Artificial intelligence for renal cancer: From imaging to histology and beyond

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.

Keywords: Artificial intelligence; Imaging; Kidney cancer; Machine learning; Technology.

Figure 1

Basic principles of supervised ML models for renal cancer. Available data from different aspects of clinical care can be used as input. Following manual annotation, ML algorithms are trained to create the models. Unused test data are used for validation and to determine the final model which can assist during care of future patients (adopted from Garrow et al. [21]). 1253 mm×714 mm (38×38 DPI). SVM, support vector machine; RF, random forest; ANN, artificial neural networks; ML, machine learning; BMI, body mass index.

Figure 2

Applications of artificial intelligence during the course of treatment. 401 mm×112 mm (38×38 DPI).

Figs1

Preparation of raw data for machine learning based analysis 106 mm × 97 mm (220 × 220 DPI).