Decoding Breast Cancer: Using Radiomics to Non-Invasively Unveil Molecular Subtypes Directly from Mammographic Images.

Breast cancer is the most commonly diagnosed cancer worldwide. The therapy used and its success depend highly on the histology of the tumor. This study aimed to explore the potential of predicting the molecular subtype of breast cancer using radiomic features extracted from screening digital mammography (DM) images.

Breast Cancer Molecular Subtype Prediction: A Mammography-Based AI Approach.

Breast 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.

PosturAll: A Posture Assessment Software for Children.

From an early age, people are exposed to risk factors that can lead to musculoskeletal disorders like low back pain, neck pain and scoliosis. Medical screenings at an early age might minimize their incidence. The study intends to improve a software that processes images of patients, using specific anatomical sites to obtain risk indicators for possible musculoskeletal problems.

Make It Less Complex: Autoencoder for Speckle Noise Removal-Application to Breast and Lung Ultrasound.

Ultrasound (US) imaging is used in the diagnosis and monitoring of COVID-19 and breast cancer. The presence of Speckle Noise (SN) is a downside to its usage since it decreases lesion conspicuity. Filters can be used to remove SN, but they involve time-consuming computation and parameter tuning.

Nanoparticle-Based Treatment in Glioblastoma.

Glioblastoma (GB) is a malignant glioma associated with a mean overall survival of 12 to 18 months, even with optimal treatment, due to its high relapse rate and treatment resistance. The standardized first-line treatment consists of surgery, which allows for diagnosis and cytoreduction, followed by stereotactic fractionated radiotherapy and chemotherapy. Treatment failure can result from the poor passage of drugs through the blood-brain barrier (BBB).

Digital Breast Tomosynthesis: Towards Dose Reduction through Image Quality Improvement.

Currently, breast cancer is the most commonly diagnosed type of cancer worldwide. Digital Breast Tomosynthesis (DBT) has been widely accepted as a stand-alone modality to replace Digital Mammography, particularly in denser breasts. However, the image quality improvement provided by DBT is accompanied by an increase in the radiation dose for the patient.

Highlighted Advances in Therapies for Difficult-To-Treat Brain Tumours Such as Glioblastoma.

Glioblastoma multiforme (GBM) remains a challenging disease, as it is the most common and deadly brain tumour in adults and has no curative solution and an overall short survival time. This incurability and short survival time means that, despite its rarity (average incidence of 3.2 per 100,000 persons), there has been an increased effort to try to treat this disease.

Avoiding Tissue Overlap in 2D Images: Single-Slice DBT Classification Using Convolutional Neural Networks.

Breast cancer was the most diagnosed cancer around the world in 2020. Screening programs, based on mammography, aim to achieve early diagnosis which is of extreme importance when it comes to cancer. There are several flaws associated with mammography, with one of the most important being tissue overlapping that can result in both lesion masking and fake-lesion appearance.

Automatic Classification of Simulated Breast Tomosynthesis Whole Images for the Presence of Microcalcification Clusters Using Deep CNNs.

Microcalcification clusters (MCs) are among the most important biomarkers for breast cancer, especially in cases of nonpalpable lesions. The vast majority of deep learning studies on digital breast tomosynthesis (DBT) are focused on detecting and classifying lesions, especially soft-tissue lesions, in small regions of interest previously selected. Only about 25% of the studies are specific to MCs, and all of them are based on the classification of small preselected regions.

AI in Breast Cancer Imaging: A Survey of Different Applications.

Breast cancer was the most diagnosed cancer in 2020. Several thousand women continue to die from this disease. A better and earlier diagnosis may be of great importance to improving prognosis, and that is where Artificial Intelligence (AI) could play a major role.

Optimization of Breast Tomosynthesis Visualization through 3D Volume Rendering.

3D volume rendering may represent a complementary option in the visualization of Digital Breast Tomosynthesis (DBT) examinations by providing an understanding of the underlying data at once. Rendering parameters directly influence the quality of rendered images. The purpose of this work is to study the influence of two of these parameters (voxel dimension in direction and sampling distance) on DBT rendered data.

Breast Cancer Risk Assessment: A Review on Mammography-Based Approaches.

Breast cancer affects thousands of women across the world, every year. Methods to predict risk of breast cancer, or to stratify women in different risk levels, could help to achieve an early diagnosis, and consequently a reduction of mortality. This paper aims to review articles that extracted texture features from mammograms and used those features along with machine learning algorithms to assess breast cancer risk.

An Enhanced Visualization of DBT Imaging Using Blind Deconvolution and Total Variation Minimization Regularization.

Digital Breast Tomosynthesis (DBT) presents out-of-plane artifacts caused by features of high intensity. Given observed data and knowledge about the point spread function (PSF), deconvolution techniques recover data from a blurred version. However, a correct PSF is difficult to achieve and these methods amplify noise.

A Novel Hybrid Nanosystem Integrating Cytotoxic and Magnetic Properties as a Tool to Potentiate Melanoma Therapy.

Cancer is a major health concern and the prognosis is often poor. Significant advances in nanotechnology are now driving a revolution in cancer detection and treatment. The goal of this study was to develop a novel hybrid nanosystem for melanoma treatment, integrating therapeutic and magnetic targeting modalities.

Total variation minimization filter for DBT imaging.

Purpose: Compressed sensing (CS) is a new approach in medical imaging which allows a sparse image to be reconstructed from undersampled data. Total variation (TV) based minimization algorithms are the one CS technique that has achieved great success due to its virtue of preserving edges while reducing image noise. The purpose of this work is to implement and evaluate the performance of a TV minimization filter able to increase the signal difference to noise ratio (SDNR) of digital breast tomosynthesis (DBT) images.

Optimization of convergent collimators for pixelated SPECT systems.

Purpose: The optimization of the collimator design is essential to obtain the best possible sensitivity in single photon emission computed tomography imaging. The aim of this work is to present a methodology for maximizing the sensitivity of convergent collimators, specifically designed to match the pitch of pixelated detectors, for a fixed spatial resolution value and to present some initial results using this approach.

Methods: Given the matched constraint, the optimal collimator design cannot be simply found by allowing the highest level of septal penetration and spatial resolution consistent with the imposed restrictions, as it is done for the optimization of conventional collimators.

Estimation of the collective dose in the Portuguese population due to medical procedures in 2010.

In a wide range of medical fields, technological advancements have led to an increase in the average collective dose in national populations worldwide. Periodic estimations of the average collective population dose due to medical exposure is, therefore of utmost importance, and is now mandatory in countries within the European Union (article 12 of EURATOM directive 97/43). Presented in this work is a report on the estimation of the collective dose in the Portuguese population due to nuclear medicine diagnostic procedures and the Top 20 diagnostic radiology examinations, which represent the 20 exams that contribute the most to the total collective dose in diagnostic radiology and interventional procedures in Europe.