Self-perception of the acquisition of transferable competencies by the participants in a research congress for undergraduate students: A cross-sectional study.

Context: Several curricular initiatives have been developed to improve the acquisition of research competencies by Health Science students. Objectives: To know how students self-perceived of whether their participation in the XIV National Research Congress for Undergraduate Students of Health Sciences had helped them in the acquisition of 36 research-related transferable competencies (TCs) common to Health Science degrees. Methods: A survey design (Cronbach's alpha = 0.924), using a self-administered questionnaire, was conducted among undergraduate students who voluntarily participated in the Congress. Data analysis was performed using SPSS 25 and Statgraphics 19. Statistical significance was considered for P < 0.05. Results: Eighty-one students from 12 Health Science degree programs responded. Key findings are presented in a structured manner, using a Likert-5 scale. Twenty-five of the competencies surveyed obtained an average ≥ 4 highlighting: "Critically evaluate and know how to use sources of clinical and biomedical information to obtain, organize, interpret, and communicate scientific and health information"; "To be able to formulate hypotheses, collect and critically evaluate information for problem solving, following the scientific method", "Critical analysis and research" and "Communicate effectively and clearly, orally and in writing with other professionals". Significance was found in 15 competencies. The development of the competencies "Teamwork", "Critical reasoning" and "Analysis and synthesis abilities" was considered to be of greater "personal utility" by the respondents. Conclusion: Participation in this event contributed to the development of research-related TCs, critical analysis and information management and communication, especially in relation to learning the sources of clinical and biomedical information, to know, following the scientific method, how to formulate hypotheses that allow students to solve problems in their professional activity. The experience was significantly influenced by the respondents' year, the type of participation in the event and the gender of the students. Limitations and suggestions regarding future research are discussed to encourage further exploration of the topic.

Resistance to Degradation of Silk Fibroin Hydrogels Exposed to Neuroinflammatory Environments.

Central nervous system (CNS) diseases represent an extreme burden with significant social and economic costs. A common link in most brain pathologies is the appearance of inflammatory components that can jeopardize the stability of the implanted biomaterials and the effectiveness of therapies. Different silk fibroin scaffolds have been used in applications related to CNS disorders. Although some studies have analyzed the degradability of silk fibroin in non-cerebral tissues (almost exclusively upon non-inflammatory conditions), the stability of silk hydrogel scaffolds in the inflammatory nervous system has not been studied in depth. In this study, the stability of silk fibroin hydrogels exposed to different neuroinflammatory contexts has been explored using an in vitro microglial cell culture and two in vivo pathological models of cerebral stroke and Alzheimer's disease. This biomaterial was relatively stable and did not show signs of extensive degradation across time after implantation and during two weeks of in vivo analysis. This finding contrasted with the rapid degradation observed under the same in vivo conditions for other natural materials such as collagen. Our results support the suitability of silk fibroin hydrogels for intracerebral applications and highlight the potentiality of this vehicle for the release of molecules and cells for acute and chronic treatments in cerebral pathologies.

Multi-vendor robustness analysis of a commercial artificial intelligence system for breast cancer detection.

Purpose: Population-based screening programs for the early detection of breast cancer have significantly reduced mortality in women, but they are resource intensive in terms of time, cost, and workload and still have limitations mainly due to the use of 2D imaging techniques, which may cause overlapping of tissues, and interobserver variability. Artificial intelligence (AI) systems may be a valuable tool to assist radiologist when reading and classifying mammograms based on the malignancy of the detected lesions. However, there are several factors that can influence the outcome of a mammogram and thus also the detection capability of an AI system. The aim of our work is to analyze the robustness of the diagnostic ability of an AI system designed for breast cancer detection. Approach: Mammograms from a population-based screening program were scored with the AI system. The sensitivity and specificity by means of the area under the receiver operating characteristic (ROC) curve were obtained as a function of the mammography unit manufacturer, demographic characteristics, and several factors that may affect the image quality (age, breast thickness and density, compression applied, beam quality, and delivered dose). Results: The area under the curve (AUC) from the scoring ROC curve was 0.92 (95% confidence interval = 0.89 - 0.95). It showed no dependence with any of the parameters considered, as the differences in the AUC for different interval values were not statistically significant. Conclusion: The results suggest that the AI system analyzed in our work has a robust diagnostic capability, and that its accuracy is independent of the studied parameters.

Proposal of an algorithm to evaluate geometric distortion and artifact spreading in digital breast tomosynthesis.

BACKGROUND: According to the European Reference Organization for Quality Assurance Breast Screening and European Diagnostic Services, the spatial accuracy of reconstructed images and reconstruction artifacts must be evaluated in digital breast tomosynthesis (DBT) quality control procedures. PURPOSE: To propose a computational algorithm to evaluate the geometric distortion and artifact spreading (GDAS) in DBT images. MATERIAL AND METHODS: The proposed algorithm analyzed tomosynthesis images of a phantom that contains aluminum spheres (1 mm in diameter) arranged in a rectangular matrix spaced 5 cm apart that was inserted in 5-mm-thick polymethylmethacrylate (PMMA). RESULTS: The obtained results were compared with the values provided by the algorithm developed by the National Coordinating Center for the Physics of Mammography (NCCPM). In the comparison, the results depended on the dimensions of the region of interest (ROI). This dependence proves the benefit of the proposed algorithm because it allows the user to select the ROI. CONCLUSION: The computational algorithm proved to be useful for the evaluation of GDAS in DBT images, in the same way as the reference algorithm (NCCPM), as well as allowing the selection of the ROI dimensions that best suit the spreading of the artifact in the analyzed images.

Assessment of breast density in women from different regions of Brazil.

In many countries, there is an interest in determining the location of the women with the highest breast density. This investigation is important for optimize screening for breast cancer for women with dense breasts as other imaging modalities since 2D mammography is not very efficient on this type of breast. The objective of this study was to evaluate the variations in breast density in Brazilian women of different regions of Brazil. The mammographic images were taken from four regions of Brazil. The images, in the cranial caudal (CC) projection, were separated into intervals of compressed breast thickness (CBT) and patient age and were analysed by the software VolparaDensity, where volumetric breast density (VBD) calculations were performed. For each interval, null hypothesis tests for the mean difference between the VBD from the four regions of Brazil were performed. The paired tests indicated that there was a significant difference in the VBD of the women in the different regions of Brazil, with variations from 11.05% to 36.73%. Higher VBD was observed for women living in the Southeast region, followed by the Midwest, Northeast, and North regions. The Brazilian IBGE data show that the most urbanised region in Brazil is the Southeast, which coincides with the second highest rate of breast cancer in Brazil, according to the Brazilian National Cancer Institute (INCA). It is also known that breast cancer is strongly related to breast density; therefore, the results of this work support the data presented by federal agencies demonstrating that women living in the most urbanised region of Brazil (e.g., Southeast) present the highest breast density.

Definition of internal target volumes based on planar X-ray fluoroscopic images for lung and hepatic stereotactic body radiation therapy. Comparison to inhale/exhale CT technique.

PURPOSE: To compare tumor motion amplitudes measured with 2D fluoroscopic images (FI) and with an inhale/exhale CT (IECT) technique MATERIALS AND METHODS: Tumor motion of 52 patients (39 lung patients and 13 liver patients) was obtained with both FI and IECT. For FI, tumor detection and tracking was performed by means of a software developed by the authors. Motion amplitude and, thus, internal target volume (ITV), were defined to cover the positions where the tumor spends 95% of the time. The algorithm was validated against two different respiratory motion phantoms. Motion amplitude in IECT was defined as the difference in the position of the centroid of the gross tumor volume in the image sets of both treatments. RESULTS: Important differences exist when defining ITVs with FI and IECT. Overall, differences larger than 5 mm were obtained for 49%, 31%, and 9.6% of the patients in Superior-Inferior (SI), Anterior-Posterior (AP), and Lateral (LAT) directions, respectively. For tumor location, larger differences were found for tumors in the liver (73.6% SI, 27.3% AP, and 6.7% in LAT had differences larger than 5 mm), while tumors in the upper lobe benefitted less using FI (differences larger than 5 mm were only present in 27.6% (SI), 36.7% (AP), and 0% (LAT) of the patients). CONCLUSIONS: Use of FI with the linac built-in CBCT system is feasible for ITV definition. Large differences between motion amplitudes detected with FI and IECT methods were found. The method presented in this work based on FI could represent an improvement in ITV definition compared to the method based on IECT due to FI permits tumor motion acquisition in a more realistic situation than IECT.

On the use of Bayesian statistics in the application of adaptive setup protocols in radiotherapy.

PURPOSE: To propose adaptive setup protocols using Bayesian statistics that facilitate, based on a prediction of coverage probability, making a decision on which patients should follow daily imaging prior to treatment delivery. MATERIALS AND METHODS: The suitability of the treatment margins was assessed combining interfraction variability measurements of the first days of treatment with previous data gathered from our patient population. From this information, we decided if a patient needs an online imaging protocol to perform daily isocenter correction before each treatment fraction. We applied our method to five different datasets. Protocol parameters were selected from each dataset based on coverage probability, the expected imaging workload of the treatment unit, and the accuracy of patient classification. Time trends were assessed and included in the proposed protocols. To validate the accuracy of the protocols, they were applied to a validation dataset of prostate cancer patients. RESULTS: Adaptive setup protocols lead expected population coverage >97% in all datasets analyzed when time trends were considered. The reduction in imaging workload ranged from 40% in lung treatments to 28.5% in prostate treatments. Results of the protocol on the validation dataset were very similar to those previously predicted. CONCLUSIONS: The adaptive setup protocols based on Bayesian statistics presented in this study enable the optimization of imaging workload in the treatment unit ensuring that appropriate dose coverage remains unchanged.

Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study.

PURPOSE: To study the feasibility of automatically identifying normal digital mammography (DM) exams with artificial intelligence (AI) to reduce the breast cancer screening reading workload. METHODS AND MATERIALS: A total of 2652 DM exams (653 cancer) and interpretations by 101 radiologists were gathered from nine previously performed multi-reader multi-case receiver operating characteristic (MRMC ROC) studies. An AI system was used to obtain a score between 1 and 10 for each exam, representing the likelihood of cancer present. Using all AI scores between 1 and 9 as possible thresholds, the exams were divided into groups of low- and high likelihood of cancer present. It was assumed that, under the pre-selection scenario, only the high-likelihood group would be read by radiologists, while all low-likelihood exams would be reported as normal. The area under the reader-averaged ROC curve (AUC) was calculated for the original evaluations and for the pre-selection scenarios and compared using a non-inferiority hypothesis. RESULTS: Setting the low/high-likelihood threshold at an AI score of 5 (high likelihood > 5) results in a trade-off of approximately halving (- 47%) the workload to be read by radiologists while excluding 7% of true-positive exams. Using an AI score of 2 as threshold yields a workload reduction of 17% while only excluding 1% of true-positive exams. Pre-selection did not change the average AUC of radiologists (inferior 95% CI > - 0.05) for any threshold except at the extreme AI score of 9. CONCLUSION: It is possible to automatically pre-select exams using AI to significantly reduce the breast cancer screening reading workload. KEY POINTS: • There is potential to use artificial intelligence to automatically reduce the breast cancer screening reading workload by excluding exams with a low likelihood of cancer. • The exclusion of exams with the lowest likelihood of cancer in screening might not change radiologists' breast cancer detection performance. • When excluding exams with the lowest likelihood of cancer, the decrease in true-positive recalls would be balanced by a simultaneous reduction in false-positive recalls.

Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists.

BACKGROUND: Artificial intelligence (AI) systems performing at radiologist-like levels in the evaluation of digital mammography (DM) would improve breast cancer screening accuracy and efficiency. We aimed to compare the stand-alone performance of an AI system to that of radiologists in detecting breast cancer in DM. METHODS: Nine multi-reader, multi-case study datasets previously used for different research purposes in seven countries were collected. Each dataset consisted of DM exams acquired with systems from four different vendors, multiple radiologists' assessments per exam, and ground truth verified by histopathological analysis or follow-up, yielding a total of 2652 exams (653 malignant) and interpretations by 101 radiologists (28 296 independent interpretations). An AI system analyzed these exams yielding a level of suspicion of cancer present between 1 and 10. The detection performance between the radiologists and the AI system was compared using a noninferiority null hypothesis at a margin of 0.05. RESULTS: The performance of the AI system was statistically noninferior to that of the average of the 101 radiologists. The AI system had a 0.840 (95% confidence interval [CI] = 0.820 to 0.860) area under the ROC curve and the average of the radiologists was 0.814 (95% CI = 0.787 to 0.841) (difference 95% CI = -0.003 to 0.055). The AI system had an AUC higher than 61.4% of the radiologists. CONCLUSIONS: The evaluated AI system achieved a cancer detection accuracy comparable to an average breast radiologist in this retrospective setting. Although promising, the performance and impact of such a system in a screening setting needs further investigation.

Quality Controls in Digital Mammography protocol of the EFOMP Mammo Working group.

This article aims to present the protocol on Quality Controls in Digital Mammography published online in 2015 by the European Federation of Organisations for Medical Physics (EFOMP) which was developed by a Task Force under the Mammo Working Group. The main objective of this protocol was to define a minimum set of easily implemented quality control tests on digital mammography systems that can be used to assure the performance of a system within a set and acceptable range. Detailed step-by-step instructions have been provided, limiting as much as possible any misinterpretations or variations by the person performing. It is intended that these tests be implemented as part of the daily routine of medical physicists and system users throughout Europe in a harmonised way so allowing results to be compared. In this paper the main characteristics of the protocol are illustrated, including examples, together with a brief summary of the contents of each chapter. Finally, instructions for the download of the full protocol and of the related software tools are provided.

Diagnostic value of the stand-alone synthetic image in digital breast tomosynthesis examinations.

OBJECTIVE: To demonstrate the non-inferiority of synthetic image (SI) mammography versus full-field digital mammography (FFDM) in breast tomosynthesis (DBT) examinations. METHODS: An observational, retrospective, single-centre, multireader blinded study was performed, using 2384 images to directly compare SI and FFDM based on Breast Imaging Reporting and Data System (BIRADS) categorisation and visibility of radiological findings. Readers had no access to digital breast tomosynthesis slices. Multiple reader, multiple case (MRMC) receiver operating characteristic (ROC) methodology was used to compare the diagnostic performance of SI and FFDM images. The kappa statistic was used to estimate the inter-reader and intra-reader reliability. RESULTS: The area under the ROC curves (AUC) reveals the non-inferiority of SI versus FFDM based on BIRADS categorisation [difference between AUC (ΔAUC), -0.014] and lesion visibility (ΔAUC, -0.001) but the differences were not statistically significant (p=0.282 for BIRADS; p=0.961 for lesion visibility). On average, 77.4% of malignant lesions were detected with SI versus 76.5% with FFDM. Sensitivity and specificity of SI are superior to FFDM for malignant lesions scored as BIRADS 5 and breasts categorised as BIRADS 1. CONCLUSIONS: SI is not inferior to FFDM when DBT slices are not available during image reading. SI can replace FFDM, reducing the dose by 45%. KEY POINTS: • Stand-alone SI demonstrated performance not inferior for lesion visibility as compared to FFDM. • Stand-alone SI demonstrated performance not inferior for lesion BIRADS categorisation as compared to FFDM. • Synthetic images provide important dose savings in breast tomosynthesis examinations.

Automated Breast Density Computation in Digital Mammography and Digital Breast Tomosynthesis: Influence on Mean Glandular Dose and BIRADS Density Categorization.

RATIONALE AND OBJECTIVES: The study aimed to compare the breast density estimates from two algorithms on full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) and to analyze the clinical implications. MATERIALS AND METHODS: We selected 561 FFDM and DBT examinations from patients without breast pathologies. Two versions of a commercial software (Quantra 2D and Quantra 3D) calculated the volumetric breast density automatically in FFDM and DBT, respectively. Other parameters such as area breast density and total breast volume were evaluated. We compared the results from both algorithms using the Mann-Whitney U non-parametric test and the Spearman's rank coefficient for data correlation analysis. Mean glandular dose (MGD) was calculated following the methodology proposed by Dance et al. RESULTS: Measurements with both algorithms are well correlated (r ≥ 0.77). However, there are statistically significant differences between the medians (P < 0.05) of most parameters. The volumetric and area breast density median values from FFDM are, respectively, 8% and 77% higher than DBT estimations. Both algorithms classify 35% and 55% of breasts into BIRADS (Breast Imaging-Reporting and Data System) b and c categories, respectively. There are no significant differences between the MGD calculated using the breast density from each algorithm. DBT delivers higher MGD than FFDM, with a lower difference (5%) for breasts in the BIRADS d category. MGD is, on average, 6% higher than values obtained with the breast glandularity proposed by Dance et al. CONCLUSIONS: Breast density measurements from both algorithms lead to equivalent BIRADS classification and MGD values, hence showing no difference in clinical outcomes. The median MGD values of FFDM and DBT examinations are similar for dense breasts (BIRADS d category).

Tomosíntesis de mama: mamografía sintetizada versus mamografía digital. Impacto en la dosis / Breast tomosynthesis: synthesized versus digital mammography. Impact on dose

Objetivo. Comparar la detectabilidad y la visibilidad de las lesiones en la imagen sintetizada y en la mamografía digital. Estimar el ahorro de dosis que supondría utilizar la imagen sintetizada en los exámenes de tomosíntesis. Pacientes y métodos. Siete observadores evaluaron la detectabilidad y la visibilidad de objetos similares a microcalcificaciones sobre las imágenes sintetizadas y mamografías digitales de un maniquí. Cuatro observadores evaluaron retrospectivamente las imágenes de 20 pacientes con lesiones histológicamente corroboradas. Se estimaron retrospectivamente los valores de dosis glandular promedio en una muestra de 50 pacientes. Resultados. La detectabilidad y la visibilidad de las microcalcificaciones sobre el fondo estructural de las imágenes del maniquí fue del 50 y del 100%, respectivamente, superior en la imagen sintetizada. La visibilidad de los hallazgos en las imágenes clínicas fue similar en ambos tipos de imagen, exceptuando las distorsiones, mejor visualizadas en la imagen sintetizada (p = 10−5). Un 16% de hallazgos malignos no se detectaron en las imágenes de mamografía digital y un 7% en las imágenes sintetizadas. La dosis glandular promedio por mama para un examen de 2 proyecciones (mama promedio) fue de 3,2 mGy (mamografía digital), 4,1 mGy (tomosíntesis) y 7,3 mGy (mamografía digital + tomosíntesis). Conclusiones. La detectabilidad y la visibilidad de la imagen sintetizada es equiparable a la mamografía digital. La sustitución de la mamografía digital por la imagen sintetizada supondría un ahorro de dosis del 44% (mama promedio) (AU)

Objective. To compare the detectability and visibility of lesions in synthesized and digital mammography. To estimate the dose saving due to the use of synthesized images in tomosynthesis examinations. Patients and methods. Seven observers scored the detectability and visibility of objects simulating microcalcifications in digital and synthesized images of a phantom. Four observers retrospectively assessed the images from a sample of 20 patients with histologically confirmed lesions. Mean glandular dose values were retrospectively estimated in a sample of 50 patients. Results. The detectability and visibility of microcalcifications in the structural background of phantom images were 50% and 100% higher, respectively, for synthesized images. The visibility of the findings in the clinical images was similar for both types of images, except for distortions, which were better visualized on synthesized images (p = 10−5). Sixteen percent of malignant findings were not detected in digital images and 7% were undetected in synthesized images. The mean glandular dose per breast for a two-view examination (average breast) was 3.2 mGy (digital mammography), 4.1 mGy (tomosynthesis) and 7.3 mGy (digital mammography + tomosynthesis). Conclusions. The detectability and visibility of synthesized images was comparable to that of digital mammography. Replacing digital mammography with synthesized imaging would result in a dose saving of 44% (average breast) (AU)

A phantom using titanium and Landolt rings for image quality evaluation in mammography.

A phantom for image quality evaluation of digital mammography systems is presented and compared to the most widely used phantoms in Europe and the US. The phantom contains objects for subjective detection of Landolt rings (four-alternative, forced-choice task) and for objective calculation of signal-difference-to-noise ratios (SDNR), both in a titanium background within a 12-step wedge. Evaluating phantom images corresponding to exposures between 15 and 160 mAs (average glandular dose between 0.2 and 2 mGy), the resulting scores were compared to the scores obtained following the European EPQC and American College of Radiology (ACR) protocols. Scores of the Landolt test equal to 19 and 8.5 and SDNR equal to 20 and 11 were found to be equivalent to the acceptable limiting values suggested by EPQC and ACR. In addition, the Landolt and SDNR tests were shown to take into account the anatomical variations in thickness and tissue density within the breast. The simplified evaluation method presented was shown to be a sensitive, efficient and reliable alternative for image quality evaluation of mammography systems.

Use of the cross-correlation component of the multiscale structural similarity metric (R* metric) for the evaluation of medical images.

PURPOSE: The aim of the present work is to analyze the potential of the cross-correlation component of the multiscale structural similarity metric (R*) to predict human performance in detail detection tasks closely related with diagnostic x-ray images. To check the effectiveness of R*, the authors have initially applied this metric to a contrast detail detection task. METHODS: Threshold contrast visibility using the R* metric was determined for two sets of images of a contrast-detail phantom (CDMAM). Results from R* and human observers were compared as far as the contrast threshold was concerned. A comparison between the R* metric and two algorithms currently used to evaluate CDMAM images was also performed. RESULTS: Similar trends for the CDMAM detection task of human observers and R* were found in this study. Threshold contrast visibility values using R* are statistically indistinguishable from those obtained by human observers (F-test statistics: p > 0.05). CONCLUSIONS: These results using R* show that it could be used to mimic human observers for certain tasks, such as the determination of contrast detail curves in the presence of uniform random noise backgrounds. The R* metric could also outperform other metrics and algorithms currently used to evaluate CDMAM images and can automate this evaluation task.

Patient dose in digital mammography.

In the present investigation, we analyze the dose of 5034 patients (20,137 images) who underwent mammographic examinations with a full-field digital mammography system. Also, we evaluate the system calibration by analyzing the exposure factors as a function of breast thickness. The information relevant to this study has been extracted from the image DICOM header and stored in a database during a 3-year period (March 2001-October 2003). Patient data included age, breast thickness, kVp, mAs, target/filter combination, and nominal dose values. Entrance surface air kerma (ESAK) without backscatter was calculated from the tube output as measured for each voltage used under clinical conditions and from the tube loading (mAs) included in the DICOM header. Mean values for the patient age and compressed breast thickness were 56 years (SD: 11) and 52 mm (SD: 13), respectively. The majority of the images was acquired using the STD (for standard) automatic mode (98%). The most frequent target/filter combination automatically selected for breast smaller than 35 mm was Mo/Mo (75%); for intermediate thicknesses between 35 and 65 mm, the combinations were Mo/Rh (54%) and Rh/Rh (38.5%); Rh/Rh was the combination selected for 91% of the cases for breasts thicker than 65 mm. A wide kVp range was observed for each target/filter combination. The most frequent values were 28 kVp for Mo/Mo, 29 kVp for Mo/Rh, and 29 and 30 kV for Rh/Rh. Exposure times ranged from 0.2 to 4.2 s with a mean value of 1.1 s. Average glandular doses (AGD) per exposure were calculated by multiplying the ESAK values by the conversion factors tabulated by Dance for women in the age groups 50 to 64 and 40 to 49. This approach is based on the dependence of breast glandularity on breast thickness and age. The total mean average glandular dose (AGD(T)) was calculated by summing the values associated with the pre-exposure and with the main exposure. Mean AGD(T) per exposure was 1.88 mGy (CI 0.01) and the mean AGD(T) per examination was 3.8 mGy, with 4 images per examination on average. The mean dose for cranio-caudal view (CC) images was 1.8 mGy, which is lower than that for medio-lateral oblique (MLO) view because the thickness for CC images was on average 10% lower than that for MLO images. Mean AGD(T) for the oldest group of women (1.90) was 3% higher than the AGD(T) for the younger group (1.85) due to the larger compressed breast thickness of women in the older group (10% on average). Differences between the corresponding AGD(T) values of each age group were lowest for breast thicknesses in the range 40-60 mm, being slightly higher for the women in the older group.