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.

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 more than 300 000 female patients who attended screening at three different sites in the United Kingdom from 2012 onward. Cancer-free screening examinations from women aged 50-70 years were performed 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 191 488 risk-negative examinations, training (n = 89 285), validation (n = 2106), and test (n = 39 351) 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, United Kingdom-specific dataset. Keywords: Deep Learning, Artificial Intelligence, Breast Cancer, Screening, Risk Prediction Supplemental material is available for this article. ©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.

Virtual Versus In-person Grand Rounds in Orthopaedics: A Framework for Implementation and Participant-reported Outcomes.

INTRODUCTION: Grand rounds have been weekly gatherings at academic orthopaedic surgery programs across the country for decades. During the 50th year of grand rounds at our institution, the COVID-19 pandemic prompted the transformation of this in-person forum into a virtual setting. The purpose of this study was to detail this initiative and to report survey data providing participant-reported perceptions and satisfaction of virtual versus in-person grand rounds. MATERIALS AND METHODS: Once in-person meetings were discontinued, virtual grand rounds commenced using the Zoom video application. At the conclusion of the 2020 to 2021 academic year, a 30-item online survey was sent to all residents, faculty, and visiting faculty to assess their perspective and satisfaction. A five-point Likert scale ranging from 1 to 5, with 5 being extremely effective, was used. A 21-item follow-up survey was sent to all speakers as well. RESULTS: Thirty-six virtual grand rounds were successfully hosted. The response rate for the survey was excellent-80 of 86 (93.0%) surveys returned completed. Respondents found that virtual grand rounds were more convenient to attend, were more convenient to obtain Continuing Medical Education, and were more satisfied with virtual grand rounds. Respondents reported that in-person grand rounds were more effective for stimulating social collegiality and networking. Speakers found that virtual grand rounds were more effective for uploading the presentation and overall convenience, whereas they were less effective at retaining audience attention and receiving audience feedback. Improved faculty attendance after the switch to virtual grand rounds was also noted. CONCLUSION: This study found that respondents across all groups appreciated the convenience of attending and obtaining Continuing Medical Educations at virtual grand rounds while also noting the merits of in-person grand rounds for promoting peer interaction, camaraderie, and departmental identity. All respondents strongly recommended continuation of this program in a hybrid format. Virtual orthopaedic grand rounds are viable, readily implemented and demonstrate improved participant satisfaction.

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 .

3D finite element analysis of nutrient distributions and cell viability in the intervertebral disc: effects of deformation and degeneration.

The intervertebral disc (IVD) receives important nutrients, such as glucose, from surrounding blood vessels. Poor nutritional supply is believed to play a key role in disc degeneration. Several investigators have presented finite element models of the IVD to investigate disc nutrition; however, none has predicted nutrient levels and cell viability in the disc with a realistic 3D geometry and tissue properties coupled to mechanical deformation. Understanding how degeneration and loading affect nutrition and cell viability is necessary for elucidating the mechanisms of disc degeneration and low back pain. The objective of this study was to analyze the effects of disc degeneration and static deformation on glucose distributions and cell viability in the IVD using finite element analysis. A realistic 3D finite element model of the IVD was developed based on mechano-electrochemical mixture theory. In the model, the cellular metabolic activities and viability were related to nutrient concentrations, and transport properties of nutrients were dependent on tissue deformation. The effects of disc degeneration and mechanical compression on glucose concentrations and cell density distributions in the IVD were investigated. To examine effects of disc degeneration, tissue properties were altered to reflect those of degenerated tissue, including reduced water content, fixed charge density, height, and endplate permeability. Two mechanical loading conditions were also investigated: a reference (undeformed) case and a 10% static deformation case. In general, nutrient levels decreased moving away from the nutritional supply at the disc periphery. Minimum glucose levels were at the interface between the nucleus and annulus regions of the disc. Deformation caused a 6.2% decrease in the minimum glucose concentration in the normal IVD, while degeneration resulted in an 80% decrease. Although cell density was not affected in the undeformed normal disc, there was a decrease in cell viability in the degenerated case, in which averaged cell density fell 11% compared with the normal case. This effect was further exacerbated by deformation of the degenerated IVD. Both deformation and disc degeneration altered the glucose distribution in the IVD. For the degenerated case, glucose levels fell below levels necessary for maintaining cell viability, and cell density decreased. This study provides important insight into nutrition-related mechanisms of disc degeneration. Moreover, our model may serve as a powerful tool in the development of new treatments for low back pain.

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

Supplemental material is available for this article.

Utility of quantitative computerized pain drawings in a sample of spinal stenosis patients.

OBJECTIVE: To evaluate the utility of quantitative computerized pain drawings (CPDs) in a sample of spine patients before and after surgery. DESIGN: Analysis of changes in quantified CPDs, the Oswestry Disability Index (ODI), the Short Form-36 Health Survey Questionnaire (SF-36), and numerical ratings of pain intensity before and after surgery. SETTING: Private clinic in large metropolitan area. Patients. Forty-six patients with spinal stenosis. Interventions. Surgery for the relief of pain due to spinal stenosis. OUTCOME MEASURES: A total points (TP) score was calculated from the CPD that reflected the total number of pixels filled by the patient, and the percentage of total pain area indicated as aching, stabbing, numbness, pins and needles, burning, and other, were each calculated separately. CPD scores, ODI score, Physical Components Summary (PCS) and Mental Components Summary scores of the SF-36, and pain intensity ratings (0-10 scale) were all recorded before and after surgical intervention. Results. After surgery, patients showed significant improvements in the extent of shaded pain area of the CPD, pain intensity ratings, ODI, and SF-36 PCS scores (paired t-test, P < or = 0.01). Changes in TP scores calculated from the CPDs were significantly correlated (P < or = 0.05) with changes in ODI scores (r = 0.34) and pain intensity ratings (r = 0.37). Changes in the percentage of total pain area covered by specific qualities of pain were not significant. CONCLUSIONS: Results from the present study provide initial support for the use of automated quantified data collected from CPDs to evaluate treatment interventions and to serve the clinician as a record of changes in spatial location, radiation or extent of pain, and the sensory quality of pain when evaluating individual patient needs.

Effect of intervertebral disc degeneration on mechanical and electric signals at the interface between disc and vertebra.

Intervertebral disc (IVD) degeneration is significantly correlated with the changes in structure and material properties of adjacent vertebral bone, possibly through mechanical and electrical interactions. However, the mechanisms underlying the alteration of the mechanical and electrical environment at the disc-vertebra interface related with disc degeneration have not been well studied. The objective of this study was to numerically investigate the long-term distributions of mechanical and electrical signals on the disc-vertebra interface with disc degeneration. A three-dimensional finite element model of a human lumbar IVD was used to study the mechanical and electric signals at the interface between disc and vertebral body. The disc degeneration was simulated by reducing the nutrition levels on the nucleus pulposus (NP)-vertebra interface and on the annulus fibrosus (AF) periphery to 30% and 60% of its normal values, respectively. In the simulation, the total external mechanical load applied to the disc-vertebra segment was assumed unchanged during disc degeneration. The simulation results showed that the compressive stress of solid matrix changed by up to ~37 kPa on the NP-vertebra interface, while it increased by up to ~32 kPa on the AF-vertebra interface. The shear stress increased by up to ~37 kPa with disc degeneration. The absolute value of the electric potential on the disc-vertebra interface of the disc slightly decreased with the disc degeneration (~0.5 mV). The knowledge of these spatial and temporal variations of the mechanical stresses and electric potential on the disc-vertebra interface is important for understanding the vertebrae adaptation and remodeling during disc degeneration.

Transport of Vancomycin and Cefepime Into Human Intervertebral Discs: Quantitative Analyses.

STUDY DESIGN: Simulation of antibiotics transport into human intervertebral disc with intravenous infusion. OBJECTIVE: The objective of this study was to quantitatively investigate antibiotic concentrations in the disc. SUMMARY OF BACKGROUND DATA: Intravenous infusion of antibiotics is typically used to treat intervertebral disc infection in clinics. However, it is difficult to evaluate the drug concentrations within discs in vivo. METHODS: A computational model was used in this study. The variation of drug charge with pH was considered in the model. Thirty-minute infusions of two commonly used antibiotics in clinic-vancomycin and cefepime-were numerically investigated. Spatial and temporal concentration distributions of these drugs in both nondegenerated and moderately degenerated discs were calculated. RESULTS: For intravenous infusion of 1 g vancomycin and 2 g cefepime in 30 minutes repeated every 12 hours, it was predicted that vancomycin concentration in the disc fluctuated between 17.0 and 31.0 times of its minimum inhibitory concentration (1 ug/mL) and cefepime concentration fluctuated between 1.1 and 4.2 times of its minimum inhibitory concentration (i.e., 8 ug/mL) in about 2 days. It was also found that vancomycin concentration in moderately degenerated disc was lower than that in the nondegenerated disc. CONCLUSION: This study provides quantitative guidance on selecting proper dosage for treating disc infection. The method used in this study could be used to provide quantitative information on transport of other antibiotics and drugs in discs as well. LEVEL OF EVIDENCE: N/A.

Effects of diurnal loading on the transport of charged antibiotics into intervertebral discs.

The objective of this study was to quantitatively analyze the effect of diurnal loading on the transport of various charged antibiotics into negatively charged human intervertebral disc (IVD). Transport of charged antibiotics into a human lumbar disc was analyzed using a 3D finite element model. The valence (z) of the electrical charge of antibiotics varied from z = +2 (positively charged) to z = -2 (negatively charged). An uncharged antibiotic (z = 0) was used as a control. Cases with transient antibiotic concentration at disc boundaries [to mimic intravenous (IV) infusion] were simulated. Our results showed that diurnal compression increased the concentrations in the nucleus pulposus (NP) region, but degreased the concentrations in the annulus fibrosus (AF) region for all charged or non-charged drugs. The overall concentration (averaged over disc) increased with diurnal compression. The diurnal compression had more effects on negatively charged antibiotics than positively charged ones. For example, at day 5 with diurnal compression, the diurnal compression increased the concentration of negatively charged drug (z = -1) in NP by 18.3%, but only by 6.6% for positively charged one (z = +1). In AF, diurnal compression decreased the concentration by 13.2% for negatively charged drug (z = -1) versus 1.2% for positively charged one (z = +1). Note these percentages are the averaged values over day 5. This study provides quantitative information on understanding the mechanisms of charged drug transport in human IVDs.

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.

How should pregnant women with spinal disease be managed?

This Practice Point commentary discusses a retrospective case series by Han et al. that investigated the management of spinal disease during pregnancy. On the basis of their experiences with 10 patients, Han et al. concluded that pregnant women who have progressive neurological deficit at 34-36 weeks' gestation or later should undergo induction of delivery or cesarean section before, or at the same time as, they undergo spinal surgery. As expressed by Han et al., MRI is the safest imaging modality with which to diagnose spinal disorders in pregnancy; however, it is our opinion that single exposure to any source of radiation from diagnostic imaging is unlikely to justify advising the pregnant patient to undergo therapeutic abortion. Spinal surgery has successfully relieved neurological symptoms in pregnant patients with spinal disorders and been followed by a successful delivery in a number of circumstances. In order to provide the optimum care for the pregnant woman and the fetus, however, a health-care team involving the spine surgeon, the obstetrician, and the anesthesiologist is necessary.

Allogeneic transfusion after predonation of blood for elective spine surgery.

UNLABELLED: The literature suggests preoperative autologous blood donation in total joint arthroplasty is associated with increased overall transfusion rates compared with nondonation and is not cost-effective for all patients. We asked whether the amount of intraoperative blood loss and blood replacement differs between autologous donors and nondonors in elective spine surgery and whether the rates of allogeneic blood transfusions differ between the two groups; we then determined the cost of wasted predonated units. We retrospectively reviewed 676 patients who underwent elective lumbar spine surgery and compared relevant data to that in a matched cohort of 51 patients who predonated blood and 51 patients who received only cell-saver blood and underwent instrumented spinal fusion. Patients who predonated blood had similar blood loss as patients who did not predonate, but they had more blood replacement (1391 cc compared with 410 cc). Patients who predonated blood also had a lower preoperative hemoglobin level and wasted a half unit of blood on average. There was no major difference in allogeneic blood transfusion rates between the two groups. Our data suggest for short, instrumented lumbar fusion surgeries in patients with a normal coagulation profile, preoperative blood donation is not beneficial. LEVEL OF EVIDENCE: Level II, therapeutic study.

Diffusion of antibiotics in intervertebral disc.

Delivering charged antibiotics to the intervertebral disc is challenging because of the avascular, negatively charged extracellular matrix (ECM) of the tissue. The purpose of this study was to measure the apparent diffusion coefficient of two clinically relevant, charged antibiotics, vancomycin (positively charged) and oxacillin (negatively charged) in IVD. A one-dimensional steady state diffusion experiment was employed to measure the apparent diffusion coefficient of the two antibiotics in bovine coccygeal annulus fibrosus (AF) tissue. The averaged apparent diffusion coefficient for vancomycin under 20% compressive strain was 7.94 ±â€¯2.00 × 10-12 m2/s (n = 10), while that of oxacillin was 2.26 ±â€¯0.68 × 10-10 m2/s (n = 10). A student's t-test showed that the diffusivity of vancomycin was significantly lower than that of oxacillin. This finding may be attributed to two factors: solute size and possible binding effects. Vancomycin is approximately 3 times larger in molecular weight than oxacillin, meaning that steric hindrance likely plays a role in the slower transport. Reversible binding between positive vancomycin and the negative ECM could also slow down the rate of diffusion. Therefore, more investigation is necessary to determine the specific relationship between net charge on antibiotic and diffusion coefficients in IVD. This study provides essential quantitative information regarding the transport rates of antibiotics in the IVD, which is critical in using computational modeling to design effective strategies to treat disc infection.

Simulation of water content distributions in degenerated human intervertebral discs.

The objective of this study was to investigate the spatial and temporal variations of water content in intervertebral discs during degeneration and repair processes. We hypothesized that the patterns of water content distribution in the discs are related to the intensity patterns observed in T2-weighted MRI images. Water content distributions in the mildly (e.g., 80% viable cells in the disc, 2.3% decrease in disc height) and moderately (e.g., 40% viable cells in the disc, 9.3% decrease in disc height) degenerated discs were predicted using a finite element model. The variation of water content in the degenerated discs treated with three biological therapies (i.e., increasing the cell density in the nucleus pulposus [Case I], increasing glycosaminoglycan synthesis rate in the nucleus pulposus [Case II], and decreasing glycosaminoglycan degradation rate in the nucleus pulposus [Case III]) were also predicted. It was found that two patterns of water content distributions, a horizontal region with lower water content at the mid-axial plane of nucleus pulposus and a spot with higher water content at the posterior region, were shown during the degeneration progress for the disc simulated in this study. These two patterns disappeared after treatment in Case I, but in Case II and Case III. The implication of these patterns for the horizontal gray band and high intensity zone in T2-weighted MRI images was discussed. This study provided new guidance to develop a novel method for diagnosing disc degeneration and assessing outcomes of biological therapies with MRI techniques. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:147-153, 2017.