Quantitative Analysis of Liver Disease Using MRI-Based Radiomic Features of the Liver and Spleen

Sack, Jordan; Nitsch, Jennifer; Meine, Hans; Kikinis, Ron; Halle, Morris; Rutherford, Anna E.

doi:10.3390/jimaging8100277

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…Yin et al found out that machine learning models incorporating CT splenic features outperformed models solely considering CT hepatic features when detecting the liver fibrosis stage [ 33 ]. A similar approach was followed by Sack et al [ 28 ], who combined MRI liver and spleen radiomic features to detect cirrhosis, and by Nitsch et al [ 26 ], who developed a predictive model of disease severity for cirrhosis compared with the existing MELD (Model for End-Stage Liver Disease) score.…”

Section: Discussionmentioning

confidence: 99%

Radiomics Applications in Spleen Imaging: A Systematic Review and Methodological Quality Assessment

et al. 2023

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The spleen, often referred to as the “forgotten organ”, plays numerous important roles in various diseases. Recently, there has been an increased interest in the application of radiomics in different areas of medical imaging. This systematic review aims to assess the current state of the art and evaluate the methodological quality of radiomics applications in spleen imaging. A systematic search was conducted on PubMed, Scopus, and Web of Science. All the studies were analyzed, and several characteristics, such as year of publication, research objectives, and number of patients, were collected. The methodological quality was evaluated using the radiomics quality score (RQS). Fourteen articles were ultimately included in this review. The majority of these articles were published in non-radiological journals (78%), utilized computed tomography (CT) for extracting radiomic features (71%), and involved not only the spleen but also other organs for feature extraction (71%). Overall, the included papers achieved an average RQS total score of 9.71 ± 6.37, corresponding to an RQS percentage of 27.77 ± 16.04. In conclusion, radiomics applications in spleen imaging demonstrate promising results in various clinical scenarios. However, despite all the included papers reporting positive outcomes, there is a lack of consistency in the methodological approaches employed.

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Section: Discussionmentioning

confidence: 99%

Radiomics Applications in Spleen Imaging: A Systematic Review and Methodological Quality Assessment

et al. 2023

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“…4 [93] successfully used conventional MRI and clinical data with machine learning algorithms to reconstruct virtual MR enterography images for assessing liver stiffness and the fibrosis category. Sack et al [94] found that MR-based liver and spleen radiomic features were highly accurate in identifying cirrhosis.…”

Section: Application Of Ai Based On Ctmentioning

confidence: 99%

“…Sack et al. [94] found that MR‐based liver and spleen radiomic features were highly accurate in identifying cirrhosis.…”

Section: Artificial Intelligence Based On Imaging For Diffuse Liver D...mentioning

confidence: 99%

Radiobioinformatics: A novel bridge between basic research and clinical practice for clinical decision support in diffuse liver diseases

Lei

et al. 2023

iRADIOLOGY

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The liver is a multifaceted organ that is responsible for many critical functions encompassing amino acid, carbohydrate, and lipid metabolism, all of which make a healthy liver essential for the human body. Contemporary imaging methodologies have remarkable diagnostic accuracy in discerning focal liver lesions; however, a comprehensive understanding of diffuse liver diseases is a requisite for radiologists to accurately diagnose or predict the progression of such lesions within clinical contexts. Nonetheless, the conventional attributes of radiological features, including morphology, size, margin, density, signal intensity, and echoes, limit their clinical utility. Radiomics is a widely used approach that is characterized by the extraction of copious image features from radiographic depictions, which gives it considerable potential in addressing this limitation. It is worth noting that functional or molecular alterations occur significantly prior to the morphological shifts discernible by imaging modalities. Consequently, the explication of potential mechanisms by multiomics analyses (encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics) is essential for investigating putative signal pathway regulations from a radiological viewpoint. In this review, we elaborate on the principal pathological categorizations of diffuse liver diseases, the evaluation of multiomics approaches pertaining to diffuse liver diseases, and the prospective value of predictive models. Accordingly, the overarching objective of this review is to scrutinize the interrelations between radiological features and bioinformatics as well as to consider the development of prediction models predicated on radiobioinformatics as integral components of clinical decision support systems for diffuse liver diseases.

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Impact of Emerging Deep Learning–Based MR Image Reconstruction Algorithms on Abdominal MRI Radiomic Features

Li,

Alves,

Pednekar

et al. 2024

J Comput Assist Tomogr

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Objective This study aims to evaluate, on one MRI vendor's platform, the impact of deep learning (DL)–based reconstruction techniques on MRI radiomic features compared to conventional image reconstruction techniques. Methods Under IRB approval and informed consent, we prospectively collected undersampled coronal T2-weighted MR images of the abdomen (1.5 T; Philips Healthcare) from 17 pediatric and adult subjects and reconstructed them using a conventional image reconstruction technique (compressed sensitivity encoding [C-SENSE]) and two DL-based reconstruction techniques (SmartSpeed [Philips Healthcare, US FDA cleared] and SmartSpeed with Super Resolution [SmartSpeed-SuperRes, not US FDA cleared to date]). Eight regions of interest (ROIs) across organs/tissues (liver, spleen, kidney, pancreas, fat, and muscle) were manually placed. Eighty-six MRI radiomic features were then extracted. Pearson's correlation coefficients (PCCs) and intraclass correlation coefficients (ICCs) were calculated between (A) C-SENSE versus SmartSpeed, and (B) C-SENSE versus SmartSpeed-SuperRes. To quantify the impact from the perspective of the whole MR image, cross-ROI mean PCCs and ICCs were calculated for individual radiomic features. The impact of image reconstruction on individual radiomic features in different organs/tissues was evaluated using ANOVA analyses. Results According to cross-ROI mean PCCs, 50 out of 86 radiomic features were highly correlated (PCC, ≥0.8) between SmartSpeed and C-SENSE, whereas only 15 radiomic features were highly correlated between SmartSpeed-SuperRes and C-SENSE reconstructions. According to cross-ROI mean ICCs, 58 out of 86 radiomic features had high agreements (ICC ≥0.75) between SmartSpeed and C-SENSE, whereas only 9 radiomic features had high agreements between SmartSpeed-SuperRes and C-SENSE reconstructions. For SmartSpeed reconstruction, the psoas muscle ROI appeared to be impacted most with the lowest median (IQR) correlation of 0.57 (0.25). The circular liver ROI was impacted most by SmartSpeed-SuperRes (PCC, 0.60 [0.22]). ANOVA analyses suggest that the impact of DL reconstruction algorithms on radiomic features varies significantly among different organs/tissues (P < 0.001). Conclusions MRI radiomic features are significantly altered by DL-based reconstruction compared to a conventional reconstruction technique. The impact of DL reconstruction algorithms on radiomic features varies significantly between different organs/tissues.

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Quantitative Analysis of Liver Disease Using MRI-Based Radiomic Features of the Liver and Spleen

Cited by 4 publications

References 25 publications

Radiomics Applications in Spleen Imaging: A Systematic Review and Methodological Quality Assessment

Radiomics Applications in Spleen Imaging: A Systematic Review and Methodological Quality Assessment

Radiobioinformatics: A novel bridge between basic research and clinical practice for clinical decision support in diffuse liver diseases

Impact of Emerging Deep Learning–Based MR Image Reconstruction Algorithms on Abdominal MRI Radiomic Features

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