

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted22 Jul 2025
  2. Study start28 Jul 2025
  3. Primary completionMar 2026 (estimated)
  4. Study completionJun 2026 (estimated)

Resources

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
130 participants (estimated)
IDs, dates and publications
ClinicalTrials.gov ID
NCT07078136
Sponsor's protocol ID
GIST-AI 2025
Status last verified
Jul 2025
First submitted
2 Jul 2025
Last updated
31 Jul 2025
Results posted
No
Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
No

Publications

  • Chinese Society of Digestive Endoscopy Tunnel Technology Collaboration Group, Endoscopist Branch of Chinese Medical Doctor Association, and Digestive Endoscopy Branch of Beijing Medical Association. Expert Consensus on Endoscopic Diagnosis and Treatment of Gastrointestinal Stromal Tumors in China (2020, Beijing). Chinese Journal of Digestive Endoscopy, 2021(07): 505-514.

  • Shen L, Cao H, Qin S, et al. Chinese Consensus on the Diagnosis and Treatment of Gastrointestinal Stromal Tumors (2017 Version). Electronic Journal of Integrated Cancer Therapy, 2018; 4(01): 31-43.

  • Gomes RSA, de Oliveira GHP, de Moura DTH, Kotinda APST, Matsubayashi CO, Hirsch BS, Veras MO, Ribeiro Jordao Sasso JG, Trasolini RP, Bernardo WM, de Moura EGH. Endoscopic ultrasound artificial intelligence-assisted for prediction of gastrointestinal stromal tumors diagnosis: A systematic review and meta-analysis. World J Gastrointest Endosc. 2023 Aug 16;15(8):528-539. doi: 10.4253/wjge.v15.i8.528.

    PubMed 37663113

  • Abe K, Tominaga K, Yamamiya A, Inaba Y, Kanamori A, Kondo M, Suzuki T, Watanabe H, Kawano M, Sato T, Yoshitake N, Ohwada T, Konno M, Hanatsuka K, Masuyama H, Goda K, Haruyama Y, Irisawa A; NUTSHELL20 Study group. Natural History of Small Gastric Subepithelial Lesions Less than 20 mm: A Multicenter Retrospective Observational Study (NUTSHELL20 Study). Digestion. 2023;104(3):174-186. doi: 10.1159/000527421. Epub 2022 Dec 5.

    PubMed 36470211

  • Standards of Practice Committee; Faulx AL, Kothari S, Acosta RD, Agrawal D, Bruining DH, Chandrasekhara V, Eloubeidi MA, Fanelli RD, Gurudu SR, Khashab MA, Lightdale JR, Muthusamy VR, Shaukat A, Qumseya BJ, Wang A, Wani SB, Yang J, DeWitt JM. The role of endoscopy in subepithelial lesions of the GI tract. Gastrointest Endosc. 2017 Jun;85(6):1117-1132. doi: 10.1016/j.gie.2017.02.022. Epub 2017 Apr 3. No abstract available.

    PubMed 28385194

  • Li J, Ye Y, Wang J, Zhang B, Qin S, Shi Y, He Y, Liang X, Liu X, Zhou Y, Wu X, Zhang X, Wang M, Gao Z, Lin T, Cao H, Shen L, Chinese Society Of Clinical Oncology Csco Expert Committee On Gastrointestinal Stromal Tumor. Chinese consensus guidelines for diagnosis and management of gastrointestinal stromal tumor. Chin J Cancer Res. 2017 Aug;29(4):281-293. doi: 10.21147/j.issn.1000-9604.2017.04.01.

    PubMed 28947860

  • Miettinen M, Sobin LH, Lasota J. Gastrointestinal stromal tumors of the stomach: a clinicopathologic, immunohistochemical, and molecular genetic study of 1765 cases with long-term follow-up. Am J Surg Pathol. 2005 Jan;29(1):52-68. doi: 10.1097/01.pas.0000146010.92933.de.

    PubMed 15613856

  • Kawanowa K, Sakuma Y, Sakurai S, Hishima T, Iwasaki Y, Saito K, Hosoya Y, Nakajima T, Funata N. High incidence of microscopic gastrointestinal stromal tumors in the stomach. Hum Pathol. 2006 Dec;37(12):1527-35. doi: 10.1016/j.humpath.2006.07.002. Epub 2006 Sep 25.

    PubMed 16996566

  • Pang T, Zhao Y, Fan T, Hu Q, Raymond D, Cao S, Zhang W, Wang Y, Zhang B, Lv Y, Zhang X, Ling T, Zhuge Y, Wang L, Zou X, Huang Q, Xu G. Comparison of Safety and Outcomes between Endoscopic and Surgical Resections of Small (</= 5 cm) Primary Gastric Gastrointestinal Stromal Tumors. J Cancer. 2019 Jul 10;10(17):4132-4141. doi: 10.7150/jca.29443. eCollection 2019.

    PubMed 31417658

  • Coe TM, Fero KE, Fanta PT, Mallory RJ, Tang CM, Murphy JD, Sicklick JK. Population-Based Epidemiology and Mortality of Small Malignant Gastrointestinal Stromal Tumors in the USA. J Gastrointest Surg. 2016 Jun;20(6):1132-40. doi: 10.1007/s11605-016-3134-y. Epub 2016 Mar 29.

    PubMed 27025710

  • Nishida T, Blay JY, Hirota S, Kitagawa Y, Kang YK. The standard diagnosis, treatment, and follow-up of gastrointestinal stromal tumors based on guidelines. Gastric Cancer. 2016 Jan;19(1):3-14. doi: 10.1007/s10120-015-0526-8. Epub 2015 Aug 15.

    PubMed 26276366

  • Casali PG, Abecassis N, Aro HT, Bauer S, Biagini R, Bielack S, Bonvalot S, Boukovinas I, Bovee JVMG, Brodowicz T, Broto JM, Buonadonna A, De Alava E, Dei Tos AP, Del Muro XG, Dileo P, Eriksson M, Fedenko A, Ferraresi V, Ferrari A, Ferrari S, Frezza AM, Gasperoni S, Gelderblom H, Gil T, Grignani G, Gronchi A, Haas RL, Hassan B, Hohenberger P, Issels R, Joensuu H, Jones RL, Judson I, Jutte P, Kaal S, Kasper B, Kopeckova K, Krakorova DA, Le Cesne A, Lugowska I, Merimsky O, Montemurro M, Pantaleo MA, Piana R, Picci P, Piperno-Neumann S, Pousa AL, Reichardt P, Robinson MH, Rutkowski P, Safwat AA, Schoffski P, Sleijfer S, Stacchiotti S, Sundby Hall K, Unk M, Van Coevorden F, van der Graaf WTA, Whelan J, Wardelmann E, Zaikova O, Blay JY; ESMO Guidelines Committee and EURACAN. Gastrointestinal stromal tumours: ESMO-EURACAN Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2018 Oct 1;29(Suppl 4):iv267. doi: 10.1093/annonc/mdy320. No abstract available.

    PubMed 30188977

  • von Mehren M, Randall RL, Benjamin RS, Boles S, Bui MM, Conrad EU 3rd, Ganjoo KN, George S, Gonzalez RJ, Heslin MJ, Kane JM 3rd, Koon H, Mayerson J, McCarter M, McGarry SV, Meyer C, O'Donnell RJ, Pappo AS, Paz IB, Petersen IA, Pfeifer JD, Riedel RF, Schuetze S, Schupak KD, Schwartz HS, Tap WD, Wayne JD, Bergman MA, Scavone J. Soft Tissue Sarcoma, Version 2.2016, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2016 Jun;14(6):758-86. doi: 10.6004/jnccn.2016.0078.

    PubMed 27283169

  • Nishida T, Hirota S, Yanagisawa A, Sugino Y, Minami M, Yamamura Y, Otani Y, Shimada Y, Takahashi F, Kubota T; GIST Guideline Subcommittee. Clinical practice guidelines for gastrointestinal stromal tumor (GIST) in Japan: English version. Int J Clin Oncol. 2008 Oct;13(5):416-30. doi: 10.1007/s10147-008-0798-7. Epub 2008 Oct 23.

    PubMed 18946752

  • Casali PG, Blay JY, Abecassis N, Bajpai J, Bauer S, Biagini R, Bielack S, Bonvalot S, Boukovinas I, Bovee JVMG, Boye K, Brodowicz T, Buonadonna A, De Alava E, Dei Tos AP, Del Muro XG, Dufresne A, Eriksson M, Fedenko A, Ferraresi V, Ferrari A, Frezza AM, Gasperoni S, Gelderblom H, Gouin F, Grignani G, Haas R, Hassan AB, Hindi N, Hohenberger P, Joensuu H, Jones RL, Jungels C, Jutte P, Kasper B, Kawai A, Kopeckova K, Krakorova DA, Le Cesne A, Le Grange F, Legius E, Leithner A, Lopez-Pousa A, Martin-Broto J, Merimsky O, Messiou C, Miah AB, Mir O, Montemurro M, Morosi C, Palmerini E, Pantaleo MA, Piana R, Piperno-Neumann S, Reichardt P, Rutkowski P, Safwat AA, Sangalli C, Sbaraglia M, Scheipl S, Schoffski P, Sleijfer S, Strauss D, Strauss SJ, Hall KS, Trama A, Unk M, van de Sande MAJ, van der Graaf WTA, van Houdt WJ, Frebourg T, Gronchi A, Stacchiotti S; ESMO Guidelines Committee, EURACAN and GENTURIS. Electronic address: clinicalguidelines@esmo.org. Gastrointestinal stromal tumours: ESMO-EURACAN-GENTURIS Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2022 Jan;33(1):20-33. doi: 10.1016/j.annonc.2021.09.005. Epub 2021 Sep 21. No abstract available.

    PubMed 34560242

Questions about this trial

What is this clinical trial studying?

An observational study of Submucosal Tumor and Gastrointestinal Stromal Tumor (GIST), run by Huazhong University of Science and Technology 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 10 inclusion and 6 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 31 Jul 2025. 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 Bin Cheng (+8613986097542, b.cheng@tjh.tjmu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

Diagnostic accuracy of a multimodal AI model for differentiating gastrointestinal stromal tumors (GISTs) from other upper gastrointestinal mesenchymal tumors (time frame: After the training process of the multimodal AI model is completed，on average per year). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT07078136, which also lists 40 linked publications.

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 31 Jul 2025.

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