Interpretability of radiomics models is improved when using feature group selection strategies for predicting molecular and clinical targets in clear-cell renal cell carcinoma: insights from the TRACERx Renal study

Interpretability of radiomics models is improved when using feature group selection strategies for predicting molecular and clinical targets in clear-cell renal cell carcinoma: insights from the TRACERx Renal study

Abstract Background The aim of this work is to evaluate the performance of radiomics predictions for a range of molecular, genomic and clinical targets in patients with clear cell renal cell carcinoma (ccRCC) and demonstrate the impact of novel feature selection strategies and sub-segmentations on m...

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Bibliographic Details
Main Authors: Matthew R. Orton, Evan Hann, Simon J. Doran, Scott T. C. Shepherd, Derfel Ap Dafydd, Charlotte E. Spencer, José I. López, Víctor Albarrán-Artahona, Francesca Comito, Hannah Warren, Joshua Shur, Christina Messiou, James Larkin, Samra Turajlic, on behalf of the TRACERx Renal Consortium, Dow-Mu Koh
Format: Article
Language:English
Published: BMC 2023-08-01
Series:Cancer Imaging
Subjects:
Radiomics
Radiogenomics
Histology
Interpretable
Machine learning
Feature selection
Online Access:https://doi.org/10.1186/s40644-023-00594-3

Internet

https://doi.org/10.1186/s40644-023-00594-3

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