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 Table of Contents  
EDITORIAL
Year : 2020  |  Volume : 13  |  Issue : 1  |  Page : 1-2

Use of artificial intelligence in the diagnosis and treatment of prostate cancer


1 Department of Urology, JN Medical College, KLE Academy of Higher Education and Research, JNMC Campus, Belagavi, Karnataka, India
2 Department of Urology, KLES Kidney Foundation, Urinary Biomarkers Research Centre, KLES Dr. Prabhakar Kore Hospital and Medical Research Centre, Belagavi, Karnataka, India

Date of Submission27-Dec-2019
Date of Acceptance05-Jan-2020
Date of Web Publication23-Jan-2020

Correspondence Address:
Dr. R B Nerli
Department of Urology, JN Medical College, KLE Academy of Higher Education and Research (Deemed-to-be-University), JNMC Campus, Belagavi - 590 010, Karnataka
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/kleuhsj.kleuhsj_298_19

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How to cite this article:
Nerli R B, Ghagane SC. Use of artificial intelligence in the diagnosis and treatment of prostate cancer. Indian J Health Sci Biomed Res 2020;13:1-2

How to cite this URL:
Nerli R B, Ghagane SC. Use of artificial intelligence in the diagnosis and treatment of prostate cancer. Indian J Health Sci Biomed Res [serial online] 2020 [cited 2020 Feb 18];13:1-2. Available from: http://www.ijournalhs.org/text.asp?2020/13/1/1/276425



Artificial intelligence (AI) is defined as the ability of a machine to perform cognitive tasks to achieve a particular goal based on provided data. This is revolutionizing and reshaping our healthcare systems today. Today, there is availability of high capability computational power, highly developed pattern recognition algorithms, and advanced image processing software, and this has led to the emergence of computer-based systems that are trained to perform complex tasks in bioinformatics, medical imaging, and medical robotics.

The defense industry was the first to use the field of AI way back in the 1950s and this has evolved over years. Currently, computer-based “deep-learning” methods have been introduced and this has prompted the physician-scientists to use this technology in the medical field so as to improve diagnostic methods.

Prostate cancer is the sixth most common cancer worldwide and the second leading cause of cancer death in American men.[1] The incidence of prostate cancer is also on the rise in India and ranks among the top ten malignancies affecting an Indian male.[2] Early detection and localization of prostate cancer at a treatable stage are desired both by the treating physician and by the patient, as excellent cancer-specific survival is expected for most locally confined diseases. Transrectal ultrasonography (TRUS)-guided biopsy of 12 regions distributed throughout the prostate is the most common method to diagnose prostate cancer. TRUS-guided biopsy is recommended in patients with an elevated prostate-specific antigen (PSA) level or an abnormal digital rectal examination.[3] The assessment of risk is primarily determined by pathologic grade based on the Gleason grading system.[4] The Gleason grading system generally captures the full range of biologic aggressiveness and has historically correlated with the risk of recurrence following definitive treatment.[5]

Cancers containing only Gleason pattern 3 (i.e., overall Gleason score assignment 3 + 3) are generally considered low risk. Gleason pattern 4 which includes poorly formed, fused, cribriform glands has shown to be associated with poor surgical and clinical outcomes, including higher rates of extraprostatic extension, positive surgical margins, biochemical recurrence, and cancer-specific mortality.[6] Gleason pattern 5, which consists of high-risk histologic patterns including sheets of tumor, individual cells, and cords of cells, carries a higher risk of progression to metastatic disease and cancer-specific mortality.[7] Prostate cancer is known to exhibit heterogeneous distribution of morphologies both within and across tumors of the same patient, with increasing number of observed morphologies in larger cancers of worsening grade.[7]

Multiparametric magnetic resonance imaging (mpMRI) has established itself as an important aid in the diagnosis of prostate cancer. mpMRI provides high-contrast, high-resolution anatomical images of the prostate and pelvic regions using T2-weighted imaging, diffusion-weighted imaging, and dynamic contrast-enhanced imaging sequences for the detection of clinically significant prostate cancers.[8] The interpretation of all mpMRI sequences requires a considerable level of expertise from radiologists. Currently, abnormal regions of the prostate on mpMRI imaging have been defined by the Prostate Imaging Reporting and Detection System Version 2.[9] This system, in spite of being widely accepted, is prone to inter-reader variability, leading to variable sensitivity, specificity, and a wide range of cancer detection rates.[10],[11] Despite these shortcomings, mpMRI has shown practice-changing value for urologists to more accurately direct needle biopsies into suspicious regions of the prostate.

The complex heterogeneity of localized prostate cancer has led to variability in clinical practice for both radiology and pathology. The use of AI applications in both radiology and pathology has increased substantially with the adoption of deep-learning techniques applied to medical imaging. AI shows great promise in decreasing reader interpretation times, increasing performance of nonexpert radiologists, and enabling large-scale screening practices without additional burden to radiologists. Applications of AI to prostate mpMRI are specifically expected to increase sensitivity of prostate cancer detection and decrease inter-reader variability.[12]

Currently, both radiology and pathology have limitations in their ability to detect and classify intermediate prostate cancers, which represent a critical point in clinical treatment decision-making. Currently, there exists a need for improvement in standardized assessment and characterization on pathologic and radiologic interpretation. The intimate connection between histology and functional imaging characteristics creates a unique opportunity for AI applications to improve detection, classification, and overall prognostication. As demonstrated across AI literature, the need for mature datasets with high-quality annotations is necessary to continue advancements in this field.



 
  References Top

1.
Grönberg H. Prostate cancer epidemiology. Lancet 2003;361:859-64.  Back to cited text no. 1
    
2.
Ghagane SC, Nerli RB, Hiremath MB, Wagh AT, Magdum PV. Incidence of prostate cancer at a single tertiary care center in North Karnataka. Indian J Cancer 2016;53:429-31.  Back to cited text no. 2
[PUBMED]  [Full text]  
3.
Costa DN, Pedrosa I, Donato F Jr., Roehrborn CG, Rofsky NM. MR imaging-transrectal US fusion for targeted prostate biopsies: Implications for diagnosis and clinical management. Radiographics 2015;35:696-708.  Back to cited text no. 3
    
4.
Gleason DF. Classification of prostatic carcinomas. Cancer Chemother Rep 1966;50:125-8.  Back to cited text no. 4
    
5.
Epstein JI, Egevad L, Amin MB, Delahunt B, Srigley JR, Humphrey PA, et al. The 2014 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma: Definition of grading patterns and proposal for a new grading system. Am J Surg Pathol 2016;40:244-52.  Back to cited text no. 5
    
6.
Kweldam CF, Wildhagen MF, Steyerberg EW, Bangma CH, van der Kwast TH, van Leenders GJ. Cribriform growth is highly predictive for postoperative metastasis and disease-specific death in Gleason score 7 prostate cancer. Mod Pathol 2015;28:457-64.  Back to cited text no. 6
    
7.
Aihara M, Wheeler TM, Ohori M, Scardino PT. Heterogeneity of prostate cancer in radical prostatectomy specimens. Urology 1994;43:60-6.  Back to cited text no. 7
    
8.
Ploussard G, Epstein JI, Montironi R, Carroll PR, Wirth M, Grimm MO, et al. The contemporary concept of significant versus insignificant prostate cancer. Eur Urol 2011;60:291-303.  Back to cited text no. 8
    
9.
Weinreb JC, Barentsz JO, Choyke PL, Cornud F, Haider MA, Macura KJ, et al. PI-RADS Prostate Imaging-Reporting and Data System: 2015, Version 2. Eur Urol 2016;69:16-40.  Back to cited text no. 9
    
10.
Rosenkrantz AB, Ginocchio LA, Cornfeld D, Froemming AT, Gupta RT, Turkbey B, et al. Interobserver reproducibility of the PI-RADS Version 2 Lexicon: A multicenter study of six experienced prostate radiologists. Radiology 2016;280:793-804.  Back to cited text no. 10
    
11.
Mehralivand S, Bednarova S, Shih JH, Mertan FV, Gaur S, Merino MJ, et al. Prospective evaluation of PI-RADS™ Version 2 using the International Society of Urological Pathology Prostate Cancer Grade Group System. J Urol 2017;198:583-90.  Back to cited text no. 11
    
12.
Greer MD, Lay N, Shih JH, Barrett T, Bittencourt LK, Borofsky S, et al. Computer-aided diagnosis prior to conventional interpretation of prostate mpMRI: An international multi-reader study. Eur Radiol 2018;28:4407-17.  Back to cited text no. 12
    




 

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