Research status and prospect of artificial intelligence technology in the diagnosis of urinary system tumors_Journal of Biomedical Engineering

Authors：

LIU Kun ^1,3,4 , ZHANG Mingyang ¹ , LI Haoran ¹ , WANG Xianghui ¹ , LI Dongming ² , LIU Shuang ^1,3,4 , YANG Kun ¹ , SUN Zhenduo ^1,4 ,  XUE Linyan ^1,4 ,  CUI Zhenyu ²

1. School of Quality and Technical Supervision, Hebei University, Baoding, Hebei 071002, P.R.China;
2. Department of Urology, Affiliated Hospital of Hebei University, Baoding, Hebei 071000, P.R.China;
3. Postdoctoral Research Station of Optical Engineering, Hebei University, Baoding, Hebei 071002, P.R.China;
4. National and Local Joint Engineering Research Center for Measuring Instruments and Systems, Baoding, Hebei 071002, P.R.China;

Corresponding author：

Keywords：

urinary system; artificial intelligence; renal cell carcinoma; bladder cancer; prostate cancer

DOI：

10.7507/1001-5515.202103010

Video：

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Abstract Full text Figures/Tables Video References Cited by

With the rapid development of artificial intelligence technology, researchers have applied it to the diagnosis of various tumors in the urinary system in recent years, and have obtained many valuable research results. The article sorted the research status of artificial intelligence technology in the fields of renal tumors, bladder tumors and prostate tumors from three aspects: the number of papers, image data, and clinical tasks. The purpose is to summarize and analyze the research status and find new valuable research ideas in the future. The results show that the artificial intelligence model based on medical data such as digital imaging and pathological images is effective in completing basic diagnosis of urinary system tumors, image segmentation of tumor infiltration areas or specific organs, gene mutation prediction and prognostic effect prediction, but most of the models for the requirement of clinical application still need to be improved. On the one hand, it is necessary to further improve the detection, classification, segmentation and other performance of the core algorithm. On the other hand, it is necessary to integrate more standardized medical databases to effectively improve the diagnostic accuracy of artificial intelligence models and make it play greater clinical value.

Citation： LIU Kun, ZHANG Mingyang, LI Haoran, WANG Xianghui, LI Dongming, LIU Shuang, YANG Kun, SUN Zhenduo, XUE Linyan, CUI Zhenyu. Research status and prospect of artificial intelligence technology in the diagnosis of urinary system tumors. Journal of Biomedical Engineering, 2021, 38(6): 1219-1228. doi: 10.7507/1001-5515.202103010 Copy

1.	Sung H, Ferlay J, Siegel R L, et al. Global cancer statistics 2020: GLOBOCAN estimates of Incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
2.	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. CA Cancer J Clin, 2018, 68(6): 394-424.
3.	Chen Jian, Remulla D, Nguyen J H, et al. Current status of artificial intelligence applications in urology and their potential to influence clinical practice. BJU Int, 2019, 124(4): 567-577.
4.	He Jianxing, Baxter S L, Xu Jie, et al. The practical implementation of artificial intelligence technologies in medicine. Nat Med, 2019, 25(1): 30-36.
5.	Duda R O, Hart P E, Stork D G. Pattern classification. New York: Wiley, 2012.
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7.	Parmar C, Grossmann P, Bussink J, al. Machine learning methods for quantitative radiomic biomarkers. Sci Rep, 2015, 5: 13087.
8.	LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444.
9.	张珺倩, 张远, 尹勇, 等. 机器学习在肿瘤放射治疗领域应用进展. 生物医学工程学杂志, 2019, 36(5): 879-884.
10.	Hosny A, Parmar C, Quackenbush J, et al. Artificial intelligence in radiology. Nat Rev Cancer, 2018, 18(8): 500-510.
11.	陈玉康. 深度神经网络结构搜索与优化方法研究. 北京: 中科院自动化所, 中国科学院大学. 2020.
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13.	Goodfellow I, Bengio Y, Courville A, et al; 赵申剑, 黎彧君, 符天凡, 等译. 深度学习. 北京: 人民邮电出版社, 2017: 66.
14.	Cui Enming, Lin Fan, Li Qing, et al. Differentiation of renal angiomyolipoma without visible fat from renal cell carcinoma by machine learning based on whole-tumor computed tomography texture features. Acta Radiol, 2019, 60(11): 1543-1552.
15.	Yang Ruimeng, Wu Jialiang, Sun Lei, et al. Radiomics of small renal masses on multiphasic CT: accuracy of machine learning–based classification models for the differentiation of renal cell carcinoma and angiomyolipoma without visible fat. Eur Radiol, 2020, 30(2): 1254-1263.
16.	Zhou Leilei, Zhang Zuoheng, Chen Yuchen, et al. A deep learning-based radiomics model for differentiating benign and malignant renal tumors. Transl Oncol, 2019, 12(2): 292-300.
17.	Xi I L, Zhao Yijun, Wang Robin, et al. Deep learning to distinguish benign from malignant renal lesions based on routine MR imaging. Clin Cancer Res, 2020, 26(8): 1944-1952.
18.	Cheville J C, Lohse C M, Zincke H, et al. Comparisons of outcome and prognostic features among histologic subtypes of renal cell carcinoma. Am J Surg Pathol, 2003, 27(5): 612-624.
19.	Kocak B, Yardimci A H, Bektas C T, et al. Textural differences between renal cell carcinoma subtypes: machine learning-based quantitative computed tomography texture analysis with independent external validation. Eur J Radiol, 2018, 107: 149-157.
20.	Zhang G M, Shi B, Xue H D, et al. Can quantitative CT texture analysis be used to differentiate subtypes of renal cell carcinoma?. Clin radiol, 2019, 74(4): 287-294.
21.	Li Zhicheng, Zhai Guangtao, Zhang Jinheng, et al. Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT: a VHL mutation perspective. Eur Radiol, 2019, 29(8): 3996-4007.
22.	Delahunt B, Cheville J C, Martignoni G, et al. The International Society of Urological Pathology (ISUP) grading system for renal cell carcinoma and other prognostic parameters. Am J Surg Pathol, 2013, 37(10): 1490-1504.
23.	Ding Jiule, Xing Zhaoyu, Jiang Zhenxing, et al. CT-based radiomic model predicts high grade of clear cell renal cell carcinoma. Eur J Radiol, 2018, 103: 51-56.
24.	Bektas C T, Kocak B, Yardimci A H, et al. Clear cell renal cell carcinoma: machine learning-based quantitative computed tomography texture analysis for prediction of Fuhrman nuclear grade. Eur Radiol, 2019, 29(3): 1153-1163.
25.	Kocak B, Durmaz E S, Ates E, et al. Unenhanced CT texture analysis of clear cell renal cell carcinomas: a machine learning-based study for predicting histopathologic nuclear grade. Am J Roentgenol, 2019, 212(6): W132-W139.
26.	Lin Fan, Ma Changyi, Xu Jinpeng, et al. A CT-based deep learning model for predicting the nuclear grade of clear cell renal cell carcinoma. Eur J Radiol, 2020, 129: 109079.
27.	Kocak B, Ates E, Durmaz E S, et al. Influence of segmentation margin on machine learning–based high-dimensional quantitative CT texture analysis: a reproducibility study on renal clear cell carcinomas. Eur Radiol, 2019, 29(9): 4765-4775.
28.	Linguraru M G, Wang Shijun, Shah F, et al. Automated noninvasive classification of renal cancer on multiphase CT. Med Phys, 2011, 38(10): 5738-5746.
29.	Yu Qian, Shi Yinghuan, Sun Jinquan, et al. Crossbar-Net: a novel convolutional neural network for kidney tumor segmentation in CT images. IEEE T Image Process, 2019, 28(8): 4060-4074.
30.	He Yuting, Yang Guanyu, Yang Jian, et al. Meta grayscale adaptive network for 3D integrated renal structures segmentation. Med Image Anal, 2021, 71: 102055.
31.	Kocak B, Durmaz E S, 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. Am J Roentgenol, 2019, 212(3): W55-W63.
32.	Ghosh P, Tamboli P, Vikram R, et al. Imaging-genomic pipeline for identifying gene mutations using three-dimensional intra-tumor heterogeneity features. J Med Imaging, 2015, 2(4): 041009.
33.	Eminaga O, Eminaga N, Semjonow A, et al. Diagnostic classification of cystoscopic images using deep convolutional neural networks. JCO Clin Cancer Info, 2018, 2: 1-8.
34.	Lorencin I, Anđelić N, Španjol J, et al. Using multi-layer perceptron with laplacian edge detector for bladder cancer diagnosis. Artif Intell Med, 2020, 102: 101746.
35.	Shkolyar E, Jia X, Chang T C, et al. Augmented bladder tumor detection using deep learning. Eur Urol, 2019, 76(6): 714-718.
36.	Sokolov I, Dokukin M E, Kalaparthi V, et al. Noninvasive diagnostic imaging using machine-learning analysis of nanoresolution images of cell surfaces: detection of bladder cancer. P Nati Acad Sci USA, 2018, 115(51): 12920-12925.
37.	Garapati S S, Hadjiiski L, Cha K H, et al. Urinary bladder cancer staging in CT urography using machine learning. Med Phys, 2017, 44(11): 5814-5823.
38.	Xu Xiaopan, Zhang Xi, Tian Qiang, et al. Three-dimensional texture features from intensity and high-order derivative maps for the discrimination between bladder tumors and wall tissues via MRI. Int J Comput Ass Rad, 2017, 12(4): 645-656.
39.	Chen Siteng, Jiang Liren, Zheng Xinyi, et al. Clinical use of machine learning-based pathomics signature for diagnosis and survival prediction of bladder cancer. Cancer Sci, 2021, 112(7): 2905-2914.
40.	Brieu N, Gavriel C G, Nearchou I P, et al. Automated tumour budding quantification by machine learning augments TNM staging in muscle-invasive bladder cancer prognosis. Sci Rep, 2019, 9(2): 1-19.
41.	McConkey D J, Choi W. Molecular subtypes of bladder cancer. Curr Oncol Rep, 2018, 20(10): 1-7.
42.	Woerl A C, Eckstein M, Geiger J, et al. Deep learning predicts molecular subtype of muscle-invasive bladder cancer from conventional histopathological slides. Eur Urol, 2020, 78(2): 256-264.
43.	Takeuchi T, Hattori-Kato M, Okuno Y, et al. Prediction of prostate cancer by deep learning with multilayer artificial neural network. Can Urol Assoc, 2019, 13(5): E145-E150.
44.	Vente C D, Vos P, Hosseinzadeh M, et al. Deep learning regression for prostate cancer detection and grading in bi-parametric MRI. IEEE T Biomed Eng, 2020, 68(2): 374-383.
45.	Ishioka J, Matsuoka Y, Uehara S, et al. Computer-aided diagnosis of prostate cancer on magnetic resonance imaging using a convolutional neural network algorithm. BJU Int, 2018, 122(3): 411-417.
46.	Kott O, Linsley D, Amin A, et al. Development of a deep learning algorithm for the histopathologic diagnosis and Gleason grading of prostate cancer biopsies: a pilot study. Eur Urol Focus, 2021, 7(2): 347-351.
47.	Sauter G, Steurer S, Clauditz T S, et al. Clinical utility of quantitative Gleason grading in prostate biopsies and prostatectomy specimens. Eur Urol, 2016, 69(4): 592-598.
48.	Nir G, Karimi D, Goldenberg S L, et al. Comparison of artificial intelligence techniques to evaluate performance of a classifier for automatic grading of prostate cancer from digitized histopathologic images. JAMA Netw open, 2019, 2(3): e190442.
49.	Bulten W, Pinckaers H, van Boven H, et al. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. Lancet Oncol, 2020, 21(2): 233-241.
50.	Yan Ke, Wang Xiuying, Kim Jinman, et al. A propagation-DNN: deep combination learning of multi-level features for MR prostate segmentation. Comput Meth Prog Bio, 2019, 170: 11-21.
51.	Liu Chang, Gardner S J, Wen Ning, et al. Automatic segmentation of the prostate on CT images using deep neural networks (DNN). Int J Radiat Oncol, 2019, 104(4): 924-932.
52.	van Sloun R J G, Wildeboer R R, Mannaerts C K, et al. Deep learning for real-time, automatic, and scanner-adapted prostate (zone) segmentation of transrectal ultrasound, for example, magnetic resonance imaging-transrectal ultrasound fusion prostate biopsy. Eur Urol Focus, 2021, 7(1): 78-85.
53.	Liu Dingyi, Peng Xin, Liu Xiaoqing, et al. A real-time system using deep learning to detect and track ureteral orifices during urinary endoscopy. Comput Biol Med, 2021, 128: 104104.
54.	Peng Xin, Liu Dingyi, Li Yiming, et al. Real-time detection of ureteral orifice in urinary endoscopy videos based on deep learning// 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Berlin: IEEE, 2019: 1637-1640.
55.	钟宇, 田芳, 邹明宇, 等. 基于PI-RADS v2. 1不同参数磁共振成像对前列腺癌诊断效能的比较. 中国医科大学学报, 2020, 49(10): 915-920.
56.	Sokolov I, Dokukin M E. Imaging of soft and biological samples using AFM ringing mode. Methods Mol Biol, 2018, 1814: 469-482.
57.	Bellmunt J. Stem-like signature predicting disease progression in early stage bladder cancer. The role of E2F3 and SOX4. Biomedicines, 2018, 6(3): 85.

1. Sung H, Ferlay J, Siegel R L, et al. Global cancer statistics 2020: GLOBOCAN estimates of Incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
2. 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. CA Cancer J Clin, 2018, 68(6): 394-424.
3. Chen Jian, Remulla D, Nguyen J H, et al. Current status of artificial intelligence applications in urology and their potential to influence clinical practice. BJU Int, 2019, 124(4): 567-577.
4. He Jianxing, Baxter S L, Xu Jie, et al. The practical implementation of artificial intelligence technologies in medicine. Nat Med, 2019, 25(1): 30-36.
5. Duda R O, Hart P E, Stork D G. Pattern classification. New York: Wiley, 2012.
6. Mohri M, Rostamizadeh A, Talwalkar A. Foundations of machine learning. London: The MIT Press, 2012.
7. Parmar C, Grossmann P, Bussink J, al. Machine learning methods for quantitative radiomic biomarkers. Sci Rep, 2015, 5: 13087.
8. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444.
9. 张珺倩, 张远, 尹勇, 等. 机器学习在肿瘤放射治疗领域应用进展. 生物医学工程学杂志, 2019, 36(5): 879-884.
10. Hosny A, Parmar C, Quackenbush J, et al. Artificial intelligence in radiology. Nat Rev Cancer, 2018, 18(8): 500-510.
11. 陈玉康. 深度神经网络结构搜索与优化方法研究. 北京: 中科院自动化所, 中国科学院大学. 2020.
12. 周志华. 机器学习. 北京: 清华大学出版社, 2016: 29-36.
13. Goodfellow I, Bengio Y, Courville A, et al; 赵申剑, 黎彧君, 符天凡, 等译. 深度学习. 北京: 人民邮电出版社, 2017: 66.
14. Cui Enming, Lin Fan, Li Qing, et al. Differentiation of renal angiomyolipoma without visible fat from renal cell carcinoma by machine learning based on whole-tumor computed tomography texture features. Acta Radiol, 2019, 60(11): 1543-1552.
15. Yang Ruimeng, Wu Jialiang, Sun Lei, et al. Radiomics of small renal masses on multiphasic CT: accuracy of machine learning–based classification models for the differentiation of renal cell carcinoma and angiomyolipoma without visible fat. Eur Radiol, 2020, 30(2): 1254-1263.
16. Zhou Leilei, Zhang Zuoheng, Chen Yuchen, et al. A deep learning-based radiomics model for differentiating benign and malignant renal tumors. Transl Oncol, 2019, 12(2): 292-300.
17. Xi I L, Zhao Yijun, Wang Robin, et al. Deep learning to distinguish benign from malignant renal lesions based on routine MR imaging. Clin Cancer Res, 2020, 26(8): 1944-1952.
18. Cheville J C, Lohse C M, Zincke H, et al. Comparisons of outcome and prognostic features among histologic subtypes of renal cell carcinoma. Am J Surg Pathol, 2003, 27(5): 612-624.
19. Kocak B, Yardimci A H, Bektas C T, et al. Textural differences between renal cell carcinoma subtypes: machine learning-based quantitative computed tomography texture analysis with independent external validation. Eur J Radiol, 2018, 107: 149-157.
20. Zhang G M, Shi B, Xue H D, et al. Can quantitative CT texture analysis be used to differentiate subtypes of renal cell carcinoma?. Clin radiol, 2019, 74(4): 287-294.
21. Li Zhicheng, Zhai Guangtao, Zhang Jinheng, et al. Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT: a VHL mutation perspective. Eur Radiol, 2019, 29(8): 3996-4007.
22. Delahunt B, Cheville J C, Martignoni G, et al. The International Society of Urological Pathology (ISUP) grading system for renal cell carcinoma and other prognostic parameters. Am J Surg Pathol, 2013, 37(10): 1490-1504.
23. Ding Jiule, Xing Zhaoyu, Jiang Zhenxing, et al. CT-based radiomic model predicts high grade of clear cell renal cell carcinoma. Eur J Radiol, 2018, 103: 51-56.
24. Bektas C T, Kocak B, Yardimci A H, et al. Clear cell renal cell carcinoma: machine learning-based quantitative computed tomography texture analysis for prediction of Fuhrman nuclear grade. Eur Radiol, 2019, 29(3): 1153-1163.
25. Kocak B, Durmaz E S, Ates E, et al. Unenhanced CT texture analysis of clear cell renal cell carcinomas: a machine learning-based study for predicting histopathologic nuclear grade. Am J Roentgenol, 2019, 212(6): W132-W139.
26. Lin Fan, Ma Changyi, Xu Jinpeng, et al. A CT-based deep learning model for predicting the nuclear grade of clear cell renal cell carcinoma. Eur J Radiol, 2020, 129: 109079.
27. Kocak B, Ates E, Durmaz E S, et al. Influence of segmentation margin on machine learning–based high-dimensional quantitative CT texture analysis: a reproducibility study on renal clear cell carcinomas. Eur Radiol, 2019, 29(9): 4765-4775.
28. Linguraru M G, Wang Shijun, Shah F, et al. Automated noninvasive classification of renal cancer on multiphase CT. Med Phys, 2011, 38(10): 5738-5746.
29. Yu Qian, Shi Yinghuan, Sun Jinquan, et al. Crossbar-Net: a novel convolutional neural network for kidney tumor segmentation in CT images. IEEE T Image Process, 2019, 28(8): 4060-4074.
30. He Yuting, Yang Guanyu, Yang Jian, et al. Meta grayscale adaptive network for 3D integrated renal structures segmentation. Med Image Anal, 2021, 71: 102055.
31. Kocak B, Durmaz E S, 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. Am J Roentgenol, 2019, 212(3): W55-W63.
32. Ghosh P, Tamboli P, Vikram R, et al. Imaging-genomic pipeline for identifying gene mutations using three-dimensional intra-tumor heterogeneity features. J Med Imaging, 2015, 2(4): 041009.
33. Eminaga O, Eminaga N, Semjonow A, et al. Diagnostic classification of cystoscopic images using deep convolutional neural networks. JCO Clin Cancer Info, 2018, 2: 1-8.
34. Lorencin I, Anđelić N, Španjol J, et al. Using multi-layer perceptron with laplacian edge detector for bladder cancer diagnosis. Artif Intell Med, 2020, 102: 101746.
35. Shkolyar E, Jia X, Chang T C, et al. Augmented bladder tumor detection using deep learning. Eur Urol, 2019, 76(6): 714-718.
36. Sokolov I, Dokukin M E, Kalaparthi V, et al. Noninvasive diagnostic imaging using machine-learning analysis of nanoresolution images of cell surfaces: detection of bladder cancer. P Nati Acad Sci USA, 2018, 115(51): 12920-12925.
37. Garapati S S, Hadjiiski L, Cha K H, et al. Urinary bladder cancer staging in CT urography using machine learning. Med Phys, 2017, 44(11): 5814-5823.
38. Xu Xiaopan, Zhang Xi, Tian Qiang, et al. Three-dimensional texture features from intensity and high-order derivative maps for the discrimination between bladder tumors and wall tissues via MRI. Int J Comput Ass Rad, 2017, 12(4): 645-656.
39. Chen Siteng, Jiang Liren, Zheng Xinyi, et al. Clinical use of machine learning-based pathomics signature for diagnosis and survival prediction of bladder cancer. Cancer Sci, 2021, 112(7): 2905-2914.
40. Brieu N, Gavriel C G, Nearchou I P, et al. Automated tumour budding quantification by machine learning augments TNM staging in muscle-invasive bladder cancer prognosis. Sci Rep, 2019, 9(2): 1-19.
41. McConkey D J, Choi W. Molecular subtypes of bladder cancer. Curr Oncol Rep, 2018, 20(10): 1-7.
42. Woerl A C, Eckstein M, Geiger J, et al. Deep learning predicts molecular subtype of muscle-invasive bladder cancer from conventional histopathological slides. Eur Urol, 2020, 78(2): 256-264.
43. Takeuchi T, Hattori-Kato M, Okuno Y, et al. Prediction of prostate cancer by deep learning with multilayer artificial neural network. Can Urol Assoc, 2019, 13(5): E145-E150.
44. Vente C D, Vos P, Hosseinzadeh M, et al. Deep learning regression for prostate cancer detection and grading in bi-parametric MRI. IEEE T Biomed Eng, 2020, 68(2): 374-383.
45. Ishioka J, Matsuoka Y, Uehara S, et al. Computer-aided diagnosis of prostate cancer on magnetic resonance imaging using a convolutional neural network algorithm. BJU Int, 2018, 122(3): 411-417.
46. Kott O, Linsley D, Amin A, et al. Development of a deep learning algorithm for the histopathologic diagnosis and Gleason grading of prostate cancer biopsies: a pilot study. Eur Urol Focus, 2021, 7(2): 347-351.
47. Sauter G, Steurer S, Clauditz T S, et al. Clinical utility of quantitative Gleason grading in prostate biopsies and prostatectomy specimens. Eur Urol, 2016, 69(4): 592-598.
48. Nir G, Karimi D, Goldenberg S L, et al. Comparison of artificial intelligence techniques to evaluate performance of a classifier for automatic grading of prostate cancer from digitized histopathologic images. JAMA Netw open, 2019, 2(3): e190442.
49. Bulten W, Pinckaers H, van Boven H, et al. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. Lancet Oncol, 2020, 21(2): 233-241.
50. Yan Ke, Wang Xiuying, Kim Jinman, et al. A propagation-DNN: deep combination learning of multi-level features for MR prostate segmentation. Comput Meth Prog Bio, 2019, 170: 11-21.
51. Liu Chang, Gardner S J, Wen Ning, et al. Automatic segmentation of the prostate on CT images using deep neural networks (DNN). Int J Radiat Oncol, 2019, 104(4): 924-932.
52. van Sloun R J G, Wildeboer R R, Mannaerts C K, et al. Deep learning for real-time, automatic, and scanner-adapted prostate (zone) segmentation of transrectal ultrasound, for example, magnetic resonance imaging-transrectal ultrasound fusion prostate biopsy. Eur Urol Focus, 2021, 7(1): 78-85.
53. Liu Dingyi, Peng Xin, Liu Xiaoqing, et al. A real-time system using deep learning to detect and track ureteral orifices during urinary endoscopy. Comput Biol Med, 2021, 128: 104104.
54. Peng Xin, Liu Dingyi, Li Yiming, et al. Real-time detection of ureteral orifice in urinary endoscopy videos based on deep learning// 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Berlin: IEEE, 2019: 1637-1640.
55. 钟宇, 田芳, 邹明宇, 等. 基于PI-RADS v2. 1不同参数磁共振成像对前列腺癌诊断效能的比较. 中国医科大学学报, 2020, 49(10): 915-920.
56. Sokolov I, Dokukin M E. Imaging of soft and biological samples using AFM ringing mode. Methods Mol Biol, 2018, 1814: 469-482.
57. Bellmunt J. Stem-like signature predicting disease progression in early stage bladder cancer. The role of E2F3 and SOX4. Biomedicines, 2018, 6(3): 85.

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