Radiomics and Deep Learning: Hepatic Applications

Park, Hyo Jung; Park, Bumwoo; Lee, Seung Soo

doi:10.3348/kjr.2019.0752

Cited by 98 publications

(59 citation statements)

References 76 publications

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“…High-order radiomics features often play an important role as predictors in radiomics model studies ( 26 – 28 ). In a previous study, high-order radiomics features with deep learning methods were applied to the differential diagnosis of fatty liver diseases and liver tumors ( 14 ). Moreover, another study proposed a high-order feature-based radiomics model to differentiate liver masses from HCCs ( 16 ).…”

Section: Discussionmentioning

confidence: 99%

“…In the evaluation of liver tumors, especially HCC, radiomics can be used for tumor detection, evaluation of stage, treatment strategy selection, and prognosis prediction. Also, a small number of studies have shown that radiomics has potential predictive value for tumor classification ( 14 ). At first, Raman et al ( 15 ) found the differentially expressed texture features in HCC, focal nodular hyperplasia and hepatic adenomas could be used differential diagnosis of these blood-rich lesions.…”

Section: Introductionmentioning

confidence: 99%

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Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models

et al. 2020

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Background: We conduct a study in developing and validating two radiomics-based models to preoperatively distinguish hepatic epithelioid angiomyolipoma (HEAML) from hepatic carcinoma (HCC) as well as focal nodular hyperplasia (FNH). Methods: Totally, preoperative contrast-enhanced computed tomography (CT) data of 170 patients and preoperative contrast-enhanced magnetic resonance imaging (MRI) data of 137 patients were enrolled in this study. Quantitative texture features and wavelet features were extracted from the regions of interest (ROIs) of each patient imaging data. Then two radiomics signatures were constructed based on CT and MRI radiomics features, respectively, using the random forest (RF) algorithm. By integrating radiomics signatures with clinical characteristics, two radiomics-based fusion models were established through multivariate linear regression and 10-fold cross-validation. Finally, two diagnostic nomograms were built to facilitate the clinical application of the fusion models. Results: The radiomics signatures based on the RF algorithm achieved the optimal predictive performance in both CT and MRI data. The area under the receiver operating characteristic curves (AUCs) reached 0.996, 0.879, 0.999, and 0.925 for the training as well as test cohort from CT and MRI data, respectively. Then, two fusion models simultaneously integrated clinical characteristics achieved average AUCs of 0.966 (CT data) and 0.971 (MRI data) with 10-fold cross-validation. Through decision curve analysis, the fusion models were proved to be excellent models to distinguish HEAML from HCC and FNH in comparison between the clinical models and radiomics signatures. Conclusions: Two radiomics-based models derived from CT and MRI images, respectively, performed well in distinguishing HEAML from HCC and FNH and might be potential diagnostic tools to formulate individualized treatment strategies.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models

et al. 2020

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“…Radiomics is a computerized image analysis process that provides diagnostic and prognostic information from radiologic images 2 . Unlike conventional image interpretation, wherein image features are qualitatively and visually assessed by radiologists, radiomics extracts a large number of quantitative features from images, selects relevant features, and using these image features, makes a decision by modeling an outcome of interest.…”

Section: Radiomicsmentioning

confidence: 99%

“…In contrast, deep learning algorithms are based on representation learning, in which no predefined feature engineering is used. Instead, the algorithm learns on how its own features are extracted, processed, and incorporated to classify the provided training data 1–3 . With sufficient training data, an algorithm based on representation learning performs better compared with a classic machine learning algorithm 3 .…”

Section: Introductionmentioning

confidence: 99%

Radiomics and deep learning in liver diseases

Sung

Park

et al. 2021

J of Gastro and Hepatol

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Recently, radiomics and deep learning have gained attention as methods for computerized image analysis. Radiomics and deep learning can perform diagnostic or predictive tasks using high‐dimensional image‐derived features and have the potential to expand the capabilities of liver imaging beyond the scope of traditional visual image analysis. Recent research has demonstrated the potential of these techniques in various fields of liver imaging, including staging of liver fibrosis, prognostication of malignant liver tumors, automated detection and characterization of liver tumors, automated abdominal organ segmentation, and body composition analysis. However, because most of the previous studies were preliminary and focused mainly on technical feasibility, further clinical validation is required for the application of radiomics and deep learning in clinical practice. In this review, we introduce the technical aspects of radiomics and deep learning and summarize the recent studies on the application of these techniques in liver radiology.

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“…Radiomics is emerging as a promising tool that allows to quantify lesion heterogeneity, extracting additional quantitative data from radiological imaging that cannot be evaluated by human eyes[ 15 , 16 ]. In recent years, multiple researches have explored the performance of radiomics models in abdominal oncologic applications, with significant results for lesions characterization, evaluation of therapeutic response and prediction of patients’ survival after surgical or systemic treatments[ 17 - 22 ]. The application of radiomics in GISTs could be used to further improve the patients’ management and provide new advances in quantitative lesion evaluation due to the unique clinical, genetic, and imaging characteristics of these tumors.…”

Section: Introductionmentioning

confidence: 99%

New advances in radiomics of gastrointestinal stromal tumors

Cannella¹,

Grutta²,

Midiri³

et al. 2020

WJG

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Gastrointestinal stromal tumors (GISTs) are uncommon neoplasms of the gastrointestinal tract with peculiar clinical, genetic, and imaging characteristics. Preoperative knowledge of risk stratification and mutational status is crucial to guide the appropriate patients’ treatment. Predicting the clinical behavior and biological aggressiveness of GISTs based on conventional computed tomography (CT) and magnetic resonance imaging (MRI) evaluation is challenging, unless the lesions have already metastasized at the time of diagnosis. Radiomics is emerging as a promising tool for the quantification of lesion heterogeneity on radiological images, extracting additional data that cannot be assessed by visual analysis. Radiomics applications have been explored for the differential diagnosis of GISTs from other gastrointestinal neoplasms, risk stratification and prediction of prognosis after surgical resection, and evaluation of mutational status in GISTs. The published researches on GISTs radiomics have obtained excellent performance of derived radiomics models on CT and MRI. However, lack of standardization and differences in study methodology challenge the application of radiomics in clinical practice. The purpose of this review is to describe the new advances of radiomics applied to CT and MRI for the evaluation of gastrointestinal stromal tumors, discuss the potential clinical applications that may impact patients’ management, report limitations of current radiomics studies, and future directions.

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Radiomics and Deep Learning: Hepatic Applications

Cited by 98 publications

References 76 publications

Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models

Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models

Radiomics and deep learning in liver diseases

New advances in radiomics of gastrointestinal stromal tumors

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