Artificial Intelligence for Renal Cancer: From Imaging to Histology and Beyond

Overview

Journal Asian J Urol

Specialty Urology

Date 2022 Aug 29

PMID 36035341

Authors

Karl-Friedrich Kowalewski

Luisa Egen

Chanel E Fischetti

Stefano Puliatti

Gomez Rivas Juan

Mark Taratkin

Rivero Belenchon Ines

Marie Angela Sidoti Abate

Julia Muhlbauer

Frederik Wessels

Enrico Checcucci

Giovanni Cacciamani

Affiliations

Soon will be listed here.

Abstract

Artificial intelligence (AI) has made considerable progress within the last decade and is the subject of contemporary literature. This trend is driven by improved computational abilities and increasing amounts of complex data that allow for new approaches in analysis and interpretation. Renal cell carcinoma (RCC) has a rising incidence since most tumors are now detected at an earlier stage due to improved imaging. This creates considerable challenges as approximately 10%-17% of kidney tumors are designated as benign in histopathological evaluation; however, certain co-morbid populations (the obese and elderly) have an increased peri-interventional risk. AI offers an alternative solution by helping to optimize precision and guidance for diagnostic and therapeutic decisions. The narrative review introduced basic principles and provide a comprehensive overview of current AI techniques for RCC. Currently, AI applications can be found in any aspect of RCC management including diagnostics, perioperative care, pathology, and follow-up. Most commonly applied models include neural networks, random forest, support vector machines, and regression. However, for implementation in daily practice, health care providers need to develop a basic understanding and establish interdisciplinary collaborations in order to standardize datasets, define meaningful endpoints, and unify interpretation.

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References

Hallscheidt P, Fink C, Haferkamp A, Bock M, Luburic A, Zuna I . Preoperative staging of renal cell carcinoma with inferior vena cava thrombus using multidetector CT and MRI: prospective study with histopathological correlation. J Comput Assist Tomogr. 2005; 29(1):64-8. DOI: 10.1097/01.rct.0000146113.56194.6d. View

Formisano E, De Martino F, Valente G . Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning. Magn Reson Imaging. 2008; 26(7):921-34. DOI: 10.1016/j.mri.2008.01.052. View

Porpiglia F, Checcucci E, Amparore D, Piramide F, Volpi G, Granato S . Three-dimensional Augmented Reality Robot-assisted Partial Nephrectomy in Case of Complex Tumours (PADUA ≥10): A New Intraoperative Tool Overcoming the Ultrasound Guidance. Eur Urol. 2020; 78(2):229-238. DOI: 10.1016/j.eururo.2019.11.024. View

Kowalewski K, Hendrie J, Schmidt M, Garrow C, Bruckner T, Proctor T . Development and validation of a sensor- and expert model-based training system for laparoscopic surgery: the iSurgeon. Surg Endosc. 2016; 31(5):2155-2165. DOI: 10.1007/s00464-016-5213-2. View

Haifler M, Pence I, Sun Y, Kutikov A, Uzzo R, Mahadevan-Jansen A . Discrimination of malignant and normal kidney tissue with short wave infrared dispersive Raman spectroscopy. J Biophotonics. 2018; 11(6):e201700188. DOI: 10.1002/jbio.201700188. View

Jewett M, Mattar K, Basiuk J, Morash C, Pautler S, Siemens D . Active surveillance of small renal masses: progression patterns of early stage kidney cancer. Eur Urol. 2011; 60(1):39-44. DOI: 10.1016/j.eururo.2011.03.030. View

Singh N, Bapi R, Vinod P . Machine learning models to predict the progression from early to late stages of papillary renal cell carcinoma. Comput Biol Med. 2018; 100:92-99. DOI: 10.1016/j.compbiomed.2018.06.030. View

Amparore D, Pecoraro A, Checcucci E, Piramide F, Verri P, De Cillis S . Three-dimensional Virtual Models' Assistance During Minimally Invasive Partial Nephrectomy Minimizes the Impairment of Kidney Function. Eur Urol Oncol. 2021; 5(1):104-108. DOI: 10.1016/j.euo.2021.04.001. View

Almassi N, Gill B, Rini B, Fareed K . Management of the small renal mass. Transl Androl Urol. 2017; 6(5):923-930. PMC: 5673824. DOI: 10.21037/tau.2017.07.11. View

10.

Hashimoto D, Rosman G, Rus D, Meireles O . Artificial Intelligence in Surgery: Promises and Perils. Ann Surg. 2018; 268(1):70-76. PMC: 5995666. DOI: 10.1097/SLA.0000000000002693. View

11.

Goh V, Ganeshan B, Nathan P, Juttla J, Vinayan A, Miles K . Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker. Radiology. 2011; 261(1):165-71. DOI: 10.1148/radiol.11110264. View

12.

Nazari M, Shiri I, Zaidi H . Radiomics-based machine learning model to predict risk of death within 5-years in clear cell renal cell carcinoma patients. Comput Biol Med. 2020; 129:104135. DOI: 10.1016/j.compbiomed.2020.104135. View

13.

Tabibu S, Vinod P, Jawahar C . Pan-Renal Cell Carcinoma classification and survival prediction from histopathology images using deep learning. Sci Rep. 2019; 9(1):10509. PMC: 6642160. DOI: 10.1038/s41598-019-46718-3. View

14.

Tsili A, Andriotis E, Gkeli M, Krokidis M, Stasinopoulou M, Varkarakis I . The role of imaging in the management of renal masses. Eur J Radiol. 2021; 141:109777. DOI: 10.1016/j.ejrad.2021.109777. View

15.

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

16.

Xu Q, Zhu Q, Liu H, Chang L, Duan S, Dou W . Differentiating Benign from Malignant Renal Tumors Using T2- and Diffusion-Weighted Images: A Comparison of Deep Learning and Radiomics Models Versus Assessment from Radiologists. J Magn Reson Imaging. 2021; 55(4):1251-1259. DOI: 10.1002/jmri.27900. View

17.

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

18.

Wong N, Lam C, Patterson L, Shayegan B . Use of machine learning to predict early biochemical recurrence after robot-assisted prostatectomy. BJU Int. 2018; 123(1):51-57. DOI: 10.1111/bju.14477. View

19.

Schiavina R, Bianchi L, Chessa F, Barbaresi U, Cercenelli L, Lodi S . Augmented Reality to Guide Selective Clamping and Tumor Dissection During Robot-assisted Partial Nephrectomy: A Preliminary Experience. Clin Genitourin Cancer. 2020; 19(3):e149-e155. DOI: 10.1016/j.clgc.2020.09.005. View

20.

Abel E, Culp S, Matin S, Tamboli P, Wallace M, Jonasch E . Percutaneous biopsy of primary tumor in metastatic renal cell carcinoma to predict high risk pathological features: comparison with nephrectomy assessment. J Urol. 2010; 184(5):1877-81. PMC: 4809737. DOI: 10.1016/j.juro.2010.06.105. View