人工智能在泌尿系统肿瘤诊断中的应用

doi:10.3969/j.issn.1006-7795.2026.02.002

摘要/Abstract

摘要： 泌尿系统肿瘤的早期诊断对实现精准治疗和改善患者预后至关重要。近年来，人工智能（artificial intelligence，AI）技术在医学影像分析、病理图像识别、生物标志物挖掘及预后预测等方面展现出了巨大潜力。本文系统综述了AI应用于前列腺癌、膀胱癌和肾癌三大常见泌尿系统肿瘤诊断的现状与进展。在提升诊疗效率、减轻医生负担、优化临床决策等方面，AI具有广阔的应用前景。随着算法优化以及多中心数据的不断积累，未来AI有望在泌尿系统肿瘤的精准诊疗中发挥更加重要的作用。

关键词: 人工智能, 泌尿系统肿瘤, 前列腺癌, 膀胱癌, 肾细胞癌, 诊断

Abstract: Early diagnosis of urologic neoplasms is pivotal for delivering precision therapy and optimizing patient outcomes. In recent years, artificial intelligence (AI) has demonstrated remarkable potential in medical-image analysis, histopathologic image recognition, biomarker discovery, and prognostic prediction. This article systematically reviews the current status and advances of AI applications in the diagnosis of the three most common urologic cancers, including prostate cancer, bladder cancer, and renal cell cancer. By enhancing diagnostic efficiency, alleviating physicians' workload, and optimizing clinical decision-making, AI offers broad prospects. With continuous algorithmic refinement and the accumulation of multicentric data, AI is expected to play an even greater role in the precision management of urologic tumors.

Key words: artificial intelligence, urologic neoplasms, prostate cancer, bladder cancer, renal cell cancer, diagnosis

中图分类号:

R737

尚雅欣, 赵有权, 熊天宇, 牛亦农, 谢萍. 人工智能在泌尿系统肿瘤诊断中的应用[J]. 首都医科大学学报, doi: 10.3969/j.issn.1006-7795.2026.02.002.

Shang Yaxin, Zhao Youquan, Xiong Tianyu, Niu Yinong, Xie Ping. Application of artificial intelligence in the diagnosis of urological tumors[J]. Journal of Capital Medical University, doi: 10.3969/j.issn.1006-7795.2026.02.002.

参考文献

［1］Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries［J］. CA Cancer J Clin, 2024, 74(3): 229-263.
［2］Haue A D, Hjaltelin J X, Holm P C, et al. Artificial intelligence-aided data mining of medical records for cancer detection and screening［J］. Lancet Oncol, 2024, 25(12): e694－e703.
［3］McSweeney S T, Werneburg G T, Vasavada S P. Artificial intelligence in the business of urology［J］. Urology, 2025, 203: 83-85.
［4］Hung A L Y, Zheng H X, Miao Q, et al. CAT-Net: a cross-slice attention transformer model for prostate zonal segmentation in MRI［J］. IEEE Trans Med Imaging, 2023, 42(1): 291-303.
［5］Wang X Y, Zhao J H, Marostica E, et al. A pathology foundation model for cancer diagnosis and prognosis prediction［J］. Nature, 2024, 634(8035): 970-978.
［6］Lee Y J, Moon H W, Choi M H, et al. MRI-based deep learning algorithm for assisting clinically significant prostate cancer detection: a bicenter prospective study［J］. Radiology, 2025, 314(3): e232788.
［7］Twilt J J, Saha A, Bosma J S, et al. AI-assisted vs unassisted identification of prostate cancer in magnetic resonance images［J］. JAMA Netw Open, 2025, 8(6): e2515672.
［8］Lorusso V, Kabre B, Pignot G, et al. External validation of the computerized analysis of TRUS of the prostate with the ANNA/C-TRUS system: a potential role of artificial intelligence for improving prostate cancer detection［J］. World J Urol, 2023, 41(3): 619-625.
［9］Duan J W, Bernard M, Downes L, et al. Evaluating the clinical acceptability of deep learning contours of prostate and organs-at-risk in an automated prostate treatment planning process［J］. Med Phys, 2022, 49(4): 2570-2581.
［10］Lindgren Belal S, Frantz S, Minarik D, et al. Applications of artificial intelligence in PSMA PET/CT for prostate cancer imaging［J］. Semin Nucl Med, 2024, 54(1): 141-149.
［11］Trägårdh E, Enqvist O, Ulén J, et al. Freely available artificial intelligence for pelvic lymph node metastases in PSMA PET-CT that performs on par with nuclear medicine physicians［J］. Eur J Nucl Med Mol Imaging, 2022, 49(10): 3412-3418.
［12］Ström P, Kartasalo K, Olsson H, et al. Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study［J］. Lancet Oncol, 2020, 21(2): 222-232.
［13］Cancer Genome Atlas Research Network. The molecular taxonomy of primary prostate cancer［J］. Cell, 2015, 163(4): 1011-1025.
［14］You S Y, Knudsen B S, Erho N, et al. Integrated classification of prostate cancer reveals a novel luminal subtype with poor outcome［J］. Cancer Res, 2016, 76(17): 4948-4958.
［15］Javadi G, Samadi S, Bayat S, et al. Multiple instance learning combined with label invariant synthetic data for guiding systematic prostate biopsy: a feasibility study［J］. Int J Comput Assist Radiol Surg, 2020, 15(6): 1023-1031.
［16］Chiu P K F, Shen X, Wang G J, et al. Enhancement of prostate cancer diagnosis by machine learning techniques: an algorithm development and validation study［J］. Prostate Cancer Prostatic Dis, 2022, 25(4): 672-676.
［17］Gontero P, Birtle A, Capoun O, et al. European Association of Urology guidelines on non-muscle-invasive bladder cancer (TaT1 and carcinoma in situ)—a summary of the 2024 guidelines update［J］. Eur Urol, 2024, 86(6): 531-549.
［18］Wu S X, Chen X, Pan J X, et al. An artificial intelligence system for the detection of bladder cancer via cystoscopy: a multicenter diagnostic study［J］. J Natl Cancer Inst, 2022, 114(2): 220-227.
［19］Shkolyar E, Jia X, Chang T C, et al. Augmented bladder tumor detection using deep learning［J］. Eur Urol, 2019, 76(6): 714-718.
［20］Wu P, Wu K, Li Z, et al. Multimodal investigation of bladder cancer data based on computed tomography, whole slide imaging, and transcriptomics［J］. Quant Imaging Med Surg, 2023, 13(2): 1023-1035.
［21］Wu S X, Hong G B, Xu A B, et al. Artificial intelligence-based model for lymph node metastases detection on whole slide images in bladder cancer: a retrospective, multicentre, diagnostic study［J］. Lancet Oncol, 2023, 24(4): 360-370.
［22］Wu S X, Shen R N, Hong G B, et al. Development and validation of an artificial intelligence-based model for detecting urothelial carcinoma using urine cytology images: a multicentre, diagnostic study with prospective validation［J］. EClinicalMedicine, 2024, 71: 102566.
［23］Lebret T, Pignot G, Colombel M, et al. Artificial intelligence to improve cytology performances in bladder carcinoma detection: results of the VisioCyt test［J］. BJU Int, 2022, 129(3): 356-363.
［24］Ma X Y, Hadjiiski L M, Wei J, et al. U-Net based deep learning bladder segmentation in CT urography［J］. Med Phys, 2019, 46(4): 1752-1765.
［25］O'Sullivan N J, Temperley H C, Corr A, et al. Current role of radiomics and radiogenomics in predicting oncological outcomes in bladder cancer［J］. Curr Urol, 2025, 19(1): 43-48.
［26］Xu X P, Zhang X, Tian Q, et al. Quantitative identification of nonmuscle-invasive and muscle-invasive bladder carcinomas: a multiparametric MRI radiomics analysis［J］. J Magn Reson Imaging, 2019, 49(5): 1489-1498.
［27］Woerl A C, Eckstein M, Geiger J, et al. Deep learning predicts molecular subtype of muscle-invasive bladder cancer from conventional histopathological slides［J］. Eur Urol, 2020, 78(2): 256-264.
［28］Loeffler C M L, Ortiz Bruechle N, Jung M, et al. Artificial intelligence-based detection of FGFR3 mutational status directly from routine histology in bladder cancer: a possible preselection for molecular testing?［J］. Eur Urol Focus, 2022, 8(2): 472-479.
［29］Sharma V, Fadel A, Tollefson M K, et al. Artificial intelligence-based assessment of preoperative body composition is associated with early complications after radical cystectomy［J］. J Urol, 2025, 213(2): 228-237.
［30］Suartz C V, Martinez L M, Cordeiro M D, et al. Artificial intelligence for predicting response to neoadjuvant chemotherapy for bladder cancer A comprehensive systematic review and meta-analysis［J］. Can Urol Assoc J, 2024, 18(9): E276-E284.
［31］Lotan Y, Krishna V, Abuzeid W M, et al. Predicting response to intravesical Bacillus Calmette-Guérin in high-risk nonmuscle-invasive bladder cancer using an artificial intelligence-powered pathology assay: development and validation in an international 12-center cohort［J］. J Urol, 2025, 213(2): 192-204.
［32］Manitz J, Gerhold-Ay A, Kieslich P, et al. Avelumab first-line maintenance in advanced urothelial carcinoma: complete screening for prognostic and predictive factors using machine learning in the JAVELIN bladder 100 phase 3 trial［J］. Cancer Med, 2024, 13(12): e7411.
［33］Xiong Y, Yao L P, Lin J L, et al. Artificial intelligence links CT images to pathologic features and survival outcomes of renal masses［J］. Nat Commun, 2025, 16(1): 1425.
［34］Silverman S G, Gan Y U, Mortele K J, et al. Renal masses in the adult patient: the role of percutaneous biopsy［J］. Radiology, 2006, 240(1): 6-22.
［35］Rasmussen R G, Xi Y, Sibley R C 3rd, et al. Association of clear cell likelihood score on MRI and growth kinetics of small solid renal masses on active surveillance［J］. AJR Am J Roentgenol, 2022, 218(1): 101-110.

［36］Etem T, Teke M. Enhanced deep learning based decision support system for kidney tumour detection［J］. BenchCouncil Trans Benchmarks Stand Eval, 2024, 4(2): 100174.

［37］Alzu'bi D, Abdullah M, Hmeidi I, et al. Kidney tumor detection and classification based on deep learning approaches: a new dataset in CT scans［J］. J Healthc Eng, 2022, 2022: 3861161.

［38］Kowalewski K F, Egen L, Fischetti C E, et al. Artificial intelligence for renal cancer: from imaging to histology and beyond［J］. Asian J Urol, 2022, 9(3): 243-252.
［39］Fenstermaker M, Tomlins S A, Singh K, et al. Development and validation of a deep-learning model to assist with renal cell carcinoma histopathologic interpretation［J］. Urology, 2020, 144: 152-157.
［40］Holdbrook D A, Singh M, Choudhury Y, et al. Automated renal cancer grading using nuclear pleomorphic patterns［J］. JCO Clin Cancer Inform, 2018, 2: 1-12.
［41］Singh N P, Bapi R S, Vinod P K. Machine learning models to predict the progression from early to late stages of papillary renal cell carcinoma［J］. Comput Biol Med, 2018, 100: 92-99.
［42］Shao L Z, Liang C, Yan Y, et al. An MRI-pathology foundation model for noninvasive diagnosis and grading of prostate cancer［J］. Nat Cancer, 2025, 6(10): 1621-1637.
［43］Kwong J C C, Al-Daqqaq Z, Chelliahpillai Y, et al. Development and international evaluation of an artificial intelligence-based model (PROGRxN-BCa) using the World Health Organization 2004/2022 grading system to predict progression risk and improve substratification for non-muscle-invasive bladder cancer［J/OL］. Eur Urol, (2025-10-07)［2025-10-09］. https://pubmed.ncbi.nlm.nih.gov/41062388/.
［44］Harmath C. AI in prostate cancer diagnosis with MRI: a friend not a foe［J］. Radiology, 2025, 314(3): e250525.

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