Bladder CancerMICS-BCNCT07051083
Bladder Cancer Staging and Prediction of New Adjuvant Chemotherapy Efficacy Based on Deep Learning and Transfer Learning in Ultrasound-Magnetic Resonance-Pathology Multimodal Multiscale
An observational study of Bladder Cancer and Staging, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 1 site in China.
Trial summary
Bladder cancer is the most common malignant tumor of the urinary system. The presence or absence of muscle invasion in early bladder cancer is an independent prognostic factor. The involvement of muscle invasion affects the choice of surgical methods and treatment. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Bladder Cancer, Staging, Deep Learning
- What is being tested?
- Risk Stratification
- How many people
- 480 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2024to 31 Dec 2026
- How long will I take part?
- 5 Years
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: 480 patients who were diagnosed with bladder cancer based on postoperative pathology at Sun Yat-sen Memorial Hospital, Sun Yat-sen University will be included between January 2023 and December 2026.
Who may be able to join
- Ultrasound and other imaging examinations (CT, MR, etc.) suggest bladder masses and are suspicious for bladder cancer patients.
- The bladder is well filled, and no allergic reactions to ultrasound contrast agents are found.
- No surgery or radiotherapy/chemotherapy has been performed.
- Patients who meet the indications for surgical resection and are planned for surgical treatment, including one of the following:
- Clinical symptoms consistent with suspected bladder cancer (such as gross hematuria, etc.);
- Patients with confirmed primary or recurrent bladder cancer by cystoscopic biopsy;
- Rapid urine cytology and urine cytology FISH testing suggest malignancy.
Who cannot join
- Individuals unable to tolerate surgery;
- Individuals allergic to ultrasound contrast agents, unable to undergo ultrasound contrast examination;
- Unsuccessful preoperative ultrasound contrast examination or non-compliant patients;
- Postoperative pathology does not indicate bladder cancer;
- Patients who have undergone chemotherapy or radiation therapy.
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
Sun Yat-sen Memorial Hospital, Sun Yat-sen University
Guangzhou, Guangdong, 510288
Ou, Dr., (86)020-34071020, ouqy5@mail.sysu.edu.cn
About this study
Official title: Intelligent Diagnosis of Bladder Cancer Staging and Prediction of New Adjuvant Chemotherapy Efficacy Based on Deep Learning and Transfer Learning in Ultrasound-Magnetic Resonance-Pathology Multimodal Multiscale
Bladder cancer is the most common malignant tumor of the urinary system. The presence or absence of muscle invasion in early bladder cancer is an independent prognostic factor. The involvement of muscle invasion affects the choice of surgical methods and treatment. Preoperatively, the precise assessment of bladder cancer staging has important practical value. A more accurate preoperative assessment of bladder cancer staging can reduce overtreatment and provide a favorable basis for clinicians to choose more reasonable and effective surgical methods. Clinically, there has been a longstanding desire to diagnose the staging of bladder cancer through a simple, convenient, effective, and non-invasive examination. As relevant research progresses, a multi-omics diagnostic model will be beneficial in improving diagnostic efficiency. This project aims to establish a multi-omics artificial intelligence system based on deep learning and transfer learning to accurately diagnose the staging of bladder cancer and predict the efficacy of neoadjuvant chemotherapy. This system will assist in clinical treatment decision-making.
Read the detailed description
Research Content
- Establishment of Ultrasound and Magnetic Resonance Imaging-Pathology-Clinical Dataset: This project aims to include medical records diagnosed through ultrasound and magnetic resonance imaging. Combining results from cytopathology or tissue pathology, the project will collect ultrasound images of the bladder, magnetic resonance images of the bladder, pathology images, and clinically relevant follow-up data. Tumor tissue specimens will be collected for immunohistochemical fluorescence staining. Participants with indistinct imaging findings or those with other primary malignant tumors will be excluded. The goal is to establish an ultrasound and magnetic resonance imaging-pathology-clinical dataset.
- Establishment of an Artificial Intelligence System for Precise Diagnosis of Bladder Cancer Staging and Prognosis based on Ultrasound Imaging: Collecting clinical baseline information, surgical pathology, and ultrasound images of enrolled subjects. The ultrasound images will undergo homogenization processing. Utilizing algorithms based on U-Net, Transformer, and attention mechanisms for tissue segmentation and feature extraction, a deep learning model based on convolutional neural networks will be established for precise diagnosis of bladder cancer staging and prognosis: Construction of a mathematical model for staging bladder cancer using ultrasound contrast: Using convolutional neural networks for deep learning to build a mathematical model for staging bladder cancer. Developing an artificial intelligence diagnostic system for ultrasound contrast images based on deep learning and mathematical models to determine whether bladder cancer has muscle invasion. Construction of a mathematical model to discriminate prognosis features of bladder cancer using ultrasound imaging: Automatically delineating target areas and extracting ultrasound image features of bladder cancer lesions using new artificial intelligence technology - convolutional neural networks to build a model for evaluating the prognosis of bladder cancer lesions and achieving accurate prognosis diagnosis.
- Establishment of an Artificial Intelligence System for the Joint Diagnosis of Bladder Cancer and Staging based on Ultrasound and Magnetic Resonance Imaging with Pathology: Based on bladder ultrasound, magnetic resonance imaging, and pathology image data, the baseline information of study subjects will be digitized. Ultrasound images, magnetic resonance images, and pathology images will undergo homogenization processing. Utilizing algorithms based on U-Net, Transformer, and attention mechanisms for segmentation and feature extraction of bladder ultrasound images, magnetic resonance images, and pathology images, a deep learning model based on convolutional neural networks will be established for the precise diagnosis of bladder cancer staging and prognosis: Construction of a mathematical model for joint pathology-based staging of bladder cancer using ultrasound and magnetic resonance imaging: Using convolutional neural networks for deep learning to build a mathematical model for staging bladder cancer. Based on the mathematical model, continuously optimizing algorithms, developing multi-omics, multidimensional artificial intelligence diagnostic systems based on ultrasound images, magnetic resonance images, pathology images, and clinical features, achieving accurate diagnosis of bladder cancer staging and prognosis prediction models. Construction of a mathematical model to discriminate prognosis features of bladder cancer using joint ultrasound, magnetic resonance, and pathology: Automatically delineating target areas and extracting features of bladder cancer lesions using convolutional neural networks and new artificial intelligence technology. Building a mathematical model to evaluate the prognosis of bladder cancer lesions. After the completion of the multi-omics, multidimensional artificial intelligence precise prediction model, validate the reliability of the model in prospective observational cohort study data and implement an intelligent system to assist in improving the efficiency of doctor diagnosis.
- Establishment of an Artificial Intelligence System based on Dynamic Quantitative Immune Cell Maps using Ultrasound Imaging: Based on bladder ultrasound and pathological immunohistochemistry data, artificial intelligence algorithms will accurately determine the expression of immune cells through ultrasound images. Visualization of 2D spatial distribution heatmaps will be conducted to explore the intercorrelation features between ultrasound images and immune cells. Study subjects will be divided into training and validation cohorts. The baseline information of study subjects will be digitized. Ultrasound images will undergo homogenization processing. Utilizing algorithms based on U-Net, Transformer, attention mechanisms, and generative adversarial networks for tissue segmentation, feature extraction, and model establishment:
Construction of a mathematical model for quantitative immune cell maps using ultrasound images: Using new artificial intelligence technology - convolutional neural networks for deep learning to build a mathematical model for predicting the expression of immune cells. Developing an artificial intelligence diagnostic system for quantifying the microenvironment of ultrasound contrast images, determining the expression of immune cells in bladder cancer lesions.
The model will combine the immune cells with the 2D ultrasound image prediction heatmap, forming a visual 2D ultrasound image-immune cell heatmap. Exploring the spatial location of immune cells in ultrasound images. Using single-cell and spatial transcriptome sequencing methods to verify the accuracy of the spatial distribution of ultrasound image-quantified immune cells.
- Conditions
- Bladder Cancer, Staging, Deep Learning, Neoadjuvant Chemotherapy, Contrast Enhanced Ultrasound
- Being tested
- Risk Stratification
- Keywords
- Bladder Cancer, Multi-omics, Staging, Deep Learning, Neoadjuvant Chemotherapy, Contrast Enhanced Ultrasound
- 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.
Risk StratificationDiagnostic test
Risk Stratification for Assessing Muscle Infiltration in Bladder Cancer.
| Study group | Role | What they receive |
|---|---|---|
| Training Group | Approximately 300 patients, which are used for the training dataset. Receives: Diagnostic Test: Risk Stratification | |
| Internal Validation Group | Approximately 100 patients, which are used for internal validation to assess the accuracy of the model. Receives: Diagnostic Test: Risk Stratification | |
| External Validation Group | Approximately 100 patients，which are used for external validation to assess the accuracy of the model. Receives: Diagnostic Test: Risk Stratification |
What is being measured
The primary outcome is the main question the study is designed to answer.
Detection of Muscle Invasion in Bladder Cancer
To evaluate the preoperative staging of bladder cancer，whether it is non-muscle invasive bladder cancer (NMIBC) or muscle invasive bladder cancers (MIBC)
Measured: Perform contrast-enhanced ultrasound (CEUS) examination within 2 weeks before the procedure.
Secondary outcomes (1)
Evaluate the Diagnostic Performance of Deep Learning and Transfer Learning in Preoperative Prediction of Muscle-Invasive Status in Bladder Cancer
The diagnostic performance of the Deep Learning and Transfer Learning Model was primarily evaluated using the statistical metrics of AUC (Area Under the ROC Curve), sensitivity, specificity, accuracy, negative predictive value (NPV), and positive predictive value (PPV).
Measured: The patient should return for follow-up at 3 months postoperatively
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Jul 2025
- Study start1 Jan 2024
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (13)Papers the sponsor has linked to this study.
- Other trials for Bladder CancerStudies of the same condition, open and under way.
- More trials run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityThe 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 480 participants (estimated)
- Target duration
- 5 Years
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Qiyun Ou, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- Investigators
- Qiyun Ou, Dr., Principal investigator, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07051083
- Sponsor's protocol ID
- SYSKY-2023-1306-01
- Other IDs
- 2024A03J1194
- Acronym
- MICS-BC
- Status last verified
- Jun 2025
- First submitted
- 6 Jan 2024
- Last updated
- 3 Jul 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes
Publications
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4.
PubMed 33538338Antoni S, Ferlay J, Soerjomataram I, Znaor A, Jemal A, Bray F. Bladder Cancer Incidence and Mortality: A Global Overview and Recent Trends. Eur Urol. 2017 Jan;71(1):96-108. doi: 10.1016/j.eururo.2016.06.010. Epub 2016 Jun 28.
PubMed 27370177Humphrey PA, Moch H, Cubilla AL, Ulbright TM, Reuter VE. The 2016 WHO Classification of Tumours of the Urinary System and Male Genital Organs-Part B: Prostate and Bladder Tumours. Eur Urol. 2016 Jul;70(1):106-119. doi: 10.1016/j.eururo.2016.02.028. Epub 2016 Mar 17.
PubMed 26996659Flaig TW, Spiess PE, Agarwal N, Bangs R, Boorjian SA, Buyyounouski MK, Chang S, Downs TM, Efstathiou JA, Friedlander T, Greenberg RE, Guru KA, Guzzo T, Herr HW, Hoffman-Censits J, Hoimes C, Inman BA, Jimbo M, Kader AK, Lele SM, Michalski J, Montgomery JS, Nandagopal L, Pagliaro LC, Pal SK, Patterson A, Plimack ER, Pohar KS, Preston MA, Sexton WJ, Siefker-Radtke AO, Tward J, Wright JL, Gurski LA, Johnson-Chilla A. Bladder Cancer, Version 3.2020, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2020 Mar;18(3):329-354. doi: 10.6004/jnccn.2020.0011.
PubMed 32135513Grossman HB, Natale RB, Tangen CM, Speights VO, Vogelzang NJ, Trump DL, deVere White RW, Sarosdy MF, Wood DP Jr, Raghavan D, Crawford ED. Neoadjuvant chemotherapy plus cystectomy compared with cystectomy alone for locally advanced bladder cancer. N Engl J Med. 2003 Aug 28;349(9):859-66. doi: 10.1056/NEJMoa022148.
PubMed 12944571Yin M, Joshi M, Meijer RP, Glantz M, Holder S, Harvey HA, Kaag M, Fransen van de Putte EE, Horenblas S, Drabick JJ. Neoadjuvant Chemotherapy for Muscle-Invasive Bladder Cancer: A Systematic Review and Two-Step Meta-Analysis. Oncologist. 2016 Jun;21(6):708-15. doi: 10.1634/theoncologist.2015-0440. Epub 2016 Apr 6.
PubMed 27053504Winquist E, Kirchner TS, Segal R, Chin J, Lukka H; Genitourinary Cancer Disease Site Group, Cancer Care Ontario Program in Evidence-based Care Practice Guidelines Initiative. Neoadjuvant chemotherapy for transitional cell carcinoma of the bladder: a systematic review and meta-analysis. J Urol. 2004 Feb;171(2 Pt 1):561-9. doi: 10.1097/01.ju.0000090967.08622.33.
PubMed 14713760Seiler R, Ashab HAD, Erho N, van Rhijn BWG, Winters B, Douglas J, Van Kessel KE, Fransen van de Putte EE, Sommerlad M, Wang NQ, Choeurng V, Gibb EA, Palmer-Aronsten B, Lam LL, Buerki C, Davicioni E, Sjodahl G, Kardos J, Hoadley KA, Lerner SP, McConkey DJ, Choi W, Kim WY, Kiss B, Thalmann GN, Todenhofer T, Crabb SJ, North S, Zwarthoff EC, Boormans JL, Wright J, Dall'Era M, van der Heijden MS, Black PC. Impact of Molecular Subtypes in Muscle-invasive Bladder Cancer on Predicting Response and Survival after Neoadjuvant Chemotherapy. Eur Urol. 2017 Oct;72(4):544-554. doi: 10.1016/j.eururo.2017.03.030. Epub 2017 Apr 5.
PubMed 28390739Makboul M, Farghaly S, Abdelkawi IF. Multiparametric MRI in differentiation between muscle invasive and non-muscle invasive urinary bladder cancer with vesical imaging reporting and data system (VI-RADS) application. Br J Radiol. 2019 Dec;92(1104):20190401. doi: 10.1259/bjr.20190401. Epub 2019 Oct 8.
PubMed 31573328Sidhu PS, Cantisani V, Dietrich CF, Gilja OH, Saftoiu A, Bartels E, Bertolotto M, Calliada F, Clevert DA, Cosgrove D, Deganello A, D'Onofrio M, Drudi FM, Freeman S, Harvey C, Jenssen C, Jung EM, Klauser AS, Lassau N, Meloni MF, Leen E, Nicolau C, Nolsoe C, Piscaglia F, Prada F, Prosch H, Radzina M, Savelli L, Weskott HP, Wijkstra H. The EFSUMB Guidelines and Recommendations for the Clinical Practice of Contrast-Enhanced Ultrasound (CEUS) in Non-Hepatic Applications: Update 2017 (Long Version). Ultraschall Med. 2018 Apr;39(2):e2-e44. doi: 10.1055/a-0586-1107. Epub 2018 Mar 6.
PubMed 29510439Hu FB. Metabolic profiling of diabetes: from black-box epidemiology to systems epidemiology. Clin Chem. 2011 Sep;57(9):1224-6. doi: 10.1373/clinchem.2011.167056. No abstract available.
PubMed 21690202Ou Q, Yu Y, Li A, Chen J, Yu T, Xu X, Xie X, Chen Y, Lin D, Zeng Q, Zhang Y, Tang X, Yao H, Luo B. Association of survival and genomic mutation signature with immunotherapy in patients with hepatocellular carcinoma. Ann Transl Med. 2020 Mar;8(5):230. doi: 10.21037/atm.2020.01.32.
PubMed 32309377Yu Y, Zeng D, Ou Q, Liu S, Li A, Chen Y, Lin D, Gao Q, Zhou H, Liao W, Yao H. Association of Survival and Immune-Related Biomarkers With Immunotherapy in Patients With Non-Small Cell Lung Cancer: A Meta-analysis and Individual Patient-Level Analysis. JAMA Netw Open. 2019 Jul 3;2(7):e196879. doi: 10.1001/jamanetworkopen.2019.6879.
PubMed 31290993
Study at a glance
From the registry record, last updated by the sponsor on 3 Jul 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Intelligent Diagnosis of Bladder Cancer Staging and Prediction of New Adjuvant Chemotherapy Efficacy Based on Deep Learning and Transfer Learning in Ultrasound-Magnetic Resonance-Pathology Multimodal Multiscale
- ClinicalTrials.gov ID
- NCT07051083
- Acronym / sponsor ID
- MICS-BC / SYSKY-2023-1306-01
- Conditions
- Bladder Cancer, Staging, Deep Learning, Neoadjuvant Chemotherapy, Contrast Enhanced Ultrasound
- Keywords
- Bladder Cancer, Multi-omics, Staging, Deep Learning, Neoadjuvant Chemotherapy, Contrast Enhanced Ultrasound
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 480 participants (estimated)
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityUniversity, hospital or other
- Study start
- 1 Jan 2024 (actual)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 3 Jul 2025 (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.
Bladder cancer is the most common malignant tumor of the urinary system. The presence or absence of muscle invasion in early bladder cancer is an independent prognostic factor. The involvement of muscle invasion affects the choice of surgical methods and treatment. Preoperatively, the precise assessment of bladder cancer staging has important practical value. A more accurate preoperative assessment of bladder cancer staging can reduce overtreatment and provide a favorable basis for clinicians to choose more reasonable and effective surgical methods. Clinically, there has been a longstanding desire to diagnose the staging of bladder cancer through a simple, convenient, effective, and non-invasive examination. As relevant research progresses, a multi-omics diagnostic model will be beneficial in improving diagnostic efficiency. This project aims to establish a multi-omics artificial intelligence system based on deep learning and transfer learning to accurately diagnose the staging of bladder cancer and predict the efficacy of neoadjuvant chemotherapy. This system will assist in clinical treatment decision-making.
Detailed description
Research Content
- Establishment of Ultrasound and Magnetic Resonance Imaging-Pathology-Clinical Dataset: This project aims to include medical records diagnosed through ultrasound and magnetic resonance imaging. Combining results from cytopathology or tissue pathology, the project will collect ultrasound images of the bladder, magnetic resonance images of the bladder, pathology images, and clinically relevant follow-up data. Tumor tissue specimens will be collected for immunohistochemical fluorescence staining. Participants with indistinct imaging findings or those with other primary malignant tumors will be excluded. The goal is to establish an ultrasound and magnetic resonance imaging-pathology-clinical dataset.
- Establishment of an Artificial Intelligence System for Precise Diagnosis of Bladder Cancer Staging and Prognosis based on Ultrasound Imaging: Collecting clinical baseline information, surgical pathology, and ultrasound images of enrolled subjects. The ultrasound images will undergo homogenization processing. Utilizing algorithms based on U-Net, Transformer, and attention mechanisms for tissue segmentation and feature extraction, a deep learning model based on convolutional neural networks will be established for precise diagnosis of bladder cancer staging and prognosis: Construction of a mathematical model for staging bladder cancer using ultrasound contrast: Using convolutional neural networks for deep learning to build a mathematical model for staging bladder cancer. Developing an artificial intelligence diagnostic system for ultrasound contrast images based on deep learning and mathematical models to determine whether bladder cancer has muscle invasion. Construction of a mathematical model to discriminate prognosis features of bladder cancer using ultrasound imaging: Automatically delineating target areas and extracting ultrasound image features of bladder cancer lesions using new artificial intelligence technology - convolutional neural networks to build a model for evaluating the prognosis of bladder cancer lesions and achieving accurate prognosis diagnosis.
- Establishment of an Artificial Intelligence System for the Joint Diagnosis of Bladder Cancer and Staging based on Ultrasound and Magnetic Resonance Imaging with Pathology: Based on bladder ultrasound, magnetic resonance imaging, and pathology image data, the baseline information of study subjects will be digitized. Ultrasound images, magnetic resonance images, and pathology images will undergo homogenization processing. Utilizing algorithms based on U-Net, Transformer, and attention mechanisms for segmentation and feature extraction of bladder ultrasound images, magnetic resonance images, and pathology images, a deep learning model based on convolutional neural networks will be established for the precise diagnosis of bladder cancer staging and prognosis: Construction of a mathematical model for joint pathology-based staging of bladder cancer using ultrasound and magnetic resonance imaging: Using convolutional neural networks for deep learning to build a mathematical model for staging bladder cancer. Based on the mathematical model, continuously optimizing algorithms, developing multi-omics, multidimensional artificial intelligence diagnostic systems based on ultrasound images, magnetic resonance images, pathology images, and clinical features, achieving accurate diagnosis of bladder cancer staging and prognosis prediction models. Construction of a mathematical model to discriminate prognosis features of bladder cancer using joint ultrasound, magnetic resonance, and pathology: Automatically delineating target areas and extracting features of bladder cancer lesions using convolutional neural networks and new artificial intelligence technology. Building a mathematical model to evaluate the prognosis of bladder cancer lesions. After the completion of the multi-omics, multidimensional artificial intelligence precise prediction model, validate the reliability of the model in prospective observational cohort study data and implement an intelligent system to assist in improving the efficiency of doctor diagnosis.
- Establishment of an Artificial Intelligence System based on Dynamic Quantitative Immune Cell Maps using Ultrasound Imaging: Based on bladder ultrasound and pathological immunohistochemistry data, artificial intelligence algorithms will accurately determine the expression of immune cells through ultrasound images. Visualization of 2D spatial distribution heatmaps will be conducted to explore the intercorrelation features between ultrasound images and immune cells. Study subjects will be divided into training and validation cohorts. The baseline information of study subjects will be digitized. Ultrasound images will undergo homogenization processing. Utilizing algorithms based on U-Net, Transformer, attention mechanisms, and generative adversarial networks for tissue segmentation, feature extraction, and model establishment:
Construction of a mathematical model for quantitative immune cell maps using ultrasound images: Using new artificial intelligence technology - convolutional neural networks for deep learning to build a mathematical model for predicting the expression of immune cells. Developing an artificial intelligence diagnostic system for quantifying the microenvironment of ultrasound contrast images, determining the expression of immune cells in bladder cancer lesions.
The model will combine the immune cells with the 2D ultrasound image prediction heatmap, forming a visual 2D ultrasound image-immune cell heatmap. Exploring the spatial location of immune cells in ultrasound images. Using single-cell and spatial transcriptome sequencing methods to verify the accuracy of the spatial distribution of ultrasound image-quantified immune cells.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 5 Years
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Training Group | – | Diagnostic Test: Risk StratificationApproximately 300 patients, which are used for the training dataset. |
| Internal Validation Group | – | Diagnostic Test: Risk StratificationApproximately 100 patients, which are used for internal validation to assess the accuracy of the model. |
| External Validation Group | – | Diagnostic Test: Risk StratificationApproximately 100 patients，which are used for external validation to assess the accuracy of the model. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- 480 patients who were diagnosed with bladder cancer based on postoperative pathology at Sun Yat-sen Memorial Hospital, Sun Yat-sen University will be included between January 2023 and December 2026.
Key inclusion criteria (7 listed)
- Ultrasound and other imaging examinations (CT, MR, etc.) suggest bladder masses and are suspicious for bladder cancer patients.
- The bladder is well filled, and no allergic reactions to ultrasound contrast agents are found.
- No surgery or radiotherapy/chemotherapy has been performed.
- Patients who meet the indications for surgical resection and are planned for surgical treatment, including one of the following:
Key exclusion criteria (5 listed)
- Individuals unable to tolerate surgery;
- Individuals allergic to ultrasound contrast agents, unable to undergo ultrasound contrast examination;
- Unsuccessful preoperative ultrasound contrast examination or non-compliant patients;
- Postoperative pathology does not indicate bladder cancer;
- Patients who have undergone chemotherapy or radiation therapy.
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 |
|---|---|---|
| Risk StratificationRisk Stratification for Assessing Muscle Infiltration in Bladder Cancer. | Diagnostic test | External Validation Group, Internal Validation Group, Training Group |
Primary outcome
Detection of Muscle Invasion in Bladder Cancer
Time frame: Perform contrast-enhanced ultrasound (CEUS) examination within 2 weeks before the procedure.
To evaluate the preoperative staging of bladder cancer，whether it is non-muscle invasive bladder cancer (NMIBC) or muscle invasive bladder cancers (MIBC)
1 secondary outcome
Evaluate the Diagnostic Performance of Deep Learning and Transfer Learning in Preoperative Prediction of Muscle-Invasive Status in Bladder Cancer
Time frame: The patient should return for follow-up at 3 months postoperatively
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 3 Jul 2025.
- Background: PubMed 33538338Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4.
- Background: PubMed 27370177Antoni S, Ferlay J, Soerjomataram I, Znaor A, Jemal A, Bray F. Bladder Cancer Incidence and Mortality: A Global Overview and Recent Trends. Eur Urol. 2017 Jan;71(1):96-108. doi: 10.1016/j.eururo.2016.06.010. Epub 2016 Jun 28.
- Background: PubMed 26996659Humphrey PA, Moch H, Cubilla AL, Ulbright TM, Reuter VE. The 2016 WHO Classification of Tumours of the Urinary System and Male Genital Organs-Part B: Prostate and Bladder Tumours. Eur Urol. 2016 Jul;70(1):106-119. doi: 10.1016/j.eururo.2016.02.028. Epub 2016 Mar 17.
- Background: PubMed 32135513Flaig TW, Spiess PE, Agarwal N, Bangs R, Boorjian SA, Buyyounouski MK, Chang S, Downs TM, Efstathiou JA, Friedlander T, Greenberg RE, Guru KA, Guzzo T, Herr HW, Hoffman-Censits J, Hoimes C, Inman BA, Jimbo M, Kader AK, Lele SM, Michalski J, Montgomery JS, Nandagopal L, Pagliaro LC, Pal SK, Patterson A, Plimack ER, Pohar KS, Preston MA, Sexton WJ, Siefker-Radtke AO, Tward J, Wright JL, Gurski LA, Johnson-Chilla A. Bladder Cancer, Version 3.2020, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2020 Mar;18(3):329-354. doi: 10.6004/jnccn.2020.0011.
- Background: PubMed 12944571Grossman HB, Natale RB, Tangen CM, Speights VO, Vogelzang NJ, Trump DL, deVere White RW, Sarosdy MF, Wood DP Jr, Raghavan D, Crawford ED. Neoadjuvant chemotherapy plus cystectomy compared with cystectomy alone for locally advanced bladder cancer. N Engl J Med. 2003 Aug 28;349(9):859-66. doi: 10.1056/NEJMoa022148.
- Background: PubMed 27053504Yin M, Joshi M, Meijer RP, Glantz M, Holder S, Harvey HA, Kaag M, Fransen van de Putte EE, Horenblas S, Drabick JJ. Neoadjuvant Chemotherapy for Muscle-Invasive Bladder Cancer: A Systematic Review and Two-Step Meta-Analysis. Oncologist. 2016 Jun;21(6):708-15. doi: 10.1634/theoncologist.2015-0440. Epub 2016 Apr 6.
- Background: PubMed 14713760Winquist E, Kirchner TS, Segal R, Chin J, Lukka H; Genitourinary Cancer Disease Site Group, Cancer Care Ontario Program in Evidence-based Care Practice Guidelines Initiative. Neoadjuvant chemotherapy for transitional cell carcinoma of the bladder: a systematic review and meta-analysis. J Urol. 2004 Feb;171(2 Pt 1):561-9. doi: 10.1097/01.ju.0000090967.08622.33.
- Background: PubMed 28390739Seiler R, Ashab HAD, Erho N, van Rhijn BWG, Winters B, Douglas J, Van Kessel KE, Fransen van de Putte EE, Sommerlad M, Wang NQ, Choeurng V, Gibb EA, Palmer-Aronsten B, Lam LL, Buerki C, Davicioni E, Sjodahl G, Kardos J, Hoadley KA, Lerner SP, McConkey DJ, Choi W, Kim WY, Kiss B, Thalmann GN, Todenhofer T, Crabb SJ, North S, Zwarthoff EC, Boormans JL, Wright J, Dall'Era M, van der Heijden MS, Black PC. Impact of Molecular Subtypes in Muscle-invasive Bladder Cancer on Predicting Response and Survival after Neoadjuvant Chemotherapy. Eur Urol. 2017 Oct;72(4):544-554. doi: 10.1016/j.eururo.2017.03.030. Epub 2017 Apr 5.
- Background: PubMed 31573328Makboul M, Farghaly S, Abdelkawi IF. Multiparametric MRI in differentiation between muscle invasive and non-muscle invasive urinary bladder cancer with vesical imaging reporting and data system (VI-RADS) application. Br J Radiol. 2019 Dec;92(1104):20190401. doi: 10.1259/bjr.20190401. Epub 2019 Oct 8.
- Background: PubMed 29510439Sidhu PS, Cantisani V, Dietrich CF, Gilja OH, Saftoiu A, Bartels E, Bertolotto M, Calliada F, Clevert DA, Cosgrove D, Deganello A, D'Onofrio M, Drudi FM, Freeman S, Harvey C, Jenssen C, Jung EM, Klauser AS, Lassau N, Meloni MF, Leen E, Nicolau C, Nolsoe C, Piscaglia F, Prada F, Prosch H, Radzina M, Savelli L, Weskott HP, Wijkstra H. The EFSUMB Guidelines and Recommendations for the Clinical Practice of Contrast-Enhanced Ultrasound (CEUS) in Non-Hepatic Applications: Update 2017 (Long Version). Ultraschall Med. 2018 Apr;39(2):e2-e44. doi: 10.1055/a-0586-1107. Epub 2018 Mar 6.
- Background: PubMed 21690202Hu FB. Metabolic profiling of diabetes: from black-box epidemiology to systems epidemiology. Clin Chem. 2011 Sep;57(9):1224-6. doi: 10.1373/clinchem.2011.167056. No abstract available.
- Background: PubMed 32309377Ou Q, Yu Y, Li A, Chen J, Yu T, Xu X, Xie X, Chen Y, Lin D, Zeng Q, Zhang Y, Tang X, Yao H, Luo B. Association of survival and genomic mutation signature with immunotherapy in patients with hepatocellular carcinoma. Ann Transl Med. 2020 Mar;8(5):230. doi: 10.21037/atm.2020.01.32.
- Individual participant dataSharing planned: Yes
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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.
Qiyun Ou, Dr. principal investigator
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
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 Cancer and Staging, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen 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 7 inclusion and 5 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 3 Jul 2025. 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 Qiyun Ou, Dr. ((86)020-34071020, ouqy5@mail.sysu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Detection of Muscle Invasion in Bladder Cancer (time frame: Perform contrast-enhanced ultrasound (CEUS) examination within 2 weeks before the procedure.). 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 NCT07051083, which also lists 13 linked publications.
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 Jul 2025.