ArticleTomography (Ann Arbor, Mich.)2021
Identifying Robust Radiomics Features for Lung Cancer by Using In-Vivo and Phantom Lung Lesions.
Article in Tomography (Ann Arbor, Mich.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Delta radiomics: a systematic review.La Radiologia medica · 2021Pooled it
- Multifaceted Preprocessing Optimization for Post-Mortem Radiomics Analysis: A Pilot Study on Forensic Age Estimation Using Proximal Femur Radiomics.Journal of imaging informatics in medicine · 2026Article
- Enhancing NSCLC recurrence prediction with PET/CT habitat imaging, ctDNA, and integrative radiogenomics-blood insights.Nature communications · 2024Article
- Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling.Military Medical Research · 2023Review
- Harnessing imaging tools to guide immunotherapy trials: summary from the National Cancer Institute Cancer Imaging Steering Committee workshop.The Lancet. Oncology · 2023Review
- Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies.Tomography (Ann Arbor, Mich.) · 2021Article
- Stability of Radiomic Features across Different Region of Interest Sizes-A CT and MR Phantom Study.Tomography (Ann Arbor, Mich.) · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
We propose a novel framework for determining radiomics feature robustness by considering the effects of both biological and noise signals. This framework is preliminarily tested in a study predicting the epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients. Pairs of CT images (baseline, 3-week post therapy) of 46 NSCLC patients with known EGFR mutation status were collected and a FDA-customized anthropomorphic thoracic phantom was scanned on two vendors' scanners at four different tube currents. Delta radiomics features were extracted from the NSCLC patient CTs and reproducible, non-redundant, and informative features were identified. The feature value differences between EGFR mutant and EGFR wildtype patients were quantitatively measured as the biological signal. Similarly, radiomics features were extracted from the phantom CTs. A pairwise comparison between settings resulted in a feature value difference that was quantitatively measured as the noise signal. Biological signals were compared to noise signals at each setting to determine if the distributions were significantly different by two-sample
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