Area of research
Radiology, Nuclear Medicine and Imaging · Pediatrics, Perinatology and Child Health
Research interest
Research interests include Radiomics and Machine Learning in Medical Imaging, Neonatal and fetal brain pathology, Advanced X-ray and CT Imaging, and Medical Imaging Techniques and Applications.
Radiomics Quality Score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine
Uncertainties in outcome modelling in radiation oncology
AutoFRS: an externally validated, annotation-free approach to computational preoperative complication risk stratification in pancreatic surgery – an experimental study
METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII
Comparative analysis of radiomics and deep-learning algorithms for survival prediction in hepatocellular carcinoma
MIRP: A Python package for standardisedradiomics
Radiomics for residual tumour detection and prognosis in newly diagnosed glioblastoma based on postoperative [11C] methionine PET and T1c-w MRI
Artificial intelligence for response prediction and personalisation in radiation oncology
CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII
Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy
Longitudinal and Multimodal Radiomics Models for Head and Neck Cancer Outcome Prediction
Radiomics in liver surgery: defining the path toward clinical application
Standardisation and harmonisation efforts in quantitative imaging
Multitask Learning with Convolutional Neural Networks and Vision Transformers Can Improve Outcome Prediction for Head and Neck Cancer Patients
Joint EANM/SNMMI guideline on radiomics in nuclear medicine
Analysis of MRI and CT-based radiomics features for personalized treatment in locally advanced rectal cancer and external validation of published radiomics models
Building reliable radiomic models using image perturbation
Radiomics-based tumor phenotype determination based on medical imaging and tumor microenvironment in a preclinical setting
Integrated radiogenomics analyses allow for subtype classification and improved outcome prognosis of patients with locally advanced HNSCC
Modelling for Radiation Treatment Outcome
An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication
Test–Retest Data for the Assessment of Breast <scp>MRI</scp> Radiomic Feature Repeatability
Do We Need Complex Image Features to Personalize Treatment of Patients with Locally Advanced Rectal Cancer?
The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping
2D and 3D convolutional neural networks for outcome modelling of locally advanced head and neck squamous cell carcinoma
Comprehensive Analysis of Tumour Sub-Volumes for Radiomic Risk Modelling in Locally Advanced HNSCC
Definition and validation of a radiomics signature for loco-regional tumour control in patients with locally advanced head and neck squamous cell carcinoma
An Integrative Analysis of Image Segmentation and Survival of Brain Tumour Patients
Comparison of patient stratification by computed tomography radiomics and hypoxia positron emission tomography in head-and-neck cancer radiotherapy
Pictures worth more than a thousand words: Prediction of survival in medulloblastoma patients