Artificial Intelligence in Uropathology

Overview

Journal Diagnostics (Basel)

Specialty Radiology

Date 2024 Oct 25

PMID 39451602

Authors

Katia Ramos Moreira Leite

Petronio Augusto de Souza Melo

Affiliations

Soon will be listed here.

Abstract

The global population is currently at unprecedented levels, with an estimated 7.8 billion people inhabiting the planet. We are witnessing a rise in cancer cases, attributed to improved control of cardiovascular diseases and a growing elderly population. While this has resulted in an increased workload for pathologists, it also presents an opportunity for advancement. The accurate classification of tumors and identification of prognostic and predictive factors demand specialized expertise and attention. Fortunately, the rapid progression of artificial intelligence (AI) offers new prospects in medicine, particularly in diagnostics such as image and surgical pathology. This article explores the transformative impact of AI in the field of uropathology, with a particular focus on its application in diagnosing, grading, and prognosticating various urological cancers. AI, especially deep learning algorithms, has shown significant potential in improving the accuracy and efficiency of pathology workflows. This comprehensive review is dedicated to providing an insightful overview of the primary data concerning the utilization of AI in diagnosing, predicting prognosis, and determining drug responses for tumors of the urinary tract. By embracing these advancements, we can look forward to improved outcomes and better patient care.

References

Cornford P, van den Bergh R, Briers E, Van den Broeck T, Brunckhorst O, Darraugh J . EAU-EANM-ESTRO-ESUR-ISUP-SIOG Guidelines on Prostate Cancer-2024 Update. Part I: Screening, Diagnosis, and Local Treatment with Curative Intent. Eur Urol. 2024; 86(2):148-163. DOI: 10.1016/j.eururo.2024.03.027. View

Marco-Ruiz L, Hernandez M, Ngo P, Makhlysheva A, Svenning T, Dyb K . A multinational study on artificial intelligence adoption: Clinical implementers' perspectives. Int J Med Inform. 2024; 184:105377. DOI: 10.1016/j.ijmedinf.2024.105377. View

Bulten W, Pinckaers H, van Boven H, Vink R, de Bel T, van Ginneken B . Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. Lancet Oncol. 2020; 21(2):233-241. DOI: 10.1016/S1470-2045(19)30739-9. View

Tian K, Rubadue C, Lin D, Veta M, Pyle M, Irshad H . Automated clear cell renal carcinoma grade classification with prognostic significance. PLoS One. 2019; 14(10):e0222641. PMC: 6776313. DOI: 10.1371/journal.pone.0222641. View

Marostica E, Barber R, Denize T, Kohane I, Signoretti S, Golden J . Development of a Histopathology Informatics Pipeline for Classification and Prediction of Clinical Outcomes in Subtypes of Renal Cell Carcinoma. Clin Cancer Res. 2021; 27(10):2868-2878. DOI: 10.1158/1078-0432.CCR-20-4119. View

Moch H, Amin M, Berney D, Comperat E, Gill A, Hartmann A . The 2022 World Health Organization Classification of Tumours of the Urinary System and Male Genital Organs-Part A: Renal, Penile, and Testicular Tumours. Eur Urol. 2022; 82(5):458-468. DOI: 10.1016/j.eururo.2022.06.016. View

Bulten W, Kartasalo K, Chen P, Strom P, Pinckaers H, Nagpal K . Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge. Nat Med. 2022; 28(1):154-163. PMC: 8799467. DOI: 10.1038/s41591-021-01620-2. View

Strom P, Kartasalo K, Olsson H, Solorzano L, Delahunt B, Berney D . Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study. Lancet Oncol. 2020; 21(2):222-232. DOI: 10.1016/S1470-2045(19)30738-7. View

Baxi V, Lee G, Duan C, Pandya D, Cohen D, Edwards R . Association of artificial intelligence-powered and manual quantification of programmed death-ligand 1 (PD-L1) expression with outcomes in patients treated with nivolumab ± ipilimumab. Mod Pathol. 2022; 35(11):1529-1539. PMC: 9596372. DOI: 10.1038/s41379-022-01119-2. View

10.

Holdbrook D, Singh M, Choudhury Y, Kalaw E, Koh V, Tan H . Automated Renal Cancer Grading Using Nuclear Pleomorphic Patterns. JCO Clin Cancer Inform. 2019; 2:1-12. DOI: 10.1200/CCI.17.00100. View

11.

Noorbakhsh J, Farahmand S, Foroughi Pour A, Namburi S, Caruana D, Rimm D . Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images. Nat Commun. 2020; 11(1):6367. PMC: 7733499. DOI: 10.1038/s41467-020-20030-5. View

12.

Yeh F, Parwani A, Pantanowitz L, Ho C . Automated grading of renal cell carcinoma using whole slide imaging. J Pathol Inform. 2014; 5(1):23. PMC: 4141422. DOI: 10.4103/2153-3539.137726. View

13.

Yin P, Kc K, Wei S, Yu Q, Li R, Haake A . Histopathological distinction of non-invasive and invasive bladder cancers using machine learning approaches. BMC Med Inform Decis Mak. 2020; 20(1):162. PMC: 7367328. DOI: 10.1186/s12911-020-01185-z. View

14.

Lucas M, Jansen I, Dilara Savci-Heijink C, Meijer S, de Boer O, van Leeuwen T . Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies. Virchows Arch. 2019; 475(1):77-83. PMC: 6611751. DOI: 10.1007/s00428-019-02577-x. View

15.

Pinckaers H, van Ipenburg J, Melamed J, De Marzo A, Platz E, van Ginneken B . Predicting biochemical recurrence of prostate cancer with artificial intelligence. Commun Med (Lond). 2022; 2:64. PMC: 9177591. DOI: 10.1038/s43856-022-00126-3. View

16.

Melo P, Neubarth Estivallet C, Srougi M, Nahas W, Leite K . Detecting and grading prostate cancer in radical prostatectomy specimens through deep learning techniques. Clinics (Sao Paulo). 2021; 76:e3198. PMC: 8527555. DOI: 10.6061/clinics/2021/e3198. View

17.

van Leenders G, van der Kwast T, Grignon D, Evans A, Kristiansen G, Kweldam C . The 2019 International Society of Urological Pathology (ISUP) Consensus Conference on Grading of Prostatic Carcinoma. Am J Surg Pathol. 2020; 44(8):e87-e99. PMC: 7382533. DOI: 10.1097/PAS.0000000000001497. View

18.

Faryna K, Tessier L, Retamero J, Bonthu S, Samanta P, Singhal N . Evaluation of Artificial Intelligence-Based Gleason Grading Algorithms "in the Wild". Mod Pathol. 2024; 37(11):100563. DOI: 10.1016/j.modpat.2024.100563. View

19.

Carlo M, Voss M, Motzer R . Checkpoint inhibitors and other novel immunotherapies for advanced renal cell carcinoma. Nat Rev Urol. 2016; 13(7):420-31. PMC: 5532875. DOI: 10.1038/nrurol.2016.103. View

20.

Jiang Y, Yang M, Wang S, Li X, Sun Y . Emerging role of deep learning-based artificial intelligence in tumor pathology. Cancer Commun (Lond). 2020; 40(4):154-166. PMC: 7170661. DOI: 10.1002/cac2.12012. View