# Finding Reproducible and Prognostic Radiomic Features in Variable Slice Thickness Contrast Enhanced CT of Colorectal Liver Metastases

arXiv: https://arxiv.org/abs/2501.11221 · HTML: https://mlsanity.com/paper/2501.11221/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 56 references that are in the map.
- First posted on arXiv 20 Jan 2025.

## Builds on (most cited first, 5 of 5)

- [Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer](https://mlsanity.com/paper/1703.08516/) (2017)
- [Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma Segmentation](https://mlsanity.com/paper/2107.03442/) (2021)
- [Towards Optimal Patch Size in Vision Transformers for Tumor Segmentation](https://mlsanity.com/paper/2308.16598/) (2023)
- [Attention-based CT Scan Interpolation for Lesion Segmentation of Colorectal Liver Metastases](https://mlsanity.com/paper/2308.15932/) (2023)
- [Are Quantitative Features of Lung Nodules Reproducible at Different CT Acquisition and Reconstruction Parameters?](https://mlsanity.com/paper/1908.05667/) (2019)

## Built on by (most cited first, 2 of 2)

- [A Dynamic Prognostic Prediction Method for Colorectal Cancer Liver Metastasis](https://mlsanity.com/paper/2505.03123/) (2025)
- [A Metabolic-Imaging Integrated Model for Prognostic Prediction in Colorectal Liver Metastases](https://mlsanity.com/paper/2507.19734/) (2025)

## Abstract (arXiv)

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 (CCC ≥ 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.