Applying the "positive predictive value-recall diagram" to monitor performance and provide recommendations for screening radiologists.

Objectives: To evaluate the suitability of "positive predictive value-recall" (PPV-recall) diagrams for monitoring performance and providing recommendations for groups of radiologists (RUs or reading units) in breast cancer screening.

Materials And Methods: This retrospective study used datasets from triennial quality assurance audits within the Dutch screening programme. The recall rate (RR), cancer detection rate (CDR), and PPV between 2010 and 2019 were plotted in PPV-recall diagrams separately for initial and subsequent screening.

Software Article: A generalized cascaded linear system model implementation for x-ray detectors.

Purpose: Cascaded linear models are widely used for the development and optimization of x-ray imaging systems, yet no publicly available Python implementation currently exists. We introduce CASYMIR, a flexible and open-source Python package capable of modeling direct and indirect-conversion x-ray imaging detectors under various acquisition conditions.

Methods: We employed a modular software design with generalized frequency-domain expressions for each process in the detection chain, which can be implemented as serial or parallel blocks.

Automatic analysis of carpal angles using dynamic CT imaging: Reference values in healthy wrists and assessment of scapholunate ligament injuries.

Introduction: Dynamic CT imaging is a promising modality for evaluating wrist pathologies like scapholunate ligament (SL) injuries. The primary objective of this study is to extract carpal angles from dynamic CT datasets using an automated motion analysis algorithm to provide reference values for healthy wrist motion. Secondly, the feasibility of this automatic method to detect SL ligament pathology was evaluated.

AI Should Read Mammograms Only When Confident: A Hybrid Breast Cancer Screening Reading Strategy.

Background Quantifying the uncertainty in artificial intelligence (AI)-based interpretations of mammograms could help AI integration in screening. Purpose To evaluate the reduction in radiologists' reading workload in mammographic screening while maintaining performance by incorporating an AI model that includes uncertainty quantification. Materials and Methods An AI model was introduced that outputs a probability of malignancy (PoM) and a measure of its uncertainty.

AI as an independent second reader in detection of clinically relevant breast cancers within a population-based screening programme in the Netherlands: a retrospective cohort study.

Background: Breast cancer screening programmes have shown to reduce mortality, but current methods face challenges such as limited mammographic sensitivity, limited resources, and variability in radiologist expertise. Artificial intelligence (AI) offers potential to improve screening accuracy and efficiency. This study simulated different screening scenarios, evaluating the performance of population-based breast cancer screening when using an AI system as a stand-alone reader or second reader.

The influence of anatomical shape variations of wrist bones on bone orientation values in CT scans.

Introduction: Four-Dimensional Computed Tomography (4DCT) shows promise in diagnosing scapholunate ligament (SL) lesions. Wrist motion analysis requires local coordinate systems (LCS) for carpal bones, which might be affected by bone shape variations. These variations can affect the extraction of scapholunate angle (SLA) and capitolunate angle (CLA), indicative for SL lesions.

An Exploration of Discrepant Recalls Between AI and Human Readers of Malignant Lesions in Digital Mammography Screening.

The integration of artificial intelligence (AI) in digital mammography (DM) screening holds promise for early breast cancer detection, potentially enhancing accuracy and efficiency. However, AI performance is not identical to that of human observers. We aimed to identify common morphological image characteristics of true cancers that are missed by either AI or human screening when their interpretations are discrepant.

More than density: validating a mammographic masking prediction model in Dutch breast cancer screening.

Objectives: To validate a lesion masking prediction model, Mammatus, previously developed on a North American cohort, on a larger retrospective breast cancer screening cohort from a single center in the Netherlands.

Materials And Methods: Mammatus was applied to all digital mammography screening examinations with a unilateral invasive breast cancer that was either diagnosed at screening or within 24 months after a negative screening, called interval cancers. All mammograms were retrospectively evaluated for the visibility of malignant masses using all available imaging and clinical information.

Four-Dimensional Similarity Filter for Cerebral CT Perfusion Maps in Acute Stroke.

Background Brain CT perfusion is used for diagnosing ischemic stroke and treatment planning. Purpose To optimize the four-dimensional similarity filter (4DSF), a noise-reducing filter, for CT perfusion in patients with acute stroke. Materials and Methods To reduce the noise of dynamic perfusion images, the 4DSF averages voxels with the most similar time-intensity curves.

Results of a Geant4 benchmarking study for bio-medical applications, performed with the G4-Med system.

Background: Geant4, a Monte Carlo Simulation Toolkit extensively used in bio-medical physics, is in continuous evolution to include newest research findings to improve its accuracy and to respond to the evolving needs of a very diverse user community. In 2014, the G4-Med benchmarking system was born from the effort of the Geant4 Medical Simulation Benchmarking Group, to benchmark and monitor the evolution of Geant4 for medical physics applications. The G4-Med system was first described in our Medical Physics Special Report published in 2021.

4D Dynamic contrast-enhanced breast CT: Phantom-based reconstruction parameter optimization for iodine quantification.

Background: Four-dimensional dynamic contrast-enhanced breast CT (4D DCE-bCT) offers promising high-resolution spatial and temporal imaging capabilities for the characterization and monitoring of breast tumors. However, the optimal combination of parameters for iodine quantification in image space remains to be determined.

Purpose: This study aims to optimize a dedicated bCT system to perform long dynamic contrast-enhanced scans with high spatio-temporal resolution while maintaining a reasonable radiation dose.

Comparative image quality and dosimetric performance of two generations of dedicated breast CT systems.

Background: Dedicated breast computed tomography (bCT) systems offer detailed imaging for breast cancer diagnosis and treatment. As new bCT generations are developed, it is important to evaluate their imaging performance and dose efficiency to understand differences over previous models.

Purpose: To characterize the imaging performance and dose efficiency of a second-generation (GEN2) bCT system and compare them to those of a first-generation (GEN1) system.

The Screening Tomosynthesis Trial with Advanced Reader Methods (STREAM): design and rationale of a population-based breast cancer screening trial.

Objectives: It is uncertain what the effects of introducing digital breast tomosynthesis (DBT) in the Dutch breast cancer screening programme would be on detection, recall, and interval cancers (ICs), while reading times are expected to increase. Therefore, an investigation into the efficiency and cost-effectiveness of DBT screening while optimising reading is required.

Materials And Methods: The Screening Tomosynthesis trial with advanced REAding Methods (STREAM) aims to include 17,275 women (age 50-72 years) eligible for breast cancer screening in the Netherlands for two biennial DBT screening rounds to determine the short-, medium-, and long-term effects and acceptability of DBT screening and identify an optimised strategy for reading DBT.

In-line spectroscopy for iodine quantification in dynamic contrast-enhanced dedicated breast CT.

Background: In 4D dynamic contrast-enhanced dedicated breast computed tomography (4D DCE-bCT), the functional properties of the breast will be characterized by monitoring the uptake and washout of iodine-based contrast agents over time. This information could be valuable in breast cancer treatment. However, prior to clinical implementation, it is crucial to validate the quantitative estimates of iodine concentrations at each time point during acquisition.

Performance evaluation of a 4D similarity filter for dynamic CT angiography imaging of the liver.

Background: Dynamic computed tomography (CT) angiography of the abdomen provides perfusion information and characteristics of the tissues present in the abdomen. This information could potentially help characterize liver metastases. However, radiation dose has to be relatively low for the patient, causing the images to have very high noise content.

Pain Experience and Image Quality with Curved versus Standard Compression for Breast Cancer Screening Mammography: A Randomized Controlled Trial.

Background A curve-shaped compression paddle could reduce the pain experienced by some women at breast cancer screening. Purpose To compare curved and standard compression systems in terms of pain experience and image quality in mammography screening. Materials and Methods In this randomized controlled trial conducted between October 2021 and February 2022, participants screened at three screening sites in the Netherlands were randomized to either a curved-paddle or sham-paddle group.

Modeling Radiologists' Assessments to Explore Pairing Strategies for Optimized Double Reading of Screening Mammograms.

Purpose: To develop a model that simulates radiologist assessments and use it to explore whether pairing readers based on their individual performance characteristics could optimize screening performance.

Methods: Logistic regression models were designed and used to model individual radiologist assessments. For model evaluation, model-predicted individual performance metrics and paired disagreement rates were compared against the observed data using Pearson correlation coefficients.

A nonparametric measure of contrast in x-ray images.

We propose a nonparametric figure of merit, the contrast equivalent distance CED, to measure contrast directly from clinical images.A relative brightness distanceis calculated by making use of the order statistic of the pixel values. By multiplyingwith the grey value range, the mean brightness distance MBD is obtained.

Deep learning-based low-dose CT simulator for non-linear reconstruction methods.

Background: Computer algorithms that simulate lower-doses computed tomography (CT) images from clinical-dose images are widely available. However, most operate in the projection domain and assume access to the reconstruction method. Access to commercial reconstruction methods may often not be available in medical research, making image-domain noise simulation methods useful.

Artificial Intelligence for breast cancer detection: Technology, challenges, and prospects.

Purpose: This review provides an overview of the current state of artificial intelligence (AI) technology for automated detection of breast cancer in digital mammography (DM) and digital breast tomosynthesis (DBT). It aims to discuss the technology, available AI systems, and the challenges faced by AI in breast cancer screening.

Methods: The review examines the development of AI technology in breast cancer detection, focusing on deep learning (DL) techniques and their differences from traditional computer-aided detection (CAD) systems.

Technical note: Characterization, validation, and spectral optimization of a dedicated breast CT system for contrast-enhanced imaging.

Background: The development of a new imaging modality, such as 4D dynamic contrast-enhanced dedicated breast CT (4D DCE-bCT), requires optimization of the acquisition technique, particularly within the 2D contrast-enhanced imaging modality. Given the extensive parameter space, cascade-systems analysis is commonly used for such optimization.

Purpose: To implement and validate a parallel-cascaded model for bCT, focusing on optimizing and characterizing system performance in the projection domain to enhance the quality of input data for image reconstruction.

The prognostic potential of mammographic growth rate of invasive breast cancer in the Nijmegen breast cancer screening cohort.

Objectives: Insight into the aggressiveness of potential breast cancers found in screening may optimize recall decisions. Specific growth rate (SGR), measured on mammograms, may provide valuable prognostic information. This study addresses the association of SGR with prognostic factors and overall survival in patients with invasive carcinoma of no special type (NST) from a screened population.

Deep learning-based breast region segmentation in raw and processed digital mammograms: generalization across views and vendors.

Purpose: We developed a segmentation method suited for both raw (for processing) and processed (for presentation) digital mammograms (DMs) that is designed to generalize across images acquired with systems from different vendors and across the two standard screening views.

Approach: A U-Net was trained to segment mammograms into background, breast, and pectoral muscle. Eight different datasets, including two previously published public sets and six sets of DMs from as many different vendors, were used, totaling 322 screen film mammograms (SFMs) and 4251 DMs (2821 raw/processed pairs and 1430 only processed) from 1077 different women.

Joint AAPM Task Group 282/EFOMP Working Group Report: Breast dosimetry for standard and contrast-enhanced mammography and breast tomosynthesis.

Currently, there are multiple breast dosimetry estimation methods for mammography and its variants in use throughout the world. This fact alone introduces uncertainty, since it is often impossible to distinguish which model is internally used by a specific imaging system. In addition, all current models are hampered by various limitations, in terms of overly simplified models of the breast and its composition, as well as simplistic models of the imaging system.