Quantitative Features of Liver Lesions, Lung Nodules, and Renal Stones at Multi–Detector Row CT Examinations: Dependency on Radiation Dose and Reconstruction Algorithm

Solomon, Justin; Mileto, Achille; Rc, Nelson; K, Roy Choudhury; Samei, Ehsan

doi:10.1148/radiol.2015150892

Cited by 95 publications

(92 citation statements)

References 19 publications

(22 reference statements)

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“…In contrast, the segmentation-based parameters depend not only on the stone but also on the delineation between the urinary stone and the background, which can be achieved differently with or without human interaction [21–25]. …”

Section: Discussionmentioning

confidence: 99%

A new method for predicting uric acid composition in urinary stones using routine single-energy CT

Lidén

2017

Urolithiasis

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Urinary stones composed of uric acid can be treated medically. Prediction of uric acid stone type is, therefore, desirable when a urinary stone is diagnosed with unenhanced CT. The purpose of the present study was to describe single-energy thin slice quantitative CT parameters of urinary stones correlated to chemical stone type and to develop a method to distinguish pure uric acid stones (UA) from other stones (non-UA/Mix). Unenhanced thin slice single-energy CT images of 126 urinary stones (117 patients) with known chemical stone type were retrospectively included in the study. Among the included stones, 22 were UA and 104 were non-UA/Mix. The included CT images and Laplacian filtered images of the stones were quantitatively analyzed using operator-independent methods. A post hoc classification method for pure UA stones was created using a combination of cutoff values for the peak attenuation and peak point Laplacian. The stone types differed in most quantitative image characteristics including mean attenuation (p < 0.001), peak attenuation (p < 0.001), and peak point Laplacian (p < 0.001). The sensitivity for the post hoc-developed peak attenuation—peak point Laplacian method for classifying pure UA stones was 95% [21/22, 95% CI (77–100%)] and the specificity was 99% [103/104, 95% CI (95–100%)]. In conclusion, quantitative image analysis of thin slice routine single-energy CT images is promising for predicting pure UA content in urinary stones, with results comparable to double energy methods.

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Section: Discussionmentioning

confidence: 99%

A new method for predicting uric acid composition in urinary stones using routine single-energy CT

Lidén

2017

Urolithiasis

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“…Further, only images reconstructed with traditional filtered back projection were considered. Iterative reconstruction (IR) algorithms are increasingly being used in practice and are known to produce images with differing textures 19,34 . However, due to the proprietary nature and continuous evolution of these IR algorithms, developing a consistent set of radiomics features that accurately describe the tumor microenvironments, or developing correction factors from images reconstructed using IR may not be practically feasible.…”

Section: Discussionmentioning

confidence: 99%

“…For example, in patients with lung adenocarcinoma, Coroller, et al 6 reported 35 radiomic features to be prognostic for distant metastasis and 12 features that predict survival. However, any value and practicality of noninvasively quantifying tumor characteristics is largely dependent on the repeatability and reproducibility of quantitative imaging features across medical imaging devices 6–19, 32 . As such, prior to the clinical application of radiomic features for patient care, they must be found to be repeatable and reproducible under the conditions that might be expected in clinical practice or a clinical trial with multiple scanners or sites 6–19, 32 .…”

Section: Introductionmentioning

confidence: 99%

“…He showed that the variability calculated from different CT scanners is comparable to that found in CT images of NSCLC tumors. In a retrospective clinical trial Solomon, et al 19 reported on the degree to which radiation dose and iterative reconstruction algorithms affect the robustness of radiomic features extracted from tumors. However, the study was only conducted on General Electric (GE) CT scanners.…”

Section: Introductionmentioning

confidence: 99%

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Investigating the Robustness Neighborhood Gray Tone Difference Matrix and Gray Level Co-occurrence Matrix Radiomic Features on Clinical Computed Tomography Systems Using Anthropomorphic Phantoms: Evidence From a Multivendor Study

Mahmood

Apte²,

Deasy³

et al. 2017

J Comput Assist Tomogr

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Objective To determine if optimized imaging protocols across multiple computed tomography (CT) vendors could result in reproducible radiomic features calculated from an anthropomorphic phantom. Methods Materials with varying degrees of heterogeneity were placed throughout the lungs of the phantom. Twenty scans of the phantom were acquired on 3 CT manufacturers with chest CT protocols that had optimized protocol parameters. Scans were reconstructed using vendor-specific standards and lung kernels. The Concordance Correlation Coefficient (CCC) was used to calculate reproducibility between features. For features with high CCC values, Bland-Altman analysis was also used to quantify agreement. Results The mean Hounsfield unit (HU) was 32.93 HU (141.7 to −26.5 HU) for the rubber insert and 347.2 HU (−320.9 to −347.7 HU) for the wood insert. Low CCC values < 0.9 were calculated for all features across all scans. Conclusions Radiomic features that are derived from the spatial distribution of voxel intensities should be particularly scrutinized for reproducibility in a multi-vendor environment.

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“…Indeed, the same study referenced above that compared features computed from multiple segmentations also indicated that implementations from separate institutions of purportedly the same feature sometimes produced different values. 61,68 Thus, a predictive model built for lung nodule characterization may be validated in one cohort and fail in another because of differences in the data acquisition and reconstruction. [11][12][13] In addition to the challenges raised by segmentation, a final challenge is the sensitivity of radiomics features to image acquisition and reconstruction (ie, the heterogeneity of image acquisitions).…”

Section: Challenges To Conventional Radiomicsmentioning

confidence: 99%

Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

Napel

Jardim‐Perassi

et al. 2018

Cancer

141

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Although cancer often is referred to as “a disease of the genes,” it is indisputable that the (epi)genetic properties of individual cancer cells are highly variable, even within the same tumor. Hence, preexisting resistant clones will emerge and proliferate after therapeutic selection that targets sensitive clones. Herein, the authors propose that quantitative image analytics, known as “radiomics,” can be used to quantify and characterize this heterogeneity. Virtually every patient with cancer is imaged radiologically. Radiomics is predicated on the beliefs that these images reflect underlying pathophysiologies, and that they can be converted into mineable data for improved diagnosis, prognosis, prediction, and therapy monitoring. In the last decade, the radiomics of cancer has grown from a few laboratories to a worldwide enterprise. During this growth, radiomics has established a convention, wherein a large set of annotated image features (1‐2000 features) are extracted from segmented regions of interest and used to build classifier models to separate individual patients into their appropriate class (eg, indolent vs aggressive disease). An extension of this conventional radiomics is the application of “deep learning,” wherein convolutional neural networks can be used to detect the most informative regions and features without human intervention. A further extension of radiomics involves automatically segmenting informative subregions (“habitats”) within tumors, which can be linked to underlying tumor pathophysiology. The goal of the radiomics enterprise is to provide informed decision support for the practice of precision oncology.

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Quantitative Features of Liver Lesions, Lung Nodules, and Renal Stones at Multi–Detector Row CT Examinations: Dependency on Radiation Dose and Reconstruction Algorithm

Abstract: Radiation dose settings and reconstruction algorithms affect the extraction and analysis of quantitative imaging features in lesions at multi-detector row CT.

Cited by 95 publications

References 19 publications

A new method for predicting uric acid composition in urinary stones using routine single-energy CT

A new method for predicting uric acid composition in urinary stones using routine single-energy CT

Investigating the Robustness Neighborhood Gray Tone Difference Matrix and Gray Level Co-occurrence Matrix Radiomic Features on Clinical Computed Tomography Systems Using Anthropomorphic Phantoms: Evidence From a Multivendor Study

Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

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