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

The GMOX science case: resolving galaxies through cosmic time

We present the key scientific questions that can be addressed by GMOX, a Multi-Object Spectrograph selected for feasibility study as a 4th generation instrument for the Gemini telescopes. Using commercial digital micro-mirror devices (DMDs) as slit selection mechanisms, GMOX can observe hundreds of sources at R∼5000 between the U and K band simultaneously. Exploiting the narrow PSF delivered by the Gemini South GeMS MCAO module, GMOX can synthesize slits as small as 40mas reaching extremely faint magnitude limits, and thus enabling a plethora of applications and innovative science. Our main scientific driver in developing GMOX has been Resolving galaxies through cosmic time: GMOX 40mas slit (at GeMS) corresponds to 300 pc at z ∼ 1:5, where the angular diameter distance reaches its maximum, and therefore to even smaller linear scales at any other redshift. This means that GMOX can take spectra of regions smaller than 300 pc in the whole observable Universe, allowing to probe the growth and evolution of galaxies with unprecedented detail. GMOXs multi-object capability and high angular resolution enable efficient studies of crowded fields, such as globular clusters, the Milky Way bulge, the Magellanic Clouds, Local Group galaxies and galaxy clusters. The wide-band simultaneous coverage and the very fast slit configuration mechanisms also make GMOX ideal for follow-up of LSST transients

Radiation Dose of Contrast-Enhanced Mammography: A Two-Center Prospective Comparison

The radiation dose associated with contrast-enhanced mammography (CEM) has been investigated only by single-center studies. In this retrospective study, we aimed to compare the radiation dose between two centers performing CEM within two prospective studies, using the same type of equipment. The CEM mean glandular dose (MGD) was computed for low energy (LE) and high energy (HE) images and their sum was calculated for each view. MGD and related parameters (entrance dose, breast thickness, compression, and density) were compared between the two centers using the Mann–Whitney test. Finally, per-patient MGD was calculated by pooling the two datasets and determining the contribution of LE and HE images. A total of 348 CEM examinations were analyzed (228 from Center 1 and 120 from Center 2). The median total MGD per view was 2.33 mGy (interquartile range 2.19–2.51 mGy) at Center 1 and 2.46 mGy (interquartile range 2.32–2.70 mGy) at Center 2, with a 0.15 mGy median difference (p < 0.001) equal to 6.2%. LE-images contributed between 64% and 77% to the total patient dose in CEM, with the remaining 23–36% being associated with HE images. The mean radiation dose for a two-view bilateral CEM exam was 4.90 mGy, about 30% higher than for digital mammography

Radiation dose with digital breast tomosynthesis compared to digital mammography: per-view analysis.

The objective of this study was to compare radiation dose delivered by digital mammography (FFDM) and breast tomosynthesis (DBT) for a single view. 4,780 FFDM and 4,798 DBT images from 1,208 women enrolled in a screening trial were used to ground dose comparison. Raw images were processed by an automatic software to determine volumetric breast density (VBD) and were used together with exposure data to compute the mean glandular dose (MGD) according to Dance’s model. DBT and FFDM were compared in terms of operation of the automatic exposure control (AEC) and MGD level

Phantom-based analysis of variations in automatic exposure control across three mammography systems: implications for radiation dose and image quality in mammography, DBT, and CEM

Abstract Background Automatic exposure control (AEC) plays a crucial role in mammography by determining the exposure conditions needed to achieve specific image quality based on the absorption characteristics of compressed breasts. This study aimed to characterize the behavior of AEC for digital mammography (DM), digital breast tomosynthesis (DBT), and low-energy (LE) and high-energy (HE) acquisitions used in contrast-enhanced mammography (CEM) for three mammography systems from two manufacturers. Methods Using phantoms simulating various breast thicknesses, 363 studies were acquired using all available AEC modes 165 DM, 132 DBT, and 66 LE-CEM and HE-CEM. AEC behaviors were compared across systems and modalities to assess the impact of different technical components and manufacturers’ strategies on the resulting mean glandular doses (MGDs) and image quality metrics such as contrast-to-noise ratio (CNR). Results For all systems and modalities, AEC increased MGD for increasing phantom thicknesses and decreased CNR. The median MGD values (interquartile ranges) were 1.135 mGy (0.772–1.668) for DM, 1.257 mGy (0.971–1.863) for DBT, 1.280 mGy (0.937–1.878) for LE-CEM, and 0.630 mGy (0.397–0.713) for HE-CEM. Medians CNRs were 14.2 (7.8–20.2) for DM, 4.91 (2.58–7.20) for a single projection in DBT, 11.9 (8.0–18.2) for LE-CEM, and 5.2 (3.6–9.2) for HE-CEM. AECs showed high repeatability, with variations lower than 5% for all modes in DM, DBT, and CEM. Conclusions The study revealed substantial differences in AEC behavior between systems, modalities, and AEC modes, influenced by technical components and manufacturers’ strategies, with potential implications in radiation dose and image quality in clinical settings. Relevance statement The study emphasized the central role of automatic exposure control in DM, DBT, and CEM acquisitions and the great variability in dose and image quality among manufacturers and between modalities. Caution is needed when generalizing conclusions about differences across mammography modalities. Key points • AEC plays a crucial role in DM, DBT, and CEM. • AEC determines the “optimal” exposure conditions needed to achieve specific image quality. • The study revealed substantial differences in AEC behavior, influenced by differences in technical components and strategies. Graphical Abstrac

Quantitative Breast Density in Contrast-Enhanced Mammography

Contrast-enhanced mammography (CEM) demonstrates a potential role in personalized screening models, in particular for women at increased risk and women with dense breasts. In this study, volumetric breast density (VBD) measured in CEM images was compared with VBD obtained from digital mammography (DM) or tomosynthesis (DBT) images. A total of 150 women who underwent CEM between March 2019 and December 2020, having at least a DM/DBT study performed before/after CEM, were included. Low-energy CEM (LE-CEM) and DM/DBT images were processed with automatic software to obtain the VBD. VBDs from the paired datasets were compared by Wilcoxon tests. A multivariate regression model was applied to analyze the relationship between VBD differences and multiple independent variables certainly or potentially affecting VBD. Median VBD was comparable for LE-CEM and DM/DBT (12.73% vs. 12.39%), not evidencing any statistically significant difference (p = 0.5855). VBD differences between LE-CEM and DM were associated with significant differences of glandular volume, breast thickness, compression force and pressure, contact area, and nipple-to-posterior-edge distance, i.e., variables reflecting differences in breast positioning (coefficient of determination 0.6023; multiple correlation coefficient 0.7761). Volumetric breast density was obtained from low-energy contrast-enhanced spectral mammography and was not significantly different from volumetric breast density measured from standard mammograms

Optimal follow-up intervals in active surveillance of renal masses in patients with von Hippel-Lindau disease.

Objectives: To estimate an optimal follow-up (FU) interval for von Hippel-Lindau (VHL) patients with renal masses (RMs) by determining tumour growth rates from growth curves. Methods: Thirty lesions (47.6 %) were classified as solid tumours (STs) and 33 (52.4 %) as complex cysts (CCs). Variations in lesion volume over time were analyzed. For 53 lesions, we calculated the growth rate during the period when the volume of the lesion changed most rapidly, and called this the fast growth rate (FGR). Results: The STs initially grew fast, followed by a period of slower growth. The CCs varied in volume over time, associated with variable amounts of their fluid component. The FGR correlated better with the latest volume for STs (r = 0.905) than for CCs (r = 0.780). An optimal FU interval between 3 and 12 months was derived by combining the FGR calculated from the curve with the latest volume measured. Conclusions: Analyzing growth curves and related kinetic parameters for RMs in VHL patients could be useful with a view to optimizing the subsequent FU interval and improving the active surveillance program

Growth curves of renal masses from patients with von Hippel-Lindau disease

Purpose: To analyse the progression curves of renal masses (RMs) in patients with von Hippel-Lindau disease (VHL), and propose some changes in their management. Methods and Materials: This retrospective study included 26 VHL patients who underwent periodical magnetic resonance imaging (MRI) examinations as follow-up for renal masses. Thirty-one of total 60 RMs (51.7%) were classified as solid tumours (STs), and remaining 29 of 60 (48.3%) as complex cysts (CCs). For each lesion, the volume was estimated at each MRI occurrence using the ellipsoid approximation, and individual growth curves were traced. Variations in volume over time were analysed, and the mean growth rates (MGRs) calculated. MGRs were used to compare STs and CCs. Results: In cases for which several imaging occurrences were available, the analysis of growth curves showed that solid tumours had an initial fast growth, followed by slower growth, according to the Gompertzian model. Otherwise, complex cysts showed some fluctuating progression curves, with spontaneous regressions in volume. Medians of the MGRs were 1.28 cm3/y for STs and 0.37 cm3/y for CCs, respectively, leading to statistically significant difference (P=0.0023), and indicating a growing process substantially slower for CCs than for STs. Conclusion: This study showed that short-term MRI follow-up and multiple lesion volume measurements in VHL patients, would allow a more accurate estimation of the tumour growth curve, providing useful information to possibly postpone the lesion treatment. Less aggressive approach seems to be possible for complex cysts versus solid tumours

Smart sensor for detection of derailment on freight trains

In this paper a sensor node able to monitor the growth of a crack in a wheelset, by means of axle box acceleration measurements, is described and the first results of experimental field tests are presented. The sensor node is equipped with a wireless transmission system and a bimorph piezoelectric energy harvester. Moreover, the node is inserted in a specific suspension system, which allows the measurement of low frequency accelerations (around wheel revolution and its first multiples) and amplifies the energy recovered by the harvester. The first tests on a freight convoy along a traditional Italian railway line show that the designed sensor node is able to acquire, analyse and transmit data with a frequency which depends on the power needed by the operation cycle