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

• Published in: Cancer imaging 2021-01, Vol.21 (1), p.17-17, Article 17
• Main Authors: Lennartz, Simon, Mager, Alina, Große Hokamp, Nils, Schäfer, Sebastian, Zopfs, David, Maintz, David, Reinhardt, Hans Christian, Thomas, Roman K, Caldeira, Liliana, Persigehl, Thorsten
• Format: Article
• Language: English
• Subjects: Accuracy; Attenuation; Cancer therapies; Chest; Classification; Classifiers; CT imaging; Datasets; Detectors; Diagnosis; Differentiation; Dual-energy CT; Entropy; Female; Fluorodeoxyglucose F18 - therapeutic use; Histochemistry; Humans; Image acquisition; Image classification; Image segmentation; Iodine; Iodine - metabolism; K-nearest neighbors algorithm; Kurtosis; Lung cancer; Lung Neoplasms - classification; Lung Neoplasms - diagnostic imaging; Lung nodules; Lungs; Male; Medical imaging; Metastasis; Middle Aged; Nodules; PET imaging; Pixels; Positron Emission Tomography Computed Tomography - methods; Spectral detector CT; Staging; Texture; Tomography, X-Ray Computed - methods
• ISSN: 1470-7330; 1740-5025; 1470-7330
• DOI: 10.1186/s40644-020-00374-3

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. 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, F-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 iodine maps, average, standard deviation, entropy, kurtosis, mean of the positive pixels (MPP), skewness, uniformity and uniformity of the positive pixels (UPP) within the volumes of interests were calculated. All acquired parameters were transferred to a KNN classifier. Differentiation 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). First-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.