ArticleInternational journal of medical physics, clinical engineering and radiation oncology2018
Technical Note: Identification of CT Texture Features Robust to Tumor Size Variations for Normal Lung Texture Analysis.
Article in International journal of medical physics, clinical engineering and radiation oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 22 citations in OpenAlex.
- Detecting Clinically Significant Prostate Cancer in PI-RADS 3 Lesions Using T2w-Derived Radiomics Feature Maps in 3T Prostate MRI.Current oncology (Toronto, Ont.) · 2024Article
- The role of parametric feature maps to correct different volume of interest sizes: an in vivo liver MRI study.European radiology experimental · 2023Article
- The Use of Radiomic Features to Predict Human Papillomavirus (HPV) Status in Head and Neck Tumors: A Review.Cureus · 2023Review
- Enhancing the stability of CT radiomics across different volume of interest sizes using parametric feature maps: a phantom study.European radiology experimental · 2022Article
- Stability of Radiomic Features across Different Region of Interest Sizes-A CT and MR Phantom Study.Tomography (Ann Arbor, Mich.) · 2021Article
- Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype.Lung cancer (Amsterdam, Netherlands) · 2020Review
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (RILD). For these features to be clinically useful, they should be robust to tumor size variations and not correlated with the normal lung volume of interest, i.e., the volume of the peri-tumoral region (PTR). CT images of 14 lung cancer patients were studied. Different sizes of gross tumor volumes (GTVs) were simulated and placed in the lung contralateral to the tumor. 27 texture features [nine from intensity histogram, eight from the gray-level co-occurrence matrix (GLCM) and ten from the gray-level run-length matrix (GLRM)] were extracted from the PTR. The Bland-Altman analysis was applied to measure the normalized range of agreement (nRoA) for each feature when GTV size varied. A feature was considered as robust when its nRoA was less than the threshold (100%). Sixteen texture features were identified as robust. None of the robust features was correlated with the volume of the PTR. No feature showed statistically significant differences (P<0.05) on GTV locations. We identified 16 robust normal lung CT texture features that can be further examined for the prediction of RILD.
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