Physical measurement and computational planning
Radiation physics links the transport of energy through matter to quantities that can be measured, reconstructed and controlled. This theme brings together dose and range estimation, imaging methods and computational optimisation. Research on prompt gamma time imaging investigates how secondary radiation signals can provide information about particle range, tissue properties and dose. Detector measurements and reconstruction methods form part of the same problem: inferring a spatially resolved physical quantity from incomplete observations.
Computational planning studies address how irradiation configurations can be selected under multiple constraints. Work on beam-angle optimisation investigates iterative model-based approaches, including language-model methods inspired by reinforcement learning. Related research examines the transformation of multimodal models into systems that can propose planning actions. The methodological focus is on connecting a proposed action to its physical consequences and to an explicit criterion for evaluating the resulting plan.
Image features, temporal information and tissue response
Radiomics and computer vision extract quantitative descriptions of image structure, intensity and heterogeneity. These methods can be applied to anatomical images, dynamic PET measurements and tissue microscopy, but the interpretation of a feature depends on how the image was acquired and processed. Our work includes analysis of dynamic PET patterns and computational pathology, linking image-derived variation to molecular or biological measurements. Temporal features extend this analysis beyond a static representation of tissue.
A shared challenge is to determine which features and model outputs remain stable under changes in acquisition, reconstruction or data composition. Parameter estimation, uncertainty analysis and evaluation across datasets help assess that stability. Research on ultrasound-assisted nanoparticle delivery provides a further connection to studies of radiation response, where material distribution and tissue effects must be measured together. Across the theme, physical modelling and quantitative image analysis support the interpretation and optimisation of experimentally observable processes.
Research resources
Profiles by research interest
Additional profiles by research interest (2)
Publications
2025
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Brain metastases (BM), along with primary central nervous system lymphomas and glioblastomas, represent the majority of malignant brain tumors encountered in clinical neuro-oncology, driving a need for advanced imaging techniques and post-processing methods to improve their characterization and treatment monitoring. In particular, stereotactic radiosurgery (…Read the full abstractCollapse abstract
Brain metastases (BM), along with primary central nervous system lymphomas and glioblastomas, represent the majority of malignant brain tumors encountered in clinical neuro-oncology, driving a need for advanced imaging techniques and post-processing methods to improve their characterization and treatment monitoring. In particular, stereotactic radiosurgery (SRS), a cornerstone treatment for BM, delivers high-dose, focused radiation (>20 Gy) to target lesions with minimal impact on surrounding tissues. Despite its efficacy, radiation-induced effects such as early radiation effects (ERE) and adverse radiation effects (ARE) complicate diagnosis and management, with ARE occurring in up to 30% of patients, often presenting as ring-enhancing T2/FLAIR hyperintensities. To address these challenges, we aimed to compare standard radiomics-based machine learning approaches with pretrained generative models for assessing ERE in BM lesions. A cohort of 21 patients for a total of 35 lesions (17 treatment-naïve and 18 post-SRS +/- combination therapy) who underwent multiparametric 18F-FPIA PET/MRI was analyzed. The study investigated: 1) Multiparametric analysis of PET and MRI diffusion/perfusion parameters (ADC, Ktrans, CBF, K1, vt); 2) MRI-based radiomics; 3) static PET radiomics; 4) Dynomics; 5) a combination of PET and MRI radiomics; and 6) low-level embeddings from a pretrained generative diffusion model applied to full T1, static PET, and their combination. Using manually contoured lesion masks for analyses 1-5 and lesion-free embeddings for analysis 6, multiple classifiers (SVM, XGBoost, Linear regressor) were applied after feature standardization and principal component analysis (retaining 90% variance). Fivefold cross-validation demonstrated comparable performances across radiomic approaches (Accuracy: 71.95±0.05%, AUC: 0.72±0.05%), while the pretrained generative model achieved significantly higher performance (Accuracy: 83.82±0.01%, AUC: 0.83±0.01%) without requiring lesion segmentation in assessing ERE in BM lesions.Clinical Relevance-This study shows the potential of generative models to streamline and enhance the assessment of early radiation effects in parenchymal metastatic lesions without need of lesion segmentation.
2025
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This study proposes an automated approach to radiotherapy treatment planning by integrating a reinforcement-learning-style iterative framework with a multimodal Large Language Model (LLM). We specifically investigate the problem of Beam Angle Optimization, a high-dimensional and non-convex subproblem of Treatment Planning. Our system employs GPT-4V to select…Read the full abstractCollapse abstract
This study proposes an automated approach to radiotherapy treatment planning by integrating a reinforcement-learning-style iterative framework with a multimodal Large Language Model (LLM). We specifically investigate the problem of Beam Angle Optimization, a high-dimensional and non-convex subproblem of Treatment Planning. Our system employs GPT-4V to select candidate beam angles and analyze three-dimensional dose distributions generated by Monte Carlo simulations within the MatRAD environment. Iterative plan refinement is guided by a reward function that encourages target dose conformity and penalizes excessive dose to organs at risk. We incorporate exploration-exploitation principles to strike a balance between investigating diverse action proposals and refining promising solutions. Experimental results on prostate cancer cases demonstrate that our LLM-based framework offers superior performance compared to random beam selection and can outperform the quality of deep reinforcement learning baselines, indicating the potential for LLMs to assist in complex radiotherapy treatment planning tasks.Clinical relevance-This approach is designed to alleviate the significant effort of manual treatment planning by assisting medical physicists in exploring beam configurations and systematically refining plans to improve dose coverage and protect healthy tissues.
2025
arXiv
Radiotherapy is a crucial cancer treatment that demands precise planning to balance tumor eradication and preservation of healthy tissue. Traditional treatment planning (TP) is iterative, time-consuming, and reliant on human expertise, which can potentially introduce variability and inefficiency. We propose a novel framework to transform a large multimodal f…Read the full abstractCollapse abstract
Radiotherapy is a crucial cancer treatment that demands precise planning to balance tumor eradication and preservation of healthy tissue. Traditional treatment planning (TP) is iterative, time-consuming, and reliant on human expertise, which can potentially introduce variability and inefficiency. We propose a novel framework to transform a large multimodal foundation model (MLM) into an action model for TP using a few-shot reinforcement learning (RL) approach. Our method leverages the MLM's extensive pre-existing knowledge of physics, radiation, and anatomy, enhancing it through a few-shot learning process. This allows the model to iteratively improve treatment plans using a Monte Carlo simulator. Our results demonstrate that this method outperforms conventional RL-based approaches in both quality and efficiency, achieving higher reward scores and more optimal dose distributions in simulations on prostate cancer data. This proof-of-concept suggests a promising direction for integrating advanced AI models into clinical workflows, potentially enhancing the speed, quality, and standardization of radiotherapy treatment planning.
2023
Journal of Personalized Medicine
Traditional imaging techniques for breast cancer (BC) diagnosis and prediction, such as X-rays and magnetic resonance imaging (MRI), demonstrate varying sensitivity and specificity due to clinical and technological factors. Consequently, positron emission tomography (PET), capable of detecting abnormal metabolic activity, has emerged as a more effective tool…Read the full abstractCollapse abstract
Traditional imaging techniques for breast cancer (BC) diagnosis and prediction, such as X-rays and magnetic resonance imaging (MRI), demonstrate varying sensitivity and specificity due to clinical and technological factors. Consequently, positron emission tomography (PET), capable of detecting abnormal metabolic activity, has emerged as a more effective tool, providing critical quantitative and qualitative tumor-related metabolic information. This study leverages a public clinical dataset of dynamic 18F-Fluorothymidine (FLT) PET scans from BC patients, extending conventional static radiomics methods to the time domain-termed as 'Dynomics'. Radiomic features were extracted from both static and dynamic PET images on lesion and reference tissue masks. The extracted features were used to train an XGBoost model for classifying tumor versus reference tissue and complete versus partial responders to neoadjuvant chemotherapy. The results underscored the superiority of dynamic and static radiomics over standard PET imaging, achieving accuracy of 94% in tumor tissue classification. Notably, in predicting BC prognosis, dynomics delivered the highest performance, achieving accuracy of 86%, thereby outperforming both static radiomics and standard PET data. This study illustrates the enhanced clinical utility of dynomics in yielding more precise and reliable information for BC diagnosis and prognosis, paving the way for improved treatment strategies.
2021
Seminars in Cancer Biology
Deep Learning (DL) algorithms are a set of techniques that exploit large and/or complex real-world datasets for cross-domain and cross-discipline prediction and classification tasks. DL architectures excel in computer vision tasks, and in particular image processing and interpretation. This has prompted a wave of disruptingly innovative applications in medic…Read the full abstractCollapse abstract
Deep Learning (DL) algorithms are a set of techniques that exploit large and/or complex real-world datasets for cross-domain and cross-discipline prediction and classification tasks. DL architectures excel in computer vision tasks, and in particular image processing and interpretation. This has prompted a wave of disruptingly innovative applications in medical imaging, where DL strategies have the potential to vastly outperform human experts. This is particularly relevant in the context of histopathology, where whole slide imaging (WSI) of stained tissue in conjuction with DL algorithms for their interpretation, selection and cancer staging are beginning to play an ever increasing role in supporting human operators in visual assessments. This has the potential to reduce everyday workload as well as to increase precision and reproducibility across observers, centers, staining techniques and even pathologies. In this paper we introduce the most common DL architectures used in image analysis, with a focus on histopathological image analysis in general and in breast histology in particular. We briefly review how, state-of-art DL architectures compare to human performance on across a number of critical tasks such as mitotic count, tubules analysis and nuclear pleomorphism analysis. Also, the development of DL algorithms specialized to pathology images have been enormously fueled by a number of world-wide challenges based on large, multicentric image databases which are now publicly available. In turn, this has allowed most recent efforts to shift more and more towards semi-supervised learning methods, which provide greater flexibility and applicability. We also review all major repositories of manually labelled pathology images in breast cancer and provide an in-depth discussion of the challenges specific to training DL architectures to interpret WSI data, as well as a review of the state-of-the-art methods for interpretation of images generated from immunohistochemical analysis of breast lesions. We finally discuss the future challenges and opportunities which the adoption of DL paradigms is most likely to pose in the field of pathology for breast cancer detection, diagnosis, staging and prognosis. This review is intended as a comprehensive stepping stone into the field of modern computational pathology for a transdisciplinary readership across technical and medical disciplines.