AI Detection of Bladder Tumors Under Endoscopy

NCT ID: NCT06474338

Last Updated: 2024-06-25

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 1000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-01-01
• Study Completion Date: 2034-12-31

Brief 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. 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

1. 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.

2. 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.

3. Research Methods This is a multicenter, retrospective, observational study.

4. 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.

5. 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

Study Design

• Observational Model Type: CASE_ONLY
• Study Time Perspective: RETROSPECTIVE

Study Groups

detection by artificial intelligence

The patient with bladder lesion confirmed by cystoscopy, these bladder lesion(s) are detected by artificial intelligence algorithm.

detection by artificial intelligence

Intervention Type: DIAGNOSTIC_TEST

The bladder lesion(s) are detected by artificial intelligence algorithm.

detection by urologists.

The patient with bladder lesion confirmed by cystoscopy, these bladder lesion(s) are detected by urologist.

detection by urologists

Intervention Type: DIAGNOSTIC_TEST

The bladder lesion(s) are detected by urologists.

Interventions

detection by artificial intelligence

The bladder lesion(s) are detected by artificial intelligence algorithm.

Intervention Type: DIAGNOSTIC_TEST

detection by urologists

The bladder lesion(s) are detected by urologists.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

The patient who receives cystoscopy and the cystoscopy video is available, and one or multiple bladder lesions can be observed in the cystoscopy.

Exclusion Criteria

The patient whose cystoscopy is not clear enough to analyze.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 100 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Peking Union Medical College Hospital

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Zixing Ye

Role: PRINCIPAL_INVESTIGATOR

Peking Union Medical College Hospital

Locations

Peking Union Medical College Hospital

Beijing, Beijing Municipality, China

Site Status: RECRUITING

Countries

China

Central Contacts

Zixing Ye

Role: CONTACT

yezixing@pumch.cn

+86-18611385866

Facility Contacts

Zixing Ye, Dr

Role: primary

yezixing@pumch.cn

+86-18611385866

Provided Documents

Document Type: Study Protocol and Statistical Analysis Plan

View Document

Document Type: Informed Consent Form

View Document

Other Identifiers

K4574

Identifier Type: -

Identifier Source: org_study_id