A5021509460- Radiomics and Machine Learning in Medical Imaging
- Medical Imaging Techniques and Applications
- Advanced Radiotherapy Techniques
- Advanced X-ray and CT Imaging
- Lung Cancer Diagnosis and Treatment
- Esophageal Cancer Research and Treatment
- Radiopharmaceutical Chemistry and Applications
- Radiation Therapy and Dosimetry
- Radiation Dose and Imaging
- Advances in Oncology and Radiotherapy
- Gastric Cancer Management and Outcomes
- Pancreatic and Hepatic Oncology Research
- AI in cancer detection
- Colorectal Cancer Surgical Treatments
- Radiation Detection and Scintillator Technologies
- Brain Tumor Detection and Classification
- Medical Imaging and Pathology Studies
- MRI in cancer diagnosis
- Artificial Intelligence in Healthcare and Education
- Head and Neck Cancer Studies
- Radiation Effects and Dosimetry
- Advanced MRI Techniques and Applications
- Colorectal and Anal Carcinomas
- Digital Radiography and Breast Imaging
- Glioma Diagnosis and Treatment
Cardiff University
2016-2025
Velindre Cancer Centre
2014-2024
University Hospital Carl Gustav Carus
2020-2024
Helmholtz-Zentrum Dresden-Rossendorf
2020-2024
TU Dresden
2020-2024
National Center for Tumor Diseases
2020-2024
German Cancer Research Center
2020-2024
Velindre NHS Trust
2002-2024
Azienda Sanitaria Unità Locale di Reggio Emilia
2024
University of Pennsylvania
2024
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,...
The purpose of this educational report is to provide an overview the present state-of-the-art PET auto-segmentation (PET-AS) algorithms and their respective validation, with emphasis on providing user help in understanding challenges pitfalls associated selecting implementing a PET-AS algorithm for particular application.A brief description different types provided using classification based method complexity type. advantages limitations current are highlighted publications existing...
In this study we investigated the characteristics of a commercial ion chamber array and its performance in verification radiotherapy plans. The device was 2D Array Seven29™ model (PTW, Freiburg, Germany). This is two-dimensional detector with 729 ionization chambers uniformly arranged 27 × matrix an active area cm2. short-, medium- long-term reproducibility have been tested through extensive set repeated measurements. Short-term well within 0.2%. Medium- were 1%, including set-up errors...
Background and purposeAccess to healthcare data is indispensable for scientific progress innovation. Sharing time-consuming notoriously difficult due privacy regulatory concerns. The Personal Health Train (PHT) provides a privacy-by-design infrastructure connecting FAIR (Findable, Accessible, Interoperable, Reusable) sources allows distributed analysis machine learning. Patient never leaves institute.Materials methodsLung cancer patient-specific databases (tumor staging post-treatment...
Purpose Radiomics is a growing field of image quantitation, but it lacks stable and high‐quality software systems. We extended the capabilities Computational Environment for Radiological Research (CERR) to create comprehensive, open‐source, MATLAB‐based platform with an emphasis on reproducibility, speed, clinical integration radiomics research. Method The tools in CERR were designed specifically quantitate medical images combination CERR's core functionalities radiological data import,...
The conversion of computed tomography (CT) numbers into material composition and mass density data influences the accuracy patient dose calculations in Monte Carlo treatment planning (MCTP). aim our work was to develop a CT scheme by performing stoichiometric calibration. Fourteen dosimetrically equivalent tissue subsets (bins), which ten bone bins, were created. After validating proposed on phantoms, it compared conventional five bin with only one bin. This resulted distributions D 14 5 for...
The European directive on basic safety standards (Council 2013/59 Euratom) mandates dosimetry-based treatment planning for radiopharmaceutical therapies. comes into operation February 2018, and the aim of a report produced by Internal Dosimetry Task Force Association Nuclear Medicine is to address this aspect directive. A summary presented. brief review five most common therapy procedures included in current text, focused potential perform patient-specific dosimetry. In full report, 11...
Currently, the implementation of dosimetry in molecular radiotherapy (MRT) is not well investigated, and view Council Directive (2013/59/Euratom), there a need to understand current availability dosimetry-based MRT clinical practice research studies. The aim this study was assess across European countries. An electronic questionnaire distributed This addressed 18 explicitly considered therapies, for each therapy, similar set questions were included. Questions covered number patients...
Abstract Radiomic studies link quantitative imaging features to patient outcomes in an effort personalise treatment oncology. To be clinically useful, a radiomic feature must robust image processing steps, which has made robustness testing necessity for many technical aspects of extraction. We assessed the stability interpolation and categorised based on stable, systematic, or unstable responses. Here, 18 F-fluorodeoxyglucose ( F-FDG) PET images 441 oesophageal cancer patients (split: = 353,...
This retrospective cohort study developed a prognostic model incorporating PET texture analysis in patients with oesophageal cancer (OC). Internal validation of the was performed. Consecutive OC (n = 403) were chronologically separated into development 302, September 2010-September 2014, median age 67.0, males 227, adenocarcinomas 237) and cohorts 101, 2014-July 2015, 69.0, 78, 79). Texture metrics obtained using machine-learning algorithm for automatic segmentation. A Cox regression...
Purpose: The purpose of this work is to characterize the x‐ray volume imager (XVI), cone‐beam computed tomography (CBCT) unit mounted on Elekta Synergy linac, with F1 bowtie filter and calculate three‐dimensional dose delivered patients using volumetric acquisition. Methods: XVI modeled in detail a new Monte Carlo (MC) code, BEAMPP , under development at National Research Council Canada. In investigation, component module developed accurately model unit's used conjunction available beam...
We developed and validated a Monte-Carlo-based application (RAYDOSE) to generate patient-specific 3D dose maps on the basis of pre-treatment imaging studies. A CT DICOM image is used model patient geometry, while repeated PET scans are employed assess radionuclide kinetics distribution at voxel level. In this work, we describe structure present tests performed validate it against reference data experiments. spheres NEMA phantom calculate S values total doses. The comparison with from...
PET/CT has recently been shown to be a viable alternative traditional post-infusion imaging methods providing good quality images of 90Y-laden microspheres after selective internal radiation therapy (SIRT). In the present paper, first we assessed quantitative accuracy 90Y-PET using an anthropomorphic phantom provided with lungs, liver, spine, and cylindrical homemade lesion located into hepatic compartment. Then, explored different computational approaches on dose calculation, including (I)...
Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- interobserver variability, but there currently no consensus the optimal method to use, as different algorithms appear perform better types of tumours. This work aimed develop a predictive model, trained automatically select apply best PET-AS method, according characteristics.
Purpose: To develop and validate an MRI-based radiomic model for predicting overall survival (OS) in patients diagnosed with glioblastoma multiforme (GBM), utilizing a retrospective dataset from multiple institutions. Materials Methods: Pre-treatment MRI images of 289 GBM were collected. From each patient’s tumor volume, 660 features (RFs) extracted subjected to robustness analysis. The initial prognostic minimum RFs was subsequently enhanced by including clinical variables. final...
Background/Objectives: Pancreatic cancer is a very aggressive disease with poor prognosis, even when diagnosed at an early stage. This study aimed to validate and refine radiomic-based [18F]FDG-PET model predict distant relapse-free survival (DRFS) in patients unresectable locally advanced pancreatic (LAPC). Methods: A Cox regression incorporating two radiomic features (RFs) stage (III vs. IV) was temporally validated using larger cohort (215 treated between 2005–2022). Patients received...
Purpose: In this study, we develop and validate an interpretable machine learning (ML) model that integrates a hybrid Swarm Intelligence (SI)-based feature selection method with Magnetic Resonance Imaging (MRI)-derived radiomic features (RFs) to estimate overall survival (OS) in Glioblastoma Multiforme (GBM) patients. This study seeks enhance the generalizability of developed prognostic its potential for clinical integration by emphasizing reproducibility leveraging multi-institutional...