1,845 results match your criteria radiomic

Benchmarking Various Radiomic Toolkit Features While Applying the Image Biomarker Standardization Initiative toward Clinical Translation of Radiomic Analysis.

J Digit Imaging 2021 Sep 20. Epub 2021 Sep 20.

Department of Radiology, University of Southern California, 1500 San Pablo Street, Los Angeles, CA, 90033, USA.

The image biomarkers standardization initiative (IBSI) was formed to address the standardization of extraction of quantifiable imaging metrics. Despite its effort, there remains a lack of consensus or established guidelines regarding radiomic feature terminology, the underlying mathematics and their implementation across various software programs. This creates a scenario where features extracted using different toolboxes cannot be used to build or validate the same model leading to a non-generalization of radiomic results. Read More

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September 2021

Multi-level multi-modality (PET and CT) fusion radiomics: Prognostic modeling for non-small cell lung carcinoma.

Phys Med Biol 2021 Sep 20. Epub 2021 Sep 20.

Division of Nuclear Medicine, University Hospital of Geneva, 24 Rue Micheli-du-Crest, 1211 Geneva 4, Geneva, SWITZERLAND.

We developed multi-modality radiomic models by integrating information extracted from 18F-FDG PET and CT images using feature- and image-level fusions, toward improved prognosis for non-small cell lung carcinoma (NSCLC) patients. Two independent cohorts of NSCLC patients from two institutions (87 and 95 patients) were cycled as training and testing datasets. Fusion approaches were applied at two levels, namely feature- and image-levels. Read More

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September 2021

Robustness of dual-energy CT-derived radiomic features across three different scanner types.

Eur Radiol 2021 Sep 20. Epub 2021 Sep 20.

Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114, USA.

Objectives: To investigate the robustness of radiomic features between three dual-energy CT (DECT) systems.

Methods: An anthropomorphic body phantom was scanned on three different DECT scanners, a dual-source (dsDECT), a rapid kV-switching (rsDECT), and a dual-layer detector DECT (dlDECT). Twenty-four patients who underwent abdominal DECT examinations on each of the scanner types during clinical follow-up were retrospectively included (n = 72 examinations). Read More

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September 2021

Zonal anatomy of the prostate using magnetic resonance imaging, morphometrics, and radiomic features: impact of age-related changes.

Br J Radiol 2021 Sep 19:20210156. Epub 2021 Sep 19.

Academic Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, Paris, France.

Objective: To evaluate the impact of age on the zonal anatomy of the prostate by MRI using morphometric and textural analysis.

Methods: A total of 154 men (mean age: 63 years) who underwent MRI due to a high prostate-specific antigen (PSA) level were included retrospectively. At each MRI examination the following variables were measured: overall dimensions of the prostate (whole gland (WG), transitional zone (TZ), and peripheral zone (PZ)), and thickness of the anterior fibromuscular stroma (AFMS) and the periprostatic venous plexus (PPVP) on weighted images. Read More

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September 2021

A Radiomic-based Machine Learning Algorithm to Reliably Differentiate Benign Renal Masses from Renal Cell Carcinoma.

Eur Urol Focus 2021 Sep 16. Epub 2021 Sep 16.

USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA; Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.

Background: A substantial proportion of patients undergo treatment for renal masses where active surveillance or observation may be more appropriate.

Objective: To determine whether radiomic-based machine learning platforms can distinguish benign from malignant renal masses.

Design, Setting, And Participants: A prospectively maintained single-institutional renal mass registry was queried to identify patients with a computed tomography-proven clinically localized renal mass who underwent partial or radical nephrectomy. Read More

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September 2021

Radiomics in PET Imaging:: A Practical Guide for Newcomers.

PET Clin 2021 Oct;16(4):597-612

Institut Curie Centre de Recherche, Centre Universitaire, Bat 101B, Rue Henri Becquerel, CS 90030, 91401 Orsay Cedex, France.

Radiomics has undergone considerable development in recent years. In PET imaging, very promising results concerning the ability of handcrafted features to predict the biological characteristics of lesions and to assess patient prognosis or response to treatment have been reported in the literature. This article presents a checklist for designing a reliable radiomic study, gives an overview of the steps of the pipeline, and outlines approaches for data harmonization. Read More

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October 2021

The Use of Radiomic Analysis of Magnetic Resonance Imaging in Predicting Distant Metastases of Rectal Carcinoma following Surgical Resection - A Systematic Review & Meta-Analysis.

Colorectal Dis 2021 Sep 18. Epub 2021 Sep 18.

Discipline of Surgery, Galway University Hospitals, Galway, H91 YR71, Ireland.

Background: Estimating prognosis in rectal carcinoma (RC) is challenging, with distant recurrence (DR) occurring in up to 30% of cases. Radiomics is a novel field using diagnostic imaging to investigate the tumour heterogeneity of cancers and may have the potential to predict DR.

Aims: To perform a systematic review of current literature evaluating the use of radiomics in predicting DR in patients with resected RC. Read More

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September 2021

Magnetic resonance imaging radiomics signatures for predicting endocrine resistance in hormone receptor-positive non-metastatic breast cancer.

Breast 2021 Sep 11;60:90-97. Epub 2021 Sep 11.

Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China; Department of Medical Oncology, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China. Electronic address:

Background: One-third of patients with hormone receptor (HR)-positive breast cancers fail to respond to hormone therapy, and some patients even progress within two years of adjuvant endocrine therapy (ET) toward primary endocrine resistance. However, there is no effective way to predict endocrine resistance.

Objective: To build a model that incorporates the radiomic signature of pretreatment magnetic resonance imaging (MRI) with clinical information to predict endocrine resistance. Read More

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September 2021

A guide to ComBat harmonization of imaging biomarkers in multicenter studies.

J Nucl Med 2021 Sep 16. Epub 2021 Sep 16.

Institut Curie, Universite PSL, Inserm, U1288 LITO, Universite Paris Saclay, France.

The impact of PET image acquisition and reconstruction parameters on SUV measurements or radiomic feature values is widely documented. This "scanner" effect is detrimental to the design and validation of predictive or prognostic models and limits the use of large multicenter cohorts. To reduce the impact of this scanner effect, the ComBat method has been proposed and is now used in various contexts. Read More

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September 2021

A Combined Radiomics and Machine Learning Approach to Overcome the Clinicoradiologic Paradox in Multiple Sclerosis.

AJNR Am J Neuroradiol 2021 Sep 16. Epub 2021 Sep 16.

From the Departments of Advanced Biomedical Sciences (G.P., L.U., E.T., S.C.).

Background And Purpose: Conventional MR imaging explains only a fraction of the clinical outcome variance in multiple sclerosis. We aimed to evaluate machine learning models for disability prediction on the basis of radiomic, volumetric, and connectivity features derived from routine brain MR images.

Materials And Methods: In this retrospective cross-sectional study, 3T brain MR imaging studies of patients with multiple sclerosis, including 3D T1-weighted and T2-weighted FLAIR sequences, were selected from 2 institutions. Read More

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September 2021

An Integrated Radiomics Model Incorporating Diffusion-Weighted Imaging and F-FDG PET Imaging Improves the Performance of Differentiating Glioblastoma From Solitary Brain Metastases.

Front Oncol 2021 30;11:732704. Epub 2021 Aug 30.

Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Background: The effectiveness of conventional MRI (cMRI)-based radiomics in differentiating glioblastoma (GBM) from solitary brain metastases (SBM) is not satisfactory enough. Therefore, we aimed to develop an integrated radiomics model to improve the performance of differentiating GBM from SBM.

Methods: One hundred patients with solitary brain tumors (50 with GBM, 50 with SBM) were retrospectively enrolled and randomly assigned to the training set ( = 80) or validation set ( = 20). Read More

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Radiomics Based Bayesian Inversion Method for Prediction of Cancer and Pathological Stage.

IEEE J Transl Eng Health Med 2021 30;9:4300208. Epub 2021 Aug 30.

Department of Electrical and Computer EngineeringMichigan State University East Lansing MI 48824 USA.

Objective: To develop a Bayesian inversion framework on longitudinal chest CT scans which can perform efficient multi-class classification of lung cancer.

Methods: While the unavailability of large number of training medical images impedes the performance of lung cancer classifiers, the purpose built deep networks have not performed well in multi-class classification. The presented framework employs particle filtering approach to address the non-linear behaviour of radiomic features towards benign and cancerous (stages I, II, III, IV) nodules and performs efficient multi-class classification (benign, early stage cancer, advanced stage cancer) in terms of posterior probability function. Read More

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Knowing When to Use Stereotactic Ablative Radiation Therapy in Oligometastatic Cancer.

Cancer Manag Res 2021 7;13:7009-7031. Epub 2021 Sep 7.

Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Milan, Italy.

Oligometastatic patients are a heterogeneous and yet not well-defined population. The actual definition identifies as oligometastatic, patients with 1-5 metastases in 1-3 different organs. However, only a proportion of these patients are "true" oligometastatic and therefore derive some kinds of benefit from local ablative approaches like stereotactic ablative radiation therapy (SABR). Read More

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September 2021

Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples.

Phys Med 2021 Sep 11;90:13-22. Epub 2021 Sep 11.

Physics and Chemistry Department "Emilio Segrè", University of Palermo, Palermo, Italy; National Institute for Nuclear Physics (INFN), Catania Division, Catania, Italy.

Predictive models based on radiomics and machine-learning (ML) need large and annotated datasets for training, often difficult to collect. We designed an operative pipeline for model training to exploit data already available to the scientific community. The aim of this work was to explore the capability of radiomic features in predicting tumor histology and stage in patients with non-small cell lung cancer (NSCLC). Read More

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September 2021

Predicting clinical outcomes in COVID-19 using radiomics on chest radiographs.

Br J Radiol 2021 Oct 14;94(1126):20210221. Epub 2021 Sep 14.

Keck School of Medicine, University of Southern California, CA, USA.

Objectives: For optimal utilization of healthcare resources, there is a critical need for early identification of COVID-19 patients at risk of poor prognosis as defined by the need for intensive unit care and mechanical ventilation. We tested the feasibility of chest X-ray (CXR)-based radiomics metrics to develop machine-learning algorithms for predicting patients with poor outcomes.

Methods: In this Institutional Review Board (IRB) approved, Health Insurance Portability and Accountability Act (HIPAA) compliant, retrospective study, we evaluated CXRs performed around the time of admission from 167 COVID-19 patients. Read More

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October 2021

Preliminary Computed Tomography Radiomics Model for Predicting Pretreatment CD8+ T-Cell Infiltration Status for Primary Head and Neck Squamous Cell Carcinoma.

J Comput Assist Tomogr 2021 Jul-Aug 01;45(4):629-636

Department of Radiology, The University of Chicago, Chicago, IL.

Purpose: Immunotherapy has emerged as a treatment option for head and neck squamous cell carcinoma (HNSCC), with tumor response being linked to the CD8+ T-cell inflammation. The purpose of this study is to assess whether computed tomography (CT) radiomic analysis can predict CD8+ T-cell enrichment in HNSCC primary tumors.

Methods: This retrospective study included 71 patients from a head and neck cancer genomics cohort with CD8+ T-cell enrichment status. Read More

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September 2021

Deep learning and radiomics analysis for prediction of placenta invasion based on T2WI.

Math Biosci Eng 2021 07;18(5):6198-6215

Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China.

The purpose of this study was to explore whether the Nomogram, which was constructed by combining the Deep learning and Radiomic features of T2-weighted MR images with Clinical factors (NDRC), could accurately predict placenta invasion. This retrospective study included 72 pregnant women with pathologically confirmed placenta invasion and 40 pregnant women with normal placenta. After 24 gestational weeks, all participants underwent magnetic resonance imaging. Read More

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Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regression.

Brainlesion 2021 27;12658:157-167. Epub 2021 Mar 27.

Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.

Glioblastoma ( ) is arguably the most aggressive, infiltrative, and heterogeneous type of adult brain tumor. Biophysical modeling of GBM growth has contributed to more informed clinical decision-making. However, deploying a biophysical model to a clinical environment is challenging since underlying computations are quite expensive and can take several hours using existing technologies. Read More

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Quantitative image features from radiomic biopsy differentiate oncocytoma from chromophobe renal cell carcinoma.

J Med Imaging (Bellingham) 2021 Sep 7;8(5):054501. Epub 2021 Sep 7.

Mayo Clinic Arizona, Department of Radiology, Phoenix, Arizona, United States.

: To differentiate oncocytoma and chromophobe renal cell carcinoma (RCC) using radiomics features computed from spherical samples of image regions of interest, "radiomic biopsies" (RBs). : In a retrospective cohort study of 102 CT cases [68 males (67%), 34 females (33%); mean age ± SD, ], we pathology-confirmed 42 oncocytomas (41%) and 60 chromophobes (59%). A board-certified radiologist performed two RB rounds. Read More

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September 2021

Differentiation Between Glioblastoma Multiforme and Metastasis From the Lungs and Other Sites Using Combined Clinical/Routine MRI Radiomics.

Front Cell Dev Biol 2021 26;9:710461. Epub 2021 Aug 26.

Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.

Background: Differentiation between cerebral glioblastoma multiforme (GBM) and solitary brain metastasis (MET) is important. The existing radiomic differentiation method ignores the clinical and routine magnetic resonance imaging (MRI) features.

Purpose: To differentiate between GBM and MET and between METs from the lungs (MET-lung) and other sites (MET-other) through clinical and routine MRI, and radiomics analyses. Read More

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Enhanced CT-Based Radiomics to Predict Micropapillary Pattern Within Lung Invasive Adenocarcinoma.

Front Oncol 2021 27;11:704994. Epub 2021 Aug 27.

Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou, China.

Objective: We aimed to investigate whether enhanced CT-based radiomics can predict micropapillary pattern (MPP) of lung invasive adenocarcinoma (IAC) in the pre-op phase and to develop an individual diagnostic predictive model for MPP in IAC.

Methods: 170 patients who underwent complete resection for pathologically confirmed lung IAC were included in our study. Of these 121 were used as a training cohort and the other 49 as a test cohort. Read More

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MRI-based Delta-Radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy.

Radiother Oncol 2021 Sep 7. Epub 2021 Sep 7.

Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum, München, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich.

Purpose: In high-grade soft-tissue sarcomas (STS) the standard of care encompasses multimodal therapy regimens. While there is a growing body of evidence for prognostic pretreatment radiomic models, we hypothesized that temporal changes in radiomic features following neoadjuvant treatment ("delta-radiomics") may be able to predict the pathological complete response (pCR).

Methods: MRI scans (T1-weighted with fat-saturation and contrast-enhancement (T1FSGd) and T2-weighted with fat-saturation (T2FS)) of patients with STS of the extremities and trunk treated with neoadjuvant therapy were gathered from two independent institutions (training: 103, external testing: 53 patients). Read More

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September 2021

Radiomic signature of DWI-FLAIR mismatch in large vessel occlusion stroke.

J Neuroimaging 2021 Sep 10. Epub 2021 Sep 10.

Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA.

Background And Purpose: Ischemic diffusion-weighted imaging-fluid-attenuated inversion recovery (DWI-FLAIR) mismatch may be useful in guiding acute stroke treatment decisions given its relationship to onset time and parenchymal viability; however, it relies on subjective grading. Radiomics is an emerging image quantification methodology that may objectively represent continuous image characteristics. We propose a novel radiomics approach to characterize DWI-FLAIR mismatch. Read More

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September 2021

MR Denoising Increases Radiomic Biomarker Precision and Reproducibility in Oncologic Imaging.

J Digit Imaging 2021 Sep 10. Epub 2021 Sep 10.

Grupo de Investigación Biomédica en Imagen, Instituto de Investigación Sanitaria La Fe, Avenida Fernando Abril Martorell, 106 Torre A 7planta, 46026, Valencia, Spain.

Several noise sources, such as the Johnson-Nyquist noise, affect MR images disturbing the visualization of structures and affecting the subsequent extraction of radiomic data. We evaluate the performance of 5 denoising filters (anisotropic diffusion filter (ADF), curvature flow filter (CFF), Gaussian filter (GF), non-local means filter (NLMF), and unbiased non-local means (UNLMF)), with 33 different settings, in T2-weighted MR images of phantoms (N = 112) and neuroblastoma patients (N = 25). Filters were discarded until the most optimal solutions were obtained according to 3 image quality metrics: peak signal-to-noise ratio (PSNR), edge-strength similarity-based image quality metric (ESSIM), and noise (standard deviation of the signal intensity of a region in the background area). Read More

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September 2021

Diagnostic classification of coronavirus disease 2019 (COVID-19) and other pneumonias using radiomics features in CT chest images.

Sci Rep 2021 09 9;11(1):17885. Epub 2021 Sep 9.

Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, 510317, People's Republic of China.

We propose a classification method using the radiomics features of CT chest images to identify patients with coronavirus disease 2019 (COVID-19) and other pneumonias. The chest CT images of two groups of participants (90 COVID-19 patients who were confirmed as positive by nucleic acid test of RT-PCR and 90 other pneumonias patients) were collected, and the two groups of data were manually drawn to outline the region of interest (ROI) of pneumonias. The radiomics method was used to extract textural features and histogram features of the ROI and obtain a radiomics features vector from each sample. Read More

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September 2021

Radiomics of MRI for the Prediction of the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer Patients: A Single Referral Centre Analysis.

Cancers (Basel) 2021 Aug 25;13(17). Epub 2021 Aug 25.

Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy.

Objectives: We aimed to determine whether radiomic features extracted from a highly homogeneous database of breast MRI could non-invasively predict pathological complete responses (pCR) to neoadjuvant chemotherapy (NACT) in patients with breast cancer.

Methods: One hundred patients with breast cancer receiving NACT in a single center (01/2017-06/2019) and undergoing breast MRI were retrospectively evaluated. For each patient, radiomic features were extracted within the biopsy-proven tumor on T1-weighted (T1-w) contrast-enhanced MRI performed before NACT. Read More

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Machine learning-based CT radiomics model distinguishes COVID-19 from non-COVID-19 pneumonia.

BMC Infect Dis 2021 Sep 8;21(1):931. Epub 2021 Sep 8.

Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), No. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, People's Republic of China.

Background: To develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spreading coronavirus disease 2019 (COVID-19).

Methods: In this retrospective study, a total of 326 chest CT exams from 134 patients (63 confirmed COVID-19 patients and 71 non-COVID-19 patients) were collected from January 20 to February 8, 2020. A semi-automatic segmentation procedure was used to delineate the volume of interest (VOI), and radiomic features were extracted. Read More

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September 2021

Radiation therapy for prostate cancer: What's the best in 2021.

Urologia 2021 Sep 8:3915603211042335. Epub 2021 Sep 8.

UOC di Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy.

Radiotherapy is highly involved in the management of prostate cancer. Its features and potential applications experienced a radical evolution over last decades, as they are associated to the continuous evolution of available technology and current oncological innovations. Some application of radiotherapy like brachytherapy have been recently enriched by innovative features and multidisciplinary dedications. Read More

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September 2021

CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal Adenocarcinoma: A Pilot Study.

Radiology 2021 Sep 7:210699. Epub 2021 Sep 7.

From the Departments of Radiology (F.R., K.J.L., M.M., P.L., Y.D., F.R.S., E.S., D.M.) and Radiation Oncology (K.J.L.), Duke University Medical Center, 2301 Erwin Rd, Box 3808, Durham, NC 27710; Multi-Dimensional Image Processing Laboratory, Duke Radiology, Duke University School of Medicine, Durham, NC (F.R., M.M., P.L., Y.D., F.R.S., D.M.); progettoDiventerò, Bracco Foundation, Milan, Italy (F.R.); Carl E. Ravin Advanced Imaging Laboratories (J.H., E.S.), Department of Biostatistics and Bioinformatics (R.L., S.L.), and Duke Electrical and Computer Engineering (K.J.L.), Duke University, Durham, NC; Department of Biostatistics, Yale University, New Haven, Conn (C.L.); Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, Hamburg, Germany (M.M.); Department of Radiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, People's Republic of China (P.L.); Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China (Y.D.); Duke Cancer Center, Duke Health, Durham, NC (N.B.M., S.Z.); and Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (D.E.M.).

Background Current imaging methods for prediction of complete margin resection (R0) in patients with pancreatic ductal adenocarcinoma (PDAC) are not reliable. Purpose To investigate whether tumor-related and perivascular CT radiomic features improve preoperative assessment of arterial involvement in patients with surgically proven PDAC. Materials and Methods This retrospective study included consecutive patients with PDAC who underwent surgery after preoperative CT between 2012 and 2019. Read More

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September 2021

Prediction of TERTp-mutation status in IDH-wildtype high-grade gliomas using pre-treatment dynamic [F]FET PET radiomics.

Eur J Nucl Med Mol Imaging 2021 Sep 7. Epub 2021 Sep 7.

Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.

Purpose: To evaluate radiomic features extracted from standard static images (20-40 min p.i.), early summation images (5-15 min p. Read More

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September 2021