Pancreatic Ductal AdenocarcinomaNCT05476978
Artificial Intelligence in EUS for Diagnosing Pancreatic Solid Lesions
An observational study of Pancreatic Ductal Adenocarcinoma and Pancreatitis, Chronic, run by Huazhong University of Science and Technology at 1 site in China.
Trial summary
We aim to develop an EUS-AI model which can facilitate clinical diagnosis by analyzing EUS pictures and clinical parameters of patients. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Pancreatic Ductal Adenocarcinoma, Pancreatitis, Chronic, Pancreatic Neuroendocrine Tumor
- What is being tested?
- EUS-AI model
- How many people
- 130 participants
- Where
- 1 siteChina
- Study dates
- 1 Jul 2022to 24 Jan 2024
- 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
- No healthy volunteers
- Adult, Older adult
Study population: The cohort will be selected from Tongji Hospital, Tongji Medical College, HUST.
Who may be able to join
- Patients who underwent EUS using a curved line array echoendoscope (GF-UCT260; Olympus Medical Systems) since 2014 in our affiliation.
- For each patient, all available native EUS pictures are included.
- Patients' diagnosis are validated by surgical outcomes or fine-needle aspiration (FNA) findings and have a compatible clinical course with a follow-up period of more than 6 months.
Who cannot join
- The image is of poor quality.
- The images contain unique marks which can potentially bias the model, such as the biopsy needle.
Trial locations and contacts
1 site in 1 country.
China1 site
Tongji hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan, Hubei, 430030
About this study
Official title: Utilization of Artificial Intelligence for the Development of an EUS-convolution Neural Network Model Trained to Differentiate Pancreatic Cancer From Other Pancreatic Solid Lesions
We aim to develop an EUS-AI model which can facilitate clinical diagnosis by analyzing EUS pictures and clinical parameters of patients.
Read the detailed description
EUS is considered to be a more sensitive modality than CT in detecting pancreatic solid lesions due to its high spatial resolution. However, the diagnostic performance is largely dependent on the experience and the technical abilities of the practitioners. Therefore, we aim to develop an objective EUS diagnostic model based on the convolutional neural network, an artificial intelligence technique. In addition, clinical parameters such as risk factors, tumor biomarkers and radiology findings are also added to this artificial intelligence model in order to mimic the actual clinical diagnosis procedures and to increase the performance of this model.
- Conditions
- Pancreatic Ductal Adenocarcinoma, Pancreatitis, Chronic, Pancreatic Neuroendocrine Tumor, Autoimmune Pancreatitis
- Being tested
- EUS-AI model
- Keywords
- artificial intelligence, endoscopic ultrasound
- Medical subject headings
- Pancreatitis, Chronic, Adenoma, Islet Cell, Autoimmune Pancreatitis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
EUS-AI modelDiagnostic test
The test subset (approximately 20% of total patients) is reserved for the final evaluation of the EUS-AI model. Clinical parameters and EUS pictures of each patient in the test subset will be inputed into the trained EUS-AI model, and the most possible diagnosis will be given by the model.
| Study group | Role | What they receive |
|---|---|---|
| Pancreas-EUS | Patients since 2014 with EUS pictures of normal pancreas or pancreatic solid lesions have been included in this cohort. Receives: Diagnostic Test: EUS-AI model |
What is being measured
The primary outcome is the main question the study is designed to answer.
The model's ability to differentiate pancreatic cancer from other pancreatic solid lesion
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 EUS-AI model is completed
Secondary outcomes (1)
The model's ability to specify the pancreatic solid lesions such as pancreatic cancer, CP, AIP and NET
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 EUS-AI model is completed
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Jul 2022
- Study start1 Jul 2022
- Primary completion30 Jun 2023
- Study completion24 Jan 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Pancreatic Ductal AdenocarcinomaStudies 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
- 130 participants (actual)
Sponsor and investigators
- Lead sponsor
- Huazhong University of Science and Technology
- Sponsor type
- University, hospital or other
- Collaborators
- The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, LanZhou University
- Responsible party
- Principal investigator, Bin Cheng, Huazhong University of Science and Technology
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05476978
- Sponsor's protocol ID
- EUS-AI 2022
- Status last verified
- Apr 2024
- First submitted
- 25 Jul 2022
- Last updated
- 3 Apr 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Cui H, Zhao Y, Xiong S, Feng Y, Li P, Lv Y, Chen Q, Wang R, Xie P, Luo Z, Cheng S, Wang W, Li X, Xiong D, Cao X, Bai S, Yang A, Cheng B. Diagnosing Solid Lesions in the Pancreas With Multimodal Artificial Intelligence: A Randomized Crossover Trial. JAMA Netw Open. 2024 Jul 1;7(7):e2422454. doi: 10.1001/jamanetworkopen.2024.22454.
PubMed 39028670
Study at a glance
From the registry record, last updated by the sponsor on 3 Apr 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Utilization of Artificial Intelligence for the Development of an EUS-convolution Neural Network Model Trained to Differentiate Pancreatic Cancer From Other Pancreatic Solid Lesions
- ClinicalTrials.gov ID
- NCT05476978
- Acronym / sponsor ID
- EUS-AI 2022
- Conditions
- Pancreatic Ductal Adenocarcinoma, Pancreatitis, Chronic, Pancreatic Neuroendocrine Tumor, Autoimmune Pancreatitis
- Keywords
- artificial intelligence, endoscopic ultrasound
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 130 participants (actual)
- Lead sponsor
- Huazhong University of Science and TechnologyUniversity, hospital or other
- Collaborators
- The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, LanZhou University
- Study start
- 1 Jul 2022 (actual)
- Primary completion
- 30 Jun 2023 (actual)
- Study completion
- 24 Jan 2024 (actual)
- First posted
- 27 Jul 2022 (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.
We aim to develop an EUS-AI model which can facilitate clinical diagnosis by analyzing EUS pictures and clinical parameters of patients.
Detailed description
EUS is considered to be a more sensitive modality than CT in detecting pancreatic solid lesions due to its high spatial resolution. However, the diagnostic performance is largely dependent on the experience and the technical abilities of the practitioners. Therefore, we aim to develop an objective EUS diagnostic model based on the convolutional neural network, an artificial intelligence technique. In addition, clinical parameters such as risk factors, tumor biomarkers and radiology findings are also added to this artificial intelligence model in order to mimic the actual clinical diagnosis procedures and to increase the performance of this model.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Pancreas-EUS | – | Diagnostic Test: EUS-AI modelPatients since 2014 with EUS pictures of normal pancreas or pancreatic solid lesions have been included in this cohort. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The cohort will be selected from Tongji Hospital, Tongji Medical College, HUST.
Key inclusion criteria (3 listed)
- Patients who underwent EUS using a curved line array echoendoscope (GF-UCT260; Olympus Medical Systems) since 2014 in our affiliation.
- For each patient, all available native EUS pictures are included.
- Patients' diagnosis are validated by surgical outcomes or fine-needle aspiration (FNA) findings and have a compatible clinical course with a follow-up period of more than 6 months.
Key exclusion criteria (2 listed)
- The image is of poor quality.
- The images contain unique marks which can potentially bias the model, such as the biopsy needle.
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 |
|---|---|---|
| EUS-AI modelThe test subset (approximately 20% of total patients) is reserved for the final evaluation of the EUS-AI model. Clinical parameters and EUS pictures of each patient in the test subset will be inputed into the trained EUS-AI model, and the most possible diagnosis will be given by the model. | Diagnostic test | Pancreas-EUS |
Primary outcome
The model's ability to differentiate pancreatic cancer from other pancreatic solid lesion
Time frame: After the training process of the EUS-AI model is completed
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.
1 secondary outcome
The model's ability to specify the pancreatic solid lesions such as pancreatic cancer, CP, AIP and NET
Time frame: After the training process of the EUS-AI model is completed
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 3 Apr 2024.
- Derived from this study: PubMed 39028670Cui H, Zhao Y, Xiong S, Feng Y, Li P, Lv Y, Chen Q, Wang R, Xie P, Luo Z, Cheng S, Wang W, Li X, Xiong D, Cao X, Bai S, Yang A, Cheng B. Diagnosing Solid Lesions in the Pancreas With Multimodal Artificial Intelligence: A Randomized Crossover Trial. JAMA Netw Open. 2024 Jul 1;7(7):e2422454. doi: 10.1001/jamanetworkopen.2024.22454.
Related trials for Pancreatic Ductal Adenocarcinoma
RecruitingPhase 1
A Study to Learn About Study Medicine ALTA3263 in Adults With Advanced Solid Tumors With KRAS Mutations
Cancer, PDAC - Pancreatic Ductal Adenocarcinoma
Active, not recruitingPhase 1
Vaccine Therapy and Sargramostim in Treating Patients With Pancreas Cancer That Cannot Be Removed By Surgery
Locally Advanced Pancreatic Adenocarcinoma, Pancreatic Acinar Cell Carcinoma
Active, not recruitingPhase 2
Chiauranib in Patients With Locally Advanced or Metastatic Pancreatic Ductal Adenocarcinoma
Pancreatic Ductal Adenocarcinoma
RecruitingPhase 1/2
Study to Evaluate the Safety, Tolerability & Efficacy of TNG462 in Combination in PDAC & NSCLC Patients
PDAC, PDAC - Pancreatic Ductal Adenocarcinoma
RecruitingPhase 1/2
A Phase 1/2 Study of VS-7375 in Patients With KRAS G12D-Mutated Solid Tumors
Pancreatic Ductal Adenocarcinoma, Non Small Cell Lung Cancer
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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Pancreatic Ductal Adenocarcinoma and Pancreatitis, Chronic, 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 3 inclusion and 2 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 3 Apr 2024. 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?
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?
The model's ability to differentiate pancreatic cancer from other pancreatic solid lesion (time frame: After the training process of the EUS-AI model is completed). 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 NCT05476978, which also lists 1 linked publication.
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 3 Apr 2024; AzTrials last refreshed its copy on 27 Oct 2026.