ArticleMedical physics2022
Synergistic impact of motion and acquisition/reconstruction parameters on
Article in Medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Assessing Respiratory Motion Stability of NovelDiagnostics (Basel, Switzerland) · 2026Article
- Reducing acquisition time in O-(2-Quantitative imaging in medicine and surgery · 2025Article
- A narrative review of imaging intratumor heterogeneity in non-small cell lung cancer: current advances.Translational lung cancer research · 2025Review
- Enhancing radiomics robustness using bayesian penalized likelihood PET reconstruction: application to Phantom and non-small cell lung cancer patient studies.BMC medical imaging · 2025Article
- Effects of data-driven respiratory gating on visualization and quantification of breast and upper abdominal cancers in FDG PET/CT examinations.Annals of nuclear medicine · 2025Article
- Impact of harmonization and oversampling methods on radiomics analysis of multi-center imbalanced datasets: application to PET-based prediction of lung cancer subtypes.EJNMMI physics · 2025Article
- Robust vs. Non-robust radiomic features: the quest for optimal machine learning models using phantom and clinical studies.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Quantitative Physiologic MRI Combined with Feature Engineering for Developing Machine Learning-Based Prediction Models to Distinguish Glioblastomas from Single Brain Metastases.Diagnostics (Basel, Switzerland) · 2024Article
- Impact of harmonization on the reproducibility of MRI radiomic features when using different scanners, acquisition parameters, and image pre-processing techniques: a phantom study.Medical & biological engineering & computing · 2024Article
- The effect of harmonization on the variability of PET radiomic features extracted using various segmentation methods.Annals of nuclear medicine · 2024Article
- Multicentric study on the reproducibility and robustness of PET-based radiomics features with a realistic activity painting phantom.PloS one · 2024Article
- Impact of respiratory motion onJournal of applied clinical medical physics · 2023Article
- Article
- Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to NasopharyngealCancers · 2023Article
- PreoperativeComputational and structural biotechnology journal · 2023Article
- Current status and prospect of PET-related imaging radiomics in lung cancer.Frontiers in oncology · 2023Review
- Synergistic impact of motion and acquisition/reconstruction parameters onMedical physics · 2022Article
- Machine Learning Radiomics Model for External and Internal Respiratory Motion Correlation Prediction in Lung Tumor.Technology in cancer research & treatmentArticle
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7 authors.
Funding
Abstract
objectivesThis study is aimed at examining the synergistic impact of motion and acquisition/reconstruction parameters on
methodsAn in-house developed thoracic phantom incorporating lesions with different sizes was used with different reconstruction settings, including various reconstruction algorithms, number of subsets and iterations, full-width at half-maximum of post-reconstruction smoothing filter and acquisition parameters, including injected activity and test-retest with and without motion simulation. To simulate motion, a special motor was manufactured to simulate respiratory motion based on a normal patient in two directions. The lesions were delineated semi-automatically to extract 174 radiomic features. All radiomic features were categorized according to the coefficient of variation (COV) to select robust features. A cohort consisting of 40 NSCLC patients with adenocarcinoma (n = 20) and squamous cell carcinoma (n = 20) was retrospectively analyzed. Statistical analysis was performed to discriminate robust features in differentiating histopathology subtypes of NSCLC lesions.
resultsOverall, 29% of radiomic features showed a COV ≤5% against motion. Forty-five percent and 76% of the features showed a COV ≤ 5% against the test-retest with and without motion in large lesions, respectively. Thirty-three percent and 45% of the features showed a COV ≤ 5% against different reconstruction parameters with and without motion, respectively. For NSCLC histopathological subtype differentiation, statistical analysis showed that 31 features were significant (p-value < 0.05). Two out of the 31 significant features, namely, the joint entropy of GLCM (AUC = 0.71, COV = 0.019) and median absolute deviation of intensity histogram (AUC = 0.7, COV = 0.046), were robust against the motion (same reconstruction setting).
conclusionsMotion, acquisition, and reconstruction parameters significantly impact radiomic features, just as their synergies. Radiomic features with high predictive performance (statistically significant) in differentiating histopathological subtype of NSCLC may be eliminated due to non-reproducibility.
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