Medical Physics, Radiobiology & Radiation Protection Group

The Medical Physics, Radiobiology & Radiation Protection Group was formed in the beginning of 2008 housed by the IPO-Porto Research Center. Its members are mainly physicists and radiobiologists but also include other expertises such as radiopharmacy and clinical practitioners. It is the only Medical Physics and Radiobiology research group in Portugal whose activities are developed entirely in a hospital environment.
The group has two Full Member (J.A.M. Santos and P.P. Teles) in the European Radiation Dosimetry Group (Eurados), and two Corresponding Member (J. Lencart and A. G. Dias), in the Workgroup 9 and 12, WG9 (Radiation Protection in Radiotherapy) and is involved in several national and international collaborations.

SCIENTIFIC COORDINATOR

João António Miranda dos Santos, PhD
João António Miranda dos Santos, PhD
ORCID ID: 0000-0003-2465-5143
CIENCIA ID: 2514-D31E-2F02
Medical Physicist, Group Leader, Member of the Ethics Committee
Email: joao.santos@ipoporto.min-saude.pt

TEAM

Senior Investigators

Anabela Gregório Dias, PhD
ORCID ID: 0000-0002-2777-321X/ CIENCIA ID: 4C12-5EAB-0C33
Medical Physicist
Email: anabela.dias@ipoporto.min-saude.pt

Isabel Maria Guedes Bravo, PhD
ORCID ID: 0000-0001-6445-6443
CIENCIA ID: 031C-23F5-F5B4
Assistant Researcher/ Radiobiologist
Email: isabel.bravo@ipoporto.min-saude.pt

Pedro Peixoto Teles, PhD
ORCID ID: 6D11-4C85-387C/ CIENCIA ID: C61C-4934-500F
Assistant Professor; FCUP/UP
E-mail: ppteles@fc.up.pt

Sofia Isabel de Castro e Silva, PhD
ORCID ID: 0000-0002-0056-6034/ CIENCIA ID: 6D11-4C85-387C
Medical Physicist, IPO Coimbra
Email: sofia.i.silva@ipocoimbra.min-saude.pt

Invited Researchers

Inês Campos Monteiro Sabino Domingues, PhD
ORCID ID: 0000-0002-2334-7280/ CIENCIA ID: 971F-F25B-1E79
Assistant Professor; ISEC
E-mail: inesdomingues@gmail.com

PhD Students

Bárbara Adélia Meireles Barbosa, MSc
ORCID ID: 0000-0003-0686-0397/ CIENCIA ID: 721A-00E6-BAC5
Radiotherapy Technologist
Email: barbara.barbosa@ipoporto.min-saude.pt

Bruno Miguel Ferreira Mendes, MSc
ORCID ID: 0000-0002-7574-7630
Email: brunomendes81@gmail.com

Sara Filipa Coelho Guerreiro, MSc
ORCID ID: 0000-0002-2334-7280/ CIENCIA ID: 971F-F25B-1E79
School of Health of the Polytechnic of Leiria
Email: sara.f.guerreiro@ipleiria.pt

MSc Students

Amizito Luís Rajabo
ORCID ID: 0009-0008-3439-7276/ CIENCIA ID: BF11-7036-81F9
Email: up202110020@edu.fc.up.pt

Rúben Diogo Oliveira Sousa
ORCID: 0000-0002-0776-5212/ CIÊNCIA ID: 2310-F2F9-4178
Email: ruben.d.sousa@ipoporto.min-saude.pt

Other Collaborators

Alexandre Baptista Mendes Pereira, BSc
ORCID ID: 0000-0001-8965-274X/ CIENCIA ID: 301D-A86A-60C0
Medical Physicist
Email: apereira@ipoporto.min-saude.pt

António Luís Soares
CIENCIA ID: A817-239C-7AC4
Medical Physicist
Email: Antonio.soares@ipoporto.min-saude.pt

Carla Isabel Vaz Tavares Figueiredo Capelo, BSc
CIENCIA ID: 9D1A-6422-C7F7
Radiopharmacist
Email: carla.capelo@ipoporto.min-saude.pt

Diana Jorge Pimparel Alves Nuno Pinto, BSc
ORCID ID: 0000-0003-2559-3407/ CIENCIA ID: D31C-57A9-559A
Medical Physicist
Email: diana.pinto@ipoporto.min-saude.pt

Filipe Augusto Madeira Dias, MSc
ORCID ID: 0000-0002-2992-5096/ CIENCIA ID: 5412-D0F2-2595
Medical Physicist
Email: filipe.dias@ipoporto.min-saude.pt

Inês Magalhães da Silva de Lucena e Sampaio, MD, MSc
ORCID ID: 0000-0003-4578-2280/ CIENCIA ID: 3918-4909-EB6E
Nuclear Medicine Physician
Email: ines.lucena@ipoporto.min-saude.pt

Joana Borges Lencart e Silva, BSc
ORCID ID: 0000-0001-7078-7257/ CIENCIA ID: 7A1A-E9DB-BECE
Medical Physics
Email: joana.lencart@ipoporto.min-saude.pt

Jorge Barbosa Pereira, MSc
ORCID ID: 0000-0002-7216-1191/ CIENCIA ID: BA13-D305-14A8
Medical Physicist
Email: jorge.b.pereira@ipoporto.min-saude.pt

Luís Hugo da Silva Trindade Duarte, MD, BSc
ORCID ID: 0000-0002-6867-1180
Nuclear Medicine Physician
Email: hugo.duarte@ipoporto.min-saude.pt

Luís Paulo Teixeira Cunha, MSc
ORCID ID: 0000-0001-6012-6893/ CIENCIA ID: 7112-7B32-F43F
Medical Physicist
Email: luis.cunha@ipoporto.min-saude.pt

Pedro Filipe Conde Andrade Silva, MSc
ORCID ID: 0000-0003-3962-4040/ CIENCIA ID: 161C-6CE9-1867
Radiology and Radiotherapy Technologist
Email: pedro.andrade.silva@ipoporto.min-saude.pt

Rita Correia da Silva Alçada Albergueiro
ORCID: 0009-0009-3211-9501/ CIÊNCIA ID: F610-FB91-C98A
Medical Physicist
Email: ritaalbergueiro@gmail.com

Rogéria Maria Craveiro Pereira, Msc
ORCID ID: 0000-0002-5786-2096/ CIENCIA ID: 0116-CFE3-35C7
Radiobiologist
Email: rogeriapereira@ipoporto.min-saude.pt

Sara Patrícia de Almeida Pinto
ORCID ID: 0000-0002-9863-2078/ CIENCIA ID: A214-DDBF-CA6E
Medical Physicist
Email: sara.pinto@ipoporto.min-saude.pt

Susana Margarida Oliveira Gonçalves
ORCID ID: 0000-0003-3036-5847/ CIENCIA ID: C119-01F8-B6F2
Dosimetrist technologist
Email: susanamg@ipoporto.min-saude.pt

Vera Catarina Marques Antunes
ORCID ID: 0000-0001-7195-8791/ CIENCIA ID: 8516-92A5-7DE5
Medical Physicist
Email: vera.antunes@ipoporto.min-saude.pt

AIMS

The group focuses on the application of the methodology of physics and radiobiology to solve specific problems related to health care, especially ionizing radiation, both from the perspective of the patient procedures optimization or in the perspective of the protection in the event of professional exposure to ionizing radiation. It has already embraced critical personal exposure due to highly heterogeneous radiation fields with Monte Carlo simulations in CT-fluoroscopy and patient exposure during intra-operatory radiotherapy. This methodology, with increasing computer power over the last years, is becoming a benchmark method to simulate procedures in ionizing radiation physics, where exposure of subjects must be very well justified.

PROJECTS WITH INTERNAL FUNDING

TIPTOP – Artificial Intelligence applied to image based oncological prognosis
PI 145-CI-IPOP-133-2020; Budget: 21.000.00€ (2019 – ongoing); PI: Prof. Inês Domingues

Artificial intelligence methods, namely Machine learning and its Deep Learning component, can be used to anticipate the prognosis and response to therapy. From the imaging modalities available in the treatment and diagnosis of a cancer patient, CT and CBCT are widely used. Radiographic findings have shown a correlation to significant differences in protein expression patterns. In this context, extracting features from radiographic images using data-characterization algorithms (Radiomics) may provide a valuable tool for cancer evaluation during treatment. The hypothesis behind radiomics is that quantitative analysis of medical images may have a similar prognosis power as phenotypes and gene protein signatures. The idea is to predict the aggressiveness of Prostate Cancer from Ct images and the effectiveness of radiation therapy from CBCT images. However, features are extracted from a region of interest previously delimited. To address this issue, several image segmentation methods will be explored for the prostate and organs at risk, such as the bladder and the rectum. Clustering, U-Net, Active Contours and Graph-Based are a few example methods that will be explored. Also, a multi-class segmentation scenario is also interesting since it mimics the holistic manual segmentation of medical experts.

This project intends to explore the use of artificial intelligence methods and augment medical images with data, potentially aiding in detection, diagnosis, prognosis, treatment responses and disease monitoring.

Obra3HT – Optimizing Breast Cancer treatment planning workflow: from 3DCRT to hybrid techniques
PI199-CI-IPOP-35-2023; Budget: 15.000.00€ (2023 – ongoing); PI: Dr. Joana Lencart

Three-dimensional conformal radiation therapy (3DCRT) and intensity modulated radiation therapy (IMRT) are common techniques used in breast cancer radiotherapy. 3DCRT employs multiple radiation beams aimed at the target from different angles, using a multileaf collimator to shape the beam and minimize the dose to nearby critical organs at risk (OAR). IMRT modulates the intensity of each beam to better conform the dose to the target’s shape, often resulting in lower doses to OARs. Hybrid radiotherapy (RT), combining both techniques, is used in Whole Breast (WB) irradiation and Simultaneous Integrated Boost (SIB). This hybrid approach is particularly useful when the target’s concavity involves an OAR, such as the heart, or when the tumor is large or complex, posing challenges for 3DCRT alone in achieving adequate target coverage while keeping OAR doses within tolerance levels.

The project aims to validate and propose the integration of hybrid techniques (3DCRT-IMRT or 3DCRT-VMAT) into institutional radiotherapy protocols for WB irradiation and assess their applicability to other anatomical sites. A selection of breast cancer patients previously treated with 3DCRT, including both left and right breast cases, will be randomly chosen. Clinically valid plans will be generated using hybrid techniques with the service Treatment Planning System (Varian Eclipse v16.1) and EZFluence software to optimize the 3DCRT component. Both AAA and Acuros algorithms will be used for plan calculations. The plans’ validation involves three steps using commercial software and quality control equipment: 1) ClearCheck (Radformation) will automatically extract dosimetric characteristics from the TPS for target coverage and OAR doses; 2) ClearCalc (Radformation) will serve as an independent calculation tool to verify accuracy; 3) Primo, a Monte Carlo treatment planning tool validated in prior projects, will provide additional independent calculations. Furthermore, 729 matrix and Verisoft (PTW), along with other methods, will be used as patient-specific QA tools. The validated plans will be grouped by technique, and a statistical analysis of relevant dosimetric parameters will be performed to compare the techniques.

DoReMi – Development and implementation of a MRI-only guided radiotherapy workflow
PI200-CI-IPOP-36-2023; Budget: 13.000.00€ (2023 – ongoing); PI: Dr. Sara Pinto

In the last decade, radiotherapy (RT) planning has increasingly used both computed tomography (CT) and magnetic resonance imaging (MRI). CT provides electron density values for dose calculation, while MRI offers superior soft tissue contrast for better delineation of target volumes and organs at risk (OAR). MRI-only RT is becoming more common as it reduces healthy tissue exposure to CT-derived ionizing radiation, eliminates co-registration errors, and decreases resource use and costs, reducing duplicated efforts between diagnostic radiology and radiotherapy departments.

Planning MRI differs from diagnostic MRI by using larger diameter scanners, flat table tops for MR-compatible immobilization devices, external laser systems, and dedicated quality control and imaging protocols. Introducing MRI-only RT into clinical practice presents challenges such as patient movement during acquisition and ensuring geometric accuracy. Geometric accuracy is crucial for assessing MRI scanners’ suitability for RT planning, especially for radiosurgery/stereotactic radiotherapy (SRS/SRT). Image processing in diagnostic radiology focuses on qualitative tissue contrast rather than millimeter-level anatomical precision. Consequently, radioncologists may use uncorrected or partially corrected MRI scans for SRS/SRT planning. A comprehensive standard procedure for correcting MRI distortion has not been established, though the American Association of Physicists in Medicine (AAPM) is working on guidelines. A clinically viable QA protocol is needed to measure MRI images’ geometric precision and assess long-term stability.

Optimizing MRI protocols for RT planning involves maximizing spatial resolution and tissue contrast, minimizing acquisition time, and maintaining anatomical congruence despite motion. Compromises are necessary as simultaneous optimization of all factors is impossible. The signal-to-noise ratio (SNR) affects the perception of structures in low-resolution images and the distinction of small objects. Since patients are diagnosed before MRI for RT, a weaker SNR may be acceptable if other image quality parameters improve.

High spatial resolution, particularly slice thickness, is needed to distinguish small lesions or structures. However, high resolution alone does not guarantee visibility without sufficient image contrast or SNR. Image optimization requires balancing spatial resolution for adequate SNR and the ability to see fine details while maintaining a reasonable acquisition time. Radiology departments focus on accurate diagnosis, while radiotherapy requires images that support effective, safe treatment. Optimizing MRI for RT is time-consuming, often doubling acquisition time, and may necessitate sacrificing SNR for patient comfort during acquisition.

RaAITo – Development of a Chatbot for Radiotherapy using Artificial Intelligence Tools
PI201-CI-IPOP-37-2023; Budget: 15.000.00€ (2023 – ongoing); PI: Dr. Bárbara Barbosa

Faced with the fourth industrial revolution, more specifically the era of the digitalization of society and economy, the digital transition is considered to be an instrument of sustainability for our country, aligned with the development interests/investments of the European Union. In this context, it becomes imperative to develop programs and strategies aimed at boosting digital and economic competitiveness. Recent literature points to Artificial Intelligence (AI) as an increasingly influential scientific area in solving problems in the healthcare sector. AI methods enable problem-solving in clinical settings and support the decision-making process in an automated and intelligent way. Currently, given the growing demand of radiotherapy users to receive quick and efficient answers to their questions when contacting the institution and the high amount of time allocated to specialists, the creation of a chatbot using AI tools is suggested. Conversational chatbots use Natural Language Processing (NLP) and Natural Language Understanding (NLU), applications of AI that enable machines to understand human language and intentions.
For the implementation of this project the action plan would be the development of a chatbot by the research team with the collaboration of a Master’s student and a grant holder.
There are currently several solutions that are highly customizable and easy to implement. For example, Google offers the Healthcare Natural Language API, which is specifically designed to analyze unstructured medical text and is also flexible enough to easily integrate with an institution’s existing internal systems.
As a general rule, the implementation of this technology will require the creation of a dictionary of intentions (database) by the multidisciplinary team of the radiotherapy department that will allow the integration of the different expertise of professionals in this field. Each conversation intent contains:
• a tag (defining what the intent is);
• patterns (sentence patterns for the neural network text classifier);
• possible responses.
The conversational intent definitions are transformed into a model, and a vocabulary structure is built for the chatbot to process the responses.
To increase the quality and scope of the construction of this dictionary of intentions, a survey will also be carried out by professionals from different specialities. In order to ensure the relevance and continuity of the project, quality control and impact measures will be implemented. One possible approach would be a star rating of the chatbot’s performance by the user at the end of the conversation.
approach would be a star rating of the chatbot’s performance by the user at the end of the conversation.
A multidisciplinary approach is required for the management of patients with cancer. A team of physicians, physicists, technicians, and nurses provides patient-centered care and enables decision-making at different points in their treatment. Support services, such as customer service to answer questions, have also become an important part of cancer care. Improving these services means ensuring the quality of the services provided and the continuity of a close relationship between the institution and the patient. This proximity will strengthen the image of excellence and innovation of the IPO Porto, based on the national strategy of administrative modernization and digital transformation.

LuPET – Lu-177 radiotherapy outcome prediction using image data from Ga-68 PET/CT scans
PI202-CI-IPOP-38-2023; Budget: 10.000.00€ (2023 – ongoing); PI: Prof. João Santos

The project aims to predict outcomes of Lutetium-177 (Lu-177) radiotherapy in neuroendocrine tumor (NET) patients using image data from Gallium-68 (Ga-68) PET/CT scans. NETs are diverse malignancies originating from neuroendocrine cells, and Peptide Receptor Radionuclide Therapy (PRRT) with Lu-177 DOTA-TATE is an effective treatment for inoperable or metastatic cases. However, patient responses vary, necessitating the identification of predictive markers for treatment response. Radiomics, which extracts quantitative features from medical imaging data, offers a promising approach. The project will involve the following steps: Data Collection: Retrospective collection of Ga-68 PET/CT scans and clinical data from NET patients treated with Lu-177 PRRT. Ensure ethical approvals and patient consent for data usage. Image Preprocessing: Standardize the Ga-68 PET/CT images by performing necessary preprocessing steps such as normalization, segmentation, and registration to ensure uniformity and accuracy in feature extraction. Feature Extraction: Utilize radiomics software to extract quantitative features from the preprocessed images. These features may include texture, shape, intensity, and wavelet-based characteristics that describe the tumor’s heterogeneity. Data Analysis: Integrate the extracted radiomic features with clinical data, such as patient demographics, treatment parameters, and outcomes. Perform statistical analysis to identify features significantly correlated with therapy response. Model Development: Develop predictive models using machine learning algorithms. Train the models on a portion of the dataset, validating and testing on separate subsets to ensure robustness. Techniques like cross-validation, regularization, and feature selection will be employed to optimize model performance. Validation: Validate the predictive models using an independent dataset to confirm their generalizability and accuracy. Performance metrics such as accuracy, sensitivity, specificity, and area under the curve (AUC) will be calculated. Interpretation and Reporting: Analyze the model outputs to identify key predictive features and their clinical relevance. Prepare comprehensive reports and visualizations to communicate the findings to clinical stakeholders. Implementation: Develop guidelines and tools for implementing the predictive models in clinical practice. Train clinicians on the use of these tools to enhance treatment planning and decision-making. Evaluation: Continuously evaluate the models’ performance in a clinical setting, making necessary adjustments based on feedback and new data. The successful completion of this project could significantly improve the personalization of Lu-177 PRRT, leading to better outcomes for NET patients.

TBIVMAT – Implementation of total body irradiation using volumetric modulated arc therapy as part of an institutional hematopoietic stem cell transplantation program
PI203-CI-IPOP-39-2023; Budget: 45.000.00€ (2023 – ongoing)
PI : Dra. Anabela Dias

Total body irradiation (TBI) with megavoltage photon beams is used in treating multiple myeloma, leukemias, lymphomas, and some solid tumors, often in combination with chemotherapy as part of conditioning before allogeneic hematopoietic stem cell transplantation. TBI delivers a uniform dose to the entire body, reaching areas like the central nervous system and testicles, which chemotherapy alone cannot. It also allows for customized dosing by shielding or boosting specific regions. Conventional TBI (cTBI) uses large fields with lung blocks to irradiate the entire body in standing or lying-on-the-side positions at extended distances, requiring large, costly vaults. cTBI has long application times and cannot individually spare organs at risk (OARs), leading to significant toxicity.
Advanced techniques aim to selectively target hematopoietic tissues with malignant cells while sparing healthy tissues. Total marrow irradiation (TMI) is a conformal treatment of the skeleton and an alternative to TBI, improving dose homogeneity with inverse optimization algorithms. TMI with volumetric modulated arc therapy (VMAT) avoids cTBI complications while maintaining effectiveness. However, limited preclinical models mean little understanding of the biological differences between TBI and TMI and their effects on bone marrow engraftment.
Studies show VMAT techniques reduce TBI treatment time and improve dose homogeneity, though their use is not widespread. VMAT enhances conformality and homogeneity in dose distribution, optimizing multiple arcs simultaneously for complex plans. Using VMAT for TBI aims to achieve optimal target coverage and sparing of OARs like the lungs, potentially reducing treatment side effects. The challenge is the large planning target volume (PTV), requiring multiple overlapping arc treatments and repositioning due to couch limitations.
This project aims to implement TBI using VMAT in a hematopoietic stem cell transplantation program. A full-body phantom is essential, necessitating the creation of RANDO phantom limbs. Although TBI is delivered supine, patients need repositioning due to couch limits. A secondary objective is to develop a rotatable tabletop system for multi-isocenter plans without repositioning patients. This could lead to high-precision 3D treatment, offering a homogeneous total body dose and sparing critical organs compared to conventional methods.

SyNTHaX – Synergistic effect of gold Nanoparticles in cancer hybrid Therapeutics: hyperthermia coupled with X-ray irradiation
PI204-CI-IPOP-40-2023; Budget: 22.600.00€ (2023 – ongoing); PI: Prof. Pedro Teles

In the SyNTHaX project, we will test the synergistic effects of combining GNRT and hyperthermia mediated by AuNPs in MCF-7 cell cultures, which are epithelial cells isolated from the mammary tissue of a 69-year-old white female patient with metastatic adenocarcinoma. This will be done in four different modalities in addition to the control group. In one modality, the cells will be irradiated with 60 keV X-rays and then excited with a pulsed laser optimized for heating the AuNPs. In the next modality, the cells will first be excited with the same laser and then irradiated with 60 keV X-rays. In the third modality, the cells will be irradiated only with 60 keV X-rays, and finally, in the fourth modality, only the laser will be used. Cell viability will be assessed using the Alamar Blue assay. Tissue responses and nanoparticle distributions will be measured spatially and temporally using 3D vascularized microfluidic breast cancer platforms. The AuNPs will be synthesized and characterized at IFIMUP, which already has excellent expertise in the fabrication and characterization of NPs. Their morphology will be optimized for laser heating. Dosimetry calculations will be performed through Monte Carlo simulations using state-of-the-art codes, which will allow for the determination of dose enhancement factors that can be correlated with cell viability.

Publications

Amorim JP; Abreu P.H.; Santos, J.; Müller, H., Evaluating Post-hoc Interpretability with Intrinsic Interpretability, arXiv arXiv:2305.03002, https://doi.org/10.48550/arXiv.2305.03002 [IF: n.a.]

Amorim JP, Abreu PH, Santos J, Cortes M, Vila V, Evaluating the faithfulness of saliency maps in explaining deep learning models using realistic perturbations, Information Processing and Management, 60 (2023) 103225, https://doi.org/10.1016/j.ipm.2022.103225 [IF: 8.6]

Barbosa B, Oliveira C, Bravo I, Couto JG, Antunes L, McFadden S, Hughes C, McClure P, Rodrigues J, Dias A. An investigation of Digital Skills of Therapeutic Radiographers/Radiation Therapists: A European survey of proficiency level and future educational needs. Radiography.2023;29(3):479–88 https://doi.org/10.1016/j.radi.2023.02.009 [IF: 2.6]

Catarina Macedo-Silva, Vera Miranda-Gonçalves, Nuno Tiago Tavares, Daniela Barros-Silva, Joana Lencart, João Lobo, Ângelo Oliveira, Margareta P. Correia, Lucia Altucci, Carmen Jerónimo, Epigenetic regulation of TP53 is involved in prostate cancerradioresistance and DNA damage response signaling, Signal Transduction and Targeted Therapy, 2023, 8:395. [IF 39.3]

Silva, MM, Canha M, Salazar D, Neves JS, Ferreira G, Carvalho D, Duarte H, Efficacy, Toxicity, and Prognostic Factors of Re-treatment With [177Lu]Lu-DOTA-TATE in Patients With Progressing Neuroendocrine Tumors: The Experience of a Single Center, Cureus, 2023 Oct 23;15(10):e47506. doi: 10.7759/cureus.47506 [IF: 1.2]

Flood T, O’Neill A, Oliveira C, Barbosa B, Soares A, Muscat K, Guille S, McClure P, Hughes C, McFadden S. Patients’ perspectives of the skills and competencies of Therapy Radiographers/Radiation Therapists (TRs/RTTs) in the UK, Portugal and Malta; a qualitative study from the SAFEEUROPE project. Radiography. 2023;29:S117-S127. https://doi.org/10.1016/j.radi.2023.03.002 [IF: 2.6]

Flood, T., O’Neill, A., Oliveira, C., Barbosa, B., Soares, M. A. L., Muscat, M. K., Guille, S., Mc Clure, P., Hughes, C., & McFadden, S. 2023. “Patients’ perspectives of the skills and competencies of therapy radiographers/radiation therapists (TRs/RTTs) in the UK, Portugal and Malta; a qualitative study from the SAFE Europe project”. Radiography, 29, 1-11. https://doi.org/10.1016/j.radi.2023.03.002 [IF: 2.6]

Julian Malicki, Carla Lopes Castro, Magdalena Fundowicz, Marco Krengli5, Carmen Llacer-Moscardo, Sebastian Curcean, Carles Muñoz Montplet, Luisa Carvalho,Ewelina Konstanty, Tania Hernandez Barragan, Carla Pisani, Istvan Laszlo, Miquel Macià Garau, Marta Kruszyna-Mochalska, Joana Lencart, Dorota Zwierzchowska, Alvar Rosello Serrano, Adelina Brezae, Eva Loureiro Varela, Piotr Milecki, Micol Zannetti, Ovidiu Coza, Eva Gonzalez, Debora Beldì, Ferran Guedea, IROCA-TES: Improving Quality in Radiation Oncology through Clinical Audits — Training and Educationfor Standardization, Reports of Radiotherapy and Oncology, 2023, 28(3):429-432 [IF: 1.2]

Katarina Sjögreen-Gleisner, Glenn Flux, Klaus Bacher, Carlo Chiesa, Robin de Nijs, George C. Kagadis, Thiago Lima, Maria Lyra Georgosopoulou, Pablo Minguez Gabiña, Stephan Nekolla, Steffie Peters, Joao Santos, Bernhard Sattler, Caroline Stokke, Johannes Tran-Gia, Paddy Gilligan, Manuel Bardiès, EFOMP policy statement nº 19: Dosimetry in nuclear medicine therapy – Molecular radiotherapy, European Journal of Medical Physics, 116 (2023) 103166, https://doi.org/10.1016/j.ejmp.2023.103166 [IF: 3.4]

Mariana Morais, Vera Machado, Patrícia Figueiredo, Francisca Dias, Rogéria Craveiro, Joana Lencart, Carlos Palmeira, Kirsi S. Mikkonen, Ana Luísa Teixeira, Rui Medeiros, Silver Nanoparticles (AgNPs) as Enhancers of Everolimus and Radiotherapy Sensitivity on Clear Cell Renal Cell Carcinoma, Antioxidants, 2023, 12(12):2051. [IF 7.0]

Maurício J, I. Domingues, J. Bernardino. Comparing Vision Transformers and Convolutional Neural Networks for Image Classification: A Literature Review. Applied Sciences. 2023; 13(9): 5521. [IF: 2.7]

Mendes B, I. Domingues, F. Dias, J. Santos. Cone Beam Computed Tomography Radiomics for Prostate Cancer: Favourable vs Unfavourable Prognosis Prediction. Applied Sciences. 2023; 13(3):1378. [IF: 2.7]

Miriam Seoane Santos, Pedro Henriques Abreu, Nathalie Japkowicz, Alberto Fernández, João Santos, A unifying view of class overlap and imbalance: Key concepts, multi-view panorama, and open avenues for research, Information Fusion 89 (2023) 228–253, https://doi.org/10.1016/j.inffus.2022.08.017 [IF: 18.6]

O Neil A, Hughes C, McClure P, Barbosa B, Muscat K, Oliveira C, Soares AL, McFadden S. 2023. Patient-reported perspectives of therapeutic radiographers when undergoing radiotherapy. A European multi-centre study. Radiography 29:S32-S39. doi:10.1016/j.radi.2023.01.027 [IF: 2.6]

Oliveira C, Barbosa B, Couto JG, Bravo I, Hughes C, McFadden S, Khine R, McNair H. Advanced practice roles of therapeutic radiographers/radiation therapists: An European survey. .Radiography. 2023;29 (2): 261-273. DOI: 10.1016/j.radi.2022.12.003 [IF: 2.6]

Oliveira C, Barbosa B, Couto JG, Bravo I, Hughes C, McFadden S, Khine R, McNair H. Advanced practice amongst therapeutic radiographers/radiation therapists: Exploring the potential of the four pillars: preliminary results from a European perspective. J. Med. Imaging Rad Science 53 (4), S26_27 DOI: 10.1016/j.jmir.2022.10.088 [IF: 1.8]

Pires AM L. Carvalho, A.C. Santos, A.M. Vilaça, A.R. Coelho, F. Fernandes, L. Moreira,J. Lima, R. Vieira, M.J. Ferraz, M. Silva, P. Silva, R. Matias, S. Zorro, S. Costa, S. Sarandão,A.F. Barros, Radiotherapy skin marking with lancets versus electric marking pen (CONFORTATTOO)- 6 Months Results on Cosmesis, Fading, and Patients’ Satisfaction From a Randomized, Doubçe-Blind Trial, Radiography, VOLUME 9, ISSUE 3, 101404, MARCH, 2024 (Published:November 05, 2023) [IF: 2.6]

Pires AM, Borges F, Dias AG, Lencart J, Matos M, Gonçalves J, Paraffin gauze bolus as tissue compensator on photon irradiation for mycosis fungoides – regarding a case study, Journal of Radiotherapy in Practice 22 (e82) 2023. https://doi.org/10.1017/S1460396923000109 [IF: 0.148]

Rodrigues A, N. Rodrigues, J. Santinha, M. V. Lisitskaya, A. Uysal, C. Matos, I. Domingues, N. Papanikolaou. Value of handcrafted and deep radiomic features towards training robust machine learning classifiers for Prediction of Prostate Cancer Disease Aggressiveness. Scientific Reports. 2023; 13(6206). [IF: 4.6]

Soares AL, Buttigieg SC, Bak B, McFadden S, McClure P, Couto JG, Bravo I. A Review of the Applicability of Current Green Practices in Healthcare Facilities. Int J Health Policy Manag. 2023;12:6947. doi: 10.34172/ijhpm.2023.6947 [IF: 2.9]

Soares AL, Buttigieg SC, Couto JG, Bak B, McFadden S, Bravo I. An evaluation of knowledge of circular economy among Therapeutic Radiographers/Radiation Therapists (TR/RTTs): Results of a European survey to inform curriculum design. Radiography (Lond). 2023 Mar;29(2):274-283. doi:10.1016/j.radi.2022.12.006 [IF: 2.6]

Ana Arriaga; Cláudia Gonçalves; P. Teles; Joana Santos; Paula Simãozinho; Patrick Sousa, Establishment of local diagnostic reference levels for abdomen and chest radiographies in the region of Algarve, Portugal, European Journal of Radiology, 170 (2023) 111248, DOI: 10.1016/j.ejrad.2023.111248 [IF: 3.3]

Books, & Book Chapters

M. El Amine Bechar, N. Settouti, and I. Domingues, Deep Learning vs. Super Pixel Classification for Breast Masses Segmentation, Deep Learning for Biomedical Applications, Boca Raton: CRC Press, 2021, pp. 121–156, eBook ISBN9780367855611

Selected publications (up to five)

Mendes B, I. Domingues, F. Dias, J. Santos. Cone Beam Computed Tomography Radiomics for Prostate Cancer: Favourable vs Unfavourable Prognosis Prediction. Applied Sciences. 2023; 13(3):1378. [IF: 2.7]

NATIONAL COLLABORATIONS

Faculty of Sciences of the University of Porto (FCUP)
Institute of Biomedical Sciences Abel Salazar of the University of Porto (ICBAS)
Higher Technical Institute (IST/CTN)
INESC Porto
University of Aveiro (Department of Mechanical Engineering)
University of Coimbra (Center for Informatics and Systems of the University of Coimbra; LIP)
Faculty of Sciences of the University of Lisbon
University of Minho (MEMS)

INTERNATIONAL COLLABORATIONS

Institute of Nuclear Physics PAN; Krakow, Polónia
Institution: Radiation Chemistry and Dosimetry Laboratory; Country: Bijenička c. 54, HR-10000 Zagreb, Croatia
Greek Atomic Energy Commission (EEAE), Dosimetry and Calibration Department; Atenas, Greece
Belgian Nuclear Research Centre, Unit Research in Dosimetric Applications; Belgium
Safe and Free Exchange of EU Radiography Professionals across Europe (SAFE EUROPE); Multi-institutional; European Consortium
European Radiation Dosimetry Group – EURADOS; EURADOS e. V.

Medical Physics, Radiobiology & Radiation Protection Group

SCIENTIFIC COORDINATOR

TEAM

Senior Investigators

Invited Researchers

PhD Students

MSc Students

Other Collaborators

AIMS

PROJECTS WITH INTERNAL FUNDING

Publications

Books, & Book Chapters

Selected publications (up to five)

NATIONAL COLLABORATIONS

INTERNATIONAL COLLABORATIONS

equipa

contactos

telefone

email

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