Instituto de Biofísica e Engenharia Biomédica - IBEB
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- Best Practices for Accurate Results Using Numerical Solvers for Microwave Body ScreeningPublication . Martins, Raquel A.; Godinho, Daniela M.; Felício, João M.; Savazzi, Matteo; Costa, Jorge R.; Conceição, Raquel C.; Fernandes, Carlos A.In this paper, we indicate best practices that should be observed when using numerical solvers for microwave body sensing. We show the impact of not minding these aspects in the case of microwave breast scanning, using the Computer Simulation Technology software tool. To this end we simulate a homogeneous breast with a 5-mm radius spherical tumor placed inside. The breast is illuminated by a broadband antenna that operates in the 2-6 GHz band. The scattering parameters are then processed to reconstruct the reflectivity map of the breast. The results highlight that the conclusions drawn from simulations may be misleading or meaningless when the solver type or positioning of model elements (body and antennas) are not carefully applied. This is particularly critical when considering more complex scenarios, such as inhomogeneous or multilayer body models.
- Breast Cancer Molecular Subtype Prediction: A Mammography-Based AI ApproachPublication . Mota, Ana M.; Mendes, João; Matela, NunoBreast cancer remains a leading cause of mortality among women, with molecular subtypes significantly influencing prognosis and treatment strategies. Currently, identifying the molecular subtype of cancer requires a biopsy—a specialized, expensive, and time-consuming procedure, often yielding to results that must be supported with additional biopsies due to technique errors or tumor heterogeneity. This study introduces a novel approach for predicting breast cancer molecular subtypes using mammography images and advanced artificial intelligence (AI) methodologies. Using the OPTIMAM imaging database, 1397 images from 660 patients were selected. The pretrained deep learning model ResNet-101 was employed to classify tumors into five subtypes: Luminal A, Luminal B1, Luminal B2, HER2, and Triple Negative. Various classification strategies were studied: binary classifications (one vs. all others, specific combinations) and multi-class classification (evaluating all subtypes simultaneously). To address imbalanced data, strategies like oversampling, undersampling, and data augmentation were explored. Performance was evaluated using accuracy and area under the receiver operating characteristic curve (AUC). Binary classification results showed a maximum average accuracy and AUC of 79.02% and 64.69%, respectively, while multi-class classification achieved an average AUC of 60.62% with oversampling and data augmentation. The most notable binary classification was HER2 vs. non-HER2, with an accuracy of 89.79% and an AUC of 73.31%. Binary classification for specific combinations of subtypes revealed an accuracy of 76.42% for HER2 vs. Luminal A and an AUC of 73.04% for HER2 vs. Luminal B1. These findings highlight the potential of mammography-based AI for non-invasive breast cancer subtype prediction, offering a promising alternative to biopsies and paving the way for personalized treatment plans.
- Detection of Microcalcifications in Digital Breast Tomosynthesis using Faster R-CNN and 3D Volume RenderingPublication . Matela, Nuno; Almeida, Pedro; Clarkson, Matthew; Mota, AnaMicrocalcification clusters (MCs) are one of the most important biomarkers for breast cancer and Digital Breast Tomosynthesis (DBT) has consolidated its role in breast cancer imaging. As there are mixed observations about MCs detection using DBT, it is important to develop tools that improve this task. Furthermore, the visualization mode of MCs is also crucial, as their diagnosis is associated with their 3D morphology. In this work, DBT data from a public database were used to train a faster region-based convolutional neural network (R-CNN) to locate MCs in entire DBT. Additionally, the detected MCs were further analyzed through standard 2D visualization and 3D volume rendering (VR) specifically developed for DBT data. For MCs detection, the sensitivity of our Faster R-CNN was 60% with 4 false positives. These preliminary results are very promising and can be further improved. On the other hand, the 3D VR visualization provided important information, with higher quality and discernment of the detected MCs. The developed pipeline may help radiologists since (1) it indicates specific breast regions with possible lesions that deserve additional attention and (2) as the rendering of the MCs is similar to a segmentation, a detailed complementary analysis of their 3D morphology is possible.
- Detection of Microcalcifications in Digital Breast Tomosynthesis using Faster R-CNN and 3D Volume RenderingPublication . Mota, Ana M.; Clarkson, Matthew J.; Almeida, Pedro; Matela, NunoMicrocalcification clusters (MCs) are one of the most important biomarkers for breast cancer and Digital Breast Tomosynthesis (DBT) has consolidated its role in breast cancer imaging. As there are mixed observations about MCs detection using DBT, it is important to develop tools that improve this task. Furthermore, the visualization mode of MCs is also crucial, as their diagnosis is associated with their 3D morphology. In this work, DBT data from a public database were used to train a faster region-based convolutional neural network (R-CNN) to locate MCs in entire DBT. Additionally, the detected MCs were further analyzed through standard 2D visualization and 3D volume rendering (VR) specifically developed for DBT data. For MCs detection, the sensitivity of our Faster R-CNN was 60% with 4 false positives. These preliminary results are very promising and can be further improved. On the other hand, the 3D VR visualization provided important information, with higher quality and discernme nt of the detected MCs. The developed pipeline may help radiologists since (1) it indicates specific breast regions with possible lesions that deserve additional attention and (2) as the rendering of the MCs is similar to a segmentation, a detailed complementary analysis of their 3D morphology is possible.
- Development of 3D MRI-Based Anatomically Realistic Models of Breast Tissues and Tumours for Microwave Imaging DiagnosisPublication . Pelicano, Ana Catarina; Gonçalves, Maria C. T.; Godinho, Daniela M.; Castela, Tiago; Orvalho, M. Lurdes; Araújo, Nuno A. M.; Porter, Emily; Conceição, Raquel C.Breast cancer diagnosis using radar-based medical MicroWave Imaging (MWI) has been studied in recent years. Realistic numerical and physical models of the breast are needed for simulation and experimental testing of MWI prototypes. We aim to provide the scientific community with an online repository of multiple accurate realistic breast tissue models derived from Magnetic Resonance Imaging (MRI), including benign and malignant tumours. Such models are suitable for 3D printing, leveraging experimental MWI testing. We propose a pre-processing pipeline, which includes image registration, bias field correction, data normalisation, background subtraction, and median filtering. We segmented the fat tissue with the region growing algorithm in fat-weighted Dixon images. Skin, fibroglandular tissue, and the chest wall boundary were segmented from water-weighted Dixon images. Then, we applied a 3D region growing and Hoshen-Kopelman algorithms for tumour segmentation. The developed semi-automatic segmentation procedure is suitable to segment tissues with a varying level of heterogeneity regarding voxel intensity. Two accurate breast models with benign and malignant tumours, with dielectric properties at 3, 6, and 9 GHz frequencies have been made available to the research community. These are suitable for microwave diagnosis, i.e., imaging and classification, and can be easily adapted to other imaging modalities.
- Development of a 3D Anthropomorphic Phantom Generator for Microwave Imaging Applications of the Head and Neck RegionPublication . Pelicano, Ana Catarina; Conceição, Raquel C.The development of 3D anthropomorphic head and neck phantoms is of crucial and timely importance to explore novel imaging techniques, such as radar-based MicroWave Imaging (MWI), which have the potential to accurately diagnose Cervical Lymph Nodes (CLNs) in a neoadjuvant and non-invasive manner. We are motivated by a significant diagnostic blind-spot regarding mass screening of LNs in the case of head and neck cancer. The timely detection and selective removal of metastatic CLNs will prevent tumor cells from entering the lymphatic and blood systems and metastasizing to other body regions. The present paper describes the developed phantom generator which allows the anthropomorphic modelling of the main biological tissues of the cervical region, including CLNs, as well as their dielectric properties, for a frequency range from 1 to 10 GHz, based on Magnetic Resonance images. The resulting phantoms of varying complexity are well-suited to contribute to all stages of the development of a radar-based MWI device capable of detecting CLNs. Simpler models are essential since complexity could hinder the initial development stages of MWI devices. Besides, the diversity of anthropomorphic phantoms resulting from the developed phantom generator can be explored in other scientific contexts and may be useful to other medical imaging modalities.
- Development of a Transmission-Based Open-Ended Coaxial-Probe Suitable for Axillary Lymph Node Dielectric MeasurementsPublication . Savazzi, Matteo; Porter, Emily; OHalloran, Martin; Costa, Jorge R.; FERNANDES, CARLOS; M. Felício, João; Conceicao, Raquel C.We assess the feasibility of a transmission-based open-ended coaxial-probe for tissue dielectric properties estimation. The ultimate goal is to use it for axillary lymph node dielectric measurement, which is not trivial when applying the state-of-the-art reflection-based open-ended coaxial-probe. The proposed technique consists in placing the material under test between two opposite open-ended coaxial-probes and record the transmission coefficient. We numerically assess three coaxial probe configurations, in order to ensure adequate transmission and sensing volume. The final setup allows for enough propagation through a 5mm sample (which will be sufficient for the measurements of axillary lymph nodes), while confining the sensing volume to the region of interest. Experimental tests on two materials of different permittivity ranges showed good agreement between the measured and numerical transmission coefficient. Moreover, we observed that the transmission coefficient can highlight the contrast between materials with different dielectric properties. The promising initial results motivate the further application of the method to the case of axillary lymph nodes.
- Development of an Anthropomorphic Phantom of the Axillary Region for Microwave Imaging AssessmentPublication . Savazzi, Matteo; Abedi, Soroush; Ištuk, Niko; Joachimowicz, Nadine; Roussel, Hélène; Porter, Emily; O’Halloran, Martin; Costa, Jorge R.; Fernandes, Carlos A.; Felício, João M.; Conceição, Raquel C.We produced an anatomically and dielectrically realistic phantom of the axillary region to enable the experimental assessment of Axillary Lymph Node (ALN) imaging using microwave imaging technology. We segmented a thoracic Computed Tomography (CT) scan and created a computer-aided designed file containing the anatomical configuration of the axillary region. The phantom comprises five 3D-printed parts representing the main tissues of interest of the axillary region for the purpose of microwave imaging: fat, muscle, bone, ALNs, and lung. The phantom allows the experimental assessment of multiple anatomical configurations, by including ALNs of different size, shape, and number in several locations. Except for the bone mimicking organ, which is made of solid conductive polymer, we 3D-printed cavities to represent the fat, muscle, ALN, and lung and filled them with appropriate tissue-mimicking liquids. Existing studies about complex permittivity of ALNs have reported limitations. To address these, we measured the complex permittivity of both human and animal lymph nodes using the standard open-ended coaxial-probe technique, over the 0.5 GHz-8.5 GHz frequency band, thus extending current knowledge on dielectric properties of ALNs. Lastly, we numerically evaluated the effect of the polymer which constitutes the cavities of the phantom and compared it to the realistic axillary region. The results showed a maximum difference of 7 dB at 4 GHz in the electric field magnitude coupled to the tissues and a maximum of 10 dB difference in the ALN response. Our results showed that the phantom is a good representation of the axillary region and a viable tool for pre-clinical assessment of microwave imaging technology.
- Development of MRI‐based axillary numerical models and estimation of axillary lymph node dielectric properties for microwave imagingPublication . Godinho, Daniela M.; Felício, João M.; Castela, Tiago; Silva, Nuno A.; Orvalho, Maria de Lurdes; Fernandes, Carlos A.; Conceição, Raquel C.Purpose: Microwave imaging (MWI) has been studied as a complementary imaging modality to improve sensitivity and specificity of diagnosis of axillary lymph nodes (ALNs), which can be metastasized by breast cancer. The feasibility of such a system is based on the dielectric contrast between healthy and metastasized ALNs. However, reliable information such as anatomically realistic numerical models and matching dielectric properties of the axillary region and ALNs, which are crucial to develop MWI systems, are still limited in the literature. The purpose of this work is to develop a methodology to infer dielectric properties of structures from magnetic resonance imaging (MRI), in particular, ALNs. We further use this methodology, which is tailored for structures farther away from MR coils, to create MRI- based numerical models of the axillary region and share them with the scientific community, through an open- access repository. Methods: We use a dataset of breast MRI scans of 40 patients, 15 of them with metastasized ALNs. We apply image processing techniques to minimize the artifacts in MR images and segment the tissues of interest. The background, lung cavity, and skin are segmented using thresholding techniques and the remaining tissues are segmented using a K- means clustering algorithm. The ALNs are segmented combining the clustering results of two MRI sequences. The performance of this methodology was evaluated using qualitative criteria. We then apply a piecewise linear interpolation between voxel signal intensities and known dielectric properties, which allow us to create dielectric property maps within an MRI and consequently infer ALN properties. Finally, we compare healthy and metastasized ALN dielectric properties within and between patients, and we create an open- access repository of numerical axillary region numerical models which can be used for electromagnetic simulations. Results: The proposed methodology allowed creating anatomically realistic models of the axillary region, segmenting 80 ALNs and analyzing the corresponding dielectric properties. The estimated relative permittivity of those ALNs ranged from 16.6 to 49.3 at 5 GHz. We observe there is a high variability of dielectric properties of ALNs, which can be mainly related to the ALN size and, consequently, its composition. We verified an average dielectric contrast of 29% between healthy and metastasized ALNs. Our repository comprises 10 numerical models of the axillary region, from five patients, with variable number of metastasized ALNs and body mass index. Conclusions: The observed contrast between healthy and metastasized ALNs is a good indicator for the feasibility of a MWI system aiming to diagnose ALNs. This paper presents new contributions regarding anatomical modeling and dielectric properties' characterization, in particular for axillary region applications.
- Digital Guardian Angel Supported by an Artificial Intelligence System to Improve Quality of Life, Well-being, and Health Outcomes of Patients With Cancer (ONCORELIEF): Protocol for a Single Arm Prospective Multicenter Pilot StudyPublication . Reis, Joaquim; Travado, Luzia; Scherrer, Alexander; Kosmidis, Thanos; Venios, Stefanos; Laras, Paris Emmanouil; Oestreicher, Gabrielle; Moehler, Markus; Parolini, Margherita; Passardi, Alessandro; Meggiolaro, Elena; Martinelli, Giovanni; Petracci, Elisabetta; Zingaretti, Chiara; Diamantopoulos, Sotiris; Plakia, Maria; Vassiliou, Charalampos; Mousa, Suheib; Zifrid, Robert; Sullo, Francesco Giulio; Gallio, ChiaraBackground: According to Europe’s Beating Cancer Plan, the number of cancer survivors is growing every year and is now estimated at over 12 million in Europe. A main objective of the European Commission is to ensure that cancer survivors can enjoy a high quality of life, underlining the role of digital technology and eHealth apps and tools to achieve this. Objective: The main objective of this study is the development of a user-centered artificial intelligence system to facilitate the input and integration of patient-related biopsychosocial data to improve posttreatment quality of life, well-being, and health outcomes and examine the feasibility of this digitally assisted workflow in a real-life setting in patients with colorectal cancer and acute myeloid leukemia. Methods: A total of 60 patients with colorectal cancer and 30 patients with acute myeloid leukemia will be recruited from 2 clinical centers: Universitätsmedizin der Johannes Gutenberg-Universität Mainz (Mainz, Germany) and IRCCS Istituto Romagnolo per lo Studio dei Tumori “Dino Amadori” (IRST, Italy). Psychosocial data (eg, emotional distress, fatigue, quality of life, subjective well-being, sleep problems, and appetite loss) will be collected by questionnaires via a smartphone app, and physiological data (eg, heart rate, skin temperature, and movement through step count) will be collected by a customizable smart wrist-worn sensor device. Each patient will be assessed every 2 weeks over their 3-month participation in the ONCORELIEF study. Inclusion criteria include patients with the diagnosis of acute myeloid leukemia or colorectal cancer, adult patients aged 18 years and older, life expectancy greater than 12 months, Eastern Cooperative Oncology Group performance status ≤2, and patients who have a smartphone and agree to use it for the purpose of the study. Exclusion criteria include patients with a reduced cognitive function (such as dementia) or technological illiteracy and other known active malignant neoplastic diseases (patients with a medical history of treated neoplastic disease are included). Results: The pilot study started on September 1, 2022. As of January 2023, we enrolled 33 patients with colorectal cancer and 7 patients with acute myeloid leukemia. As of January 2023, we have not yet started the data analysis. We expect to get all data in June 2023 and expect the results to be published in the second semester of 2023. Conclusions: Web-based and mobile apps use methods from mathematical decision support and artificial intelligence through a closed-loop workflow that connects health professionals and patients. The ONCORELIEF system has the potential of continuously identifying, collecting, and processing data from diverse patient dimensions to offer health care recommendations, support patients with cancer to address their unmet needs, and optimize survivorship care.