Radiomics Analysis of Gadoxetic Acid–enhanced MRI for Staging                     Liver Fibrosis

Park, Hyo Jung; Lee, Seung Soo; Park, Bumwoo; Yun, Jun-Won; Sung, Yu Sub; Shim, Woo Hyun; Shin, Yong Moon; Kim, So Yeon; Lee, So Jung; Lee, Moon-Gyu

doi:10.1148/radiol.2018181197

Cited by 105 publications

(94 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given that not all features would be useful for the classification task, we followed a two‐step methodology to dimensionality reduction, feature selection, and recognition of the optimal set of features for the final radiomics signature construction. Particularly, to overcome the selection bias and overfitting in the traditional logistic regression algorithm, the Kendall correlation coefficient was calculated between each feature and the pathologic outcome as previously described . The feature would be eliminated if the corresponding coefficient was less than 0.2.…”

Section: Methodsmentioning

confidence: 99%

“…Particularly, to overcome the selection bias and overfitting in the traditional logistic regression algorithm, the Kendall correlation coefficient was calculated between each feature and the pathologic outcome as previously described. 25 The feature would be eliminated if the corresponding coefficient was less than 0.2. Second, a least absolute shrinkage and selection operator (LASSO) regression algorithm was used to evaluate the remaining radiomics features.…”

Section: Radiomics Analysismentioning

confidence: 99%

See 1 more Smart Citation

MRI‐Based Radiomics Signature for the Preoperative Prediction of Extracapsular Extension of Prostate Cancer

Xie

Wang

et al. 2019

Magnetic Resonance Imaging

View full text Add to dashboard Cite

Background Radiomics approaches based on multiparametric MRI (mp‐MRI) have shown high accuracy in prostate cancer (PCa) management. However, there is a need to apply radiomics to the preoperative prediction of extracapsular extension (ECE). Purpose To develop and validate a radiomics signature to preoperatively predict the probability of ECE for patients with PCa, compared with the radiologists' interpretations. Study Type Retrospective. Population In total, 210 patients with pathology‐confirmed ECE status (101 positive, 109 negative) were enrolled. Field Strength/Sequence T2‐weighted imaging (T2WI), diffusion‐weighted imaging, and dynamic contrast‐enhanced imaging were performed on two 3.0T MR scanners. Assessment A radiomics signature was constructed to predict the probability of ECE prior to radical prostatectomy (RP). In all, 17 stable radiomics features of 1619 extracted features based on T2WI were selected. The same images were also evaluated by three radiologists. The predictive performance of the radiomics signature was validated and compared with radiologists' interpretations. Statistical Tests A radiomics signature was developed by a least absolute shrinkage and selection operator (LASSO) regression algorithm. Samples enrolled were randomly divided into two groups (143 for training and 67 for validation). Discrimination, calibration, and clinical usefulness were validated by analysis of the receiver operating characteristic (ROC) curve, calibration curve, and the decision curve, respectively. The predictive performance was then compared with visual assessments of three radiologists. Results The radiomics signature yielded an AUC of 0.902 and 0.883 in the training and validation cohort, respectively, and outperformed the visual assessment (AUC: 0.600–0.697) in the validation cohort. Pairwise comparisons demonstrated that the radiomics signature was more sensitive than the radiologists (75.00% vs. 46.88%–50.00%, all P < 0.05), but obtained comparable specificities (91.43% vs. (88.57%–94.29%); P ranged from 0.64–1.00). Data Conclusion A radiomics signature was developed and validated that outperformed the radiologists' visual assessments in predicting ECE status. Level of Evidence: 4 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1914–1925.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Radiomics Analysismentioning

confidence: 99%

MRI‐Based Radiomics Signature for the Preoperative Prediction of Extracapsular Extension of Prostate Cancer

Xie

Wang

et al. 2019

Magnetic Resonance Imaging

View full text Add to dashboard Cite

show abstract

“…https://doi.org/10.3348/kjr.2019.0752 kjronline.org (mean), dispersion (standard deviation), asymmetry (skewness), peakedness or flatness (kurtosis), randomness (entropy), uniformity (energy and uniformity), and dispersion relative to the magnitude (coefficient of variation) of gray-level pixel values. These histogram features describe the distribution pattern of gray-level pixel values within a VOI as a whole, but cannot address the spatial relationship among pixels or the textural pattern (4)(5)(6) (Fig. 2).…”

Section: Radiomicsmentioning

confidence: 99%

“…Radiomics features can be extracted in 2D or 3D using inhouse software (6,(9)(10)(11)(12)(13) or commercial software (14,15 (16), the analysis of high-dimensional features may lead to problems of multicollinearity and overfitting. A recent phantom study revealed that the information provided by multiple radiomics features could be summarized using only 10 features because of redundancy (16).…”

Section: Process Of Radiomics Analysismentioning

confidence: 99%

“…Feature selection and classification can be performed together as a single process or separately using different algorithms. Unreliable features may be excluded prior to feature selection and classification, based on the results of interor intra-observer agreement or test-retest repeatability analyses (6,11,(17)(18)(19)(20)(21). To reduce redundancy in the features, informative features showing a high dynamic range may be selected among the correlated features in hierarchical feature clustering (18,22).…”

Section: Process Of Radiomics Analysismentioning

confidence: 99%

See 1 more Smart Citation

Radiomics and Deep Learning: Hepatic Applications

2020

Self Cite

View full text Add to dashboard Cite

Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. Recent research has demonstrated the potential utility of radiomics and deep learning in staging liver fibroses, detecting portal hypertension, characterizing focal hepatic lesions, prognosticating malignant hepatic tumors, and segmenting the liver and liver tumors. In this review, we outline the basic technical aspects of radiomics and deep learning and summarize recent investigations of the application of these techniques in liver disease.

show abstract

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

Lei

et al. 2023

iRADIOLOGY

View full text Add to dashboard Cite

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.

show abstract

Radiomics Analysis of Gadoxetic Acid–enhanced MRI for Staging Liver Fibrosis

Cited by 105 publications

References 34 publications

MRI‐Based Radiomics Signature for the Preoperative Prediction of Extracapsular Extension of Prostate Cancer

MRI‐Based Radiomics Signature for the Preoperative Prediction of Extracapsular Extension of Prostate Cancer

Radiomics and Deep Learning: Hepatic Applications

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

Contact Info

Product

Resources

About