Welcome to visit Zhongnan Medical Journal Press Series journal website!

Home Articles Vol 37,2024 No.6 Detail

Research progress of artificial intelligence in prostate cancer

Published on Jun. 28, 2024Total Views: 1155 times Total Downloads: 349 times Download Mobile

Author: LEI Bixiu 1 LI Qing 2

Affiliation: 1. School of Clinical Sciences, Xiangnan University, Chenzhou 423000, Hunan Province, China 2. Department of Urology, Affiliated Hospital of Xiangnan University, Chenzhou 423000, Hunan Province, China

Keywords: Prostate cancer Artificial intelligence Diagnosis Treatment Prognosis Screening

DOI: 10.12173/j.issn.1004-4337.202403093

Reference: Lei BX, Li Q. Research progress of artificial intelligence in prostate cancer[J]. Journal of Mathematical Medicine, 2024, 37(6): 453-461. DOI: 10.12173/j.issn.1004-4337.202403093[Article in Chinese]

  • Abstract
  • Full-text
  • References
Abstract

Prostate cancer (PCa) has become the most common malignant tumor in the male urogenital system in China, which has received extensive clinical attention. With the progress of technology and the deepening of multidisciplinary cooperation, artificial intelligence (AI) has gradually shown its unique advantages and application value in the medical field. In order to help clinicians better understand the relevant AI technology and select the appropriate AI algorithm model more accurately and efficiently, this article briefly introduces the commonly used AI technologies in the field of PCa, reviews its application in the diagnosis, treatment, prognosis and early screening of PCa in recent years.

Full-text
Please download the PDF version to read the full text: download
References

1.Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249. DOI: 10.3322/caac.21660.

2.李星, 曾晓勇. 中国前列腺癌流行病学研究进展[J]. 肿瘤防治研究, 2021, 48(1): 98-102. [Li X, Zeng XY. Advances in epidemiology of prostate cancer in China[J]. Cancer Research on Prevention and Treatment, 2021, 48(1): 98-102.] DOI: 10.3971/j.issn.1000-8578.2021.20.0370.

3.任靖文, 喻婷, 罗光恒, 等. MRI联合人工智能在前列腺癌诊断中的应用进展[J]. 中国CT和MRI杂志, 2023, 21(3): 184-186. [Ren JW, Yu T, Luo GH, et al. Application progress of MRI combined with artificial intelligence in diagnosis of prostate cancer[J]. Chinese Journal of CT and MRI, 2023, 21(3): 184-186.] DOI: 10.3969/j.issn.1672-5131.2023.03.063.

4.张驰, 郭媛, 黎明. 人工神经网络模型发展及应用综述[J]. 计算机工程与应用, 2021, 57(11): 57-69. [Zhang C, Guo Y, Li M. Review of development and application of artificial neural network models[J]. Computer Engineering and Applications, 2021, 57(11): 57-69.] DOI: 10.3778/j.issn.1002-8331.2102-0256.

5.季长清, 高志勇, 秦静, 等. 基于卷积神经网络的图像分类算法综述[J]. 计算机应用, 2022, 42(4): 1044-1049. [Ji CQ, Gao ZY, Qin J, et al. Review of image classification algorithms based on convolutional neural network[J]. Journal of Computer Applications, 2022, 42(4): 1044-1049.] DOI: 10.11772/j.issn.1001-9081.2021071273.

6.张珂, 冯晓晗, 郭玉荣, 等. 图像分类的深度卷积神经网络模型综述[J]. 中国图象图形学报, 2021, 26(10): 2305-2325. [Zhang K, Feng XH, Guo YR, et al. Overview of deep convolutional neural networks for image classification[J]. Journal of Image and Graphics, 2021, 26(10): 2305-2325.] https://www.cnki.com.cn/Article/CJFDTotal-ZGTB202110001.htm.

7.European Commission. Artificial intelligence in Europe: outlook for 2019 and beyond[EB/OL]. (2019-05-17). https://docslib.org/doc/8955391/artificial-intelligence-in-europe-outlook-for-2019-and-beyond#google_vignette.

8.郑闪, 孙丰龙, 张慧娟, 等. 人工智能在肿瘤组织病理学的研究现状[J]. 中华肿瘤杂志, 2018, 40(12): 885-889. [Zheng S, Sun FL, Zhang HJ, et al. Current applications of artificial intelligence in tumor histopathology[J]. Chinese Journal of Oncology, 2018, 40(12): 885-889.] DOI: 10.3760/cma.j.issn.0253-3766. 2018.12.002.

9.胡金玥, 白志明, 王刚, 等. 影像组学在前列腺癌诊治中的应用现状及展望[J]. 中国CT和MRI杂志, 2023, 21(12): 175-178. [Hu JY, Bai ZM, Wang G, et al. Application status and prospects of radiomics in the diagnosis and treatment of prostate cancer[J]. Chinese Journal of CT and MRI, 2023, 21(12): 175-178.] DOI: 10.3969/j.issn.1672-5131.2023.12.054.

10.Ali N, Bolenz C, Todenhöfer T, et al. Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors[J]. Sci Rep, 2021, 11(1): 11629. DOI: 10.1038/s41598-021-91081-x.

11.许克新, 丁泽华. 人工智能在功能泌尿外科的应用[J].北京大学学报(医学版), 2023, 55(5): 771-774. [Xu KX, Ding ZH. Application of artificial intelligence in functional urology[J]. Journal of Peking University (Health Sciences), 2023, 55(5): 771-774.] DOI: 10.19723/j.issn.1671-167X.2023.05.001.

12.徐文浩, 田熙, 艾合太木江·安外尔, 等. 人工智能在泌尿系统肿瘤中的应用研究进展[J]. 中国癌症杂志, 2022, 32(1): 68-74. [Xu WH, Tian X, Aihetaimujiang A, et al. A systematic review of current advancements of artificial intelligence in genitourinary cancers[J]. China Oncology, 2022, 32(1): 68-74] DOI: 10.19401/j.cnki.1007-3639.2022.01.009.

13.梁晓秋, 曹凌玲, 陈溢旭. 经直肠超声造影引导前列腺穿刺活检诊断前列腺癌[J]. 中国介入影像与治疗学, 2020, 17(2): 93-97. [Liang XQ, Cao LL, Chen YX. Contrast-enhanced transrectal ultrasonography in guiding prostate biopsy for diagnosis of prostate cancer[J]. Chinese Journal of Interventional Imaging and Therapy, 2020, 17(2): 93-97.] DOI: 10.13929/j.issn.1672-8475.2020.02.008.

14.Li S, Ye X, Tian H, et al. An artificial intelligence model based on transrectal ultrasound images of biopsy needle tract tissues to differentiate prostate cancer[J]. Postgrad Med J, 2024, 100(1182): 228-236. DOI: 10.1093/postmj/qgad127.

15.肖志伟. 基于Transformer的经直肠超声图像分割技术研究[D]. 广州: 广东技术师范大学, 2024. [Xiao ZW. Research on the technology about segmentation of transrectal ultrasound image based on transformer[D]. Guangzhou: Guangdong Polytechnic Normal University, 2024.] DOI: 10.27729/d.cnki.ggdjs.2023.000237.

16.Norris JM, Kinnaird A, Margolis DJ, et al. Developments in MRI-targeted prostate biopsy[J]. Curr Opin Urol, 2020, 30(1): 1-8. DOI: 10.1097/MOU.0000000000000683.

17.Mehralivand S, Yang D, Harmon SA, et al. Deep learning-based artificial intelligence for prostate cancer detection at biparametric MRI[J]. Abdom Radiol(NY), 2022, 47(4): 1425-1434. DOI: 10.1007/s00261-022-03419-2.

18.Akamine Y, Ueda Y, Ueno Y, et al. Application of hierarchical clustering to multi-parametric MR in prostate: differentiation of tumor and normal tissue with high accuracy[J]. Magn Reson Imaging, 2020, 74: 90-95. DOI: 10.1016/j.mri.2020.09.011.

19.许雨锶, 陈锐, 常易凡, 等. 人工智能在前列腺癌诊治及预后判断中的应用和展望[J]. 中华泌尿外科杂志, 2023, 44(2): 152-156. [Xu YS, Chen R, Chang YF, et al. Application and prospect of artificial intelligence in diagnosis, treatment and prognosis of prostate cancer[J]. Chinese Journal of Urology, 2023, 44(2): 152-156.] DOI: 10.3760/cma.j.cn112330-20220930-00527.

20.Litjens G, Sánchez CI, Timofeeva N, et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis[J]. Sci Rep, 2016, 6: 26286. DOI: 10.1038/srep26286.

21.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. DOI: 10.1016/S1470-2045(19)30738-7.

22.Yamada Y, Fujii Y, Kakutani S, et al. Development of risk-score model in patients with negative surgical margin after robot-assisted radical prostatectomy[J]. Sci Rep, 2024, 14(1): 7607. DOI: 10.1038/s41598-024-58279-1.

23.程晓锋, 王共先. 根治性前列腺切除术手术入路创新进展与加速康复外科[J]. 机器人外科学杂志(中英文), 2022, 3(6): 500-510. [Cheng XF, Wang GX. Innovative advances of surgical approach in radical prostatectomy and enhanced recovery after surgery[J]. Chinese Journal of Robotic Surgery, 2021, 3(6): 500-510.] DOI: 10.12180/j.issn.2096-7721.2022.06.011.

24.崔曦雯, 陈芳, 韩博轩, 等.虚拟内窥镜图像增强膝关节镜手术导航系统[J]. 中国生物医学工程学报, 2019, 38(5): 558-565. [Cui XW, Chen F, Han BX, et al. Image-guided arthroscopic surgery based on virtual endoscopic technology[J]. Chinese Journal of Biomedical Engineering, 2019, 38(5): 558-565.] DOI: 10.3969/j.issn.0258-8021. 2019.05.006.

25.方滢, 叶哲伟, 陈孝平. 科学技术对现代外科学发展的影响[J]. 临床外科杂志, 2024, 32(1): 1-5. [Fang Y, Ye ZW, Chen XP. Effect of science and technology on the development of modern surgery[J]. Journal of Clinical Surgery, 2024, 32(1): 1-5.] DOI: 10.3969/j.issn.1005-6483.2024.01.001.

26.张宇辰. 混合现实环境下的前列腺粒子植入机器人的手势交互研究[D]. 哈尔滨:哈尔滨理工大学, 2023. [Zhang YC. Research on gesture interaction of prostate seed implantation robot in mixed reality environment[D]. Harbin: Harbin University of Science and Technology, 2023.] DOI: 10.27063/d.cnki.ghlgu.2023.000194.

27.张天宇, 李彩虹, 昌志刚, 等. 锥形束CT下膀胱充盈度对前列腺癌实际剂量分布影响[J]. 中国CT和MRI杂志, 2023, 21(5): 102-104. [Zhang TY, Li CH, Chang ZG, et al. Actual dose distribution of volumetric changes of bladder filling during prostate cancer radiotherapy by cone beam CT[J]. Chinese Journal of CT and MRI, 2023, 21(5): 102-104.] DOI: 10.3969/j.issn.1672-5131.2023.05.035.

28.Hyer DE, Caster J, Smith B, et al. A technique to enable efficient adaptive radiation therapy: automated contouring of prostate and adjacent organs[J]. Adv Radiat Oncol, 2023, 9(1): 101336. DOI: 10.1016/j.adro.2023.101336.

29.Tahri S, Texier B, Nunes JC, et al. A deep learning model to generate synthetic CT for prostate MR-only radiotherapy dose planning: a multicenter study[J]. Front Oncol, 2023, 13: 1279750. DOI: 10.3389/fonc.2023.1279750.

30.吴双, 周育夫. 人工智能在肿瘤放射治疗中的应用[J]. 中国医疗设备, 2020, 35(8): 161-166. [Wu S, Zhou YF. Application of artificial intelligence in tumor radiotherapy[J]. China Medical Devices, 2020, 35(8): 161-166.] DOI: 10.3969/j.issn.1674-1633.2020.08.041.

31.Simon G, DiNardo CD, Takahashi K, et al. Applying artificial intelligence to address the knowledge gaps in cancer care[J]. Oncologist, 2019, 24(6): 772-782. DOI: 10.1634/theoncologist.2018-0257.

32.苏海志, 李其锋, 李斌. 专家系统和大数据在职业病的应用分析思考[J]. 中国医疗器械信息, 2021, 27(15): 29-30, 133. [Su HZ, Li QF, Li B. Analysis and reflection on the application of expert system and big data in occupational diseases[J]. China Medical Device Information, 2021, 27(15): 29-30, 133.] DOI: 10.3969/j.issn.1006-6586.2021.15.012.

33.Campanella G, Hanna MG, Geneslaw L, et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images[J]. Nat Med, 2019, 25(8): 1301-1309. DOI: 10.1038/s41591-019-0508-1.

34.Xiang J, Wang X, Wang X, et al. Automatic diagnosis and grading of prostate cancer with weakly supervised learning on whole slide images[J]. Comput Biol Med, 2023, 152: 106340. DOI: 10.1016/j.compbiomed.2022.106340.

35.Vazzano J, Johansson D, Hu K, et al. Evaluation of a computer-aided detection software for prostate cancer prediction: excellent diagnostic accuracy independent of preanalytical factors[J]. Lab Invest. 2023, 103(12): 100257. DOI: 10.1016/j.labinv.2023.100257.

36.朱学华, 邵立智, 刘振宇, 等. MRI相关影像组学模型用于前列腺癌诊断、侵袭性和预后评估(英文)[J]. 浙江大学学报B辑(生物医学与生物技术)(英文版), 2023, 24(8): 663-682. [Zhu XH, Shao LZ, Liu ZY, et al. MRI-derived radiomics models for diagnosis, aggressiveness, and pronosis evaluation in prostate cancer[J]. Journal of Zhejiang University-Science B (Biomedicine & Biotechnology), 2023, 24(8): 663-682.] https://www.cnki.com.cn/Article/CJFDTOTAL-ZDYW202308001.htm.

37.Guerra A, Negrão E, Papanikolaou N, et al. Machine learning in predicting extracapsular extension (ECE) of prostate cancer with MRI: a protocol for a systematic literature review[J]. BMJ Open, 2022, 12(5): e052342.  DOI: 10.1136/bmjopen-2021-052342.

38.Yan Y, Shao L, Liu Z, et al. Deep learning with quantitative features of magnetic resonance images to predict biochemical recurrence of radical prostatectomy: a multi-center study[J]. Cancers (Basel), 2021, 13(12): 3098. DOI: 10.3390/cancers13123098.

39.Jin C, Bian Z, Mo F, et al. Establishment and validation of coagulation factor-based nomogram for predicting the recurrence-free survival of prostate cancer[J]. Urol Int, 2022, 106(9): 954-962. DOI: 10.1159/000519329.

40.Zhao Y, Tao Z, Li L, et al. Predicting biochemical-recurrence-free survival using a three-metabolic-gene risk score model in prostate cancer patients[J]. BMC Cancer, 2022, 22(1): 239. DOI: 10.1186/s12885-022-09331-8.

41.Hu Q, Hong X, Xu L, et al. A nomogram for accurately predicting the pathological upgrading of prostate cancer, based on 68Ga-PSMA PET/CT[J]. Prostate, 2022, 82(11): 1077-1087. DOI: 10.1002/pros.24358.

42.曹毛毛, 陈万青. 中国癌症筛查现状[J]. 科技导报, 2023, 41(18): 11-17. [Cao MM, Chen WQ. The status of cancer screening in China[J]. Science and Technology Review, 2023, 41(18): 11-17.] https://www.cnki.com.cn/Article/CJFDTOTAL-KJDB202318003.htm.

43.Zhou K, Arslanturk S, Craig DB, et al. Discovery of primary prostate cancer biomarkers using cross cancer learning[J]. Sci Rep, 2021, 11(1): 10433. DOI: 10.1038/s41598-021-89789-x.

44.Deniffel D, Abraham N, Namdar K, et al. Using decision curve analysis to benchmark performance of a magnetic resonance imaging-based deep learning model for prostate cancer risk assessment[J]. Eur Radiol, 2020, 30(12): 6867-6876. DOI: 10.1007/s00330-020-07030-1.

45.Dwivedi AK. Artificial neural network model for effective cancer classification using microarray gene expression data[J]. Neural Comput Appl, 2018, 29(12): 1545-1554. DOI: 10.1007/s00521-016-2701-1.

46.赫捷, 陈万青, 李霓, 等. 中国前列腺癌筛查与早诊早治指南(2022, 北京)[J]. 中国肿瘤, 2022, 31(1): 1-30. [He J, Chen WQ, Li N, et al. China guideline for the screening and early detection of prostate cancer (2022, Beijing)[J]. China Cancer, 2022, 31(1): 1-30.] DOI: 10.11735/j.issn.1004-0242.2022.01.A001.

47.Song SH, Kim H, Kim JK, et al. A smart, practical, deep learning-based clinical decision support tool for patients in the prostate-specific antigen gray zone: model development and validation[J]. J Am Med Inform Assoc, 2022, 29(11): 1949-1957. DOI: 10.1093/jamia/ocac141.

48.Karageorgos GM, Cho S, McDonough E, et al. Deep learning-based automated pipeline for blood vessel detection and distribution analysis in multiplexed prostate cancer images[J]. Front Bioinform, 2024, 3: 1296667. DOI: 10.3389/fbinf.2023.1296667.

49.李骁, 孙志远, 张龙江. 影像人工智能在肺炎筛查、诊断及预测领域的应用研究进展[J]. 山东大学学报(医学版), 2023, 61(12): 13-20. [Li X, Sun ZY, Zhang LJ. Research advances of artificial intelligence-based medical imaging in the screening,diagnosis and prediction of pneumonia[J]. Journal of Shandong University (Health Science), 2023, 61(12): 13-20.] DOI: 10.6040/j.issn.1671-7554.0.2023.0803.

50.张姝艳, 皮婷婷. 医疗领域中人工智能应用的可解释性困境与治理[J]. 医学与哲学, 2023, 44(3): 25-29, 35. [Zhang SY, Pi TT. Interpretability dilemma and governance of artificial intelligence application in medical field[J]. Medicine and Philosophy, 2023, 44(3): 25-29, 35.] DOI: 10.12014/j.issn.1002-0772.2023.03.06.

Popular papers
Last 6 months