A5009159721- Advanced Radiotherapy Techniques
- Radiomics and Machine Learning in Medical Imaging
- Medical Imaging Techniques and Applications
- Lung Cancer Diagnosis and Treatment
- Medical Imaging and Analysis
- Radiation Dose and Imaging
- Radiation Therapy and Dosimetry
- AI in cancer detection
- Medical Image Segmentation Techniques
- Advanced X-ray and CT Imaging
- Advanced MRI Techniques and Applications
- Prostate Cancer Diagnosis and Treatment
- Advances in Oncology and Radiotherapy
- Head and Neck Cancer Studies
- Breast Cancer Treatment Studies
- Radiation Effects and Dosimetry
- Hepatocellular Carcinoma Treatment and Prognosis
- Prostate Cancer Treatment and Research
- Lung Cancer Treatments and Mutations
- Cardiac Imaging and Diagnostics
- Digital Radiography and Breast Imaging
- Dysphagia Assessment and Management
- Brain Tumor Detection and Classification
- Anatomy and Medical Technology
- Statistical Methods in Clinical Trials
University of Manchester
2017-2025
The Christie NHS Foundation Trust
2018-2025
Cancer Research UK Manchester Institute
2018-2025
Universidad Nacional Autónoma de México
2025
National Health Service
2025
Manchester Academic Health Science Centre
2017-2022
Nestlé (France)
2020
European Society for Therapeutic Radiology and Oncology
2020
German Cancer Research Center
2018
Helmholtz-Zentrum Dresden-Rossendorf
2018
For patients with lung cancer treated radiation therapy, a dose to the heart is associated excess mortality; however, it often not feasible spare whole heart. Our aim define cardiac substructures and thresholds that optimally reduce early mortality.Fourteen were delineated on 5 template representative anatomies. One thousand one hundred sixty-one non-small cell registered nonrigidly these anatomies, their therapy doses mapped. Mean maximum each substructure extracted, means evaluated as...
Deformable image registration (DIR) is a versatile tool used in many applications radiotherapy (RT). DIR algorithms have been implemented commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of can be large difficult to quantify, resulting barriers clinical practice. Currently, there no agreement RT community on how quantify these uncertainties determine thresholds that distinguish good result from poor one. This review...
Modern radiotherapy requires assessment of patient anatomical changes. By using unidirectional registration methods, the quantified changes are asymmetric, i.e., depend on direction registration. Moreover, is challenged by large and complex organ deformations that can occur in, e.g., cervical cancer patients. The aim this work was to develop, test, validate a symmetric feature-based nonrigid method handle organs with large-scale deformations.A version thin plate spline robust point matching...
Technical improvements in planning and dose delivery verification of patient positioning have substantially widened the therapeutic window for radiation treatment cancer. However, changes anatomy during limit exploitation these new techniques. To further improve treatments, anatomical need to be modeled accounted for. Nonrigid registration can used this purpose. This article describes design, implementation, validation a framework nonrigid radiotherapy applications. The core is an improved...
Prediction of clinical complete response in rectal cancer before neoadjuvant chemo-radiotherapy treatment enables selection. Patients predicted to have could chemo-radiotherapy, and others additional doublet chemotherapy at this stage their improve overall outcome. This work investigates the role variables predicting response. Using UK-based OnCoRe database (2008 2019), we performed a propensity-score matched study 322 patients who received chemoradiotherapy. We collected pre-treatment...
Automatic segmentation of organs-at-risk in radiotherapy planning computed tomography (CT) scans using convolutional neural networks (CNNs) is an active research area. Very large datasets are usually required to train such CNN models. In radiotherapy, large, high-quality scarce and combining data from several sources can reduce the consistency training segmentations. It therefore important understand impact quality on performance auto-segmentation models for radiotherapy.
Purpose Dysphagia is a common toxicity after head and neck (HN) radiotherapy that negatively affects quality of life. We explored the relationship between dose to normal HN structures dysphagia one year treatment using image-based datamining (IBDM), voxel-based analysis technique. Materials Methods used data from 104 oropharyngeal cancer patients treated with definitive (Chemo)RT. Swallow function was assessed pre-treatment 1 three validated measures: M.D. Anderson Inventory (MDADI),...
Growing evidence suggests that spatial dose variations across the rectal surface influence toxicity risk after radiotherapy. Existing methodologies employ a fixed, arbitrary physical extent for mapping, limiting their analysis. We developed method to standardise rectum contours, unfold them into 2D cylindrical maps, and identify subregions where higher doses increase toxicities. Data of 1,048 patients with prostate cancer from REQUITE study were used. Deep learning based automatic...
Missing tissue presents a big challenge for dose mapping, e.g., in the reirradiation setting. We propose pipeline to identify missing on intra-patient structure meshes using previously trained geometric-learning correspondence model. For our application, we relied prediction discrepancies between forward and backward correspondences of input meshes, quantified correspondence-based Inverse Consistency Error (cICE). optimised threshold applied cICE points dataset 35 simulated mandible...
This study describes the process and outcomes of a Patient Public Involvement Engagement (PPIE) event designed to incorporate patient perspectives into application Natural Language Processing (NLP) for analyzing unstructured free-text cancer medical notes. The analysis routinely collected data aims provide evidence support clinical decision making in groups that are often under-represented conventional trials, highlighting critical role PPIE responsibly implementing AI within healthcare....