Radiomics and Artificial Intelligence for Renal Mass

Radiol. Clin. North Am. · Sep 2020

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Radiomics and Artificial Intelligence for Renal Mass Characterization.

Radiomics allows for high throughput extraction of quantitative data from images. This is an area of active research as groups try to capture and quantify imaging parameters and convert these into descriptive phenotypes of organs or tumors. ⋯ This is used with or without associated machine learning classifiers or a deep learning approach is applied to similar types of data. These tools have shown utility in characterizing renal masses, renal cell carcinoma, and assessing response to targeted therapeutic agents in metastatic renal cell carcinoma.

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- Meghan G Lubner.
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Sciences Center, 600 Highland Avenue, Madison, WI 53792, USA. Electronic address: mlubner@uwhealth.org.
- Radiol. Clin. North Am. 2020 Sep 1; 58 (5): 995-1008.
AbstractRadiomics allows for high throughput extraction of quantitative data from images. This is an area of active research as groups try to capture and quantify imaging parameters and convert these into descriptive phenotypes of organs or tumors. Texture analysis is one radiomics tool that extracts information about heterogeneity within a given region of interest. This is used with or without associated machine learning classifiers or a deep learning approach is applied to similar types of data. These tools have shown utility in characterizing renal masses, renal cell carcinoma, and assessing response to targeted therapeutic agents in metastatic renal cell carcinoma.Copyright © 2020 Elsevier Inc. All rights reserved.

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