Articles with "radiomic features" as a keyword



MRI‐based radiomic features for identifying recurrent prostate cancer after proton radiation therapy

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Published in 2024 at "Journal of Applied Clinical Medical Physics"

DOI: 10.1002/acm2.14293

Abstract: Abstract Purpose Magnetic Resonance Imaging (MRI) evaluation of recurrent prostate cancer (PCa) following proton beam therapy is challenging due to radiation‐induced tissue changes. This study aimed to evaluate MRI‐based radiomic features so as to identify… read more here.

Keywords: recurrent prostate; cancer; proton; therapy ... See more keywords

Clinical and radiomic features for predicting the treatment response of repetitive transcranial magnetic stimulation in major neurocognitive disorder: Results from a randomized controlled trial

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Published in 2022 at "Human Brain Mapping"

DOI: 10.1002/hbm.26032

Abstract: Image‐guided repetitive transcranial magnetic stimulation (rTMS) has shown clinical effectiveness in senior adults with co‐occurring depression and cognitive impairment, yet the imaging markers for predicting the treatment response are less investigated. In this clinical trial,… read more here.

Keywords: transcranial magnetic; rtms; repetitive transcranial; treatment ... See more keywords

Assessment of severity in neuropsychiatric disorders based on radiomic features with prior shape level set and metaheuristic algorithms

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Published in 2019 at "International Journal of Imaging Systems and Technology"

DOI: 10.1002/ima.22306

Abstract: The diagnostic and clinical overlap between schizophrenia (SZ) and schizoaffective (SA) is vital oncological issue in psychiatry disorder. This study is designed to examine tissue variation in cerebellum region. Shape prior levelset method is used… read more here.

Keywords: radiomic features; shape; features extracted; cerebellum ... See more keywords

Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi‐institutional study

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Published in 2017 at "Journal of Magnetic Resonance Imaging"

DOI: 10.1002/jmri.25562

Abstract: To evaluate in a multi‐institutional study whether radiomic features useful for prostate cancer (PCa) detection from 3 Tesla (T) multi‐parametric MRI (mpMRI) in the transition zone (TZ) differ from those in the peripheral zone (PZ). read more here.

Keywords: multi institutional; institutional study; radiomic features; prostate cancer ... See more keywords

Reliability of tumor segmentation in glioblastoma: impact on the robustness of MRI-radiomic features.

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Published in 2019 at "Medical physics"

DOI: 10.1002/mp.13624

Abstract: PURPOSE The use of radiomic features as biomarkers of treatment response and outcome or as correlates to genomic variations requires that the computed features are robust and reproducible. Segmentation, a crucial step in radiomic analysis,… read more here.

Keywords: radiomic features; mri radiomic; segmentation; method ... See more keywords

Machine learning prediction of axillary lymph node metastasis in breast cancer: 2D versus 3D radiomic features.

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Published in 2020 at "Medical physics"

DOI: 10.1002/mp.14538

Abstract: PURPOSE The purpose of this study was to distinguish axillary lymph node (ALN) status using preoperative breast DCE-MRI radiomics and compare the effects of 2D and 3D analysis. METHODS A retrospective study including 154 breast… read more here.

Keywords: radiomic features; machine learning; breast; analysis ... See more keywords
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Identification of reproducible radiomic features from on-board volumetric images: A multi-institutional phantom study.

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Published in 2023 at "Medical physics"

DOI: 10.1002/mp.16376

Abstract: BACKGROUND Radiomics analysis using on-board volumetric images has attracted research attention as a method for predicting prognosis during treatment; however, the lack of standardization is still one of the main concerns. PURPOSE This study investigated… read more here.

Keywords: filter based; volumetric images; radiomic features; treatment ... See more keywords

Evaluation of the stability of radiomic features of non‐irradiated organs utilizing fan‐beam kilovoltage computed tomography

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Published in 2025 at "Medical Physics"

DOI: 10.1002/mp.17914

Abstract: Although radiation oncologists obtain non‐contrast computed tomography (CT) images in every treatment, their quality is often insufficient for radiomic analyses for monitoring response over the course of treatment. Newer linear accelerators, such as RefleXion X1,… read more here.

Keywords: features non; stability radiomic; radiomic features; non irradiated ... See more keywords

Deep learning image enhancement algorithms in PET/CT imaging: a phantom and sarcoma patient radiomic evaluation

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Published in 2025 at "European Journal of Nuclear Medicine and Molecular Imaging"

DOI: 10.1007/s00259-025-07149-7

Abstract: PET/CT imaging data contains a wealth of quantitative information that can provide valuable contributions to characterising tumours. A growing body of work focuses on the use of deep-learning (DL) techniques for denoising PET data. These… read more here.

Keywords: image; radiomic features; medicine; usepackage ... See more keywords

A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images

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Published in 2017 at "European Radiology"

DOI: 10.1007/s00330-017-5154-8

Abstract: ObjectivesTo develop a model using radiomic features extracted from MR images to distinguish radiation necrosis from tumour progression in brain metastases after Gamma Knife radiosurgery.MethodsWe retrospectively identified 87 patients with pathologically confirmed necrosis (24 lesions)… read more here.

Keywords: radiomic features; tumour progression; radiation necrosis; necrosis tumour ... See more keywords

Differentiation between pilocytic astrocytoma and glioblastoma: a decision tree model using contrast-enhanced magnetic resonance imaging-derived quantitative radiomic features

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Published in 2018 at "European Radiology"

DOI: 10.1007/s00330-018-5706-6

Abstract: ObjectiveTo differentiate brain pilocytic astrocytoma (PA) from glioblastoma (GBM) using contrast-enhanced magnetic resonance imaging (MRI) quantitative radiomic features by a decision tree model.MethodsSixty-six patients from two centres (PA, n = 31; GBM, n = 35)… read more here.

Keywords: tree model; radiomic features; model; contrast enhanced ... See more keywords