Colorectal NeoplasmsNCT05576506
Application of Hyperspectral Imaging Analysis Technology in the Diagnosis of Colorectal Cancer Based on Colonoscopic Biopsy
An observational study of Colorectal Neoplasms and Colorectal Polyp, run by Shandong University at 1 site in China.
Trial summary
The purpose of this study is to develop and validate a deep learning algorithm for the diagnosis of colorectal cancer other colorectal disease by marking and analyzing the characteristics of hyperspectral images based on the pathological results of colonoscopic biopsy, so as to improve the objectiveness and… Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Colorectal Neoplasms, Colorectal Polyp, Colorectal Adenoma
- How many people
- 86 participants
- Where
- 1 siteChina
- Study dates
- 8 Oct 2022to 31 Dec 2022
- 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 to 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: patients aged 18-75 years who undergo the colonoscopy examination and biopsy;
Who may be able to join
- patients aged 18-75 years who undergo the colonoscopy examination and biopsy
Who cannot join
- patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric disorders who cannot participate in colonoscopy
- patients with previous surgical procedures on the gastrointestinal tract.
- patients with contraindications to biopsy
- patients who refuse to sign the informed consent form
Trial locations and contacts
1 site in 1 country.
About this study
The purpose of this study is to develop and validate a deep learning algorithm for the diagnosis of colorectal cancer other colorectal disease by marking and analyzing the characteristics of hyperspectral images based on the pathological results of colonoscopic biopsy, so as to improve the objectiveness and intelligence of early colorectal cancer diagnosis.
Read the detailed description
Prospectively collect the hyperspectral image information of ordinary colonoscopic biopsy tissue. The colonoscopic biopsy tissue is from the Endoscopy Center of Qilu Hospital of Shandong University. The hyperspectral images are marked based on the biopsy pathological results, and the deep convolutional neural network (DCNN) model is used. With training and verification, develop the Hyperspectral Imaging Artificial Intelligence Diagnostic System (HSIAIDS) .A portion of colonoscopic biopsy tissue will be collected as a prospective test set to prospectively test the diagnostic performance of the HSIAIDS algorithm.
- Keywords
- Hyperspectral imaging, Artifitial intelligence, colorectal cancer
- Medical subject headings
- Colorectal Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Deep learning algorithm group | After the patient has passed the screening, a routine colonoscopy will be performed, and the target tissue with suspected inflammation or neoplasia will be biopsied. The clinical investigators use the hyperspectral microscope to collect image information of the biopsy tissue in the endoscopy room. After collecting information, biopsy specimens will be routinely processed and sent for pathological diagnosis. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of HSI artificial intelligence model to identify colorectal adenoma and cancer
Accuracy of hyperspectral imaging (HSI) artificial intelligence model to identify colorectal hyperplastic polyp, adenoma, SSL and colorectal cancer. Accuracy of artificial intelligence models Accuracy = (true positives + true negatives) / total number of subjects * 100%
Measured: 1 year
Sensitivity
Sensitivity of HSI artificial intelligence model Sensitivity = number of true positives / (number of true positives + number of false negatives) * 100%.
Measured: 1 year
Specificity
Specificity of HSI Artificial Intelligence Model Specificity = number of true negatives / (number of true negatives + number of false positives))*100%
Measured: 1 year
Negative predictive values(NPV)
Negative predictive values for HSI artificial intelligence model = number of true negatives / (number of true negatives + number of false negatives)*100%
Measured: 1 year
AUC (95% CI)
area under the receiver operating characteristic curve (AUC)
Measured: 1 year
Secondary outcomes (1)
To record and evaluate any unknown risks and adverse events of hyperspectral imaging in specimen image acquisition
To record and evaluate any unknown risks and adverse events of hyperspectral imaging in specimen image acquisition
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Oct 2022
- Study start8 Oct 2022
- Primary completion31 Dec 2022
- Study completion31 Dec 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Colorectal NeoplasmsStudies 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
- 86 participants (actual)
Sponsor and investigators
- Lead sponsor
- Shandong University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Xiuli Zuo, Shandong University
- Investigators
- Xiuli Zuo, MD,PhD, Study chair, Study Principal investigator
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05576506
- Sponsor's protocol ID
- 2022-SDU-QILU-G003
- Status last verified
- Jul 2024
- First submitted
- 8 Oct 2022
- Last updated
- 31 Jul 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 31 Jul 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Application of Hyperspectral Imaging Analysis Technology in the Diagnosis of Colorectal Cancer Based on Colonoscopic Biopsy
- ClinicalTrials.gov ID
- NCT05576506
- Acronym / sponsor ID
- 2022-SDU-QILU-G003
- Keywords
- Hyperspectral imaging, Artifitial intelligence, colorectal cancer
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 86 participants (actual)
- Lead sponsor
- Shandong UniversityUniversity, hospital or other
- Study start
- 8 Oct 2022 (actual)
- Primary completion
- 31 Dec 2022 (actual)
- Study completion
- 31 Dec 2022 (actual)
- First posted
- 12 Oct 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.
The purpose of this study is to develop and validate a deep learning algorithm for the diagnosis of colorectal cancer other colorectal disease by marking and analyzing the characteristics of hyperspectral images based on the pathological results of colonoscopic biopsy, so as to improve the objectiveness and intelligence of early colorectal cancer diagnosis.
Detailed description
Prospectively collect the hyperspectral image information of ordinary colonoscopic biopsy tissue. The colonoscopic biopsy tissue is from the Endoscopy Center of Qilu Hospital of Shandong University. The hyperspectral images are marked based on the biopsy pathological results, and the deep convolutional neural network (DCNN) model is used. With training and verification, develop the Hyperspectral Imaging Artificial Intelligence Diagnostic System (HSIAIDS) .A portion of colonoscopic biopsy tissue will be collected as a prospective test set to prospectively test the diagnostic performance of the HSIAIDS algorithm.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Deep learning algorithm group | – | –After the patient has passed the screening, a routine colonoscopy will be performed, and the target tissue with suspected inflammation or neoplasia will be biopsied. The clinical investigators use the hyperspectral microscope to collect image information of the biopsy tissue in the endoscopy room. After collecting information, biopsy specimens will be routinely processed and sent for pathological diagnosis. |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- patients aged 18-75 years who undergo the colonoscopy examination and biopsy;
Key inclusion criteria (1 listed)
- patients aged 18-75 years who undergo the colonoscopy examination and biopsy
Key exclusion criteria (4 listed)
- patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric disorders who cannot participate in colonoscopy
- patients with previous surgical procedures on the gastrointestinal tract.
- patients with contraindications to biopsy
- patients who refuse to sign the informed consent form
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Accuracy of HSI artificial intelligence model to identify colorectal adenoma and cancer
Time frame: 1 year
Accuracy of hyperspectral imaging (HSI) artificial intelligence model to identify colorectal hyperplastic polyp, adenoma, SSL and colorectal cancer. Accuracy of artificial intelligence models Accuracy = (true positives + true negatives) / total number of subjects * 100%
Sensitivity
Time frame: 1 year
Sensitivity of HSI artificial intelligence model Sensitivity = number of true positives / (number of true positives + number of false negatives) * 100%.
Specificity
Time frame: 1 year
Specificity of HSI Artificial Intelligence Model Specificity = number of true negatives / (number of true negatives + number of false positives))*100%
Negative predictive values(NPV)
Time frame: 1 year
Negative predictive values for HSI artificial intelligence model = number of true negatives / (number of true negatives + number of false negatives)*100%
AUC (95% CI)
Time frame: 1 year
area under the receiver operating characteristic curve (AUC)
1 secondary outcome
To record and evaluate any unknown risks and adverse events of hyperspectral imaging in specimen image acquisition
Time frame: 1 year
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 31 Jul 2024.
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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.
Xiuli Zuo, MD,PhD study chair
Study Principal investigator
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 Colorectal Neoplasms and Colorectal Polyp, run by Shandong University 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 1 inclusion and 4 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 31 Jul 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?
Accuracy of HSI artificial intelligence model to identify colorectal adenoma and cancer (time frame: 1 year). The study lists 5 primary outcomes in all. 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 31 Jul 2024.