Texture analysis of iodine maps and conventional images for k-nearest neighbor classification of benign and metastatic lung nodules.

ABSTRACT:

Background

The 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.

Methods

183 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, ¹⁸F-FDG-PET-CT or unequivocal change during treatment. Semi-automatic 3D segmentation of BLN/LM was performed, and volumetric HU attenuation and iodine concentration were acquired. For conventional images and io

SUBMITTER: Lennartz S 

PROVIDER: S-EPMC7836145 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature