切换至 "中华医学电子期刊资源库"

中华腔镜泌尿外科杂志(电子版) ›› 2024, Vol. 18 ›› Issue (02) : 178 -182. doi: 10.3877/cma.j.issn.1674-3253.2024.02.012

综述

人工智能辅助临床决策在泌尿系肿瘤的应用进展
杨龙雨禾1, 王跃强1, 招云亮1, 金溪1,(), 卫娜1, 杨智明1, 张贵福1   
  1. 1. 661100 蒙自,云南省滇南中心医院(红河州第一人民医院)泌尿外科
  • 收稿日期:2023-04-06 出版日期:2024-04-01
  • 通信作者: 金溪

Progress in the application of artificial intelligence assisted clinical decision-making in urological tumors

Longyuhe Yang, Yueqiang Wang, Yunliang Zhao   

  • Received:2023-04-06 Published:2024-04-01
引用本文:

杨龙雨禾, 王跃强, 招云亮, 金溪, 卫娜, 杨智明, 张贵福. 人工智能辅助临床决策在泌尿系肿瘤的应用进展[J]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(02): 178-182.

Longyuhe Yang, Yueqiang Wang, Yunliang Zhao. Progress in the application of artificial intelligence assisted clinical decision-making in urological tumors[J]. Chinese Journal of Endourology(Electronic Edition), 2024, 18(02): 178-182.

疾病的诊断和治疗,尤其对于肿瘤,需要依靠影像学等辅助资料,同时根据患者自身病情、预后等选择不同的个体化治疗方案。这往往依赖于医师的临床经验及准确判断,需要大量的时间和经验积累。而在人工智能(artificial intelligence,AI)中,机器学习(machine learning,ML)和深度学习(deep learning,DL)技术使用卷积神经网络(convolutional neural networks,CNN),以发现大型的、高维的数据集,例如医学影像中复杂的结构及模式。它们可根据经验进行微调,通过对数据进行收集、整合,继而准确地得出我们能理解的结果。这一特点使它们成为模式识别、分类和预测的有力工具。近年来,泌尿系肿瘤,如肾细胞癌、膀胱癌和前列腺癌等呈高发的趋势,提高其早期诊断率、降低误诊率、选择精准的治疗方案成为临床诊疗中面临的难题,通过ML和DL进行图像识别诊断、个性化医疗和临床决策等提高诊断准确率、加快临床诊疗进程和降低人力资源成本已成为可行性强的研究热点[12]。本文的目的是介绍ML和DL在常见泌尿系肿瘤中的应用情况,及AI在临床决策中的作用,为今后AI在泌尿外科领域的运用提供更多研究思路。

[1]
Hameed BMZ, S Dhavileswarapu AVL, Raza S Z, et al. Artificial intelligence and its impact on urological diseases and management: a comprehensive review of the literature[J]. J Clin Med, 2021, 10(9): 1864.
[2]
Rashidian N, Abu Hilal M. Applications of machine learning in surgery: ethical considerations[J]. Artif Intell Surg, 2022, 2(1):18-23.
[3]
Kolben Y, Azmanov H, Gelman R, et al. Using chronobiology-based second-generation artificial intelligence digital system for overcoming antimicrobial drug resistance in chronic infections[J]. Ann Med, 2023, 55(1): 311-318.
[4]
Wang YF, Mao L, Chen HJ, et al. Predicting cognitive impairment in chronic kidney disease patients using structural and functional brain network: an application study of artificial intelligence[J]. Prog Neuropsychopharmacol Biol Psychiatry, 2023, 122: 110677.
[5]
Manimaran M, Arora A, Lovejoy CA, et al. Role of artificial intelligence and machine learning in haematology[J]. J Clin Pathol, 2022, 75(9): 585-587.
[6]
Biswas N, Chakrabarti S. Artificial intelligence (AI)-based systems biology approaches in multi-omics data analysis of cancer[J]. Front Oncol, 2020, 10: 588221.
[7]
Bertoni G, Rotunno E, Marsmans D, et al. Near-real-time diagnosis of electron optical phase aberrations in scanning transmission electron microscopy using an artificial neural network[J]. Ultramicroscopy, 2023, 245: 113663.
[8]
De Fauw J, Ledsam JR, Romera-Paredes B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease[J]. Nat Med, 2018, 24(9): 1342-1350.
[9]
Delpino FM, Costa ÂK, Farias S R, et al. Machine learning for predicting chronic diseases: a systematic review[J]. Public Health, 2022, 205: 14-25.
[10]
Hinton G. Deep learning-a technology with the potential to transform health care[J]. JAMA, 2018, 320(11): 1101-1102.
[11]
Ferlay J, Colombet M, Soerjomataram I, et al. Cancer incidence and mortality patterns in Europe: estimates for 40 countries and 25 major cancers in 2018[J]. Eur J Cancer, 2018, 103: 356-387.
[12]
Padala SA, Barsouk A, Thandra KC, et al. Epidemiology of renal cell carcinoma[J]. World J Oncol, 2020, 11(3): 79-87.
[13]
Kunapuli G, Varghese BA, Ganapathy P, et al. A decision-support tool for renal mass classification[J]. J Digit Imaging, 2018, 31(6): 929-939.
[14]
Li X, Ma Q, Nie P, et al. A CT-based radiomics nomogram for differentiation of renal oncocytoma and chromophobe renal cell carcinoma with a central scar-matched study[J]. Br J Radiol, 2022, 95(1129): 20210534.
[15]
Xu Q, Zhu Q, Liu H, et al. Differentiating benign from malignant renal tumors using T2- and diffusion-weighted images: a comparison of deep learning and radiomics models versus assessment from radiologists[J]. J Magn Reson Imaging, 2022, 55(4): 1251-1259.
[16]
Kocak B, Durmaz ES, Ates E, et al. Radiogenomics in clear cell renal cell carcinoma: machine learning-based high-dimensional quantitative CT texture analysis in predicting PBRM1 mutation status[J]. AJR Am J Roentgenol, 2019, 212(3): W55-W63.
[17]
Ding J, Xing Z, Jiang Z, et al. CT-based radiomic model predicts high grade of clear cell renal cell carcinoma[J]. Eur J Radiol, 2018, 103: 51-56.
[18]
Zhao B, Waterman RS, Urman RD, et al. A machine learning approach to predicting case duration for robot-assisted surgery[J]. J Med Syst, 2019, 43(2): 32.
[19]
Haifler M, Pence I, Ristau B, et al. Discrimination of malignant and benign kidney tissue with 1064 nm dispersive Raman spectroscopy[J]. Eur Urol Suppl, 2017, 16(3): e1345-e1346.
[20]
Li P, Ren H, Zhang Y, et al. Fifteen-gene expression based model predicts the survival of clear cell renal cell carcinoma[J]. Medicine, 2018, 97(33): e11839.
[21]
Kim H, Lee SJ, Park SJ, et al. Machine learning approach to predict the probability of recurrence of renal cell carcinoma after surgery: prediction model development study[J]. JMIR Med Inform, 2021, 9(3): e25635.
[22]
Buchner A, Kendlbacher M, Nuhn P, et al. Outcome assessment of patients with metastatic renal cell carcinoma under systemic therapy using artificial neural networks[J]. Clin Genitourin Cancer, 2012, 10(1): 37-42.
[23]
Teoh JYC, Huang J, Ko WYK, et al. Global trends of bladder cancer incidence and mortality, and their associations with tobacco use and gross domestic product per capita[J]. Eur Urol, 2020, 78(6): 893-906.
[24]
Chang TC, Marcq G, Kiss B, et al. Image-guided transurethral resection of bladder tumors - current practice and future outlooks[J]. Bladder Cancer, 2017, 3(3): 149-159.
[25]
Shkolyar E, Jia X, Chang TC, et al. Augmented bladder tumor detection using deep learning[J]. Eur Urol, 2019, 76(6): 714-718.
[26]
Zhang X, Xu X, Tian Q, et al. Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging[J]. J Magn Reson Imaging, 2017, 46(5): 1281-1288.
[27]
Eminaga O, Eminaga N, Semjonow A, et al. Diagnostic classification of cystoscopic images using deep convolutional neural networks[J]. JCO Clin Cancer Inform, 2018, 2: 1-8.
[28]
Posielski N, Koenig H, Ho O, et al. Use of neoadjuvant chemotherapy in elderly patients with muscle-invasive bladder cancer: a population-based study, 2006-2017[J]. Oncology, 2022, 36(1): 21-33.
[29]
Patel HD, Patel SH, Blanco-Martinez E, et al. Four versus 3 cycles of neoadjuvant chemotherapy for muscle-invasive bladder cancer: implications for pathological response and survival[J]. J Urol, 2022, 207(1): 77-85.
[30]
Cha KH, Hadjiiski LM, Cohan RH, et al. Diagnostic accuracy of CT for prediction of bladder cancer treatment response with and without computerized decision support[J]. Acad Radiol, 2019, 26(9): 1137-1145.
[31]
Cha KH, Hadjiiski LM, Samala RK, et al. Bladder cancer segmentation in CT for treatment response assessment: application of deep-learning convolution neural network-a pilot study[J]. Tomography, 2016, 2(4): 421-429.
[32]
Bhambhvani HP, Zamora A, Shkolyar E, et al. Development of robust artificial neural networks for prediction of 5-year survival in bladder cancer[J]. Urol Oncol, 2021, 39(3): 193.e7-193193.e12.
[33]
Hasnain Z, Mason J, Gill K, et al. Machine learning models for predicting post-cystectomy recurrence and survival in bladder cancer patients[J]. PLoS One, 2019, 14(2): e0210976.
[34]
Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2018, 68(6): 394-424.
[35]
程贤文, 邹晓凤, 赖伟建. 弥散加权成像纹理特征分析对不同级别前列腺癌鉴别诊断的临床价值[J]. 泌尿外科杂志(电子版), 2023, 15(3): 18-21.
[36]
Ishioka J, Matsuoka Y, Uehara S, et al. Computer-aided diagnosis of prostate cancer on magnetic resonance imaging using a convolutional neural network algorithm[J]. BJU Int, 2018, 122(3): 411-417.
[37]
Epstein JI. An update of the Gleason grading system[J]. J Urol, 2010, 183(2): 433-440.
[38]
Bulten W, Pinckaers H, van Boven H, et al. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study[J]. Lancet Oncol, 2020, 21(2): 233-241.
[39]
Nagpal K, Foote D, Liu Y, et al. Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer[J]. NPJ Digit Med, 2019, 7:2:48.
[40]
Auffenberg GB, Ghani KR, Ramani S, et al. askMUSIC: leveraging a clinical registry to develop a new machine learning model to inform patients of prostate cancer treatments chosen by similar men[J]. Eur Urol, 2019, 75(6): 901-907.
[41]
Abdollahi H, Mofid B, Shiri I, et al. Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, gleason score and stage in prostate cancer[J]. Radiol Med, 2019, 124(6): 555-567.
[42]
Hung AJ, Chen J, Che Z, et al. Utilizing machine learning and automated performance metrics to evaluate robot-assisted radical prostatectomy performance and predict outcomes[J]. J Endourol, 2018, 32(5): 438-444.
[43]
Hung AJ, Chen J, Ghodoussipour S, et al. A deep-learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot-assisted radical prostatectomy[J]. BJU Int, 2019, 124(3): 487-495.
[44]
Wong NC, Lam C, Patterson L, et al. Use of machine learning to predict early biochemical recurrence after robot-assisted prostatectomy[J]. BJU Int, 2019, 123(1): 51-57.
[45]
Chen J, Remulla D, Nguyen JH, et al. Current status of artificial intelligence applications in urology and their potential to influence clinical practice[J]. BJU Int, 2019, 124(4): 567-577.
[46]
Schneider L, Laiouar-Pedari S, Kuntz S, et al. Integration of deep learning-based image analysis and genomic data in cancer pathology: a systematic review[J]. Eur J Cancer, 2022, 160: 80-91.
[1] 洪东琪, 叶臻. 真实世界研究在骨关节炎领域的研究进展[J]. 中华关节外科杂志(电子版), 2024, 18(01): 137-141.
[2] 戴雨霖, 张新春. 人工智能在口腔修复诊疗中的应用与进展[J]. 中华口腔医学研究杂志(电子版), 2024, 18(01): 65-69.
[3] 黄艺承, 梁海祺, 何其焕, 韦发烨, 杨舒博, 谭舒婷, 翟高强, 程继文. 机器学习模型评估RAS亚家族基因对膀胱癌免疫治疗的作用[J]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(02): 131-140.
[4] 张铭星, 刘文倩, 王以然, 赵泽恬, 袁欣怡, 丁留成. 江苏地区腹腔镜下前列腺癌根治术后一年夜尿症发生率及相关危险因素多中心回顾性研究[J]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(02): 141-145.
[5] 李俊涛, 张天佑, 叶雷, 郭强, 吴坚坚, 尧冰, 王德娟, 邱剑光. 保留"尿道系膜"的腹腔镜下前列腺根治性切除术后尿控情况的研究[J]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(01): 19-24.
[6] 李俊涛, 张天佑, 叶雷, 郭强, 吴坚坚, 尧冰, 王德娟, 邱剑光. 保留"尿道系膜"的腹腔镜下前列腺根治性切除术后尿控情况的研究[J]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(01): 19-24.
[7] 王浩, 王卓, 王琦, 高金莉, 田新涛, 张文元, 蒋文惠, 陆佳荪, 杨国胜, 温机灵. 经尿道激光操作架直视推拨法铥激光整块切除术治疗非肌层浸润性膀胱癌的初步经验[J]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(01): 31-35.
[8] 张琳, 吴波, 王东文. 前列腺癌特异性近红外荧光探针的研究进展与展望[J]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(01): 6-11.
[9] 张逸, 张继, 栾成明, 张传猛. 肿瘤定量参数对VI-RADS评分系统评估膀胱癌的辅助价值[J]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(01): 41-45.
[10] 邓瑞锋, 程璐, 周宇林, 刘远灵, 江文聪, 江敏耀, 江福能, 习明. TGF-β1诱导骨髓间充质干细胞外泌体分泌miR-424-3p促进前列腺癌细胞增殖及转移[J]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(01): 82-89.
[11] 加素尔·巴吐尔, 徐铭泽, 唐钵, 曾浩, 苏力坦·乌斯曼, 陈羽. 广东省医学会泌尿外科疑难病例多学科会诊(第14期)——左肾原发罕见恶性肿瘤并全身多处转移[J]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(01): 110-113.
[12] 邓新军, 李正明, 李文彬. 广东省医学会泌尿外科疑难病例多学科会诊(第14期)——左肾原发恶性肿瘤并发于肺癌并脑转移[J]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(01): 114-117.
[13] 邱凯锋, 王则远, 何志超, 付凯利, 梅童霖, 关英杰, 高飞, 伍俊妍. 人工智能技术在超说明书用药循证中的应用研究[J]. 中华临床医师杂志(电子版), 2023, 17(12): 1212-1218.
[14] 陈健, 张子豪, 卢勇达, 夏开建, 王甘红, 刘罗杰, 徐晓丹. 基于深度学习构建结直肠息肉诊断自动分类模型[J]. 中华诊断学电子杂志, 2024, 12(01): 9-17.
[15] 吕泉龙, 史文杰, 孙文国. 免疫检查点抑制剂在治疗转移性去势抵抗性前列腺癌中的研究进展[J]. 中华诊断学电子杂志, 2024, 12(01): 69-72.
阅读次数
全文


摘要