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Assessing robustness of quantitative susceptibility-based MRI radiomic features in patients with multiple sclerosis
Multiple Sclerosis (MS) is an autoimmune demyelinating disease characterised by changes in iron and myelin content. These biomarkers are detectable...
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Prediction of clinically relevant postoperative pancreatic fistula using radiomic features and preoperative data
Clinically relevant postoperative pancreatic fistula (CR-POPF) can significantly affect the treatment course and outcome in pancreatic cancer...
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Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence
The primary objective of the present study was to identify a subset of radiomic features extracted from primary tumor imaged by computed tomography...
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AutoComBat: a generic method for harmonizing MRI-based radiomic features
The use of multicentric data is becoming essential for develo** generalizable radiomic signatures. In particular, Magnetic Resonance Imaging (MRI)...
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Enhancing foveal avascular zone analysis for Alzheimer’s diagnosis with AI segmentation and machine learning using multiple radiomic features
We propose a hybrid technique that employs artificial intelligence (AI)-based segmentation and machine learning classification using multiple...
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The prognostic value of radiomic features from pre- and post-treatment 18F-FDG PET imaging in patients with nasopharyngeal carcinoma
Positron emission tomography/computed tomography (PET/CT) with 18 F-fluorodeoxyglucose (FDG) is widely used for management of nasopharyngeal carcinoma...
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Improved generalized ComBat methods for harmonization of radiomic features
Radiomic approaches in precision medicine are promising, but variation associated with image acquisition factors can result in severe biases and low...
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Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality
As portable chest X-rays are an efficient means of triaging emergent cases, their use has raised the question as to whether imaging carries...
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Predicting social anxiety in young adults with machine learning of resting-state brain functional radiomic features
Social anxiety is a symptom widely prevalent among young adults, and when present in excess, can lead to maladaptive patterns of social behavior....
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Feasibility and intra-and interobserver reproducibility of quantitative susceptibility map** with radiomic features for intracranial dissecting intramural hematomas and atherosclerotic calcifications
Quantitative susceptibility map** (QSM) for 61 patients with dissecting intramural hematomas ( n = 36) or atherosclerotic calcifications ( n = 25) in...
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Feasibility and sensitivity study of radiomic features in photoacoustic imaging of patient-derived xenografts
Photoacoustic imaging is an increasingly popular method of exploring the tumour microenvironment, which can provide insight into tumour oxygenation...
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Deep learning-based reconstruction on cardiac CT yields distinct radiomic features compared to iterative and filtered back projection reconstructions
We aimed to determine the effects of deep learning-based reconstruction (DLR) on radiomic features obtained from cardiac computed tomography (CT) by...
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Value of handcrafted and deep radiomic features towards training robust machine learning classifiers for prediction of prostate cancer disease aggressiveness
There is a growing piece of evidence that artificial intelligence may be helpful in the entire prostate cancer disease continuum. However, building...
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MRI-based delta-radiomic features for prediction of local control in liver lesions treated with stereotactic body radiation therapy
Real-time magnetic resonance image guided stereotactic ablative radiotherapy (MRgSBRT) is used to treat abdominal tumors. Longitudinal data is...
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Radiomic profiles improve prognostication and reveal targets for therapy in cervical cancer
Cervical cancer (CC) is a major global health problem with 570,000 new cases and 266,000 deaths annually. Prognosis is poor for advanced stage...
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Image resampling and discretization effect on the estimate of myocardial radiomic features from T1 and T2 map** in hypertrophic cardiomyopathy
Radiomics is emerging as a promising and useful tool in cardiac magnetic resonance (CMR) imaging applications. Accordingly, the purpose of this study...
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Multivariate testing and effect size measures for batch effect evaluation in radiomic features
While precision medicine applications of radiomics analysis are promising, differences in image acquisition can cause “batch effects” that reduce...
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Comparing effectiveness of image perturbation and test retest imaging in improving radiomic model reliability
Image perturbation is a promising technique to assess radiomic feature repeatability, but whether it can achieve the same effect as test–retest...
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Generalized ComBat harmonization methods for radiomic features with multi-modal distributions and multiple batch effects
Radiomic features have a wide range of clinical applications, but variability due to image acquisition factors can affect their performance. The...
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The ImSURE phantoms: a digital dataset for radiomic software benchmarking and investigation
In radiology and oncology, radiomic models are increasingly employed to predict clinical outcomes, but their clinical deployment has been hampered by...
