Public parks in the city of Rio de Janeiro, southeast Brazil, and the risk of parasitosis transmission by freshwater gastropods.

Urban parks are not only important for the wellbeing of the human population, but are also widely considered to be potentially important sites for the conservation of biodiversity. However, they may offer risk parasitic infections, such as schistosomiasis and fascioliasis, which are both transmitted by freshwater snails. The present study investigated the occurrence of freshwater gastropods in urban parks of the Brazilian city of Rio de Janeiro, and their possible infection by helminths of medical-veterinary importance. Gastropods were collected from six parks (2021 - 2022) and examined for the presence of larval helminths. In all, 12 gastropod species from different families were collected: Ampullariidae, Assimineidae, Burnupidae, Lymnaeidae, Physidae, Planorbidae, Succineidae, and Thiaridae. The parasitological examination revealed cercaria of three types in five snail species, with the Pleurolophocerca cercariae type in Melanoides tuberculata (the most abundant species), Echinostoma cercariae in Physella acuta and Pomacea maculata, and Virgulate cercariae, in Pomacea sp. and Pomacea maculata. None of the Biomphalaria tenagophila and Pseudosuccinea columella (the most frequent species) specimens were parasitized by Schistosoma mansoni or Fasciola hepatica, respectively. Even so, some parks may represent a considerable potential risk for transmission of both Schistosoma mansoni and Fasciola hepatica, given the presence of these gastropod vectors and the frequent contact of visitors with the waterbodies.

A deep learning approach to estimate x-ray scatter in digital breast tomosynthesis: From phantom models to clinical applications.

BACKGROUND: Digital breast tomosynthesis (DBT) has gained popularity as breast imaging modality due to its pseudo-3D reconstruction and improved accuracy compared to digital mammography. However, DBT faces challenges in image quality and quantitative accuracy due to scatter radiation. Recent advancements in deep learning (DL) have shown promise in using fast convolutional neural networks for scatter correction, achieving comparable results to Monte Carlo (MC) simulations. PURPOSE: To predict the scatter radiation signal in DBT projections within clinically-acceptable times and using only clinically-available data, such as compressed breast thickness and acquisition angle. METHODS: MC simulations to obtain scatter estimates were generated from two types of digital breast phantoms. One set consisted of 600 realistically-shaped homogeneous breast phantoms for initial DL training. The other set was composed of 80 anthropomorphic phantoms, containing realistic internal tissue texture, aimed at fine tuning the DL model for clinical applications. The MC simulations generated scatter and primary maps per projection angle for a wide-angle DBT system. Both datasets were used to train (using 7680 projections from homogeneous phantoms), validate (using 960 and 192 projections from the homogeneous and anthropomorphic phantoms, respectively), and test (using 960 and 48 projections from the homogeneous and anthropomorphic phantoms, respectively) the DL model. The DL output was compared to the corresponding MC ground truth using both quantitative and qualitative metrics, such as mean relative and mean absolute relative differences (MRD and MARD), and to previously-published scatter-to-primary (SPR) ratios for similar breast phantoms. The scatter corrected DBT reconstructions were evaluated by analyzing the obtained linear attenuation values and by visual assessment of corrected projections in a clinical dataset. The time required for training and prediction per projection, as well as the time it takes to produce scatter-corrected projection images, were also tracked. RESULTS: The quantitative comparison between DL scatter predictions and MC simulations showed a median MRD of 0.05% (interquartile range (IQR), -0.04% to 0.13%) and a median MARD of 1.32% (IQR, 0.98% to 1.85%) for homogeneous phantom projections and a median MRD of -0.21% (IQR, -0.35% to -0.07%) and a median MARD of 1.43% (IQR, 1.32% to 1.66%) for the anthropomorphic phantoms. The SPRs for different breast thicknesses and at different projection angles were within ± 15% of the previously-published ranges. The visual assessment showed good prediction capabilities of the DL model with a close match between MC and DL scatter estimates, as well as between DL-based scatter corrected and anti-scatter grid corrected cases. The scatter correction improved the accuracy of the reconstructed linear attenuation of adipose tissue, reducing the error from -16% and -11% to -2.3% and 4.4% for an anthropomorphic digital phantom and clinical case with similar breast thickness, respectively. The DL model training took 40 min and prediction of a single projection took less than 0.01 s. Generating scatter corrected images took 0.03 s per projection for clinical exams and 0.16 s for one entire projection set. CONCLUSIONS: This DL-based method for estimating the scatter signal in DBT projections is fast and accurate, paving the way for future quantitative applications.

Generative compressed breast shape model for digital mammography and digital breast tomosynthesis.

BACKGROUND: Modelling of the 3D breast shape under compression is of interest when optimizing image processing and reconstruction algorithms for mammography and digital breast tomosynthesis (DBT). Since these imaging techniques require the mechanical compression of the breast to obtain appropriate image quality, many such algorithms make use of breast-like phantoms. However, if phantoms do not have a realistic breast shape, this can impact the validity of such algorithms. PURPOSE: To develop a point distribution model of the breast shape obtained through principal component analysis (PCA) of structured light (SL) scans from patient compressed breasts. METHODS: SL scans were acquired at our institution during routine craniocaudal-view DBT imaging of 236 patients, creating a dataset containing DBT and SL scans with matching information. Thereafter, the SL scans were cleaned, merged, simplified, and set to a regular grid across all cases. A comparison between the initial SL scans after cleaning and the gridded SL scans was performed to determine the absolute difference between them. The scans with points in a regular grid were then used for PCA. Additionally, the correspondence between SL scans and DBT scans was assessed by comparing features such as the chest-to-nipple distance (CND), the projected breast area (PBA) and the length along the chest-wall (LCW). These features were compared using a paired t-test or the Wilcoxon signed rank sum test. Thereafter, the PCA shape prediction and SL scans were evaluated by calculating the mean absolute error to determine whether the model had adequately captured the information in the dataset. The coefficients obtained from the PCA could then parameterize a given breast shape as an offset from the sample means. We also explored correlations of the PCA breast shape model parameters with certain patient characteristics: age, glandular volume, glandular density by mass, total breast volume, compressed breast thickness, compression force, nipple location, and centre of the chest-wall. RESULTS: The median value across cases for the 90th and 99th percentiles of the interpolation error between the initial SL scans after cleaning and the gridded SL scans was 0.50 and 1.16 mm, respectively. The comparison between SL and DBT scans resulted in small, but statistically significant, mean differences of 1.6 mm, 1.6 mm, and 2.2 cm2 for the LCW, CND, and PBA, respectively. The final model achieved a median mean absolute error of 0.68 mm compared to the scanned breast shapes and a perfect correlation between the first PCA coefficient and the patient breast compressed thickness, making it possible to use it to generate new model-based breast shapes with a specific breast thickness. CONCLUSION: There is a good agreement between the breast shape coverage obtained with SL scans used to construct our model and the DBT projection images, and we could therefore create a generative model based on this data that is available for download on Github.

Impact of Artificial Intelligence Decision Support Using Deep Learning on Breast Cancer Screening Interpretation with Single-View Wide-Angle Digital Breast Tomosynthesis.

Background The high volume of data in digital breast tomosynthesis (DBT) and the lack of agreement on how to best implement it in screening programs makes its use challenging. Purpose To compare radiologist performance when reading single-view wide-angle DBT images with and without an artificial intelligence (AI) system for decision and navigation support. Materials and Methods A retrospective observer study was performed with bilateral mediolateral oblique examinations and corresponding synthetic two-dimensional images acquired between June 2016 and February 2018 with a wide-angle DBT system. Fourteen breast screening radiologists interpreted 190 DBT examinations (90 normal, 26 with benign findings, and 74 with malignant findings), with the reference standard being verified by using histopathologic analysis or at least 1 year of follow-up. Reading was performed in two sessions, separated by at least 4 weeks, with a random mix of examinations being read with and without AI decision and navigation support. Forced Breast Imaging Reporting and Data System (categories 1-5) and level of suspicion (1-100) scores were given per breast by each reader. The area under the receiver operating characteristic curve (AUC) and the sensitivity and specificity were compared between conditions by using the public-domain iMRMC software. The average reading times were compared by using the Wilcoxon signed rank test. Results The 190 women had a median age of 54 years (range, 48-63 years). The examination-based reader-averaged AUC was higher when interpreting results with AI support than when reading unaided (0.88 [95% CI: 0.84, 0.92] vs 0.85 [95% CI: 0.80, 0.89], respectively; P = .01). The average sensitivity increased with AI support (64 of 74, 86% [95% CI: 80%, 92%] vs 60 of 74, 81% [95% CI: 74%, 88%]; P = .006), whereas no differences in the specificity (85 of 116, 73.3% [95% CI: 65%, 81%] vs 83 of 116, 71.6% [95% CI: 65%, 78%]; P = .48) or reading time (48 seconds vs 45 seconds; P = .35) were detected. Conclusion Using a single-view digital breast tomosynthesis (DBT) and artificial intelligence setup could allow for a more effective screening program with higher performance, especially in terms of an increase in cancers detected, than using single-view DBT alone. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Chan and Helvie in this issue.

Evaluation of irregular breathing effects on internal target volume definition for lung cancer radiotherapy.

PURPOSE: Irregular breathing may compromise the treated volume for free-breathing lung cancer patients during radiotherapy. We try to find a measure based on a breathing amplitude surrogate that can be used to select the patients who need further investigation of tumor motion to ensure that the internal target volume (ITV) provides reliant coverage of the tumor. MATERIAL AND METHODS: Fourteen patients were scanned with four-dimensional computed tomography (4DCT) during free-breathing. The breathing motion was detected by a pneumatic bellows device used as a breathing amplitude surrogate. In addition to the 4DCT, a breath-hold (BH) scan and three cine CT imaging sessions were acquired. The cine images were taken at randomized intervals at a rate of 12 per minute for 8 minutes to allow tumor motion determination during a typical hypo-fractionated treatment scenario. A clinical target volume (CTV) was segmented in the BH CT and propagated over all cine images and 4DCT bins. The center-of-volume of the translated CTV (CTVCOV ) in the ten 4DCT bins were interconnected to define the 4DCT determined tumor trajectory (4DCT-TT). The volume of CTV inside ITV for all cine CTs was calculated and reported at the 10th percentile (VCTV10% ). The deviations between CTVCOV in the cine CTs and the 4DCT-TT were calculated and reported at its 90th percentile (d90% ). The standard deviation of the bellows amplitude peaks (SDP) and the ratio between large and normal inspirations, κrel , were tested as surrogates for VCTV10% and d90% . RESULTS: The values of d90% ranged from 0.6 to 5.2 mm with a mean of 2.2 mm. The values of VCTV10% ranged from 59-93% with a mean of 78 %. The SDP had a moderate correlation (r = 0.87) to d90% . Less correlation was seen between SDP and VCTV10% (r = 0.77), κrel and d90% (r = 0.75) and finally κrel and VCTV10% (r = 0.75). CONCLUSIONS: The ITV coverage had a large variation for some patients. SDP seems to be a feasible surrogate measure to select patients that needs further tumor motion determination.