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. 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.

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. Here, a method based on 2D Total Variation (2D TV) minimization to improve image quality without the need to increase the dose was proposed. Two phantoms were used to acquire data at different dose ranges (0.88-2.19 mGy for Gammex 156 and 0.65-1.71 mGy for our phantom). A 2D TV minimization filter was applied to the data, and the image quality was assessed through contrast-to-noise ratio (CNR) and the detectability index of lesions before and after filtering. The results showed a decrease in 2D TV values after filtering, with variations of up to 31%, increasing image quality. The increase in CNR values after filtering showed that it is possible to use lower doses (-26%, on average) without compromising on image quality. The detectability index had substantial increases (up to 14%), especially in smaller lesions. So, not only did the proposed approach allow for the enhancement of image quality without increasing the dose, but it also improved the chances of detecting small lesions that could be overlooked.

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. Classifying the whole image according to the presence or absence of MCs is a difficult task due to the size of MCs and all the information present in an entire image. A completely automatic and direct classification, which receives the entire image, without prior identification of any regions, is crucial for the usefulness of these techniques in a real clinical and screening environment. The main purpose of this work is to implement and evaluate the performance of convolutional neural networks (CNNs) regarding an automatic classification of a complete DBT image for the presence or absence of MCs (without any prior identification of regions). In this work, four popular deep CNNs are trained and compared with a new architecture proposed by us. The main task of these trainings was the classification of DBT cases by absence or presence of MCs. A public database of realistic simulated data was used, and the whole DBT image was taken into account as input. DBT data were considered without and with preprocessing (to study the impact of noise reduction and contrast enhancement methods on the evaluation of MCs with CNNs). The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance. Very promising results were achieved with a maximum AUC of 94.19% for the GoogLeNet. The second-best AUC value was obtained with a new implemented network, CNN-a, with 91.17%. This CNN had the particularity of also being the fastest, thus becoming a very interesting model to be considered in other studies. With this work, encouraging outcomes were achieved in this regard, obtaining similar results to other studies for the detection of larger lesions such as masses. Moreover, given the difficulty of visualizing the MCs, which are often spread over several slices, this work may have an important impact on the clinical analysis of DBT images.

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. When no information is available about the PSF, blind deconvolution can be used. Additionally, Total Variation (TV) minimization algorithms have achieved great success due to its virtue of preserving edges while reducing image noise. This work presents a novel approach in DBT through the study of out-of-plane artifacts using blind deconvolution and noise regularization based on TV minimization. Gradient information was also included. The methodology was tested using real phantom data and one clinical data set. The results were investigated using conventional 2D slice-by-slice visualization and 3D volume rendering. For the 2D analysis, the artifact spread function (ASF) and Full Width at Half Maximum (FWHMMASF) of the ASF were considered. The 3D quantitative analysis was based on the FWHM of disks profiles at 90°, noise and signal to noise ratio (SNR) at 0° and 90°. A marked visual decrease of the artifact with reductions of FWHMASF (2D) and FWHM90° (volume rendering) of 23.8% and 23.6%, respectively, was observed. Although there was an expected increase in noise level, SNR values were preserved after deconvolution. Regardless of the methodology and visualization approach, the objective of reducing the out-of-plane artifact was accomplished. Both for the phantom and clinical case, the artifact reduction in the z was markedly visible.

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 z direction and sampling distance) on DBT rendered data. Both parameters were studied with a real phantom and one clinical DBT data set. The voxel size was changed from 0.085 × 0.085 × 1.0 mm3 to 0.085 × 0.085 × 0.085 mm3 using ten interpolation functions available in the Visualization Toolkit library (VTK) and several sampling distance values were evaluated. The results were investigated at 90º using volume rendering visualization with composite technique. For phantom quantitative analysis, degree of smoothness, contrast-to-noise ratio, and full width at half maximum of a Gaussian curve fitted to the profile of one disk were used. Additionally, the time required for each visualization was also recorded. Hamming interpolation function presented the best compromise in image quality. The sampling distance values that showed a better balance between time and image quality were 0.025 mm and 0.05 mm. With the appropriate rendering parameters, a significant improvement in rendered images was achieved.

Dissolved gaseous mercury formation and mercury volatilization in intertidal sediments.

Intertidal sediments of Tagus estuary regularly experiences complex redistribution due to tidal forcing, which affects the cycling of mercury (Hg) between sediments and the water column. This study quantifies total mercury (Hg) and methylmercury (MMHg) concentrations and fluxes in a flooded mudflat as well as the effects on water-level fluctuations on the air-surface exchange of mercury. A fast increase in dissolved Hg and MMHg concentrations was observed in overlying water in the first 10min of inundation and corresponded to a decrease in pore waters, suggesting a rapid export of Hg and MMHg from sediments to the water column. Estimations of daily advective transport exceeded the predicted diffusive fluxes by 5 orders of magnitude. A fast increase in dissolved gaseous mercury (DGM) concentration was also observed in the first 20-30min of inundation (maximum of 40pg L-1). Suspended particulate matter (SPM) concentrations were inversely correlated with DGM concentrations. Dissolved Hg variation suggested that biotic DGM production in pore waters is a significant factor in addition to the photochemical reduction of Hg. Mercury volatilization (ranged from 1.1 to 3.3ngm-2h-1; average of 2.1ngm-2h-1) and DGM production exhibited the same pattern with no significant time-lag suggesting a fast release of the produced DGM. These results indicate that Hg sediment/water exchanges in the physical dominated estuaries can be underestimated when the tidal effect is not considered.

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. METHODS: Assuming a Poisson noise model, the authors present a practical methodology, based on Rudin, Osher, and Fatemi model, which directly applies a TV minimization filter to real phantom and clinical DBT images. Different moments of filter application (before and after image reconstruction) and the suitable Lagrange multiplier (λ) to be used in filter equation are studied. Also, the relationship between background standard deviation (σB) of unfiltered images and optimal λ values is determined, in order to maximize the SDNR. Qualitative and quantitative analyses are conducted between unfiltered and filtered images and between the different moments of filter application. The proposed methodology is also tested with one clinical DBT data set. RESULTS: Using phantom data, when the filter is applied to the projections, the authors observed a decrease of 31.34% in TV and an increase of 5.29% and 5.44% in SDNR and full width at half maximum (FWHM), respectively. When applied after reconstruction, a decrease of 35.48% and 2.59% was achieved for TV and FWHM, respectively, and an increase of 8.32% for SDNR. For each moment of filter application, the optimal λ value found through a comprehensive study was λ = 85 and λ = 60 when the filter is applied before and after reconstruction, respectively. The best fit found for the relationship between σB and the corresponding λ values that allowed the highest filtered SDNR was the logarithmic adjustment. The difference between the λ values obtained by the first approach and the logarithmic adjustment ranges from 0.11% (filter applied before reconstruction) to 2.54% (filter applied after reconstruction). On the other hand, a decrease of 37.63% and 2.42% in TV and FWHM, respectively, and an increase of 24.39% in SDNR were obtained when the filter is applied to clinical data. This great minimization is present through a visual inspection of unfiltered and filtered clinical images, where areas with higher noise level become smoother while preserving edges and details of the structures. CONCLUSIONS: An optimized digital filter for TV minimization in DBT imaging has been presented. The reliability of a logarithmic relation found between σB and λ values was confirmed and can be used in future work. Both quantitative and qualitative analyses performed in a clinical DBT image confirmed the relevance of this approach in improving image quality in DBT imaging. The results obtained are very encouraging about increasing SDNR in a short time and preserving the principal variations in image, the structures' boundary.

Depuration kinetics of paralytic shellfish toxins in Mytilus galloprovincialis exposed to Gymnodinium catenatum: laboratory and field experiments.

The kinetics of paralytic shellfish toxins in Mytilus galloprovincialis, previously exposed to Gymnodinium catenatum, was studied under depuration laboratory conditions and over a declining bloom of the dinoflagellate in the field. The variation of the levels observed throughout the laboratory experiment was characterized by a fast depuration of B1, C1 + 2, dcSTX and dcGTX2 + 3, possibly due to the gut evacuation of unassimilated toxins or microalgae cells, or loss during digestive mechanisms. Subsequent enhancements were observed for all compounds with emphasis to dcSTX and dcGTX2 + 3, pointing to biotransformation of the assimilated toxins. Then levels decreased gradually. A first-order depuration kinetic model fitted well to the decrease of B1, C1 + 2 and dcGTX2 + 3 concentrations, but not for dcSTX. Mussels exposed to a declining bloom of Gymnodinium catenatum exhibited a loss of toxins following the same pattern. Despite the low abundance of this dinoflagellate, a similar kinetic model was applied to the field data. The depuration rate of dcGTX2 + 3 in the field experiment (0.153 ± 0.03 day(-1)) significantly exceeded the value calculated in the laboratory (0.053 ± 0.01 day(-1)), while smaller differences were obtained for B1 (0.071 ± 0.02 and 0.048 ± 0.01 day(-1)) and similar values for C1 + 2 (0.082 ± 0.03 and 0.080 ± 0.03 day(-1)). The slower depuration rate of dcGTX2 + 3 in the heavily contaminated mussels at the laboratory may be related to a more effective contribution of C1 + 2 biotransformation.