Pancreatic CancerNCT04892329
A Multi-center Study on the Efficacy and Safety of AI-assisted Navigation System for Biliopancreatic EUS
A study testing Artificial intelligence assistant system in people with Pancreatic Cancer, run by Renmin Hospital of Wuhan University at 2 sites in China.
Trial summary
Pancreatic cancer is one of the most lethal cancers. Survival rates vary largely depending on the stage at which it is diagnosed. EUS is considered one of the most sensitive modalities for pancreatic cancer detection. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Pancreatic Cancer
- What is being tested?
- Artificial intelligence assistant system
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 285 participants
- Where
- 2 sitesChina
- Study dates
- 12 May 2021to 30 Nov 2022
- Phase
- Interventional
- 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
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- All patients meeting the following criteria will be considered for participation in the study:
- Male or female aged 18 or above;
- EUS is needed to further clarify the characteristics of biliopancreatic diseases;
- Patients able to give informed consent were eligible to participate.
- Able and willing to comply with all study process.
Who cannot join
- All patients meeting the following criteria will not be considered for participation in the study:
- Has participated in other clinical trials, signed informed consent and was in the follow-up period of other clinical trials.
- Has participated in clinical trials of the drug and is in the elution period of the experimental drug or control drug.
- Drug or alcohol abuse or psychological disorder in the last 5 years.
- Patients in pregnancy or lactation.
- A history of Upper Gastrointestinal surgery.
Show all 9Show fewer
- Patients with anatomical abnormalities of the upper gastrointestinal tract due to advanced neoplasia
- Patients in whom the presence of clearly defined vital anatomical structures cannot be observed
- Researchers believe that the patient is not suitable to participate in the trial.
Trial locations and contacts
2 sites in 1 country.
China2 sites, 2 recruiting
- Not yet recruiting
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan, Hubei, 430000
Rong Lin, Doctor
- Recruiting
Renmin Hospital of Wuhan University
Wuhan, 430060
Honggang Yu, Doctor, 02788041911, whdxrmyy@126.com
About this study
Official title: A Multi-center Study on the Efficacy and Safety of Artificial Intelligence-assisted Navigation System for Biliopancreatic Endoscopic Ultrasonography
Pancreatic cancer is one of the most lethal cancers. Survival rates vary largely depending on the stage at which it is diagnosed. EUS is considered one of the most sensitive modalities for pancreatic cancer detection. To avoid a missed diagnosis of the pancreatic cancer, the continuity and integrity of EUS needs to be ensured as much as possible. The station approach in pancreatic EUS has been established as the standard scanning procedure. Complete anatomical scanning is helpful for the identification of standard stations, and its imaging findings can assist in the diagnosis of pancreatic lesions and guide patient treatment and prognosis. But EUS is highly operator-dependent and the learning curve is steep. In this study, we constructed a deep learning-based pancreatic scanning navigation system in EUS, which can assist in identifying important anatomical structures adjacent to the pancreas in real time. In order to improve the quality of EUS and reduce the missed diagnosis of pancreatic lesions.
Read the detailed description
Pancreatic cancer is a malignant tumor of the digestive system with insidious onset, rapid progression and very poor prognosis. According to the latest cancer data in China in 2020 released by the International Agency for Research on Cancer (IARC) of the World Health Organization, there are about 120,000 new cases of pancreatic cancer in China, with a mortality rate close to 100%, which seriously endangers the national health. Early diagnosis of pancreatic cancer can be achieved by surgical resection with a 5-year survival rate of 58%, Once advanced pancreatic cancer develops, patient survival is 7.2%. As a rapidly developing deadly cancer, missed diagnosis of pancreatic cancer may have extremely serious consequences for patients.How to improve the diagnostic rate of early pancreatic cancer is an urgent problem to be solved.
EUS(endoscopic ultrasonography) is considered one of the most sensitive modalities for pancreatic cancer detection. It has a much higher diagnostic accuracy than MRI and CT for the diagnosis of pancreatic cancer, especially early pancreatic cancer < 1 cm in diameter (EUS-FNA 95.6% vs CT 77.4%, MRI 76.2%). EUS is the modality of choice for the early diagnosis of pancreatic tumors. To avoid a missed diagnosis of the pancreatic cancer, the continuity and integrity of EUS needs to be ensured as much as possible. But EUS is highly operator-dependent and the learning curve is steep, and the quality of the examination is highly dependent on the operator's technique. Therefore, it is necessary to develop a system that can effectively assist the full scanning of EUS.
The station approach in pancreatic EUS has been established as the standard scanning procedure. The principle of completing the station approach is to find the anatomical landmarks of this station, Such as organs (kidney, spleen), blood vessels (such as splenic artery, splenic vein, portal vein), ducts (pancreatic duct, bile duct), etc.The scanning of these anatomical landmarks is the basis for an accurate assessment of the entire pancreas。 At the same time, the type of pancreatic lesions and the development of the course have abnormal imaging findings of different anatomical structure. For example, ultrasound images of pancreatic cancer will show vascular invasion, deformation of the biliopancreatic duct, and metastasis of adjacent organs. The guidelines clearly require that the choice of surgical approach for pancreatic cancer needs to be based on the degree of invasion of the cancer to adjacent important anatomical structures, to maximize the volume sparing of functional pancreatic parenchyma. Complete anatomical scanning can assist in the diagnosis of pancreatic lesions and guide patient treatment and prognosis.
In recent years, artificial intelligence (AI) has been successfully applied in multiple medical fields. At present, there have been studies of AI-based endoscopic ultrasonography for the identification of pancreatic lesions, However, there are no studies of AI-based navigation system for pancreatic endoscopic ultrasonography. Previously, we have successfully developed a standard station scanning navigation system for the pancreas and bile ducts. This system can improve the recognition accuracy of endoscopists for standard stations and enhance the cognitive ability of endoscopic ultrasonography images.
Based on the previous, we constructed a deep learning-based pancreatic scanning navigation system in EUS, which can assist in identifying important anatomical structures adjacent to the pancreas in real time. and verify its auxiliary performance for endoscopists in clinical practice. In order to improve the quality of EUS and reduce the missed diagnosis of pancreatic lesions.
- Conditions
- Pancreatic Cancer
- Being tested
- Artificial intelligence assistant system
- Medical subject headings
- Pancreatic Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligence assistant systemOther
The endoscopists in the experimental group will be assisted by EndoAngel, which can assist in identifying important anatomical structures adjacent to the pancreas in real time. The system is an non-invasive AI system .
| Study group | Role | What they receive |
|---|---|---|
| EUS AI navigation system augmentation | Experimental | The endoscopists in the experimental group will be assisted by EndoAngel, which can assist in identifying important anatomical structures adjacent to the pancreas in real time. The system is an non-invasive AI system . Receives: Other: Artificial intelligence assistant system |
| without EUS AI navigation system augmentation | No intervention | The endoscopists in the contrpl group performs the examination routinely without special prompts. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Missed scanning rate of adjacent important anatomical structures in pancreatic endoscopic ultrasonography
It was calculated by dividing the number of important anatomy that is not scanned in the actual EUS pancreas by the number of EUS.
Measured: Six month
Secondary outcomes (6)
Pancreatic lesions detection rate
It was calculated by dividing the total number of patients being detected pancreatic lesions by the number of EUS
Measured: Six month
Cholangiopancreatic duct lesions detection rate
It was calculated by dividing the total number of patients being detected cholangiopancreatic duct lesions by the number of EUS.
Measured: Six month
The average number of scanning in the pancreatic standard station of endoscopic ultrasonography
It was calculated by dividing the total number of scanning in the pancreatic standard station by the number of EUS.
Measured: Six month
Detection rate of lesions in different pancreatic standard stations of endoscopic ultrasonography
It was calculated by dividing the number of patients with pancreatic lesions and Cholangiopancreatic duct lesions in the different standard stations by the number of EUS.
Measured: Six month
Missed scanning rate of adjacent vital anatomical structures in different pancreatic standard stations of endoscopic ultrasonography
It was calculated by dividing the number of important anatomy that is not scanned in the different standard stations by the number of EUS.
Measured: Six month
Mean scanning time in different pancreatic standard stations of endoscopic ultrasonography
It was calculated by dividing the total scanning time by the number of EUS.
Measured: Six month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 May 2021
- Study start12 May 2021
- Primary completion29 Nov 2022 (estimated)
- Study completion30 Nov 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Pancreatic CancerStudies 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind
- Who is masked
- Participant, Outcomes assessor
- Enrolment
- 285 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Renmin Hospital of Wuhan University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Honggang Yu, Doctor, Principal investigator, Renmin Hospital of Wuhan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04892329
- Sponsor's protocol ID
- EA-19-003-09
- Status last verified
- Jun 2022
- First submitted
- 10 May 2021
- Last updated
- 5 Jul 2022
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 5 Jul 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Multi-center Study on the Efficacy and Safety of Artificial Intelligence-assisted Navigation System for Biliopancreatic Endoscopic Ultrasonography
- ClinicalTrials.gov ID
- NCT04892329
- Acronym / sponsor ID
- EA-19-003-09
- Conditions
- Pancreatic Cancer
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 285 participants (estimated)
- Lead sponsor
- Renmin Hospital of Wuhan UniversityUniversity, hospital or other
- Study start
- 12 May 2021 (actual)
- Primary completion
- 29 Nov 2022 (estimated)
- Study completion
- 30 Nov 2022 (estimated)
- First posted
- 19 May 2021 (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.
Pancreatic cancer is one of the most lethal cancers. Survival rates vary largely depending on the stage at which it is diagnosed. EUS is considered one of the most sensitive modalities for pancreatic cancer detection. To avoid a missed diagnosis of the pancreatic cancer, the continuity and integrity of EUS needs to be ensured as much as possible. The station approach in pancreatic EUS has been established as the standard scanning procedure. Complete anatomical scanning is helpful for the identification of standard stations, and its imaging findings can assist in the diagnosis of pancreatic lesions and guide patient treatment and prognosis. But EUS is highly operator-dependent and the learning curve is steep. In this study, we constructed a deep learning-based pancreatic scanning navigation system in EUS, which can assist in identifying important anatomical structures adjacent to the pancreas in real time. In order to improve the quality of EUS and reduce the missed diagnosis of pancreatic lesions.
Detailed description
Pancreatic cancer is a malignant tumor of the digestive system with insidious onset, rapid progression and very poor prognosis. According to the latest cancer data in China in 2020 released by the International Agency for Research on Cancer (IARC) of the World Health Organization, there are about 120,000 new cases of pancreatic cancer in China, with a mortality rate close to 100%, which seriously endangers the national health. Early diagnosis of pancreatic cancer can be achieved by surgical resection with a 5-year survival rate of 58%, Once advanced pancreatic cancer develops, patient survival is 7.2%. As a rapidly developing deadly cancer, missed diagnosis of pancreatic cancer may have extremely serious consequences for patients.How to improve the diagnostic rate of early pancreatic cancer is an urgent problem to be solved.
EUS(endoscopic ultrasonography) is considered one of the most sensitive modalities for pancreatic cancer detection. It has a much higher diagnostic accuracy than MRI and CT for the diagnosis of pancreatic cancer, especially early pancreatic cancer < 1 cm in diameter (EUS-FNA 95.6% vs CT 77.4%, MRI 76.2%). EUS is the modality of choice for the early diagnosis of pancreatic tumors. To avoid a missed diagnosis of the pancreatic cancer, the continuity and integrity of EUS needs to be ensured as much as possible. But EUS is highly operator-dependent and the learning curve is steep, and the quality of the examination is highly dependent on the operator's technique. Therefore, it is necessary to develop a system that can effectively assist the full scanning of EUS.
The station approach in pancreatic EUS has been established as the standard scanning procedure. The principle of completing the station approach is to find the anatomical landmarks of this station, Such as organs (kidney, spleen), blood vessels (such as splenic artery, splenic vein, portal vein), ducts (pancreatic duct, bile duct), etc.The scanning of these anatomical landmarks is the basis for an accurate assessment of the entire pancreas。 At the same time, the type of pancreatic lesions and the development of the course have abnormal imaging findings of different anatomical structure. For example, ultrasound images of pancreatic cancer will show vascular invasion, deformation of the biliopancreatic duct, and metastasis of adjacent organs. The guidelines clearly require that the choice of surgical approach for pancreatic cancer needs to be based on the degree of invasion of the cancer to adjacent important anatomical structures, to maximize the volume sparing of functional pancreatic parenchyma. Complete anatomical scanning can assist in the diagnosis of pancreatic lesions and guide patient treatment and prognosis.
In recent years, artificial intelligence (AI) has been successfully applied in multiple medical fields. At present, there have been studies of AI-based endoscopic ultrasonography for the identification of pancreatic lesions, However, there are no studies of AI-based navigation system for pancreatic endoscopic ultrasonography. Previously, we have successfully developed a standard station scanning navigation system for the pancreas and bile ducts. This system can improve the recognition accuracy of endoscopists for standard stations and enhance the cognitive ability of endoscopic ultrasonography images.
Based on the previous, we constructed a deep learning-based pancreatic scanning navigation system in EUS, which can assist in identifying important anatomical structures adjacent to the pancreas in real time. and verify its auxiliary performance for endoscopists in clinical practice. In order to improve the quality of EUS and reduce the missed diagnosis of pancreatic lesions.
Study design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind (participant, outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| EUS AI navigation system augmentation | Experimental | Other: Artificial intelligence assistant systemThe endoscopists in the experimental group will be assisted by EndoAngel, which can assist in identifying important anatomical structures adjacent to the pancreas in real time. The system is an non-invasive AI system . |
| without EUS AI navigation system augmentation | No intervention | –The endoscopists in the contrpl group performs the examination routinely without special prompts. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (5 listed)
- All patients meeting the following criteria will be considered for participation in the study:
Key exclusion criteria (9 listed)
- All patients meeting the following criteria will not be considered for participation in the study:
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 |
|---|---|---|
| Artificial intelligence assistant systemThe endoscopists in the experimental group will be assisted by EndoAngel, which can assist in identifying important anatomical structures adjacent to the pancreas in real time. The system is an non-invasive AI system . | Other | EUS AI navigation system augmentation |
Primary outcome
Missed scanning rate of adjacent important anatomical structures in pancreatic endoscopic ultrasonography
Time frame: Six month
It was calculated by dividing the number of important anatomy that is not scanned in the actual EUS pancreas by the number of EUS.
6 secondary outcomes
Pancreatic lesions detection rate
Time frame: Six month
Cholangiopancreatic duct lesions detection rate
Time frame: Six month
The average number of scanning in the pancreatic standard station of endoscopic ultrasonography
Time frame: Six month
Detection rate of lesions in different pancreatic standard stations of endoscopic ultrasonography
Time frame: Six month
Missed scanning rate of adjacent vital anatomical structures in different pancreatic standard stations of endoscopic ultrasonography
Time frame: Six month
Mean scanning time in different pancreatic standard stations of endoscopic ultrasonography
Time frame: Six month
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Jul 2022.
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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.
Honggang Yu, Doctor principal investigator
Renmin Hospital of Wuhan University
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Artificial intelligence assistant system in people with Pancreatic Cancer, run by Renmin Hospital of Wuhan University at 2 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 9 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: status not confirmed, as of the sponsor's last update on 5 Jul 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Honggang Yu, Doctor (+862788041911, whdxrmyy@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Missed scanning rate of adjacent important anatomical structures in pancreatic endoscopic ultrasonography (time frame: Six month). 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 5 Jul 2022.