A5055844896- Radiomics and Machine Learning in Medical Imaging
- Advanced X-ray and CT Imaging
- Medical Imaging Techniques and Applications
- Lung Cancer Diagnosis and Treatment
- AI in cancer detection
- MRI in cancer diagnosis
- Pancreatic and Hepatic Oncology Research
- Gastric Cancer Management and Outcomes
- Head and Neck Cancer Studies
- COVID-19 diagnosis using AI
- Sarcoma Diagnosis and Treatment
- Lung Cancer Treatments and Mutations
- Glioma Diagnosis and Treatment
- Colorectal and Anal Carcinomas
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence in Healthcare
- Colorectal Cancer Surgical Treatments
- Advanced Radiotherapy Techniques
- Radiation Dose and Imaging
- Ferroptosis and cancer prognosis
- Medical Imaging and Pathology Studies
- Colorectal Cancer Treatments and Studies
- Cancer, Hypoxia, and Metabolism
- Cancer Genomics and Diagnostics
- Privacy-Preserving Technologies in Data
Maastricht University
2013-2024
Maastro Clinic
2013-2022
Maastricht University Medical Centre
2013-2021
University of Liège
2021
University Hospital Carl Gustav Carus
2020
OncoRay
2020
Helmholtz-Zentrum Dresden-Rossendorf
2020
Johns Hopkins University
2020
TU Dresden
2020
National Center for Tumor Diseases
2020
Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes applying a large number quantitative image features. Here we present radiomic analysis 440 features quantifying intensity, shape and texture, which are extracted from computed tomography data 1,019 patients with lung or head-and-neck cancer. We find have prognostic power in independent sets cancer patients, many...
The image biomarker standardisation initiative (IBSI) is an independent international collaboration which works towards standardising the extraction of biomarkers from acquired imaging for purpose high-throughput quantitative analysis (radiomics). Lack reproducibility and validation studies considered to be a major challenge field. Part this lies in scantiness consensus-based guidelines definitions process translating into biomarkers. IBSI therefore seeks provide nomenclature definitions,...
Due to advances in the acquisition and analysis of medical imaging, it is currently possible quantify tumor phenotype. The emerging field Radiomics addresses this issue by converting images into minable data extracting a large number quantitative imaging features. One main challenges segmentation. Where manual delineation time consuming prone inter-observer variability, has been shown that semi-automated approaches are fast reduce variability. In study, semiautomatic region growing...
Abstract Radiomics provides a comprehensive quantification of tumor phenotypes by extracting and mining large number quantitative image features. To reduce the redundancy compare prognostic characteristics radiomic features across cancer types, we investigated cancer-specific feature clusters in four independent Lung Head & Neck (H&N) cohorts (in total 878 patients). Radiomic were extracted from pre-treatment computed tomography (CT) images. Consensus clustering resulted eleven...
Purpose. Besides basic measurements as maximum standardized uptake value (SUV)max or SUVmean derived from 18F-FDG positron emission tomography (PET) scans, more advanced quantitative imaging features (i.e. “Radiomics” features) are increasingly investigated for treatment monitoring, outcome prediction, potential biomarkers. With these prospected applications of Radiomics features, it is a requisite that they provide robust and reliable measurements. The aim our study was therefore to perform...
Abstract FDG-PET-derived textural features describing intra-tumor heterogeneity are increasingly investigated as imaging biomarkers. As part of the process quantifying heterogeneity, image intensities (SUVs) typically resampled into a reduced number discrete bins. We focused on implications manner in which this discretization is implemented. Two methods were evaluated: (1) R D , dividing SUV range equally spaced bins, where intensity resolution (i.e. bin size) varies per image; and (2) B...
Medical imaging can visualize characteristics of human cancer noninvasively. Radiomics is an emerging field that translates these medical images into quantitative data to enable phenotypic profiling tumors. While radiomics has been associated with several clinical endpoints, the complex relationships radiomics, factors, and tumor biology are largely unknown. To this end, we analyzed two independent cohorts respectively 262 North American 89 European patients lung cancer, consistently...
Background: Radiomic analyses of CT images provide prognostic information that can potentially be used for personalized treatment. However, heterogeneity acquisition- and reconstruction protocols influences robustness radiomic analyses. The aim this study was to investigate the influence different CT-scanners, slice thicknesses, exposures gray-level discretization on feature values their stability.Material methods: A texture phantom with ten inserts scanned nine CT-scanners varying tube...
Background. Oropharyngeal squamous cell carcinoma (OPSCC) is one of the fastest growing disease sites head and neck cancers. A recently described radiomic signature, based exclusively on pre-treatment computed tomography (CT) imaging primary tumor volume, was found to be prognostic in independent cohorts lung cancer patients treated Netherlands. Here, we further validate this signature a large North American cohort OPSCC patients, also considering CT artifacts.Methods. total 542 were...
Radiomics is an objective method for extracting quantitative information from medical images. However, in radiomics, standardization, overfitting, and generalization are major challenges to be overcome. Test–retest experiments can used select robust radiomic features that have minimal variation. Currently, it unknown whether they should identified each disease (disease specific) or only imaging device-specific (computed tomography [CT]-specific). Here, we performed a test–retest analysis on...
In this study we investigated the interchangeability of planning CT and cone-beam (CBCT) extracted radiomic features. Furthermore, a previously described based prognostic signature for non-small cell lung cancer (NSCLC) patients using CBCT features was validated.One training dataset 132 two validation datasets 62 94stage I-IV NSCLC were included. Interchangeability assessed by performing linear regression on A two-step correction applied prior to model published signature. Results 13.3% (149...
Human papillomavirus (HPV) positive oropharyngeal cancer (oropharyngeal squamous cell carcinoma, OPSCC) is biologically and clinically different from HPV negative OPSCC. Here, we evaluate the use of a radiomic approach to identify status OPSCC.Four independent cohorts, totaling 778 OPSCC patients with determined by p16 were collected. We randomly assigned 80% all data for model training (N = 628) 20% validation 150). On pre-treatment CT images, 902 features calculated gross tumor volume....
The central hypothesis of "radiomics" is that imaging features reflect tumor phenotype and genotype. Until now only correlative studies have been performed. main objective our study to determine whether a causal relationship exists between genetic changes image features. secondary assess the combination with radiotherapy (RT) influences these features.HCT116 doxycycline (dox) inducible GADD34 cells were grown as xenografts in flanks NMRI-nu mice. overexpression decreases hypoxic fraction....
Background Solid components of part-solid nodules (PSNs) at CT are reflective invasive adenocarcinoma, but studies describing radiomic features PSNs and the perinodular region lacking. Purpose To develop to validate signatures diagnosing lung adenocarcinoma in compared with Brock, clinical-semantic features, volumetric models. Materials Methods This retrospective multicenter study (https://ClinicalTrials.gov, NCT03872362) included 291 patients (median age, 60 years; interquartile range,...