Whole liver CT texture analysis to predict the development of colorectal liver metastases − a multicentre study

Beckers, Roeland C.Y.; Lambregts, Doenja M J; Schnerr, R. S.; Maas, Marissa; Rao, Shengxiang; Kessels, Alfons; Thywissen, Thomas; Beets, G.; Houwers, Janneke B.; Dejong, C. H. C.; Verhoef, Cornelis; Beets-Tan, R.

doi:10.1016/s0959-8049(17)30241-1

Cited by 9 publications

(16 citation statements)

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“…Feature extraction is a crucial step in radiomics and comprises the computation of texture, density, and shape from predefined regions of interest (ROIs). Radiomics offers the advantage of an objective quantification of tissue characteristics and enables the detection of abnormalities in radiological images not depicted by routine visual analysis [16][17][18][19]. Due to the high objectivity and reliability of data, radiomics shows great potential as support for clinical decision-making [20].…”

Section: Introductionmentioning

confidence: 99%

Applicability of radiomics in interstitial lung disease associated with systemic sclerosis: proof of concept

et al. 2020

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Objective To retrospectively evaluate if texture-based radiomics features are able to detect interstitial lung disease (ILD) and to distinguish between the different disease stages in patients with systemic sclerosis (SSc) in comparison with mere visual analysis of high-resolution computed tomography (HRCT). Methods Sixty patients (46 females, median age 56 years) with SSc who underwent HRCT of the thorax were retrospectively analyzed. Visual analysis was performed by two radiologists for the presence of ILD features. Gender, age, and pulmonary function (GAP) stage was calculated from clinical data (gender, age, pulmonary function test). Data augmentation was performed and the balanced dataset was split into a training (70%) and a testing dataset (30%). For selecting variables that allow classification of the GAP stage, single and multiple logistic regression models were fitted and compared by using the Akaike information criterion (AIC). Diagnostic accuracy was evaluated from the area under the curve (AUC) from receiver operating characteristic (ROC) analyses, and diagnostic sensitivity and specificity were calculated. Results Values for some radiomics features were significantly lower (p < 0.05) and those of other radiomics features were significantly higher (p = 0.001) in patients with GAP2 compared with those in patients with GAP1. The combination of two specific radiomics features in a multivariable model resulted in the lowest AIC of 10.73 with an AUC of 0.96, 84% sensitivity, and 99% specificity. Visual assessment of fibrosis was inferior in predicting individual GAP stages (AUC 0.86; 83% sensitivity; 74% specificity). Conclusion The correlation of radiomics with GAP stage, but not with the visually defined features of ILD-HRCT, implies that radiomics might capture features indicating severity of SSc-ILD on HRCT, which are not recognized by visual analysis. Key Points • Radiomics features can predict GAP stage with a sensitivity of 84% and a specificity of almost 100%. • Extent of fibrosis on HRCT and a combined model of different visual HRCT-ILD features perform worse in predicting GAP stage. • The correlation of radiomics with GAP stage, but not with the visually defined features of ILD-HRCT, implies that radiomics might capture features on HRCT, which are not recognized by visual analysis.

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Section: Introductionmentioning

confidence: 99%

Applicability of radiomics in interstitial lung disease associated with systemic sclerosis: proof of concept

et al. 2020

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“…Texture analysis is used to quantify the pixel variation on portal venous phase in Liver CT scans [11]. In CT images, with some adjustments in entropy and consistency, some of texture features can highlight the possible separation between with and without colorectal liver metastases stages in patients [12].…”

Section: Texture Analysismentioning

confidence: 99%

Non-Invasive Technique to Classify Cirrhotic Liver Using Texture Parameters

Nisa

Buzdar

Riaz

et al. 2021

Mehran Univ. res. j. eng. technol.

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Texture analysis is an outstanding and fundamental task being used in many medical and computer vision applications. Malfunctioning of the human liver upsets almost all the other organs in the human body. Usually liver infections are difficult to analyze because of inconclusive side effects. More often, the liver could be confronting critically but it may not be significantly unveiled. The main objective of this research work is to provide some standard liver diagnostic measures to minimize the risk factors, as better diagnosis is essential requirement in radiology. The Computerized Tomography (CT) contributes important information to the clinical evaluation of diffuse liver diseases. Haralick texture parameters have been computed on the selected Regions of Interest. B11 is used for discrimination and interpretation of normal and cirrhotic liver diseases. Normal and diseased Liver CT images were collected from Bahawal Victoria Hospital. Normal and cirrhotic liver samples of clinically verified patients were obtained and total 900 Regions of Interest (ROIs) were taken from the selected data. Training of the classes was next step after texture parameter computation. In this work, supervised classification method was used to classify the selected images. In this way, the classes were trained in a supervised manner. The maximum accuracy obtained during this research work was 100%, linear dimensionality was 1 and the linear separability was 0.99%. Results of this research work suggested that texture parameters have high degree of reliability to automatically discriminate similar tissue textures, when regions are obvious. This framework separates benign from malignant liver tumors with moderately high precision and is therefore link up the psychophysics with machine vision to outline, recognize, categorize or discriminate textures.

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“…Some studies have not identified strong predictors and other reports have noted opposite results relative to the baseline heterogeneity (15,77,78). Some studies have reported that entropy in the liver is higher with an occult malignancy than a liver that has no metastatic disease (79)(80)(81)(82). There is a pathological correlation between homogeneity and tumor grading that can be explained by dense cellularity of the tumor (77).…”

Section: Ctmentioning

confidence: 99%

Current state of the art imaging approaches for colorectal liver metastasis

Hazhirkarzar

Khoshpouri

Shaghaghi

et al. 2020

Hepatobiliary Surg Nutr

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One of the most common cancers worldwide, colorectal cancer (CRC) has been associated with significant morbidity and mortality and therefore represents an enormous burden to the health care system. Recent advances in CRC treatments have provided patients with primary and metastatic CRC a better long-term prognosis. The presence of synchronous or metachronous metastasis has been associated, however, with worse survival. The most common site of metastatic disease is the liver. A variety of treatment modalities aimed at targeting colorectal liver metastases (CRLM) has been demonstrated to improve the prognosis of these patients. Loco-regional approaches such as surgical resection and tumor ablation (operative and percutaneous) can provide patients with a chance at long-term disease control and even cure in select populations. Patient selection is important in defining the most suitable treatment option for CRLM in order to provide the best possible survival benefit while avoiding unnecessary interventions and adverse events.Medical imaging plays a crucial role in evaluating the characteristics of CRLMs and disease resectability.Size of tumors, proximity to adjacent anatomical structures, and volume of the unaffected liver are among the most important imaging parameters to determine the suitability of patients for surgical management or other appropriate treatment approaches. We herein provide a comprehensive overview of current-state-ofthe-art imaging in the management of CRLM, including staging, treatment planning, response and survival assessment, and post-treatment surveillance. Computed tomography (CT) scan and magnetic resonance imaging (MRI) are two most commonly used techniques, which can be used solely or in combination with functional imaging modalities such as positron emission tomography (PET) and diffusion weighted imaging (DWI). Providing up-to-date evidence on advantages and disadvantages of imaging modalities and tumor assessment criteria, the current review offers a practice guide to assist providers in choosing the most suitable imaging approach for patients with CRLM.

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Whole liver CT texture analysis to predict the development of colorectal liver metastases − a multicentre study

Cited by 9 publications

References 0 publications

Applicability of radiomics in interstitial lung disease associated with systemic sclerosis: proof of concept

Applicability of radiomics in interstitial lung disease associated with systemic sclerosis: proof of concept

Non-Invasive Technique to Classify Cirrhotic Liver Using Texture Parameters

Current state of the art imaging approaches for colorectal liver metastasis

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