Exploring the Use of Artificial Intelligence in the Management of Prostate Cancer

被引：14
作者：
Chu, Timothy N. N. [1 ]
Wong, Elyssa Y. Y. [1 ]
Ma, Runzhuo [1 ]
Yang, Cherine H. H. [1 ]
Dalieh, Istabraq S. [1 ]
Hung, Andrew J. J. [1 ]
机构：
[1] Univ Southern Calif, USC Inst Urol, Ctr Robot Simulat & Educ, Dept Urol, Catherine & Joseph Aresty1441 Eastlake Ave Suite 7, Los Angeles, CA 90089 USA
来源：
CURRENT UROLOGY REPORTS | 2023年 / 24卷 / 05期
关键词：
Artificial intelligence; Machine learning; Prostate cancer; Radiomics; Pathomics; RADIOMICS; BIOPSIES;
D O I：
10.1007/s11934-023-01149-6
中图分类号：
R5 [内科学]; R69 [泌尿科学（泌尿生殖系疾病）];
学科分类号：
1002 ; 100201 ;
摘要：
Purpose of ReviewThis review aims to explore the current state of research on the use of artificial intelligence (AI) in the management of prostate cancer. We examine the various applications of AI in prostate cancer, including image analysis, prediction of treatment outcomes, and patient stratification. Additionally, the review will evaluate the current limitations and challenges faced in the implementation of AI in prostate cancer management.Recent FindingsRecent literature has focused particularly on the use of AI in radiomics, pathomics, the evaluation of surgical skills, and patient outcomes.AI has the potential to revolutionize the future of prostate cancer management by improving diagnostic accuracy, treatment planning, and patient outcomes. Studies have shown improved accuracy and efficiency of AI models in the detection and treatment of prostate cancer, but further research is needed to understand its full potential as well as limitations.
引用
收藏
页码：231 / 240
页数：10
相关论文
共 49 条
[1]   A data-driven ultrasound approach discriminates pathological high grade prostate cancer [J].
Akatsuka, Jun ;
Numata, Yasushi ;
Morikawa, Hiromu ;
Sekine, Tetsuro ;
Kayama, Shigenori ;
Mikami, Hikaru ;
Yanagi, Masato ;
Endo, Yuki ;
Takeda, Hayato ;
Toyama, Yuka ;
Yamaguchi, Ruri ;
Kimura, Go ;
Kondo, Yukihiro ;
Yamamoto, Yoichiro .
SCIENTIFIC REPORTS, 2022, 12 (01)
[2]   Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists [J].
Antonelli, Michela ;
Johnston, Edward W. ;
Dikaios, Nikolaos ;
Cheung, King K. ;
Sidhu, Harbir S. ;
Appayya, Mrishta B. ;
Giganti, Francesco ;
Simmons, Lucy A. M. ;
Freeman, Alex ;
Allen, Clare ;
Ahmed, Hashim U. ;
Atkinson, David ;
Ourselin, Sebastien ;
Punwani, Shonit .
EUROPEAN RADIOLOGY, 2019, 29 (09) :4754-4764
[3]   Automated Gleason grading of prostate cancer tissue microarrays via deep learning [J].
Arvaniti, Eirini ;
Fricker, Kim S. ;
Moret, Michael ;
Rupp, Niels ;
Hermanns, Thomas ;
Fankhauser, Christian ;
Wey, Norbert ;
Wild, Peter J. ;
Ruschoff, Jan H. ;
Claassen, Manfred .
SCIENTIFIC REPORTS, 2018, 8
[4]   askMUSIC: Leveraging a Clinical Registry to Develop a New Machine Learning Model to Inform Patients of Prostate Cancer Treatments Chosen by Similar Men [J].
Auffenberg, Gregory B. ;
Ghani, Khurshid R. ;
Ramani, Shreyas ;
Usoro, Etiowo ;
Denton, Brian ;
Rogers, Craig ;
Stockton, Benjamin ;
Miller, David C. ;
Singh, Karandeep .
EUROPEAN UROLOGY, 2019, 75 (06) :901-907
[5]   A computer vision technique for automated assessment of surgical performance using surgeons' console-feed videos [J].
Baghdadi, Amir ;
Hussein, Ahmed A. ;
Ahmed, Youssef ;
Cavuoto, Lora A. ;
Guru, Khurshid A. .
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, 2019, 14 (04) :697-707
[6]   Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients [J].
Bhargava, Hersh K. ;
Leo, Patrick ;
Elliott, Robin ;
Janowczyk, Andrew ;
Whitney, Jon ;
Gupta, Sanjay ;
Fu, Pingfu ;
Yamoah, Kosj ;
Khani, Francesca ;
Robinson, Brian D. ;
Rebbeck, Timothy R. ;
Feldman, Michael ;
Lal, Priti ;
Madabhushi, Anant .
CLINICAL CANCER RESEARCH, 2020, 26 (08) :1915-1923
[7]   Development and Validation of an Interpretable Artificial Intelligence Model to Predict 10-Year Prostate Cancer Mortality [J].
Bibault, Jean-Emmanuel ;
Hancock, Steven ;
Buyyounouski, Mark K. ;
Bagshaw, Hilary ;
Leppert, John T. ;
Liao, Joseph C. ;
Xing, Lei .
CANCERS, 2021, 13 (12)
[8]   Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study [J].
Bulten, Wouter ;
Pinckaers, Hans ;
van Boven, Hester ;
Vink, Robert ;
de Bel, Thomas ;
van Ginneken, Bram ;
van der Laak, Jeroen ;
Hulsbergen-van de Kaa, Christina ;
Litjens, Geert .
LANCET ONCOLOGY, 2020, 21 (02) :233-241
[9]   Artificial intelligence and computational pathology [J].
Cui, Miao ;
Zhang, David Y. .
LABORATORY INVESTIGATION, 2021, 101 (04) :412-422
[10]   Machine learning-based analysis of [18F]DCFPyL PET radiomics for risk stratification in primary prostate cancer [J].
Cysouw, Matthijs C. F. ;
Jansen, Bernard H. E. ;
van de Brug, Tim ;
Oprea-Lager, Daniela E. ;
Pfaehler, Elisabeth ;
de Vries, Bart M. ;
van Moorselaar, Reindert J. A. ;
Hoekstra, Otto S. ;
Vis, Andre N. ;
Boellaard, Ronald .
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2021, 48 (02) :340-349
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