Area of research
Radiology, Nuclear Medicine and Imaging · Artificial Intelligence
Research interest
Research focused on Breast cancer and Radiogenomics, with related work in Radiomics, Renal cell carcinoma, Artificial intelligence. Notable publications include 'Imaging breast cancer using hyperpolarized carbon-13 MRI', 'Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma—a systematic review and meta-analysis', and 'Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer'.
Image normalization techniques and their effect on the robustness and predictive power of breast MRI radiomics
ESUR consensus MRI for endometriosis: indications, reporting, and classifications
ESUR consensus MRI for endometriosis: protocol, lexicon, and compartment-based analysis
Quantifying the tumour vasculature environment from CD-31 immunohistochemistry images of breast cancer using deep learning based semantic segmentation
A large‐scale retrospective study in metastatic breast cancer patients using circulating tumour <scp>DNA</scp> and machine learning to predict treatment outcome and progression‐free survival
Assessment of early response to neoadjuvant chemotherapy in multi-site high-grade serous ovarian cancer using hyperpolarized-13C MRI
Abstract LB339: SYNERGIA Breast Cancer - Revolutionizing breast cancer care with multi-modal data integration for personalised treatment and future trials
Multi-task deep learning for automatic image segmentation and treatment response assessment in metastatic ovarian cancer
26P Integrative pathomic-radiomic factor analysis to predict first-line treatment response in high-grade serous ovarian carcinoma
Perceptions of radiologists on structured reporting for cancer imaging—a survey by the European Society of Oncologic Imaging (ESOI)
Neoadjuvant Radiotherapy and Endocrine Therapy for Oestrogen Receptor Positive Breast Cancers: The Neo-RT Feasibility Study
Source-detector trajectory optimization for FOV extension in dental CBCT imaging
A Self-supervised Image Registration Approach for Measuring Local Response Patterns in Metastatic Ovarian Cancer
AUGMENT: a framework for robust assessment of the clinical utility of segmentation algorithms
Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer
Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers
Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation
Deep learning-based segmentation of multisite disease in ovarian cancer
Position statement on clinical evaluation of imaging AI
Hyperpolarized Carbon-13 MRI in Breast Cancer
Hyperpolarised 13C-MRI using 13C-pyruvate in breast cancer: A review
Deep learning-based Segmentation of Multi-site Disease in Ovarian Cancer
PET/MRI of hypoxia and vascular function in ER-positive breast cancer: correlations with immunohistochemistry
Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study
A large-scale retrospective study in metastatic breast cancer patients using circulating tumor DNA and machine learning to predict treatment outcome and progression-free survival
Hyperpolarized 13C-Pyruvate Metabolism as a Surrogate for Tumor Grade and Poor Outcome in Renal Cell Carcinoma—A Proof of Principle Study
Clinically Interpretable Radiomics-Based Prediction of Histopathologic Response to Neoadjuvant Chemotherapy in High-Grade Serous Ovarian Carcinoma
Staging Breast Cancer with MRI, the T. A Key Role in the Neoadjuvant Setting
3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients
Semi-automated and interactive segmentation of contrast-enhancing masses on breast DCE-MRI using spatial fuzzy clustering