Bladder TumorNCT06474338
AI Detection of Bladder Tumors Under Endoscopy
An observational study of Bladder Tumor, run by Peking Union Medical College Hospital at 1 site in China.
Trial summary
The goal of this clinical trial is to learn if the AI algorithm can detect bladder tumors better than urologists under cystoscopy. It will also train the AI algorithm for bladder tumor detection. Read more about this study
- Who is it for?
- Ages 18 to 100 years
- Conditions the study is for
- Bladder Tumor
- What is being tested?
- detection by artificial intelligence and detection by urologists
- How many people
- 1,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2024to 31 Dec 2034
- 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 to 100 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The patients with benign or malignant bladder lesion.
Who may be able to join
- The patient who receives cystoscopy and the cystoscopy video is available, and one or multiple bladder lesions can be observed in the cystoscopy.
Who cannot join
- The patient whose cystoscopy is not clear enough to analyze.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Peking Union Medical College Hospital
Beijing, Beijing Municipality
Zixing Ye, Dr, +86-18611385866, yezixing@pumch.cn
About this study
Official title: Artificial Intelligence Detection of Bladder Tumors Under Endoscopy
The goal of this clinical trial is to learn if the AI algorithm can detect bladder tumors better than urologists under cystoscopy. It will also train the AI algorithm for bladder tumor detection. The main question it aims to answer is:
Can AI algorithm achieve IOU value, precision, recall, false negative rate of bladder tumor detection similar to that of urologists? The cystoscopy video will be annotated by AI and urologists. Researchers will compare AI algorithm to urologists to see if Al algorithm has a similar capability as urologists do.
Read the detailed description
- Research Background Bladder cancer is the ninth most common malignancy worldwide, with an estimated 430,000 new cases diagnosed annually. The standard diagnosis and monitoring of bladder cancer rely on white light cystoscopy (WLC), with over 2 million cystoscopies performed annually in the United States and Europe. Due to the high recurrence rate of bladder cancer, frequent monitoring and intervention are necessary. Early detection and complete resection of non-muscle invasive bladder cancer can reduce recurrence and progression. However, up to 40% of patients with multifocal disease do not achieve complete resection during the initial transurethral resection of bladder tumor (TURBT). Many papillary tumors and flat lesions are difficult to identify through WLC. There is an urgent need for cost-effective, non-invasive, and user-friendly adjunct imaging technologies to address the diagnostic deficiencies of WLC. Recent advancements in deep learning-based automated image processing may provide new solutions to the limitations of cystoscopy. Convolutional neural networks (CNNs) possess the ability to learn complex relationships and integrate existing knowledge into models, showing potential applications across various fields, including bladder tumor diagnosis. We employed the HRNet algorithm, a convolutional neural network, for enhanced bladder tumor detection.
- Research Objectives We aim to explore the potential application of AI in urological tumors by collecting cystoscopy videos from patients undergoing cystoscopy. These videos include bladder tumors will be annotated manually by urologists, then, the AI algorithm will be used to recognize the bladder tumors.
- Research Methods This is a multicenter, retrospective, observational study.
- Research Process 4.1 Patient Cohort Inclusion criteria: 1. The patients who had bladder tumor and received WLC or TURBT, and the full-length surgery video is available. Exclusion criteria: 1. The video is too blurry to distinguish the normal bladder wall and bladder tumor. 2. Lack of the appearance of bladder tumor before resection. 3. Lack of informed consent. Patient information in the videos will not be shown. Videos from the initially recruited 200 bladder tumor patients will be used for algorithm development. Videos from an additional 100 patients are used for algorithm validation. 4.2 Data Preprocessing To reduce the data volume, we extract the frame at a ratio of 1:4. Two urologists outline the boundary of bladder tumors in each frame seperately and check for each other. AI algorithm is used to contour the same bladder tumors. The outlines of bladder tumor annotated by urologists and algorithm are compared, the IOU value, precision, sensitivity and false negative rate are analyzed. 4.3 Algorithm Development This study uses semantic segmentation to identify bladder tumors in WLC. The D-LinkNet network structure used a pre-trained ResNet34 on the ImageNet dataset as its encoder, with the central part utilizing dilated convolutions with different dilation rates in a cascaded manner, and upsampling performed using deconvolution. The original resolution of all images are 1920×1080, downsampled by 2 to 960×540, and zero-padded in the width direction to obtain the image with a resolution of 960×544. The RGB images of this size were normalized, mean-subtracted, and variance-divided before being input into the network. The images undergo five encoding processes, dilated convolutions, and five decoding processes, ultimately producing a prediction result of 960×544, which was further post-processed. In this research, the parameter settings were as follows: batch size of 8, Adam optimizer, a learning rate of 0.001. The training environment was an NVIDIA TITAN Xp GPU. 4.4 Results Interpretation The intersection over union (IOU) is a crucial standard for evaluating single-frame image recognition capability in image recognition. When IOU is above the threshold, it suggests that the model detects the object successfully, indicating a true positive. When IOU can not reach the threshold, it suggests that the model fails to detect the object, and indicating a false negative. If a prediction appears without ground truth in the image, it is considered a false positive. We calculate the model's sensitivity and precision in the test set. The Dice coefficient measures the similarity between two samples. A higher average Dice coefficient indicates a better detection performance of the model. 4.5 Observation Indicators ① Video annotation and classification: annotation status and RLN recognition discernibility classification results for training and test set surgery videos; ② After grouping and training, observe the model's sensitivity, precision, false negative rate, false positive rate, and average Dice coefficient at IoU thresholds of 0.1 and 0.5 in the test set under different discernibility groups. 4.6 Statistical Methods Analysis will be analyzed by R 4.0.2 software, the data will be expressed as absolute numbers or percentages.
- Data Management and Confidentiality All data in this study is properly stored to ensure security without loss or leakage. Sensitive information and patient information will not be uploaded to public platforms. During data processing, patients' personal information will be anonymized, and patient identification codes will be used to replace patient names and IDs. If technical services are needed, data will be appropriately encrypted, and a confidentiality agreement will be signed.
- Conditions
- Bladder Tumor
- Being tested
- detection by artificial intelligence, detection by urologists
- Medical subject headings
- Urinary Bladder Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
detection by artificial intelligenceDiagnostic test
The bladder lesion(s) are detected by artificial intelligence algorithm.
detection by urologistsDiagnostic test
The bladder lesion(s) are detected by urologists.
| Study group | Role | What they receive |
|---|---|---|
| detection by artificial intelligence | The patient with bladder lesion confirmed by cystoscopy, these bladder lesion(s) are detected by artificial intelligence algorithm. Receives: Diagnostic Test: detection by artificial intelligence | |
| detection by urologists. | The patient with bladder lesion confirmed by cystoscopy, these bladder lesion(s) are detected by urologist. Receives: Diagnostic Test: detection by urologists |
What is being measured
The primary outcome is the main question the study is designed to answer.
intersection over union
The overlapping area of the actual bladder lesion and detected bladder lesion divided by their combined areas.
Measured: From Jan 1st 2024 to Dec 31st 2033
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Jun 2024
- Study start1 Jan 2024
- Primary completion31 Dec 2033 (estimated)
- Study completion31 Dec 2034 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Bladder TumorStudies of the same condition, open and under way.
- More trials run by Peking Union Medical College HospitalThe sponsor's other registered studies.
- 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
- Case only
- Time perspective
- Retrospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking Union Medical College Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Zixing Ye, Principal investigator, Peking Union Medical College Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06474338
- Sponsor's protocol ID
- K4574
- Status last verified
- Jun 2024
- First submitted
- 19 Jun 2024
- Last updated
- 25 Jun 2024
- 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 25 Jun 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence Detection of Bladder Tumors Under Endoscopy
- ClinicalTrials.gov ID
- NCT06474338
- Acronym / sponsor ID
- K4574
- Conditions
- Bladder Tumor
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Peking Union Medical College HospitalUniversity, hospital or other
- Study start
- 1 Jan 2024 (actual)
- Primary completion
- 31 Dec 2033 (estimated)
- Study completion
- 31 Dec 2034 (estimated)
- First posted
- 25 Jun 2024 (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 goal of this clinical trial is to learn if the AI algorithm can detect bladder tumors better than urologists under cystoscopy. It will also train the AI algorithm for bladder tumor detection. The main question it aims to answer is:
Can AI algorithm achieve IOU value, precision, recall, false negative rate of bladder tumor detection similar to that of urologists? The cystoscopy video will be annotated by AI and urologists. Researchers will compare AI algorithm to urologists to see if Al algorithm has a similar capability as urologists do.
Detailed description
- Research Background Bladder cancer is the ninth most common malignancy worldwide, with an estimated 430,000 new cases diagnosed annually. The standard diagnosis and monitoring of bladder cancer rely on white light cystoscopy (WLC), with over 2 million cystoscopies performed annually in the United States and Europe. Due to the high recurrence rate of bladder cancer, frequent monitoring and intervention are necessary. Early detection and complete resection of non-muscle invasive bladder cancer can reduce recurrence and progression. However, up to 40% of patients with multifocal disease do not achieve complete resection during the initial transurethral resection of bladder tumor (TURBT). Many papillary tumors and flat lesions are difficult to identify through WLC. There is an urgent need for cost-effective, non-invasive, and user-friendly adjunct imaging technologies to address the diagnostic deficiencies of WLC. Recent advancements in deep learning-based automated image processing may provide new solutions to the limitations of cystoscopy. Convolutional neural networks (CNNs) possess the ability to learn complex relationships and integrate existing knowledge into models, showing potential applications across various fields, including bladder tumor diagnosis. We employed the HRNet algorithm, a convolutional neural network, for enhanced bladder tumor detection.
- Research Objectives We aim to explore the potential application of AI in urological tumors by collecting cystoscopy videos from patients undergoing cystoscopy. These videos include bladder tumors will be annotated manually by urologists, then, the AI algorithm will be used to recognize the bladder tumors.
- Research Methods This is a multicenter, retrospective, observational study.
- Research Process 4.1 Patient Cohort Inclusion criteria: 1. The patients who had bladder tumor and received WLC or TURBT, and the full-length surgery video is available. Exclusion criteria: 1. The video is too blurry to distinguish the normal bladder wall and bladder tumor. 2. Lack of the appearance of bladder tumor before resection. 3. Lack of informed consent. Patient information in the videos will not be shown. Videos from the initially recruited 200 bladder tumor patients will be used for algorithm development. Videos from an additional 100 patients are used for algorithm validation. 4.2 Data Preprocessing To reduce the data volume, we extract the frame at a ratio of 1:4. Two urologists outline the boundary of bladder tumors in each frame seperately and check for each other. AI algorithm is used to contour the same bladder tumors. The outlines of bladder tumor annotated by urologists and algorithm are compared, the IOU value, precision, sensitivity and false negative rate are analyzed. 4.3 Algorithm Development This study uses semantic segmentation to identify bladder tumors in WLC. The D-LinkNet network structure used a pre-trained ResNet34 on the ImageNet dataset as its encoder, with the central part utilizing dilated convolutions with different dilation rates in a cascaded manner, and upsampling performed using deconvolution. The original resolution of all images are 1920×1080, downsampled by 2 to 960×540, and zero-padded in the width direction to obtain the image with a resolution of 960×544. The RGB images of this size were normalized, mean-subtracted, and variance-divided before being input into the network. The images undergo five encoding processes, dilated convolutions, and five decoding processes, ultimately producing a prediction result of 960×544, which was further post-processed. In this research, the parameter settings were as follows: batch size of 8, Adam optimizer, a learning rate of 0.001. The training environment was an NVIDIA TITAN Xp GPU. 4.4 Results Interpretation The intersection over union (IOU) is a crucial standard for evaluating single-frame image recognition capability in image recognition. When IOU is above the threshold, it suggests that the model detects the object successfully, indicating a true positive. When IOU can not reach the threshold, it suggests that the model fails to detect the object, and indicating a false negative. If a prediction appears without ground truth in the image, it is considered a false positive. We calculate the model's sensitivity and precision in the test set. The Dice coefficient measures the similarity between two samples. A higher average Dice coefficient indicates a better detection performance of the model. 4.5 Observation Indicators ① Video annotation and classification: annotation status and RLN recognition discernibility classification results for training and test set surgery videos; ② After grouping and training, observe the model's sensitivity, precision, false negative rate, false positive rate, and average Dice coefficient at IoU thresholds of 0.1 and 0.5 in the test set under different discernibility groups. 4.6 Statistical Methods Analysis will be analyzed by R 4.0.2 software, the data will be expressed as absolute numbers or percentages.
- Data Management and Confidentiality All data in this study is properly stored to ensure security without loss or leakage. Sensitive information and patient information will not be uploaded to public platforms. During data processing, patients' personal information will be anonymized, and patient identification codes will be used to replace patient names and IDs. If technical services are needed, data will be appropriately encrypted, and a confidentiality agreement will be signed.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| detection by artificial intelligence | – | Diagnostic Test: detection by artificial intelligenceThe patient with bladder lesion confirmed by cystoscopy, these bladder lesion(s) are detected by artificial intelligence algorithm. |
| detection by urologists. | – | Diagnostic Test: detection by urologistsThe patient with bladder lesion confirmed by cystoscopy, these bladder lesion(s) are detected by urologist. |
Who may qualify
- Ages
- 18 to 100 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The patients with benign or malignant bladder lesion.
Key inclusion criteria (1 listed)
- The patient who receives cystoscopy and the cystoscopy video is available, and one or multiple bladder lesions can be observed in the cystoscopy.
Key exclusion criteria (1 listed)
- The patient whose cystoscopy is not clear enough to analyze.
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 |
|---|---|---|
| detection by artificial intelligenceThe bladder lesion(s) are detected by artificial intelligence algorithm. | Diagnostic test | detection by artificial intelligence |
| detection by urologistsThe bladder lesion(s) are detected by urologists. | Diagnostic test | detection by urologists. |
Primary outcome
intersection over union
Time frame: From Jan 1st 2024 to Dec 31st 2033
The overlapping area of the actual bladder lesion and detected bladder lesion divided by their combined areas.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Jun 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.
Zixing Ye principal investigator
Peking Union Medical College Hospital
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 Bladder Tumor, run by Peking Union Medical College Hospital 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 1 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 25 Jun 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?
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 Zixing Ye (+86-18611385866, yezixing@pumch.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
intersection over union (time frame: From Jan 1st 2024 to Dec 31st 2033). 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 25 Jun 2024.