Abstract: Importance Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results Overall, 144231 screening mammograms from 85580 US women (952 cancer positive <= 12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166578 examinations from 68008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation. Question How do deep learning algorithms perform compared with radiologists in screening mammography interpretation? Findings In this diagnostic accuracy study using 144231 screening mammograms from 85580 women from the United States and 166578 screening mammograms from 68008 women from Sweden, no single artificial intelligence algorithm outperformed US community radiologist benchmarks; including clinical data and prior mammograms did not improve artificial intelligence performance. However, combining best-performing artificial intelligence algorithms with single-radiologist assessment demonstrated increased specificity. Meaning Integrating artificial intelligence to mammography interpretation in single-radiologist settings could yield significant performance improvements, with the potential to reduce health care system expenditures and address resource scarcity experienced in population-based screening programs. This diagnostic accuracy study evaluates whether artificial intelligence can overcome human mammography interpretation limits with a rigorous, unbiased evaluation of machine learning algorithms.

Abstract: The gamma rays and conversion electrons emitted in the beta decay of (78)Sr to levels in (78)Rb have been studied using Ge detectors and a mini-orange spectrometer. A reliable level scheme based on the results of these experiments has been established. The properties of the levels in (78)Rb have been compared with calculations based on deformed Hartree-Fock with Skyrme interactions and pairing correlations in the BCS approximation. This has allowed an interpretation of the nature of the observed sets of levels in the odd-odd nucleus (78)Rb.

Abstract: Background: The 14N(n, p) 14C reaction is of interest in neutron capture therapy, where nitrogen-related dose is the main component due to low-energy neutrons, and in astrophysics, where 14N acts as a neutron poison in the s process. Several discrepancies remain between the existing data obtained in partial energy ranges: thermal energy, keV region, and resonance region. Purpose: We aim to measure the 14N(n, p) 14C cross section from thermal to the resonance region in a single measurement for the first time, including characterization of the first resonances, and provide calculations of Maxwellian averaged cross sections (MACS). Method: We apply the time-of-flight technique at Experimental Area 2 (EAR-2) of the neutron time-of-flight (n_TOF) facility at CERN. 10B(n, & alpha;) 7Li and 235U(n, f ) reactions are used as references. Two detection systems are run simultaneously, one on beam and another off beam. Resonances are described with the R-matrix code SAMMY. Results: The cross section was measured from subthermal energy to 800 keV, resolving the first two resonances (at 492.7 and 644 keV). A thermal cross section was obtained (1.809 & PLUSMN; 0.045 b) that is lower than the two most recent measurements by slightly more than one standard deviation, but in line with the ENDF/B-VIII.0 and JEFF-3.3 evaluations. A 1/v energy dependence of the cross section was confirmed up to tens of keV neutron energy. The low energy tail of the first resonance at 492.7 keV is lower than suggested by evaluated values, while the overall resonance strength agrees with evaluations. Conclusions: Our measurement has allowed determination of the 14N(n, p) cross section over a wide energy range for the first time. We have obtained cross sections with high accuracy (2.5%) from subthermal energy to 800 keV and used these data to calculate the MACS for kT = 5 to kT = 100 keV.

Abstract: The neutron-induced fission cross section of Th-230 has been measured at the neutron time-of-flight facility n_TOF located at CERN. The experiment was performed at the experimental area EAR-1 with a neutron flight path of 185 m, using Micromegas detectors for the detection of the fission fragments. The Th-230(n, f ) cross section was determined relative to the U-235(n, f ) one, covering the energy range from the fission threshold up to 400 MeV. The results from the present work are compared with existing cross-section datasets and the observed discrepancies are discussed and analyzed. Finally, using the code EMPIRE 3.2.3 a theoretical study, based on the statistical model, was performed leading to a satisfactory reproduction of the experimental results with the proper tuning of the respective parameters, while for incident neutron energy beyond 200 MeV the fission of( 230)Th was described by Monte Carlo simulations.

Abstract: This article presents a few selected developments and future ideas related to the measurement of (n, gamma ) data of astrophysical interest at CERN n_TOF. The MC-aided analysis methodology for the use of low-efficiency radiation detectors in time-of-flight neutron-capture measurements is discussed, with particular emphasis on the systematic accuracy. Several recent instrumental advances are also presented, such as the development of total-energy detectors with gamma- ray imaging capability for background suppression, and the development of an array of small-volume organic scintilla tors aimed at exploiting the high instantaneous neutron-flux of EAR2. Finally, astrophysics prospects related to the intermediate i neutron-capture process of nucleosynthesis are discussed in the context of the new NEAR activation area.