Design and fabrication of heterogeneous lung nodule phantoms for assessing the accuracy and variability of measured texture radiomics features in CT.

We aimed to design and fabricate synthetic lung nodules with patient-informed internal heterogeneity to assess the variability and accuracy of measured texture features in CT. To that end, 190 lung nodules from a publicly available database of chest CT images (Lung Image Database Consortium) were selected based on size ( > 3 mm ) and malignancy. The texture features of the nodules were used to train a statistical texture synthesis model based on clustered lumpy background. The model parameters were ascertained based on a genetic optimization of a Mahalanobis distance objective function. The resulting texture model defined internal heterogeneity within 24 anthropomorphic lesion models which were subsequently fabricated into physical phantoms using a multimaterial three-dimensional (3-D) printer. The 3-D-printed lesions were imbedded in an anthropomorphic chest phantom and imaged with a clinical scanner using different acquisition parameters including slice thickness, dose level, and reconstruction kernel. The imaged lesions were analyzed in terms of texture features to ascertain the impact of CT imaging on lesion texture quantification. The texture modeling method produced lesion models with low and stable Mahalanobis distance between real and synthetic textures. The virtual lesions were successfully printed into 3-D phantoms. The accuracy and variability of the measured features extracted from the CT images of the phantoms showed notable influence from the imaging acquisition parameters with contrast, energy, and texture entropy exhibiting most sensitivity in terms of accuracy, and contrast, dissimilarity, and texture entropy most variability. Thinner slice thicknesses yielded more accurate and edge reconstruction kernels more stable results. We conclude that printed textured models of lesions can be developed using a method that can target and minimize the mathematical distance between real and synthetic lesions. The synthetic lesions can be used as the basis to investigate how CT imaging conditions might affect radiomics features derived from CT images.

Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In-Phase and Opposed-Phase MRI?

OBJECTIVE: The purpose of this study is to determine whether second-order texture analysis can be used to distinguish lipid-poor adenomas from malignant adrenal nodules on unenhanced CT, contrast-enhanced CT (CECT), and chemical-shift MRI. MATERIALS AND METHODS: In this retrospective study, 23 adrenal nodules (15 lipid-poor adenomas and eight adrenal malignancies) in 20 patients (nine female patients and 11 male patients; mean age, 59 years [range, 15-80 years]) were assessed. All patients underwent unenhanced CT, CECT, and chemical-shift MRI. Twenty-one second-order texture features from the gray-level cooccurrence matrix and gray-level run-length matrix were calculated in 3D. The mean values for 21 texture features and four imaging features (lesion size, unenhanced CT attenuation, CECT attenuation, and signal intensity index) were compared using a t test. The diagnostic performance of texture analysis versus imaging features was also compared using AUC values. Multivariate logistic regression models to predict malignancy were constructed for texture analysis and imaging features. RESULTS: Lesion size, unenhanced CT attenuation, and the signal intensity index showed significant differences between benign and malignant adrenal nodules. No significant difference was seen for CECT attenuation. Eighteen of 21 CECT texture features and nine of 21 unenhanced CT texture features revealed significant differences between benign and malignant adrenal nodules. CECT texture features (mean AUC value, 0.80) performed better than CECT attenuation (mean AUC value, 0.60). Multivariate logistic regression models showed that CECT texture features, chemical-shift MRI texture features, and imaging features were predictive of malignancy. CONCLUSION: Texture analysis has a potential role in distinguishing benign from malignant adrenal nodules on CECT and may decrease the need for additional imaging studies in the workup of incidentally discovered adrenal nodules.

Interchangeability between real and three-dimensional simulated lung tumors in computed tomography: an interalgorithm volumetry study.

Using hybrid datasets consisting of patient-derived computed tomography (CT) images with digitally inserted computational tumors, we establish volumetric interchangeability between real and computational lung tumors in CT. Pathologically-confirmed malignancies from 30 thoracic patient cases from the RIDER database were modeled. Tumors were either isolated or attached to lung structures. Patient images were acquired on one of two CT scanner models (Lightspeed 16 or VCT; GE Healthcare) using standard chest protocol. Real tumors were segmented and used to inform the size and shape of simulated tumors. Simulated tumors developed in Duke Lesion Tool (Duke University) were inserted using a validated image-domain insertion program. Four readers performed volume measurements using three commercial segmentation tools. We compared the volume estimation performance of segmentation tools between real tumors in actual patient CT images and corresponding simulated tumors virtually inserted into the same patient images (i.e., hybrid datasets). Comparisons involved (1) direct assessment of measured volumes and the standard deviation between simulated and real tumors across readers and tools, respectively, (2) multivariate analysis, involving segmentation tools, readers, tumor shape, and attachment, and (3) effect of local tumor environment on volume measurement. Volume comparison showed consistent trends (9% volumetric difference) between real and simulated tumors across all segmentation tools, readers, shapes, and attachments. Across all cases, readers, and segmentation tools, an intraclass correlation coefficient = 0.99 indicates that simulated tumors correlated strongly with real tumors ( p = 0.95 ). In addition, the impact of the local tumor environment on tumor volume measurement was found to have a segmentation tool-related influence. Strong agreement between simulated tumors modeled in this study compared to their real counterparts suggests a high degree of similarity. This indicates that, volumetrically, simulated tumors embedded into patient CT data can serve as reasonable surrogates to real patient data.