Finding Reproducible and Prognostic Radiomic Features in Variable Slice Thickness Contrast Enhanced CT of Colorectal Liver Metastases
Journal:
arXiv
Published Date:
Jan 20, 2025
Abstract
Establishing the reproducibility of radiomic signatures is a critical step in
the path to clinical adoption of quantitative imaging biomarkers; however,
radiomic signatures must also be meaningfully related to an outcome of clinical
importance to be of value for personalized medicine. In this study, we analyze
both the reproducibility and prognostic value of radiomic features extracted
from the liver parenchyma and largest liver metastases in contrast enhanced CT
scans of patients with colorectal liver metastases (CRLM). A prospective cohort
of 81 patients from two major US cancer centers was used to establish the
reproducibility of radiomic features extracted from images reconstructed with
different slice thicknesses. A publicly available, single-center cohort of 197
preoperative scans from patients who underwent hepatic resection for treatment
of CRLM was used to evaluate the prognostic value of features and models to
predict overall survival. A standard set of 93 features was extracted from all
images, with a set of eight different extractor settings. The feature
extraction settings producing the most reproducible, as well as the most
prognostically discriminative feature values were highly dependent on both the
region of interest and the specific feature in question. While the best overall
predictive model was produced using features extracted with a particular
setting, without accounting for reproducibility, (C-index = 0.630
(0.603--0.649)) an equivalent-performing model (C-index = 0.629 (0.605--0.645))
was produced by pooling features from all extraction settings, and thresholding
features with low reproducibility ($\mathrm{CCC} \geq 0.85$), prior to feature
selection. Our findings support a data-driven approach to feature extraction
and selection, preferring the inclusion of many features, and narrowing feature
selection based on reproducibility when relevant data is available.
