Study on the Staging and Prognosis Model of Bladder Cancer

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.

Sex

All

Minimum age

18 Years

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:

* 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.

• RESULTGe 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
• RESULTTataru 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
• RESULTFerro 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
• RESULTArdila 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
• RESULTLiu 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
• RESULTVente 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.