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  1. Dench E, Bond-Smith D, Darcey E, Lee G, Aung YK, Chan A, et al.
    BMJ Open, 2019 Dec 31;9(12):e031041.
    PMID: 31892647 DOI: 10.1136/bmjopen-2019-031041
    INTRODUCTION: For women of the same age and body mass index, increased mammographic density is one of the strongest predictors of breast cancer risk. There are multiple methods of measuring mammographic density and other features in a mammogram that could potentially be used in a screening setting to identify and target women at high risk of developing breast cancer. However, it is unclear which measurement method provides the strongest predictor of breast cancer risk.

    METHODS AND ANALYSIS: The measurement challenge has been established as an international resource to offer a common set of anonymised mammogram images for measurement and analysis. To date, full field digital mammogram images and core data from 1650 cases and 1929 controls from five countries have been collated. The measurement challenge is an ongoing collaboration and we are continuing to expand the resource to include additional image sets across different populations (from contributors) and to compare additional measurement methods (by challengers). The intended use of the measurement challenge resource is for refinement and validation of new and existing mammographic measurement methods. The measurement challenge resource provides a standardised dataset of mammographic images and core data that enables investigators to directly compare methods of measuring mammographic density or other mammographic features in case/control sets of both raw and processed images, for the purposes of the comparing their predictions of breast cancer risk.

    ETHICS AND DISSEMINATION: Challengers and contributors are required to enter a Research Collaboration Agreement with the University of Melbourne prior to participation in the measurement challenge. The Challenge database of collated data and images are stored in a secure data repository at the University of Melbourne. Ethics approval for the measurement challenge is held at University of Melbourne (HREC ID 0931343.3).

  2. Hayrapetyan A, Tumasyan A, Adam W, Andrejkovic JW, Bergauer T, Chatterjee S, et al.
    Phys Rev Lett, 2024 Nov 08;133(19):191902.
    PMID: 39576923 DOI: 10.1103/PhysRevLett.133.191902
    The first search for soft unclustered energy patterns (SUEPs) is performed using an integrated luminosity of 138  fb^{-1} of proton-proton collision data at sqrt[s]=13  TeV, collected in 2016-2018 by the CMS detector at the LHC. Such SUEPs are predicted by hidden valley models with a new, confining force with a large 't Hooft coupling. In events with boosted topologies, selected by high-threshold hadronic triggers, the multiplicity and sphericity of clustered tracks are used to reject the background from standard model quantum chromodynamics. With no observed excess of events over the standard model expectation, limits are set on the cross section for production via gluon fusion of a scalar mediator with SUEP-like decays.
  3. Hayrapetyan A, Tumasyan A, Adam W, Andrejkovic JW, Bergauer T, Chatterjee S, et al.
    Phys Rev Lett, 2024 Nov 15;133(20):201803.
    PMID: 39626745 DOI: 10.1103/PhysRevLett.133.201803
    Searches for pair-produced multijet signatures using data corresponding to an integrated luminosity of 128  fb^{-1} of proton-proton collisions at sqrt[s]=13  TeV are presented. A data scouting technique is employed to record events with low jet scalar transverse momentum sum values. The electroweak production of particles predicted in R-parity violating supersymmetric models is probed for the first time with fully hadronic final states. This is the first search for prompt hadronically decaying mass-degenerate higgsinos, and extends current exclusions on R-parity violating top squarks and gluinos.
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