ArticleCancer imaging : the official publication of the International Cancer Imaging Society2021
Texture analysis of iodine maps and conventional images for k-nearest neighbor classification of benign and metastatic lung nodules.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2021. 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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6 citing papers in PubMed, 21 citations in OpenAlex.
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- Dual-Energy CT in Oncologic Imaging.Tomography (Ann Arbor, Mich.) · 2024Review
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- Differentiating pulmonary metastasis from benign lung nodules in thyroid cancer patients using dual-energy CT parameters.European radiology · 2022Article
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundThe purpose of this study was to analyze if the use of texture analysis on spectral detector CT (SDCT)-derived iodine maps (IM) in addition to conventional images (CI) improves lung nodule differentiation, when being applied to a k-nearest neighbor (KNN) classifier.
methods183 cancer patients who underwent contrast-enhanced, venous phase SDCT of the chest were included: 85 patients with 146 benign lung nodules (BLN) confirmed by either prior/follow-up CT or histopathology and 98 patients with 425 lung metastases (LM) verified by histopathology,
resultsDifferentiation between BLN and LM was most accurate, when using all CI-derived features combined with the most significant IM-derived feature, entropy (Accuracy:0.87; F1/Dice:0.92). However, differentiation accuracy based on the 4 most powerful CI-derived features performed only slightly inferior (Accuracy:0.84; F1/Dice:0.89, p=0.125). Mono-parametric lung nodule differentiation based on either feature alone (i.e. attenuation or iodine concentration) was poor (AUC=0.65, 0.58, respectively).
conclusionsFirst-order texture feature analysis of contrast-enhanced staging SDCT scans of the chest yield accurate differentiation between benign and metastatic lung nodules. In our study cohort, the most powerful iodine map-derived feature slightly, yet insignificantly increased classification accuracy compared to classification based on conventional image features only.
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