Study on the Staging and Prognosis Model of Bladder Cancer

NCT06565923 | Active Not Recruiting DMC

• 1 other identifier: 2024-SR-386
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Bladder Cancer

Keywords

Multimodal omics features; artificial intelligence; staging and prognostic models; Bladder cancer

Outcome Measures

Primary Outcomes (3)

• Overall survival (OS)

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

  2013-

• 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

  2013-

• Recurrence-Free Survival (RFS)

  Recurrence-Free Survival (RFS)

  2013-

Secondary Outcomes (2)

• Tumor Infiltration Status

  2013-

• Lymph node metastasis status

  2013-

Other Outcomes (1)

• Neoadjuvant and Adjuvant Treatment Effects

  2013-

Study Arms (1)

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

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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;

You may qualify if:

• \. 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;

You may not qualify if:

• \. 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.

Sponsors & Collaborators

• The First Affiliated Hospital with Nanjing Medical University: lead
• Suzhou Municipal Hospital: collaborator
• Yixing People's Hospital: collaborator
• Union Hospital, Tongji Medical College, Huazhong University of Science and Technology: collaborator
• Huai an First People Hospital: collaborator
• Jiangsu Province Hospital of Traditional Chinese Medicine: collaborator
• The First Affiliated Hospital of Zhengzhou University: collaborator
• The second affiliated hospital of Xuzhou medical university: collaborator

Study Sites (1)

The First Affiliated Hospital with Nanjing Medical University

Nanjing, Jiangsu, China

Location

Related Publications (13)

• 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.

  PMID: 31850202 RESULT

• 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.

  PMID: 33672608 RESULT

• 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.

  PMID: 35814914 RESULT

• 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.

  PMID: 31110349 RESULT

• 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.

  PMID: 33328124 RESULT

• 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.

  PMID: 32396068 RESULT

• 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.

  PMID: 29730602 RESULT

• 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.

  PMID: 14734003 RESULT

• 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.

  PMID: 32360052 RESULT

• 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.

  PMID: 23982601 RESULT

• 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.

  PMID: 35020204 RESULT

• 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.

  PMID: 34511303 RESULT

• 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.

  PMID: 33538338 RESULT

Biospecimen

Retention: SAMPLES WITH DNA

Blood, urine, bladder tissue

MeSH Terms

Conditions

Urinary Bladder Neoplasms

Condition Hierarchy (Ancestors)

Urologic Neoplasms; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Urinary Bladder Diseases; Urologic Diseases; Male Urogenital Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Year
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: principal investigator

Study Record Dates

• First Submitted: August 20, 2024
• First Posted: August 22, 2024
• Study Start: March 18, 2024
• Primary Completion: July 18, 2025
• Study Completion: July 18, 2025
• Last Updated: August 22, 2024

Record last verified: 2024-08

Locations

CN (1)