- Radiomics Analysis of Magnetic Resonance Proton Density Fat Fraction for the Diagnosis of Hepatic Steatosis in Patients With Suspected NonAlcoholic Fatty Liver Disease
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Sim KC, Kim MJ, Cho Y, Kim HJ, Park BJ, Sung DJ, Han NY, Han YE, Kim TH, Lee YJ
- KMID: 2537043
- J Korean Med Sci.
- 2022 Dec;37(49):e339.
- doi: 10.3346/jkms.2022.37.e339
Background: This study aimed to assess the diagnostic feasibility of radiomics analysis based on magnetic resonance (MR)-proton density fat fraction (PDFF) for grading hepatic steatosis in patients with suspected non-alcoholic... -
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- Development and Validation of MRI-Based Radiomics Models for Diagnosing Juvenile Myoclonic Epilepsy
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Kim KM, Hwang H, Sohn B, Park K, Han K, Ahn SS, Lee W, Chu MK, Heo K, Lee SK
- KMID: 2536345
- Korean J Radiol.
- 2022 Dec;23(12):1281-1289.
- doi: 10.3348/kjr.2022.0539
Objective: Radiomic modeling using multiple regions of interest in MRI of the brain to diagnose juvenile myoclonic epilepsy (JME) has not yet been investigated. This study aimed to develop and... -
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- Association of Fluorodeoxyglucose Positron Emission Tomography Radiomics Features with Clinicopathological Factors and Prognosis in Lung Squamous Cell Cancer
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Erol M, Önner H, Küçükosmanoğlu
- KMID: 2537090
- Nucl Med Mol Imaging.
- 2022 Dec;56(6):306-312.
- doi: 10.1007/s13139-022-00774-2
Aim To evaluate the role of fluorine-18 fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) radiomics features (RFs) for predicting clinicopathological factors (CPFs) and prognosis in patients with resected lung squamous... -
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- Development and Validation of a Model Using Radiomics Features from an Apparent Diffusion Coefficient Map to Diagnose Local Tumor Recurrence in Patients Treated for Head and Neck Squamous Cell Carcinoma
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Kim M, Lee JH, Joo L, Jeong B, Kim S, Ham S, Yun J, Kim N, Chung SR, Choi YJ, Baek JH, Lee JY, Kim Jh
- KMID: 2534420
- Korean J Radiol.
- 2022 Nov;23(11):1078-1088.
- doi: 10.3348/kjr.2022.0299
Objective: To develop and validate a model using radiomics features from apparent diffusion coefficient (ADC) map to diagnose local tumor recurrence in head and neck squamous cell carcinoma (HNSCC). Materials and... -
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- Evolution of Radiological Treatment Response Assessments for Cancer Immunotherapy: From iRECIST to Radiomics and Artificial Intelligence
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Kim N, Lee ES, Won SE, Yang M, Lee AJ, Shin Y, Ko Y, Pyo J, Park HJ, Kim KW
- KMID: 2534421
- Korean J Radiol.
- 2022 Nov;23(11):1089-1101.
- doi: 10.3348/kjr.2022.0225
Immunotherapy has revolutionized and opened a new paradigm for cancer treatment. In the era of immunotherapy and molecular targeted therapy, precision medicine has gained emphasis, and an early response assessment... -
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- Combination of 18 F-Fluorodeoxyglucose PET/CT Radiomics and Clinical Features for Predicting Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma
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Li S, Li Y, Zhao M, Wang P, Xin J
- KMID: 2532659
- Korean J Radiol.
- 2022 Sep;23(9):921-930.
- doi: 10.3348/kjr.2022.0295
Objective: To identify epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma based on 18 F-fluorodeoxyglucose (FDG) PET/CT radiomics and clinical features and to distinguish EGFR exon 19 deletion (19... -
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- Feasibility of a Clinical-Radiomics Model to Predict the Outcomes of Acute Ischemic Stroke
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Zhou Y, Wu D, Yan S, Xie Y, Zhang S, Lv W, Qin Y, Liu Y, Liu C, Lu J, Li J, Zhu H, Liu WV, Liu H, Zhang G, Zhu W
- KMID: 2532203
- Korean J Radiol.
- 2022 Aug;23(8):811-820.
- doi: 10.3348/kjr.2022.0160
Objective: To develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes. Materials and Methods: Data from 522 AIS patients (382 male [73.2%]; mean... -
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- Artificial Intelligence in Neuroimaging: Clinical Applications
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Choi KS, Sunwoo L
- KMID: 2533866
- Investig Magn Reson Imaging.
- 2022 Mar;26(1):1-9.
- doi: 10.13104/imri.2022.26.1.1
Artificial intelligence (AI) powered by deep learning (DL) has shown remarkable progress in image recognition tasks. Over the past decade, AI has proven its feasibility for applications in medical imaging.... -
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- Prediction of Cognitive Progression in Individuals with Mild Cognitive Impairment Using Radiomics as an Improvement of the ATN System: A Five-Year Follow-Up Study
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Song R, Wu X, Liu H, Guo D, Tang L, Zhang W, Feng J, Li C
- KMID: 2523739
- Korean J Radiol.
- 2022 Jan;23(1):89-100.
- doi: 10.3348/kjr.2021.0323
Objective: To improve the N biomarker in the amyloid/tau/neurodegeneration system by radiomics and study its value for predicting cognitive progression in individuals with mild cognitive impairment (MCI). Materials and Methods: A... -
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- Quality of Radiomics Research on Brain Metastasis: A Roadmap to Promote Clinical Translation
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Park CJ, Park YW, Ahn SS, Kim D, Kim EH, Kang SG, Chang JH, Kim SH, Lee SK
- KMID: 2523738
- Korean J Radiol.
- 2022 Jan;23(1):77-88.
- doi: 10.3348/kjr.2021.0421
Objective: Our study aimed to evaluate the quality of radiomics studies on brain metastases based on the radiomics quality score (RQS), Transparent Reporting of a multivariable prediction model for Individual... -
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- Radiomics and Deep Learning in Brain Metastases: Current Trends and Roadmap to Future Applications
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Park YW, Lee N, Ahn SS, Chang JH, Lee SK
- KMID: 2526480
- Investig Magn Reson Imaging.
- 2021 Dec;25(4):266-280.
- doi: 10.13104/imri.2021.25.4.266
Advances in radiomics and deep learning (DL) hold great potential to be at the forefront of precision medicine for the treatment of patients with brain metastases. Radiomics and DL can aid... -
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- Computed tomography-based radiomic model predicts radiological response following stereotactic body radiation therapy in early-stage non-small-cell lung cancer and pulmonary oligo-metastases
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Cheung BMF, Lau KS, Lee VHF, Leung TW, Kong FMS, Luk MY, Yuen KK
- KMID: 2523727
- Radiat Oncol J.
- 2021 Dec;39(4):254-264.
- doi: 10.3857/roj.2021.00311
Purpose: Radiomic models elaborate geometric and texture features of tumors extracted from imaging to develop predictors for clinical outcomes. Stereotactic body radiation therapy (SBRT) has been increasingly applied in the... -
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- Prediction of Prognosis in Glioblastoma Using Radiomics Features of Dynamic Contrast-Enhanced MRI
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Pak E, Choi KS, Choi SH, Park CK, Kim TM, Park SH, Lee JH, Lee ST, Hwang I, Yoo RE, Kang KM, Yun TJ, Kim JH, Sohn CH
- KMID: 2519437
- Korean J Radiol.
- 2021 Sep;22(9):1514-1524.
- doi: 10.3348/kjr.2020.1433
Objective: To develop a radiomics risk score based on dynamic contrast-enhanced (DCE) MRI for prognosis prediction in patients with glioblastoma. Materials and Methods: One hundred and fifty patients (92 male [61.3%];... -
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- Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data
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Purkayastha S, Xiao Y, Jiao Z, Thepumnoeysuk R, Halsey K, Wu J, Tran TML, Hsieh B, Choi JW, Wang D, Vallières M, Wang R, Collins S, Feng X, Feldman M, Zhang PJ, Atalay M, Sebro R, Yang L, Fan Y, Liao Wh, Bai HX
- KMID: 2518657
- Korean J Radiol.
- 2021 Jul;22(7):1213-1224.
- doi: 10.3348/kjr.2020.1104
Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials... -
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- Breast Ultrasound Microvascular Imaging and Radiogenomics
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Park AY, Seo BK, Han MR
- KMID: 2515056
- Korean J Radiol.
- 2021 May;22(5):677-687.
- doi: 10.3348/kjr.2020.1166
Microvascular ultrasound (US) techniques are advanced Doppler techniques that provide high sensitivity and spatial resolution for detailed visualization of low-flow vessels. Microvascular US imaging can be applied to breast lesion... -
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- Performance of Prediction Models for Diagnosing Severe Aortic Stenosis Based on Aortic Valve Calcium on Cardiac Computed Tomography: Incorporation of Radiomics and Machine Learning
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Kang Ng, Suh YJ, Han K, Kim YJ, Choi BW
- KMID: 2513104
- Korean J Radiol.
- 2021 Mar;22(3):334-343.
- doi: 10.3348/kjr.2020.0099
Objective: We aimed to develop a prediction model for diagnosing severe aortic stenosis (AS) using computed tomography (CT) radiomics features of aortic valve calcium (AVC) and machine learning (ML) algorithms. Materials... -
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- Noncontrast Computed Tomography-Based Radiomics Analysis in Discriminating Early Hematoma Expansion after Spontaneous Intracerebral Hemorrhage
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Song Z, Guo D, Tang Z, Liu H, Li X, Luo S, Yao X, Song W, Song J, Zhou Z
- KMID: 2513112
- Korean J Radiol.
- 2021 Mar;22(3):415-424.
- doi: 10.3348/kjr.2020.0254
Objective: To determine whether noncontrast computed tomography (NCCT) models based on multivariable, radiomics features, and machine learning (ML) algorithms could further improve the discrimination of early hematoma expansion (HE) in... -
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- Quality Reporting of Radiomics Analysis in Mild Cognitive Impairment and Alzheimer’s Disease: A Roadmap for Moving Forward
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Won SY, Park YW, Park M, Ahn SS, Kim J, Lee SK
- KMID: 2509562
- Korean J Radiol.
- 2020 Dec;21(12):1350-1359.
- doi: 10.3348/kjr.2020.0715
Objective: To evaluate radiomics analysis in studies on mild cognitive impairment (MCI) and Alzheimer’s disease (AD) using a radiomics quality score (RQS) system to establish a roadmap for further improvement... -
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- Squamous Cell Carcinoma and Lymphoma of the Oropharynx: Differentiation Using a Radiomics Approach
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Bae S, Choi YS, Sohn B, Ahn SS, Lee SK, Yang J, Kim J
- KMID: 2506857
- Yonsei Med J.
- 2020 Oct;61(10):895-900.
- doi: 10.3349/ymj.2020.61.10.895
The purpose of this study was to evaluate the diagnostic performance of magnetic resonance (MR) radiomics-based machine learning algorithms in differentiating squamous cell carcinoma (SCC) from lymphoma in the oropharynx.... -
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- Evaluating Focal 18F-FDG Uptake in Thyroid Gland with Radiomics
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Aksu A, Şen NPK, Acar E, Kaya G
- KMID: 2507741
- Nucl Med Mol Imaging.
- 2020 Oct;54(5):241-248.
- doi: 10.1007/s13139-020-00659-2
Purpose: The aim of this study was to evaluate the ability of 18F-FDG PET/CT texture analysis to predict the exact pathological outcome of thyroid incidentalomas. Methods: 18F-FDG PET/CT images between March... -
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