Short-term reproducibility of radiomic features in liver parenchyma and liver malignancies on contrast-enhanced CT imaging

Perrin, Thomas; Midya, Abhishek; Yamashita, Rikiya; Chakraborty, Jayasree; Saidon, Tome; Jarnagin, William R.; Gönen, Mithat; Simpson, Amber L.; Richard, K. G.

doi:10.1007/s00261-018-1600-6

Cited by 50 publications

(32 citation statements)

References 35 publications

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“…The spatial variations in image brightness may be enhanced by the intravenously injected contrast agent, which may then result in the variability of radiomics features. This is supported by the findings of previous studies, which suggested that hepatic texture parameters correlate with hepatic vascularity [32], and the proportion of reproducible radiomics features decreases with the increasing variations in contrast injection rates in liver cancer patients [21]. Therefore, our study indicated that radiomic features may be affected by the phase (arterial or venous) of acquisition, and the reproducibility and stability of radiomics features due to image acquisition should be investigated extensively before using these features for classifying models.…”

Section: Discussionsupporting

confidence: 88%

“…Previous research [20] indicated that contrast-enhancement could affect the performance of radiomic models in diagnosing solitary pulmonary nodules. For example, Perrin's study [21] also showed that the proportion of reproducible features decreases with the increasing variations in contrast injection rates in liver cancer patients who underwent consecutive CECTs. In diagnostic radiology, a large proportion of CT images are contrast-enhanced.…”

Section: Introductionmentioning

confidence: 98%

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Radiomics for Classifying Histological Subtypes of Lung Cancer Based on Multiphasic Contrast-Enhanced Computed Tomography

Linning¹,

Lin

et al. 2019

J Comput Assist Tomogr

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Objectives-To evaluate the performance of radiomics method on classifying histological subtypes of lung cancer in multiphasic contrast-enhanced computed tomography (CT) images. Materials and Methods-A total of 229 patients with pathologically confirmed lung cancer were recruited. All recruited patients underwent non-enhanced and dual phase chest contrastenhanced CT. 1160 quantitative radiomic features were extracted from CT images to build radiomic classification models. The performance of the classification models was evaluated by the area under the receiver operating characteristic curve (AUC).

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Section: Discussionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 98%

Radiomics for Classifying Histological Subtypes of Lung Cancer Based on Multiphasic Contrast-Enhanced Computed Tomography

Linning¹,

Lin

et al. 2019

J Comput Assist Tomogr

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“…In lung cancer, the diagnostic performance of a radiomic signature for solitary pulmonary nodules fluctuates based on slice thickness, contrast enhancement, and the convolutional kernel [196]. Changes in pixel resolution and contrast injection rates decreased the proportion of reproducible features in liver cancer [197]. Reproducibility testing is becoming more routine in the radiomic biomarker identification process [187,[198][199][200][201][202][203].…”

Section: Reproducibility Of Radiomic Featuresmentioning

confidence: 99%

Radiogenomics: bridging imaging and genomics

et al. 2019

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From diagnostics to prognosis to response prediction, new applications for radiomics are rapidly being developed. One of the fastest evolving branches involves linking imaging phenotypes to the tumor genetic profile, a field commonly referred to as "radiogenomics." In this review, a general outline of radiogenomic literature concerning prominent mutations across different tumor sites will be provided. The field of radiogenomics originates from image processing techniques developed decades ago; however, many technical and clinical challenges still need to be addressed. Nevertheless, increasingly accurate and robust radiogenomic models are being presented and the future appears to be bright.

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“…In general, CT-based radiomics studies have shown that the variation of image acquisition parameters such as slice thickness, reconstruction algorithms, image resolution, contrast medium, and scanner type has the most significant influence on texture quantification [9][10][11]. In particular, reconstruction algorithms, pixel resolution, changes in contrast injection rates, and scanner models have been specifically implicated in HCC radiomics [9,10,12]. Regarding slice thickness, thinner image slices (1.25 and 2.5 mm) yield more quantitative texture information than thick slices (5 mm) [11].…”

Section: Ct-based Radiomicsmentioning

confidence: 99%

The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges

et al. 2020

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Recently, radiomic texture quantification of tumors has received much attention from radiologists, scientists, and stakeholders because several results have shown the feasibility of using the technique to diagnose and manage oncological conditions. In patients with hepatocellular carcinoma, radiomics has been applied in all stages of tumor evaluation, including diagnosis and characterization of the genotypic behavior of the tumor, monitoring of treatment responses and prediction of various clinical endpoints. It is also useful in selecting suitable candidates for specific treatment strategies. However, the clinical validation of hepatocellular carcinoma radiomics is limited by challenges in imaging protocol and data acquisition parameters, challenges in segmentation techniques, dimensionality reduction, and modeling methods. Identification of the best segmentation and optimal modeling methods, as well as texture features most stable to imaging protocol variability would go a long way in harmonizing HCC radiomics for personalized patient care. This article reviews the process of HCC radiomics, its clinical applications, associated challenges, and current optimization strategies.

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Short-term reproducibility of radiomic features in liver parenchyma and liver malignancies on contrast-enhanced CT imaging

Abstract: A greater number of liver malignancy radiomic features were reproducible compared to liver parenchyma features, but the proportion of reproducible features decreased with increasing variations in contrast injection rates and pixel resolution.

Cited by 50 publications

References 35 publications

Radiomics for Classifying Histological Subtypes of Lung Cancer Based on Multiphasic Contrast-Enhanced Computed Tomography

Radiomics for Classifying Histological Subtypes of Lung Cancer Based on Multiphasic Contrast-Enhanced Computed Tomography

Radiogenomics: bridging imaging and genomics

The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges

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