ArticleApplied sciences (Basel, Switzerland)2022
The Impact of Image Acquisition Parameters and ComBat Harmonization on the Predictive Performance of Radiomics: A Renal Cell Carcinoma Model.
Article in Applied sciences (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 14 citations in OpenAlex.
- CT-Based Radiomics in the Characterization of Solid Renal Tumors: A Systematic Review.Cancers · 2026Review
- Assessing the multi-software robustness of radiomic biomarkers: a three-tool evaluation.Frontiers in oncology · 2026Article
- Scaling of Haralick features with image bit depth and gray level co-occurrence matrix displacement vector for linear gradients.Computers in biology and medicine · 2025Article
- Radiomics-based kidney lesion classification: Mitigating batch effect with nested combat harmonization.Medical physics · 2025Article
- Variability of HCC Tumor Diameter and Density Measurements on Dynamic Contrast-Enhanced Computed Tomography.Tomography (Ann Arbor, Mich.) · 2025Article
- Myocardial perfusion SPECT radiomic features reproducibility assessment: Impact of image reconstruction and harmonization.Medical physics · 2025Article
- MRI-based radiomics for noninvasive prediction of T790M resistance mutation in lung cancer spinal metastases: an exploratory study.Frontiers in cell and developmental biology · 2025Article
- Differential Radiomics-Based Signature Predicts Lung Cancer Risk Accounting for Imaging Parameters in NLST Cohort.Cancer medicine · 2024Article
- Impact of Preprocessing Parameters in Medical Imaging-Based Radiomic Studies: A Systematic Review.Cancers · 2024Review
- Article
- Machine learning-based identification of contrast-enhancement phase of computed tomography scans.PloS one · 2024Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Radiomics, one of the potential methods for developing clinical biomarker, is one of the exponentially growing research fields. In addition to its potential, several limitations have been identified in this field, and most importantly the effects of variations in imaging parameters on radiomic features (RFs). In this study, we investigate the potential of RFs to predict overall survival in patients with clear cell renal cell carcinoma, as well as the impact of ComBat harmonization on the performance of RF models. We assessed the robustness of the results by performing the analyses a thousand times. Publicly available CT scans of 179 patients were retrospectively collected and analyzed. The scans were acquired using different imaging vendors and parameters in different medical centers. The performance was calculated by averaging the metrics over all runs. On average, the clinical model significantly outperformed the radiomic models. The use of ComBat harmonization, on average, did not significantly improve the performance of radiomic models. Hence, the variability in image acquisition and reconstruction parameters significantly affect the performance of radiomic models. The development of radiomic specific harmonization techniques remain a necessity for the advancement of the field.
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Registered trials
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