A5004371367- Particle physics theoretical and experimental studies
- High-Energy Particle Collisions Research
- Radiomics and Machine Learning in Medical Imaging
- Particle Detector Development and Performance
- Quantum Chromodynamics and Particle Interactions
- Ovarian cancer diagnosis and treatment
- MRI in cancer diagnosis
- Medical Imaging Techniques and Applications
- AI in cancer detection
- Dark Matter and Cosmic Phenomena
- Cancer Genomics and Diagnostics
- Advanced X-ray and CT Imaging
- Cancer, Hypoxia, and Metabolism
- Renal cell carcinoma treatment
- Lung Cancer Diagnosis and Treatment
- Renal and related cancers
- Cancer-related molecular mechanisms research
- Gene expression and cancer classification
- Colorectal Cancer Screening and Detection
- Mathematical Biology Tumor Growth
- Cosmology and Gravitation Theories
- Single-cell and spatial transcriptomics
- Machine Learning and Data Classification
- Endometrial and Cervical Cancer Treatments
- Digital Imaging for Blood Diseases
University of Cambridge
2017-2025
Cancer Research UK Cambridge Center
2019-2024
Cancer Research UK
2017-2023
Georgetown University
2020
Memorial Sloan Kettering Cancer Center
2016-2020
Georgetown University Medical Center
2020
University of Oxford
2014-2017
Aarhus University Hospital
2017
The University of Adelaide
2014-2016
Universidade Federal do Rio de Janeiro
2015
Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment
Rapid detection of compact binary coalescence (CBC) with a network advanced gravitational-wave detectors will offer unique opportunity for multi-messenger astronomy. Prompt alerts the astronomical community might make it possible to observe onset electromagnetic emission from CBC. We demonstrate computationally practical filtering strategy that could produce early-warning triggers before gravitational radiation final merger has arrived at detectors.
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,...
Abstract High grade serous ovarian carcinoma (HGSOC) is a highly heterogeneous disease that typically presents at an advanced, metastatic state. The multi-scale complexity of HGSOC major obstacle to predicting response neoadjuvant chemotherapy (NACT) and understanding critical determinants response. Here we present framework predict the patients NACT integrating baseline clinical, blood-based, radiomic biomarkers extracted from all primary lesions. We use ensemble machine learning model...
Differentiating aggressive clear cell renal carcinoma (ccRCC) from indolent lesions is challenging using conventional imaging. This work prospectively compared the metabolic imaging phenotype of tumors carbon-13 MRI following injection hyperpolarized [1-
Uncertainty quantification in automated image analysis is highly desired many applications. Typically, machine learning models classification or segmentation are only developed to provide binary answers; however, quantifying the uncertainty of can play a critical role for example active human interaction. especially difficult when using deep learning-based models, which state-of-the-art imaging The current approaches do not scale well high-dimensional real-world problems. Scalable solutions...
Cancer typically exhibits genotypic and phenotypic heterogeneity, which can have prognostic significance influence therapy response. Computed Tomography (CT)-based radiomic approaches calculate quantitative features of tumour heterogeneity at a mesoscopic level, regardless macroscopic areas hypo-dense (i.e., cystic/necrotic), hyper-dense calcified), or intermediately dense soft tissue) portions.
Background: Early and accurate grading of renal cell carcinoma (RCC) improves patient risk stratification has implications for clinical management mortality. However, current diagnostic approaches using imaging mass biopsy have limited specificity may lead to undergrading. Methods: This study explored the use hyperpolarised [1-13C]pyruvate MRI (HP 13C-MRI) identify most aggressive areas within tumour patients with clear (ccRCC) as a method guide targeting reduce Six ccRCC underwent...
BACKGROUND Immunotherapy has improved patient survival for multiple cancer types, including melanoma. While a variety of molecular features have been linked to response immune checkpoint inhibitors (ICI) treatment, clinically established biomarkers, such as tumour mutation burden (TMB) and PD-L1 expression, shown limitations in accurately categorising responders versus non-responders. Due the complex nature ICI response, which includes intrinsic extrinsic within microenvironment (TME), using...
Background Pathological response to neoadjuvant treatment for patients with high-grade serous ovarian carcinoma (HGSOC) is assessed using the chemotherapy score (CRS) omental tumor deposits. The main limitation of CRS that it requires surgical sampling after initial (NACT) treatment. Earlier and non-invasive predictors could improve patient stratification. We developed computed tomography (CT) radiomic measures predict before NACT as a gold standard. Methods Omental CT-based radiomics...
Abstract Purpose To develop a precision tissue sampling technique that uses computed tomography (CT)–based radiomic tumour habitats for ultrasound (US)-guided targeted biopsies can be integrated in the clinical workflow of patients with high-grade serous ovarian cancer (HGSOC). Methods Six suspected HGSOC scheduled US-guided biopsy before starting neoadjuvant chemotherapy were included this prospective study from September 2019 to February 2020. The segmentation was performed manually on...
Develop an integrated intra-site and inter-site radiomics-clinical-genomic marker of high grade serous ovarian cancer (HGSOC) outcomes explore the biological basis radiomics with respect to molecular signaling pathways tumor microenvironment (TME).
Tumor hypoxia increases resistance to radiotherapy and systemic therapy. Our aim was develop validate a disease-agnostic disease-specific CT (+FDG-PET) based radiomics classification signature.A total of 808 patients with imaging data were included: N = 100 training/N 183 external validation cases for signature, 76 39 the H&N signature 62 36 Lung signature. The primary gross tumor volumes (GTV) manually defined by experts on CT. In order dichotomize between hypoxic/well-oxygenated tumors...
Abstract Measurements of water diffusion with MRI have been used as a biomarker tissue microstructure and heterogeneity. In this study, kurtosis tensor imaging (DKTI) the brain was undertaken in 10 healthy volunteers at clinical field strength 3 T. Diffusion metrics were measured regions-of-interest on resulting maps compared quantitative analysis normal post-mortem histology from separate age-matched donors. White matter regions showed low (0.60 ± 0.04 × –3 mm 2 /s) high (1.17 0.06),...
Governments and medical associations across the world, including US Food Drug Administration, UK Medicines Healthcare products Regulatory Agency, Royal College of Radiologists, European Society Radiology, believe advent health technologies associated with artificial intelligence (AI) will be most radical change in how care is delivered our lifetime.1Royal RadiologistsRCR position statement on intelligence.http://www.rcr.ac.uk/posts/rcr-position-statement-artificial-intelligenceDate: 2018Date...
Abstract Spatial quantification is a critical step in most computational pathology tasks, from guiding pathologists to areas of clinical interest discovering tissue phenotypes behind novel biomarkers. To circumvent the need for manual annotations, modern methods have favoured multiple-instance learning approaches that can accurately predict whole-slide image labels, albeit at expense losing their spatial awareness. We prove mathematically model using instance-level aggregation could achieve...
Abstract Purpose To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods. Methods A learning model for the two most common disease sites high-grade serous (pelvis/ovaries omentum) was developed compared against well-established “no-new-Net” framework unrevised trainee radiologist segmentations. total 451 CT scans collected from four different institutions were used training (...
Spatial heterogeneity of tumors is a major challenge in precision oncology. The relationship between molecular and imaging still poorly understood because it relies on the accurate coregistration medical images tissue biopsies. Tumor molds can guide localization biopsies, but their creation time consuming, technologically challenging, difficult to interface with routine clinical practice. These hurdles have so far hindered progress area multiscale integration tumor data.