Prioritizing cases from a multi-institutional cohort for a dataset of pathologist annotations.

Objective: With the increasing energy surrounding the development of artificial intelligence and machine learning (AI/ML) models, the use of the same external validation dataset by various developers allows for a direct comparison of model performance. Through our High Throughput Truthing project, we are creating a validation dataset for AI/ML models trained in the assessment of stromal tumor-infiltrating lymphocytes (sTILs) in triple negative breast cancer (TNBC).

Materials And Methods: We obtained clinical metadata for hematoxylin and eosin-stained glass slides and corresponding scanned whole slide images (WSIs) of TNBC core biopsies from two US academic medical centers.

Growth and properties of hybrid Au-CoNinanowires embedded in SrTiO/SrTiO(001).

We present a sequential growth scheme based on pulsed laser deposition, which yields dense arrays of ultrathin, match-shaped Au/CoNi nanopillars, vertically embedded in SrTiOthin films. Analysis of the magnetic properties of these nanocomposites reveals a pronounced out-of-plane anisotropy. We show that the latter not only results from the peculiar nanoarchitecture of the hybrid films but is further enhanced by strong magneto-structural coupling of the wires to the surrounding matrix.

Femtosecond Drift Photocurrents Generated by an Inversely Designed Plasmonic Antenna.

Photocurrents play a crucial role in various applications, including light detection, photovoltaics, and THz radiation generation. Despite the abundance of methods and materials for converting light into electrical signals, the use of metals in this context has been relatively limited. Nanostructures supporting surface plasmons in metals offer precise light manipulation and induce light-driven electron motion.

Training pathologists to assess stromal tumour-infiltrating lymphocytes in breast cancer synergises efforts in clinical care and scientific research.

A growing body of research supports stromal tumour-infiltrating lymphocyte (TIL) density in breast cancer to be a robust prognostic and predicive biomarker. The gold standard for stromal TIL density quantitation in breast cancer is pathologist visual assessment using haematoxylin and eosin-stained slides. Artificial intelligence/machine-learning algorithms are in development to automate the stromal TIL scoring process, and must be validated against a reference standard such as pathologist visual assessment.

Reproducible Reporting of the Collection and Evaluation of Annotations for Artificial Intelligence Models.

This work puts forth and demonstrates the utility of a reporting framework for collecting and evaluating annotations of medical images used for training and testing artificial intelligence (AI) models in assisting detection and diagnosis. AI has unique reporting requirements, as shown by the AI extensions to the Consolidated Standards of Reporting Trials (CONSORT) and Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) checklists and the proposed AI extensions to the Standards for Reporting Diagnostic Accuracy (STARD) and Transparent Reporting of a Multivariable Prediction model for Individual Prognosis or Diagnosis (TRIPOD) checklists. AI for detection and/or diagnostic image analysis requires complete, reproducible, and transparent reporting of the annotations and metadata used in training and testing data sets.