Breast cancer detection rates using four different types of mammography detectors.

OBJECTIVE: To compare the performance of different types of detectors in breast cancer detection. METHODS: A mammography image set containing subtle malignant non-calcification lesions, biopsy-proven benign lesions, simulated malignant calcification clusters and normals was acquired using amorphous-selenium (a-Se) detectors. The images were adapted to simulate four types of detectors at the same radiation dose: digital radiography (DR) detectors with a-Se and caesium iodide (CsI) convertors, and computed radiography (CR) detectors with a powder phosphor (PIP) and a needle phosphor (NIP). Seven observers marked suspicious and benign lesions. Analysis was undertaken using jackknife alternative free-response receiver operating characteristics weighted figure of merit (FoM). The cancer detection fraction (CDF) was estimated for a representative image set from screening. RESULTS: No significant differences in the FoMs between the DR detectors were measured. For calcification clusters and non-calcification lesions, both CR detectors' FoMs were significantly lower than for DR detectors. The calcification cluster's FoM for CR NIP was significantly better than for CR PIP. The estimated CDFs with CR PIP and CR NIP detectors were up to 15% and 22% lower, respectively, than for DR detectors. CONCLUSION: Cancer detection is affected by detector type, and the use of CR in mammography should be reconsidered. KEY POINTS: The type of mammography detector can affect the cancer detection rates. CR detectors performed worse than DR detectors in mammography. Needle phosphor CR performed better than powder phosphor CR. Calcification clusters detection is more sensitive to detector type than other cancers.

The effect of image processing on the detection of cancers in digital mammography.

OBJECTIVE. The objective of our study was to investigate the effect of image processing on the detection of cancers in digital mammography images. MATERIALS AND METHODS. Two hundred seventy pairs of breast images (both breasts, one view) were collected from eight systems using Hologic amorphous selenium detectors: 80 image pairs showed breasts containing subtle malignant masses; 30 image pairs, biopsy-proven benign lesions; 80 image pairs, simulated calcification clusters; and 80 image pairs, no cancer (normal). The 270 image pairs were processed with three types of image processing: standard (full enhancement), low contrast (intermediate enhancement), and pseudo-film-screen (no enhancement). Seven experienced observers inspected the images, locating and rating regions they suspected to be cancer for likelihood of malignancy. The results were analyzed using a jackknife-alternative free-response receiver operating characteristic (JAFROC) analysis. RESULTS. The detection of calcification clusters was significantly affected by the type of image processing: The JAFROC figure of merit (FOM) decreased from 0.65 with standard image processing to 0.63 with low-contrast image processing (p = 0.04) and from 0.65 with standard image processing to 0.61 with film-screen image processing (p = 0.0005). The detection of noncalcification cancers was not significantly different among the image-processing types investigated (p > 0.40). CONCLUSION. These results suggest that image processing has a significant impact on the detection of calcification clusters in digital mammography. For the three image-processing versions and the system investigated, standard image processing was optimal for the detection of calcification clusters. The effect on cancer detection should be considered when selecting the type of image processing in the future.

canSAR: an integrated cancer public translational research and drug discovery resource.

canSAR is a fully integrated cancer research and drug discovery resource developed to utilize the growing publicly available biological annotation, chemical screening, RNA interference screening, expression, amplification and 3D structural data. Scientists can, in a single place, rapidly identify biological annotation of a target, its structural characterization, expression levels and protein interaction data, as well as suitable cell lines for experiments, potential tool compounds and similarity to known drug targets. canSAR has, from the outset, been completely use-case driven which has dramatically influenced the design of the back-end and the functionality provided through the interfaces. The Web interface at http://cansar.icr.ac.uk provides flexible, multipoint entry into canSAR. This allows easy access to the multidisciplinary data within, including target and compound synopses, bioactivity views and expert tools for chemogenomic, expression and protein interaction network data.

Deep Learning for Breast Cancer Risk Prediction: Application to a Large Representative UK Screening Cohort.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop an artificial intelligence (AI) deep learning tool capable of predicting future breast cancer risk from a current negative screening mammographic examination and to evaluate the model on data from the UK National Health Service Breast Screening Program. Materials and Methods The OPTIMAM Mammography Imaging Database contains screening data, including mammograms and information on interval cancers, for > 300,000 women who attended screening at three different sites in the UK from 2012 onward. Cancer-free screening examinations from women aged 50-70 years were obtained and classified as risk-positive or risk-negative based on the occurrence of cancer within 3 years of the original examination. Examinations with confirmed cancer and images containing implants were excluded. From the resulting 5264 risk-positive and 191488 risk-negative examinations, training (n = 89285) validation (n = 2106) and test (n = 39351) datasets were produced for model development and evaluation. The AI model was trained to predict future cancer occurrence based on screening mammograms and patient age. Performance was evaluated on the test dataset using the area under the receiver operating characteristic curve (AUC) and compared across subpopulations to assess potential biases. Interpretability of the model was explored, including with saliency maps. Results On the hold-out test set, the AI model achieved an overall AUC of 0.70 (95% CI: 0.69, 0.72). There was no evidence of a difference in performance across the three sites, between patient ethnicities or across age-groups Visualization of saliency maps and sample images provided insights into the mammographic features associated with AI-predicted cancer risk. Conclusion The developed AI tool showed good performance on a multisite, UK-specific dataset. ©RSNA, 2024.

Lessons learned from independent external validation of an AI tool to detect breast cancer using a representative UK data set.

OBJECTIVE: To pilot a process for the independent external validation of an artificial intelligence (AI) tool to detect breast cancer using data from the NHS breast screening programme (NHSBSP). METHODS: A representative data set of mammography images from 26,000 women attending 2 NHS screening centres, and an enriched data set of 2054 positive cases were used from the OPTIMAM image database. The use case of the AI tool was the replacement of the first or second human reader. The performance of the AI tool was compared to that of human readers in the NHSBSP. RESULTS: Recommendations for future external validations of AI tools to detect breast cancer are provided. The tool recalled different breast cancers to the human readers. This study showed the importance of testing AI tools on all types of cases (including non-standard) and the clarity of any warning messages. The acceptable difference in sensitivity and specificity between the AI tool and human readers should be determined. Any information vital for the clinical application should be a required output for the AI tool. It is recommended that the interaction of radiologists with the AI tool, and the effect of the AI tool on arbitration be investigated prior to clinical use. CONCLUSION: This pilot demonstrated several lessons for future independent external validation of AI tools for breast cancer detection. ADVANCES IN KNOWLEDGE: Knowledge has been gained towards best practice procedures for performing independent external validations of AI tools for the detection of breast cancer using data from the NHS Breast Screening Programme.

ImmunoGrid, an integrative environment for large-scale simulation of the immune system for vaccine discovery, design and optimization.

Vaccine research is a combinatorial science requiring computational analysis of vaccine components, formulations and optimization. We have developed a framework that combines computational tools for the study of immune function and vaccine development. This framework, named ImmunoGrid combines conceptual models of the immune system, models of antigen processing and presentation, system-level models of the immune system, Grid computing, and database technology to facilitate discovery, formulation and optimization of vaccines. ImmunoGrid modules share common conceptual models and ontologies. The ImmunoGrid portal offers access to educational simulators where previously defined cases can be displayed, and to research simulators that allow the development of new, or tuning of existing, computational models. The portal is accessible at .

OPTIMAM Mammography Image Database: A Large-Scale Resource of Mammography Images and Clinical Data.

Supplemental material is available for this article.

Towards a lightweight generic computational grid framework for biological research.

BACKGROUND: An increasing number of scientific research projects require access to large-scale computational resources. This is particularly true in the biological field, whether to facilitate the analysis of large high-throughput data sets, or to perform large numbers of complex simulations - a characteristic of the emerging field of systems biology. RESULTS: In this paper we present a lightweight generic framework for combining disparate computational resources at multiple sites (ranging from local computers and clusters to established national Grid services). A detailed guide describing how to set up the framework is available from the following URL: http://igrid-ext.cryst.bbk.ac.uk/portal_guide/. CONCLUSION: This approach is particularly (but not exclusively) appropriate for large-scale biology projects with multiple collaborators working at different national or international sites. The framework is relatively easy to set up, hides the complexity of Grid middleware from the user, and provides access to resources through a single, uniform interface. It has been developed as part of the European ImmunoGrid project.

Toward the atomistic simulation of T cell epitopes automated construction of MHC: peptide structures for free energy calculations.

Epitopes mediated by T cells lie at the heart of the adaptive immune response and form the essential nucleus of anti-tumour peptide or epitope-based vaccines. Antigenic T cell epitopes are mediated by major histocompatibility complex (MHC) molecules, which present them to T cell receptors. Calculating the affinity between a given MHC molecule and an antigenic peptide using experimental approaches is both difficult and time consuming, thus various computational methods have been developed for this purpose. A server has been developed to allow a structural approach to the problem by generating specific MHC:peptide complex structures and providing configuration files to run molecular modelling simulations upon them. A system has been produced which allows the automated construction of MHC:peptide structure files and the corresponding configuration files required to execute a molecular dynamics simulation using NAMD. The system has been made available through a web-based front end and stand-alone scripts. Previous attempts at structural prediction of MHC:peptide affinity have been limited due to the paucity of structures and the computational expense in running large scale molecular dynamics simulations. The MHCsim server (http://igrid-ext.cryst.bbk.ac.uk/MHCsim) allows the user to rapidly generate any desired MHC:peptide complex and will facilitate molecular modelling simulation of MHC complexes on an unprecedented scale.

The relationship between cancer detection in mammography and image quality measurements.

PURPOSE: To investigate the relationship between image quality measurements and the clinical performance of digital mammographic systems. METHODS: Mammograms containing subtle malignant non-calcification lesions and simulated malignant calcification clusters were adapted to appear as if acquired by four types of detector. Observers searched for suspicious lesions and gave these a malignancy score. Analysis was undertaken using jackknife alternative free-response receiver operating characteristics weighted figure of merit (FoM). Images of a CDMAM contrast-detail phantom were adapted to appear as if acquired using the same four detectors as the clinical images. The resultant threshold gold thicknesses were compared to the FoMs using a linear regression model and an F-test was used to find if the gradient of the relationship was significantly non-zero. RESULTS: The detectors with the best image quality measurement also had the highest FoM values. The gradient of the inverse relationship between FoMs and threshold gold thickness for the 0.25mm diameter disk was significantly different from zero for calcification clusters (p=0.027), but not for non-calcification lesions (p=0.11). Systems performing just above the minimum image quality level set in the European Guidelines for Quality Assurance in Breast Cancer Screening and Diagnosis resulted in reduced cancer detection rates compared to systems performing at the achievable level. CONCLUSIONS: The clinical effectiveness of mammography for the task of detecting calcification clusters was found to be linked to image quality assessment using the CDMAM phantom. The European Guidelines should be reviewed as the current minimum image quality standards may be too low.

Objective assessment of cancer genes for drug discovery.

Selecting the best targets is a key challenge for drug discovery, and achieving this effectively, efficiently and systematically is particularly important for prioritizing candidates from the sizeable lists of potential therapeutic targets that are now emerging from large-scale multi-omics initiatives, such as those in oncology. Here, we describe an objective, systematic, multifaceted computational assessment of biological and chemical space that can be applied to any human gene set to prioritize targets for therapeutic exploration. We use this approach to evaluate an exemplar set of 479 cancer-associated genes, reveal the tension between biological relevance and chemical tractability, and describe major gaps in available knowledge that could be addressed to aid objective decision-making. We also propose drug repurposing opportunities and identify potentially druggable cancer-associated proteins that have been poorly explored with regard to the discovery of small-molecule modulators, despite their biological relevance.

A computational Grid framework for immunological applications.

We have developed a computational Grid that enables us to exploit through a single interface a range of local, national and international resources. It insulates the user as far as possible from issues concerning administrative boundaries, passwords and different operating system features. This work has been undertaken as part of the European Union ImmunoGrid project whose aim is to develop simulations of the immune system at the molecular, cellular and organ levels. The ImmunoGrid consortium has members with computational resources on both sides of the Atlantic. By making extensive use of existing Grid middleware, our Grid has enabled us to exploit consortium and publicly available computers in a unified way, notwithstanding the diverse local software and administrative environments. We took 40 000 polypeptide sequences from 4000 avian and mammalian influenza strains and used a neural network for class I T-cell epitope prediction tools for 120 class I alleles and haplotypes to generate over 14 million high-quality protein-peptide binding predictions that we are mapping onto the three-dimensional structures of the proteins. By contrast, the Grid is also being used for developing new methods for class T-cell epitope predictions, where we have running batches of 120 molecular dynamics free-energy calculations.