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Active, not recruitingNCT065659232024-SR-386Updated Aug 22, 2024

Study on the Staging and Prognosis Model of Bladder Cancer

An observational study in Bladder Cancer, sponsored by The First Affiliated Hospital with Nanjing Medical University. Active, not recruiting at 1 site in China. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2024-08-22.

Sponsored by The First Affiliated Hospital with Nanjing Medical University · Observational

From the registry’s dates

  • Primary completion was expected by Jul 2025, 1 year 2 months ago, but the record still lists the study as active, not recruiting.
Study type
Observational
Model
Cohort
Time perspective
Prospective
Enrollment
200
Ages
18 Years and older
Sex
All
01

Study summary

Firstly, we retrospectively gathered the patient information who compliant with the criteria from 2012 to 2023, encompassing basic information, clinical information, along with MRI images, blood/urine samples, and tissue samples, for conducting relevant analyses of radiomics. Subsequently, based on artificial intelligence technology, deep learning and machine learning models were established on the basis of MRI radiomics and pathological histomics. Ultimately, the following research aims were accomplished: 1. Primary research objective: To explore the role of artificial intelligence and multimodal omics features in the staging and prognosis monitoring of bladder cancer. 2. Secondary objective: To explore the correlations among radiomics, case histomics, and test omics.

02

Conditions studied

  • Bladder Cancer

Keywords

  • Multimodal omics features
  • artificial intelligence
  • staging and prognostic models
  • Bladder cancer
03

In context

Urinary Bladder Neoplasms

1,616 studies on the registry are indexed under Urinary Bladder Neoplasms; 421 are open to participants now.

This study's planned enrollment of 200 is above the median of 180 across 374 observational studies indexed under Urinary Bladder Neoplasms.

Browse Urinary Bladder Neoplasms studies →

Lead sponsor

The First Affiliated Hospital with Nanjing Medical University is the lead sponsor of 543 studies on the registry; 301 are open to participants now.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
18 Years and older
Sexes eligible
All
Accepts healthy volunteers
No
Sampling method
Non-probability sample

Study population

  1. Patients with bladder cancer in preoperative examination; 2. Gender is not limited; 3. Age≥ 18 years old; 4. Be able to provide MRI images, pathological data and laboratory examination data before the operation; 5. Agree to provide basic personal clinical information and pathological and imaging data for scientific research use, and sign the informed consent form; 6. Agree to provide monitoring results during follow-up recurrence monitoring;

Inclusion criteria

    1. Patients with bladder cancer in preoperative examination; 2. Gender is not limited; 3. Age≥ 18 years old; 4. Be able to provide MRI images, pathological data and laboratory examination data before the operation; 5. Agree to provide basic personal clinical information and pathological and imaging data for scientific research use, and sign the informed consent form; 6. Agree to provide monitoring results during follow-up recurrence monitoring;

Exclusion criteria

Exclusion Criteria:

    1. Incomplete clinicopathological data; 2. Combined with upper tract urothelial carcinoma or previously diagnosed upper tract urothelial carcinoma; 3. Is participating in the rest of the clinical studies; Unable to cooperate with the relevant examinations of this project, and do not agree to sign the informed consent form.
05

Study design

Observational model
Cohort
Time perspective
Prospective
Enrollment
200 participants (estimated)
Target follow-up
1 Year
Patient registry
Yes
Biospecimen retention
Samples with dna

Groups and cohorts

  • Adult bladder cancer patients with MRI, pathology, and laboratory data provided
06

What researchers measure

Primary outcomes

  1. Overall survival (OS)

    Overall survival (OS) is defined as the duration from surgery to death or the date of the last follow-up.

    Time frame: 2013-

  2. Progression-free survival (PFS)

    Progression-free survival (PFS) refers to the time from surgery until disease progression, the date of the last follow-up, or death from causes other than disease recurrence

    Time frame: 2013-

  3. Recurrence-Free Survival (RFS)

    Recurrence-Free Survival (RFS)

    Time frame: 2013-

Secondary outcomes

  1. Tumor Infiltration Status

    Tumor Infiltration Status

    Time frame: 2013-

  2. Lymph node metastasis status

    Lymph node metastasis status

    Time frame: 2013-

Other outcomes

  1. Neoadjuvant and Adjuvant Treatment Effects

    Neoadjuvant and Adjuvant Treatment Effects

    Time frame: 2013-

07

Study locations

1 site
  • The First Affiliated Hospital with Nanjing Medical University
    Nanjing, Jiangsu, China
08

References and documents

Publications

  • Ge L, Chen Y, Yan C, Zhao P, Zhang P, A R, Liu J. Study Progress of Radiomics With Machine Learning for Precision Medicine in Bladder Cancer Management. Front Oncol. 2019 Nov 28;9:1296. doi: 10.3389/fonc.2019.01296. eCollection 2019. PubMed 31850202 ↗
  • Tataru OS, Vartolomei MD, Rassweiler JJ, Virgil O, Lucarelli G, Porpiglia F, Amparore D, Manfredi M, Carrieri G, Falagario U, Terracciano D, de Cobelli O, Busetto GM, Del Giudice F, Ferro M. Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives. Diagnostics (Basel). 2021 Feb 20;11(2):354. doi: 10.3390/diagnostics11020354. PubMed 33672608 ↗
  • Ferro M, de Cobelli O, Musi G, Del Giudice F, Carrieri G, Busetto GM, Falagario UG, Sciarra A, Maggi M, Crocetto F, Barone B, Caputo VF, Marchioni M, Lucarelli G, Imbimbo C, Mistretta FA, Luzzago S, Vartolomei MD, Cormio L, Autorino R, Tataru OS. Radiomics in prostate cancer: an up-to-date review. Ther Adv Urol. 2022 Jul 4;14:17562872221109020. doi: 10.1177/17562872221109020. eCollection 2022 Jan-Dec. PubMed 35814914 ↗
  • Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, Tse D, Etemadi M, Ye W, Corrado G, Naidich DP, Shetty S. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019 Jun;25(6):954-961. doi: 10.1038/s41591-019-0447-x. Epub 2019 May 20. Erratum In: Nat Med. 2019 Aug;25(8):1319. doi: 10.1038/s41591-019-0536-x. PubMed 31110349 ↗
  • Liu KL, Wu T, Chen PT, Tsai YM, Roth H, Wu MS, Liao WC, Wang W. Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation. Lancet Digit Health. 2020 Jun;2(6):e303-e313. doi: 10.1016/S2589-7500(20)30078-9. PubMed 33328124 ↗
  • Vente C, Vos P, Hosseinzadeh M, Pluim J, Veta M. Deep Learning Regression for Prostate Cancer Detection and Grading in Bi-Parametric MRI. IEEE Trans Biomed Eng. 2021 Feb;68(2):374-383. doi: 10.1109/TBME.2020.2993528. Epub 2021 Jan 20. PubMed 32396068 ↗
  • Wang K, Lu X, Zhou H, Gao Y, Zheng J, Tong M, Wu C, Liu C, Huang L, Jiang T, Meng F, Lu Y, Ai H, Xie XY, Yin LP, Liang P, Tian J, Zheng R. Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study. Gut. 2019 Apr;68(4):729-741. doi: 10.1136/gutjnl-2018-316204. Epub 2018 May 5. PubMed 29730602 ↗
  • Nishiyama H, Habuchi T, Watanabe J, Teramukai S, Tada H, Ono Y, Ohshima S, Fujimoto K, Hirao Y, Fukushima M, Ogawa O. Clinical outcome of a large-scale multi-institutional retrospective study for locally advanced bladder cancer: a survey including 1131 patients treated during 1990-2000 in Japan. Eur Urol. 2004 Feb;45(2):176-81. doi: 10.1016/j.eururo.2003.09.011. PubMed 14734003 ↗
  • Witjes JA, Bruins HM, Cathomas R, Comperat EM, Cowan NC, Gakis G, Hernandez V, Linares Espinos E, Lorch A, Neuzillet Y, Rouanne M, Thalmann GN, Veskimae E, Ribal MJ, van der Heijden AG. European Association of Urology Guidelines on Muscle-invasive and Metastatic Bladder Cancer: Summary of the 2020 Guidelines. Eur Urol. 2021 Jan;79(1):82-104. doi: 10.1016/j.eururo.2020.03.055. Epub 2020 Apr 29. PubMed 32360052 ↗
  • Xylinas E, Kent M, Kluth L, Pycha A, Comploj E, Svatek RS, Lotan Y, Trinh QD, Karakiewicz PI, Holmang S, Scherr DS, Zerbib M, Vickers AJ, Shariat SF. Accuracy of the EORTC risk tables and of the CUETO scoring model to predict outcomes in non-muscle-invasive urothelial carcinoma of the bladder. Br J Cancer. 2013 Sep 17;109(6):1460-6. doi: 10.1038/bjc.2013.372. Epub 2013 Aug 27. PubMed 23982601 ↗
  • Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022 Jan;72(1):7-33. doi: 10.3322/caac.21708. Epub 2022 Jan 12. PubMed 35020204 ↗
  • Babjuk M, Burger M, Capoun O, Cohen D, Comperat EM, Dominguez Escrig JL, Gontero P, Liedberg F, Masson-Lecomte A, Mostafid AH, Palou J, van Rhijn BWG, Roupret M, Shariat SF, Seisen T, Soukup V, Sylvester RJ. European Association of Urology Guidelines on Non-muscle-invasive Bladder Cancer (Ta, T1, and Carcinoma in Situ). Eur Urol. 2022 Jan;81(1):75-94. doi: 10.1016/j.eururo.2021.08.010. Epub 2021 Sep 10. PubMed 34511303 ↗
  • 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 33538338 ↗

Individual participant data

Plan to share: Undecided

09

Updates

Tracking since Sep 25, 2026
No changes since tracking began. The registry record was last updated on Aug 22, 2024, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
10

Registry details

Key details

Study ID
NCT06565923
Lead sponsor
The First Affiliated Hospital with Nanjing Medical University
Collaborators
Suzhou Municipal Hospital, Yixing People's Hospital, Wuhan Union Hospital, China, Huai an First People Hospital, Jiangsu Province Hospital of Chinese Medicine, The First Affiliated Hospital of Zhengzhou University, The second affiliated hospital of Xuzhou medical university
Responsible party
Qiang Lv (principal investigator, The First Affiliated Hospital with Nanjing Medical University) — Principal investigator
First posted
Aug 22, 2024
Start date
Mar 18, 2024
Primary completion
Jul 18, 2025 (estimated)
Completion
Jul 18, 2025 (estimated)
Last update
Aug 22, 2024

Oversight

Data monitoring committee
Yes
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

Not currently enrolling

This study is active, not recruiting, as verified in Aug 2024. You cannot join it, but the record below documents what was studied.

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